text:
To each member of that small but loyal band of Aristos readers who responded yet again to our annual appeal for support, we extend our heartfelt thanks. All who read these pages are in your debt. - The Editors
An Urgent Appeal
Care about the arts? Value our unique perspective? If the answer to both is yes, and you haven't already done so, please join that "small but loyal band of Aristos readers" who support our work by contributing to the Aristos Foundation at whatever level you can afford, via PayPal or by check. You won't regret it.
Viewing Images Online
Firefox is the optimal browser for viewing images of art (or of anything else) because it centers them on a dark background. Make Firefox your permanent alternate browser for use when visiting this website (or perhaps even set it as your default browser).
Celebrating the Birthday of Winslow Homer
The Metropolitan Museum of Art's Facebook post on Homer's birthday included a link to a brief bio and slide show of 12 paintings, as well as to information about the featured painting (The Studio) of a cellist and violinist making music together. In our entry at the end of the comments section, we included this link to hundreds more images of Homer's paintings and drawings. For much more about him, see his illustrated Wikipedia page.
An Avant-Gardist's Take on Mr. Turner (the Movie)
Los Angeles Times art critic Christopher Knight is an advocate par excellence of the artworld principle "anything goes." In a review of the exhibition J.M.W. Turner: Painting Set Free now at the J. Paul Getty Museum, he raves about the recent movie Mr. Turner (on which, see our January Notes & Comments). According to Knight, the film is "the most convincing cinematic portrayal of an artist since 'Basquiat' nearly 20 years ago" (see Basquiat's "art"). Knight continues: "[Director Mike] Leigh, like the earlier film's director, artist Julian Schnabel [see his "art," too], understands that when it comes to making worthwhile art, the only workable attitude is: Do whatever it takes" (emphasis ours). Comments by Louis Torres follow a Facebook post on Knight's review.
Cubism Mythologized at the Met
On its page for the recent exhibition Cubism: The Leonard A. Lauder Collection, the Metropolitan Museum of Art proclaims:
Cubism, the most influential art movement of the early twentieth century, still resonates today. It destroyed traditional illusionism in painting and radically changed the way we see the world.
Hardly. While Cubism may have changed the way the Met's director and its modern and "contemporary" curators see the world, the pretentious "we" does surely not include the many living practitioners and admirers of "traditional illusionism," past and present. See, for example, "Classical Realism: A Living Artistic Tradition" by Stephen Gjertson and the Mission Statement of the Florence Academy of Art--not to mention the pages of Aristos (search for "classical realism").
"Puzzling Out" Cubism
Truth be told, even avowed "devotees" of Cubism have their problems with it. In comments on the Met's recent exhibition (see above note), critic Lee Rosenbaum, aka "CultureGrrl," writes:
Anyone (including devotees of Analytic Cubism's complexities, like me) who spent considerable time with Lauder's textbook collection [consisting of eighty-one works of painting, collage, drawing, and sculpture] . . . was probably staggered by a sense of Cubist overload from trying to puzzle out so many dense, challenging compositions in one uninterrupted session.
"Even veteran critic Peter Schjeldahl," she adds, "found scrutinizing the collection in its entirety a somewhat arduous task"--and quotes him as follows:
"If you're not tired after seeing the show, you weren't trying. . . . You may find that after an hour spent in the avant-garde boot camp of Cubism, looking at almost any other art will seem a breeze."
As for us, we made no attempt to "puzzle out" the Cubist works on view, for we understand all too well that the fragmentation of objects in Cubism [more] flouts the requirements of human perception. So we weren't "tired" after seeing the show, just bored and irritated.
Assessing the Legacy of Gardner Museum Director
In Arts Beat, a weblog of the New York Times, critic Randy Kennedy has nothing but praise for Anne Hawley, the director of the Isabella Stewart Gardner Museum in Boston, who is retiring after 25 years at its helm. As observed in a comment by Louis Torres on Kennedy's weblog post, however, Hawley's signature accomplishment was opening the doors of the museum---not of the original building, but of the new modernist wing grafted onto Mrs. Gardner's Venetian-inspired mansion)---to "contemporary art."
Isabella Stewart Gardner (1840-1924) was a famed collector of Renaissance art, as well as of work from nineteenth-century France and America, among other cultures and eras. She was also "a patron and friend of leading artists . . . of her time," as her museum's website notes. But the artists she befriended and supported most notably included John Singer Sargent [see her portrait by him]--not the leading modernists of the day, whom she disliked.
As emphasized by Boston Globe art critic Sebastian Smee, Gardner had "a hostile relationship with modernity," held "anti-modern convictions," and was "committed to preserving the past" ("New Look Rewrites Museum Founder's Vision for Her Guests," January 21, 2010). But that didn't stop Hawley from brazenly turning Gardner's museum into a haven for avant-gardists--most recently, for Jean-Michel Othoniel (see his Entry of Apollo, a "fountain-sculpture" awaiting installation on, of all places, the magnificent grounds of Versailles, France). Moreover, she completely ignored contemporary Classical Realist painters who carry on Sargent's legacy.
Books a Choreographer Reads
ArtsJournal (an online digest of links to articles) recently cited an interview with Cuban-born Boston-based choreographer José Mateo [more] in the Books section of the Boston Globe. The subject was what he reads in his spare time, when not creating dances or directing his ballet company and school, the José Mateo Dance Theatre [more].
"I'm always reading a number of books," Mateo says, "but, though I'm a well-intended reader, I just don't get through a lot of books."
Does he read many books on dance? "[D]ance books tend to be about fitness or glamorize one company or one dancer," he explains. "I like to read about trends so I look at writers like Selma Jeanne Cohen, who wrote Dance as a Theatre Art, about the history of dance, and the New Yorker dance critic Arlene Croce."
We found of particular interest "what else" Mateo (who had earned a degree in art history from Princeton University) reads. "I read about the philosophy of art and beauty," he replied, adding: "I recently got a copy of What Art Is by Louis Torres about the esthetic theory of Ayn Rand." Nearly 15 years after our book's publication, we're happy to know it still finds discriminating readers like Mateo, so we'll gladly forgive him for not remembering at the instant the name of its co-author (and Aristos co-editor), Michelle Kamhi.
Is This Political Activist also an Artist?
Search for the name "Ai Weiwei" on the web and you'll no doubt find this famous Chinese dissident characterized as, among other things, a "contemporary artist." His official website biography says simply: "Ai Weiwei is an artist." Born in 1957, Ai is undoubtedly contemporary. But is he an artist? For Michelle Kamhi's answer, see the recent weblog post on her website.
Misplacing the Bard in a Brooklyn Brownstone
In the past, Aristos has enthusiastically praised the generally excellent Pearl Theatre Company. But its current staging of Shakespeare's Winter's Tale, in a contemporary setting, prompted Michelle Kamhi to post the following remarks on the company's Facebook page--in response to a review lauding the "clever, rejuvenating staging":
As one of the Pearl's devoted fans, I beg to differ. To my dismay, I found the updated conception of this production jarringly inappropriate, especially in the profoundly dramatic and often moving early scenes. Why, I kept wondering, would a director choose to have Shakespeare's elevated diction spoken by actors portraying royalty in banal contemporary dress in a brownstone-like setting [more] that conjured up a scene from The Godfather? If he was afraid we otherwise wouldn't "get" the family relationships, he gives neither Shakespeare nor us enough credit. Better to have no setting at all than one at war with the text. The Pearl has been admirably free of such gratuitous updatings until now, and I earnestly hope that such an approach won't be repeated.
Updatings of this kind have been all too common these days, especially in the realm of opera. As a first-rate repertory company devoted to intelligent production of the classics, the Pearl should eschew this now-fashionable but misguided practice.
Must Reading
In case you missed it, our January 2005 issue featured an amply illustrated article on the noteworthy twentieth-century American sculptor Anna Hyatt Huntington, the first poem we've published, and much more of interest. Be sure to read it!
We invite readers to comment on any of the items on this or other pages. See information on Letters to the Editors, as well as archived letters [look for "LETTERS" in all caps].


text:
(function(window){
    'use strict';
    function dragSelector(config) {
        return new DSInternal(config);
    }

    // getTransformToElement polyfill for Chrome
    SVGElement.prototype.getTransformToElement = SVGElement.prototype.getTransformToElement || function(toElement) {
        return toElement.getScreenCTM().inverse().multiply(this.getScreenCTM());
    };

    dragSelector.version = "0.0.1";

    function DSInternal(config) {
        if (typeof window.d3 === 'undefined') {
            throw Error("d3.js must exist in order to use the d3.js drag selector plugin. Ensure that it is placed above this file when referencing your scripts.")
        }
        this.d3 = window.d3;
        config = config || {};
        this.config = {};
        this.config.selectNode = config.selectNode;
        this.config.selectFilter = config.selectFilter;
        this.config.selectedClass = config.selectedClass || "";
        this.config.rectangleClass = config.rectangleClass || "";
        this.config.rectTranslateNode = (config.rectTranslateNode && config.rectTranslateNode.select) ? config.rectTranslateNode.node() : config.rectTranslateNode;
        this.config.onSelect = config.onSelect;
        this.config.onDragStart = config.onDragStart;
        this.config.onDragEnd = config.onDragEnd;
        this.config.multiSelectKey = config.multiSelectKey || "ctrl";
        this.config.preventDragBubbling = config.preventDragBubbling === true ? true : false; // default to false - do not prevent drag bubbling
        this.config.bindToWindow = config.bindToWindow === false ? false : true;     // defaults to true - do bind to the window
        this.config.useDblClick = config.useDblClick === true ? true : false;     // defaults to false - don't use double click for rect drawing
    }

    var fn = DSInternal.prototype;

    fn.selectNode = function(nodeTag) {
        if (!arguments.length) return this.config.selectNode;
        this.config.selectNode = nodeTag;
        return this;
    };
    fn.selectFilter = function(filter) {
        if (!arguments.length) return this.config.selectFilter;
        this.config.selectFilter = filter;
        return this;
    };
    fn.selectedClass = function(className) {
        if (!arguments.length) return this.config.selectedClass;
        this.config.selectedClass = className;
        return this;
    };
    fn.rectangleClass = function(className) {
        if (!arguments.length) return this.config.rectangleClass;
        this.config.rectangleClass = className;
        return this;
    };
    fn.rectTranslateNode = function(node) {
        if (!arguments.length) return this.config.rectTranslateNode;
        this.config.rectTranslateNode = (node && node.select) ? node.node() : node;
        return this;
    };
    fn.onSelect = function(cb) {
        if (!arguments.length) return this.config.onSelect;
        this.config.onSelect = cb;
        return this;
    };
    fn.onDragStart = function(cb) {
        if (!arguments.length) return this.config.onDragStart;
        this.config.onDragStart = cb;
        return this;
    };
    fn.onDragEnd = function(cb) {
        if (!arguments.length) return this.config.onDragEnd;
        this.config.onDragEnd = cb;
        return this;
    };
    fn.multiSelectKey = function(key) {
        if (!arguments.length) return this.config.multiSelectKey;
        this.config.multiSelectKey = key;
        return this;
    };
    fn.preventDragBubbling = function(bool) {
        if (!arguments.length) return this.config.preventDragBubbling;
        this.config.preventDragBubbling = bool;
        return this;
    };
    fn.bindToWindow = function(bool) {
        if (!arguments.length) return this.config.bindToWindow;
        this.config.bindToWindow = bool;
        return this;
    };
    fn.useDblClick = function(bool) {
        if (!arguments.length) return this.config.useDblClick;
        this.config.useDblClick = bool;
        return this;
    };

    fn.selector = function() {
        var $$ = this;
        if (!(($$.config.selectNode === "path") || ($$.config.selectNode === "rect") || ($$.config.selectNode === "circle") || ($$.config.selectNode === "line"))) {
            throw Error("The \"selectNode\" configuration option must be either \"path\", \"rect\", \"circle\" or \"line\".");
        }
        if (!$$.config.selectedClass) {
            throw Error("The \"selectedClass\" configuration option must be specified and must not be falsy");
        }
        if ($$.config.selectNode === "path" && (!SVGPathElement.prototype.getPathData || !SVGPathElement.prototype.setPathData)) {
            throw Error("If the \"selectNode\" configuration option is set to \"path\" then the getPathData polyfill must be used. The polyfill can be acquired at https://github.com/jarek-foksa/path-data-polyfill.js")
        }
        return function(node) {
            internalSelectInit.call($$, node);
        };
    };

    function internalSelectInit(node) {
        var $$ = this,
            nodeSelector = $$.config.selectNode + ($$.config.selectFilter || ""),
            targets = node.selectAll(nodeSelector),
            currentSelection,
            clickedNode,
            scan = 0,
            dblClicked = false;
        node.on("mousedown", function(d, i, a) {
            if ($$.config.useDblClick && !dblClicked) {
                dblClicked = true;
                setTimeout(function() { dblClicked = false; }, 300);
                return;
            } else if ($$.config.useDblClick) {
                $$.d3.event.stopImmediatePropagation(); // this prevents other mousedown events firing afterwards on our "node" selection - it is required to prevent panning when using d3.behavior.zoom(), particularly for maps
                dblClicked = false;
            }
            var reset = false;
            if (($$.config.multiSelectKey === "ctrl" && $$.d3.event.ctrlKey) ||
                ($$.config.multiSelectKey === "shift" && $$.d3.event.shiftKey) ||
                ($$.config.multiSelectKey === "alt" && $$.d3.event.altKey)) {
                targets = targets.filter(":not(." + $$.config.selectedClass + ")"); // set the targets (the things to be searched) to exclude those already selected
            } else {
                targets = node.selectAll(nodeSelector); // else reset - we search all
                reset = true;
            }
            $$.d3.event.preventDefault(); // this prevents text from being selected - this should not be configurable due to how necessary it is
            if ($$.rect) {
                $$.rect.remove();
                delete $$.rect;
            }
            if ($$.config.onDragStart) $$.config.onDragStart.call(this);
            $$.rect = new Rect($$.d3.mouse($$.config.rectTranslateNode || this),$$.config.rectTranslateNode ? $$.d3.select($$.config.rectTranslateNode).attr("transform") : "translate(0,0)", $$.config.rectangleClass);
            $$.rect.appendTo(node);
            targets.each(function(d, i, a) {
                    if ($$.d3.event.target === this) { // ought this be currentTarget?
                        clickedNode = $$.d3.select(this);
                    } else if (reset) {
                        $$.d3.select(this).classed($$.config.selectedClass, false);
                    }
                });
            currentSelection = $$.d3.select(null);
        });
        (function() {
            if (!$$.config.bindToWindow) return node;
            return $$.d3.select(window);
        })()
        .on("mousemove", function(d, i, a) {
            if ($$.rect) {
                if ($$.config.preventDragBubbling) pauseEvent($$.d3.event);
                $$.rect.reDraw($$.d3.mouse($$.config.rectTranslateNode || node.node()));
                if ((scan++ !== 3)) {
                    return; // scan only every 3rd event - this is fine due to frequency that this event occurs
                }
                scan = 0;
                if ($$.config.selectNode === "circle") {
                    circleSearch.call($$, getTargetsToScan(targets, $$.config.selectedClass, $$.rect.bothDimensionsIncreasing(), $$.rect.bothDimensionsDecreasing()) , $$.rect.getDimensions(), $$.rect.node);
                } else if ($$.config.selectNode === "rect") {
                    rectSearch.call($$, getTargetsToScan(targets, $$.config.selectedClass, $$.rect.bothDimensionsIncreasing(), $$.rect.bothDimensionsDecreasing()), $$.rect.getDimensions(), $$.rect.node);
                } else if ($$.config.selectNode === "path") {
                    pathSearch.call($$, getTargetsToScan(targets, $$.config.selectedClass, $$.rect.bothDimensionsIncreasing(), $$.rect.bothDimensionsDecreasing()), $$.rect.getDimensions(), $$.rect.node);
                } else if ($$.config.selectNode === "line") {
                    lineSearch.call($$, getTargetsToScan(targets, $$.config.selectedClass, $$.rect.bothDimensionsIncreasing(), $$.rect.bothDimensionsDecreasing()), $$.rect.getDimensions(), $$.rect.node);
                }
                if (clickedNode) clickedNode.classed($$.config.selectedClass,true);
                if ($$.config.onSelect) {
                    var foundThisScan = node.selectAll(nodeSelector).filter("." + $$.config.selectedClass); // do not filter "targets" here due to "targets" possibly not containing all elements (in case of multiselect)
                    if (currentSelection.size() !== foundThisScan.size()) {
                        $$.config.onSelect.call(node, foundThisScan/*, currentSelection*/); // leaving this out for now
                    }
                    currentSelection = foundThisScan;
                }
            }
        })
        .on("mouseup", function(d, i, a) {
            if ($$.rect)  {
                $$.rect.remove();
                delete $$.rect; // delete to prevent entering mousemove event's main body after finishing, and this event
                clickedNode = null;
                if ($$.config.onDragEnd) {
                    var selected = node.selectAll(nodeSelector).filter("." + $$.config.selectedClass); // do not filter "targets" here due to "targets" possibly not containing all elements (in case of multiselect)
                    $$.config.onDragEnd.call(selected, selected);
                }
            }
            scan = 3;
        });
    }

    function Rect(anchorPoint, transform, className) {
        this.anchor = { x: anchorPoint[0], y: anchorPoint[1] };
        this.transform = transform;
        this.className = className;
        this.d3 = window.d3;
        this.dimensions = {};
        this.node = null;
        this.selection = null;
        this.widthIncreasing = false;
        this.heightIncreasing = false;
    }

    var r_fn = Rect.prototype;

    r_fn.getDimensions = function() {
        return this.dimensions;
    };
    r_fn.remove = function() {
        this.selection.remove();
    };
    r_fn.appendTo = function(node) {
        var d3Node = (node.select) ? node : this.d3.select(node);
        this.dimensions.x = this.anchor.x;
        this.dimensions.y = this.anchor.y;
        this.dimensions.width = 0;
        this.dimensions.height = 0;
        this.selection = d3Node
                        .append("rect")
                        .attr(this.dimensions)
                        .attr("transform", this.transform)
                        .style("pointer-events", "none !important")
                        .classed(this.className, true);
        this.node = this.selection.node();
    };
    r_fn.reDraw = function(mouseCoordinates) {
        var movement = {
                x: mouseCoordinates[0] - this.dimensions.x,
                y: mouseCoordinates[1] - this.dimensions.y
        };
        var oldHeight = this.dimensions.height;
        var oldWidth = this.dimensions.width;
        if (movement.x < 1 || movement.x * 2 < this.dimensions.width) {
            this.dimensions.width = Math.abs(this.anchor.x - mouseCoordinates[0]); // abs stops errors when moving extremely fast
            this.dimensions.x = mouseCoordinates[0];
        } else {
            this.dimensions.x = this.anchor.x;
            this.dimensions.width = movement.x;
        }
        if (movement.y < 1 || movement.y * 2 < this.dimensions.height) {
            this.dimensions.height = Math.abs(this.anchor.y - mouseCoordinates[1]); // abs stops errors when moving extremely fast
            this.dimensions.y = mouseCoordinates[1];
        } else {
            this.dimensions.y = this.anchor.y;
            this.dimensions.height = movement.y;
        }
        this.widthIncreasing = oldWidth <= this.dimensions.width;
        this.heightIncreasing = oldHeight <= this.dimensions.height;
        this.selection.attr(this.dimensions);
    };
    r_fn.bothDimensionsIncreasing = function() {
        return this.heightIncreasing && this.widthIncreasing;
    };
    r_fn.bothDimensionsDecreasing = function() {
        return !this.heightIncreasing && !this.widthIncreasing;
    };

    function getTargetsToScan(allTargets, filterClass, isRectIncreasing, isRectDecreasing) {
        if (isRectIncreasing) {
            return allTargets.filter(":not(." + filterClass + ")");
        }
        if (isRectDecreasing) {
            return allTargets.filter("." + filterClass);
        }
        return allTargets;
    }

    // This function is a fast and lazy approximation - for the purposes of what this tool is designed for, it works
    // It will not universally work - e.g. it works with translations, but not rotations or skews
    function applyMatrixTransformToRect(CTMmatrix, rect) {
        var xt = CTMmatrix.e + rect.x*CTMmatrix.a + rect.y*CTMmatrix.c,
            yt = CTMmatrix.f + rect.y*CTMmatrix.d + rect.x*CTMmatrix.b;
        return { x: xt, y: yt, width: rect.width * CTMmatrix.a, height: rect.height * CTMmatrix.d }; // width and height do not take rotation into account here...
    }

    function circleSearch(targetCircles, rect, rectNode) {
        var $$ = this;
        targetCircles
            .each(function(d, i, a) {
                var thisCircle = $$.d3.select(this);
                if (thisCircle.attr("r") !== "0") {
                    if (circleWithinArea({ x: thisCircle.attr("cx"), y: thisCircle.attr("cy"), r: thisCircle.attr("r") }, applyMatrixTransformToRect(rectNode.getTransformToElement(this), rect))) {
                        thisCircle.classed($$.config.selectedClass, true);
                    } else {
                    thisCircle.classed($$.config.selectedClass, false);
                    }
                } else {
                    if (rectWithinArea({ x: thisCircle.attr("cx")|0, y: thisCircle.attr("cy")|0, width: 0, height: 0 }, applyMatrixTransformToRect(rectNode.getTransformToElement(this), rect))) {
                        thisCircle.classed($$.config.selectedClass, true);
                    } else {
                    thisCircle.classed($$.config.selectedClass, false);
                    }
                }
        });
    }

    function rectSearch(targetRects, rect, rectNode) {
        var $$ = this;
        targetRects
            .each(function(d, i, a) {
                var thisRect = $$.d3.select(this);
                if (rectWithinArea(
                        { x: thisRect.attr("x")|0, y: thisRect.attr("y")|0, width: thisRect.attr("width")|0, height: thisRect.attr("height")|0 },
                        applyMatrixTransformToRect(rectNode.getTransformToElement(this), rect)
                        )
                   ) {
                    thisRect.classed($$.config.selectedClass, true);
                } else {
                    thisRect.classed($$.config.selectedClass, false);
                }
        });
    }

    function lineSearch(targetLines, rect, rectNode) {
        var $$ = this;
        targetLines.each(function(d, i, a) {
            var xformRect = applyMatrixTransformToRect(rectNode.getTransformToElement(this), rect),
                lineSelection = $$.d3.select(this);
            if (!rectWithinArea(this.getBBox(),xformRect)) { // if it isn't within the boundary box, don't bother scanning
                    lineSelection.classed($$.config.selectedClass, false);
                    return;
                }
                if (lineWithinArea( [
                        [ (lineSelection.attr("x1")|0)||0, (lineSelection.attr("y1")|0)||0 ],
                        [ (lineSelection.attr("x2")|0)||0, (lineSelection.attr("y2")|0)||0 ]
                    ], xformRect)) {
                    lineSelection.classed($$.config.selectedClass, true);
                    return;
                } else {
                    lineSelection.classed($$.config.selectedClass, false);
                }
        });
    }

    function pathSearch(targetPath, rect, rectNode) {
        var $$ = this;
        targetPath
            .each(function(d, i, a) {
                var xformRect = applyMatrixTransformToRect(rectNode.getTransformToElement(this), rect);
                if (!rectWithinArea(this.getBBox(),xformRect)) { // if it isn't within the boundary box, don't bother scanning
                    $$.d3.select(this).classed($$.config.selectedClass, false);
                    return;
                }
                var lineSegments = this.getPathData(),
                    previousPoint = lineSegments[0],
                    curSubPath = lineSegments[0],
                    lineSelection = $$.d3.select(this),
                    l = lineSegments.length, j = 0;
                while (++j < l) {
                    var segment = lineSegments[j];
                    switch (segment.type) {
                        case /*SVGPathSeg.PATHSEG_MOVETO_ABS*/"M":
                            curSubPath = segment;
                            break;
                        case /*SVGPathSeg.PATHSEG_LINETO_ABS*/"L":
                            if (lineWithinArea([previousPoint.values, segment.values], xformRect)) {
                                lineSelection.classed($$.config.selectedClass, true);
                                return;
                            }
                            break;
                        case /*SVGPathSeg.PATHSEG_CLOSEPATH*/"Z":
                            if (lineWithinArea([previousPoint.values, curSubPath.values], xformRect)) {
                                lineSelection.classed($$.config.selectedClass, true);
                                return;
                            }
                            break;
                        default:
                            var sampledLines = getResampledLine(this.cloneNode(), previousPoint, segment);
                            if (sampledLines.some(function(e) { return lineWithinArea(e, xformRect); })) {
                                lineSelection.classed($$.config.selectedClass, true);
                                return;
                            }
                    }
                    previousPoint = segment;
                }
                lineSelection.classed($$.config.selectedClass, false);
            });
    }

    function circleWithinArea(circle, area) {
        var half = { x: area.width/2, y: area.height/2 },
            center = {
            x: circle.x - (area.x+half.x),
            y: circle.y - (area.y+half.y)
        },
            side = {
            x: Math.abs (center.x) - half.x,
            y: Math.abs (center.y) - half.y
        };
        if (side.x >  circle.r || side.y >  circle.r)
            return false;
        if (side.x < -circle.r && side.y < -circle.r)
            return true;
        if (side.x < 0 || side.y < 0)
            return true;

        return side.x*side.x + side.y*side.y  < circle.r*circle.r;
    }

    function rectWithinArea(rect, area) {
        return rect.x < (area.x + area.width) &&
               (rect.x + rect.width) > area.x &&
               rect.y < (area.y + area.height) &&
               (rect.y + rect.height) > area.y;
    }

    function lineWithinArea(line, area) {
        var tl = { x: area.x, y: area.y + area.height },
            tr = { x: area.x + area.width, y: area.y + area.height },
            br = { x: area.x + area.width, y: area.y },
            bl = { x: area.x, y: area.y };
        return (endProject(line[0], line[1], tl, tr, br, bl) && cornersSameSide([bl,br,tr,tl], line[0], line[1])); // pass the cornors in array from bottom left c-clockwise to top left
    }

    function cornersSameSide(cornorsArr, lineStart, lineEnd) {
        var xC = lineStart[0] - lineEnd[0],
            yC = lineEnd[1] - lineStart[1],
            os = lineEnd[0] * lineStart[1] - lineStart[0] * lineEnd[1],
            v = cornorsArr[3].x * yC + cornorsArr[3].y * xC + os,
            sign = (v < 0 ? -1 : (v > 0 ? 1 : 0)),
            i = 3;
        if (v < 0) {
            sign = -1;
        }
        else if (v > 0) {
            sign = 1;
        }
        else {
            sign = 0;
        }
        while (i--) {
            v = cornorsArr[i].x * yC + cornorsArr[i].y * xC + os;
            if (v<0) {
                if (sign>0) {
                    return true;
                }
            } else if (v>0) {
                if (sign < 0) {
                    return true;
                }
            }
        }
        return false;
    }
    function endProject(lineStart, lineEnd, tl, tr, br, bl) {
        if (lineStart[1] > tr.y && lineEnd[1] > tr.y) return false;
        if (lineStart[1] < bl.y && lineEnd[1] < bl.y) return false;
        if (lineStart[0] > tr.x && lineEnd[0] > tr.x) return false;
        if (lineStart[0] < bl.x && lineEnd[0] < bl.x) return false;
        return true;
    }

    function getResampledLine(clonedNode, start, segment) {
        var lines = [],
            points = [],
            i = 0,
            segmentLength = getSegmentLength(clonedNode, start.values, segment),
            precision = 8; // increasing this will degrade performance but will increase the accuracy of the resampling. ** hard coded for the present **
        points.push(clonedNode.getPointAtLength(0));
        while ((i+=(1/precision)) <= 1) {
            var curPoint = clonedNode.getPointAtLength(i * segmentLength),
                pi = points.length - 1;
            lines.push([ [ points[pi].x, points[pi].y ], [ curPoint.x, curPoint.y ] ]);
            points.push(curPoint);
        }
        return lines;
    }

    function getSegmentLength(node, start, segment) {
        node.setPathData([{ type: "M", values: start }, segment]);
        return node.getTotalLength();
    }

    function pauseEvent(e){
        if(e.stopPropagation) e.stopPropagation();
        if(e.preventDefault) e.preventDefault();
        e.cancelBubble=true;
        e.returnValue=false;
        return false;
    }

    window.d3dragSelector = dragSelector;

})(window);

redpajama_set_name: RedPajamaGithub


Whether you're making a gift for upcoming Mother's Day or declaring yourself springtime royalty, all are welcome to create a custom flower or greenery crown. Grades 6-12. Required registration begins March 26.


text:
PriceChecker is a UK price comparison website allows you to compare prices of electronics, entertainment, household goods, fashion and more. Store owners can benefit from having their products featured on the website as not all products have competition. Some products on the site only have one retail price while others allow customers to compare prices.
Cruises.com via ShermansCruise offers a Carnival Cruise 5-Night Bahamas Cruise for Two with prices starting at $508. (On the ShermansCruise landing page, scroll down and click "Book Now" next to the December 3 sailing date.) That's the lowest price we could find for such a cruise in Decemberby $50. This roundtrip cruise aboard the Carnival Elation departs from Jacksonville, FL, on December 3. Book this travel deal by November 30.
This essential book helps you find exactly what you want on the Web in no time flat--without having to struggle with slow downloads or the endless, irrelevant listings on search engines. From well-known giants like Amazon.com to promising upstarts like Plato's Toybox, these sites run the gamut of products and services, size and price. But they all have one thing in common: they're the best the Web has to offer. Inside you'll find
Looking for a portable hard drive for your laptop or PC? You might be interested to know that Amazon currently has the LaCie Porsche Design 2TB Mobile Hard Drive on sale for $94.99. That's a savings of $55 off the drive's $150 list price. With USB-C and USB 3.0 compatibility, this drive offers simple plug and play installation with virtually any PC or Mac from the past or future. When you're on the go and time is scarce, fast transfer speeds up to 5GB/s lets you drag and drop files quickly. This hard drive packs a generous 2TB of storage making it perfect for photographers, gamers, graphics artists, and video editors.
All Better via Amazon offers the QQwow Beard Grooming Kit for $16.99. Coupon code "ILYRWJMO" cuts that to $10.19. Plus, Prime members bag free shipping. That's the lowest price we could find now by $2, although we saw it for slightly less in July. It includes a 2-oz. bottle of beard oil, 2.8-oz. tin of beard balm, and sandalwood comb. Deal ends October 31.
HomeCrate HC-2340- The Amazon leaves design brings the lush beauty of the tropics to your home, the stunning leaves design brings a bright, fresh, sunny look to your table, this fresh contemporary design will surely brighten up your home decor. Features: -Amazon collection.-Made of 100% polyester water repellent spills bead up fabric for indoor, outdoor use, with a hard wearing, seamless construction, that won't easily fray even after long term use.-Dress up your table top and update your home with this attractive tablecloth that will enhance and brighten up your home, and will surely add a beautiful touch to your table decor.-Machine wash cold with like colors.-Only non-chlorine bleach when needed.-Tumble dry low heat.-Cool iron if necessary.-It is fade resistant and will retain the... Less
Certified Refurbished Amazon Fire TV with Alexa Voice Remote Features A Certified Refurbished Fire TV Stick is refurbished, tested, and certified to look and work like new Amazon Fire TV now supports 4K Ultra HD for true-to-life picture quality. Watch high-definition 1080p streams on Amazon Video, Netflix, Hulu and more, even without a 4K TV. Now with Alexa, use your Fire TV Voice Remote to check sports scores or the weather, play music, and more - instantly Enjoy over 3,000 channels, apps, and games including access to over 250,000 TV episodes and movies on Netflix, Amazon Video, HBO GO, Hulu, and more. Cutting the cord? Watch the best of live TV on NBC News, NBA, and Sling TV, which includes ESPN, CNN, HGTV, AMC, A&E, Cartoon Network, and more. Now with 75% more processing power, a... Less
template test Product Carrying Case for Amazon Tap Features Customized dress for your Amazon Tap. Premium synthetic leather with soft non-scratch microfiber interior. Provides protection for the aluminum body against scratches, greasy dirt and abrasions. Integrated with durable nylon fabrics which act as acoustic vents to deliver all the high-quality sound expected from the Amazon Tap. Removable carrying strap and keychain design makes it easy to move your Tap. Aluminum button closure adds stability. Easy to install and secure. Exquisite design and available in a variety of bright, fun colors. Can be placed anywhere as a decoration. Payment We only accept PayPal at this time. PayPal is quick, convenient and safe. Your payment is traceable and sellers do not have access to your credit... Less
Since its founding, the company has attracted criticism and controversy from multiple sources over its actions. These include: supplying law enforcement with facial recognition surveillance tools;[154] forming cloud computing partnerships with the CIA;[155] luring customers away from the site's brick and mortar competitors;[156] placing a low priority on warehouse conditions for workers; participating in anti-unionization efforts; remotely deleting content purchased by Amazon Kindle users; taking public subsidies; claiming that its 1-Click technology can be patented; engaging in anti-competitive actions and price discrimination;[157] and reclassifying LGBT books as adult content.[158][159] Criticism has also concerned various decisions over whether to censor or publish content such as the WikiLeaks website, works containing libel and material facilitating dogfight, cockfight, or pedophile activities. In December 2011, Amazon faced a backlash from small businesses for running a one-day deal to promote its new Price Check app. Shoppers who used the app to check prices in a brick-and-mortar store were offered a 5% discount to purchase the same item from Amazon.[160] Companies like Groupon, eBay and Taap.it countered Amazon's promotion by offering $10 off from their products.[161][162] The company has also faced accusations of putting undue pressure on suppliers to maintain and extend its profitability. One effort to squeeze the most vulnerable book publishers was known within the company as the Gazelle Project, after Bezos suggested, according to Brad Stone, "that Amazon should approach these small publishers the way a cheetah would pursue a sickly gazelle."[122] In July 2014, the Federal Trade Commission launched a lawsuit against the company alleging it was promoting in-app purchases to children, which were being transacted without parental consent.[163]
:: eBay Listing Template :: MoKo Case for All-New Amazon Fire HD 8 Tablet (7th Generation, 2017 Release Only)-Lightweight Slim Shell Stand Cover with Translucent Frosted Back for Fire HD 8, BLACK (with Auto Wake/Sleep) For lots of other items, visit My Store Description Compatibility: Custom designed for your precious All-New Amazon Fire HD 8 2017 Tablet, this MoKo case features a combination of functionality and style. Well built to protect your All-New Amazon Fire HD 8 2017 Tablet for the years to come. Product Description: Automatically wakes or puts All-New Amazon Fire HD 8 2017 Tablet to sleep when the lid is opened and closed. Access to all features and controls. Slim lightweight hardback adds minimal bulk while protecting your precious device. High Quality, Stylish and Durable,... Less
At Verizon Wireless, buy one iPhone XR, XS Max, XS, or X with monthly device payments and get an iPhone XR or X 64GB phone for free. (Alternately, get $750 off any other XR model.) Plus, all orders receive free shipping. A new line is required for one of these phones, and you'll receive a $31.25 monthly bill credit for 24 months, beginning one to two billing cycles after purchase.
Our favorite is DealNews.com, which has a team of deal hunters who keep their eyes on a million products at more than 2,000 reputable online retailers and update the site with new deals at least 200 times a day. Plus, it works with merchants to offer exclusive deals you won't find elsewhere. There's a picture of each product and ample information, including the original price, sale price and whether it's the lowest price DealNews has found for the product. You can sign up for e-mail alerts for products or stores you're interested in and get shopping advice from the site's buying guides.
BuyDig offers the Sony 69.5" 4K HDR Flat LED Ultra HD Smart Television, model no. XBR70X830F, bundled with a $150 Visa Gift Card for $1,598 with free shipping. (The gift card will be emailed within six to eight weeks.) Assuming you'll use the gift card, that's $52 under our mention from two weeks ago and the lowest price we could find now by $150. This 2018 model features:
In November 2013, Amazon.com announced a partnership with the United States Postal Service to begin delivering orders on Sundays. The service, included in Amazon's standard shipping rates, initiated in metropolitan areas of Los Angeles and New York due to the high-volume and inability to deliver timely, with plans to expand into Dallas, Houston, New Orleans and Phoenix by 2014.[79]
Amazon has separate retail websites for the United States, the United Kingdom and Ireland, France, Canada, Germany, Italy, Spain, Netherlands, Australia, Brazil, Japan, China, India, Mexico, Singapore, and Turkey. In 2016, Dutch, Polish, and Turkish language versions of the German Amazon website were also launched.[7][8][9] Amazon also offers international shipping of some of its products to certain other countries.[10]

redpajama_set_name: RedPajamaCommonCrawl


If you really want long and healthy nails then you are at right place. There is no easy way to speed up the nail growth. Nails grow naturally about 1 millimeter per month. Having long, strong and beautiful nail is desire of every young lady. However because of some negative behavior of pattern, bad eating habits, the kind of work, using poor products, after this maintaining healthy and good looking nails can be a bit hard. The cuticle is the most important part of your nail as it fights against infection. Cuticle damage can also damage your nail.
If you are getting sick of having short little nails then don't worry we have some natural ways by which you can grow your nails faster within a week. This remedy will improve your nail quality and helps in making them strong and long and healthy.
Take a bowl and add lemon juice and garlic together to it and then leave the solution for about 5 minutes. After this let your nails soaked in this solution for about 10 minutes and then take out your nail and rub it with garlic as garlic improves the nail strength. Lemon has an antioxidant property which is very beneficial in improving the nail quality and also helps your nail to grow fast. It also contains citric acid which maintains the smoothness of your nail.
Take some coconut in a bowl and massage your nail with it for about 5 minutes and then let your nail soak in it for 5 minutes. After take out your finger and pat dry it. Repeat both the steps once in a day for more effective result.


text:
Paul W. Smith
Frank Beckmann
Guy Gordon
Specials @ 7
Weekend Experts
News Anchors & Reporters
Dick Haefner
Marie Osborne
Gene Fogel
Ken Rogulski
Dayna Clark
WJR Auto Assembly Line
Gaylord Area Winter Update
WJR Golf
WJR Travel Club
WJR's Harbor Tour
WJR News
Advertise on WJR
GridLine Sports ~ Fridays 7 – 8 p.m.
GridLine Sports brings you all of the important topics in the world of sports. Join WJR's Steve Courtney and former Detroit Lions Offensive Tackle Lomas Brown as they cover both local and national pro, college, and high school sports. Lions, Tigers, Wings, Pistons, Spartans, Wolverines, and more … they're all here!
MORE PODCASTS>>>
Born and raised in Detroit, Steve Courtney is a lifetime Detroit fan in what he refers to as, "America's Greatest Sports City."
Steve signed on at News/Talk 760 WJR-AM in 1998, having previously worked as an on-air DJ at several other Detroit radio stations.
Known for his humor and positive attitude, Steve's sports reporting is infused with both information and entertainment; a style that has earned him respect and likeability by players, coaches, guests, and colleagues.
Lomas Brown is a former first round pick from the University of Florida in the 1985 NFL Draft. The Detroit Lions selected Brown sixth overall and he played for the Lions for 11 of his 18 NFL seasons. Brown also won a Super Bowl with Tampa Bay in 2002, and was named to the Pro Bowl Team seven straight seasons from 1990 to 1996.
Today, Lomas is a as color analyst for the Lions' game day broadcasts on News/Talk 760 WJR


A member of the Harbro Group, Harbro Ltd provides innovative high quality livestock solutions, focused on improving animal performance and its customers' profitability.
Privately-owned, the business trades throughout the UK and has developed an increasingly strong export market through its international division. With a manufacturing capacity of 280,000 tonnes from sites based in Scotland, the business produces high quality livestock feeds for beef, sheep, dairy, pig and poultry.
In addition, Harbro Ltd also has a network of 19 retail country stores throughout Scotland from the Borders to Shetland supplying both the farming and wider rural community with animal health products, farming supplies, equestrian and pet food and accessories.
The Harbro Group also includes Strathclyde Nutrition Ltd, which contract manufactures animal feed buckets and blocks; and Transpan (Scotland) Ltd.
Up until 2010 Harbro was using a semi-mainframe enterprise system to help run its business. System 21 had been in place for over 20 years. To supplement this, they were using additional databases with web front ends and spreadsheets.
This way of working caused a number of issues and the system was proving expensive in terms of annual maintenance costs. Furthermore, it was not delivering the reporting and the data they required to inform their decision making and effectively run the business.
Due to its age, the number of add-ons that had been developed over time and various infrastructure issues, it was not an option to purely upgrade the existing system. Harbro needed something new that was flexible to meet the needs of an ever evolving business and capable of dealing with the large number of growing transitions.
Harbro considered several ERP solutions before narrowing their search to Dynamics AX.
They also evaluated different Dynamics AX partners with experience and modules in food manufacturing as they had a number of requirements, including complex pricing of packaged products, which they thought would be hard to meet out of the box.
In parallel, Incremental Group invested time and the right local people to collaborate with Harbro in order to truly understanding their issues and requirements. Incremental Group thought creatively about how to use standard features of AX, not originally designed to manage animal feed, to deliver a smart solution. Incremental set up a demo, using Harbro data, to demonstrate how their specific requirements could be accommodated in Dynamics AX without the need for any add on modules. Addressing all requirements leveraging standard AX functionality would be more cost effective than having to purchase additional modules and meant that they were not tied in to the provider.
Harbro subsequently selected Dynamics AX as their new ERP system and Incremental Group as their delivery partner for design, implementation and support.
The new system has transformed working right across Harbro and them become a more efficient, informed and flexible organisation.
Basic tasks like the completion of monthly accounts used to take 2 weeks to complete. These are now closed off in 2 days meaning the time can be better put to more valuable use.
Harbro has to make complex changes to pricing every month, and when markets are volatile, even more frequently. This previously labour intensive process which used to take several days is now automated to complete in a few minutes.
The new stock control capabilities and integrated store planning inform manufacturing – Harbro now know exactly how much they need to be producing to meet their orders. Consequently, the volume of feed and medicines that goes to waste has been slashed. Furthermore, the business is no longer under producing during peak periods, resulting in additional sales.
Harbro now has an established standard model in which to easily and quickly integrate acquisitions, regardless of size or international location.
In addition to rolling the system out to recently acquired stores, Harbro is in the process of integrating all counter sales and manufacturing.
This year Harbro started to expand its operations internationally, with the acquisition of a distribution company in Italy. Dynamics AX is fully geared up this with its country specific capabilities for managing localised requirements including tax and banking rules. The system is in the process of being rolled out to the Italian operation.
Harbro is also currently evaluating moving into China to expand its manufacturing operations.


Concierges are hotel, apartment or office building employees who typically assist guests with personal services. Responsibilities may include taking messages, dispensing advice on transportation or local sites of interest, and monitoring guest requests for maintenance and assistance. Becoming a concierge typically requires a high school diploma and some previous work experience. To become successful in this field, concierges should possess a penchant for working with people and multitasking.
As an employee in guest services, concierges must be prepared to meet guests' needs. A service-oriented personality is essential, as they need to anticipate guests' desires and concerns. Customer service responsibilities include making dining reservations and travel arrangements, running errands and providing information about the local area. Concierges may also be expected to fulfill unusual guest requests, such as searching for hard-to-find items or booking exotic services, like a hot-air balloon ride, according to ONetOnline.org.
Concierges should possess above-average communication skills. Their attitude, speaking and listening skills will be a reflection on their employers. They must be able to convey information effectively and with care, whether they're offering recommendations on nearby restaurants or trying to solve guest problems. They'll also need to relay requests from guests to other staff members.
Any given day working in a hotel will likely be different from the next. Concierges handle a number of standard tasks, but will likely have to deal with different guests, who come with their own set of unique needs. As such, they should be skilled in time management and planning. They need to be available to all guests for questions and concerns during their shifts, while also carrying out the requests of other guests. For example, a concierge might need to arrange for the replacement of a traveler's missing items, while someone else is asking for a delivery to her room. Being able to juggle these requests efficiently is a testament to a concierge's multitasking ability.
Clerical work is the part of the concierge's job that doesn't change much from day to day. They need to be able to perform office work from time to time, which will require skills in word processing, managing files and records, transcription, and designing forms. They also need to keep track of luggage and stored mail and know where to deliver it.
April Marie has written and edited for newspapers, magazines and newsletters. She covers business communication, management, technology, art, health and fashion. Marie earned her B.A. in journalism from New York University.
What Are the Duties of a Doorman?
Marie, April. "Skills to Work as a Concierge." , https://careertrend.com/info-8656842-skills-work-concierge.html. 05 July 2017.
What Are the Duties of a Concierge?


text:
U.S. WW1 Army Helmet Shell - 326th Infantry
U.S. WW1 326th Infantry Patrotic painted helmet shell. Appears to be named on interior (Matthew Lewis). Chinstrap metal guiding loops held on by solid machined rivets. Only a portion of chinstrap left that been cut down and stapled together appears to be for display. Stamped on interior rim of helmet with the heating lot number of ZH 81.
Additional Notes: The United States Army purchased the 400,000 available British Mk. I helmets from England and issued them to the American Expeditionary Forces already in Europe until the production of the U.S. M-1917 (P17) could start. Production began on the M-1917 helmets in the fall of 1917. By the end of November 1917, large quantities of U.S. M-1917 helmets became available for the U.S. Army. The U.S. M-1917 helmet was very similar to the British Mk. I helmet. One differnce was riveted to the steel bowl were two flexible guiding loops for the chin strap. For this, the U.S. M-1917 helmet differed from the British Mk. I helmet in that on U.S. helmets the loops were secured by solid machined rivets, where the British Mk. I helmet used split rivets. Also, both the helmets may have markings on the underside of the helmet skirt. On British Brodie helmets this is typically a series of Letters (for the steel manufacturer) follow by followed by a series of numbers (for the lot number). On U.S. WW1 M1917 (P17) helmets typically there are heating lot numbers, usually, starting with a Z followed by another letter and ending with a series of numbers. The known heat-stamps for WW1 American helmet include, but not limited to: ZA, ZB, ZC, ZD, ZF, ZH, ZJ, YJ, UC, and XH. Also, the American helmets are made of a thicker guage steel and therefore heavier then their British counterparts as well.
See more U.S. Military Helmets
Item # HU-946


text:
The Shaker Hymn Drop New Single
By: Lucas Dean
The band's upcoming EP will be out April 10.
Cork-based The Shaker Hymn has released a new single, 'Brother (Love in Flight)'.
The Shaker Hymn's indie rock flare is on full display in 'Brother (Love in Flight)'. the first single off of their upcoming EP. The release of The Shape Of The Sea will follow the band's previous albums Do You Think You're Clever? and Rascal's Antique.
The five-track EP is made of song that were written while the band were on a tour in the US, as well a new songs. It will be released on April 10. The Shaker Hymn's will be performing several acoustic shows across Ireland, including The Workman's Club in Dublin on April 13, Coughlan's Bar in Cork on April 14 and The Record Room in Limerick on May 3.
Listen to 'Brother (Love in Flight)' below:
Album Review: SYML, SYML
Live Report: PowPig And Junior Brother At The Grand Social
Interpol Release New Single 'The Weekend'
Beck And Cage The Elephant Release Single 'Night Running'
New Single and Video 'Coldest Water' from Walking on Cars
Live Report: Bastille At The Olympia Theatre
Premiere: Fox Jaw Release New Single Madeline
Album Review: Jon Fratelli, Bright Night Flowers
Empire Circus: The Dublin alt-rock band talk getting things right on album No. 2


text:
\section{Introduction}

As a fundamental NLP task,  semantic role labeling (SRL), also known as shallow semantic parsing, aims to capture the major semantic information of a sentence based on predicate-argument  structure. Basically, SRL tries to answer ``who did what to whom where and when'' \citep{marquez-2008-semantic}. Previous works have shown that SRL can help various downstream tasks, including information extraction \citep{Emanuele-jssp-ie}, plagiarism detection \citep{Paul-etal-2015-Plagiarism}, machine translation \citep{shi-etal-2016-knowledge}, reading comprehension
\citep{Zhang-etal-2020-Semantics-aware}, etc.

Figure \ref{fig:raw-srl} gives two examples of SRL structure.
According to the definition of semantic roles, there  exist two typical representation forms, i.e.,  the word-based and the span-based.
This work adopts the word-based form, in which an argument corresponds to a single word.
In contrast, span-based SRL, adopted by most previous datasets, takes a word span as an argument. The  direction of arcs is from predicates to arguments, and the labels indicate the types of semantic roles. For example, the arc from ``买 (bought)'' to ``裙子 (dress)'' with a label ``patient'' means that the semantic role between the predicate ``买  (bought)'' and the argument ``裙子 (dress)'' is ``patient''.

\input{images/example-srl.tex}

Recently, Chinese SRL research has achieved tremendous  progress, thanks to the rise of deep learning methods \citep{Marcheggiani2017ASA,  He-2018-SyntaxFS, cai-2018A-srl}, especially of powerful pre-trained language models (PLMs) \citep{shi-2019-simple, conia-navigli-2020-bridging, paolini-2021-structured}.
However,
existing studies on Chinese SRL mainly focus on the in-domain setting, where training and test data are from the same domain \citep{ZhenWang2015ChineseSR, Guo2016AUA, Xia-2017-CSB}. SRL performance drops dramatically when the domain of test data is different from that of the training data, known as the domain adaptation problem.

Meanwhile, with the rapid growth of user-generated web  data, cross-domain SRL has become an important and challenging task in realistic NLP systems \citep{Jiang-2007-InstanceWF, Ramponi-2020-NeuralUDA}.
However, due to the scarcity of
multi-domain labeled data, recent research on SRL
makes very limited progress in the domain adaptation scenario.

As far as we know, there are three publicly available  Chinese SRL  datasets, i.e., Chinese Proposition Bank (CPB) \citep{Xue-2007-CPB}, Chinese NomBank (CNB) \citep{Xue-2006-CNB}, and Chinese SemBank (CSB) \citep{Xia-2017-CSB}.
All these datasets mainly contain canonical texts from newspapers or magazine/textbook articles.

\input{images/example-2.tex}

In order to facilitate research on cross-domain SRL,  this paper presents MuCPAD, a multi-domain Chinese  predicate-argument dataset, consisting of 30,897 sentences and 92,051 predicates, from 6  different domains.
Overall, MuCPAD has the following important features.
\begin{enumerate}[label =(\arabic*),leftmargin=*]
\item Following CSB instead of CPB and CNB, we adopt a frame-free annotation methodology,  considering that it requires a very high level of linguistic background to define new frames for new  predicates or new senses, and
a lot of new predicates or new senses may appear in \textcolor{black}{multi-domain texts}.
\item \textcolor{black}{As shown in Figure \ref{fig:raw-srl-2}, we explicitly annotate omitted core arguments with two special labels, i.e., ``hidden-subject'' and ``hidden-object'', in order to capture richer semantics expressed by  predicates.
It is ubiquitous that people try to avoid repetition by omitting previous content in context, especially in non-canonical Chinese texts.
}

\item We adopt strict double annotation for all sentences in order to improve quality. If two annotators submit inconsistent results, a senior annotator determines the final answer.
We also compile 53 pages of annotation guidelines to be studied and referred to by the annotators.
\end{enumerate}

Based on our newly annotated MuCPAD, we conduct preliminary cross-domain SRL experiments and analysis.
We enhance the basic SRL model by exploiting CPB2.0 as a heterogeneous dataset under the multi-task learning (MTL) framework, and by utilizing powerful
contextualized word representations from pretrained language models (PLMs).

\textcolor{black}{We release MuCPAD along with our annotation guidelines for research usage at   \href{https://github.com/SUDA-LA/MuCPAD}{https://github.com/SUDA-LA/MuCPAD}.}

\iffalse
In real texts, especially Chinese ones, there are often omissions in sentences to avoid repetition and highlight new information in context. As shown in Figure \ref{fig:raw-srl-2}, the sentence is the answer to the other sentence ``Yesterday, I bought a new dress, how about you ?''. In this context, ``衣服 (dress)'' is the omitted arguments for the predicate ``买 (bought)'' in context. To label the omitted arguments can help reasoning of dialogue, subject recovery in machine translation, etc.
With the surge of web data, word ellipsis phenomena appear in more and more domain texts.
However, none of the above corpora annotate the omitted core arguments. Though the omitted core arguments in CPB and CNB can be implicitly inferred from the frames, the definition of frames is both time and labor-intensive.
To reduce the loss of information, we propose the new guidelines, which use two hidden labels to label the omitted core argument: ``hidden-subject'' and ``hidden-obj'' for helping model to  understand the meaning of the sentence better.
\fi

\section{Approach}
Based on our newly annotated multi-domain Chinese SRL data, we conduct preliminary experiments, aiming to provide benchmark results.
Specifically, we present a simple basic SRL model and enhance the model with the contextualized word representations from BERT for further improvements.
Besides, we also present a MTL framework to improve the SRL performance by learning from multiple heterogeneous datasets simultaneously \citep{conia-etal-2021-unifying}.

In this work, we focus on the \emph{predicate-given} setting, which means we do argument identification and classification according to the given predicates in one sentence.

Following previous works \citep{cai-2018A-srl, zhang-etal-2019-syntax}, we treat the predicate-given SRL task as a word pair  classification problem and try to find the predicate-argument structure $\hat{\bm{y}}$ with the highest score:
\begin{equation}
\hat{\bm{y}} = \argmax_{\bm{y} \in \mathcal{Y}(\bm{x})} score(\bm{x}, \bm{y})
\end{equation}
where $\mathcal{Y}(\bm{x})=\{(i,j,r)| i\in \mathcal{P}, 1\leq j \leq n, r \in \mathcal{R}\}$ represents the set of all possible predicate-argument pairs.
$\mathcal{P}$ is the set of given predicates, $n$ is the number of sentence, and
$\mathcal{L}$ is the semantic role label set, which contains 24 semantic role labels and an extra ``None'' label to indicate there is no semantic relationship between  the given predicate and the $j$-th word.

\input{images/base-model.tex}
\subsection{Basic SRL Model}
Inspired by previous works \cite{cai-2018A-srl, zhang-etal-2019-syntax}, we build a basic SRL model that utilizes the biaffine attention mechanism \cite{dozat2016deep} to score each candidate predicate-argument pair.
Figure \ref{fig:base-model} shows the architecture of the basic model.
During both training and evaluation, multiple predicates in the same sentence are handled simultaneously. First, the input sentence is encoded; then, scores between predicates and all other words are computed; finally, the roles of each predicate are determined via local classification.

\textbf{The input vector} is the concatenation of the pre-trained word embedding $\mathbf{e}_{i}^{pre}$, the randomly initialized word embedding $\mathbf{e}_{i}^{r}$, the character-based  word representation $\mathbf{r}_{i}^{c}$, and the  predicate indicator embedding $\mathbf{e}_{i}^{p}$.
$$\mathbf{x}_i =  \mathbf{e}_{i}^{pre}\oplus \mathbf{e}_{i}^{r} \oplus  \mathbf{r}_{i}^{c} \oplus  \mathbf{e}_{i}^{p}$$
where $\mathbf{r}_{i}^{c}$ is produced by CNN, and the Boolean predicate indicator is true only for words that are given predicates.

\textbf{A three-layer BiLSTM} is applied to obtain  context-aware representation of each word, i.e., $\mathbf{h}_{i}$.

\textbf{Two separate MLPs} are applied over $\mathbf{h}_{i}$ to get two lower-dimensional representation $\mathbf{h}_{i}^{pred}$ (as predicate) and $\mathbf{h}_{i}^{arg}$ (as candidate argument).

\textbf{Biaffines} are used to compute scores of  labels between a predicate and a word.

During training, we adopt the local cross-entropy loss.
To obtain cross-domain results on the basic SRL model, we train the model on source domain data and make predictions on target domain data.

\subsection{Enhancing with BERT}
Recently proposed PLMs,
such as BERT \citep{Devlin-2019-BERT},
have shown the great power in learning and capturing  contextualized representations and have proven to be beneficial in a  variety of NLP tasks, such as information retrieval \citep{Yang-2019-SimpleAO}, question answering \citep{Yang-2019-EndtoEndOQ}, and word segmentation \citep{Hung-2020-TowardsFA}.
In this work, we extract the fixed contextualized representations from BERT for words and treat them as additional features to augment the input representation, i.e., $\mathbf{x}_i =  \mathbf{e}_{i}^{pre}\oplus \mathbf{e}_{i}^{r} \oplus    \mathbf{r}_{i}^{c} \oplus  \mathbf{e}_{i}^{p} \oplus  \mathbf{e}_{i}^{BERT} $.

\input{images/mtl}
\input{images/tbl-data-statistics}

\subsection
Utilizing Heterogeneous Data
with MTL}
MTL is a commonly used method to improve the model performance by learning the underlying knowledge from multiple related tasks or datasets \citep{Collobert-2008-AUA, Guo-2016-AUF, Li-2019-SemisupervisedDA}.
In this work, we design a MTL framework to utilize heterogeneous SRL datasets to boost the  SRL model performance.

\input{images/result-baseline}
As shown in Figure \ref{fig:mtl}, we extend the basic SRL model to the MTL framework.
Specifically, the SRL parsing on MuCPAD data and CPB2.0 data are considered as two separate tasks. They share the same word/predicate embeddings and BiLSTM parameters. Over the shared BiLSTMs, two separate MLPs and biaffines are employed for MuCPAD and CPB2.0 SRL parsing respectively.

\section{Model}

To facilitate our presentation in the following sections, we first formulate our basic SRL model. Following \cite{cai-2018A-srl}, we treat the SRL parsing task as a sequence labeling problem and introduce a "None" label to indicate that there is no semantic relationship between word and predicate.
As shown in Figure \ref{fig:base-model}, the model mainly contains a deep BiLSTM encoder and a biaffine attentional scorer, which is described as follows.
\input{images/base-model}
\textbf{Input layer.}
 Given an input sequence $s = w_1, w_2, ..., w_n$, for each token $w_i$, we concatenate the pre-trained word embedding $\mathbf{e}_{i}^{pre}$, the randomly initialized word embedding $\mathbf{e}_{i}^{r}$, the char embedding $\mathbf{r}_{i}^{c}$ and the predicate indicator embedding $\mathbf{e}_{i}^{p}$ as our basic model input, where $\mathbf{r}_{i}^{c}$ are encoded from word characters with CNNs and $\mathbf{e}_{i}^{p}$ is used to indicate whether the word is predicate. Besides, since \citep{cai-2018A-srl} demonstrated that the impact of part-of-speech (POS) tag embedding is less compared to the previous works for this task, we remove it from the word representation. The i-th word is represented as follows:
\begin{equation} \label{eq:concate}
\mathbf{x}_i =  \mathbf{e}_{i}^{pre}\oplus \mathbf{e}_{i}^{r} \oplus    \mathbf{r}_{i}^{c} \oplus  \mathbf{e}_{i}^{p}
\end{equation}

\textbf{BiLSTM encoder.}
The model applies three-layers BiLSTM as an encoder to obtain context-aware representations of each $\mathbf{x}_{i}$, in which the output is denoted as $\mathbf{h}_{i}$.

\textbf{Biaffine attention role scorer.}
Two separate MLPs are applied to each $\mathbf{h}_{i}$, then, we will obtain two vectors with smaller dismensionality $\mathbf{h}_{i}^{pred}$ (as predicate) and $\mathbf{h}_{i}^{arg}$ (as candidate arguments). Finally, the scores of the roles between predicate and argument is computed via a biaffine operation with two distinguish bias terms

\textbf{Training loss.}
For each $\mathbf{w}_{i}$ and its predicates $\mathbf{w}_{j}$ and semamtic role label $\mathbf{l}$, we minimize the negative log likelihood of the gold structure and label.

\section{Related Work}

\paragraph{English SRL Data.}

Large-scale annotated data is a prerequisite to develop high-performance SRL systems \citep{Frstenau-2009-SemiSupervisedSRL, xia-etal-2020-semantic}.
The most representative ones in English are FrameNet \citep{FrameNet-1998}, PropBank \citep{Kingsbury-2002-PropBank}, and NomBank \citep{Meyers-2004-NomBank}.
FrameNet is a large-scale manually annotated semantic lexicon resource and uses semantic frames to represent meanings of words. A frame corresponds to a sense of a word, and defines the specific meanings of its core roles (i.e., ``A0-A5''). In other words, labels for core semantic roles have predicate-sense-specific  meanings.

PropBank and NomBank are built by adding  predicate-argument structures to the constituents of syntactic parser trees in Penn Treebank \cite{Marcus-1993-treebank}.
Their semantic roles are naturally span-based, instead of word-based.
PropBank considers verbal predicates, while NomBank supplements nominal predicates.
Following FrameNet, PropBank and NomBank use frames to represent semantic meanings of predicates.
However, the development of frames is both time-consuming and labor-intensive, and requires annotators to be equipped with strong linguistic background.

The texts of PropBank and NomBank are mainly from the news domain, i.e., Wall Street Journal, except 426 sentences from the Brown corpus, which is usually used as an out-of-domain section of PropBank.

It is also noteworthy that there are PropBank-style SRL data for other languages, such as Portuguese \citep{Duran-2011-PropbankBrAB, Duran-2012-PropbankBrAB}\textcolor{black}{, Arabic \citep{pradhan-etal-2012-conll}, Finnish \citep{Haverinen-2015-TheFP}, and Turkish \citep{Sahin2018AnnotationOS}.}

\input{images/tbl-label.tex}

\paragraph{Chinese SRL Data.}
CPB \citep{Xue-2007-CPB}, CNB \citep{Xue-2006-CNB}, and CSB \citep{Xia-2017-CSB} are the three publicly available SRL datasets in Chinese.
CPB and CNB, corresponding to PropBank and NomBank in English respectively, \textcolor{black}{add  predicate-argument structure of verbal predicates and  nominal predicates} into Penn Chinese Treebank \citep{Marcus-1993-treebank}.
The semantic roles are based on pre-defined frames as well.
Moreover, sentences in
CPB and CNB
mainly come from canonical texts, such Xinhua newswire, Hong Kong news, and Sinorama Magazine \citep{Hajic-2009-TheCS}.

In contrast, CSB uses general-purpose role labels, such as ``agent'' and ``patient'', and the sentences are mainly from canonical texts such as online articles and news as well.

\paragraph{Domain adaptation.}
Domain adaptation has been an important and challenging research topic in NLP \citep{daume-p07-feature-sharing, Ganin-15-ICML-Gan-ADV,
Guo-2016-AUF,
Kim-acl2017-adversary-synthetic-stale, kevin-2018-emnlp-coview, zhao-18-ICLR-Adv-multi-source}.

\citet{kim-16-fa-conling} proposed a neural shared-private model for the cross-domain slot sequence tagging task, which utilizes separate BiLSTM encoders to obtain domain-invariant and domain-specific representations, achieving significant improvements on all domains.
\citet{acl2019-Jiachen-ner-pgn} proposed parameter generation networks for cross-domain NER. They idea is dynamically generate parameters of network modules (such as BiLSTMs) according to predicted domain distribution.

To facilitate cross-domain Chinese dependency parsing research, \citet{Li-2019-SemisupervisedDA} proposed a large-scale multi-domain dataset for Chinese dependency parsing. They organized the NLPCC-2019 shared task on cross-domain dependency parsing \cite{Peng-2019-OverviewOT}.  \citet{Li-2019-CrossDomainTL} rank the first place in the shared task, based on a tri-training approach.

However, possibly due to the lack of multi-domain data, research on cross-domain SRL is scarce so far. We hope our newly annotated MuCPAD can promote future research in this direction.

\section{Analysis on MuCPAD}\label{sec:analysis}
In this section, we analyze the MuCPAD dataset from different perspectives
to gain more insights.
\input{images/consistency-ratio}
\paragraph{Annotation consistency.}
As aforementioned, each task is assigned to two annotators. If the two submissions are inconsistent, a third expert annotator is asked to handle the inconsistency and decide the final results. The first major row in Table \ref{data-consistency-accuracy-ratio-target} shows the predicate- and argument-wise annotation consistency ratios \cite{marcus-1994-penn, 2018-Annotation-ljg} in all domains.

The predicate-wise consistency ratio is defined as $\frac{\#\texttt{Pred}_{\texttt{annoA}\cap \texttt{annoB}}}{\#\texttt{Pred}_{\texttt{annoA}\cup\texttt{annoB}}}$, where the denominator is the total number of predicates submitted by all annotators, and the numerator is the number of predicates with consistent arguments from all annotator pairs.
We can see that the predicate-wise annotation consistency ratios in most domains are lower than 60\%. Even the highest predicate-wise consistency ratio, which is achieved in PC domain, is only 71.23\%. This means that more than a quarter of the annotation tasks need to be further checked by a third expert annotator, demonstrating the difficulty of the SRL annotation task and the importance of performing strict double annotation to guarantee data quality.

In addition, it is worth noting that the predicate-wise consistency ratio in PC domain is much higher than that in the other five domains.
We believe this is related to the average number of arguments per predicate. For further investigation, we calculate the average number of arguments and find the number of average arguments per predicate is the lowest in PC domain. Therefore, it is relatively easier for the annotators to recognize the arguments in PC domain.

The argument-wise consistency ratio is defined as $\frac{\#\texttt{Arg}_{\texttt{annoA}\cap \texttt{annoB}}}{\#\texttt{Arg}_{\texttt{annoA}\cup\texttt{annoB}}}$, where the denominator is the total number of arguments submitted by all annotator pairs, and the numerator is the number of arguments that receive the same arcs and labels from the annotator pairs.
As shown in Table \ref{data-consistency-accuracy-ratio-target}, the argument-wise consistency ratios in most of the domains are lower than 75\%, except that PC achieves the highest argument-wise consistency ratio of 83.63\%.

\paragraph{Annotation accuracy.}
In the second major row of Table \ref{data-consistency-accuracy-ratio-target}, we present the argument-wise annotation accuracy. The overall argument-wise annotation accuracy is defined as $\frac{\sum_{i=1}^{n}\texttt{\#}\texttt{Arg}_{\texttt{correct}_{i}}}{2 \times \texttt{\#}\texttt{Arg}_{\texttt{gold}}}$, where the numerator is the sum of the number of correctly annotated arguments submitted by all annotators; the denominator is the total number of all gold arguments; $n$ is the number of annotators. The reason for ``$2 \times$'' in the denominator is that each task is annotated twice since it is assigned to two annotators for double annotation. The annotation accuracies in all the domains are more than 80\%, indicating that our guidelines are reasonable, which ensures the quality of annotation data.

To gain more insights into the accuracy regarding different labels, we calculate the accuracy of 5 core labels and 2 non-core labels with high proportions for further analysis, which is shown in the third major row of Table \ref{data-consistency-accuracy-ratio-target}. The argument-wise annotation accuracy for each label is calculated by $\frac{\sum_{i=1}^{n}\texttt{\#}\texttt{Arg}_{\texttt{correct}_{i}}^{{l}}}{2 \times \texttt{\#}\texttt{Arg}_{\texttt{gold}}^{{l}}}$,
where the numerator is the sum of the number of correctly
annotated arguments with the concerned label $l$ submitted by all annotators, the denominator is the total number of all gold arguments with the concerned label $l$; $n$ is the number of annotators.
As we can see, ``hidden-subject'' achieves the highest average accuracy, demonstrating the omitted subject is easy to recognize. The lowest average accuracy is 82.78\% on ``loc'', probably because it is a non-core label with the lowest proportion of all labels and is prone to be ignored by the annotators.

\input{images/data-analysis}
\paragraph{Label distribution.}
Figure \ref{fig:label-distribution} illustrates the label distribution in the 6 domains. The labels in Figure \ref{fig:label-distribution} are sorted in descending order by their proportion in News data. We choose 2 core labels and 2 non-core labels with the highest proportions in News data. Besides, we also analyze ``hidden-subject'' and ``hidden-object'' since their distributions in different domains are specific. As shown in Figure \ref{fig:label-distribution}, the label distributions are vary across different domains.

For PC, both ``hidden-subject'' and ``hidden-object'' account for the largest proportion compared with that for all the other 5 domains, which means PC contains the most omitted core arguments. The reason is that PC is user-generated comments on a concerned product, and people tend to directly write the comment of the concerned product and omit the product name and the personal pronoun. For example, in the sentence ``很~(very)~喜欢~(like), 买~(buy)~了~好~(very)~多~(much)'', both the hidden-subjects and the hidden-objects of the predicates ``喜欢~(like)'' and ``买~(buy)'' are omitted.
The omitted core arguments in PB also take relatively large proportion, similar to the reason for PC.

For ZX, it has the most ``agent'' roles and the least ``hidden-subject'' roles compared with other domains. This is owing to the genre of ZX texts, which are extracted from a popular Chinese fantasy novel with a lot of fictional characters. In order to make the story more understandable by readers, the names of the fictional characters are often explicitly written in the sentences, leading to more ``agent'' roles and fewer ``hidden-subject'' roles.

For LAW, it has more ``time'' roles and ``loc'' roles than other domains,
since the elements (i.e., time and location) of the cases are usually frequently occurred to provide more accurate information.

For MED, the number of ``hidden-subject'' label accounts for a large proportion among all the labels in MED, only fewer than that of ``agent'' label,
mainly because the descriptions of symptoms in MED usually omit the subjects. For example, in the sentence ``酒精 (alcohol) 中毒 (poisoning): 发生 (occur) 昏迷 (coma) 不能 (cannot) 催吐 (induce vomiting), the subjects of the predicates ``中毒 (poisoning)'', ``发生 (poisoning)'', ``昏迷 (coma)'', ``不能 (cannot)'' are all omitted.

Looking into the distribution of ``hidden-subject'' and ``hidden-object'' labels in all the domains, we find that hidden labels exist in all the domains, especially in non-canonical texts like PC and PB, demonstrating the necessity of annotating hidden labels.
In addition, ``hidden-subject'' takes a higher proportion than ``hidden-object'' in all the 6 domains, reflecting that the subject of the predicate in Chinese sentences is often omitted.

\paragraph{Annotation difficulties.}
To understand difficulties during annotation, we calculate the proportion of the arguments with the same arcs but different labels from two annotators among all the arguments with the same arcs.
We find that the confusion pattern ``{agent, expe}'' accounts for the largest proportion of 22.23\%, which means the label ``agent'' is prone to be confused with ``expe''.
This is possibly because the POS for some predicates is subtle and vague in Chinese, causing the confusion of the argument labels.
Taking the sentence ``纽扣 (Buttons) 一天 (a day)  坏 (getting broken) 一个 (one)'' as an example, ``坏 (getting broken)'' may be misunderstood as an adjective and thus the argument ``纽扣 (buttons)'' is incorrectly annotated as ``expe''. Actually, ``坏 (getting broken)'' acts as a verb in this sentence and the correct label of ``纽扣 (buttons)'' is ``agent''.
The second confusion pattern is ``{patient, pred-patient}'', with a proportion of 12.6\%, due to the misunderstanding of the POS of the argument.
It is also difficult for annotators to distinguish ``agent'' and ``patient''. For example, in the sentence ``新 (new) 衣服 (cloths) \underline{被 (was)} 弄脏了 (soiled)'', the preposition ``被 (was)'' is omitted. As a result, the label of ``新 (new) 衣服 (cloths)'', which is ``patient'', may be confused with ``agent'' due to the omission.

\section*{Acknowledgements}

The authors would like to thank the anonymous reviewers for the helpful comments. We are greatly grateful to all our annotators for their hard work in data annotation. This work was supported by the National Natural Science Foundation of China (Grant No. 62176173, 61876116), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
\section{Conclusions}
This paper presents a multi-domain Chinese predicate-argument dataset, named MuCPAD, which consists of 30,897 sentences with 92,051 predicates and covers 6 different domains.
In particular, we adopt a frame-free annotation  methodology, which does not require high-level linguistic background for defining frames for large amounts of new predicates or new senses in multi-domain data.
Besides, considering that omission of content words is  ubiquitous in Chinese, we explicitly annotate omitted core arguments with two special designed labels ``hidden-subject'' and ``hidden-object''
for better semantic understanding.
To ensure annotation quality, we adopt strict double  annotation and ask a third expert to handle annotation inconsistency.
We also perform analysis on MuCPAD from different perspectives.
Finally, we conduct preliminary cross-domain experiments and analysis on MuCPAD.

\section{Data Annotation}\label{section:data-annotation}
This section describe the annotation methodology and annotation process of MuCPAD in detail.

\paragraph{Annotation guidelines.} After an extensive survey of previous works on SRL data annotation, we compile 53 pages of annotation guidelines.
We adopt 24 fine-grained general-purpose role labels to  capture the semantic relationships between predicates and arguments, as shown in Table \ref{table:summary relation}, most of which are borrowed from the guidelines of CSB.
In particular, we introduce two special labels, i.e.,  ``hidden-subject'' and ``hidden-object'',  to explicitly annotate omitted core arguments.
Our guidelines illustrate each label in detail using concrete examples, and are gradually
improved according to feedback of annotators during the course of the annotation project.

\paragraph{Data selection.}

We select the data to annotate from six domains, i.e., news, product blogs (PB), product comments (PC), web fictions (ZX), legals (LAW), and medical (MED) domains. Table \ref{data-statistic-anno} shows the data statistics.

News consists of the sentences in Chinese SemBank \citep{Xia-2017-CSB} and CoNLL-2009 Chinese dataset \citep{Hajic-2009-TheCS}.
Specifically, we choose all 10.3K sentences with 16.5K predicates from Chinese SemBank \citep{Xia-2017-CSB} and randomly select 6.7K sentences with 24.5K predicates from CoNLL-2009 Chinese dataset \citep{Hajic-2009-TheCS}. Both PB and PC are non-canonical data from Taobao\footnote{http://www.taobao.com}, where PB is from Taobao headline website, and PC is comments on products written by users.
ZX is selected from a popular Chinese fantasy novel called ``Zhuxian'' (ZX, known as ``Jade Dynasty'').
LAW is extracted from the China artificial intelligence law challenge 2018.\footnote{http://cail.cipsc.org.cn:2018/}
MED is crawled from the medical section of People's Daily Online\footnote{http://paper.people.com.cn} and Sina.com\footnote{https://news.sina.com.cn}.

After selecting the sentences, we also need to select the concerned predicates in the sentences for annotators to annotate their corresponding arguments.
For news domain, we directly choose the predicates in Chinese SemBank and CoNLL-2009 Chinese dataset. For other 5 domains, we choose the predicates according to several pre-defined rules which are carefully designed by considering both the dependency tree structures of the sentences and a frame dictionary extracted from the Chinese frames\footnote{https://verbs.colorado.edu/chinese/cpb/html\_frames}. For example, the root words of dependency trees are considered as predicates; the head words with ``subject'' or ``object'' dependency labels are considered as predicates; all the words that can be matched in the frame dictionary are considered as predicates.
\begin{table}[tb]
\setlength{\tabcolsep}{3pt}
\begin{center}
\begin{small}
\begin{tabular}{l l l l l l l }
\toprule
& News &PB &PC&ZX&LAW&MED \\
\midrule
\#Sent & 16,974 &  3,753 & 3,890 & 1,575 & 2,813 & 1,892  \\
\#Pred & 40,989 & 11,317 & 17,074 & 5,891 & 11,156 & 5,624 \\
\bottomrule
\end{tabular}
\end{small}
\caption{Statistics of annotated data. ``\#Pred'' and ``\#Sent'' represent the number of predicates and sentences.}
\label{data-statistic-anno}
\end{center}
\end{table}

\paragraph{Quality Control.}
We employ 20 undergraduate students as annotators, and select 5 experienced annotators as expert annotators to handle annotation inconsistency issues.
All the annotators are paid for their work, and the salary is determined by their annotation quantity and quality. The average salary is 28 RMB per hour.

Before real annotation, each annotator is trained for several hours to be familiar with our guidelines and our annotation tool. During the annotation process, we adopt a strict double annotation workflow to guarantee the annotation quality. Specifically, each task is randomly assigned to two annotators to annotate independently. If the submissions from the pair of two annotators are the same, the consistent answer is taken as the final answer. Otherwise, the task is assigned to a third expert annotator to decide the final answer by comparing and analyzing the inconsistent submissions.

\paragraph{Annotation tool.}
We build a browser-based annotation tool to support the double annotation workflow. For each annotation sentence,
the annotation tool highlights the predicate in the sentence for the annotators to annotate all the arguments of the highlighted predicate.
We also design a ``not-predicate'' checkbox in the annotation tool and ask annotators to click this checkbox to inform us when the highlighted word is out of the range of the predicate types in our guidelines.

\section{Experiments}

\paragraph{Data.}
\textcolor{black}{Our experiments mainly focus on zero-shot single-source domain adaptation, that is, we have labeled training data for the source domain, and do not have labeled training data for the target domain.
Specifically, we use the News domain of MuCPAD as the source domain, and the other five domains as target domains.}
The data statistics for source and target domains are shown in Table \ref{data-statistic-target}.
For the auxiliary data used in the MTL framework, we randomly select 13,170 sentences with 72,616 predicates from CPB2.0 \cite{Xue-2006-SemanticRL}, which belongs to the same newswire genre with the source domain data.

\paragraph{Evaluation metric.}
We adopt the standard precision ($\frac{\#\texttt{Arg}_{\texttt{correct}}}{\#\texttt{Arg}_\texttt{pred}}$), recall ($\frac{\#\texttt{Arg}_{\texttt{correct}}}{\#\texttt{Arg}_\texttt{gold}}$), and F1 score ($\frac{2PR}{P+R}$) for SRL evaluation.

\paragraph{Settings.}
We implement the basic SRL model and MTL framework with PyTorch\footnote{https://pytorch.org/} and mainly follow the hyperparameters of \citet{cai-2018A-srl}, such as the dimensions of embeddings, learning rate, and dropout ratios. We use bert-base-chinese\footnote{https://huggingface.co/bert-base-chinese} to obtain contextualized representations for words, and the dimension of the BERT representations is 768.
During training, early stopping is triggered if the peak performance in dev data does not increase in 50 consecutive iterations.

\textbf{Results of the basic model.}
The first row of Table \ref{tbl:baseline-mtl} presents the results in the source/target domain dev/test data using the basic SRL model trained on the source data.

First, it is obvious that the performance in all the five target domains drops dramatically compared with the results on source data, with the gap of more than 18\% in F1. This indicates that the model trained on source data has a challenge in making reliable predictions on target domain data due to the distributional mismatch between different domains.
Second, we find that the basic SRL model performs better on ZX and LAW compared with the other three target domains data, i.e., PB, PC, and MED. The probable reason is that ZX and LAW are novel and legal case, respectively, which are more canonical in text.
Third, PB has the lowest F1 score in both dev and test. This can be explained by the fact that PB is non-canonical data from Taobao headline website. The dissimilarity between the source training data and PB target data causes the low performance.

\textbf{Results with BERT.}
The second row of Table \ref{tbl:baseline-mtl} shows the results of the baseline with BERT representations.
We can see that the results of ``Baseline+BERT'' consistently increase by large margins compared with the corresponding baseline models without BERT (as shown in the first major row of table \ref{tbl:baseline-mtl}), demonstrating the great power of BERT in contextualized representation.

\textbf{Results with heterogeneous CPB2.0.}
As shown in the third row of Table \ref{tbl:baseline-mtl}, benefiting from the additional semantic information provided by the auxiliary CPB2.0 data using the MTL framework, the SRL performance in all domains are improved compared with the baseline model. This indicates that the MTL framework is effective in capturing and learning the underlying common knowledge from heterogeneous data.

On the one hand, comparing the improvements brought by MTL in all domains, we find that MED data obtains the largest gains of 9.65\%/8.56\% F1 in dev/test, respectively.
The main reason is that the MED data belongs to the same newswire domain as the auxiliary CPB2.0 data.
On the other hand, the improvement in LAW is the smallest.
This can be explained by the difference in label distribution between \textcolor{black}{LAW and CPB2.0. For example, as mentioned in Section \ref{sec:analysis}, the labels ``time'' and ``loc'' in LAW account for the largest proportion (13.95\% and 6.14\% respectively) compared with other domains. However, the proportions of ``time'' and ``loc'' in CPB2.0 data are only 6.10\% and 3.40\% respectively} (about half of that in LAW). Therefore, CPB2.0 cannot provide much more valid information to increase the performance of these labels.

\textbf{Results with BERT and heterogeneous CPB2.0.}
Finally, when utilizing both BERT representations and the heterogeneous CPB2.0 data on our baseline, the enhanced model gives the best or comparable results in 5 of the 6 domains, with an average increase of 0.5\% F1, showing that the MTL framework is effective in utilizing heterogeneous data and can complement the information obtained from BERT representations.

redpajama_set_name: RedPajamaArXiv


text:
Israel ِArrests Palestinian minister in Jerusalem
Israeli police said Sunday they had detained and were questioning the Palestinian minister for Jerusalem affairs, while a source close to the official said he was arrested in his home.
Palestinian minister for Jerusalem affairs Fadi al-Hadami was detained and was being questioned for "activities in Jerusalem", Israeli police spokesman Micky Rosenfeld told AFP.
Hadami's arrest was most likely due to recent activities that have included accompanying Chile's president during a visit to Jerusalem's Al-Aqsa mosque complex, a source close to the minister told AFP.
On Tuesday, Hadami was seen alongside Chilean President Sebastian Pinera on a tour of the flashpoint holy site, enraging Israel, which said it constituted a violation of regulations and a breach of understandings reached with Santiago for the head of state's visit.
The status of Al-Aqsa, known to Jews as the Temple Mount and located above the Western Wall, is one of the most sensitive issues in the Israeli-Palestinian conflict.
It is the holiest site in Judaism and the third-holiest for Muslims after Mecca and Medina, administered by the Muslim Waqf but secured by Israeli police.
Chile later said Pinera's visit was private, with Hadami's presence not part of official protocol.
The arrest follows days of violent unrest in Jerusalem.
Overnight Saturday, Israeli police "continued dealing with riots and disturbances in a number of neighborhoods when stones were thrown at officers and fireworks fired at them," Rosenfeld said in a statement.
Two officers were wounded and six suspects arrested, Rosenfeld said.
The continued unrest follows the shooting of a 20-year-old Palestinian in the east Jerusalem neighborhood of Issawiya by Israeli police, after he had allegedly thrown fireworks at them.
The young man, identified as Mohammed Obeid, died of his wounds, according to the Palestinian health ministry.
Israel occupied east Jerusalem and the West Bank in the 1967 Six-Day War. It later annexed east Jerusalem in a move never recognized by the international community.
Israel sees all of Jerusalem as its undivided capital, while the Palestinians see east Jerusalem as the capital of their future state.


Highway 14/15 motorists in the New Ulm area need to plan for a new detour beginning as early as Monday, April 15.
Highway 14/15 motorists in the New Ulm area need to plan for a new detour beginning as early as Monday, April 15 as construction crews on the New Ulm Gateway project tackle the completion of the new Highway 14/15 interchange as well as the Minnesota River Bridge.
The new detour will consist of Nicollet County Road 5 (Fort Road) and Nicollet County Road 12 and Nicollet County Road 21 between Nicollet and Courtland due to the flooding on County Road 12 north of Highway 14.
Construction on the new Highway 14/15 interchange bridge is ahead of schedule and crews will focus on the intersecting roadways as soon as a detour can be implemented. The goal is to be setting beams in mid-April. Progress can be seen on the web cam at www.mndot.gov/new ulm.
The Highway 14/15 New Ulm Gateway project addresses intersection safety, aging bridges, poor pavement conditions and occasional road flooding around New Ulm. Construction began in 2017, with the majority of the work and detours taking place in 2018 and 2019.


Report injured deer to BC Conservation at 1-877-952-7277. BC Conservation will only respond if the deer is not mobile.
If the deer is in Saanich, Animal Control officers can assist. Contact them through the Saanich police non-emergency line at (250) 475-4321.


Maurice Miner, Director, Syndicated & Satellite Radio, has announced that he will be leaving Sony Music Nashville to pursue other endeavors. Miner, who joined the company in 2005 and reports to VP, Media & Corporate Communications Allen Brown, will continue with Sony until a to-be-determined date.
Once the position is posted, inquiries, résumé submissions and follow-up conversations will be facilitated through the Sony Music online process. Job postings can be viewed by clicking on "careers" on the home page of www.sonymusic.com.


text:
Attorney Craig R. Misci is dedicated to providing the best possible immigration legal representation to his clients throughout the entire Greater Boston, MA and New England area. By dealing one-on-one with the client throughout their entire case, he is able to provide personalized service to each of his clients.
Craig R. Misci earned his Juris Doctor (J.D.) from Suffolk University Law School in 1997 after receiving his Bachelor of Science (B.S.) in Business Administration from the University of Arizona in 1993.
After graduation, Craig R. Misci worked for almost seven years in a small law firm concentrating in immigration law, but left in 2004 to open his own office in Chelsea, MA with the hope of being able to provide better and more personal service to his clients.
Attorney Craig R. Misci is also a member of the American Immigration Lawyers Association (AILA).
Se habla espa�ol.
Copyright � 2004 - 2015. All rights reserved.
Contact Attorney Craig R. Misci


text:
What is Summary Possession?
A summary possession may occur if a tenant is accused of selling or possessing drugs.
Receiving an eviction notice does not necessarily indicate that a tenant will actually be evicted.
Summary possession typically occurs in rental situations in which a landlord wants to have a tenant physically removed from a rental property.
A landlord may issue a summary possession to a tenant who has caused serious damage to the rental property.
Written By: N. Madison
Edited By: Jenn Walker
Summary possession is a legal term that means eviction. It typically occurs in rental situations in which a landlord wants to have a tenant physically removed from his property. In many places, there are legal processes a landlord must go through in order to evict a tenant, which often involves going to court to get an official order for summary possession. The procedure a landlord must follow typically depends on the laws in the jurisdiction as well as whether or not the landlord and tenant signed a lease.
In most cases of summary possession, a landlord works to have a tenant removed from the property he owns. Often, a tenant is given a court date to defend against a summary possession notice. If the tenant doesn't win his case or fails to show up in court, a judge typically grants an eviction order. In many jurisdictions, this allows the landlord to evict the tenant, but also gives the tenant a certain number of days to vacate the premises. If the tenant fails to vacate the premises in the required amount of time, the landlord may have the right to change the locks and lock the tenant out.
There are many reasons a landlord might seek summary possession, but the most frequent is an act on a tenant's part that breaks a lease. For example, a tenant may fail to pay his rent as agreed, or he may allow another party to live in the rental property without the landlord's permission. A landlord may also want to evict a tenant for doing serious damage to the rental or interfering with other tenants' enjoyment of the property. In some cases, a landlord may even seek summary possession because the tenant has committed an illegal act on the property, such as possessing or selling drugs. Additionally, a landlord may want to evict a tenant because a rental lease has ended and the tenant refuses to move out.
While an eviction notice is a serious document, it doesn't necessarily mean a tenant will be evicted. A tenant may go to court and demonstrate to a judge that he should be permitted to maintain possession of the rental property. Likewise, a tenant may negotiate an agreement for staying in the property, and the landlord may withdraw his summary possession petition. In such a case, a tenant will typically have to get the agreement in writing to ensure its validity.
What Is a Possession Order?
What is an Estate for Years?
What is Possession of Property?
What is Summary Justice?
What is a Notice of Possession?
What is Vacant Possession?
What is a Summary Offense?