Inventors and Geography - Any ideas?
Discussion in 'Primary' started by triathlete, Apr 14, 2011.
I want to do inventors or something along those lines after Easter to tie in with explanation texts and we'll be 'inventing' telephones in Science. My problem is that I've not done much Geography this year so I'll need to do some and I can't think of an obvious link. After half term I'm going to do Journeys which will obviously take in a lot of Geography, but I'd like to do some this half term too. Anyone got any ideas?
Inventors and /or Explorers - lots of scope there!
How about early communications? For example radio? Early signals accross the channel? The laying of telegraph cables all over the planet?
The GPS satellites - they were invented and now span the globe?
Or, how about looking at Ironbridge in Shropshire? Birthplace of the industrial revolution. Lots of invention and all made possible by the geography - ie the coal, iron and water as well as it's central(ish) location in the UK?
How about ship development? Brunel's SS Great Britain? First screw driven steam ship (I think) and all to do with international trade and travel. Where was it built? Where did it go?
Aircraft? From the Wright brothers to transatlantic flight in a very short space of time.
I hope any of that helps.
Thanks for all your ideas everyone, you've given me plenty to think about.
I used these resources with Year 5 - great stuff!
Discuss the impact of mobile phones on communities in developing countries.
Look at the components in a mobile phone. Where do the raw materials come from? Where are the different components manufactured? By whom? Track the journey of the phone from source to sale in your local shop.
Will require research on your part but interesting issues raised as well as mapping opportunities.


How do you evaluate the performance of a city?
For Jake Haulk and his team at the Allegheny Institute for Public Policy, performance, in the case of Pittsburgh and the Western Pennsylvania region, lies in the data. They pour through the metrics that define the citizens of the region and shape our daily lives. They assess this data, both in comparison to its past performance and in comparison to other similar-sized cities, to provide concrete, no-frills analysis of just how well the region is doing, from education to transportation to optimizing its budget, and everything in between.
But what do their findings tell us about us?
On Sunday, Jake checks in with Your American Story Radio, as he talks with Raja about the mission of the Allegheny Institute, their latest projects and areas of research, the trends they're noticing about the data, and what individuals and businesses should do to take advantage of this data. You won't want to miss this discussion!
-And speaking of guests telling us about ourselves…..
Individually, each of us knows our story. How old we are. Where we come from. What we do. Our living and spending habits. But taken as a collective, this data can be the absolute lifeblood of business.
The demographics of our lives serve as a critical tool for businesses to plan their strategy on how to market their product or service to us. And when it comes to demographic analysis in the United States, few if any experts have the pedigree of Peter Francese. Peter is the founder of American Demographics Magazine (now a part of Ad Age Magazine), and has spent decades not only gathering the data about our lives, but analyzing that data, synthesizing it, and presenting it in a way that holds a proverbial mirror to businesses and consumers about the way we think and act.
This Sunday, Peter joins Raja to discuss the study of demographics, how business can and should use demographic analysis to get the jump on their competition, and what the future holds for our economy, based on his current analysis of demographic trends.


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HARTFORD - Authorities say a 28-year-old sex offender who escaped from furlough supervision about a week ago has been captured.
The Hartford Police Department says Dale Adams Jr., was taken into custody without incident Saturday in White River Junction. He's been turned over to the state Department of Corrections, which transported him to the Southern State Correctional Center in Springfield.
Adams is scheduled to appear in court on Monday. It wasn't immediately known if he's represented by a lawyer.
Corrections officials placed Adams on escape status May 9 after he did not return to his approved home in Perkinsville on May 6.
Authorities say Adams was under supervision for a sex-related crime and was considered at high risk for committing another sexual offense.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: I am using TestRestTemplate to Test with @RequestParam value how to execute //how to send @RequestParam value to url
enter code here@ApiRestController

public class CityController extends BaseController{
    @GetMapping("/cities")

    public ResponseEntity<CitiesResponse> getAll(
            @RequestParam(value = "pageNumber", defaultValue = "1") int pageNumber,
            @RequestParam(value = "pageSize", defaultValue = "100") int pageSize,
            @RequestParam(value = "sortBy", defaultValue = "id", required = false) String sortBy,
            @RequestParam(value = "sortDirection", defaultValue = "asc", required = false) String sortDirection,
            @RequestParam(value = "search", required = false) String search) {
        return new ResponseEntity(cityService.getAll(pageNumber, pageSize, sortBy, sortDirection, search), HttpStatus.OK);
    }
}

A: There are different ways to test in spring boot. Check the samples below:
First option:
It's more like an integration test. In this case the port will be the default 8080
@RunWith(SpringRunner.class)
@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.DEFINED_PORT)
public class DemoApplicationTests {

    private TestRestTemplate restTemplate = new TestRestTemplate();

    @Test
    public void contextLoads() {
        String url = "http://localhost:8080";
        URI uri = UriComponentsBuilder.fromHttpUrl(url).path("/books")
                .queryParam("order", "asc").build().toUri();
        this.restTemplate.getForEntity(uri, Void.class);
    }

}

Second option:
Very similar to the first option but this time it will run in an random port which can be capture by @LocalServerPort
@RunWith(SpringRunner.class)
@SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.RANDOM_PORT)
public class DemoApplicationTests {

    @LocalServerPort
    private int port;

    private TestRestTemplate restTemplate = new TestRestTemplate();

    @Test
    public void contextLoads() {
        String url = "http://localhost:" + this.port;
        URI uri = UriComponentsBuilder.fromHttpUrl(url).path("/books")
                .queryParam("order", "asc").build().toUri();
        this.restTemplate.getForEntity(uri, Void.class);
    }

}

UriComponentsBuilder has been used to build the uri in a very friendly way.

Third option:
This option doesn't involve TestRestTemplate but just involve the RestController by itself. Any dependency inside the controller should be mark with @MockBean in the test.
@RunWith(SpringRunner.class)
@WebMvcTest(BookRestController.class)
public class DemoApplicationTests {

    @Autowired
    private MockMvc mvc;

    @Test
    public void contextLoads() throws Exception {

        this.mvc.perform(MockMvcRequestBuilders.get("/books")
                .param("order", "asc"))
                .andExpect(MockMvcResultMatchers.status().isOk());
    }

}

A: To easily manipulate URLs / path / params / etc., you can use Spring's UriComponentsBuilder class. It's cleaner that manually concatenating strings and it takes care of the URL encoding for you:
UriComponentsBuilder builder = UriComponentsBuilder.fromHttpUrl(url)
        .queryParam("pageNumber", 1)
        .queryParam("pageSize", 10)
        .queryParam("sortBy", "id")
        .queryParam("sortDirection", "desc")
        .queryParam("search", "hello search");

HttpEntity<?> entity = new HttpEntity<>(headers); //Update this as per your code

HttpEntity<String> response = restTemplate.exchange(
        builder.build().encode().toUri(),
        HttpMethod.GET,
        entity,
        String.class);

redpajama_set_name: RedPajamaStackExchange


text:
State to issue new similar-school rankings, growth targets
BRADLEY WEAVER
A second wave of statewide rankings that compares schools onnational and demographic levels is due out Tuesday.
The numbers are a part of the second statewide AcademicPerformance Index, which was first released in October and includedscores based on how well students did on the Stanford AchievementTest, Ninth Edition, which second- through 11th-graders took lastspring.
The new numbers, which are a part of Gov. Gray Davis'educational reform campaign, will show how schools rank with othersof similar demographics, including ethnicity, class size, familyincome and parents' education.
The state uses the index to create two separate rankings, onecomparing each school score to that of 6,700 schools statewide, theother comparing each to 100 schools with similar socioeconomiccharacteristics. A rank of 1 is the lowest; 10 is the highest. Theindex will also show the school's new academic growth targets,based on API scores released last year.
But some educators, such as Temecula Trustee Barbara Tooker, areskeptical of the accuracy of the rankings based on demographics.She said that because not every school is identical, the rankingsmay be inaccurate.
"One good thing about the (index) is it helps parents who havebeen asking for years how their local schools compare with otherschools," Tooker said. "But I've always been truly amazed to thinkwe can be paired or matched up with other districts that have thesame demographics. It's difficult to do that because we can getwithin range, but that's about as close as we can get."
The performance index is the central part of the Public SchoolsAccountability Act of 1999, endorsed by the governor and has twomain goals: to rank California public schools by how well theyeducate students and to bring schools out of their academicdoldrums by educating students better.
In October, the index showed school scores from a low score of200 to a high of 1,000. Schools at 250 or below are considered tobe low-performing, while those with a score of 800 or above areconsidered high-performing.
Schools that show improvement in the API scores qualify for upto $150 per student, plus cash bonuses for teachers. If a school'sscores deteriorate over three years, the state could move in andrun the school.
Schools that score below 800 are expected to improve every yearby 5 percent of the gap between their scores and 800. With only afew exceptions, Southwest County schools met -- and in some casesgreatly surpassed -- their goals for improvement under the state'snew accountability system. Now they get to see how they stack upagainst their peers and the state's schools as a whole.
State Department of Education spokesman Doug Stone said heexpected schools to improve under the new system that sets growthtargets.
"Last year, 71 percent of California schools met their targetsand that's phenomenal," said Stone. "Schools are competing againstthemselves and hopefully, with these new targets, they willcontinue with the same progress and momentum."
The state for the first time is giving financial rewards toabout 4,500 schools -- about two-thirds of all the schools in thestate -- that have improved their rankings on the API.
The money is part of an enormous wave of cash -- $677 million inall -- that the governor and Legislature set aside to give publicschools that showed gains on state achievement-test scores thispast year.
The rankings, lists of similar schools, and data on individualschools will be available to the public on Wednesday atwww.cde.ca.gov/psaa/api.
Contact staff writer Bradley Weaver at (909) 676-4315, Ext.2625, or bweaver@nctimes.com.
Two Jeffrey Epstein accusers urged a judge Monday to keep the wealthy financier behind bars until he goes on trial on federal charges that he sexually abused underage girls.


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By Jonathan Lambert 10 hours ago
Scientists Say	Chemistry
Scientists Say: Osmosis
This word describes how liquids can move across membranes to equalize the amount of a substance dissolved in them
On the left, there are few molecules per volume of solution; on the right are many more. A somewhat porous membrane separates them. If microscopic holes in the membrane are big enough (but not too big), the liquid will move from left to right — through that membrane. And it will continue to do that until the concentration of the particles per unit volume is equal in the solution on each side of the membrane.
OpenStax College/Wikimedia Commons (CC BY 3.0)
By Bethany Brookshire
Osmosis (noun, "Oz-MO-sis")
The movement of a solution across a membrane — a barrier that blocks the flow of some, but not all, materials. The liquid in the solution will move from the side with fewer dissolved molecules toward the side with more. Movement will continue until the concentrations on both sides of the membrane are the same. Think of a solution low in water and high in salt next to a solution high in water and low in salt. The water will move from the high-water, low-salt solution toward the low-water, high-salt solution. The water will continue this flow until the concentration of salt in each solution is equal.
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To get to Mars, astronauts may have to depend on osmosis to purify their pee — so they can drink it again.
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(for more about Power Words, click here)
concentration (in chemistry) A measurement of how much of one substance has been dissolved into another.
dissolve To turn a solid into a liquid and disperse it into that starting liquid. For instance, sugar or salt crystals (solids) will dissolve into water. Now the crystals are gone and the solution is a fully dispersed mix of the liquid form of the sugar or salt in water
membrane A barrier which blocks the passage (or flow through of) some materials depending on their size or other features. Membranes are an integral part of filtration systems. Many serve that function on cells or organs of a body.
osmosis The movement of certain molecules within a solution across a membrane. The movement is always from the solution where the concentration of some chemical is higher to the solution where the concentration of that chemical is lower. This movement tends to continue until concentrations on each side of the membrane are the same.
permeable Having pores or openings that permit liquids or gases to pass through. Sometimes materials can be permeable for one particular type of liquid or gas (water, for example) but block others (such as oil).
semi– An adjective meaning "somewhat."
solution A liquid in which one chemical has been dissolved into another.
solute A substance dissolved in another substance, forming a solution.
solvent A material (usually a liquid) used to dissolve some other material into a solution.
About Bethany Brookshire
Bethany Brookshire is the staff writer at Science News for Students. She has a Ph.D. in physiology and pharmacology and likes to write about neuroscience, biology, climate and more. She thinks Porgs are an invasive species.
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What is a MIDI Interface? These devices are used to route this information in and out of a computer. Standard MIDI ports are a five-pin din plug configuration.
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(C) One person from representable employees not represented by employee representative units described in subparagraphs (A) and (B) of this paragraph.
(2) One member of the Senate shall be appointed by the President of the Senate and one member of the House of Representatives shall be appointed by the Speaker of the House to serve as nonvoting advisory members.
(3)(a) If the governing body of a local government elects to participate in a benefit plan offered by the board, in addition to the members appointed under subsections (1) and (2) of this section, the Governor shall appoint two voting members, one of whom represents local government management and one of whom represents local government nonmanagement employees.
(b) After the appointment of members under paragraph (a) of this subsection, if the number of eligible employees of a local government or local governments enrolled in a benefit plan or plans offered by the board exceeds 25,000, the Governor shall appoint two additional voting members, one of whom represents local government management and one of whom represents local government nonmanagement employees.
(c) After the appointment of members under paragraphs (a) and (b) of this subsection, for every additional 25,000 eligible employees of a local government or local governments enrolled in a benefit plan or plans offered by the board, the Governor shall appoint one additional voting member representing local government management and one additional voting member representing local government nonmanagement employees.
(4) A maximum of three members may be appointed to represent local government management and a maximum of three members may be appointed to represent local government nonmanagement employees.
(5) The term of office of each appointed voting member is four years, but an appointed voting member serves at the pleasure of the Governor. Before the expiration of the term of a voting member appointed by the Governor, the Governor shall appoint a successor to take office upon the date of that expiration. A member is eligible for reappointment. If there is a vacancy for any cause, the Governor shall make an appointment to become immediately effective for the unexpired term.
(6) The appointments by the Governor of voting members of the board are subject to confirmation by the Senate in the manner prescribed in ORS 171.562 (Procedures for confirmation) and 171.565 (Vote required for confirmation).
(7) Members of the board who are not members of the Legislative Assembly shall receive no compensation for their services, but shall be paid for their necessary and actual expenses while on official business in accordance with ORS 292.495 (Compensation and expenses of members of state boards and commissions). Members of the board who are members of the Legislative Assembly shall be paid compensation and expense reimbursement as provided in ORS 171.072 (Salary of members and presiding officers), payable from funds appropriated to the Legislative Assembly.
Note: 243.061 (Public Employees' Benefit Board) and 243.066 (Officers) were enacted into law by the Legislative Assembly but were not added to or made a part of ORS chapter 243 or any series therein by legislative action. See Preface to Oregon Revised Statutes for further explanation.


text:
La cuenta de Borda o recuento Borda es un proceso electoral propuesto por el matemático francés Jean-Charles de Borda en 1770. En la cuenta de Borda, cada papeleta asigna un puntaje a cada candidato, dependiendo de la posición que tengan en el orden de preferencias de esa papeleta i.e. mientras más preferente, más puntos. Por ejemplo, si hay un número m de candidatos, el primer candidato en el orden recibe m-1 puntos, el segundo m-2 puntos, así sucesivamente hasta que el último candidato en el orden recibe cero puntos. Cada papeleta genera un puntaje, y luego todos estos son sumados para obtener los puntajes finales de cada candidato.

La cuenta de Borda es fácil de implementar pero no satisface el criterio de Condorcet. Tiene una debilidad particular en que su resultado puede estar fuertemente influenciado por la nominación de candidatos que no tienen ninguna oportunidad de ser elegidos.

Proceso

Se elige un número n igual o inferior al número de candidatos. Cada elector hace una lista de n candidatos por orden de preferencia.

Al primero de la lista se le atribuyen n puntos, al segundo n - 1 y así sucesivamente, hasta el n de la lista al que se asigna 1 punto. El resultado de cada candidato es la suma de los puntos que haya obtenido. Será elegido el o los candidatos que obtengan mayor número de puntos.

En caso de que n = 1, será el sistema de elección mayoritaria a una vuelta.

Un ejemplo
Imaginemos que a cuatro poblaciones se les pide que voten para determinar en cual de entre ellas se construirá un hospital para la zona.

Imaginemos por otro lado que en la población A reside el 42% de los votantes, en la población B el 26% de los votantes, en la población C 15% de los votantes y en la población D 17% de los votantes.

Es lógico pensar que cada votante desearía que el hospital esté lo más cerca posible de su domicilio, por lo que se obtendría la siguiente clasificación:

lo que lleva a al recuento de puntos siguiente:

Mientras que el sistema mayoritario simple hubiera llevado a construir el hospital en la población A, aquí la elección la gana la población B. En el caso propuesto, la eleección coincidiría con la obtenida utilizando el sistema Condorcet, pero no siempre sería así.

Usos del recuento Borda
En las elecciones parlamentarias de Eslovenia, se usa el método Borda para elegir a los representantes de las minorías italiana y húngara.
En Nauru, para las elecciones legislativas.
En Kiribati, para las primarias presidenciales.
En el Festival de la Canción de Eurovisión se utiliza un sistema derivado, en el que cada país escoge 10 canciones y les otorga una puntuación que va desde 1 a 8, 10 y 12 puntos.

Véase también

 Voto por orden de preferencia

Enlaces externos
Complexity of Control of Borda Count Elections: tesis de Nathan F. Russell.

Notas y referencias

Sistemas electorales preferenciales
Criterios del sistema electoral

redpajama_set_name: RedPajamaWikipedia


Run Or Dye - Los Angeles is a running event on May 26, 2014 at 9:00 AM in Los Angeles, CA.
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Posts Tagged 'deborah eisenberg'
Good stuff online: Deborah Eisenberg on PEN America and Charlie Hebdo
I've only belatedly caught wind of the exchange of communications between PEN America's Executive Director Suzanne Nossel and novelist Deborah Eisenberg about PEN America's decision to bestow an award for courage in freedom on expression on the French magazine Charlie Hebdo at its annual literary gala May 6 at the Museum of Natural History. PEN America's decision was highly controversial and caused a number of prominent writers to distance themselves from the event and from the organization, which in turn brought other writers to the defense. I have felt very uneasy about this particular honoring of Charlie Hebdo myself, for reasons I couldn't put my finger on. Eisenberg, in her brilliance, nails it.
Much is made these days of exposing snarky exchanges, especially those not meant for public consumption. The exchange between Eisenberg and Nossel (pictured below) doesn't fall into that category at all. Instead, these two women model thoughtful, respectful, extremely nuanced dialogue on a topic about which they fundamentally disagree. If you've ever wondered how to manage that trick, this document is worthy of study. Check it out and let me know what you think.
These letters (and others about the PEN America controversy) were posted on The Intercept, the website co-founded by the journalist Glenn Greenwald, whom Eisenberg correctly cites as a good example of someone who truly exercises courage in service of freedom of expression.
Posted in good stuff online | Leave a Comment »
Tags: charlie hebdo, deborah eisenberg, glenn greenwald, PEN america, suzanne nossel, the intercept
Performance diary: return to THE DESIGNATED MOURNER
7.27.13 — I went back to see The Designated Mourner, and I can testify that after five viewings (the David Hare film twice and three live performances) I'm still absorbing new passages and nuances from Wallace Shawn's extraordinary play about the demise of a politically independent intelligentsia from the perspective of a fellow traveler not especially unhappy about its disappearance. Somehow I'd never paid attention to the fleeting reference by Jack, the title character (played by Shawn himself in the Andre Gregory production at the Public Theater), to the moment when "my thing started – you know, mental problems or whatever you'd call them." Suddenly, the character's wayward cognitive associations and gaps in simple human empathy became clearer and more comprehensible to me. Over drinks afterwards, Dave and Tim and I tried to imagine how George W. Bush would describe life in America during his pathetic presidency – what events he would highlight and which he would omit that anyone else would consider important. And we talked a lot about the performances, especially that of Deborah Eisenberg, who plays Jack's wife Judy. I think most people who see the play will know that she and Wally Shawn are a couple offstage (they've been together 40-some years), but not everybody knows that Eisenberg is an exceptionally gifted fiction writer herself. Recipient of many big awards (including a MacArthur Foundation fellowship), she has published several collections of short stories, many of them actually quite long, many of them first published in the New Yorker. (You can read a long interview with her in the Paris Review's legendary "The Art of Fiction" series here.) She's not a trained or especially experienced actor, but her performance in The Designated Mourner is compelling for its combination of sculptural stillness and emotional fullness. We sat in the first row directly in front of the wooden chair she occupies for most of the show's three-hour running time, which gave us a perfect vantage point to study her amazing face.
When Andy and I saw the show a few weeks ago, we arrived just after curtain time (7:00! Not 7:30!) and weren't seated until 12 minutes into the show, when Wally departs from the script to give a brief recap to the latecomers. This time, there were about 10 spectators who arrived late, and as they were ushered in Wally gave them an entirely different spiel than I'd heard before, and apparently it was new to the other actors because Eisenberg and Larry Pine were discreetly cracking up while he was improvising about the scenes the latecomers had missed. After the show, Wally observed his tradition of standing by the exit available for conversation, and he told me this performance was the best in the run so far. "Only one sleeper," he noted. (Since the three actors speak most of the time directly to the audience rather than each other, they have plenty of time to study the crowd.) A good chunk of the audience, maybe 20 out of 99, left at intermission, but that didn't bother him at all: "It was better after they left."
Posted in performance diary | 2 Comments »
Tags: andre gregory, deborah eisenberg, larry pine, the designated mourner, wallace shawn


Founded by Robin Levine, Jacqueline Loeb, and Craig Perler, Scouted, formerly Snap!, is an employment platform geared towards campus recruitment. The company has developed a matching service that streamlines and modernizes the process both for employers and college students that are entering the workforce for the very first time. According to National Association of College and Employers in 2014, 91.4% considering branding a very or extremely important component of university recruiting while average cost-per-hire is ~$3600. The future may be bright for Scouted if it can nail the branding in an industry that obviously has some room to reduce acquisition costs for employers.


text:
Enjoy the peaceful scenery of Peggy's Cove
Grand Pré National Historic Site
Nova Scotia and Prince Edward Island
The Seafaring Heritage of the Canadian Maritimes
Ten days / nine nights
Travelers will enjoy spectacular scenery and fascinating history on our customized group tours:
Visit the original land of the ACADIANS, the people of Longfellow's EVANGELINE, the French farmers and fishermen who were evicted from French Canada and migrated to Louisiana. Learn their unfortunate tale of separation and immense personal tragedy amidst the ebbs and flows of 18th century geopolitics.
Sightsee the gorgeous COUNTRYSIDE of Canada's Maritime Provinces, named by National Geographic Traveler as one of "50 SPECIAL PLACES WORLDWIDE"
Visit the home of Anne of Green Gables on PEI, the place that inspired Lucy Maud Montgomery to write her beloved Anne novels.
Tour HALIFAX, with its magnificent harbor, Citadel fortress, and Maritime Museum featuring the city's connection to the Titanic. One of the world's most vibrant waterfronts, full of activity. Your schedule is leisurely paced to allow time to explore, shop and soak it all in.
Tour PRINCE EDWARD ISLAND, with its lush countryside and charming capital city of Charlottetown. Complete with a fresh lobster dinner.
Drive the CABOT TRAIL on CAPE BRETON ISLAND, voted by Conde Nast readers as THE MOST BEAUTIFUL ISLAND in the WORLD! Includes the delightful village of CHETICAMP.
Be amazed by the world's largest tides at the BAY OF FUNDY, over 40+ feet! You will be astounded at the HOPEWELL ROCKS, the flowerpot formations carved by the sea.
Enjoy museums featuring the life of ALEXANDER GRAHAM BELL, the mining and industrialization HISTORY of the Maritimes, and the FISHING and SAILING heritage of the provinces.
Visit the picturesque village of PEGGY'S COVE, often photographed, with its world famous lighthouse. Plus the UNESCO Heritage Site of LUNENBURG.
To include the stirring HALIFAX MILITARY TATTOO in your itinerary, go in June!


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Marcus Fisher | What Is It Like For You? 2000
Ashery went to meet the artist and barber Faisal Abdu'Allah at his hairdressing shop in Willesden. Together they designed a hair cut that will combine the Star-of-David symbol with a pattern that is commonly used by black young people coming to his shop (see also After Duchamp). Footage from this encounter features in the video.
Also in the video we see Ashery's orthodox Jew alter ego Marcus Fisher, running around Central Saint Martins College of Art & Design building in Charing Cross Road with a large portfolio, putting work up in the studio, dancing on the roof, sniffing hard boiled egg in the toilet, leaving a tag 'MF waz ere', and masturbating in one of the photographic darkrooms. This video was filmed during Ashery's MA at the art school.
Links/Downloads:
Additional project information: Video extracts can be viewed through the 7 Acts of Love project archive
Review: Cherry Smyth, 'Say Cheese, Home – Salon 17′, Circa (Spring 2002)


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Recently, Dan Sperber alerted us to ancedotal observations of grieving in non-human animals (see blog here – by the way, "anecdotal" is not derogatory here – our observations of grieving in humans are anecdotal too). Are the dead chimp's compagnons as baffled and shaken by their friend and relative's death as we would be?
In any case, bereavement in humans is difficult enough to describe and explain. This is an important topic for cognition and culture for many reasons – because it is of obvious interest to all human beings, because it is universal, because it is seemingly framed in such different ways in different places, and because the psychology is not well understood so far.
This used to be a topos for cultural anthropologists, who charted the many similarities in beliefs about death, and even more strikingly, the similarities in people's ritualization of behaviour around death.
The tradition of condidering the relevant psychological and social dynamics runs from Robert Hertz (A Contribution to the Study of the Collective Representation of Death), to a collection edited by Maurice Bloch and Jonathan Parry (Death and the regeneration of life). As with other important topics to do with cognition and recurrent features of human cultures, this was then more or less abandoned by mainstream cultural anthropologists.
There is no general human response to death, or mortality, as such. Indeed, our reactions vary from great pleasure (at an enemy's demise in battle, at a prey's final throes) to polite indifference (when reading most obituaries in the papers) to excruciating psychic pain (at the loss of a loved one). Evolutionary considerations would concur that there is no reason we should react to all mortality in the same way, since not all instances of death affect our fitness in the same way. We and other predators should derive positive rewards from our prey's death, but we and other social animals should experience an ally's death as a personal loss, which is indeed what we call it.
All this is fine, but probably not sufficient to explain the specific processes engaged in bereavement. To cultural anthropologists, the main question is, Why do people tend to engage in ritualized behavior following the death of a group member? People do all sorts of amazing things with corpses, and generally do them in a socially acquired, normative manner. For early sociologists like Hertz, this was because the loss affected "the group" and therefore had to be handled though "collective representations". Burials were a rite of passage to turn corpses into ancestors. But we do not believe in group intentionality or collective representations anymore, and the rite of passage is no explanation, since it itself is what we should try to explain. By the same token, let's don't say that these ritualized sequences provide solace or closure – these are question-begging labels, not proper explanations. What makes prescriptions of ritualized behavior attention-grabbing and compelling in these particular circumstances?
There is no straightforward answer to this, which is not too surprising as we have only a very rudimentary understanding of ritualized behavior in general (see this for a general survey and reasons why most anthropological "theories of ritual" are unsatisfactory). Moreover, we know surprisingly little about the cognitive processes triggered by the death of a friend or relative or acquaintance. Elsewhere I speculated that such events may throw a spanner in the works of our evolved cognitive systems. The main idea was this. The animacy components of death are early developed and result, as Clark Barrett demonstrated, from our understanding of predator-prey interaction. Most preschoolers find death unproblematic, if framed as the result of predation. In this sense our animacy systems have no trouble inferring that the corpse has no thoughts or sensations, does not move by itself, does not require food, and that these are permanent states. However, it is also clear that our social interaction systems (however many and different they are) are not hindered by physical constraints. We have rich and complex social interaction with imaginary friends, absent lovers, fictional characters or historical heroes, and some people add imagined agents like spirits and gods to the list. Part of the oddity and fascination of corpses – note that ritualized behaviors are overwhelmingly about what to do with the corpse – may stem from this strange situation in which different mental systems produce incompatible assumptions about the same object. In this sense a corpse would seem to constitute a rare case of a genuinely counter-intuitive physical object, about which different systems undermine each other's assumptions. But this is just speculation.
Another possibility, of course, is that we are disposed to entertain some notion of the afterlife, as Jesse Bering has suggested. I have nothing against this suggestion (or rather, I have too many arguments against it to fit in the margins of this discussion), but it seems more focused on what we would call ancestors than on corpses. The difference is that ancestors are what you typically get, in many human societies, after a second burial, once the meat-like substance of the corpse has been disposed of. What compels ritualized behavior is not ancestors but the corpse itself, or rather, our mental representations of the corpse – that is where the sting is.
I really like the idea developed by Pascal in Explaining religion that different mental systems produce incompatible assumptions about corpes. I'd like to add another system: morality. People may feel that they have duties toward the dead: taking care of them, respecting their dignity, etc. At the same time, they intuitively know that they can't fulfill their duties in the usual way. This counter-intuitive aspect of the relation with dead people may explain the cultural stabilization of ritualized and reflective behaviours.
Institutions again - What is a primitive society?
Pascal Boyer published "So you're saying … we should live like lobsters?" or: Why does politics make us stupid?
Pascal Boyer published Is the moral-economic fallacy universal?
Pascal Boyer published Why don't people like markets?
Pascal Boyer published What it is about women?
Pascal Boyer published Why are human beings so interested in explaining misfortune?
Pascal Boyer published Do people ever engage in "magical thinking" ?
Pascal Boyer published Death, where is thy sting ?
Pascal Boyer published Institutions again - What is a primitive society?
Pascal Boyer published What is an institution, that people may participate in it?


text:
ZOA Energy Named Official and Exclusive Energy Drink of the XFL
Stephan Rachuk· December 15, 2022
ZOA to serve as presenting sponsor of XFL kickoff in February and host in-stadium experiences for fans at all XFL games throughout the 2023 season
Arlington, TX, December 15, 2022 – The XFL today announced a partnership with ZOA Energy, the delicious better-for-you energy drink, establishing ZOA as the Official and Exclusive Energy Drink of the XFL. The XFL's much-anticipated return begins on February 18th, with ZOA as the presenting sponsor for the kickoff weekend and opening day games across the U.S.
We are thrilled to add ZOA Energy, a rapidly growing and exciting brand, to our list of League partners," said Russ Brandon, President of the XFL. "XFL fans always bring the energy on game day. In partnership with ZOA, we can ignite that energy and their passion for football with exclusive experiences and events throughout the season. The ZOA brand aligns strongly with ours – purpose-driven, fan-focused and innovative – and to collaborate in a way that connects with fans and players alike is exactly what we look for in a partner."
I couldn't be more excited to celebrate two of our dynamic brands coming together for the XFL's February kick-off," said Dany Garcia, Chairwoman and Owner of the XFL and ZOA Energy Founder. "Through this growing partnership, the XFL and ZOA Energy are opening the door for fans to participate in immersive activations and events – providing our community even more opportunities to engage with the league. We are thrilled to watch them bring these 360-degree fan experiences to stadiums across the country."
Throughout the season, ZOA and the XFL will roll out curated fan-first experiences and activations, both in and out of the stadiums. From pregame tailgates and fanfests to branded giveaways, tastings, and engaging promotions, loyal fans will have access to exclusive ticket offers for all home games. Further, every end zone in each stadium will be transformed into the official "ZOA Energy Fan Zone," where the most spirited fan will be honored as the "ZOA Energy Fan of the House."
As part of the partnership, ZOA+ pre-workout supplement will be sponsoring the XFL Weight Room, capturing content that provides fans across the country with a sneak peek into the players' performance and training at the XFL's Hub in Arlington. Intentionally crafted to prepare the body and mind for greatness, ZOA+ pre-workout supplement is designed for those who want to unleash their full potential.
Exciting news as our two passionate and disruptive brands of the XFL and ZOA Energy converge into this great partnership," said Dwayne Johnson, Owner of the XFL and ZOA Energy Founder. "Our ZOA Energy team will be immersed in each XFL city and stadium across the country as we proudly help create one-of-a-kind experiences that fans, players, coaches, and everyone will love and enjoy."
XFL names Fred Harner as SVP Content and Media
Stephan Rachuk· June 17, 2019
NewsPress ReleaseXFL
Launched in 2021, ZOA is the fastest-growing, health-minded energy drink brand in the U.S. market and cuts through the noise of the industry with one clear focus: better-for-you energy that gets you where you want to be. Designed with natural caffeine for energy, each 12-ounce can deliver vitamins, electrolytes, and amino acids for sustained energy with immune system support.
The XFL 2023 season will kick off on February 18th in partnership with its exclusive broadcast partner, The Walt Disney Company and ESPN. For more information on tickets, please visit, XFL.com/tickets.
About XFL
The XFL's ownership group, led by Dany Garcia, Dwayne Johnson, and Gerry Cardinale's RedBird Capital Partners, is building a fan-first, fast-paced global professional football league with innovative rules and enhanced 360 game experience. The XFL will bring entertainment to world class football, with the goal of advancing the game of football and expanding player opportunities when it launches in February 2023. For more information, XFL.com and follow us on Twitter, Facebook, and Instagram.
About ZOA Energy
ZOA Energy is here to activate the world's potential: giving people the fuel they need to show up as their best selves. Developed by strength and conditioning coach, Dave Rienzi; global icon and businessman, Dwayne Johnson; global entrepreneur and chairwoman, Dany Garcia; and entrepreneur and investor, John Shulman, ZOA is taking the category to new heights. The ZOA portfolio features both better-for-you energy drinks and a pre-workout supplement––all of which are made with high quality ingredients, natural flavors and colors, and natural caffeine from green tea and green coffee for clean energy. For more information, please visit ZOAEnergy.com. Follow us on Twitter, Instagram and Facebook.
XFLXFL 2023ZOA
Stephan Rachuk
Owner/Editor XFL Newsroom
Originally from Detroit, I grew up a Lions fan, which has now led me on a lifetime journey to find better football. I'm now living in the greater Houston area and I am one of the few journalists covering spring football. Contact: stephan@rachuk.com
Christian Paolantonio· February 7, 2023
ColumnsXFL
Stephan Rachuk· February 6, 2023


On the day when the great John Hughes, director of one of my favourite films sadly passed away, Nat and I went to the Campbell Lace Beta Breakfast Club for Campaign bloggers and commenters. It was great fun, the food was delicious and we learned many things.
One, that you don't need a fancy sign above the door to open an agency. Two, that Campbell Lace have a row of awesome little thinking pods, where, once installed, you're on full display to all Carnaby shoppers while you brainstorm. And three, that 'thumbs up' and 'double thumbs up' are now back in fashion – according to fellow ex-BBHer, Mark Threlfall (pictured in action).
Thanks very much for having us guys. Nice to meet you in person!
And good to put a face to a name with Campaign's Colin, our long-suffering web-editor, who has been there for us in times of techno-mares for over a year now (6th pic down on the left).
P.s. Does Barry Manilow know that you raid his wardrobe?


text:
## Continuous Target Data ($y\in \mathbb{R}$)

```python
import numpy as np
import matplotlib.pylab as plt
%matplotlib inline
```

```python
from sklearn.datasets import load_boston

boston = load_boston()
```

```python
boston.keys()
```

    ['data', 'feature_names', 'DESCR', 'target']

```python
print(boston.DESCR)
```

    Boston House Prices dataset

    Notes
    ------
    Data Set Characteristics:

        :Number of Instances: 506

        :Number of Attributes: 13 numeric/categorical predictive

        :Median Value (attribute 14) is usually the target

        :Attribute Information (in order):
            - CRIM     per capita crime rate by town
            - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
            - INDUS    proportion of non-retail business acres per town
            - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
            - NOX      nitric oxides concentration (parts per 10 million)
            - RM       average number of rooms per dwelling
            - AGE      proportion of owner-occupied units built prior to 1940
            - DIS      weighted distances to five Boston employment centres
            - RAD      index of accessibility to radial highways
            - TAX      full-value property-tax rate per $10,000
            - PTRATIO  pupil-teacher ratio by town
            - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
            - LSTAT    % lower status of the population
            - MEDV     Median value of owner-occupied homes in $1000's

        :Missing Attribute Values: None

        :Creator: Harrison, D. and Rubinfeld, D.L.

    This is a copy of UCI ML housing dataset.
    http://archive.ics.uci.edu/ml/datasets/Housing

    This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.

    The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
    prices and the demand for clean air', J. Environ. Economics & Management,
    vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics
    ...', Wiley, 1980.   N.B. Various transformations are used in the table on
    pages 244-261 of the latter.

    The Boston house-price data has been used in many machine learning papers that address regression
    problems.

    **References**

       - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
       - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
       - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)

```python
print(boston.feature_names)
```

    ['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'
     'B' 'LSTAT']

```python
print(boston.data.dtype)
```

    float64

```python
print(boston.target.dtype)
```

    float64

```python
train = boston.data
test = boston.target
```

### Linear Regression
Linear regression fits the data to the follow equation

$\hat{y}_i=\beta_0+\sum_j^p\beta_jx_{i,j}=\beta_0+\mathbf{x}^T\beta$

where $i=1,...,n$ and $p$ is the number of predictors

The error produced by this equation is

$\epsilon_i=\hat{y_i}-y_i$

For linear regression, the goal is the sum of the errors by using least squares

$\min\left|\sum_i^n\epsilon_i^2\right|$

```python
from sklearn import linear_model
lin_reg = linear_model.LinearRegression()

model = lin_reg.fit(train, test)
print(model.score(train, test))
```

    0.740607742865

```python
plt.plot(model.predict(train), test, 'ro')
plt.plot([0,50],[0,50], 'k', linewidth = 2)
plt.xlabel('Predicted Value')
plt.ylabel('True Value')
plt.axis('square')
plt.ylim([-10, 55])
plt.xlim([-10, 55])
```

    (-10, 55)

![png](output_12_1.png)

### Ridge Regression
Sometimes there is an issue where the model over-fits the data.  For example, the following figure shows a sine curve fit with a 3 and 9 degree polynomial

When this happens, the solution is not always clear.  So one way is to use ridge regression, which can be used to surpress certain features of the model that cause overfitting.  Rigde regression uses the same model as linear regression but instead of using least squares, ridge regression attempts to minimize

$\min\left|\sum_i^n\epsilon_i^2 + \lambda\sum_j^p\beta_j^2\right|$

Clearly, compared to least squares, this penalizes the squared value of the coefficients.  when this is used, the previous figure has the following result

In terms of using this in sklearn the following code can be used.

```python
from sklearn import linear_model
ridge_reg = linear_model.Ridge(alpha=1.0,  # regularization
                               normalize=True,  # normalize X regressors
                               solver='auto')  # options = 'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag'

model = ridge_reg.fit(train, test)
print(model.score(train, test))
```

    0.635014072122

```python
plt.plot(model.predict(train), test, 'ro')
plt.plot([0,50],[0,50], 'k', linewidth = 2)
plt.xlabel('Predicted Value')
plt.ylabel('True Value')
plt.axis('square')
plt.ylim([-10, 55])
plt.xlim([-10, 55])
```

    (-10, 55)

![png](output_16_1.png)

The error rate actually decreased, by making a simplier model.

### Lasso Regression
A very similar method to ridge regression is lasso regression.  The difference is that the function to minimize is

$\min\left|\sum_i^n\epsilon_i^2 + \lambda\sum_j^p|\beta_j|\right|$

Thus, lasso penalizes the abosulte value of the coeffiecients.  These two methods are commonly visualized as

In sklearn this can be used as

```python
from sklearn import linear_model
lasso_reg = linear_model.Lasso(alpha=.10,  # regularization
                               normalize=True)  # normalize X regressors

model = lasso_reg.fit(train, test)
print(model.score(train, test))
```

    0.582783031405

```python
plt.plot(model.predict(train), test, 'ro')
plt.plot([0,50],[0,50], 'k', linewidth = 2)
plt.xlabel('Predicted Value')
plt.ylabel('True Value')
plt.axis('square')
plt.ylim([-10, 55])
plt.xlim([-10, 55])
```

    (-10, 55)

![png](output_20_1.png)

### Elastic Net
Another method to minimize overfitting is the elastic net method.  This is a combination of lasso and ridge.  Thus, this minimizes the following

$\min\left|\sum_i^n\epsilon_i^2 + \lambda_1\sum_j^p|\beta_j|+\lambda_2\sum_j^p\beta_j^2\right|$

This is in sklearn as

```python
elastic_reg = linear_model.ElasticNet(alpha=1.0,  # penalty, 0 is OLS
                               selection='cyclic')  # or 'random', which converges faster

model = elastic_reg.fit(train, test)
print(model.score(train, test))
```

    0.686093731552

```python
plt.plot(model.predict(train), test, 'ro')
plt.plot([0,50],[0,50], 'k', linewidth = 2)
plt.xlabel('Predicted Value')
plt.ylabel('True Value')
plt.axis('square')
plt.ylim([-10, 55])
plt.xlim([-10, 55])
```

    (-10, 55)

![png](output_23_1.png)

### Polynomial Fits
While linear regression is nice, there is often a need for higher order parameters and cross terms, such as $x^2$ or $x_1x_2$.  In this case the following programs can be used to add the polynomial terms.

```python
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline

model = make_pipeline(PolynomialFeatures(3), ridge_reg)
model.fit(train, test)
print(model.score(train, test))
```

    0.824145841871

```python
plt.plot(model.predict(train), test, 'ro')
plt.plot([0,50],[0,50], 'k', linewidth = 2)
plt.xlabel('Predicted Value')
plt.ylabel('True Value')
plt.axis('square')
plt.ylim([-10, 55])
plt.xlim([-10, 55])
```

    (-10, 55)

![png](output_26_1.png)

### Random Forest

```python
from sklearn import ensemble

rf_reg = ensemble.RandomForestRegressor(n_estimators=10,  # number of trees
                                        criterion='mse',  # how to measure fit
                                        max_depth=None,  # how deep tree nodes can go
                                        min_samples_split=2,  # samples needed to split node
                                        min_samples_leaf=1,  # samples needed for a leaf
                                        min_weight_fraction_leaf=0.0,  # weight of samples needed for a node
                                        max_features='auto',  # max feats
                                        max_leaf_nodes=None,  # max nodes
                                        n_jobs=1)  # how many to run parallel

model = rf_reg.fit(train, test)
print(model.score(train, test))
```

    0.97957237416

### Boosting

```python
ab_reg = ensemble.AdaBoostRegressor(base_estimator=None,  # default is DT
                                    n_estimators=50,  # number to try before stopping
                                    learning_rate=1.0,  # decrease influence of each additional estimator
                                    loss='linear')  # also 'square', 'exponential'

model = ab_reg.fit(train, test)
print(model.score(train, test))
```

    0.900938875655

## Discrete Target Data (classification problem)
### Logistic Regression
For this type of problem the target data takes on discretet values, for example the target be 'yes' or 'no' for a disease, or equivalently '1' or '0'

```python
n_samples = 100
train = np.random.normal(size=n_samples) # make random data set
target = (train > 0).astype(np.float) # make half the values true/1/yes
train[train > 0] *= 2
train += .3 * np.random.normal(size=n_samples) # create abiguity near the transition

train = np.reshape(train, [n_samples, 1])
target = np.reshape(target, [n_samples, 1])
```

```python
plt.plot(train, target, 'o')
plt.ylim([-.1,1.1])
plt.xlabel('Predictor')
plt.ylabel('Target')
```

    <matplotlib.text.Text at 0x11558ff90>

![png](output_33_1.png)

The data above shows that the predictor can estimate the target.  However, if we fit the data with a linear fit, the solution is rather ambigous.

```python
lin_reg = linear_model.LinearRegression()

model = lin_reg.fit(train, target)
```

```python
plt.plot(train, target, 'o')
plt.plot(train, model.coef_*train + model.intercept_, 'r-', linewidth=2)
plt.ylim([-.1,1.1])
plt.xlabel('Predictor')
plt.ylabel('Target')
```

    <matplotlib.text.Text at 0x113835450>

![png](output_36_1.png)

With a linear model, it is hard to really descide what value for the predictor results in a '1' or '0'.  Also hard to really make sense of the negative target predictions.

So, instead we can use logistic regression which fits the following equation

$P(x) = \frac{1}{1+\exp(-\mathbf{x}^T\beta)}$

where again we are trying to fit the coefficeints $\beta$ by using least squares.  Here P(x) can be understood as the probability of predicting a '1'

```python
log_reg = linear_model.LogisticRegression()
target = np.reshape(target, [n_samples, ])
logistic_fit = log_reg.fit(train,target)

logistic_line  = 1/(1+np.exp(-(logistic_fit.coef_*sorted(train)+logistic_fit.intercept_)))
```

```python
plt.plot(train, target, 'o')
plt.plot(train, model.coef_*train + model.intercept_, 'r-', linewidth=2)
plt.plot(sorted(train), logistic_line, 'k-', linewidth=2)
plt.ylim([-.1,1.1])
plt.xlabel('Predictor')
plt.ylabel('Target')
plt.legend(('data', 'linear', 'logistic'), loc="lower right")
```

    <matplotlib.legend.Legend at 0x115a6e890>

![png](output_39_1.png)

Logistic regression removed negative values and values greater than 1.  Also, thresholding is more intuitive since we are predicting the probability now.  Thus, if we were doing disease prediction, we could threshold depending on how many false positives we want for example by increasing or decreasing the threshold.

However, logistic regression becomes more complicated when there is more than 2 classifications, say even three.  One method is to do logistic regression for each classification.  But usually, it is better to use other methods.

## Multi-class dataset

```python
import sklearn.datasets
```

```python
num_classes = 4
train, target = sklearn.datasets.make_classification(n_samples=100, n_features=2,
                                                     n_informative=2, n_redundant=0, n_repeated=0,
                                                     n_clusters_per_class=1, n_classes=num_classes)
```

```python
for group in range(num_classes):
    plt.plot(train[target==group][:,0],train[target==group][:,1], 'o')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
```

    <matplotlib.text.Text at 0x113835550>

![png](output_43_1.png)

Here I have plotted each of the classes as a different color.

### Naive Bayes
The first method to approach this is naive bayes, which got its name for using bayes theorem to predict the classifer.
The goal of the method is to find the classifier of a point based on the maximum of the probabilies

$P(y|\mathbf{x})$

which with bayes theorem becomes

$P(y|\mathbf{x}) = \frac{P(y_i)P(\mathbf{x}|y)}{P(\mathbf{x})}$

if we assume independence between the different features of x, then

$P(\mathbf{x}|y) = \prod_i^nP(x_i|y)$

Thus, we can make our prediction based on

$\hat{y} = \max P(y)\prod_i^nP(x_i|y)$

And lastly, we can then assume the shape of $P(x_i|y)$, often assumed to be a multivariate gaussian.

```python
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()

gnb_fit = gnb.fit(train, target)

gnb_pred = gnb_fit.predict(train)

print((gnb_pred!=target).sum()/100.)
```

    0.19

```python
for group in range(num_classes):
    plt.plot(train[target==group][:,0],train[target==group][:,1], 'o')
plt.plot(train[gnb_pred!=target][:,0], train[gnb_pred!=target][:,1], 'kx', markersize = 10)
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
```

    <matplotlib.text.Text at 0x1161c7dd0>

![png](output_46_1.png)

```python
side = np.linspace(-3,3,100)
X,Y = np.meshgrid(side,side)

X = np.reshape(X, [-1])
Y = np.reshape(Y, [-1])

points = np.column_stack((X,Y))

pred = gnb_fit.predict(points)

for i in range(num_classes):
    plt.plot(points[pred==i][:,0],points[pred==i][:,1],'o')
```

![png](output_47_0.png)

The nice about this method is that it is fairly simple to excute and understand.  However it is only accurate when classes are well defined locally, which may not always be the case.

### K-nearest neighbors
The next method that could be used is k nearest neighbors, or knn.  This method can also work very well for continues data, but I think it is easier to visualize and explain as a classification model.

This method works on the idea that if you look to the k train data points nearest to the point x, then you can determine the class based on the most common class of the nearest points.  Often, euclidean distance is used for this, but other methods of deciding the nearest neighbor is possible, such as weighting the strength of nearest neighbor based on its distance.

If k == 1, this is equivalent to using a step function.

```python
from sklearn import neighbors
nbrs = neighbors.KNeighborsClassifier(n_neighbors=1)
nbrs_fit = nbrs.fit(train, target)

nbrs_pred = nbrs_fit.predict(train)

print((nbrs_pred!=target).sum()/100.)
```

    0.0

```python
for group in range(num_classes):
    plt.plot(train[target==group][:,0],train[target==group][:,1], 'o')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
```

    <matplotlib.text.Text at 0x111b99cd0>

![png](output_50_1.png)

```python
pred = nbrs_fit.predict(points)

for i in range(num_classes):
    plt.plot(points[pred==i][:,0],points[pred==i][:,1],'o')
```

![png](output_51_0.png)

However, using k = 1 is overfitting the data. We will try using 15 now.  It will increase the number of incorrect train data that are mislabeled, but should increase the test error rate

```python
from sklearn import neighbors
nbrs = neighbors.KNeighborsClassifier(n_neighbors=15)
nbrs_fit = nbrs.fit(train, target)

nbrs_pred = nbrs_fit.predict(train)

print((nbrs_pred!=target).sum()/100.)

pred = nbrs_fit.predict(points)

for i in range(num_classes):
    plt.plot(points[pred==i][:,0],points[pred==i][:,1],'o')
```

    0.21

![png](output_53_1.png)

While k nearest neighbors is a very powerful classifing algorithm, it suffers from dimensionality.  This method works great for the above example because we only have 2 features, but as the feature size increases to large numbers, the euclidean space increases by power law.

### SVM (support vector machine)
A method that overcomes the dimensionality issue of knn, and overcomes the simpliest of naive bayes, is SVM, support vector machines.  It is probably one of the most popular machine learning algorithms.  It can be used for both classification problems and continues target problems.  The disadvantage is the computational cost of the method.

SVM is another model that can be used for continous target problems, but I feel is easier to understand on a classification problem.

First, the idea behind SVM is to fit a hyperplane that divides the different classes.  However, determining the best hyperplane can be complex as shown in the following figure

Clearly, the decision is arbitrary.  SVM thus tries to maximize the distance of the points to the plane, by using the maximal margin classifier.  However, this idea clearly will only work when the points are serperable by a plane.  To correct for this, SVM was created with a generalized method that can use any K(x) kernel.  The previous example, used a linear kernel, but gaussian, polynomial, and many other kernels also exist.  The effects of kernel can be seen in the following image

This method has had great success.  To use it in sklearn is similar to the previous methods. By default, this uses RBF kernel

```python
from sklearn import svm
svm_method = svm.SVC()
svm_fit = svm_method.fit(train, target)
```

```python
for group in range(num_classes):
    plt.plot(train[target==group][:,0],train[target==group][:,1], 'o')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
```

    <matplotlib.text.Text at 0x111d4e3d0>

![png](output_58_1.png)

```python
pred = svm_fit.predict(points)

for i in range(num_classes):
    plt.plot(points[pred==i][:,0],points[pred==i][:,1],'o')
```

![png](output_59_0.png)

This used the gaussian kernel.

## Organizing Your Data
### Train/test data sets
A crucial part of machine learning is obtaining a training data set to build the model and a testing data set to verify the success of the model.  The purpose of a new test data set that was not used in building the model is that if the model is tested against the training data, the model may be overfit.  For example, if the model fits the train data perfectly, there is a good chance that the predictions on new data are poor.

The above figure shows how a high order polynomial can overfit a simple line.  Thus, it is important to split the data into train and test data, and a strong model will have a low test data error rate.

To do this with sklearn, there is a package to split data sets into train and test

```python
from sklearn.cross_validation import train_test_split

boston = load_boston()

X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target,
                                                    train_size=0.8, test_size=0.2)
```

The above function splits the data into 80% train and 20% test data.  Generally, it is recommended to use somewhere around a 80:20 split.  Now in all the previous models, the predict function can be used on the X_test data and the error can be tested on y_test.  This method helps prevent against over fitted modeling.

### Cross-validation
One issue with the above method of splitting the data is that we did it randomly and blindly.  This can lead to high variance in the models, if the split accidentally places all of one class in the train and all of a different class in the test data.  Another issue is, by splittling the data 80:20, we have reduced the sample size of the data we are using to build our model.  What if we want to keep the same sample size but also have a test set?  Similaly, if the dataset is originally small, we may want to not waste train data.

One way to overcome both of these issues is to use cross validation.  In cross validation, we split the data into train data and test data, but we do this multiple times such that we build a model with all data.  We then get a final model by averaging the multiple models.

Several methods for this exist, one such method is called leave-one-out cross-validation.  This splits the data into train and test sets, where the test set only has one case.  The model is created for each of the n-1 possible train sets.  This works well until n becomes very large.

Another common validation method is k-fold cross validation, this splits the data set into k even groups, and makes k models with each group used as a test set once.  The final model is then averaged. Using cross-validation helps greatly with the bias-variance trade-off in machine learning.

To do cross-validation in sklearn, the model is first chosen then provided to the following method

```python
from sklearn.model_selection import cross_val_predict
lin_reg = linear_model.LinearRegression()

predicted_values = cross_val_predict(lin_reg, boston.data, boston.target, cv=5)

```

    ---------------------------------------------------------------------------

    ImportError                               Traceback (most recent call last)

    <ipython-input-42-62b486a49643> in <module>()
    ----> 1 from sklearn.model_selection import cross_val_predict
          2 lin_reg = linear_model.LinearRegression()
          3
          4 predicted_values = cross_val_predict(lin_reg, boston.data, boston.target, cv=5)

    ImportError: No module named model_selection

The previous code uses 5-fold cross-validation to predict the values for the boston data with a linear model.

### PCA
Sometimes, there are too many features to describe the target, maybe some features are duplicates or combinations of other features, or maybe some features just dont correlate well with the target.  In these cases it is good to reduce the feature space, because the lower the dimension the easier it is to create a model and the easier it is for us to comprehend with our simple brains.

A common way to reduce the feature space is to use principle component analysis.  PCA computes the directions that best describe the data.  Here is a 2-d exmaple

This shows that pca will change from x,y to vectors that better describe the data.  PCA in sklearn is determined by SVD and can be used with the following commands

```python
from sklearn.decomposition import PCA
pca = PCA(n_components=None) #returns the same number of features as the original set
pca_fit = pca.fit(boston.data)
print(pca.explained_variance_ratio_)

```

    [  8.05814643e-01   1.63050854e-01   2.13486883e-02   6.95695814e-03
       1.30786367e-03   7.27742761e-04   4.20024207e-04   2.48721489e-04
       8.53904192e-05   3.08110199e-05   6.65495193e-06   1.56785857e-06
       7.96571657e-08]

The printed values show the significance of the principle compnent.  Thus, we can reduce the feature space by say only keeping the principle components that explain more than 90% of the data, which means we would only keep the first two principle compenents here.

```python
pca = PCA(n_components=3) #returns 3 componenets
pca_fit = pca.fit(boston.data)
print(pca.explained_variance_ratio_)
```

    [ 0.80581464  0.16305085  0.02134869]

Now we can transform the data to use in the models

```python
new_boston = pca.fit_transform(boston.data)
```

Now we can use this in our models.  We now only have 2 features, thus building the models will be far simplier.

```python
lin_reg = linear_model.LinearRegression()

model = lin_reg.fit(new_boston, test)
print(model.score(new_boston, test))

plt.plot(model.predict(new_boston), test, 'ro')
plt.plot([0,50],[0,50], 'k', linewidth = 2)
plt.xlabel('Predicted Value')
plt.ylabel('True Value')
plt.axis('square')
plt.ylim([-10, 55])
plt.xlim([-10, 55])
```

    0.287702290199

    (-10, 55)

![png](output_75_2.png)

The error rate may have increased, but we can see that a majority of the prediction has been maintained while significantly decreasing the complexity of the model.

## Auto ML
For a lot of these methods, there are a of parameters that need to be tuned in order to best optimize the model.  One way to do this is simply to iterate over a range of parameters to try and find a optimize model.  However, there are packages created that will do this for us.  One such package is

from autosklearn.regression import AutoSklearnRegressor

This package will auto tune the parameters to best fit the model.

## Ensemble averaging
As a last note I will point out that one of the popular and successful methods to use in machine learning in ensemble averaging.  For this, one would great several cross validated models for the training data, meaning for a classification problem, one might use naive bayes, knn, svm, LDA, etc.  Then they obtain the predicted classes for each model, and average over the models (sometimes the average is weighted to prioritize different models).  I have personally seen the test error rate significantly decreases by simply ensembling different methods.

The error decreases because different models have different strengths and different weaknesses, and by averaging over them, the errors of each model can be decreased.

There are packages that can do this ensembling for you, or you can just run each model and average yourself.

## Last Thoughts of Sklearn
As I mentioned, I originally learned all these methods for R, however, I can safely say that learning to use these methods is python was not very difficult, I believe it is because of the better documentation and snytax of sklearn, but it could be because of prior knowledge from using R.  Overall, I would say that sklearn is a lot nicer to use though than R.

```python

```

```python

```

redpajama_set_name: RedPajamaGithub


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I would like to focus at this moment on the study of the rivet pattern made by Jay Vincelli. The attachment shows the CAD model overlaid upon the image of the "bumps" from the 2010 High Definition Video.
May I call your attention, please, to the one rivet where the center in the model differs slightly (less than a rivet's radius) from the center of the perceived rivet in the video. The CAD model, of course, was developed from the rivet pattern under the pilot's window of Grace McGuire's Lockheed 10E Electra. It should not be surprising if the rivets on one Electra differ slightly from those on another Electra: after all, I think it is safe to say that all the riveting was done by hand in the 1930s, and not by robotic machines.
In his study, Jay placed the pattern of the CAD at the forward end of the line of rivets under the pilot's left window. But the rivet with the offset center might be anywhere along the entire length of the window frame. So I have asked Jay to see if, from actual photos of the Earhart Electra, he could determine if there actually existed a matching "off-center-rivet". If so, we might be able to attain higher fit than the 84% that was calculated with respect to the McGuire Electra window frame.
Not that the 84% is shabby, of course.
The interpretation of underwater photographs has to this point been accomplished using the analysis described by Jeff Glickman in TIGHAR's Research Bulletin #63 printed in 2013. The nine criteria used by Jeff in analyzing the potential of an artifact are shown in the attachment hereto.
1. Differences in organic (natural) vs. man-made shapes. My opinion is that the parallel sides to the track, as well as the regular, alternating, pattern of the bumps, puts this artifact squarely in the man-made column.
2. Texture differences between objects within a neighborhood. The second attachment, showing the artifact, demonstrates vivid contrast with the surrounding surface and objects.
3. Pattern differences between objects within a neighborhood. Ditto. Nothing nearby looks anything like the artifact in question.
4. Color differences within multiple color spaces within a neighborhood. The maroonish color of the artifact contrasts vividly with all surrounding objects.
5. Color absorption differences (spectrometry) between specific objects. I do not have the equipment or expertise to make any determination in this case.
6. Temporal similarities vs. differences between video frames. The integrity of the artifact is not diminished either frame-to-frame or between various sets of frames (the artifact appears on multiple occasions in the 8.55 minute High Definition video from 2010: in the two attachments below, the respective time stamps are 13:41:04;12 and 13:43:48;24).
7. Object size and scale information which may be relative, absolute and ratiometric (mensuration). The third attachment places the artifact in context: it sits to the left of the open cockpit of the Electra (see third attachment) wherein the pilot's seat cushion is clearly visible (IMHO) and the relative sizes of seat cushion and window slide are reasonably comparable.
8. Object interaction with its environment. The artifact rests on the side of the fuselage, as I interpret the photos, outside the aircraft's cockpit.
9. Location information including absolute position, relative position, and context. Absolute position is at a depth of 255 meters, down-slope from the presumed position of the "Bevington Object", as sought by the ROV operators on the Niku VI expedition. Relative position and context have been covered above.
I am convinced that this artifact is a section of the window slide from the pilot's window of the Lockheed Electra, registration NR16020, lost in July of 1937.
I'll grant you that is an interesting pattern, although I still have major reservations.
I love the process you are working at with this though. At the end of the day it's that determination to deliberately keep cutting to the truth that counts.
Thanks for contributing to that process. I'm betting you will get some interesting discussion going.
I've outlined the vertical 'line of rivets' in yellow, and an area where some sort of junction seems suggested with green.
Can't tell for sure, but the vertical line may be rubble - although there is an fascinating pattern there. Something about the shadowing really makes me think at least some of this is natural - maybe some subtle irregularities among shapes that suggest a man-made pattern. But it is an interesting view.
Had not thought of a sea cucumber, interesting too. It is a complex world down there after all.
Jeff, the Supplemental Report hereto attached shows that the piece you have circled is the window track in question. I have not posted it till today because I was hoping to wait for the final report that may have some further refinements of the analysis. The close-up of the rivets shown at the top of this thread comes from that same "vertical" piece.
Anyone please correct me, but isn't this depth below the reach of sunlight and natural photosynthesis needed for such as sea cucumbers, etc.? Maybe Tim could "enlighten" us re exact depth. If it's in the range of say 900' it would be totally dark – I think the base of the photosynthesis zone is somewhere around 600'??
The object lies at 255 meters of depth according to the 2010 Standard Definition video. That corresponds to 837 feet.
Flora is possible at this depth, but if it were a sea cucumber, I think we would see other examples hither and yon in the other 2010 and 2012 underwater videos. I dare say window slides (if that 's what it turns out to be) are not nearly so ubiquitous.
I guess I'm still trying to figure out how the 'horizontal' piece at the 'top' fits into the 'vertical' piece (what is their relationship). They do not appear to be juxtaposed in a way that fits the window frame / slide arrangement as we see it on the Electra (your report pictures are very nice). So I guess that leaves me wondering how this neatly 'crossed T' fits what we observe on the Electra cockpit windows. If there was a vertical element on the airplane that intersected the horizontal channel then it would make more sense to me - have I missed something?
With regard to the discussion of photosynthesis and flora at depth: A sea cucumber is an animal, not a plant. No photosynthesis. They are found as deep as 2000 meters (6500 feet), according to this website: http://www.divernet.com/Marine-Life/951174/the_secret_lives_of_sea_cucumbers.html.
So there is no reason why it cannot be a sea cucumber at this depth. Whether it is, and whether those are rivets or bumps on an echinoderm, or something else entirely, is another matter of course.
The "vertical" piece in the picture is presumed to be the horizontal track in which the pilot's side window slides back and forth.
The "horizontal" piece in the photo has not been placed on the airplane. It may or may not actually be attached to the vertical piece. But it is clearly of similar genre, that is to say, evenly spaced rivet-like bumps in a strip of similar material, presumably metal, possibly aluminum.
And the bumps on the surface of sea cucumbers just don't seem as regular as those in the photo object.
It is the horizontal piece that is such an eye-grabber to me, regarding the 'rivet' placement consideration. I just couldn't account for how it related to the vertical piece - but as you noted, we don't know that they are connected.


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text:
Les Fraises sauvages () est un film suédois réalisé par Ingmar Bergman, sorti en 1957.

Synopsis
La veille de la cérémonie qui doit honorer et célébrer sa longue carrière de médecin, le professeur Isak Borg fait un rêve étrange où il est confronté à sa propre mort. Le lendemain, il décide de partir en voiture à l'université de Lund en compagnie de Marianne, sa belle-fille. Durant le trajet, le vieux professeur fait le bilan d'une vie gâchée par l'égoïsme. Il revoit sa jeunesse avec "le coin des fraises sauvages" où l'entraînait sa cousine. Puis il évoque ses souvenirs de sa vie de médecin de campagne.

Tandis que Marianne conduit, Isak s'endort et fait un rêve où il s'adresse des reproches. Après avoir été déclaré "coupable de culpabilité", il se voit reprocher sa froideur. Puis il revoit l'infidélité de sa femme quarante ans auparavant...

Après un dernier reproche que Marianne adresse à son beau-père au sujet d'un enfant qu'elle aurait voulu garder malgré l'opposition de son mari, Isak est solennellement couronné par l'université de Lund. Avant de s'endormir, il tente de réconcilier son fils et sa belle-fille. Puis il rêve aux scènes heureuses de son enfance.

Fiche technique
 Titre : Les Fraises sauvages
 Titre original :
 Réalisation : Ingmar Bergman
 Scénario : Ingmar Bergman
 Musique : Erik Nordgren et Göte Lovén
 Photographie : Gunnar Fischer
 Montage : Oscar Rosander
 Décors : Gittan Gustafsson
 Costumes : Millie Ström
 Son : Aaby Wadin et Lennart Wadin
 Production : Allan Ekelund
 Société de production : Svensk Filmindustri
 Pays d'origine :
 Langue originale : suédois
 Format : noir et blanc - 1,37:1 - 35 mm - mono
 Genre : comédie dramatique
 Durée : 91 minutes
 Dates de sortie :
 Suède :
 France :

Distribution
 Victor Sjöström  (V.F : Paul Ville) : Isak Borg
 Bibi Andersson : Sara
 Ingrid Thulin  (V.F : Jacqueline Porel) : Marianne, la femme d'Evald
 Gunnar Björnstrand  (V.F : Gabriel Cattand) : Evald, le fils d'Isak
 Folke Sundquist (V.F : Michel François)  : Anders
 Björn Bjelvenstam : Viktor
 Max von Sydow  (V.F : Jacques Thebault) : Henrik Åkerman
 Gunnel Lindblom : Charlotta
 Jullan Kindahl  (V.F : Yvette Andréyor)  : Agda, la gouvernante d'Isak
 Gunnar Sjöberg  (V.F : Georges Aminel) : Sten Alman
 Per Sjöstrand  (V.F : Hubert Noel) : Sigfrid Borg
 Sif Ruud : la tante Olga
 Gertrud Fridh : Karin, la femme d'Isak
 Naïma Wifstrand : la mère d'Isak

Commentaires

Dans la filmographie d'Ingmar Bergman, les Fraises sauvages suit Le Septième Sceau (Det sjunde inseglet, 1957). Les deux films sont un succès immense pour le réalisateur qui a acquis une véritable reconnaissance avec Sourires d'une nuit d'été (Sommarnattens leende, 1955).

Le film a pour acteur principal Victor Sjöström, par ailleurs pionnier du cinéma suédois. Ingmar Bergman lui avait déjà confié un rôle dans Vers la joie (Till glädje). Les Fraises sauvages est sa dernière contribution cinématographique.

Les rêves répétés, les discussions avec sa bru ou sa gouvernante, les rencontres (Sara, une autostoppeuse, jouée par Bibi Andersson qui tient aussi le rôle d'un amour de jeunesse), font prendre conscience au vieux médecin de son égoïsme, de sa solitude et de l'échec de sa vie, vie qui doit bientôt s'achever. Cependant, les moments de lucidité douloureuse sont suivis d'épisodes ou de pensées plus réconfortants, le vieil Isak Borg n'ayant plus le temps de changer ce qui a été, mais pouvant néanmoins faire la paix avec lui-même, tout en adoptant une attitude plus ouverte et généreuse avec son entourage, parfois maladroitement (l'idée du tutoiement entre lui et sa gouvernante), sans être toujours compris (son fils ne lui permet pas d'achever son idée de renoncer au remboursement du prêt qu'il lui a fait, mais sa belle-fille a compris et lui en est reconnaissante ; au moment des adieux des autostoppeurs, la dernière phrase du professeur ne peut être entendue par eux). En effet, il serait invraisemblable que le vieil homme puisse apprendre en un seul jour à communiquer avec les autres d'une manière aisée.

Le film se caractérise par sa fluidité, les rêves utilisent les personnages et les situations de la vie réelle de la journée, les passages entre le présent, les souvenirs ou les irruptions de l'inconscient dans les rêves sont toujours amenés d'une façon à la fois naturelle et sans ambiguïté.

L'utilisation de la première personne dans les commentaires en voix off facilite l'identification du spectateur avec le personnage principal. D'autre part, la similarité des initiales d'Ingmar Bergman et d'Isak Borg peut suggérer une identification du réalisateur à son personnage principal.

Box-office

  :

Distinctions

Récompenses
 Festival de Berlin 1958 : Ours d'Or
 Prix FIPRESCI de la Berlinale pour Victor Sjöstrom
 Golden Globe du meilleur film étranger en 1959
 Prix du meilleur film au Festival international du film de Mar del Plata en 1959

Sélection
 Oscars du cinéma 1960 : Nomination à l'Oscar du meilleur scénario original pour Ingmar Bergman

Notes et références

Voir aussi

Bibliographie
 Jean d'Yvoire, Téléciné, 82, Paris, Fédération des Loisirs et Culture Cinématographique (FLECC), avril-,
 Fereydoun Hoveyda, Les Cahiers du Cinéma, 95, Paris, Éditions Cahiers du Cinéma,
 Jean Collet, Téléciné, 85, Paris, F.L.E.C.C., , fiche 356,
 François D. Guyon, Ingmar Bergman, Premier Plan 3, Lyon, SERDOC, p. 34-35
 Jacques Siclier, Ingmar Bergman, Paris, Éditions universitaires, 1960, p.153-157, 159, 160.
 Leonard Maltin, 'Wild Strawberries', Leonard Maltin's 2001 Movie & Video Guide, New York, Signet, 2000, p.1567-1568,
 Jean Tulard,  Guide des Films. F-O, Paris, Éditions Robert Laffont (Collection : Bouquins), 2005, p.1353,

Liens externes

Film suédois sorti en 1957
Comédie dramatique suédoise
Road movie
Film à flash-back
Film suédois en noir et blanc
Film réalisé par Ingmar Bergman
Film sur les rêves
Ours d'or
Prix FIPRESCI à la Berlinale
Film nommé aux BAFTA Awards


text:
Q: caliper error: CICompilerCount of 1 is invalid; must be at least 2 i have a caliper benchmark (1.0-beta-2):
import com.google.caliper.Benchmark;
import com.google.caliper.runner.CaliperMain;

public class MyBenchmark {

    @Benchmark public int a(int rep) {
        return 0;
    }

    public static void main(String[] args) {
        CaliperMain.main(MyBenchmark.class, args);
    }
}

i run it from eclipse or from command line with:
mvn exec:java -Dexec.mainClass="com.google.caliper.runner.CaliperMain" -Dexec.args="MyBenchmark"

in both cases i got an error:
ERROR: Trial failed to complete (its results will not be included in the run):
  The worker exited without producing data. It has likely crashed. Inspect /tmp/1427055470061-0/trial-1.log to see any worker output.

in this file i see:
Trial Number: 1
Trial Id: d663a0b5-55b4-43c3-97d8-93f14f436342
Experiment: {instrument=allocation, benchmarkMethod=a, vm=default, parameters={}}

[stderr] CICompilerCount of 1 is invalid; must be at least 2
[stderr] Error: Could not create the Java Virtual Machine.
[stderr] Error: A fatal exception has occurred. Program will exit.

ubuntu 14.04, java:
java version "1.8.0_40"
Java(TM) SE Runtime Environment (build 1.8.0_40-b25)
Java HotSpot(TM) 64-Bit Server VM (build 25.40-b25, mixed mode)

any ideas how to fix it?

A: It's a bug in Caliper. It uses -XX:CICompilerCount=1 as a default JVM argument. However, when Tiered compilation is on, there must be at least 2 compiler threads (one for C1 and one for C2).
Try overriding -XX:CICompilerCount manually with a larger value.

A: i found a solution. flag -XX:-TieredCompilation helps. it can be used directly in microbenchmark java class as:
import com.google.caliper.api.VmOptions;

@VmOptions("-XX:-TieredCompilation")
public class MyMicrobenchmark {
...


This afternoon Mayor Jackson formally addressed the Republican National Convention welcoming the event and it's participants to the city of Cleveland.
The Mayor urged the thousands of convention goers gathered in Q arena to take in all the city had to offer both downtown and in Cleveland's neighborhoods. He spoke to Cleveland's hospitality and the city's desire to make all who visit to feel welcomed.
The Mayor followed the Chairman of the RNC Rance Priebus, and was the first voice not formally associated with the party to address the Convention.
City of Cleveland Daily Media Update – July 18, 2016 at 7:00 p.m.


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Newer PostNeed Emergency Generator Service?


text:
Look Out For … Frankenstein: The 1818 Text (Penguin Classics)
5 Must Read Horror Articles 15 Jan 2018
News Round-up Week Ending 12 January 2018
Here's a small selection of the horror and genre news that caught our eye during the last week …
Broken Shells, from Michael Patrick Hicks, out 6 February
On 6 February, the new novella, Broken Shells, from dark fiction writer Michael Patrick Hicks (Convergence, The Marque) is released through High Fever Books. The novella is a story of a man, Antoine DeWitt, who is down on his luck, so desperate that he walks head first into what he knows is likely a scam. There to capitalise on his misfortune is Joe Dangle, a man with buried secrets and a will to exploit people like Antoine for everything they can. What unfolds is DeWitt's struggle to escape with his life. With strong critical responses to many of Hicks' prior work, this is sure to be an entertaining ride. You can pre-order your copy of Broken Shells, here.
Judging panel announced for Frightfest Glasgow's ninety-second film challenge
Aspiring horror movie makers take note. Frightfest Glasgow is just around the corner (23 February) and the ninety-second film challenge now has a full compliment of judges. Andy Nyman and Jeremy Dyson, the writer and director of Ghost Stories, join Hex Studio's Laurie Brewster, Frightfest's own Paul McEvoy and Allison Gardener, head of the Glasgow Film Festival. The deadline for submissions is 13 February, with selected entries to be screened at the festival on 23 February. Get all the information, terms and conditions, etc here.
10th Hellraiser film, Hellraiser: Judgement, coming 13 February
Great news for fans of Clive Barker's Cenobites, as the tenth film in the Hellraiser series, Hellraiser: Judgement, is scheduled for a 13 February release on VOD and DVD. Those seeking a sneak preview need look no further as we have the trailer, below.
KEV HARRISON
Permanent link to this article: https://www.thisishorror.co.uk/news-round-up-week-ending-12-january-2018/