text:
The 1995 Critérium du Dauphiné Libéré was the 47th edition of the cycle race and was held from 4 June to 11 June 1995. The race started in Évian-les-Bains and finished in Chambéry. The race was won by Miguel Induráin of the Banesto team.

Teams
Fourteen teams, containing a total of 109 riders, participated in the race:

 Aguardiente Antioqueño–Lotería de Medellín

 Collstrop–Lystec

 Le Groupement

Route

Stages

Prologue
4 June 1995 – Évian-les-Bains,  (ITT)

Stage 1
5 June 1995 – Évian-les-Bains to Montalieu-Vercieu,

Stage 2
6 June 1995 – Charbonnières-les-Bains to Guilherand-Granges,

Stage 3
7 June 1995 – Tain-l'Hermitage to Tain-l'Hermitage,  (ITT)

Stage 4
8 June 1995 – Guilherand-Granges to Carpentras,

Stage 5
9 June 1995 – Avignon to Gap,

Stage 6
10 May 1995 – Briançon to Vaujany,

Stage 7
11 June 1995 – Vaujany to Chambéry,

General classification

References

1995
1995 in French sport
June 1995 sports events in Europe

redpajama_set_name: RedPajamaWikipedia


text:
Robert 'Bobby' Tennison…
Robert 'Bobby' Tennison still missing in S. Humboldt; holidays heighten painful loss
By Redwood Times |
PUBLISHED: November 25, 2009 at 12:00 am | UPDATED: July 30, 2018 at 2:46 am
Franklin Stover
Humboldt Beacon
A 37-year old man from the town of Keyes in Stanislaus County came to the Garberville area almost a year ago to get paid for a construction job and mysteriously vanished shortly after arriving. Dropped off in Alderpoint on Monday, Jan. 19, family members have not seen or heard of Robert Joseph Tennison for ten months. According to his mother, Sandy Tennison, he was supposed to return home on Sunday, Jan. 25. This year will mark their first Thanksgiving without him.
The father of four sons and a member of a large extended family, 'Bobby' Tennison was born on Sept. 11, 1971 in Turlock, Calif. and raised in Keyes by his mother and an adoptive father. Tennison is now 38. Keyes is located about 15 minutes south of Modesto on Highway 99 where most of Tennison's family resides.
When Tennison got out of high school, he worked in the construction trade and lived with his mother doing odd jobs to earn a living. Although Tennison never married, he fathered four sons, aged 5, 8, 11, and 17. A few weeks before his disappearance, Tennison reunited with his birth father, Johnnie Tennison at a family gathering in Manteca in December of 2008.
According to Tammy Lankford, one of Tennison's cousins, his ties to Humboldt County began around the time two friends from Keyes moved to Alderpoint. Introduced to friends of theirs, Tennison learned about building projects in the area and worked freelance while staying in touch with family. At least two projects in an Alderpoint neighborhood occupied Tennison's time: a bathroom remodel at one house, and a kitchen remodel at another.
Tennison was in the habit of taking the bus to Garberville and meeting up with acquaintances who would provide a lift. Being a traveling carpenter, tools were made available to him wherever he worked. He was there long enough to pick up the nick name, Builder Bob, as a clerk at the Alderpoint General Store on 4th Street called him. "I thought they found him," she said, momentarily confusing Tennison with Garberville resident Sam Cushman, whose remains were discovered on Oct. 29 in Trinity County.
But this time, Tennison left Keyes with his eldest son and a friend by truck, appearing at an Alderpoint address where he had completed a job not long ago.Tennison cleared things with contacts in Alderpoint before coming north, and his plans were to complete a job he started, get paid, and give his traveling friend Andrew, a chance to sell a dirt bike to an interested party.
Not long after the three arrived, Tennison showed his son the completed kitchen remodel and all had lunch together. Tennison stayed on, as was the plan, while the other two returned to Keyes. Lankford said Tennison reassured his son he would return by bus in a week, but Sergeant Ken Swithenbank of the Sheriff's Substation in Garberville said, without going into detail, that Tennison probably had plans to stay longer.
As far as family members were concerned, however, Tennison was expected to be home on Jan. 25, and they began to worry when they didn't hear from him by the appointed date.
"What happened after he arrived has been the hardest puzzle to put together. All leads of information lead to a dead end," Lankford said.
Days turned into weeks, and Tennison's family contacted Stanislaus and Humboldt County Sheriffs to lodge missing person's reports. The first report was filed in Stanislaus County on Feb. 9, according to Tennison's mother. But in no time, Lankford began hearing horror stories of her cousin. "Immediately after his disappearance, rumors started to spread that Bobby was murdered," she said.
"On March 13, Calvin (not his real name) came on March 13 and told my aunt that Bobby had been murdered," Lankford said.
Tennison's mother was told by one person in Alderpoint that her son walked off the job and disappeared somewhere in an area known as Murder Mountain which was by some accounts, covered in snow in mid-January.
Another story has Tennison in a scuffle with several other individuals, resulting in a blow to the head by a blunt object, killing him. Other stories collected by family members include one about him leaving a Garberville home in an altered state of mind, never to be seen again – though the family insists that drugs were not an issue.
Irrespective of hearsay and innuendo, police have not concluded that there is anything in the disappearance that could be considered suspicious. According to Humboldt County Sheriff's investigator Dan Paris, the case "is listed under the missing persons format and is labeled under 'unknown circumstances.' It was reported to us on March 19, 2009."
Paris indicated that he would be talking to Sgt. Swithenbank about the case to determine what course the follow-up investigation will take. Paris referred to Swithenbank as a valuable resource, and "is quite familiar with many of the involved people."
Swithenbank remarked by email that he interviewed several people regarding the disappearance, but said that no suspects have been named.
"I have never heard (with any validity) that Robert was murdered. Several family members have speculated murder but have no real grounds or viable information to follow it up with. Some locals have offered that he may have wandered into someone's marijuana garden and been killed because of that. That's always the first rumor that comes up when someone goes missing in the SoHum area, and once again there is no information at all to back up that rumor – just pure speculation," Swithenbank said.
"As I told the family," he added, "my investigation leads me to believe that the disappearance is an unfortunate accident."
Anna Fletcher, another cousin of Tennison's wrote that her family is looking for closure. "It's not easy losing someone you love but it's even harder not knowing what has happened to them."
Robert Tennison is described as a caucasian with long sandy brown hair, shoulder-length or longer. His left eye is blue and his right eye is green. Tennison has a broken tooth in the front, and a tattoo of a woman's face on his abdomen. He is generally seen wearing a baseball cap and stands at 5 feet, 6 inches tall. Also, his right heel was broken from an old accident. Tennison weighs between 150 to 160 pounds.
Jamie Dunn, mother of Tennison's oldest son, Robert, said that Tennison was easy going and calm. "Bobby was not a trouble maker…he is a kind and loving man. And he never stayed away from his family, especially his mother. He would always keep in touch because as a mother, he knew how she would worry."
Recalling fond memories of her cousin, Cindy Tennison wrote he would go lizard hunting with his boys. "Bobby liked doing that kind of stuff and the boys love it too. As for him doing anything out of character, no. Bobby never flipped out or even raised his voice."
If you have information or tips about Robert 'Bobby' Tennison, call Dan Paris at the Humboldt County Sheriff's Department at 445-7251 or Sgt. Ken Swithenbank in Garberville at 923-2761.
1. Robert 'Bobby' Tennison, at a December 2008 family gathering in a Manteca Burger King. A resident of Stanislaus County, Tennison arrived in Alderpoint last January to collect a debt and has not been seen since. While some family members suspect fowl play, the Humboldt County Sheriff's Office says the circumstances behind his disappearance are 'unknown.'
2. Tennison with one of his sons, taken in 2001 in Keyes, Calif.
I would be glad to understand this better
Local Internet marketing company sold
What to do if you are bitten by a rattlesnake
Sunnyvale: Developers proposing 130-room Hilton hotel on El Camino Real
Menlo Park's oldest retail business, Beltramo's, closing after 134 years
Inn, pub closed, at least temporarily
Redwood Times
Eureka city manager Greg Sparks to retire, sends letter to staff to announce decision
Suspect nabbed in connection with home invasion, another man sought

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Why is Core Data shm file not removed in journal_mode DELETE? I'm using the following code to to consolidate my Core Data sqlite store files, which current consists of the database itself, a -wal and a -shm file. I am doing this so I can manipulate it as a single file using NSFileManager.
NSPersistentStoreCoordinator *tmpPSC = [[NSPersistentStoreCoordinator alloc]
      initWithManagedObjectModel...];

NSDictionary *options = @{NSSQLitePragmasOption:
                        @{@"journal_mode": @"DELETE"}};

NSPersistentStore *persistentStore = [tmpPSC
   addPersistentStoreWithType:NSSQLiteStoreType configuration:nil
   URL:storeURL options:options error:NULL];

if (persistentStore) {
   [tmpPSC removePersistentStore:persistentStore error:NULL];
   tmpPSC = nil;
}

However, after the above code execute, I am seeing the -wal file disappear, but the -shm file is still there.
Any idea why this is so?

A: Never mind, found my answer here:
How to save the content of a MOC to a file, with no -wal and no -shm?
shm file can safely be deleted, which is what I did.

redpajama_set_name: RedPajamaStackExchange


Today I tried giving the Nova folk even more suffering with those pesky Lumens. Not only would Elodie kill Togami through the earthquake, she would have summoned the Kraken as well, so that the nature responds with those magical outcasts twice, wreaking havok on the land as well as on the sea.
Went pretty smoothly. Economics and Faith got trained in the early game, so that I could get the crystal early, recruit Lucille (Meditation so that Elodie asks about Charlotte at the gala). That way I had not issue with the birthday or the chocolate. Since Faith included lore, I also could take Briony hostage to fight off Arisse, but it wasn't even needed.
After those two skill categories, I maxed swimming, so that I could survive the naval battle without having Military skills. I also trained Lumen, though I was cheap with Wield Magic. After all, I don't want to win the naval battle.
So Elodie destroyed a part of the fleet with a Lumen Concert, but she lost the battle, since she had no military skills and I never got soldiers. The Kraken should rise a bit later. Togami came and she retreated, just to kill him with the earth-quake. Lumen and Meditation was sufficient. The earthquake arose the monsters and Father was not happy about it. But the Kraken? Nope, no one mentioned it. Not even in the epilogue. As if it didn't happen at all.
Seems the new way to defeat Togami that was added in one mayor update overrode the Kraken one. What a mistake.
In the new versions, there is a new way to defeat Togami. You need Serene as your mentor, so send Julianna away at the beginning. Get your crystal and raise Lumen and Meditation. When confronting Togami, retreat. Serene calls Julianna and they help you creating an earthquake that kills Togami. Just like awakening the Kraken, it has some bad side effects that effect the last weeks and some endings.


text:
\section{Introduction}

Object affordances play an essential role in object perception \cite{gibson2014ecological}. Experiments show that object affordances are more compelling in infants' conceptual development of object perception than colors, textures, and other perceptual cues \cite{oakes2008function, nelson1973some}. Gibson \cite{gibson2014ecological} contends that predicting object affordances is more important than predicting object class labels. This is also true from a robotic perspective. For instance, when a humanoid robot is confronted with an object classified as a chair, the label of  ``chair" would not provide the robot with the knowledge of how to sit on it; while knowing if it is ``sittable" and the pose which affords the sitting function offer more informative cues for object perception and interaction.

\begin{figure}[!htp]
\centering
\includegraphics[scale = 0.21]{figure1_0827_compressed.pdf}
\caption{Chair Affordance Imagination. Our method \textit{imagines} the object affordance as a chair by physically simulating a human agent sitting on it. The agent is simplified as an articulated human body. The agent and the object are subject to constraints of geometry and physics (\textit{e.g.}, gravity, friction, collision, and inertia). More details can be found on our project page: \url{https://chirikjianlab.github.io/chairimagination/}}
\label{figure1}
\vspace{-0.2cm}
\end{figure}

Despite the advantages of affordance-based object perception, the majority of work on object perception is appearance-based. These methods can be limiting when considering object classes with a large intra-class appearance variation such as chairs. Moreover, in the case where an object appears similar to a class but fails to afford the most salient functionality of the class, the appearance-based method would be challenged. For example, consider a ``broken chair" without a seat (Figure \ref{figure2}) or a ``toy chair" for dollhouse play.

\begin{figure*}[!htp]
\centering
\includegraphics[scale = 0.545]{figure2_0407_compressed.pdf}
\caption{Imagination Pipeline. The imagination consists of two steps: stable pose imagination and functional pose imagination. Given an arbitrarily oriented object, we first imagine its stable poses. Then, we proceed to imagine the functional poses by performing a sitting interaction between a human agent and the object in the stable poses found in the first step. The \textit{sitting quality} for each stable pose is reasoned to check if there exists any functional pose which affords sitting. The three objects shown in the figure include a chair, a broken chair without a seat, and a table. Only the chair has functional poses (the green box in the Functional Pose Imagination) which afford sitting.}
\label{figure2}
\vspace{-0.2cm}
\end{figure*}
 Our primary contribution is a novel method which \textit{imagines} an object's affordance as a chair by physically simulating a human agent interacting with the object (Figure \ref{figure1}). Our method is inspired by the fact that human brains perform mental simulations to reason high-level physical interactions of complex systems \cite{hegarty2004mechanical, gentner2014mental, battaglia2013simulation}. We want to endow robots with an analogous ``mental" capability which \textit{imagines} possible physical interactions of a class and deduces an object's affordance as the class accordingly. Therefore, we first define the physical interaction to imagine for the class of chair.

The Merriam-Webster\footnote{https://www.merriam-webster.com/dictionary/chair} dictionary defines a chair as: ``a seat typically having four legs and a back for one person". However, this human-centric definition does not provide any affordance information or guidance on how the object can be interacted with. To be more robot-centric, we propose an \textit{interaction-based definition} of a chair:
\begin{adjustwidth}{1cm}{1cm}
``an object which can be stably placed on a flat horizontal surface in such a way that a typical human is able to \textit{sit}\footnote{https://www.collinsdictionary.com/dictionary/english/sit} ($\rightarrow$ to adopt or rest in a posture in which the body is supported on the buttocks and thighs and the torso is more or less upright) stably above the ground."

\end{adjustwidth}
This definition provides affordance information on how to interact with an object, \textit{i.e.}, if the object is a chair, one can sit on it. Moreover, if the object is a chair, it is able to afford sitting on a flat surface in some poses which are stable. We define this type of poses as the \textit{functional pose}. These poses are associated with the designed upright orientation of the object. Figure \ref{figure2} shows our imagination pipeline. Using this imagination, we are able to quantify the sitting affordance of an object. This quantification can be further used as a cue for chair vs non-chair classification (hereafter referred to as chair classification). In addition, the functional pose which affords sitting can also be predicted if the object is categorized as a chair.
We demonstrate our method's performance on the chair classification and functional pose prediction tasks. For chair classification, we compare with two state-of-the-art appearance-based deep learning methods. Results show that our method outperforms the state of the art on synthetic 3D model data when the upright orientation of the model is not assumed. For functional pose prediction, we show that our method's predictions align well with human judgments on both synthetic models and real objects scanned by a consumer-grade depth camera.

\section{Related Work}

\textbf{Affordance-based Object Classification.}
There is a growing interest in classifying objects with object affordances \cite{ho1987representing, yu2015fill, hinkle2013predicting, grabner2011makes, bar2006functional, seib2016detecting, sawatzky2017weakly, roy2016multi, zhu2014reasoning}. Hinkle and Olson \cite{hinkle2013predicting} simulate dropping spheres onto objects and classify objects based on the final configuration of the spheres using the support vector machine (SVM). This differs from our approach which introduces a simulated human agent for exploring human-centric affordances. Grabner \textit{et al.} \cite{grabner2011makes} fit a human mesh model onto an upright chair and estimates the vertex nearest distance and triangle intersection between them to measure the sitting affordance. In \cite{bar2006functional} and \cite{seib2016detecting}, the sitting affordance of an object is detected by searching an embodied agent's configuration space. Only collision detection is considered in the search.
Unlike \cite{grabner2011makes, seib2016detecting, bar2006functional} which are solely based on geometry, our method considers not only geometric constraints but also physical properties (mass, inertia matrix, restitution, friction, \textit{etc.}) of the agent and the interacting object. In addition, we predict the \textit{functional pose} of a chair from an arbitrary orientation while the above methods all assume upright orientation in classfication. The functional pose prediction can benefit the affordance reasoning in the case where the chair is not in the pose to afford the functionality of sitting.

\textbf{Learning-based Affordance Detection.}
Learning-based methods have been widely applied to detect object affordances \cite{nguyen2016detecting, do2018affordancenet, aldoma2012supervised, myers2015affordance, desai2013predicting, tenorth2013decomposing, fu2008upright, manuelli2019kpam, manuelli2019kpam, mar2017can}. Aldoma \textit{et al.} \cite{aldoma2012supervised} detect the ``0-order affordance" of an object in a supervised learning manner. \cite{myers2015affordance, desai2013predicting, tenorth2013decomposing} focus on detecting functional regions and parts of objects by learning geometric features.  Mar \textit{et al.} \cite{mar2017can} use a self-supervised learning method to learn tool affordances based on the 3D geometry with Self-Organizing Maps (SOMs). Manuelli \textit{et al.} \cite{manuelli2019kpam} recently propose keypoint affordances to reinforce purposeful robot manipulation. Instead of relying on learning, our work encodes object affordances by simulating physical interactions with an object to measure its potential to afford a functionality.

\textbf{Physical Reasoning and Scene Understanding.}
Object affordances have also been used to understand physics in real world scenes \cite{zhao2013scene, zhu2016inferring, zhu2015understanding, jia20133d, zheng2013beyond, battaglia2013simulation, ruiz2018can, wu2015galileo, ruiz2018what}.
Battaglia \textit{et al.} \cite{battaglia2013simulation} propose an ``intuitive physics engine" to simulate physics in natural scenes and explain human mental models for understanding the real world. \cite{ruiz2018can} and \cite{ruiz2018what} leverage deep 3D saliency and interactive tensors to detect object afforadnces in 3D scene.  Our work diverges from these approaches by using object affordances for object classification and functional pose prediction instead of physical reasoning or scene understanding.

\textbf{Affordance-based Shape Analysis.}
Object affordances have also been explored with geometry-based shape analysis\cite{pirk2017understanding, hu2016learning, kim2014shape2pose, hu2015interaction}.
Pirk \textit{et al.} \cite{pirk2017understanding} capture the interaction between a motion driver and a static object by obtaining the ``interaction landscape" with animations. Hu \textit{et al.} \cite{hu2016learning} seek to understand local object affordances by learning the correlation between local geometric properties and object functionalities. Instead of geometric analysis, our method uses physics to deduce object affordances.

\section{Method}

Given an object in arbitrary orientation, our goal is to find if there exists any \textit{functional pose} which affords the sitting function.
We attach the body frame to the center of mass of the object and align the axes parallel to those of the world frame. All objects are considered as rigid bodies. A rigid body transformation can be specified by $g=(R, \textbf{p}) \in SE(3)$. $R \in SO(3)$ is a rotation matrix which can be parameterized using the Euler angles: $R = R(\alpha, \beta, \gamma)$. We use the x-y-z extrinsic rotation convention for Euler angles throughout this paper. $\alpha, \beta, \gamma$ correspond to the roll, pitch, and yaw of the object.  $\textbf{p} \in \mathbb{R}^3$ is the translation vector which can be specified by the body frame's three coordinates in the world frame $\textbf{p} = [x, y, z]^T$.

A rigid body has infinitely many poses in $SE(3)$. It is computationally costly to finely search the whole $SE(3)$ to find functional poses. The \textit{interaction-based definition} also indicates that the functional pose is necessarily stable. Therefore, we first find a set of stable poses $G_{s} \subset SE(3)$ of the object. We then perform the sitting interaction on each stable pose $g_s \in G_{s}$ to find the functional pose $g_f \in G_{s}$.

\subsection{Stable Pose Imagination}
\label{sec:stable_pose}
We simulate dropping an object with different initial orientations on a flat plane to find the stable poses of the object. However, there can be infinitely many such poses. We notice that many of these poses are functionally equivalent. For example, all translations in the x-y plane and all rotations around the z-axis of the world frame cause no change in the functionality of a chair pose, as long as the human agent positions and orients its stance in $SE(2)$ correctly. Figure \ref{figure3}(a) shows two examples. Therefore, we say two stable poses are \textit{equivalently stable} if their roll $\alpha$, pitch $\beta$, and z-axis coordinate $z$ in the world frame are equal. In the simulation, we consider two poses to be \textit{equivalently stable} if:
\begin{equation}\label{eq:1}
\|R(\alpha, \beta, 0)-R(\alpha', \beta', 0)\| < \Delta R_{\textrm{thr}}^{\textrm{es}}
\end{equation}
\begin{equation}\label{eq:2}
|z - z'| < \Delta z_{\textrm{thr}}^{\textrm{es}}
\end{equation}
where $\|A\|$ is the Frobenius norm of the matrix $A$; $R(\alpha, \beta, 0)$ and $R(\alpha', \beta', 0)$ are the rotation matrices which represent the roll and pitch of the two poses given their Euler angles $(\alpha, \beta, \gamma)$ and $(\alpha', \beta', \gamma')$; $z$ and $z'$ are the z-axis coordinates of the two poses.
$\Delta R_{\textrm{thr}}^{\textrm{es}}, \Delta z_{\textrm{thr}}^{\textrm{es}} \in \mathbb{R}$ are two thresholds.

\begin{figure}[!htp]
\centering
\includegraphics[scale = 0.34]{figure3_0914_modify_wide_compressed.pdf}
\caption{Stable Pose Imagination. (a) A tipped over chair (red box) would not afford sitting regardless of its $\gamma$, $x$, and $y$. An upright chair (green box) can always afford sitting regardless of its $\gamma$, $x$, and $y$. (b) The upper and lower images show a chair object before and after applying $g_{\textrm{obb}}$, respectively. OBB is the minimum bounding box of which one of its surfaces is coincident with one of the surfaces of the object's convex hull. The red plane and the red arrow indicate the surface in contact with the ground when the chair is in the \textit{functional pose}. $g_{\textrm{obb}}$ transforms this surface to be normal to the z-axis in this case. (c) For each enumerated orientation, the object is dropped from a height equal to half the length of the diagonal of the OBB plus 5cm. The right figure shows the snapshots of the dropping simulation at the 0th, 100th, and 1000th time step, respectively.}
\label{figure3}
\vspace{-0.2cm}
\end{figure}

\textbf{Object Bounding Box Transformation.}
Given an arbitrarily oriented object, we first compute the minimum volume oriented bounding box (OBB) and apply the rigid body transformation $g_{\textrm{obb}}$ which aligns the center of the OBB with the origin of the world frame and rotates the object such that the edges of the OBB are parallel to the coordinate axes of the world frame (Figure \ref{figure3}(b)). We apply $g_{\textrm{obb}}$ because we notice that when chairs are in a functional pose, the surface in contact with the ground is heuristically coincident with one of the surfaces of its OBB. $g_{\textrm{obb}}$ allows the enumeration of orientations which are very close to the orientation of the functional pose and thus would stabilize to a functional pose after being dropped.

\textbf{Dropping Simulation.}
After the OBB transformation, we enumerate the orientation of the object by varying its roll $\alpha$ and pitch $\beta$ in a discrete increment $\Delta \alpha = \Delta \beta = \frac{\pi}{10}$ while keeping $\gamma=0$. Figure \ref{figure3}(c) shows the simulation setup. The simulation duration for each drop is 1000 time steps (1/240 seconds per time step). The object's pose in the last time step is considered a stable pose if in the last 50 time steps:
\begin{equation}\label{eq:3}
    \sum_{i} \|R_{i} - R_{i-1}\| \leq \Delta R_{\textrm{thr}}^{\textrm{s}}
\end{equation}
\begin{equation}
    \sum_{i} \|\textbf{p}_{i} - \textbf{p}_{i-1}\| \leq \Delta p_{\textrm{thr}}^{\textrm{s}}
\end{equation}
where $R_i$ and $\textbf{p}_{i}$ are the rotation matrix and position of the object in the i-th time step, respectively; $ \Delta R_{\textrm{thr}}^{s},  \Delta p_{\textrm{thr}}^{s} \in \mathbb{R}$ are two thresholds.
We initialize $G_s$ as an empty set. If a newly found stable pose is equivalently stable to a pose already in $G_s$, we discard it. Otherwise, we append it to the set.

\subsection{Functional Pose Imagination}
Sitting is performed on each stable pose $g_s=(R_s, \textbf{p}_s)$ to find the functional pose. We denote $R_s = R(\alpha_s, \beta_s, \gamma_s)$ and $\textbf{p}_s=[x_s, y_s, z_s]^T$. Figure \ref{figure4} shows our sitting simulation setting. We simplify the human agent as an articulated human body. We trim off the arms and feet because they are not substantial in defining a sitting configuration \cite{forssberg1994postural, kerr1997analysis}. We set appropriate limits, friction, and damping for each joint to avoid configurations which are not physiologically capable for a typical human \cite{roaas1982normal}.

For each $g_s$, the object's orientation is enumerated by fixing $\alpha = \alpha_s$, $\beta = \beta_s$ while varying $\gamma$ from $[0, 2\pi)$ in a discrete increment $\Delta \gamma = \pi/9$. The agent's pelvis is placed on a horizontal plane 15cm above the current axis-aligned bounding box (AABB) of the object. For each enumerated orientation, we sample three positions on the plane (the origin and two positions with a translation of $L_{\textrm{sit}}$ and $2L_{\textrm{sit}}$ along the x-axis, respectively) and freely drop the agent onto the object from each position. We regard each drop as a \textit{sitting trial}. In total, we conduct 54 sitting trials for each $g_s$. Since the sitting affordance depends on the agent's size and where the agent sits \cite{gibson2014ecological}, we scale the agent's size and $L_{\textrm{sit}}$ linearly with respect to the size of the object's OBB.

\subsection{Sitting Affordance Model}
The agent's resultant configuration $C_{\textrm{res}}$ in each sitting trial of a stable pose $g_s$ is compared to a key sitting configuration $C_{\textrm{key}}$ (Figure \ref{figure4}) to estimate the \textit{sitting quality} $S$ of $g_s$. The sitting affordance model estimates $S$ with four criteria: joint angle score, link rotation score, sitting height, and number of contact points.

\textbf{Joint Angle Score.}
    The joint angles of a configuration can be described with a vector $\pmb{\theta} \in \mathbb{R}^{18}$. We calculate the weighted L1 distance between the joint angle vector of $C_{\textrm{res}}$ (denoted as $\pmb{\theta}_{\textrm{res}}$) and $C_{\textrm{key}}$ (denoted as $\pmb{\theta}_{\textrm{key}}$) to obtain the joint angle score $J$ of a sitting trial:
    \begin{equation}
        J = \sum_{i} w_{J}^i |\theta_{\textrm{res}}^{i} - \theta_{\textrm{key}}^{i}|
    \end{equation}
    where $w_{J}^i$ is the weight of the i-th joint; $\theta_{\textrm{res}}^{i}$ and $\theta_{\textrm{key}}^{i}$ are the i-th element of $\pmb{\theta}_{\textrm{res}}$ and $\pmb{\theta}_{\textrm{key}}$, respectively. We assign weights based on the joint's relevance to sitting: chest-pelvis, pelvis-thigh, and thigh-calf have positive weights; other joints have zero weights. $w_{J}^i$ increases by threefold if $|\theta_{\textrm{res}}^{i} - \theta_{\textrm{key}}^{i}|$ exceeds a threshold.

\textbf{Link Rotation Score.}
    According to the \textit{interaction-based definition}, the chest and pelvis are more or less upright while the thigh are horizontal when the agent is sitting. Therefore, we consider the link's rotation of $C_{\textrm{res}}$ in the world frame to obtain the link rotation score $L$ of a sitting trial:
    \begin{equation}
        L = \sum_{i} w_{L}^{i} (1 - \textbf{z}_{\textrm{res}}^{i}\cdot\textbf{z}_{\textrm{key}}^{i})
    \label{eq:6}
    \end{equation}
    where $w_{L}^i$ is the weight for the i-th link; $\textbf{z}_{\textrm{key}}^{i}$ is the z-axis unit vector of the frame attached to the i-th link in $C_{\textrm{key}}$ (Figure \ref{figure4}); $\textbf{z}_{\textrm{res}}^{i}$ is the corresponding vector in $C_{\textrm{res}}$. We assign positive weights to the chest, pelvis and left/right thigh; zero weights for other links. $w_{L}^i$ increases by threefold if $(1 - \textbf{z}_{\textrm{res}}^{i} \cdot \textbf{z}_{\textrm{key}}^{i})$ exceeds a threshold.

\begin{figure}[!htp]
\centering
\includegraphics[scale = 0.28]{figure4_0914_modify_compressed.pdf}
\caption{Functional Pose Imagination. The object is placed by fixing the center of its OBB on the z-axis while keeping $z=z_s$. Before dropping, the agent is first set to a pre-sitting configuration $C_{\textrm{pre}}$ facing the x-axis as shown in the left figure. The middle figure shows four examples of $C_{\textrm{res}}$. The two with a check are regarded as \textit{correct} sittings. The agent is passively dropped onto the object. The right figure shows the key sitting configuration $C_{\textrm{key}}$. The articulated human body consists of 9 links with 18 joints. The three coordinate frames on the agent show the three link frames for computing the link rotation score $L$ (Equation (\ref{eq:6})).}

\label{figure4}
\vspace{-0.2cm}
\end{figure}

\textbf{Sitting Height.}
    Sitting height is an important factor in the interaction-based definition of chairs. Therefore, we measure the sitting height $H$ in each sitting trial.

\textbf{Number of Contact Points.}
    The interaction-based definition also specifies that the buttocks and the back (corresponding to the left/right thigh and chest link) are in contact with the object when sitting. Therefore, we count the number of contact points of the agent's head $P_{H}$, chest $P_{C}$, and left/right thigh $P_{LT}$,  $P_{RT}$ in each sitting trial.

\textbf{Detection.}
    The $C_{\textrm{res}}$ of a sitting trial is considered as a \textit{correct} sitting (Figure \ref{figure4}) if all the followings are satisfied:
    \begin{equation*}
        J < J_{\textrm{thr}}\\, L < L_{\textrm{thr}}\\, H \in (H_{\textrm{min}}, H_{\textrm{max}})
    \end{equation*}
    \begin{equation*}
        (P_{H} + P_{C}) \cdot P_{LT} \cdot P_{RT} > 0
    \end{equation*}
    \begin{equation*}
        P_{H} + P_{C} + P_{LT} + P_{RT} \geq P_{\textrm{thr}}
    \end{equation*}
    $J_{\textrm{thr}}$, $L_{\textrm{thr}}$, $H_{\textrm{min}}$, $H_{\textrm{max}}$, and $P_{\textrm{thr}}$ are thresholds corresponding to the four criteria. We count the number of correct sittings $N$ and calculate the mean sitting height $\xoverline{H}$ of the 54 sitting trials for each $g_s$. We select the $g_s$ with the largest value of $N\xoverline{H}$ to be the candidate functional pose $g_{\textrm{cand}}$ of the object.
The sitting quality $S_{\textrm{cand}}$ of $g_{\textrm{cand}}$ is defined as:
\begin{equation}
S_{\textrm{cand}} = \frac{N_{\textrm{cand}}\xoverline{H}_{\textrm{cand}}^{2}}{\xoverline{J}_{\textrm{cand}}\xoverline{L}_{\textrm{cand}}}
\label{eq:7}
\end{equation}
where $N_{\textrm{cand}}$ is the number of correct sittings of $g_{\textrm{cand}}$; $\xoverline{H}_{\textrm{cand}}, \xoverline{J}_{\textrm{cand}}$, and $\xoverline{L}_{\textrm{cand}}$ are the average of the sitting height, the joint angle score, and the link rotation score of all the correct sitting trials of $g_{\textrm{cand}}$.
We regard the object as a chair and $g_\textrm{cand}$ as a functional pose if:
\begin{equation*}
    ((S_{\textrm{cand}} > S_{\textrm{thr}}) \vee (N_{\textrm{cand}} \geq N_{\textrm{thr}})) \wedge (S_{\textrm{cand}} > s_{\textrm{thr}} N_{\textrm{cand}})
\end{equation*}
where $S_{\textrm{thr}}$, $N_{\textrm{thr}}$, and $s_{\textrm{thr}}$ are three thresholds.
We experimented with different definitions of $S_{\textrm{cand}}$ with the training examples and found that using a power of $\xoverline{H}_{\textrm{cand}}^{2}$ in $S_{\textrm{cand}}$ gives better performance than linear in $\xoverline{H}_{\textrm{cand}}$.
We consider $S_{\textrm{cand}} > s_{\textrm{thr}} N_{\textrm{cand}}$ because we want to discard those objects with a low average sitting quality for each correct sitting of its $g_{\textrm{cand}}$.

\section{Evaluation}

\begin{figure}[!htp]
\centering
\includegraphics[scale = 0.27]{Figure5_0305_compressed.pdf}
\caption{Data. (a) Two objects from the test set (chair class) of \cite{wu20153d}. Although their appearances are very close to chairs, one does not have a seat while the other only has two legs. (b) Scanning GUI of the Occipital Structure Sensor. We use the segmentation algorithm provided by the sensor SDK to extract chairs from the scenes. The chairs in the left and right figures correspond to the ones in the red and blue boxes in (c), respectively. (c) Examples of our scanned real chair models.}
\label{figure5}
\vspace{-0.2cm}
\end{figure}

\subsection{Physics Integration}
We use PyBullet \cite{coumanspybullet} as the physical engine for simulation. The flat plane, the object, and the agent are imported with URDFs which specify the mass, center of mass, inertia matrix, friction coefficient and joint properties. We use the default Coulomb friction model. The collision between the object and the flat plane is modelled as almost inelastic (coefficient of restitution $e_{\textrm{object}} = 0.1$). The collision between the agent and the object is modelled as perfectly inelastic ($e_\textrm{human} = 0$).

\subsection{Data}
Our synthetic models are sourced from the Princeton ModelNet40 dataset \cite{wu20153d}. We use 30 objects from the chair class of the training set for training. Our synthetic test data consist of 300 synthetic models extracted from the test set: 100 objects from the chair class and 200 objects from 20 non-chair object classes (10 objects per class). The non-chair objects are common household objects, \textit{e.g.}, TV stands, beds, desks, tables, bathtubs, and cups. Since the scale of the object is essential in determining its affordances \cite{gibson2014ecological}, we scale all the synthetic objects to an appropriate size of their corresponding classes. Our real test data consist of 50 real chairs scanned with the Occipital Structure Sensor (Figure \ref{figure5}(b)(c)). They are not scaled. As we are interested in finding the functional pose from an arbitrary initial orientation, we further randomly orient all the objects in the synthetic and real test data.

Since both synthetic and real chair models have no annotations of \textit{functional pose}, we recruited 10 volunteers to annotate the functional pose via the PyBullet GUI interface. For each chair model, the volunteer first determined whether it is sittable. A flat ground was then presented and the volunteer was asked, ``How would you place it on the ground to sit on it?" The annotated pose was recorded as the functional pose annotation.
It is worth mentioning that although the 100 chair models in the synthetic test data are labeled as chair in \cite{wu20153d}, two of them are labelled as not sittable by the human annotator (Figure \ref{figure5}(a)). In our experiment, we consider them as non-chairs because they cannot afford sitting.

\subsection{Training}
The OBB transformation is computed using the Trimesh Python Library \cite{trimesh}. Volumetric Hierarchical Approximate Convex Decomposition (V-HACD) \cite{mamou2016volumetric} is applied to decompose the transformed object into a set of convex hulls for collision detection in the simulation. We use MeshLab to compute the mass, center of mass, and inertia matrix of the object assuming a uniform density of 600 kg/m$^3$ (comparable to the density of wood).
We manually drop the agent onto the 30 training examples and obtain $C_{\textrm{pre}}$, $C_{\textrm{key}}$, $\Delta R_{\textrm{thr}}^{\textrm{es}}$, $\Delta p_{\textrm{thr}}^{\textrm{es}}$, $\Delta R_{\textrm{thr}}^{\textrm{s}}$, $\Delta p_{\textrm{thr}}^{\textrm{s}}$, $\Delta o_{\textrm{thr}}$, $J_{\textrm{thr}}$, $L_{\textrm{thr}}$, $H_{\textrm{min}}$, $H_{\textrm{max}}$, and $P_{\textrm{thr}}$ which maximize the \textit{sitting quality} $S$ and number of correct sittings $N$ for all the training examples. In detection, we set $S_{\textrm{thr}}=5\textrm{m}^2/\textrm{rad}$, $N_{\textrm{thr}}=4$, and $s_{\textrm{thr}}=0.1\textrm{m}^2/\textrm{rad}$ according to the value of $S$ and $N$ of the training examples.

\section{Results}
We implement our method with Python. We perform the evaluation on a computer running Intel Core i7-8700 @ 3.2GHz CPU. Our single-threaded unoptimized implementation takes about 1.5 minutes to analyze a single raw model. The imagination accounts for about 70 seconds of that time.

\subsection{Chair Classification}
We first perform the chair vs non-chair classification on the synthetic test data. We compare our method with two state-of-the-art appearance-based 3D object classifiers \cite{kanezaki2018rotationnet, su2018deeper}. Both methods leverage deep neural networks to classify a 3D object with multiple views of the object. \cite{kanezaki2018rotationnet} uses 20 views encompassing the object without the upright orientation assumption. \cite{su2018deeper} uses 12 views and assumes that the object is oriented upright. We note that in the original training settings of \cite{kanezaki2018rotationnet} and \cite{su2018deeper}, validation (model selection) is performed on the test set of \cite{wu20153d}. Since our synthetic test data are extracted from the test set of \cite{wu20153d}, for fair comparison, we retrain the two baselines with 80\% of the original training set and use the rest 20\% as the validation set for model selection. We use the retrained models for evaluation. The training set of \cite{wu20153d} contains 9843 models of 40 classes in total (the chair class has 889 models). We use the rendered images and the rendering code provided by the authors for training and rendering our test data for evaluation, respectively.

\
\begin{table}[!htp]
\vspace{-0.6cm}
\caption{Chair Classification Accuracy On Synthetic Data(\%)}
\centering
{
\begin{tabular}{c c  c }
\toprule
 Method & Object Orientation & Accuracy \\
 \arrayrulecolor{black}\midrule
 Kanezaki \textit{et al.} \cite{kanezaki2018rotationnet} & upright  & 99.3 \\
 Su \textit{et al.}\cite{su2018deeper} & upright     & 99.0 \\
 \arrayrulecolor{black!30}\midrule
 Kanezaki \textit{et al.} \cite{kanezaki2018rotationnet} & random  & 83.0 \\
  Su \textit{et al.} \cite{su2018deeper} & random   & 86.3 \\
 Ours & random                          & \textbf{97.0}\\
\arrayrulecolor{black}\bottomrule
\end{tabular}
}
\label{table:1}
\vspace{-0.1cm}
\end{table}

We use the overall classification accuracy (Table \ref{table:1}) and mean average precision (Figure \ref{figure6}) for quantitative evaluation. We test \cite{kanezaki2018rotationnet} and \cite{su2018deeper} with and without the upright orientation assumption. Although we only use 30 synthetic chair models for training, our algorithm achieves the highest classification accuracy when the upright orientation is \textit{not assumed}.
Note that in Table \ref{table:1}, the performance of both \cite{kanezaki2018rotationnet} and \cite{su2018deeper} drops dramatically when the upright orientation assumption is removed.

\subsection{Functional Pose Prediction}
We also evaluate the \textit{functional pose} prediction of our method on both synthetic and real test data. The rotation matrix $R$ of a rigid body transformation $g=(R, \textbf{p})$ can be specified as
$R=[\textbf{v}^{x}, \textbf{v}^{y}, \textbf{v}^{z}]^{T}$,
where $\textbf{v}^{x}$, $\textbf{v}^{y}$, $\textbf{v}^{z}$ are the unit vectors of the world frame's three axes in the body frame. Since a chair in the functional pose would be \textit{equivalently stable} regardless of its $\textbf{v}^{x}$, $\textbf{v}^{y}$, $x$, and $y$, we compare the $\textbf{v}^z$ and $z$ of the predicted functional pose (denoted as $\textbf{v}_{\textrm{pred}}^{{z}}$ and $z_{\textrm{pred}}$) to those of the functional pose annotation (denoted as $\textbf{v}_{\textrm{ann}}^z$ and $z_{\textrm{ann}}$). If $1-\textbf{v}_{\textrm{pred}}^{{z}}\textbf{v}_{\textrm{ann}}^{{z}} \leq 0.01$ and $|z_{\textrm{pred}} - z_{\textrm{ann}}| \leq 0.01\textrm{m}$, we consider the prediction correct and incorrect if otherwise. If a non-chair is classified as a chair, we count it as a false positive. If a chair is classified as a non-chair or its functional pose prediction is incorrect, we count it as a false negative. The precision and recall of the functional pose prediction on the synthetic data are both 94.9\%. For the 50 real chair models, the recall of the functional pose prediction is 100\%. We can see that our method generalizes well to the real data.

\subsection{Rotation Metric Sensitivity}
In Sec. \ref{sec:stable_pose}, we use the Frobenius norm to compute the distance between two rotations (Equation (\ref{eq:1}) and (\ref{eq:3})).
To probe our method's sensitivity against rotation metrics, we replace the Frobenius norm with a geometric-based metric  \cite{chirikjian2016harmonic} which defines the distance between two rotations to be the angle of rotation from one to the other, \textit{i.e.}, $d(R_1, R_2) = |\theta_{12}|$. The angle of rotation $\theta_{12}$ is derived from:
\begin{equation*}
    e^{\widehat{w}_{12}\theta_{12}} = R_1^{-1}R_2
\end{equation*}
\begin{equation*}
    \theta_{12} = \|(\textrm{log}(R_1^{-1}R_2))^\vee\|
\end{equation*}
where $\widehat{w}_{12} \in so(3)$ (or $w_{12} \in \mathbb{R}^3$) specifies the axis of rotation between $R_1$ and $R_2$ \cite{chirikjian2016harmonic}. The geometric structure of $SO(3)$ can be identified as an upper hemisphere of a unit sphere in $\mathbb{R}^{4}$. The Frobenius norm measures the straight-line distance between two points on the sphere while the geometric-based norm measures the arcs length. The chair classification accuracy and the precision and recall of the functional pose prediction on the corresponding test data are the same as those using the Frobenius norm. Therefore, our method is not sensitive to the choice of rotation metrics.

\begin{figure}[!htp]
    \centering
    \includegraphics[scale=0.279]{figure6_0228_compressed.pdf}
    \caption{Results. (a) Chair classification precision-recall curve. ``random" and ``upright" refer to the test object orientation. In our case, the sitting quality $S$ is used to calculate the mean average precision (mAP). Our method outperforms the deep learning methods when the upright orientation is not assumed. (b) Two examples of false positive in the chair classification owing to the single rigid body modeling. The agent's back rests on the opened closet door in the TV stand case and on the antenna in the radio case.}
    \label{figure6}
\vspace{-0.3cm}
\end{figure}

\section{Discussion and Future Work}
 In contrast to the black box nature of deep learning methods, our method is fully explainable. It is able to \textit{explain} the sitting affordance of an object by inferring the \textit{functional pose} in addition to classification. Indeed, these two tasks are closely related in terms of object perception. Our method captures the most essential cue of chairs, the sitting affordance, with only 30 training data while the compared deep learning methods use thousands of examples to distill appearance cues which may not be adaptable between different data domains (\textit{e.g.}, random vs upright). The appearance-based methods are also limited when the object appears similar to a class but fails to afford the functionality of the class. \cite{kanezaki2018rotationnet} and \cite{su2018deeper} classify the two objects in Figure \ref{figure5}(a) as chairs when tested with the upright orientation assumption. Our method classifies both as non-chairs because no \textit{functional pose} can be found for each.

Although we have incorporated many physical properties, all the test models in the simulations are considered as single rigid bodies (Figure \ref{figure6}(b)). Future studies can incorporate more detailed physical properties (\textit{e.g.}, joints and elasticity) of the object of interest in the simulation and explore more object classes with complex object affordances. Also, the dropping simulations with different initial orientations in the stable pose imagination are independent to each other. This is also true for the sitting simulations in the functional pose imagination. Future studies can parallelize these two imagination processes via multi-threading to reduce processing time.

\section{Conclusion}
In this paper, we propose a novel method for robots to \textit{imagine} an object's affordance using physical simulations. The class of chair is chosen here to illustrate a more general paradigm of interaction-based object affordance reasoning. We simulate dropping the object to find the stable poses and the sitting interaction between a simulated human agent and the object to find the \textit{functional pose} which affords the functionality of sitting. The object and the agent are both subject to physical and geometric constraints. The \textit{imagination} of object affordances is used as a cue for chair classification and functional pose prediction. In chair classification, results show that our method outperforms two state-of-the-art deep learning methods on the synthetic test data when the upright orientation of the object is not assumed. The functional pose predictions of our method on both synthetic and real data align well with human judgements. We hope that our method will serve as an effective approach to guide robot-object interaction in future research.

\section*{Acknowledgment}
This work was performed under Office of Naval Research Award N00014-17-1-2142 and National Science Foundation grant IIS-1619050.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Hall, Clinton James
House 1961-66 (District 1); House 1967-68 (District 1A)
Party when first elected: Nonpartisan Election-Conservative Caucus
Counties Served: Fillmore
Date of Birth: 10/3/1926
Birth Place: Carrington, North Dakota
Birth County: Foster
Religion: Lutheran
City of Residence (when first elected): Rushford
Occupation (when first elected): Attorney
Carrington Public Schools, North Dakota; Elementary School;
Carrington Public Schools, North Dakota; Secondary;
University of Minnesota; B.S.L.; 1953
St. Paul College of Law; LL.B.; 1955
Military: Minnesota Army National Guard, 47th Viking Division (Korean War; Fort Rucker, Alabama; Secretary to General Norman Everard Erickson); 12/1950 to 05/1952
Spouse: Ramona Mae Gronos (married in December 1950)
Children: Three children: Kathryn Patricia (daughter); Norman Everard, and Curtis James (sons)
He was a member of the Republican Party. (Obituary)
He moved to the Twin Cities in 1968.
He died of heart disease in San Diego, California.
Birth date, middle name, cause of death, spouse's name, and children's middle names provided by his daughter.
Religion provided by his daughter.
Residence: Rushford
Counties Represented: Fillmore
Party: Nonpartisan Election-Conservative Caucus
Employees Compensation
Insurance and Securities
Labor-Management Relations (Chair)
Recreation and Water Resources
Industrial and Employee Relations (Chair)
Towns and Counties
Dairy Products and Livestock
Reapportionment
Temperance and Liquor Control
"Obituaries. C. Hall, Former Legislator, Dies." St. Paul Pioneer Press, September 28, 1984.


This detached villa is built on three levels and has comfortable accommodation with three en-suite bedrooms and a large living area. Each room is air-conditioned, giving you control over the temperature. For relaxation, the villa has a sauna and a fitness equipment. The terrace has a pool, hot tub, six luxury loungers with sun umbrellas and a grill. There is also a mooring for a boat and a jetty so you can relax on the waterfront. Parking is available directly behind the house.
Living room. Dining room/kitchen. Second kitchen/dining room. 3 double bedrooms each with shower room en-suite. Washroom. Sauna.
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1 Week rental in peak season, 3 days minumum at other times. Price for odd durations by request only.


text:
Mistrovství světa ve veslování 1966 byl v pořadí 2. šampionát. Konal se na Bledském jezeře ve slovinském Bledu.

Medailové pořadí

Přehled medailí

Mužské disciplíny

Reference

Externí odkazy

1966
Veslování v roce 1966
Veslování ve Slovinsku
Sport v Bledu


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text:
March Madness and Mayo Clinic's laboratory genius
By Elizabeth Harty
It's too late this year, but before you fill out next year's NCAA college basketball tournament brackets, it may help to heed the advice in this article from Forbes magazine. If only because writer Roger Groves draws a comparison between the statistical geniuses in ESPN's numbers department and the genius of Mayo Clinic's Department of Laboratory Medicine and Pathology. "I trust the ESPN department of statisticians," Groves writes. "To me, it is the sports equivalent to Mayo Clinic's lab staffs in the basement of Rochester, Minnesota, who work exclusively on medical issues of patients."
It's not every day that we see Mayo's laboratory staff mentioned in a sports business article, but we'll take it. Especially since we know they're much more than a bunch of scientists who spend their days staring at computer screens in a dark room deep in the bowels of Mayo Clinic. To give their work proper perspective, we thought we'd do some stats work of our own. Or at least ask the department's communication guru, Andy Tofilon, to enlighten us. "The Department of Laboratory Medicine and Pathology supports physicians at Mayo Clinic and more than 4,000 health care organizations around the world," Tofilon tells us. Not only that, but about "75 percent of the objective data in a patient's medical record comes from the laboratory." (That's almost three-quarters, by our calculations.)
Here are a few more facts about the Department of Laboratory Medicine and Pathology from our friend Tofilon:
3,000 -- Employees
23 million -- Lab tests performed each year
2,800+ -- Lab tests available to Mayo Clinic physicians
150 -- New lab tests developed each year, on average
750+ -- Peer-reviewed articles authored each year
35,000 -- Specimens that arrive for testing each day, on average
6,600 -- Outside health care organizations that send tests
4.5 million -- Patients touched by these testing services each year
Now that's what we call impressive. But, if you're still wondering what stats Forbes feels you must know for your basketball brackets, here they are, direct from the geniuses at ESPN's "Numbers Never Lie" television show:
The last 25 championship teams all had a positive assists-to-turnovers ratio and a Sweet 16 appearance the previous year
The last 20 championship teams all had at least three wins against teams rated in the top 10 percent
The last 20 championship teams all had a "Top 75 Strength of Schedule" rating
All good info to have. Until, Dayton, Harvard and Mercer come along and ruin … EVERYTHING!
Give yourself an edge by being one of the first to share your comments below.
Tags: Research News
Suzanne Leaf-Brock
@suzanneleafbrock
Posts: 2 Joined: Aug 29, 2012
Posted by Suzanne Leaf-Brock @suzanneleafbrock, Mar 28, 2014
Hey Andy, Bet you never thought you'd see this type of mention in the media. And love seeing the MML box beside the basketball.


text:
The Youth Environment Council of South Australia: youth working together to make a difference
Nick Goode
Department for Heritage and Environment
Youth Environment Council (YEC) of South Australia provides advice to the State Government about the environmental perspectives of young people, and supports young people to be active in caring for the environment.
The Youth Environment Council (YEC) of South Australia is run by young people for young people. Council members range in age from 10 to 21 years.
The Ministers for Education and Environment announced the formation of the YEC at the 1997 state-wide Department of Education and Children's Services Sustainable Living Student Forum. As a Government advisory council, the YEC's role is to provide advice to the Government about the environmental perspectives of young people and to support young people to be active in caring for the environment.
In February of 2000, the Youth Environment Council of South Australia launched the 'YEC Community Plan'. This document is a set of guidelines for establishing youth driven environmental action projects.
In 2001 the YEC developed the Youth for Environmental Action Workshops (Y4EA) to ensure that the 'Community Plan' was implemented. The two-day workshop continues to operate. Like the Council, it is run by young people for young people, and includes presentation and project management sessions, and networking with environmental experts to help participants to initiate and develop their own environmental projects. Through participating in a Y4EA workshop, young people develop the leadership, project management and team work skills needed for the success of their projects.
Another workshop developed by the Youth Environment Council is the Youth Action for Sustainability (YAFS) workshop. This workshop was designed for adults who wish to work effectively with young people. Workshop participants learn about strategies that the YEC uses to engage the interest of young people. The YEC also helps to organise effective mentoring of young people to maintain their involvement and enthusiasm.
Promoting and encouraging a youth voice is a significant part of the YEC's work. The YEC believes that the opinions of young people need to be heard, and that young people need to be included in decision making processes if society is to move towards a sustainable future.
Over the past 7 years, the YEC has had many opportunities to provide a voice for young people. These opportunities have included invitations to present at national and international conferences, environmental events, roundtable forums and workshops. For example, YEC representatives have attended the World Youth Summit on Sustainability held in Holland in 2002, and the 2004 National Summit on the Future of Australia's Cities and Towns. They have also given keynote addresses at the 2002 and the 2004 Australian Association for Environmental Education biennial National conferences.
The YEC is always interested to hear from young people who are committed to caring for the environment. If you know of anyone who may want to be involved in the YEC please let us know. You may also contact:
Department for Environment and Heritage
goode.nick@saugov.sa.gov.au
Dave Butler
Department of Education and Children's Services
butler.david@saugov.sa.gov.au


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text:
<?php

/*
 * To change this license header, choose License Headers in Project Properties.
 * To change this template file, choose Tools | Templates
 * and open the template in the editor.
 */

namespace MicroCMS\DAO;

use MicroCMS\Domain\Recense;
/**
 * Description of RecenseDAO
 *
 * @author thouars
 */
class RecenseDAO  extends DAO{
    //put your code here

      public function findAll() {
        $sql = "select * from recense order by id desc";
        $result = $this->getDb()->fetchAll($sql);

        // Convert query result to an array of domain objects
        $recenses = array();
        foreach ($result as $row) {
            $recenseId = $row['id'];
            $recenses[$recenseId] = $this->buildDomainObject($row);
        }
        return $recenses;
    }

     public function find($id) {
        $sql = "select * from recense where id=?";
        $row = $this->getDb()->fetchAssoc($sql, array($id));

        if ($row) {
            return $this->buildDomainObject($row);
        } else {
            throw new \Exception("No recense matching id " . $id);
        }
    }

    public function save(Recense $recense) {
        $recenseData = array(
            'id' => $recense->getId(),
            'nom' => $recense->getNom(),
            'prenom' => $recense->getPrenom(),
            'nomUsage' => $recense->getNomUsage(),
            'dateNaissance' => $recense->getDateNaissance(),
            'adresseMail' => $recense->getAdresseMail(),
            'telephonePortable' => $recense->getTelephonePortable(),
            );

        if ($recense->getId()) {
            // The recense has already been saved : update it
            $this->getDb()->update('recense', $recenseData, array('id' => $recense->getId()));
        } else {
            // The recense has never been saved : insert it
            $this->getDb()->insert('recense', $recenseData);
            // Get the id of the newly created recense and set it on the entity.
            $id = $this->getDb()->lastInsertId();
            $recense->setId($id);
        }
    }

     public function delete($id) {
        // Delete the recense
        $this->getDb()->delete('recense', array('id' => $id));
    }

    /**
     * Creates an Recense object based on a DB row.
     *
     * @param array $row The DB row containing Recense data.
     * @return \MicroCMS\Domain\Recense
     */
    protected function buildDomainObject($row) {
        $recense = new Recense();
        $recense->setId($row['id']);
        $recense->setNom($row['nom']);
        $recense->setPrenom($row['prenom']);
        $recense->setNomUsage($row['nomUsage']);
        $recense->setDateNaissance($row['dateNaissance']);
        $recense->setAdresseMail($row['adresseMail']);
        $recense->setTelephonePortable($row['telephonePortable']);
        return $recense;
    }
}

redpajama_set_name: RedPajamaGithub


When many people by a car, they fail to think about all those extra costs, and only take into account the purchase of the vehicle. However, all those extra costs quickly add up, and can make it remarkably expensive to own a car.
Recent research carried out by affordable car hire company AutoEurope, found that compared to the cost of owing a car it's actually cheaper to hire a car when you absolutely need to.
It turns out that British motorists are paying a huge £168,880 just to keep their cars on the road.
Almost 2000 motorists were surveyed about their driving behaviours and attitudes towards motoring, along with current costs. The research showed that renting a car is much cheaper compared to the large financial commitment associated with car ownership.
Car owners spend an average of £168.46 each year on MOT and repairs. That works out to be £10,865 over a lifetime of spending. Petrol is even more expensive, with the average person spending around £89.67 each month, which works out to be a massive £69.402 over a lifetime of driving.
As a spokesman for the company said, this money could potentially buy a home in many parts of the UK, and doesn't even include insurance and tax.
There were some funny and interesting statistics revealed in the infographic as well. In a driver's lifetime, they can expect to play 1,470 games of eye spy, participate in 7,740 kisses goodbye, and visit the drive-thru 2,012 times.
Each brit will drink around 7,000 cups of coffee, and will eat around 1,935 burgers and 4,953 sandwiches.
The average Brit also owns a car worth around £12,919, and every British family owns an average of 1.6 vehicles. The average driver spends around £436 each year on car insurance, and when they go on holiday they spend around £180 on car hire.
If you think you're the only one that seems to continually have parking fails, don't worry. It turns out that most brits will mess up their parking 3.5 times a month, which adds up to a huge 2,790 times between the ages of 17 and 81.
You can also expect to clip the curb 1,702 times, scratch and scrape your car 1,006 times, and be involved in road rage around 2,709 times.
Along with the expected costs like MOT and insurance, you'll also pay for things like air freshener and screen wash, which will cost around £2000 over a lifetime.
For many people, owning a car equals freedom. However, if you're aiming to save some serious money and use public transport to commute to work, it can make much more sense to hire a car when you need one, so you can save money on all the upkeep.
While short term rentals are popular, many people are unaware that you can also hire for long-term. This can greatly decrease your hire rate per day, and it can be worth doing this instead of owning a car outright.


text:
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    <meta content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no" name="viewport">

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    <link href="<?php echo $base_url;?>css/iCheck/square/blue.css?v=<?php echo $version;?>" rel="stylesheet">

        <a href="<?php echo $base_url;?>">BelajarUjian

Masuk ke BelajarUjian

        <form action="../../index2.html" method="post">

            <input type="email" class="form-control" placeholder="Email">

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                  <input type="checkbox"> Ingat Saya

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        <a href="<?php echo $base_url;?>index.php/main/forgot">Lupa Password

        <a href="<?php echo $base_url;?>index.php/main/register" class="text-center">Daftar Baru


text:
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">

<title>Uses of Class net.lotrek.lacewing.client.packet.ReadPacket0Response0Connect</title>
<meta name="date" content="2014-07-10">
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Uses of ReadPacket0Response0Connect in net.lotrek.lacewing.client

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text:
Gisele Galante, Daughter Of Eminent Olivia De Havilland, Inherited Her Mother's Stunning Beauty
Legendary Olivia de Havilland is one of the most outstanding icons of the Hollywood Golden Age. But did you know that Olivia has a grown-up daughter who looks as stunning as her beautiful mother?
Eminent Olivia de Havilland
Olivia de Havilland is one of the greatest representatives of the Golden Age cinematography. She stole hearts of millions of fans around the globe with her iconic role in an ageless film Gone with the Wind.
READ ALSO: Olivia De Havilland, The Last Bright Star Of Hollywood's Golden Era, Turns 102: How Does She Feel About Her Age?
Olivia is 102 years old. Though she retired from acting a long time ago, devoted fans still admire her iconic roles of charming heroines in costume movies.
Her beautiful daughter
Olivia de Havilland was married twice. She has two children. Gisele Galante is her daughter with a French editor, Pierre Galante. When Olivia married Pierre, she had to relocate to Paris.
You can take just one look at Gisele to see her striking resemblance with her beautiful mother. The daughter is a young copy of the legendary actress.
In these black-and-white photos, Gisele resembles Olivia when she was the same age. No doubt, natural beauty and charm pass from generation to generation in this family.
READ ALSO: Bad Blood Between Sisters: Olivia De Havilland And Joan Fontaine's Epic Feud
Despite her busy working schedule, her children have always been at the first place for de Havilland. She even turned down a role in The Towering Inferno because she didn't want to be away from home when her daughter would be taking exams for college.
She said at that time:
I thought of my daughter, and I couldn't do it.
Olivia de Havilland is a true icon of our generation, but for her children, she will always be the best mother they could have.
READ ALSO: At 101, Olivia De Havilland Was Able To Sue FX Over Catherine Zeta-Jones' TV Portrayal Of Her As Hypocrite


text:
Improving workplace culture by managing stress and encouraging mindful drinking
Laura Willoughby
How do you create a workplace culture with a mindful attitude to drink? How do you support a team you rarely see face to face? How do you know if they are coping with the pressures of home schooling or pandemic uncertainty?
In this podcast episode, we explore how to lead your team to drinking more mindfully as a way to reduce stress and improve mental health. We touch on how to speak to your boss about your drinking and how to create a workplace culture that supports people to drink less.
The discussion is hosted by Tara Kent from Champs, a Global Wellbeing Consultancy and partner with Club Soda. They support our Mindful Drinking Festival. And we hear from Sara Gaughan from Finlay James, a technology recruitment agency based in Manchester and the US. Ross Carter from The Drinks Trust – providing support, care and assistance to the drinks industry workforce since 1886. They offer a range of support to people in the food and drinks sector including a FREE Mindful Drinking Programme designed for the sector in partnership with Club Soda and Emma Heal, Managing Director of Lucky Saint – the 0.5% Superior Unfiltered Lager.
Lucky Saint are giving all our listeners £5 off their next order. Just enter the code CLUBSODAFIVE when checking out at www.luckysaint.co
As human beings, we've all got ways of coping with stress and we tend to draw on the things that have helped us in the past, they give us immediate relief. Alcohol is one of those things. Whenever I mention the word stress of the moment, the first thing suggested to me by many people is to have a large glass of wine.
Tara Kent from Champs
Creating a workplace culture that does not revolve around booze
The Drinks Trust changed its focus to talk about mindful drinking in the past few years. After all, If the drinks sector can't talk about a healthy relationship with alcohol then who can? There is strong evidence of alcohol-related harm in the industry and they wanted to tackle that positively. It is seen as part of an overall wellbeing package and is integral to mental health.
We did a survey at the end of last year of the drinks and hospitality industry. About 10% of respondents said that their main health and wellbeing concern was alcohol. But then stress was 55%. But the two things go hand in hand. Alcohol is a coping mechanism.
Ross Carter. the Drinks Trust
Finlay James used to provide an office with a free bar that they thought was a perk. But through surveys and interviews they realised that this had actually had the opposite effect. Sara and John who run the company have used education about their story and their interest in alcohol-free drinks to actively role model. Staff now send them pictures of alcohol-free drinks they have found. By doing that they have improved their workplace culture and given staff the confidence to discuss the issue with them.
It's something that I've really noticed in our sector. In recruitment you have a lot of sales incentives that revolve around alcohol. You have corporate events that have an unlimited amount of alcohol included. I'll be honest, we've had a couple of issues along the way with that. It's finding that balance, between offering some kind of reward for your staff but also wanting to look after their well being. We started to get feedback from our people that this [lots of alcohol] is something that they didn't necessarily want anymore.
Sara Gaughan, Finlay James
Creating permission for people not to drink
Lucky Saint as a company works to create permission for people not to drink, and throughout lockdown have used their Slack Channel to openly discuss wellbeing and create team challenges. Collectively they have taken on a walking challenge. Creating shared moments that don't focus around alcohol is much stronger for team building.
It's about communication. We've put lots of touch points in place, even if you're on furlough. If you look in the detail of the furlough scheme, it's all about driving revenue, but you can still keep in touch with your team. We still have a Monday morning meeting that everyone joins.
You are not alone. The low and no movement is growing. Do lean on the new wave of alcohol-free drinks. Find something new for the end of the day drink. Most of the drinkers of low and no alcohol drinks are also moderating.
Emma Heal, Lucky Saint
How to know if someone is struggling with alcohol
With staff on furlough, some people have no contact with their team. People don't always put stress and drinking together, so staff might report stress but don't talk about what they are using to cope with it (often alcohol). So you could approach it from the stress and mental health angle. People are always scared about their job prospects if they are perceived to have drink issues, so a focus on well-being as a whole begins to create that workplace culture.
Furlough does not mean you can't keep in touch with your team. have 1-1's and do socials together. They don't have to feel alone and on a limb. Open your 1-1's with an open wellbeing question so you can understand what is going on. Give them an opportunity to talk about it and put resources in place to point them to.
Being open with your team about your drinking helps. Letting them know when you may be using it as a coping mechanism and why you're cutting down. I'm just saying "I'm honestly feel perhaps I'm drinking too much at the minute".
I think there's a lot to do around normalising not drinking. Particularly in our industry and society. If you go out and you say to somebody would you like to drink? And they say 'no, I don't drink', the immediate question is 'why? How long have you not drunk for? What do you do in these situations? Is it difficult?'. I think that we need to be able to move past that. There should be no questions that follow that statement.
In Cultures where alcohol is still prevalent there may not be signs you see but signs you hear. People start using drinking and hangovers as 'bragging rights' which is a sign of something bigger going on.Use it as a way to open the conversation. It can put you on the front foot.
Tara Kent, Champs
How to talk to your boss about your drinking
You may not want to speak to a manager about it. But a buddy system allowing someone to open up about all sorts of work stuff in confidence can be a good idea.
Where to signpost staff for support
Many employees will have an EAP scheme which is confidential and involves counselling.
The Drinks Trust can help you with financial assistance, therapeutic services, sleep and insomnia treatment, managing alcohol consumption and more. So do look at their offer if you have worked in or still work in the food and drinking sector. You can also access support from Healthy Hospo. They have great articles and videos.
The courses run by Club Soda are also brilliant. 80% of the UK are trying to make healthier choices so don't think this is something you are doing your own on.
Everyone can access local council funded services, and often people find that the hardest thing is actually making that first call. But what you've got on the other end of all of those services are people who understand, who want to help and who are willing to help. So please don't be frightened to make that call. Once you've made it, you'll probably feel a huge sense of relief.
Tasting notes: January's alcohol-free choices for the wine occasion
10 low and no alcohol craft beers that'll get you feeling festive
Kim's story: Looking back at five months sober


While millennials might share the same generational demographic, there's no doubt that we have a wide range of interests, tastes, and hobbies. But one thing any of us can get into? A variety of useful products on Amazon that save us time and money while improving the daily quality of our lives — oh, and memes. We all love memes.
Of course, there's lots of not-so-positive connotations around millennials — like spending way too much money on brunch and having an unreasonable obsession with avocados. But there should be something else this generation should be recognized for — we're conscious, well-educated consumers who feel comfortable purchasing things online, especially when our peers give a glowing recommendation.
When we decide to spend money on things other than avocado toast, we do want to make sure it's somewhat useful. Whether it's oven liners that minimize the post pizza night clean-up, or a productivity planner that's scientifically proven to increase your work capacity, these products take into account the inefficiencies of our day-to-day and provide thoughtful solutions.
In this rundown of useful Amazon products, you'll find items that appeal to eco-conscious buyers, people with overly cluttered closets, beauty gurus, and everyone in between. So whether you're finding an all-natural way to deal with stress, or just trying to save that last glass of wine for another day, there's something for every millennial to add to their shopping cart.


text:
Groenhoff, Hans
NASM.XXXX.0359
National Air and Space Museum Archives
Hans Groenhoff (1906-1985) was a celebrated American aviation photographer from the 1930s through the 1960s, also working as a pilot, journalist, editor, correspondent, and—in his retirement years—as an aviation tourism publicist for the Bahamas. This collection of 25,550 images consists of Groenhoff's collection of negatives and transparencies, spanning his career from 1933 to 1975.
Samuel P. Langley Collection
Langley, S. P. (Samuel Pierpont), 1834-1906
24.28 Cubic feet (64 boxes)
This collection includes information about Samuel P. Langley and his colleagues, as well as documentation of Langley's work. The collection includes biographies of Langley and his assistant Charles Manly, newspaper clippings, correspondence, manuscripts regarding Langley's aircraft, photographs and drawings, work requisitions for the Aerodromes, a sketchbook, specifications and measurements for Langley's experiments, the Langley Memoirs on Mechanical Flight and the Langley "Waste Books."
American Petroleum Institute Photograph and Film Collection
American Petroleum Institute.
45 Cubic feet (122 boxes)
Collection includes historic photographs, slides and films on subjects relating to all aspects of the petroleum industry, including exploration, drilling, refineries, tankers, pipelines, automobiles, trucks, aviation, refueling, buildings, coal, gasification, plants, mining, surface mining, fields, land reclamation, coastal zone management, corporate public service, educational programs, crude oil, deepwater ports, and watercraft It also documents numerous products other than gasoline produced by the petroleum industry, such as propane, lubricants, heating oil, and plastics.
American Federation of Arts records
AAA.amerfeda
The records of the American Federation of Arts (AFA) provide researchers with a complete set of documentation focusing on the founding and history of the organization from its inception through the 1960s. The collection measures 79.8 linear feet, and dates from 1895 through 1993, although the bulk of the material falls between 1909 and 1969. Valuable for its coverage of twentieth-century American art history, the collection also provides researchers with fairly comprehensive documentation of the many exhibitions and programs supported and implemented by the AFA to promote and study contemporary American art, both nationally and abroad.
Event Files
Smithsonian Institution. Office of Special Events
This accession consists of files pertaining to Smithsonian Institution events, such as luncheons, meetings, exhibition openings, dedications, receptions, and the American Folklife Festivals. Materials include correspondence, memoranda, press releases, programs, guest lists, and invitations.
Garland Fulton Collection
Fulton, Garland, 1890-1974
20.8 Cubic feet (45 boxes)
The Garland Fulton Collection consists of material gathered by Captain Garland Fulton.
Subject Files
National Museum of American History. Office of the Director
circa 1945-1987 and related records, 1920-1930
103.51 cu. ft. (100 record storage boxes) (1 12x17 box) (7 tube boxes) (oversize materials)
These records document the tenures of Frank A. Taylor, John C. Ewers, and Robert P. Multhauf as Directors of the Museum of History and Technology (NMHT); of Daniel J. Boorstin, Brooke Hindle, and Roger G. Kennedy as Directors of the National Museum of History and Technology; and of Roger G. Kennedy as Director of the National Museum of Am...
Alexander Wetmore Papers
Wetmore, Alexander, 1886-1978
circa 1848-1983 and undated
50.21 linear meters.
The papers of Alexander Wetmore were received in the Smithsonian Archives in several different accessions between 1978 and 1987. The Archives would like to thank Mrs. Beatrice T. Wetmore for her help in transferring her husband's papers to the Archives. We also appreciate the assistance of the staff of the Division of Birds, National Museum ...
Leonard Gaskin Papers
Gaskin, Mary
Gaskin, Leonard, 1920-
30 Cubic feet (129 boxes, 4 map folders)
Papers documenting the life and career of bass player Leonard Gaskin, as well as documenting the numerous other musicians he was associated with in his long career. Gaskin's career encompasses a wide range of musical genres, including gospel, rock, folk, and Caribbean music. The papers include photographs, newspaper clippings, diaries, music manusc...
Smithsonian Institution. Traveling Exhibition Service
11.69 cu. ft. (11 record storage boxes) (1 16x20 box)
This accession consists of records that document the planning, execution, administration, and promotion of traveling exhibitions. The records are arranged by exhibition title, followed by the exhibition ID number and the dates of the exhibition. Materials include correspondence, memoranda, press releases, press kits, photographs, negatives, ...


On behalf of Law Office of Perry Dean Ellis, P.C. posted in workers' compensation on Tuesday, August 21, 2018.
Georgia employers should make sure to educate their employees about the basic safety rules for handling hazardous materials in the workplace. Employees could be informed about the rules during a regularly scheduled safety meeting at work. Furthermore, workers may be encouraged to offer additional safety rules for managing hazardous materials.
On behalf of Law Office of Perry Dean Ellis, P.C. posted in car accidents on Wednesday, August 1, 2018.
Georgia became the sixteenth state to prohibit the use of hand-held mobile devices by drivers when Gov. Nathan Deal signed House Bill 673 into law. The bill, which is also known as the Hands Free Law, also bans recording video with mobile devices and reading cellphone screens while behind the wheel. Motorists in the Peach State who wish to avoid a $50 ticket must now either use hands-free devices or pull over before using their smartphones. The law went into effect on July 1.


I rrigation systems (such as automated lawn sprinkler systems) are one of the best investments you can make in your landscape. Irrigations services include backflow testing, spring start-ups, winter shut downs, repairs and improvements.
Installing an automatic irrigation system is a complex task. That's why it is best to hire qualified Palm Beach landscaping professionals, like us.