text:
package org.apache.hadoop.dynamodb.split;

import org.apache.hadoop.dynamodb.filter.DynamoDBQueryFilter;
import org.apache.hadoop.fs.Path;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;

public class DynamoDBSegmentsSplit implements DynamoDBSplit {

  private long approxItemCount;
  private int splitId;
  private List<Integer> segments;
  private int totalSegments;
  private DynamoDBQueryFilter filterPushdown;

  public DynamoDBSegmentsSplit() {
    this.segments = new ArrayList<>();
  }

  public DynamoDBSegmentsSplit(Path path, long approxItemCount, int splitId, List<Integer>
      segments, int totalSegments, DynamoDBQueryFilter filterPushdown) {
    this.approxItemCount = approxItemCount;
    this.segments = segments;
    this.splitId = splitId;
    this.totalSegments = totalSegments;
    this.filterPushdown = filterPushdown;
  }

  @Override
  public long getLength() {
    return 0;
  }

  @Override
  public void readFields(DataInput in) throws IOException {
    approxItemCount = in.readLong();
    splitId = in.readInt();
    int numSegments = in.readInt();
    this.segments = new ArrayList<>(numSegments);
    for (int i = 0; i < numSegments; i++) {
      this.segments.add(in.readInt());
    }
    totalSegments = in.readInt();
    filterPushdown = new DynamoDBQueryFilter();
  }

  @Override
  public void write(DataOutput out) throws IOException {
    out.writeLong(approxItemCount);
    out.writeInt(splitId);
    out.writeInt(segments.size());
    for (Integer segment : segments) {
      out.writeInt(segment);
    }
    out.writeInt(totalSegments);
  }

  @Override
  public String[] getLocations() throws IOException {
    return new String[0];
  }

  @Override
  public long getApproxItemCount() {
    return approxItemCount;
  }

  @Override
  public int getSplitId() {
    return splitId;
  }

  @Override
  public List<Integer> getSegments() {
    return Collections.unmodifiableList(segments);
  }

  @Override
  public int getTotalSegments() {
    return totalSegments;
  }

  @Override
  public DynamoDBQueryFilter getFilterPushdown() {
    return filterPushdown;
  }

  @Override
  public void setDynamoDBFilterPushdown(DynamoDBQueryFilter filterPushdown) {
    this.filterPushdown = filterPushdown;
  }
}


The "DiorGaia" sunglasses are characterised by an oversized square shape made from Optyl and acetate. The frame plays on volumes and shades of colours. The front is in transparent shaded brown to amber ornage, and the temples feature a gold-tone metal and red lacquered CD logo.
The shaded grey lenses ensure 100% UV protection (Category 3).


text:
<?php

declare(strict_types = 1);

namespace UaBrowserType;

use UnexpectedValueException;

final class NotFoundException extends UnexpectedValueException
{
    // nothing to do here
}

redpajama_set_name: RedPajamaGithub


The NHS 111 telephone number operates 24 hours a day, 365 days a year and is the telephone number to dial for non-emergency health care.
Patients may need to call the NHS 111 service for a number of non-emergency reasons, such as an urgent, non life threatening medical concern about yourself or someone else.
By calling 111 you will be directed to the most appropriate service that meets your needs.
They will also determine the most appropriate course of action and direct you to the right place, first time.
NHS 111 does not replace the usual direct contact you have with your GP, dentist, pharmacy or other primary care service. For less urgent matters you should still contact your GP surgery direct.
Patients who are under the care of community health services or mental health services should continue to use the number they have been given for their specific treatment.
Please complete and return the patients' feedback form below when you want to highlight an issue with the 111 service.


PORT ELIZABETH - General Motors South Africa (GMSA) confirmed two new Opel vehicles will be launched in South Africa in 2015 –the new Adam hatchback and Mokka SUV.
The new models form part of GM's global product offensive which will see 27 new vehicles and 17 engines (in applicable markets) by 2018.
Its design borrows design cue from the Monza concept car.
Cradock said: "The Adam experience is all about the freedom to create and own an individual, personalised car through which the owner can express their lifestyle preferences and tastes. In addition to customisation, the Adam offers an exciting and engaging driving experience and as with all Opel vehicles it comes packed with standard innovative technology and specification.
Engines, specifications and model derivatives will be confirmed closer to the launch.
Cradock said: "Over 240 000 Mokka orders have been placed in Europe – this is not only an exceptionally important factor in the growth of the Opel brand on a global level, but also proves that Mokka is a worthy contender in a segment which is experiencing exponential growth all over the world.
Opel reports a "resurgence" in South Africa with its vehicle sales up 54% in the first seven months of 2014. In Europe, Opel is investing heavily in the development of advanced new powertrain technology which South African consumers will experience for the first time when the Adam and Mokka reach SA.


Steve Tisch looks on as Dr. Christopher Giza and research coordinator Max Zeiger demonstrate the new football helmet sensors.
Dozens of UCLA student-athletes will participate in research by UCLA scientists that is expected to deliver new clues about the biological and genetic risk factors for sports-related concussions.
UCLA researchers in medicine and athletics have outfitted 22 Bruin football players with helmet sensors that measure the speed, intensity and location of hits to the head during all games and practices. The scientists also will track the health of 40 men's and women's soccer players. The information they gather could shed light on why concussions strike some athletes more than others, and why certain people recover faster from head injury.
The innovative studies are part of the Concussion Assessment Research and Education Grand Alliance, a landmark $30 million initiative funded by the National Collegiate Athletic Association and the U.S. Department of Defense to collect "big data" on concussions. The three-year project aims to fill critical gaps in knowledge about concussion and translate research findings into new safety guidelines for the more than 450,000 U.S. collegiate student-athletes.
In the first study, UCLA will work with 20 other universities and military academies to gather neurological and cognitive data from more than 25,000 student-athletes in all NCAA sports.
At UCLA, a partnership between medicine and athletics will enable researchers to conduct clinical studies that monitor athletes' head injuries from onset through recovery.
"Our goal is to make this research the sport-concussion equivalent of the Framingham study," Giza added, referring to a large-scale initiative launched in 1948 that identified previously unknown risk factors for heart disease.
All athletes participating in the first study will receive a preseason medical history and baseline testing for balance, memory, cognitive function and reaction time; the tests will be repeated annually. If a student suffers a concussion, he or she will repeat the tests six times at designated intervals over the following six months. To date, 5,000 students have enrolled in the study and completed baseline testing.
According to Giza, concussed athletes rarely lose consciousness or show other objective signs of brain injury. Athletic trainers and team physicians rely in part on self-reports from players, who may downplay their symptoms in order to get back in the game.
"Our goal is to develop scientific, evidence-based tools that will enable doctors to more accurately gauge when it is safe for an athlete to return to play," Giza said.
In the second study, UCLA and three other universities will blend sports science and medical technology to gather biological data from some 400 men and women competing in football, soccer, ice hockey and lacrosse — the collegiate sports with the highest concussion rates.
Investigators will examine athletes' blood samples for biomarkers that may predispose some to brain injury or help others recover faster. So far, 200 athletes have enrolled nationwide.
Football players in the study will wear helmets outfitted with sensors that measure the impact of collisions they sustain on the field. The sophisticated gadgets wirelessly transmit data to a laptop for analysis, and pagers connected to the helmets alert medical staff to what could be a significant hit.
"The helmet sensors allow us to track the frequency, magnitude and direction of hits that our football players sustain to the head," said Dr. John DiFiori, chief of sports medicine at the David Geffen School of Medicine at UCLA and UCLA's head physician for intercollegiate athletics.
According to DiFiori, the findings will be invaluable in identifying athletes who are at risk for a brain injury before one occurs.
"If the data shows that an athlete's impacts occur mostly on top of his head rather than in front or on the sides, we can take the player aside and say, 'Here's what we're seeing on our helmet impact data. We need to work on your technique,'" said DiFiori, who is also a UCLA professor of family medicine and orthopaedics.
The tests in the second study also will be repeated each year. And in an effort to detect biological changes linked to brain injury and recovery, any athlete in the study who suffers a concussion will undergo brain scans and repeat follow-up tests five times over a six-month period. Their names won't be revealed, and they may opt out of the study at any time.
According to the U.S. Centers for Disease Control, as many as 3.8 million sports concussions occur in the U.S. each year. Up to half of them are unreported because the symptoms aren't recognized. Sports are second only to motor vehicle crashes as the leading cause of traumatic brain injury among people aged 15 to 24.
Editors: B-roll of a clinical concussion exam and a demonstration of the football helmet sensors are available upon request. Please contact the UCLA media representative.


Technique from the famous American brand is always in demand, which is not surprising. After all, it combines the original design and unlimited spectrum of functionality. Now let's see what Apple TV is and how it works. As a result, each user will understand whether it makes sense to spend money on the purchase of this gadget.
access to an impressive storage of audio tracks, podcasts, images, video games.
Apple TV console works on the principle of streaming media content. The latest models of this type of device have a limited amount of internal memory. Therefore, you should store the content in the cloud or on removable media: flash drives, external hard drives.
The gadget is equipped with a built-in adapter for wireless Internet connection. Therefore, after connecting the console, the user will be able to connect the TV to the wireless home network, even if it did not initially support Wi-Fi. You will not require additional wires or adapters to configure the connection.
Analyzing the features of Apple TV, we cannot ignore the performance. Content in UHD and Full HD format is played without any delay. Multichannel sound will make watching movies even more exciting. The stream buffer is 8 GB of flash memory. These devices are equipped with a powerful Apple A5 processor.
The device operates under the iOS operating system. The desktop of the console is almost the same as iPod or iPhone. Practice shows that with an easy-to-use user interface, there are no problems. All desktop icons are signed. The only inconvenience is text entering from the remote control. It is not equipped with a keyboard. Therefore, you can type by selecting letters on the joystick, the screen displays the keyboard.
There are several ways to solve the problem described above. You can connect a wireless keyboard or smartphone to the console. You must first install the Remote application on iPhone, iPad. You can download this program from the Apple Store. In addition to entering text, you can use your smartphone as a game joystick.
The main idea of creating Apple TV was to use content from the iTunes Store and Apple Music on the TV. The console has a store with interesting movies and music, and its design is specially adjusted to your screen.
You can not only purchase, but also rent movies for 48 hours. As a rule, renting a movie is the best solution that allows you to view the movie in good quality in real time. Moreover, when you activate family access, you can view what your family members have acquired.
Many movies are free and you can watch them in online cinemas downloaded from the App Store on Apple TV.
Thanks to Apple TV, you can listen to music from iTunes from your PC connected to the same network as the Wi-fi console.
Due to the service Apple Music console is a great modern media player, which has millions of music tracks, as well as your personal music collection.
In "Radio" option you will find more than two hundred different stations and brand one – Beats 1.
Broadcasting is all day and night, the best DJs work for you, the music is always fresh, and experts selecting quality material work here. This indicates that everyone will find here something for their own interests.
Moreover, with the help of the console, you can view your photos and videos placed in iCloud on the TV screen. Also right there you can watch the latest updates of your friends.
Thanks to the app store, you can download various games and applications through the console.
Also it is necessary to say about the programs of other developers for watching streaming clips, as well as online cinemas. For example, this is YouTube, different online cinemas to play files in any format, and so on.
Playing on Apple TV is very convenient. You can play using the remote control with a sensor. It has accelerometer and gyroscope. Or you can play the game with the help of a other controller.
Air Play is the best option on Apple TV. This technology can show video or play sound through a gadget or PC. The image is broadcast from the display device online, which is quite useful for games. By the way, some programs are specially optimized for Apple TV, that is, for example, the phone shows control buttons, and an image is displayed on the television. You can also use Air Play from your PC.
You can send movies from ITunes, use the device audio system instead of a PC, or set up a TV for this purpose. This functionality is also available for Windows users but with the Air Parrot utility.
How to connect iPhone to TV?


text:
import * as React from "react"

const Tracer = require('pegjs-backtrace')

import {parseRelations} from '../parser/parsing'
import {Catalog} from '../parser/types'

const DEFAULT_INPUT = `
Sailors(sid:integer, sname:string, rating:integer, age:real)
Boats(bid:integer, bname:string, color:string)
Reserves(sid:integer, bid:integer, day:date)
`

export interface RelationsInputOutput {
  catalog: Catalog.Catalog | null
  error: null | Error
  traceback: '' | string
}

export interface RelationsInputProps {
  onUpdate: (output: RelationsInputOutput) => void
}

interface RelationsInputState {
  catalog: Catalog.Catalog | null
  text: string
}

export default class RelationsInput extends React.Component<RelationsInputProps, RelationsInputState> {

  constructor(props) {
    super(props)
    this.state = {
      catalog: null,
      text: DEFAULT_INPUT
    }

    this.run = this.run.bind(this)
    this.onChange = this.onChange.bind(this)
  }

  run(e?) {
    const text = this.state.text
    if (e) e.preventDefault()

    const tracer = new Tracer(text, {
      useColor: false,
      showTrace: true
    })

    let catalog: Catalog.Catalog|null = null
    try {
      catalog = parseRelations(text, {tracer})
      this.props.onUpdate({ catalog, error: null, traceback: '' })
    } catch (ex) {
      this.props.onUpdate({
        catalog,
        error: ex,
        traceback: tracer.getParseTreeString()
      })
    }
    this.setState({catalog})
  }

  onChange(event) {
    this.setState({text: event.target.value})
  }

  render() {
    return (
      <div id="relations-input-wrapper">
        <textarea
          id="relations-input"
          value={this.state.text}
          cols={80}
          rows={10}
          onChange={this.onChange}
        />
        <button onClick={this.run}>Parse Relations</button>
      </div>
    )
  }
}


text:
\section{Introduction}

The availability of large labeled image datasets~\cite{pascal-voc-2007,imagenet} has been one of the key factors for advances in
recognition. These datasets, which have been largely curated from internet
images, have not only helped boost performance~\cite{pedro-dpm,rcnn}, but have
also fostered the development of new techniques~\cite{alexnet,rcnn}. However, compared to images, videos seem like a more natural source of training data because of the additional temporal continuity they offer for both learning and labeling. So ideally we should have large labeled internet video datasets. In general, the human effort required for labeling these vision datasets is huge, e.g., ImageNet~\cite{imagenet} required 19 man-years to label bounding boxes in the 1.2 million images harvested from the
internet. Consider the scale of internet videos -- YouTube
has 100 hours of video (10 million frames) uploaded every minute. It seems
unlikely that human per-image labeling will scale to this amount of data.
Given this scale of data and the associated annotation problems~\cite{labelme-video,virat}, which are more pronounced in videos,
it is no surprise that richly annotated large video recognition datasets are
hard to find. In fact, the available video datasets~\cite{ucf101,hollywood,sports1m,virat} lack the kind
of annotations offered by benchmark image datasets~\cite{pascal-voc-2007,coco,imagenet}.
\begin{figure}[!t]
\centering
\includegraphics[width=0.45\textwidth]{pullup.pdf}
\vspace{-0.1in}
\caption{We present a novel formulation of semi-supervised learning for automatically learning object detectors from videos. Our method works with long video to automatically learn bounding box level annotations for multiple object instances. It does not assume exhaustive labeling of every object instance in the input videos, and from a handful of labeled instances can automatically label hundreds of thousands of instances.}
\vspace{-0.2in}
\label{fig:pullup}
\end{figure}

One way to tackle the labeling problem is using semi-supervised learning (SSL).
Starting with only a few annotated examples, the algorithm can label more
examples automatically. However, a major challenge for any kind of SSL technique is to
constrain the learning process to avoid semantic drift, i.e., added
noisy samples cause the learner to drift away from the true concept. Recent
work~\cite{abhinav-ssl,neil,lean,choi-ssl} has shown ways to constrain this learning
process for images. In this paper, we present an approach to constrain the semi-supervised learning process~\cite{abhinav-ssl} in videos. Our technique
constrains the SSL process by using \emph{multiple weak cues} - appearance,
motion, temporal etc., in video data and automatically learns \emph{diverse
new examples}.

Intuitively, algorithms dealing with videos should use appearance and temporal
cues using detection and tracking, respectively. One would expect a
simple combination of detection and tracking to constitute a semi-supervised
framework that would prevent drift since both of these processes
would ideally cancel each others' errors. However, as we show in our
experiments (Sec.~\ref{sec:experiments}), a na\"{\i}ve combination of these two
techniques performs poorly. In the long run, the errors in both detection and
tracking are amplified in a coupled system. We can also consider pure
detection approaches or pure tracking approaches for this problem.
However, pure detection ignores temporal information while pure tracking tends
to stray away over a long duration.

We present a scalable framework that discovers objects in
video using SSL (see Figure~\ref{fig:pullup}). It tackles the challenging problem of localizing new object instances in long videos starting from
only a few labeled examples. In addition, we present our algorithm in a
realistic setting of ``sparse labels''~\cite{virat}, i.e., in the few initial ``labeled''
frames, not all objects are annotated. This setting relaxes the assumption that in a given frame, all object instances have been exhaustively annotated. It implies that we do not know if any unannotated region in the frame belongs to the object category or the background, and thus cannot use any region from our input as negative data.
While much of the past work has ignored this type of sparse labeling (and \emph{lack of explicit negatives}), we show ways to overcome this handicap. Figure~\ref{fig:teaser} presents an overview of our algorithm.
Our proposed algorithm is different from the rich body of work on tracking-by-detection. Firstly, we do not attempt to solve the
problem of \emph{data association}. Our framework does not try to identify
whether it has seen a particular instance before. Secondly, since it works in
the regime of \emph{sparse annotations}, it does not assume that negative data
can be sampled from around the current box. Thirdly, we limit expensive
computation to a subset of the input frames to scale to a million frames.

\par \noindent \textbf{Contributions:} We present a semi-supervised learning framework that
\emph{localizes multiple unknown objects} in videos. Starting from few
\emph{sparsely labeled} objects, it iteratively labels new and useful training examples in the videos. Our key contributions are: 1) We tackle the SSL problem for discovering multiple objects in sparsely labeled videos; 2) We
present an approach to constrain SSL by combining multiple weak cues in
videos and exploiting decorrelated errors by modeling data in multiple feature spaces.
We demonstrate its effectiveness as compared to traditional tracking-by-detection approaches; 3) Given the redundancy in video data, we need a
method that can automatically determine the relevance of training examples to
the target detection task. We present a way to include \emph{relevance and diversity of the training examples}
in each iteration of the SSL process, leading to a scalable \emph{incremental learning} algorithm.
\begin{figure*}[!ht]
\centering
\includegraphics[width=0.9\textwidth]{teaser3.pdf}
\vspace{-0.1in}
\caption{Our approach selects samples by iteratively discovering new boxes by a careful fusion of detection, robust tracking, relocalization and multi-view modeling of positive data. It shows how an interplay between these techniques can be used to learn from large scale unlabeled video corpora.}
\vspace{-0.2in}
\label{fig:teaser}
\end{figure*}

\vspace{-0.1in}
\section{Related Work}
\vspace{-0.05in}
\label{sec:relatedWork}

The availability of web scale image and video data has made semi-supervised learning more popular in recent years. In the case of images, many methods~\cite{fergus-ssl,ensemble-projection} rely on image similarity measures, and try to assign similar labels to close-by unlabeled images. However, in the case of real-world images, obtaining good image similarity is hard and hence the simple approaches become less applicable. One major body of work~\cite{abhinav-ssl,neil,lean,choi-ssl,devi-ssl} tries to overcome this difficulty by leveraging the use of a set of pre-defined attributes for image classes~\cite{abhinav-ssl,choi-ssl} and additionally web-supervision and text~\cite{lean,neil}. While these methods have good performance for images, they are not applicable to videos mainly because they treat each image independently and do not use video constraints. One major reason for the success of attribute based methods for SSL was the relatively cheap supervision required for attributes (per image level tag). In the same spirit, weakly supervised video approaches use tags available with internet videos.

Weakly supervised video algorithms have gained popularity largely due to the abundance of video level tags on the internet. The input is a set of videos with video level tags (generally a video belongs to an object category), and the algorithm discovers the object (if present) in the video. These methods, while effective, assume a maximum of one dominant object per video~\cite{prest-video,wang-discov,tang-segment,liu-vid-discov}. Some of them additionally assume dominant motion~\cite{prest-video} or appearance saliency~\cite{prest-video,wang-discov,liu-vid-discov,tang-segment,yongjae-egocentric} for the object of interest. The methods of video co-segmentation~\cite{chen-video-coseg,jose-video-coseg,lin-video-coseg,guo-video-coseg} can be considered a subset of weakly supervised methods. They make a strong assumption that multiple videos contain the exact same object in majority of the frames.
This assumption of at most one salient object in a video is rarely satisfied by internet or real world videos. When this assumption does not hold, methods cannot strongly rely on motion based foreground/background clustering or on appearance saliency. Our proposed work deals with \emph{multiple objects} and can even discover \emph{static} object instances without strongly relying on motion/appearance saliency. However, we do require richer bounding box annotations by way of a few \emph{sparsely labeled} instances. A concurrent work~\cite{babylearning} utilizes weakly labeled video for improving detection.

A relevant thread of work which also uses bounding box annotations is that of tracking-by-detection. It has a long and rich history in computer vision and the reader is referred to~\cite{tracking-benchmark} for a survey. The tracking-by-detection algorithms start with bounding box annotation(s) of the object(s) to track the object(s) over a long period of time. The underlying assumption is that negative data can be sampled from around the object~\cite{tld,spltt,ensemble-tracking,struck,mvboost-track,sslboost-track,thrun-ssltracking} to distinguish between the object and background. This is not valid in the case of \emph{sparsely labeled} videos because the unmarked regions may contain more instances of the same object, rather than background.

Other tracking-by-detection methods~\cite{hamed-deva-tracking,k-shortest,geiger-kitti-2} do not sample negative data from the input video. They do so at the cost of using a detector trained on additional training data, e.g., ~\cite{hamed-deva-tracking} uses a DPM~\cite{pedro-dpm} trained on images from PASCAL~\cite{pascal-voc-2007}. In contrast, we do not use such additional training data for our method. This allows us to work on object categories which are not in standard datasets.

Multi-object tracking-by-detection approaches also focus on solving the problem of \emph{data association} - given object locations across multiple frames, determine which locations belong to the same object. Data association is critical for long term tracking, and is also very challenging~\cite{k-shortest}. In contrast, our goal is not to track over long periods, but to get short and reliable tracking. Moreover, we do not require these short tracklets to be associated with each other, and thus have minimal need for data association.

To summarize, our SSL framework operates in a less restrictive domain compared to existing work in weakly labeled object discovery and tracking-by-detection. The key differences are: 1) We localize multiple objects in a single frame as opposed to zero or one objects. 2) We do not assume strong motion or appearance saliency of objects, thus discovering static object instances as well. 3) We operate in the regime of \emph{sparsely labeled} videos. Thus, in any given frame, all the unmarked region may contain instances of the object. This does not allow using negative data from the input frame. 4) We \emph{do not need explicit negative data} or any pre-trained object models. 5) Finally, the aim of our approach is very different from tracking approaches. We do not want to track objects over a long period of time, but want short reliable tracklets.

\begin{figure}[t]
        \centering
        \includegraphics[width=0.48\textwidth]{kittiIms}
\caption{Sparsely labeled positives (as shown in the top row) are used to train Exemplar detectors~\cite{tomasz-esvm}. Since we do not assume exhaustive labeling of each instance in the image, we cannot sample negative data from around the input boxes. When these detectors (trained without domain negatives) are used, they may learn background features (like the co-occuring yellow stripes or road divider) and give high confidence false positives (bottom row). We address this issue by exploiting decorrelated errors (Section~\ref{sec:outlierDetails})}

\vspace{-0.2in}
\label{fig:static-back-fp}
\end{figure}
\vspace{-0.1in}
\section{Approach Overview}
\label{sec:overview}
\vspace{-0.1in}
There are two ways to detect objects in videos -- either using detection in individual frames or tracking across frames.
In a semi-supervised framework, detection plays the role of constraining the system
by providing an appearance prior for the object, while tracking generalizes by
providing newer views of the object. So one could imagine a detection and tracking combination, in which one tracks from confident detections and then updates the detector using the tracked samples. However, as we show in our
experiments (Section~\ref{sec:experiments}), such a na\"{i}ve combination does not impose enough constraints for SSL. In contrast, our approach builds
on top of this basic combination of detection and tracking to correct their mistakes.

Our algorithm starts with a few sparsely annotated video frames
($\mathcal{L}$) and iteratively discovers new instances in the large unlabeled
set of videos ($\mathcal{U}$). Simply put, we first train detectors on
annotated objects, followed by detection on input videos. We determine good
detections (removing confident false positives) which serve as starting points for
short-term tracking. The short-term tracking aims to label new and unseen examples reliably. Amongst these newly labeled examples, we identify good and diverse examples which are used to update the detector without re-training from scratch. We iteratively repeat this fusion of tracking and detection to label new examples. We now describe our algorithm (illustrated in Figure~\ref{fig:teaser}).

\par \noindent \textbf{Sparse Annotations (lack of explicit negatives):} We
start with a few sparsely annotated frames in a random subset of $\mathcal{U}$. Sparse labeling implies that unlike other
approaches~\cite{tld}, we do not assume exhaustively annotated input, and thus cannot sample negatives from the vicinity of labeled
positives. We use random images from the internet as negative data for training object detectors on these sparse labels~\cite{abhinav-data-driven}. We use these
detectors to detect objects on a \emph{subset of the video}, e.g., every 30 frames.
Training on a few positives without domain negatives can result in high confidence false positives as shown in Figure~\ref{fig:static-back-fp}. Removing such false positives is important because if we track them, we
will add more bad training examples, thus degrading the detector's performance over iterations.

\par \noindent \textbf{Temporally consistent detections:} We first remove
detections that are temporally inconsistent using a smoothness prior on the
motion of detections.

\par \noindent \textbf{Decorrelated errors:} To remove high confidence false
positives (see Figure~\ref{fig:static-back-fp}), we rely on the principle of \emph{decorrelated errors} (similar to
\emph{multi-view} SSL~\cite{coreg-multiview,rosenberg}). The intuition is that the
detector makes mistakes that are related to its feature
representation~\cite{hoggles}, and a different feature representation would
lead to different errors. Thus, if the errors in different feature spaces are
decorrelated, one can correct them and remove false positives. This gives us a filtered set of detections.

\par \noindent \textbf{Reliable tracking:}
We track these filtered detections to label new examples. Our final goal is not to track the object over a long period. Instead, our goal is to track reliably and label new and hopefully diverse examples for the object detector. To get such reliable tracks we design a conservative \emph{short-term tracking} algorithm that identifies \emph{tracking failures}. Traditional tracking-by-detection approaches~\cite{hamed-deva-tracking,geiger-kitti-2} rely heavily on the detection prior to identify tracking failures. In contrast, the goal of our tracking is to improve the (weak) detector itself. Thus, heavily relying on input from the detector defeats the purpose of using tracking in our case.

\par \noindent \textbf{Selection of diverse positives for updating the
detector:} The reliable tracklets give us a large set of automatically labeled
boxes which we use to update our detector. Previous work~\cite{prest-video}
temporally subsamples boxes from videos, treating each box with equal
importance. However, since these boxes come from videos, a large number of
them are redundant and do not have equal importance for training our detector.
Additionally, the relevance of an example added at the current iteration $i$ depends on whether similar examples were added in earlier iterations. One would ideally want
to train (make an \emph{incremental update}) only on new and diverse examples,
rather than re-train from scratch on thousands of largely redundant boxes. We
address this issue by selection and show a way of training only on diverse, new
boxes. After training detectors on diverse examples, we repeat the SSL process to iteratively label more examples.

\par \noindent \textbf{Stopping criterion of SSL:}  It is desirable to have SSL algorithms which automatically determine when they should stop. We stop our SSL once our selection algorithm indicates that it does not have any good candidates to select.

\vspace{-0.05in}
\section{Approach Details}
\label{sec:details}
\vspace{-0.1in}
We start with a small sparsely labeled set $\mathcal{L}_0$ of
bounding boxes and unlabeled input videos $\mathcal{U}$. At each iteration
$i$, using models trained on $\mathcal{L}_{i-1}$, we want to label new boxes
in the input videos $\mathcal{U}$, add them to our labeled set
$\mathcal{L} =\mathcal{L} \cup \mathcal{L}_{i}$, and iteratively repeat this procedure.
\vspace{-0.05in}
\subsection{Detections with decorrelated errors}
\label{sec:outlierDetails}
\vspace{-0.1in}
We train object detectors on our initial set of examples using random
images from Flickr as negatives~\cite{abhinav-data-driven}. We detect on a uniformly
sampled subset of the video frames and remove the temporally inconsistent detections.

Since the negative data for training the detectors comes from a different domain
than the positives, we still get consistent false positive detections because
the detector learns the wrong concept (see Figures~\ref{fig:static-back-fp} and ~\ref{fig:qualAbl}). To remove such false positives, we perform outlier removal in a feature space different from that of the detector. The intuition is that the errors made by
learning methods are correlated with their underlying feature representation,
and thus using decorrelated feature spaces might help correct them.

For outlier removal, we use unconstrained Least Squares Importance Fitting (uLSIF)~\cite{uLSIF}. uLSIF uses definite inliers (our labeled set $\mathcal{L}_{i-1}$) to identify outliers in unknown data. It scales well with the size of the input and can be computed in closed form. These final filtered detections serve as starting points for reliable short term tracking.
\subsection{Reliable Tracking}
\vspace{-0.05in}
We formulate a scalable tracking procedure that effectively capitalizes
on priors available from detection, color/texture consistency, objectness~\cite{objectness,sel-search} and optical flow. More importantly, our tracking procedure is very good at identifying its own failures. This property is vital in our semi-supervised
framework since any tracking failure will add wrong examples to the labeled set
leading to quick semantic drift (see Figure~\ref{fig:qualAbl}). The short-term tracking produces a set of labeled examples $\mathcal{L}_i$.

Our single object tracking computes sparse optical flow using Pyramidal Lucas
Kanade~\cite{bouget-lk} on Harris feature points. Since we start with a small set of labeled examples, and do not perform expensive per-frame detection, our detection prior is
weak. To prevent tracking drift in such cases we incorporate color/texture consistency
by using object proposal bounding boxes~\cite{sel-search} obtained from a
region around the tracked box. We address two failure modes of tracking:
\par \noindent \textbf{Drift due to spurious motion:} This occurs while computing optical flow
on feature points which are not on the object, e.g., points on a moving background or occlusion. To correct this, we first divide each tracked box into four quadrants and compute the mean flow in each quadrant. We weigh points in each quadrant by their agreement with the flow in the other quadrants. The final dominant motion direction for the box is the weighted mean of the flow for each point. This simple and efficient scheme helps correct the different motion of feature points not on the object.
\par \noindent \textbf{Drift due to appearance change:} This is incorporated by object
detection boxes and object proposal bounding boxes in the trellis graph formulation described below.

We formulate the tracking problem as finding the min-cost path in a graph
$\mathcal{G}$. At each frame we incorporate priors in terms of
bounding boxes, i.e., detection bounding boxes, tracked boxes and
object proposal bounding boxes. These boxes are the nodes in our graph and we
connect nodes in consecutive frames with edges forming a trellis graph. The edge weights are a linear combination
of the difference in dominant motions of the boxes (described above), spatial proximity and area change. Tracking through this trellis graph $\mathcal{G}$ is the equivalent of finding the single min-cost path, and is efficiently computed using Dynamic-Programming~\cite{hamed-deva-tracking,k-shortest}. As post-processing, we cut the path as soon as the total cost exceeds a set threshold.

\vspace{-0.05in}
\subsection{Selection algorithm:}
\label{sec:selectionDetails}
\vspace{-0.05in}
After we label thousands of boxes $\mathcal{L}_i$ for the current iteration, we
use them for improving our object detectors. Since video data is highly redundant, we label few diverse examples and many redundant ones.
Training a category detector on these thousands of (redundant) boxes, \emph{from scratch}, in
every iteration is suboptimal. We prefer an \emph{incremental training} approach that makes
incremental updates to the detector, i.e., trains only on \emph{newly added and diverse} examples rather than everything. This is especially important to prevent drift because even if we label thousands of wrong but redundant boxes, our method picks only a few of them. We find the exemplar detectors~\cite{tomasz-esvm,bharath-whitening} suitable for incremental learning as they are trained per bounding box.

For each labeled bounding box in $\mathcal{L}_i$, we compute a detection
signature~\cite{object-bank, ishan-wacv,yuxiong} using our exemplar detectors. Boxes where our
current set of detectors do not give a high response correspond to examples
which are not explained well by the existing set of detectors. Training on these
boxes increases the coverage of our detectors. Thus, we compute similarity in
this detection signature space to greedily select a set of boxes that are
neither similar to our current detectors, nor amongst themselves.

More formally, let $\mathcal{L}_{i} = \{l_1, \ldots, l_k\}$ be the set of labeled boxes at iteration $i$ and $\mathcal{E} = \cup_{j=0}^{i-1} \mathcal{D}_j$ the set of boxes $b_n$ associated with the exemplar detectors from all previous iterations ($0$ to $i-1$). We compute the $|\mathcal{L}_{i}| \times |\mathcal{E}|$ detector response matrix $R$. The entry $R(m,n)$ indicates the response of the detector associated with box $b_n$ on box $l_m$ (also called the detection signature~\cite{object-bank}). We row-normalize the matrix $R$, and denote the detection signature of box $l_m$ by its row $R(m)$. We initialize the set $\mathcal{D}_{i} \subset \mathcal{L}_{i}$ with all the boxes $l_j$ which have a low response, i.e., none of the detectors from the previous iterations are confident about detecting these boxes (ESVM score of $<-0.8$ and IOU $<0.4$).

We iteratively grow the set $\mathcal{D}_{i}$ by adding detectors which minimize the following objective criterion
\begin{align}
 t^* &= \underset{l_t \in \mathcal{L}_{i}}{\arg\!\max} \sum_{b_p \in \mathcal{D}_i, p\neq t} \mathrm{JSD}( R(t) || R(p) ) \label{eqn:kldiv-selection} \\
 D_{i} &= D_{i} \cup \{ l_{t^*} \} \nonumber
\end{align}

This objective function favors boxes that are diverse with respect to the existing detectors
using the Jensen-Shannon Divergence (JSD) of the detection signatures. At each
iteration, we limit the number of boxes selected to $10$.
When this selection approach is unable to find new boxes, we conclude that we
have reached the saturation point of our SSL. This serves as our \emph{stopping criterion.}
 \vspace{-0.05in}

\section{Experiments}
\label{sec:experiments}
 \vspace{-0.05in}
Our algorithm has a fair number of components interacting with each other across iterations. It is difficult to characterize the importance of each component by using the whole system at once.
For such component-wise characterization, we divide our experiments in two sets.
Our first set of ablative experiments is on a small subset of videos from VIRAT~\cite{virat}. In the second set of experiments, we demonstrate the scalability of our approach to a million frames from~\cite{virat}. We also show the generalization of our method to a different dataset (KITTI~\cite{geiger-kitti-1}). In both these cases we evaluate the automatically labeled data in terms of quality, coverage (recall), diversity and relevance to training an object detector. We now describe the experimental setup which is common across all our experiments.

\begin{figure}[!t]
        \centering

        \begin{subfigure}[b]{0.21\textwidth}
                \includegraphics[width=\textwidth]{abl3_1_pur.pdf}

                \label{fig:tiger}
        \end{subfigure} \hspace{-0.15in}

        \begin{subfigure}[b]{0.21\textwidth}
                \includegraphics[width=\textwidth]{abl3_1_rec.pdf}
                \label{fig:mouse}
        \end{subfigure} \hspace{-0.15in}
       \begin{subfigure}[b]{0.06\textwidth}
       \centering
                \includegraphics[width=\textwidth]{legendAblOrig.pdf}
			\begin{minipage}{0.01in}
            		\vspace{0.75in}
	         \end{minipage}
                \label{fig:mouse2}
        \end{subfigure}
        \vspace{-0.6in}
	\caption{We measure the detection performance of the ESVMs from each ablation method on our test set by computing (left) Average Purity and (right) Recall.}
\vspace{-0.2in}
  \label{fig:abl-graphs}
\end{figure}

\par \noindent \textbf{Datasets:}
Due to limited availability of large video datasets with bounding box annotations, we picked car as our object of interest, and two video datasets with a large number of cars and related objects like trucks, vans etc. We chose the VIRAT 2.0 Ground~\cite{virat} dataset for its large number of frames, and \emph{sparsely} annotated bounding boxes over all the frames. This dataset consists of long hours of surveillance videos (static camera) of roads and parking lots. It has 329 videos ($\sim$1 million frames, and $\sim$6.5 million annotated bounding boxes (partial ground truth) of cars. We also evaluate on videos from the KITTI~\cite{geiger-kitti-1} dataset which were collected by a camera mounted on a moving car. We use the set 37 videos ($\sim$12,500 frames) which have partial ground truth boxes ($\sim$41,000 boxes, small cars are not annotated).

\par \noindent \textbf{Dataset characteristics:} We chose these datasets with very different characteristics (motion, size of object etc.) to test the generalization of our method. The VIRAT and KITTI datasets both consist of outdoor scene videos with a static and moving camera respectively. The VIRAT dataset captures surveillance videos of multiple cars in parking lots. The cars in this dataset are small compared to the frame size, tightly packed together and viewed from a distance (thus no drastic perspective effects). The KITTI dataset on the other hand, consists of videos taken by a vehicle mounted camera. It has high motion, large objects, and perspective effects. Figure~\ref{fig:static-back-fp} shows examples from both the datasets demonstrating their visual differences.

\par \noindent \textbf{Detectors:} We use the Exemplar-SVM (ESVM)~\cite{tomasz-esvm} detectors with 5000 random images from Flickr as negatives~\cite{abhinav-data-driven}. Since per frame detection is expensive, we detect once every $30^\text{th}$ frame for VIRAT, and every $10^\text{th}$ frame for KITTI. We threshold detections at SVM score of $-0.75$.

\par \noindent \textbf{Multiple feature spaces for false positive removal:} We use the uLSIF~\cite{uLSIF} algorithm on Pyramidal HOG (PHOG)~\cite{phog} and color histogram (LAB color with $32\times16\times16$ bins) features computed on a resized box of $200 \times 300$ px. We set the kernel bandwidth for uLSIF by computing the $75^\text{th}$ percentile distance between random pairs of points.

\par \noindent \textbf{Object proposal windows}: We obtain 2000 windows per image using selective search~\cite{sel-search}.
\begin{table*}
    \centering
\small{
\caption{Comparison of our method with baselines as explained in Section~\ref{sec:exp-Abl}. We train an LSVM~\cite{pedro-dpm} on all the automatically labeled data and compute its detection performance on a held-out, fully annotated test set (AP for IOU 0.5).}
\label{tbl:lsvms}
\vspace{-0.1in}
\begin{tabular}[b]{@{}c@{}p{1em}@{}cccccc@{}p{1em}@{}ccc@{}}\toprule
& & \multicolumn{6}{c}{Automatic Labeling (LSVM)}  & & \multicolumn{3}{c}{Ground Truth} \\
\cmidrule{3-8}\cmidrule{10-12}
Iteration & & Boot. & Boot.+Sel. & Det+Track & O w/o Sel & O w/o Outlier & \textbf{Ours} & & Pascal LSVM & Pascal DPM & VIRAT LSVM \\
\cmidrule{1-8}\cmidrule{10-12}
10 & & 1.32 & 9.09 & 9.09 & 11.21 & 7.32 & \textbf{15.39} &  & \multirow{2}{*}{20.89} & \multirow{2}{*}{29.56} & \multirow{2}{*}{41.38}\\
30 & & 1.94 & 3.03 & 6.59 & 10.83 & 1.41 & \textbf{17.68}  & &  &  & \\
\bottomrule
\end{tabular}
}
\vspace{-0.1in}
\end{table*}

 \vspace{-0.05in}
\subsection{Ablative Analysis of each constraint}
 \vspace{-0.05in}
\label{sec:exp-Abl}
\begin{figure*}
    \centering
    \includegraphics[width=\textwidth]{acrossIter.pdf}
\caption{(a) We look at a subset of the bounding boxes used to train ESVMs across iteration. Each row corresponds to an ablation method. The top row shows the randomly chosen initial positive bounding boxes (same for each method). The other methods diverge quickly across iterations, thus showing that constraints are very important for maintaining purity.}
\vspace{-0.2in}
\label{fig:qualAbl}
\end{figure*}
To tease apart the contributions of each component described in Section~\ref{sec:overview}, we design a set of algorithms using only a subset of the components.
\par \noindent \textbf{Bootstrapping (Boot):} In this vanilla form of SSL, we train object detectors on the initial labeled set and perform detection. The confident detections are used as training examples for the next iteration detectors.

\par \noindent \textbf{Bootstrapping with Selection (Boot+Sel):} This algorithm builds upon the bootstrapping approach described above. However, diverse examples are selected (Section~\ref{sec:selectionDetails}) from confident detections to train new detectors.

\par \noindent \textbf{Detection, Tracking and Clustering (Det+Track):} In this algorithm, we use a basic combination of detection and tracking. We start tracking from the confident ESVM detections to label new examples. We then use WHO (or ELDA)~\cite{bharath-whitening} clustering~\cite{ishan-wacv} on these boxes to select training examples for the new detectors. For clustering, we use WHO features on labeled boxes after resizing, followed by $k$-means. We choose the best $k$ in the range $(5,10)$.
\par \noindent \textbf{Ours without outlier (O w/o Outlier):} This setup uses our entire algorithm except outlier removal (Section~\ref{sec:outlierDetails}). It can roughly be thought of as Detection+Tracking+Selection.
\par \noindent \textbf{Ours without selection (O w/o Sel):} This algorithm uses our algorithm except selection (Section~\ref{sec:selectionDetails}). It uses WHO clustering for selection like Det+Track.
\par \noindent \textbf{Ours:} We use our full algorithm as detailed in Sections~\ref{sec:overview}.
\par \noindent \textbf{Ablation dataset:} For these set of experiments we use an input set of 25 videos ($\sim$170,000 frames) and a separate test set of 17 videos ($\sim$105,000 frames) which we fully annotated. All algorithms start with the same sparse labels consisting of only 21 boxes spread across different videos. We run them iteratively till 30 iterations.
\vspace{-0.1in}
\subsubsection{Qualitative Results}
\vspace{-0.1in}
Figure~\ref{fig:qualAbl} shows a random set of boxes labeled by each ablation method (and used to train ESVMs for that method), along with the initial set of 21 positive examples. We notice that as iterations proceed, the labeling quality (especially ``tightness'' of the boxes) for all methods degrades. More importantly, the other methods like Boot, Det+Track etc.\ show semantic drift (Figure~\ref{fig:qualAbl} columns 2-5 at iteration 20). We also notice the importance of selection, as Ours w/o Selection loses good localization ability fairly quickly (Figure~\ref{fig:qualAbl} column 6 at iterations 10-30). We believe methods like~\cite{derek-relo} can be used to further improve the labeling quality.
\vspace{-0.1in}
\subsubsection{ESVM Detection performance}
\vspace{-0.1in}
For the input videos, we cannot measure labeling purity because of partial ground truth (refer to supplementary material for an approximation of purity). Instead, we measure the relevance of labeled boxes to detection. We consider detection performance on the test set as a proxy for good labeling. We test the ESVMs selected and trained by each method across iterations on the held out test set. A good labeling would result in an increased detection performance. Figure~\ref{fig:abl-graphs} shows Average Purity vs.\ Recall across iterations for the various methods on the test set. We use Average Purity, which is same as Average Precision~\cite{pascal-voc-2007} but does not penalize double-detections, since we are more interested in whether the ESVMs are good detectors individually, rather than as an ensemble
We consider an instance correctly labeled (or pure) if its Intersection-Over-Union (IOU) with any ground-truth instance is greater than 0.3.
Our method shows a higher purity and recall, pointing towards meaningful labeling and selection of the input data. It also shows that every component of our method is crucial for getting good performance. We stop our method at iteration 40 because of our stopping criterion (Section~\ref{sec:selectionDetails}). We got a $2$ point drop in purity from iteration 40 to 45, proving the validity of our stopping criterion. This is important since our algorithm would rather saturate than label noisy examples.
\vspace{-0.1in}
\subsubsection{Training on all the automatically labeled data}
\vspace{-0.1in}
In this section, we evaluate the effectiveness of all our labeled data.
For each algorithm, we \textbf{train an LSVM}~\cite{pedro-dpm} (only root filters, mixtures and positive latent updates of DPM~\cite{pedro-dpm}) on the data it labeled, and test it on the held-out test set. Since it is computationally expensive to train an LSVM on the thousands of boxes, we subsample the labeled boxes (5000 boxes in total for each method using $k$-means on WHO~\cite{bharath-whitening} features). We sample more boxes from the earlier iterations of each method, because their labeling purity decreases across iterations (Figure~\ref{fig:abl-graphs}). We use the same domain independent negatives~\cite{abhinav-data-driven} for all these LSVMs (left side in Table~\ref{tbl:lsvms}). Table~\ref{tbl:lsvms} shows the detection AP performance of all \textbf{LSVMs} (measured at IOU of 0.5~\cite{pascal-voc-2007}) for the data labeled at iteration 10 and 30. We see that LSVM trained on our labeled data outperforms all other LSVMs. Our performance is close to that of an LSVM trained on the PASCAL VOC 2007 dataset~\cite{pascal-voc-2007}. This validates the high quality of our automatically labeled data.

We also note that the performance of an LSVM trained on the ground truth boxes (VIRAT-LSVM) (5000 boxes from $\sim$1 million ground truth boxes using the same $k$-means as above) achieves twice the performance. The fact that all LSVMs (except the ones from PASCAL) are trained with the same domain-independent negatives, indicates that the lack of domain-negatives is not the major cause of this limitation. This suggests that automatic labeling has limitations compared to human annotations. On further scrutiny of the LSVM trained on our automatically labeled data, we found that the recall saturates after iteration 30. However, the precision was within a few points of VIRAT-LSVM. Since we work with only the confident detections/tracklets in the high precision/low recall regime, this behavior is not unexpected. This is also important since our algorithm would rather saturate than label noisy positives.
\begin{figure*}
        \centering
        \begin{subfigure}[t]{0.23\textwidth}
                \includegraphics[width=\textwidth]{kit1_1_pur.pdf}
                \caption{Purity on KITTI}

        \end{subfigure}~%

        \begin{subfigure}[t]{0.23\textwidth}
                \includegraphics[width=\textwidth]{kit1_1_rec.pdf}
                \caption{Recall on KITTI}

        \end{subfigure}~
			\begin{subfigure}[t]{0.23\textwidth}
                \includegraphics[width=\textwidth]{fs3_1_pur.pdf}
                \caption{Purity on VIRAT}

        \end{subfigure}~%

        \begin{subfigure}[t]{0.23\textwidth}
                \includegraphics[width=\textwidth]{fs3_1_rec.pdf}
                \caption{Recall on VIRAT}

        \end{subfigure}
         \vspace{-0.05in}
        \caption{We measure the detection performance of the labeled boxes for our large scale experiments. We test the ESVMs trained at each iteration on the held out test set and compute Average Purity and Recall. Our method outperforms the baselines by a significant margin. It maintains purity while substantially increasing recall.}
         \vspace{-0.05in}
        \label{fig:largeScaleGraphs}
\end{figure*}

\begin{figure*}
        \centering

        \begin{subfigure}[t]{0.16\textwidth}
                \includegraphics[width=\textwidth]{kabDetTr_init_lab.pdf}
                \caption{Initial boxes}

        \end{subfigure}~

        \begin{subfigure}[t]{0.16\textwidth}
                \includegraphics[width=\textwidth]{kabBoot3_10_lab.pdf}
                \caption{Bootstrapping}

        \end{subfigure}~
        \begin{subfigure}[t]{0.16\textwidth}
                \includegraphics[width=\textwidth]{kabEig_10_lab.pdf}
                \caption{Eigen Functions}

        \end{subfigure}~
        \begin{subfigure}[t]{0.16\textwidth}
                \includegraphics[width=\textwidth]{kabDetTr_10_lab.pdf}
                \caption{Detect+Track}

        \end{subfigure}~
        \begin{subfigure}[t]{0.16\textwidth}
           \includegraphics[width=\textwidth]{kabAll_10_lab.pdf}
            \caption{Ours}

        \end{subfigure}~
                \begin{subfigure}[t]{0.16\textwidth}
                \includegraphics[width=\textwidth]{kabDetTr_gt_lab}
                \caption{Ground truth}

        \end{subfigure}
       \vspace{-0.1in}
        \caption{Pose variation in automatic labeling of the KITTI dataset. For each algorithm, we plot the 3D pose distribution of all the boxes it labels after 30 iterations. The first and last plots show pose distribution for the initial labeled boxes and all boxes in ground truth respectively. The distribution of boxes labeled by our method is close to the ground truth distribution.}
        \label{fig:poseKitti}
        \vspace{-0.1in}
\end{figure*}

 \begin{figure*}[!tbh]
 \centering
 \includegraphics[width=\textwidth]{acrossIterLargeScale.pdf}
  \vspace{-0.2in}
 \caption{We look at the selected positives for each baseline method across iterations for both KITTI and VIRAT datasets. We notice that the purity of the labeled set drops significantly as iterations proceed. This indicates that constraints specific to video are needed to learn meaningfully over iterations.}
  \vspace{-0.25in}
 \label{fig:largeQual}
 \end{figure*}

\subsection{Large scale experiments}
\label{sec:exp-largeScale}
\vspace{-0.1in}
In this section, we evaluate the scalability of our algorithm to millions of frames and object instances. We also test its generalization on two datasets with widely different characteristics - VIRAT (static camera, small cars tightly packed) and KITTI (moving camera, high motion, large cars with perspective effects). We use the \emph{Boot} and \emph{Ours} methods described in Section~\ref{sec:exp-Abl}. As we described in Section~\ref{sec:relatedWork}, most of the existing approaches make additional assumptions that are not applicable in our setting, or do not have publicly available code. To make a fair comparison against existing methods, we adapt them to our setting.

\par \noindent \textbf{Baseline - Dense detection and association (Detect + Track):} This algorithm is inspired by Geiger et al.~\cite{geiger-kitti-2} which has state-of-the-art results on KITTI. The original algorithm uses per-frame detections, followed by a Kalman filter and data association. We make two changes - 1) To have a comparable detector across methods, we do not use a pre-trained DPM on PASCAL VOC 2007. We substitute it with the ESVMs we have at the current iteration. 2) We do not use a Kalman filter and use data association over a short term (maximum 300 frames). We select positives for the ESVMs by $k$-means on WHO features~\cite{ishan-wacv}.

\par \noindent \textbf{Baseline - Eigen Functions:} We modify the Eigen functions~\cite{fergus-ssl} method which was originally designed for image classification. This method uses distances over manifolds to label unlabeled data. We use basic detection and short term tracking to get a set of bounding boxes and use eigen functions to classify them as positive or background. The boxes from previous iterations on which we trained ESVMs are used as positives and random images from Flickr~\cite{abhinav-data-driven} as negative data. We use color histogram ($32 \times 16 \times 16$ LAB space) and PHOG~\cite{phog} features as input to eigen functions.

\par \noindent \textbf{Datasets:} Our input set consists of 312 videos ($\sim$820,000 frames) from VIRAT. We take a held out test set of 17 videos ($\sim$105,000 frames) which we fully annotated. As input, all algorithms start with the same sparse labels consisting of 43 randomly chosen bounding boxes across different videos.
For the KITTI dataset we use 30 videos ($\sim$10,000 frames) as our input and 7 videos ($\sim$2000 frames) for testing. All algorithms start with the same sparse labeled 25 bounding boxes from different videos.

\par \noindent \textbf{Qualitative Results:}
We first present qualitative results in Figure~\ref{fig:largeQual}. We notice the same trends as we did in the ablation analysis, namely, bounding boxes tend to get less tight across iterations. For the baseline methods, we notice quick divergence as an increasing number of background patches are classified as car.

\par \noindent \textbf{ESVM Detection Performance:}
Following the approach outlined in Section~\ref{sec:exp-Abl}, we compute the detection performance of the ESVMs on the held out test set. This helps us measure the relevance of our labeling to the detection task. Figure~\ref{fig:largeScaleGraphs} shows the results of these experiments. We notice that our method outperforms the baselines on both the metrics (Average Purity and Recall). This reinforces the fact that our constraints help arrest semantic drift.

\par \noindent \textbf{Diversity of labeling:}
The KITTI dataset provides the 3D pose associated with each labeled car. We use this 3D pose as a proxy for estimating the diversity of our labeled set. In this experiment, we compute the pose of the examples labeled by all methods. Figure~\ref{fig:poseKitti} demonstrates that our labeling procedure covers a diverse range of poses as opposed to baseline methods. The pose distribution of our labeling is closer to the ground truth distribution, while that of the baselines prefers the more ``popular'' poses, i.e., front/back of cars. Combined with the results of Figure~\ref{fig:largeScaleGraphs}, this points towards a diverse, and high quality labeling of the data.

 \vspace{-0.1in}
\section{Conclusions}
 \vspace{-0.1in}
We introduce a semi-supervised learning technique for training object detectors from videos. Our technique addresses the detection of multiple objects without assuming exhaustive labeling of object instances in any input frame. In addition, we introduce constraints like decorrelated errors, reliable tracking and diverse selection which are effective in arresting semantic drift. Our experiments show that such an SSL approach can start with a handful of labeled examples and label hundreds of thousands of new examples which also improve object detectors.

\smallskip
\small{
\noindent \textbf{Acknowledgments:}
The authors thank Robert Collins and the reviewers for many helpful comments. This project was supported by NSF Grant IIS1065336 and a Google Faculty Research Award. IM was also supported by the Siebel Scholarship, and AS was supported by the Microsoft Research PhD Fellowship.}

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


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became bigger. It started off blowing 20, 25 knots, it went to 30
knots, 35, 40, 45, and that is when we decided to turn back. It kept
increasing, 50, 55, 60 knots, we were about a mile offshore when the
big wave hit the boat on the side. The third crew member was 17 year-
old Mick Dieperink from Holland. He described the events in an e-mail.
Suddenly a massive wave reached the top of our mast so it had to be 50
ft high. The wind speed was up to 90 knots. We were around 360
degrees and knocked off the mast. All three were thrown into the sea.
Only Nick made it back on board. was being blown away from us and we
couldn't catch up with it. Richard's lifejacket was torn away.
Alasdair called out to make who was waving a red flare from the deck.
heard somebody calling me, not shouting but very relaxed. It was
Alasdair. He said Mick, Mick, port side, here I am, and then I saw him.
He was very calm and very, very professional. I know you can do it,
take command, get some help, then another wave came, and a lost him.
The skippers started to get very cold. He couldn't talk properly.
His words was Lord -- slurred and I can understand what he was saying.
And then he lost consciousness, and I had to swim dragging him behind
me. I didn't pick Alasdair of from the water, and everybody is telling
me that I couldn't, but I don't believe them. Richard and Alasdair
were pummelled by the waves and struggled to stay together.
Although the liferaft had inflated, it was out of reach of the two men.
Richard saw a light and realised that the soap wars breaking more
often and the water was getting shallow. -- the serve was breaking
more often. After struggling for nearly two hours, the two men
finally reach some rocks. By now, Richards was its -- exhausted from
Towyn Alasdair. I try to help him. I couldn't resuscitate him. I tried
to drag the skipper up the beach, but I wasn't strong enough. Mick
stayed with the boat until he was rescued by the French emergency
services. He also wondered whether services. He also wondered whether
he could have helped Alasdair more. I am still very angry with myself.
I will never know if I could make it and save him. I am depressed and
I find it hard not to cry. I feel so guilty. After searching for help
along the deserted coastline, Richard collapsed in an abandoned
building. I went back to the beach. He wasn't there? No, so I don't
know whether the tide came in, because I couldn't read come out of
the water. Alasdair's body was washed up near by. Nick never
recovered from the trauma. He died a few years later.
The yacht does not appear to have been correctly registered, a legal
requirement for any boat in international waters. We understand
another delivery company with boats in that area warned its crew to
stay in court -- in port. Alasdair's mother said there was so
much pressure that he changed his plans for Christmas. She said it
was pressure, pressure, pressure. An inquiry found Alasdair was
trying to reduce delays to the delivery, but as the skipper, was
ultimately responsible. Richard Heath says the tragedy could have
been prevented if basic equipment had been on board. In if we had
storm sails we would have been able to sail much, much better in those
conditions and would not have needed to turn back.
If we'd had a way of receiving weather forecasts, them potentially
we would have turned back much sooner when the weather was still
fairly reasonable. If we had had better charts maybe we would have
selected a port that was easier to get into. Alasdair Crawford was not
qualified to sail a boat like this offshore. It is a commercial
vessels run as a business, and as such has to be properly equipped
with a well qualified crew. A yacht been delivered for profit often
against the clock and in atrocious weather is considered to be a
pleasure craft. It is an unregulated industry.
The yachtswoman Dee Caffari knows only too well the dangers of the
profession, and that short cuts put lives at risk. By registering it as
a pleasure vessel, he could do the trip across, and once it gets to
the Caribbean, it was going to become a charter yacht and then
meet the requirements. That's just stinks of a way of not putting the
safety equipment required for an ocean passage. Reliance insist the
boat was probably equipped and compliant with all the local
regulations. It says there was adequate weather information and a
way of receiving it, and firmly denies that Alasdair was under
pressure. One of Reliance's most experienced
skippers was John Anstess. He had been a senior coxswain on a
lifeboat and had sold water running through his veins. His sister
crewed for him. To go through the bad weather we went through, out
there, incredibly calm, I had every faith in him and I said to him, why
aren't you panicking, and he said, you can panic if you like, it won't
help, but incredibly calm. It was a help, but incredibly calm. It was a
natural instinct for him, the sea, he could read it. Later that year,
Reliance contracted the Plymouth born sailor to sail the 44 ft
catamaran from Cape Town to Seattle catamaran from Cape Town to Seattle
catamaran from Cape Town to Seattle in the north-west coast of America.
Delivering other people's yachts is Delivering other people's yachts is
a great way to see the world and experienced the high seas. The crew
are often unpaid as they try to build their mileage. Canadian Paul
Webb wanted to get some more Webb wanted to get some more
Webb wanted to get some more offshore experience. Reliance came
offshore experience. Reliance came up mainly because it was fairly
well advertised on the internet, and had a reputation which are
thought was fairly good at the times. Paul flew to Trinidad where
he met John and Caspar Venter on board kopje -- cat shot. As they
voyage north the weather deteriorated. We were very close, I
think, to the Colombian coast, and we started get some change in the
weather. There were lots of thunderstorms and lightning, heavy
rain showers, three-four metre waves. The boat was getting
battered look pretty good. The best thing we could do was turn round
and head back to Rober and wait for a change in the weather. Reliance
criticised John for returning to port. John, Paul and Caspar motored
through the Panama Canal, but all three were worried about the
unpredictable weather in the final voyage to Seattle. John warned
Reliance about the storms. He wanted to wait until the weather
cleared, and suggested an alternative route, but Reliance
said he had retired attitude and was making too much of the weather.
Don't worry about it, they said. His two options were a sensible
route which was safe for both boat and crew, or to stop for those bad
winter months and actually went to the boat somewhere safe, and he was
in San Francisco. He asked Reliance if he could change route and go up
around Hawaii and avoid the hurricane, and he was flatly told,
no, the owner Will Go ballistic, you are taking too long and
dragging your heels. Paul felt safety was compromised and he and
Caspar decided to leave cat shot when they left California. I told
John I was not happy going further north at this time of the year.
John told Reliance Paul said their chances of survival were slim, a
view echoed by local sailors. John now needed two new crew to complete
He was told he had attitude because he was complaining. He was told to
tell the crew that there was nothing wrong with the weather.
John was joined by Richard Beck and Dave Rodman. Dave's wife knew that
her husband loved adventure but was safety-conscious. I can just
safety-conscious. I can just remember having a sick feeling when
I took him to the port. It was the last time I saw my husband. Dave
rang from California, and Carole thought she had no reason to worry.
I had nothing in his voice. I heard nothing from him other than the
fact that he was excited to go. He was finally going to go. I told him
to be safe, like I always did. last e-mail John had from Reliance
when he asked what the weather was like because he was having
difficulty getting it, the email accept that the weather was fine,
NICE like winds, See You In Seattle. There are three sailors missing of
the Oregon coast. The boat has been found. There is nobody there.
Coastguard found the catamaran. So the boat was destined for Seattle.
The crew was no way to be found. world fell apart. Carole has not
seen all the evidence contained in the US Coast Guard report on the
loss of Catshot and the three deaths. Before Catshot left San
Francisco, Paul Ward Reliance that the wage -- warned Reliance that
the voyage was dangerous. I was not leaving the boat on a whim, and I
put that in an e-mail to Nick Irving. I said that I did not feel
that the boat was equipped to sail the North Pacific in winter. For I
did not leave the vessel on a whim, the vessel was not equipped to
leave a -- sail safely in the North Pacific in November and December.
Asked John, it is is not a contract fee you and not 80 -- delivery,
would you do it? Would she do the voyage if he did not have a
contract? He replied then, I've not. When the skipper asked other
sailors, all indicated when they would not sail north at this time
of year. That is before David or hop on board. It was not just the
weather. There was concern about lack of equipment and the condition
of the vote. There was some major deficiencies on the boat. One of
the things was that there were no survival suits, no heat on the boat.
It had no heat. Their radar, no survival suits. In addition, the
whole had developed stress cracks. I do not know who said they saw
When the coastguard found the catamaran, they found the logbook.
The last entry was on Monday morning 160 miles south. Could this
have been avoided? Why did a seasoned skipper like John agreed
to put to sea? He has just spent half the money he had at the
beginning on fuel, provisions, her thin, Crewe, so he is out of pocket
and risking not getting paid. It is a hand two existence. I think it
was pressure from the company to force him to take that final leg up
to Seattle. There were gale-force winds back and huge seas, and that
boat is not designed for that. This is a cruising and catamaran.
report later revealed that Reliance told Richard Beckman's family not
to alert the authorities. They said everything was fine. They might
have been able to find them. Maybe they could have got to them.
Richard Beckman's body was washed up 130 miles from the boat. The
other bodies were never found. would have been a long-time friend.
You have that affinity with him? Sorry, even today, it is terrible.
Wendy says a Reliance employee admitted gave it a ignored the
warnings. These words will stay with me forever. I said to her that
he told it was unsafe, and she said yes, and we didn't listen. They
know. They knew that he was telling them that it was not safe. And that
was really the last we hear from Reliance. Now we are another five
years, and I miss him still. I miss his laugh. We are never going to
the dancing in the kitchen doing dishes any more. Somebody took that
away from me. They took it away from me. Mat Sandys-Winsch worked
for the lines as a skipper and in the office. He had a major falling-
out with his -- company and severed all ties. It was terrible. It was a
sombre place. It was not long before it was all back up and
running as Colonel well. -- as normal. I hear a huge fan and I
felt the birch -- boat lurched up and over. I could not believe this
was the way I was going to go out and nobody would know how it
You would think that after the loss of three men, Nick Irving would be
more attentive to what the crew had to say. But two months later, Mr
Irving ignored another skipper. was instructed quite bluntly to go
north of Bermuda, or do not work for us again. Aboard the catamaran
with Kevin Klinges was Steve Hobley and another man. They were
delivering the boat from France to Florida. Steve vowed that he would
be at his daughter's wedding in the States. He promised me that he
would be it at wedding. The French built catamaran was heading for
Fort Lauderdale at what should have been an easy passing. Five days out
of Madeira, and Steve got a text message. It said that they wanted
to change our course to Maryland. I was confused that anybody would
want us to sail to Mary and at that time of year. He did not want to do
it for the reason that this high pressure that sits with the trade
winds for being underneath, that has it depressions going across the
top of it. They fuel our winter weather as they travel across the
Atlantic and give us our all wind and rain and cold. We have the warm
Gulf Stream coming up. We have the Labrador current coming down. The
need here. That difference in temperature fuels and generates
more energy and they get more vicious. The route he was
instructed to take was north of Bermuda, which is a completely
different ball game. Basically, he had gone from the perfectly of
delivery route to being told to sail in two... Yes, bad weather.
Steve did not want to divert. It was a bad time of year to head
north. Was history about to repeat itself? They basically held at over
his head. If you do not change course and go to and a palace, and
if you decide to go on to Fort Lauderdale, we might not have
another job for you. instructions came from Reliance
back in Farnborough, but Steve got his weather information from his
children. Dad had to get his own satellite phone, even down to
training Jonathan and I to plot the weather on the internet with his
position and their text in important things that were coming
up in the weather. Things that a yacht company should have been day.
I was not a professional. catamaran capsized 200 miles from
Bermuda in 45 ft waves and Jared Payne forced winds. Steve was
becoming hypothermic. I heard him talking to his mum. It was strange.
It was sad. The notion seemed to get more aggressive. -- Ocean. And
then I was thrown off the hull with Steve. He landed on me and his head
was staring right into my face. He was underneath the water, with his
navigate and his eyes wide open. He had died and I was going to try
everything I could to give his family his body, give them
something. If I could do that. Another wave came and blue was of
the boat and I climbed back on and that holds an order for seven I
eight Jansch and there was just a lifejacket. And with my strobe
light flashing through the water, I could see Steve's face and he was
looking up at me like this... His eyes were closed and with each
blinking successive Light, he was three or four feet underneath the
water, then five or six feet, and then the last I saw him he was
around 12 feet underneath the water. He slipped into the darkness and I
came back on board. Having nothing back of debt, it leaves a wound,
like somebody has just walked out of your life. They have an appeal
for women you I in mid-conversation. It is the most awful thing.
hours after the capsize, Kevin and on the were rescued. Mat Sandys-
Winsch claims that Nick Irving gave the fatal order. I have to live
with the knowledge that I could have helped him. I could have done
something. I could have changed the course of events. But it is have to
live with it. I am Nick Irving, the director of Reliance. I want to
show you some of the extraordinary lengths we go to to prepare abodes
for delivery. We have evidence that Mr Irving lied to insurance
companies about his skippers? Experience. He had relished CVs to
shown lots of deliveries. He tripled the skipper's actual
experience. Experience can only be gained by miles that you have, but
it shows. If you have not done the miles, it shows by mistakes or not
knowing things. To enhance somebody's mileage in that position
of responsibility when they have not had that experience is a
shocking thing to do. The Maritime and Coastguard Agency asked the
Crown Prosecution Service to whether a criminal proceedings
would follow. Because Catshot was the registered abroad, they are
powerless. Reliance was sued and won. The result was damning. He has
got five deaths on his hands. I do not know how Nick Irving sees at
night. Nick Irving refused to the interview. In a state and, he said
his business had completed thousands of deliveries. He said
his skippers are highly professionals and gives them a 24
hour support. I want to ask you about the deaths of five sailors.
Why I put in so much pressure on them to say on every they did not
want to. I have answered all those questions with your producer.
owe it to the families of the sailors and skippers who lost their
husbands, brothers. The losses are great. I cannot make up for that.
But I do respond to those questions and I have answered them as best
they can. Please put any further questions in writing. We did put it
in writing, and he denied pressuring the skippers. He ignored
specific questions, so we gave him one last chance to defend himself.
Sending e-mails like this, saying that it was unsafe... And have
answered those questions to your producer. It is here and black-and-
white that you put unfair pressure on the skippers. That is your
opinion, not mine. We offered to show Mr Irving how he was avoiding
our questions. I have got your answers here. Put it in writing. I
am happy to respond in writing. asked to write a CV as well? Why
did you falsified a skipper's experience on ECB? That is
dangerous. He has been found liable in four civil cases that has failed
to pay damages as ordered. Four times a court has taught you to pay.
Why had he not done that? If a court has ruled that you should
pay... The court has ruled against the company. Which you run. Yes, I
was director at the time. Let us finish this here. Any to get back.
Have you got anything to say to the relatives of the skippers that were
lost at sea because of your actions and your company's actions?
have got something to say to them and it is personal and it will be
to them directly and not in a public arena. Boasted South found
that Nick Irving's silence speaks volumes. We had a memorial party.
send an e-mail to the office to see if anyone wanted to come and pay

redpajama_set_name: RedPajamaCommonCrawl


text:
Assam Ration Card List 2023: Download District/ Village Wise List, Status
Assam Ration Card List Download, How to Check Online NFSA Assam Ration Card New List 2023, Pdf Download | Assam Ration Card District/ Village Wise List, APL BPL List, Status
The government of Assam has launched the New Assam Ration Card List. So, in today's essay, we'll go over the key features of the Assam ration card for the year 2023. We will also offer a step-by-step approach for checking the Assam Ration Card List, which was just launched by the Assam government, in this article. In this post, we will discuss the features of the Assam ration card as well as the benefits that the public would receive as a result of the new ration card's launch.
The Assam government gives ration cards to specific families in the state under the Public Distribution Scheme (PDS) of the Nation Food Security Act, 2013. A ration card is a document that allows low-income (as decided by the PDS) or poor families to obtain food grains at a reduced price. In Assam, ration cards are now given under the NFSA Scheme.
Assam Ration Card List 2023
NFSA Assam Ration Card List Highlights
Importance of Ration Card
Ration Card Types
Antyodaya Ration Card
BPL Ration Card
APL Ration Card
Blue/Pink/Red Ration Card
Orange Ration Card
Yellow Ration Card
Assam Ration Card List Eligibility
Documents Needed for Assam Ration Card List
How to Check the Assam Ration Card List 2023
Assam Ration Card Application Procedure
A ration card is a document that is useful to state inhabitants. The ration card ensures that the poorest members of society have access to adequate food. In India, many poor people can benefit from subsidized food through the use of a ration card. Even a nationalized ration card is now provided to Indian citizens. This nationalized ration card can assist you in obtaining food supplies within the country. The ration card is, in general, the most crucial document for Indian citizens.
Assam Orunodoi Scheme
Name Assam Ration Card List
Launched by Assam Government
Beneficiaries Residents Of Assam
Objective Providing Ration Card
Official website fcsca.assam.gov.in
In our country, the ration card is quite important because it is sometimes used as proof of identity. The ration card has numerous advantages. The biggest benefit is the availability of food products at a very low cost, since, many poor people in our country cannot buy food items for their daily day-to-day lives. Ration cards allow all poor people to live happily and comfortably without having to worry about food inflation.
There are six different types of ration cards available from the government. The following is a list of these ration cards:
Antyodaya ration cards are given to families from the poorest sections of society who do not have a steady source of income. This category typically includes elderly persons, women, unemployed people, and laborers. This ration card is available to those with a monthly per capita income of less than Rs 250. To obtain this ration card, the applicant must submit an application form that has been duly attested by a municipal councilor or village sarpanch, as well as two passport-size family photographs and a duly defined affidavit. Antyodaya Ration Card holders receive 35 kg of rice at a cost of Rs 3 per kilogram.
Assam Voter List Download
A BPL Ration card can be applied for by families who fall below the poverty line. BPL families have a yearly income of less than Rs 27,000.
Citizens who are above the poverty level are eligible to apply for an APL ration card. There is no salary limit for this type of ration card, thus anyone can apply.
These ration cards are given to families who are over the poverty threshold.
These cardholders are eligible for special subsidies, such as the ability to purchase kerosene at subsidised prices from ration stores. This ration card is available to families that do not have access to gas or LPG.
Orange ration cards are also given to families who earn more than the poverty threshold. The income requirements for receiving an Orange ration card vary from state to state.
This ration card is given to residents who fall below the poverty threshold. Each state has varying yearly income criteria for receiving a Yellow ration card, depending on how the poverty line is defined.
In Assam, the following people are eligible to apply for a ration card:
Individual who does not have a ration card can apply for one.
Women in the family are eligible to apply for ration cards.
The applicant must reside in the state of Assam.
The applicant must be an Indian citizen.
The resident's annual income must be less than one lakh rupees.
Assam Swanirbhar Naari Scheme
When applying for an Assam Ration Card List, the following documents are required:
Proof of no Ration card from the village Head/Gaon panchayat president/ward Commissioner/Inspector, FCS&CA /Concerned Authority.
Copies of Birth Certificate
Voter List Copy
BPL Certificate
Tax pay receipt of Land Revenue
BPL Family SI. No
Follow the procedures below to find out who is on the Assam Ration Card List:
To begin, go to the Official Website link provided here.
After that, pick your
District Name
Tehsil Name
Village Name
The following information will appear on your screen: Unique RC ID Code, Applicant Name, Father/Spouse Name, and Ration Card Type.
To apply for an Assam ration card, go to the nearest ration shop or public distribution system government office, whichever is closest to your home. The application form is available at the counter. Fill out the form and attach the required papers. In 15 days, the ration card will be delivered to your residence.
What are the benefits of utilising this Ration Card?
This Card is a report that supports the needy in obtaining basic things at reasonable prices and applying for government assistance programmes from the Assam government.
Which of the three types of ration cards are available in Assam?
Individuals from Assam have three basic types of ration cards: APL, BPL, and AAY.
Would I be able to read the Ration Card PDS entry's individual recipient rundown?
On the Assam PDS entry, residents can see the specific recipient list of Ration cards.
Is it possible to see a breakdown of these Ration Card holders by APL, BPL, and AAY on the Assam gateway?
Yes, candidates can view a distinct list of percentage cardholders based on APL, BPL, and AAY on the PDS Assam authority website.
Categories Assam Govt Scheme
E District Kerala: Login & Registration, Download Certificate, Status
Tnvelaivaaippu Registration 2023: Renewal & Login, TN Employment Registration


text:
Q: parsing return value out of a python script run in a bash script I'm trying to write a code to extract return values from Google's GAM script.
I've written a program to see how values are returned and hopefully interact with them, but I haven't had much luck. Here's what the test code looks like:
#!/bin/bash
    gam_test(){
             OAUTHFILE=oauth.txt
            local gtestout=`~/gam/gam.py info user "$email" > test`
    }
    fname="April"
    lname="O'Neil"
    letter1=${fname:0:1}
    letter2=${lname:0:1}
    error_out="Error: 1301 - Entity AOkccp30@ga4edu.org Does Not Exist"
    concatemail=$letter1$letter2"kccp30@ga4edu.org"
    fullemail="AOkccp30@ga4edu.org"
    email=$fullemail
    gam_test
    sleep 5
    echo "---------"
    sleep 3
    echo $gtestout
    echo "---------"
    if [ "$error_out" -eq `gam_test` ]
            then
                    echo "Test 1 Success!! They are the same when you feed it emails!"
            else
                    echo "TEST 1 FAIL! WHAT DID YOU DO!?"
                    echo $error_out"/...test..../"
                    echo $gam_test"/....gam_test.../"
            fi
    email=$concatemail
    gtestout=$(python ~/gam/gam.py info user "$email" &)
    echo "---------"
    sleep 3
    echo $gtestout
    echo "---------"
    if [ "$error_out" == "$gtestout" ]
            then
                    echo "Test 2 Success!! They are the same when the program generates emails!"
            else
                    echo "TEST 2 FAIL! WHAT DID YOU DO!?"

I've tried a few other methods but I can't seem to figure it out.

redpajama_set_name: RedPajamaStackExchange


text:
Top Five Rugby Teams for 2015 Rugby World Cup
Rugby is a very popular game in the western hemisphere. Rugby was developed at the Rugby School, from one the many ways or styles of playing football that were played in the English Public Schools during the 19th century. Mainly there are two types of Rugby, Rugby League and Rugby Union. There is some difference in specific rules but objective of both types of rugby is same. Rugby Union Teams are very popular among the rugby playing nations. In this list the top five Rugby playing nations are included, they are:
Five Rugby Teams for 2015 Rugby World Cup List:
New Zealand Rugby Union Team:
New Zealand Rugby Union team currently stands in the 1st position of the World Rugby Ranking. Rugby is regarded as theirnational sport.The nationalmen's Rugby Union team of New Zealandis popularly known as "All Blacks" and the national women's Rugby Union team is known as the "Black Ferns". Both of the teams are currently World Champions. This shows how good the New Zealand Rugby Union teams are and they are the most deserving candidate of 1st position. They surely are the best Rugby Union team in the world.
World ranking: First
Coach: Steve Hansen
Captain: Richie McCaw
South African Rugby Union Team:
South African Rugby Union team is standing in the 2nd Position of World Rugby Ranking. They are popularly known as the Springboks. They were World Champions in the year 1995 and 2007. They are the best rugby team after the All Blacks and they have proved it over and over again.
World ranking: Second
Coach: Heyneke Meyer
Captain: Jean de Villiers
Ireland National Rugby Union Team:
Ireland National Rugby Union Team is currently standing in the 3rd position in World Rugby Ranking. This is their best position so far. They have played all Rugby World Cups and their best achievement is reaching the quarter finals. They are one of the four teams that form British and Irish Lions. The players, who can play for them, are also eligible for playing for Lions.
World ranking: Third
Coach: Joe Schmidt
Captain: Paul O'Connell
England National Rugby Union Team:
England National Rugby Union TeamCurrently stands in the 4thposition of the World Rugby Ranking. They were the Rugby World Champion in 2003 and they were Runners-Up in 1991 and 2007. They are a good team. They won the Six Nations Championship 26 times. Rugby is a very popular sport in England and the National team plays very well to make the whole nation proud.
World ranking: Fourth
Coach: Stuart Lancaster
Captain: Chris Robshaw
Wales National Rugby Union Team:
Wales National Rugby Union Team is currently standing in the 5th position of the World Rugby Ranking. They have participated in all Rugby World Cups and their best achievement was being 3rd in the year 1987. They have won Six Nations Championship 26 times and they are jointly with England the most successful team of the tournament.
World ranking: Fifth
Coach: Warren Gatland
Captain: Sam Warburton
These are currently the best five Rugby teams of the world and they have proved their strength over the years. They have shown that they deserve their places in the World Rugby Ranking.
◂ The Venues of England Rugby World Cup 2015
Rugby World Cup 2015 Final at Twickenham Stadium ▸