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HomeCelebrity
Peter Breck — Remembering Life and Death of 'The Big Valley' Star
By Rebelander Basilan
Known best for playing Nick Barkley, Barbara Stanwyck's most temperamental son in the 1960s western series "The Big Valley," Hollywood actor Peter Breck passed away in 2012.
Peter Breck's death was confirmed by his wife, Diana. Breck started acting shortly after serving in the navy. During his time at the University of Houston, he studied drama and English.
Originally appearing at the Alley Theater in Houston, he moved to Arena Stage in Washington, where he was seen in "Man of Destiny" and offered a part in Robert Mitchum's film, "Thunder Road."
Peter Breck wears a cowboy outfit and leans on a saddle, circa 1950s. | Photo: Getty Images
Brooks Atkinson, the theater critic of the New York Times, was also there that night. In his reviews, Breck's effectiveness in the part has been noted as witticisms, clever invention, and energy.
In 1956, Breck made his television debut in an episode of "Sheriff of Cochise," which was not a western but a contemporary drama. However, westerns seem to be his particular vocation.
Peter Breck and Barbara Stanwyck pictured in 1966. | Photo: Getty Images
In 1959, he appeared in the TV series "Black Saddle," telling the story of a gunfighter who eventually becomes a lawyer. Starting in 1961, he portrayed Doc Holliday in a half-dozen episodes of "Maverick."
Breck's career took off following his success in TV, and he built his net worth to reach $5 million.
Peter Breck and Barbara Stanywck pictured in 1965. | Photo: Getty Images
"The Big Valley" starred Breck from 1965 to 1969. The cast as a whole would go on to enjoy success, but working with Barbara Stanwyck was particularly meaningful to Breck. They developed a close bond.
During the same time period, Breck's role on "The Big Valley" paved the way for guest-starring roles in other hit series like "Fantasy Island," "The Six Million Dollar Man," and "The Dukes of Hazzard."
Cast of "The Big Valley" posing together, circa 1965. | Photo: Getty Images
By this time, the acclaimed actor had landed roles in several genres and emerged as a recognizable face in the sitcom business thanks to his role as Sham-Ir in two of the "I Dream of Jeannie" movies.
Breck's career took off following his success in TV, and he built his net worth to reach $5 million, leading him to open the Breck Academy as a place where budding stars can cultivate their craft.
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Peter Breck and Janis Hansen pictured in 1968. | Photo: Getty Images
A report stated that Breck is the fastest gun draw in Hollywood. His ability to draw his gun is said to have taken just 16/100ths of a second, making Nick Barkley the quickest shooter in television history.
After a long illness, Breck, who grew up living with his grandparents in Massachusetts, died on February 6, 2012. His was 82. In the Big Valley Writing Desk, Diana, his wife, announced his death.
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'Yellowstone' star Taylor Sheridan's Enduring 8-Year Marriage with Wife Who Left Her Career & Moved with Him
Jon Provost, Who Played Little Timmy in 'Lassie', Was 'Lucky' to Escape the Child Star Curse — He Is 71 Now
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Cindi Knight Brought Andy Griffith More Joy than Anyone He'd Ever Known


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COLON CARE, LLC - 503.222.3311
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Are Colonics Safe?
Are Colonics and Colon Cleansing Safe?
That would depend on who you ask. Like Mr Bunny. He had a wonderful colonic that inspired him to make lifestyle changes and lose weight, improve his blood pressure and most of all, feel good in his clothes! But, if you trust the mainstream media and the mainstream medical establishment, you will hear that colonics are "harmful and unnecessary". Funny thing, there has been relatively zero harm caused from colonics. My medical liability for colonics is only $400 and it used to be for the first 10 years I had a clinic, only $200. Do you know what an GYN pays? Would you believe tens of thousands a year. I read where one paid $90,000 per year in medical liability. If colonics are harmful then why are our medical liability rates so low? Which makes you wonder where did all the bad press come from. That is what is so fishy. On one day, every online news network ran the same story with basically the same headline, "Colonics are harmful and unnecessary". They presented their stories as a "new study" but actually it wasn't a study at all but opinions. Their "study" was actually a review of past studies by one team of researchers and was their opinion with no medical evidence given.
ABC News reports:
"Despite colon cleansing's long history and current popularity, the literature does not support its purported benefits," wrote the report's authors, led by Dr. Ranit Mishori, clinical faculty at Georgetown University School of Medicine in Washington, D.C. Mishori and her colleagues reviewed published case studies and found that most of them noted numerous adverse effects from colon cleansing. They ranged from mild cramping and nausea to serious effects such as kidney failure. Mishori said that she and other doctors at Georgetown University Hospital have seen an increasing number of patients who have suffered the ill effects of colon cleansing as well as patients who have asked about it, which is why she decided to undertake this research.
In addition to the side effects of colon cleansing, the authors warned that the Food and Drug Administration has not approved the devices used for colon hydrotherapy, the procedure that uses water to flush out the large intestine."
If you just blindly trust the mainstream media and medical establishment you would think the systems that colon hydrotherapists use are not approved by the FDA. No they are not because they are REGISTERED with the FDA as class II medical devices. The media left that important fact out of their articles. Another important fact the media tended to leave out of their reports was that Mishori did state that colonics was the best way to correct constipation. If that alone was all colonics did then it should have made headline news that colonics was the best way to help 63 million people per year that are constipated and save some 8 million physician office visits! To put this in perspective, roughly 13 million people per year are diagnosed with cancer. So the headlines should have read, Hurray! Colonics Correct Constipation! I think the main reason the medical establishment is so against colonics is that they don't believe having a bowel movement let alone 3 bowel movements a day is necessary. Many doctors think it is healthy to only go once a week. Further, most allopathic physicians do not believe the body is toxic or in need of internal cleansing. They believe the body cleanses itself. Well, maybe they should stop brushing their teeth and see what that gets them. If we lived in the Garden of Eden we would not need to cleanse our body and do colonics. But we live in an incredibly toxic environment with estimated over 80,000 toxins and chemicals in our air, food, water, and dont forget all the toxic medications, pain killers and antibiotics.Did the articles care to mention how many people die every year from taking the doctors medications? Roughly 130,000 people die yearly from taking their prescriptions, properly! Not only are colonics the gentlest way to correct constipation as was mentioned briefly in Mishori's original opinion that did not make it to most of the "news" reports, but also was stated as being the "best way to prepare for a colonoscopy".
This was also the finding of Dr Joseph Fiorita, MD who presented his true case study (not an opinion) to the Gastroenterology Association. Dr. Joseph Fiorita, MD (chief of the section of Gastroenterology at Danbury Hospital) says "Patients now have a choice"!
"The new method is called colon hydrotherapy, a procedure that infuses warm, filtered, chemical free water into the colon to cleanse it prior to colonoscopy. He goes on to state, "Colon Hydrotherapy is much more convenient, people don't have to worry about having to miss work, not eat for 24 hours and stay up all night because of diarrhea (which is what happens when you take the alternative salt preparation). COLON CANCER survivor Ethel Pancotti is one of the people who colon hydrotherapy has greatly helped. She is among those who could not tolerate the laxative process and would suffer loss of electrolytes, eventually passing out and ending up in the Emergency Department. "WITH COLON HYDROTHERAPY THERE IS NO PAIN OR DISCOMFORT. I was so relaxed that I fell asleep listening to music during the cleansing procedure".
Dr. Joseph Fiorita's medical case study on colon hydrotherapy as a preparation for colonoscopy was accepted and delivered as an oral presentation at the American college of Gastroenterology Meeting October 2006. (ONLY 1% to 2% of all studies submitted, from thousands of abstracts are selected for oral presentation. This is a testament to HOW IMPORTANT the ACG feels this research is).
His Medical Study showed colon hydrotherapy was good or better than alternative preparations and patients preferred colon hydrotherapy. Is colonics safe for everyone? No but neither is taking tylenol or aspirin. There are contraindications for colonics just like there are contraindications for getting your teeth cleaned. It all comes down to some common sense and where you get your information from. I personally prefer sites like Naturalnews.com, mercola.com and Foodbabe.com for health information. So, are colonics safe? After giving over 20,000 colonics to men and women from age 8 to 92, I would say so!
Rebecca Harder is a National Board Certified Colon Hydrotherapist, I-ACT Certified at the Instructor Level and Happy Owner of Colon Care, LLC. She has given over 20,000 colonics and consultations since 2003. She also offers Health Coaching at www.GastraGirl.com.
Disclaimer: Colon Care LLC and all it's representatives are not doctors and we do not diagnose, treat or prescribe. We do not know whether or not you can do colonics. Please do your own research to determine for yourself whether colonics are good for you and your health situation before making your appointment. You assume all responsibility to know whether or not you can do colonics and you assume all risk both physical and financial for doing colonics, infrared sauna or any resource Colon Care, LLC uses or suggests.

redpajama_set_name: RedPajamaCommonCrawl


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DNATIX Platform
Genetic vault
How Blockchain Can Simplify The Transfer Of Genomic Data
Posted by: Michael Kern
Category: Blockchain, Technology
For over a decade now the amount (data size) and variety of genomic data available has been increasing exponentially. This growth was met with advancements made in the technologies involved in the processes of decoding, analysing and storing the genomic data.
Completion of the Human Genome Project designated for humankind a new era in the genetic revolution. In the years that followed its completion it provided scientific guidance to the structure, function and organisation of the human genetic makeup.
With great knowledge comes great power and with great power comes great responsibility. In the age of big data, knowledge does mean power. When it comes to genetic data, preferably accompanied with medical information, this data is worth hundreds of millions of dollars. So far, this data was gathered and stored in a centralized manner: 1. Data was kept on centralized servers. 2. Data was usually centralized at commercial entities making use of this data for R&D purposes. It appears that now Blockchain is challenging these old centralization paradigms. As the worlds of blockchain tech and genetics join forces, data is now being distributed on decentralized ledgers leading eventually to genetic data being handled and managed by the end-users rather than by commercial parties.
To understand this shift we first need to clarify some basic concepts.
In a very simplified manner, a Blockchain is a spreadsheet that is duplicated across a network of computers wherein spreadsheet is regularly updated. In a more technological definition the blockchain (referred to as distributed ledger technology (DLT)) is a digitized, decentralized, ledger that can be public or private and holds records of transactions. The blockchain keeps growing as new 'completed' blocks (the recent transactions) are recorded and added to it in chronological order. The blocks that are added are linked in a manner similar to a chain and hence the name Blockchain. Each new block being added contains a time stamp and data on the transaction. Blockchains could be private and handled by a company or organization with clear rules as to who within the organization has the authority to add new blocks or could also be public with access permission for all users.
The entire genetic information of an organism is usually known as the genome (usually it also contains for example mitochondrial genetic data). The genetic material building blocks that constitute genomes are called nucleotides (A, C, G, and T for DNA genomes) that make up all the chromosomes of an individual or a species. In almost all human cells, for example, the number of these blocks reach 6X109 and contains the instructions required to make the full range of the organism's cell types, tissues and the organism in its entirety.
This is the process used to determine the DNA sequence of an organism's genomic data. When the entire genome is sequenced it is called full or whole genome sequencing (FGS and WGS respectively). Partial genome sequencing which focuses only on the regions of the genome that code for proteins (genes) is called Full Exome sequencing. Genomic data has revolutionised the fields of medicine and of genetic research and as costs of this process are dropping it is expected that significant proportion of the population will have their genome sequenced.
How do blockchain and genetics connect?
First step for brining genetics and blockchain together involves digitization of the genetic information (DNA). DNA is found in almost all our body cells encapsulated in the cell's nucleus and needs to be first extracted. The easiest (and less painful) way to get the DNA from a person is by collecting a saliva sample (DNA can also be extracted from blood). The sample is collected in a special saliva collection kit and is then sent to a DNA sequencing service provider. The DNA molecule is read and converted into a digital file that can be sent back to the user. This is only the beginning of the genetic journey one can take to understand his genetic makeup. The next step is now to find the hidden meaning in the hundreds of millions of nucleotides of the genome. The digital file with the genetic information which is huge in size (1.5 Gigabyte after some "cleaning") should now be shared with different genetic service providers that can decipher the genetic code and enrich it with data related to your health care and wellbeing. This data may include for example information on your tendency of developing metabolic diseases, the way you metabolize drugs and your inclination for developing various types of cancers. Storing the genetic information which is highly sensitive and then sharing it with 3rd parties carries many challenges in terms of data security. By properly connecting the new possibilities that are included in the blockchain infrastructure with the genetic and genomic data, new opportunities now reveal themselves for data sharing and management.
Imagine the blockchain is like a highway system that connects the different participants in the genetic ecosystem that includes, the consumers, the genetic labs, pharma companies, the genetic counsels and the health care and research institutes. On this imaginary highway genetic distributed application manage transections controlled by genetic smart contract that enable the participants to share genetic information, provide or consume genetic services and get paid or be rewarded for participation in this ecosystem. Now, add a few more important details such as that you can travel on these highways with high levels of anonymity, feeling very secured and being able to control the manner data is being transmitted and shared. This is what the new world of genetic blockchain will look like.
What are the challenges for Genomic data?
The vision of the genetic blockchain is exciting but before it turns into realty a few questions should should be addressed. The digital human genome is very large in terms of data size. One copy of the genome (in our cells there are two such copies) is in the size of ~750mb. Compared with the average size of transections occurring today on the Ethereum blockchain genetic data is several folds larger.
Building a state-of-the-art system of highways is useful only if there are enough cars to use it. To have more cars (or transections) it is required that more people will consume genetic services, and this can only happen when the prices of genetic services such as full genome sequencing and genetic testing will decline. As the prices for full genome sequencing are plummeting ($400 for non-clinical sequencing) and as genetic testing is moving from wet genetic tests to dry tests (based on algorithms that manage the digital genetic code) the need for the solutions that blockchain can bring with will significantly increase.
By Ofer A. Lidsky, Co-Founder of DNAtix
Blockchain Data Genomics
"Mind the (Genomic) gap, please"
Genetics News Round Up!
What is Heritage, Really?
10 Surprising Facts About DNA
Utilizing Precision Medicine for Diabetes Management
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Governor John Bel Edwards has responded with a letter of his own after Attorney General Jeff Landry criticized him for halting executions in the state. Edwards responded by saying executions are not being carried out, because the only drugs that the legislature has authorized for lethal injection haven't been accessible since 2010.
"It's a statutory prescribed manner of death and we have certain drugs we have to use and they are absolutely not available," Edwards said.
In a tweet, Landry says he supports the death penalty, whether it's by lethal injection, gas, hanging or firing squad.
In a letter on Wednesday to the governor, Landry suggested Edwards could do more, so executions can take place in Louisiana and provide justice to victims' families.
Edwards says the A-G is deliberately misrepresenting the facts and the last communication from Landry on this issue was in October.
"None of this makes sense and I think the Attorney General ought to take a deep breath and calm down a little bit," Edwards said.


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\section{Introduction}
Visual Data in the form of images, videos and live streams have been growing at an unprecedented rate in the last few years. While this massive data is a blessing to data science by helping improve predictive accuracy, it is also a curse since humans are unable to consume this large amount of data. Moreover, today, machine generated videos (via Drones, Dash-cams, Body-cams, Security cameras, Go-pro etc.) are being generated at a rate higher than what we as humans can process. Moreover, majority of this data is plagued with redundancy. Given this data explosion, machine learning techniques which automatically understand, organize and categorize this data are of utmost importance. Video summarization attempts to provide a highlight of the most critical and important events in the video, giving the viewer a quick glimpse of the entire video so they can decide which parts of the video is important.

What comprises of the most critical aspect of a video depends largely on the domain. What is important in a surveillance video is very different from the highlights of a soccer game. This work attempts to provide a better understanding of different summarization models in different domains. We try to make a case that the choice of the summarization model really depends on the application and domain at hand. This paper investigates several choices of summarization models -- models which capture diversity, representation, importance or relevance, and coverage. We quantitatively and qualitatively study this pattern in many different domains, including surveillance footages, dashcam, bodycams and gopro footages, movies and TV shows and sports events like soccer. We argue how different characteristics are important for these domains, and through extensive experimentation establish the benefit of using the corresponding summarization models. For example, we show that for surveillance footages, diversity is more important compared to representation or coverage, while in a movie, representation and coverage form a better fit compared to diversity. Similarly, in a sports event like soccer, importance and relevance signals are important aspects of summarization. This paper also analyzes several choices of feature representations and concepts, including faces, scenes, humans, color information, objects etc.

We study three variants of summarization: one is extractive summarization, the second is query focused summarization and the third is Entity based summarization (which we also call Concept based summarization). Entity based summarization focuses on entities, like objects, scenes, humans, faces to provide a representative yet diverse subset of these entities. This answers questions like who are the different people or what are the diverse objects and scenes in the video. Finally, we discuss several implementational details on how to create a video summarization system, including the preprocessing of features, different segmentations of shots and tricks for speeding up the optimization for near real time response times.

\subsection{Existing Work}
Several papers in the past have investigated the problems of video and image collection summarization. Video Summarization techniques differ in the way they generate the output summary. Some of these~\cite{wolf1996key, lee2012discovering} extract a set of keyframes from the video, while others focus on extracting video summaries or skims from the long video~\cite{gygli2015video, zhang2016summary}. Other forms of video summarization include creating GIF summaries from videos~\cite{gygli2016video2gif}, Montages~\cite{sun2014salient}, Visual Storyboards from videos~\cite{goldman2006schematic}, video synopses~\cite{pritch2008nonchronological} and time lapses and hyperlapse summaries~\cite{kopf2014first}. Similarly, image collection summarization involves choosing a subset of representative  images from the collection~\cite{tschiatschek2014learning}. Another line of approach, which is similar to what we call Entity based Summarization, was proposed in \cite{meng2016keyframes}, wherein the authors select representative summaries of all objects in a video. They do this by modeling the problem as that of sparse dictionary selection. Most video summarization techniques can be categorized into methods trying to model one of three properties of summaries (i) interestingness (how good is a given snippet as a summary), (ii) representativeness (how well the summary represents the entire video or image collection), and (iii) diversity (how non-redundant and diverse is the summary).

Examples of methods which model interestingness of snippets include~\cite{wolf1996key} that find summary snippets through motion analysis and optical flow, \cite{lee2012discovering} which uses humans and objects to determine interesting snippets and finally, \cite{gygli2014creating} which models interestingness through a super-frame segmentation. \cite{chu2015video} summarizes multiple videos collectively by looking at inter-video-frame similarity and posing a maximal bi-clique finding algorithm for finding summaries. Methods which only model the quality of the snippets, or equivalently the interestingness of the summaries and do not model the diversity often achieve redundant frames and snippets within their summary.

Hence a lot of recent work has focused on diversity models for video and image collection summarization. \cite{simon2007scene} used the Facility Location function with a diversity penalty for image collection summarization, while \cite{sinha2011extractive} defined a coverage function and a disparity function as a diversity model. \cite{lu2013story} attempted to find the candidate chain of sub shots that has the maximum score composed of measures of story progress between sub shots, importance of individual sub shots and diversity among sub shot transitions.   \cite{tschiatschek2014learning} was among the first to use a mixture of submodular functions learnt via human image summaries for this problem. For video summarization, \cite{li2010multi} proposed the Maximum Marginal Relevance (MMR) as a diversity model, while \cite{zhang2016summary, gong2014diverse} used a Determinantal Point Process based approach for selecting diverse summaries. \cite{zhao2014quasi} proposed an approach for video summarization based on dictionary based sparse coding, and \cite{gygli2015video} proposed using mixtures of submodular functions and supervised learning of these mixtures via max-margin training, an approach used for several other tasks including document summarization~\cite{lin2012learning} and image collection summarization~\cite{tschiatschek2014learning}.

\subsection{Our Contributions}
The goal of this work is not to achieve the best results on Video and Image summarization tasks and datasets like TVSum~\cite{tvsumm} and Summe~\cite{GygliECCV14}. Rather, we attempt to provide insights into what it takes to build a real world video summarization system. In particular, we try to understand the role of different submodular functions in different domains, and how to implement a video summarization system in practice.

As observed in prior work~\cite{gygli2015video,lin2012learning,tschiatschek2014learning} several models for diversity, representation, coverage and uniformity can be unified within the class of Submodular Optimization. We build upon this work as follows.

\begin{enumerate}
\item This paper studies the role and characteristics of different summarization models. What constitutes a good summary depends on the particular domain at hand.
\item We investigate several diversity, coverage and representation models, and demonstrate how different models are applicable in different kinds of video summarization tasks.
\item We validate our claims by empirically showing the behavior of these functions on different kinds of videos, and quantitatively prove this on several videos in each domain. For example, we show that \emph{Diversity} models focus on getting outliers in the video, which is important in domains like surveillance. On the other hand, \emph{Representation} models capture the centroids and important scenes, which is useful in Movies. We also argue how coverage functions focus on achieving a good coverage of concepts. Similarly, we show that in domains like soccer, \emph{Importance} or \emph{Relevance} plays the most important role in the summary.
\item We also discuss the computational scalability of the optimization algorithms, and point out some computational tricks including lazy evaluations and memoization, which enable optimized implementations for various submodular functions. As a result, we show that once the important visual features have been extracted (via a pre-processing step), we can obtain the summary subset of the video (or frames) in a few seconds. This allows the user to interactively obtain summaries of various lengths, types and queries in real time. We empirically demonstrate the benefit of memoization and lazy greedy implementations for various video summarization problems.
\end{enumerate}

Most past work on Video and Image collection summarization, either use a subset of hand-tuned submodular functions~\cite{simon2007scene,lin2012learning,li2010multi} or a learnt mixture of submodular functions~\cite{gygli2015video,tschiatschek2014learning,lin2012learning}. This work addresses the orthagonal aspect how do different subclasses of submodular functions model summarization and their performance in different video domains. We believe the insights gathered from this work, will help practitioners in choosing appropriate models for several real world video and image summarization tasks.

\section{Background and Main Ideas}
This section describes the building blocks of our framework, namely the Submodular Summarization Framework and the basics of Convolutional Neural Networks for Image recognitions (to extract all the objects, scenes, faces, humans etc.)
\subsection{Submodular Summarization Framework}
We assume we are given a set $V = \{1, 2, 3, \cdots, n\}$ of items which we also call the \emph{Ground Set}. Also define a utility function $f:2^V \rightarrow \mathbf{R}$, which measures how good of a summary a set $X \subseteq V$ is. Let $c :2^V \rightarrow \mathbf{R}$ be a cost function, which describes the cost of the set (for example, the size of the subset). The goal is then to have a summary set $X$ which maximizes $f$ while simultaneously minimizes the cost function $c$. In this paper, we study a special class of set functions called \emph{Submodular Functions}. Given two subsets $X \subseteq Y \subseteq V$, a set function $f$ is submodular, if $f(X \cup j) - f(X) \geq f(Y \cup j) - f(j)$, for $j \notin Y$. This is also called the diminishing returns property.  Several Diversity and Coverage functions are submodular, since they satisfy this diminishing returns property. We also call a function \emph{Monotone Submodular} if $f(X) \leq f(Y)$, if $X \subseteq Y \subseteq V$. The ground-set $V$ and the items $\{1, 2, \cdots, n\}$ depend on the choice of the task at hand. We now define a few relevant optimization problems which shall come up in our problem formulations:
\begin{align}
\mbox{Problem 1:} \max_{X \subseteq V, s(X) \leq b} f(X)
\end{align}
Problem 1 is knapsack constrained submodular maximization~\cite{sviridenko2004note}. The goal here is to find a summary with a fixed cost, and $s_1, s_2, \cdots, s_n$ denotes the cost of each element in the ground-set. A special case is cardinality constrained submodular maximization, when the individual costs are $1$~\cite{nemhauser1978analysis}. This a natural model for extracting fixed length summary videos (or a fixed number of keyframes).

\begin{align}
\mbox{Problem 2: } \min_{f(X) \geq c} s(X)
\end{align}
This problem is called the \emph{Submodular Cover Problem}~\cite{wolsey1982analysis,iyer2013submodular}. $s(X)$ is the modular cost function, and  $c$ is the coverage constraint. The goal here is to find a minimum cost subset $X$ such that the submodular coverage or representation function covers \emph{information} from the ground set. A special case of this is the set cover problem. Moreover, Problem 2 can be seen as a Dual version of Problem 1~\cite{iyer2013submodular}.

Submodular Functions have been used for several summarization tasks including Image summarization~\cite{tschiatschek2014learning}, video summarization~\cite{gygli2015video}, document summarization~\cite{lin2012learning}, training data summarization and active learning~\cite{wei2015submodularity} etc. Using a
greedy algorithm to optimize a submodular function (for selecting a subset) gives a lower-bound performance guarantee of around 63\% of optimal and in practice these greedy solutions are often within 90\% of optimal ~\cite{krause2008optimizing}. This makes it advantageous to formulate (or approximate) the objective function for data selection as a submodular function.

\subsection{CNNs for Image Feature Extraction}
Convolutional Neural Networks are critical to feature extraction in our summarization framework. We pre-process the video to extract key visual features including objects, scenes, faces, humans, etc. Convolutional Neural Networks have recently provided state of the art results for several recognition tasks including object recognition~\cite{krizhevsky2012imagenet,szegedy2015going,he2016deep}, Scene recognition~\cite{zhou2014learning}, Face Recognition~\cite{parkhi2015deep} and Object Detection and Localization~\cite{redmon2016you}. We next describe the end to end system in detail.

\section{Method}
The input to our system is a video. Our system then extracts all important features from the video and generates an analysis database. The user can then interact with the system in several ways. User can generate a video summary of a given length, or extract a set of key frames or a montage describing the video. Similarly the user can search for a query and extract video snippets of frames which are relevant to the query. Finally the user can also view a summary of all objects, scenes, humans and faces in the video along with their statistics. All these interactions are enabled on the fly (in a few seconds). The user can also define the summarization model of their choice. We investigate and compare  different submodular models, and argue the utility of different models based on the use case.
\subsection{Problem Formulation for the Multi-Faceted Visual Summarization}
We now formulate problem statements across the different summarization views. Extractive summarization considers the entire video. We can generate a summary either in terms of key frames (represent the video as a set of frames sampled at a frame-rate), or video snippets. In either case, we extract a ground set $V$, with each individual element either being a key frame or a video snippet. We then solve Problems 1 or 2 depending on the use case. Problem 1 is the right formulation if we are interested in obtaining a summary of a fixed budget. Problem 2 is useful if we don't care about the size of the video, but we are interested in the summary capturing all the \emph{information} of the video. In the case of query based summarization, we first extract the set of frames or snippets relevant to that query $q$. Denote this by $V_q$.  We then solve the submodular optimization problem on $V_q$. Finally, in the case of entity based summarization, we extract all the entities in the video, and denote the set of entities as $V_e$. $V_e$ represents, for example, all the faces of people in the video. We can then run our summarization with $V_e$ as the groundset.

In the case of Extractive or Query based summarization, the ground truth elements can be either frames of video snippets. Our video snippets can be either fixed length snippets or Shots, obtained from a shot detector. If the snippets are fixed length snippets (say, 2 or 3 seconds), we can use the cardinality constrained submodular maximization. If the snippets are shots from the video, the length of each shot can differ, and we have the more general knapsack constrained setting. While our system can handle each of these modes, we focus on the key-frame based method for our experiments, since we are interested in proving the utility of different summarization models. The insights will carry over to the other modes as well.

\begin{figure*}
\begin{center}
\includegraphics[width = 0.3\textwidth]{diversity}
\includegraphics[width = 0.3\textwidth]{representation}
\includegraphics[width = 0.3\textwidth]{coverage}
\end{center}
\caption{Illustration of the Difference between Diversity Functions, Coverage Functions and Representation Functions}
\label{divrepcov}
\end{figure*}

\subsection{Submodular Functions as Summarization Models}
This section describes the Submodular Functions used in our system. We divide these into Coverage Functions, Representation Functions and Diversity Functions.

\subsubsection{Modeling Coverage}
This class of functions model notions of coverage, i.e. try to find a subset of the ground set $X$ which covers a set of \emph{concepts}. Below are instantiations of this.

\noindent \textbf{Set Cover Function:} Denote $V$ as the ground set and let $X \subseteq V$ be a subset (of snippets or frames). Further $\mathcal U$ denotes a set of concepts, which could represent, for example, scenes or objects. Each frame (or snippet) $i \in X$ contains a subset $U_i \in \mathcal U$ set of concepts (for example, an image covers a table, chair and person). The set cover function then is
\begin{align}
f(X) = w(\cup_{i \in X} U_i),
\end{align}
where $w_u$ denotes the weight of concept $u$.

\noindent \textbf{Probabilistic Set Cover: } This is a generalization of the set cover function, to include probabilities $p_{i u_i}$ for each object $u_i$ in Image $i \in X$. For example, our convolutional neural network might output a confidence of object $u_i$ in Image $i$, and we can use that in our function. The probabilistic coverage function is defined as,
\begin{align}
f(X) = \sum_{i \in \mathcal U} w_i[1 - \prod_{i \in X}(1 - p_{ij})].
\end{align}
The set cover function is a special case of this if $p_{ij} = 1$ if Object $j$ belongs to Image $i$ (i.e. we use the hard labels instead of probabilities).

\noindent \textbf{Feature Based Functions: } Finally we investigate the class of Feature Based functions. Here, we denote an Image $i$ via a feature representation $q_i$. This could be, for example, the features extracted from the second last layer of a ConvNet. Denote $F$ as the set of features. The feature based function is defined as,
\begin{align}
f(X) = \sum_{i \in F} \psi(q_i(X))
\end{align}
where $q_i(X) = \sum_{j \in X} q_{ij}$, and $q_{ij}$ is the value of feature $i$ in Image $j$. $\psi$ is a concave function.  Examples of $\psi$ are square-root, Log and Inverse Function etc.

\subsubsection{Modeling Representation}
Representation based functions attempt to directly model representation, in that they try to find a representative subset of items, akin to centroids and mediods in clustering.

\noindent \textbf{Facility Location Function: } The Facility Location function is closely related to k-mediod clustering. Denote $s_{ij}$ as the similarity between images $i$ and $j$. We can then define $f(X) = \sum_{i \in V} \max_{j \in X} s_{ij}$. For each image $i$, we compute the representative from $X$ which is closest to $i$ and add the similarities for all images. Note that this function, requires computing a $O(n^2)$ similarity function. However, as shown in~\cite{wei2014fast}, we can approximate this with a nearest neighbor graph, which will require much smaller space requirement, and also can run much faster for large ground set sizes.

\noindent \textbf{Saturated Coverage Function: } The saturated coverage function~\cite{lin2011class} is defined as $f(X) = \min\{\sum_{i \in X} s_{ij}, \alpha \sum_{i \in V} s_{ij}\}$. This function is similar to Facility Location and attempts to model representation. This is also a Kernel based function and requires computing the similarity matrix.

\noindent \textbf{Graph Cut Functions: } We define the graph cut family of functions as $f(X) = \lambda \sum_{i \in V} \sum_{j \in X} s_{ij} - \sum_{i, j \in X} s_{ij}$. This function is similar to the Facility Location and Saturated Coverage in terms of its modeling behaviour.

\subsubsection{Modeling Diversity}
The third class of Functions are Diversity based ones, which attempt to obtain a diverse set of key points.

\noindent \textbf{Dispersion (Disparity) Functions: } Denote $d_{ij}$ as a distance measure between Images $i$ and $j$. Define a set function $f(X) = \min_{i, j \in X} d_{ij}$. This function is not submodular, but can be efficiently optimized via a greedy algorithm~\cite{dasgupta2013summarization}. It is easy to see that maximizing this function involves obtaining a subset with maximal minimum pairwise distance, thereby ensuring a diverse subset of snippets or keyframes. Similar to the Minimum Disparity, we can define two more variants. One is Disparity Sum, which can be defined as $f(X) = \sum_{i, j \in X} d_{ij}$. This is a supermodular function. Another model is, what we call, Disparity Min-Sum which is a combination of the two forms of models. Define this as $f(X) = \sum_{i \in X} \min_{j \in X} d_{ij}$. This function is submodular~\cite{chakraborty2015adaptive}.

\noindent \textbf{Determinantal Point Processes: } Another class of Functions are Determinantal Point Processes, defined as $p(X) = \mbox{Det}(S_X)$ where $S$ is a similarity kernel matrix, and $S_X$ denotes the rows and columns instantiated with elements in $X$. It turns out that $f(X) = \log p(X)$ is submodular, and hence can be efficiently optimized via the Greedy algorithm. Unlike the other choices of submodular functions investigated so far, this requires computing the determinant and is $O(n^3)$ where $n$ is the size of the ground set. This function is not computationally feasible and hence we do not use it in our system since we require near real time results in summarization.

\begin{table*}
\begin{center}
 \begin{tabular}{|| c | c | c |  c | c ||}
 \hline
 Name & $f(X)$ & $p_f(X)$ & $T^o_f$ & $T^p_f$\\ [0.5ex]
 \hline
 Facility Location & $\sum_{i \in V} \max_{k \in X} s_{ik}$ & $[\max_{k \in X} s_{ik}, i \in V]$ & $O(n^2)$ & $O(n)$\\
 \hline
 Saturated Coverage & $\sum_{i \in V} \min\{\sum_{j \in X} s_{ij}, \alpha_i\}$ & $[\sum_{j \in X} s_{ij}, i \in V]$ & $O(n^2)$ & $O(n)$\\
\hline
 Graph Cut & $\lambda \sum_{i \in V}\sum_{j \in X} s_{ij} - \sum_{i, j \in X} s_{ij}$ & $[\sum_{j \in X} s_{ij}, i \in V]$ & $O(n^2)$ & $O(n)$\\
\hline
 Feature Based & $\sum_{i \in \mathcal F} \psi(w_i(X))$ & $[w_i(X), i \in \mathcal F]$ & $O(n|\mathcal F|)$ & $O(|\mathcal F|)$ \\
 \hline
 Set Cover & $w(\cup_{i \in X} U_i)$ & $\cup_{i \in X} U_i$ & $O(n|U|$ & $|U|$\\
 \hline
 Prob. Set Cover & $\sum_{i \in \mathcal U} w_i[1 - \prod_{k \in X}(1 - p_{ik})]$ & $[\prod_{k \in X} (1 - p_{ik}), i \in \mathcal U]$ & $O(n|\mathcal U|)$ & $O(|\mathcal U|)$ \\ [1ex]
 \hline
 DPP & $\log\det(S_X))$ & SVD($S_X$) & $O(|X|^3)$ & $O(|X|^2)$\\
 \hline
 Dispersion Min & $\min_{k,l  \in X, k \neq l} d_{kl}$ & $\min_{k, l \in X, k \neq l} d_{kl}$ & $O(|X|^2)$ & $O(|X|)$\\
 \hline
 Dispersion Sum & $\sum_{k,l  \in X} d_{kl}$ & $[\sum_{k \in X} d_{kl}, l \in X]$ & $O(|X|^2)$ & $O(|X|)$\\
  \hline
 Dispersion Min-Sum& $\sum_{k \in X} \min_{l \in X} d_{kl}$ & $[\min_{k \in X} d_{kl}, l \in X]$ & $O(|X|^2)$ & $O(|X|)$\\
 \hline
\end{tabular}
\caption{List of Submodular Functions used, with the precompute statistics $p_f(X)$, gain evaluated using the precomputed statistics $p_f(X)$ and finally $T^f_o$ as the cost of evaluation the function without memoization and $T^f_p$ as the cost with memoization. It is easy to see that memoization saves an order of magnitude in computation.}
\end{center}
\end{table*}

\subsubsection{Modeling Importance and Relevance}
To model Importance or Relevance, we use Modular terms~\cite{potapov2014category}. Given a specific task, we train a supervised model to predict the important frames in that video (for example, a \emph{goal} might be considered important in a soccer video). Given this learnt model, we can predict the score of each frame, and rank the scores. This is exactly equivalent to optimize the modular function defined with these scores.

\subsubsection{Understanding Diversity, Representation and Coverage}
Figure~\ref{divrepcov} demonstrates the intuition of using diversity, representation and coverage functions. Diversity based functions attempt to find the most different set of images. The leftmost figure in Fig.~\ref{divrepcov} demonstrates this. It is easy to see that the five most diverse images are picked up by the diversity function (Disparity Min), and moreover, the summary also contains the image with a hand covering the camera (the image on the right hand side bottom), which is an outlier. The middle figure demonstrates the summary obtained via a representation function (like Facility Location). The summary does not include outliers, but rather contains one representative image from each cluster. The diversity function on the other hand, does not try to achieve representation from every cluster. The third figure demonstrates coverage functions. The summary obtained via a coverage function (like Set Cover or Feature based function), covers all the concepts contained in the images (Male, Car, Greenery, Beach etc.).

\subsection{Instantiations of the Submodular Functions}
Having discussed the choices of the submodular functions and features, we go over the specific instantiations of submodular functions considered in our system. First consider Extractive and Query Based Summarization. For the Facility Location function and the disparity min function, we define the similarity kernel as:
\begin{align*}
s_{ij} = \langle F_s^i, F_s^j \rangle + \langle F_o^i, F_o^j \rangle + \mbox{corr}(H^i, H^j)
\end{align*}
where $F_s$ represent normalized Deep Scene Features extracted using GoogleNet on Places205~\cite{zhou2014learning}, $F_o$ represents normalized Deep Object features using GoogleNet on ImageNet~\cite{szegedy2015going}, $H$ represents the normalized color histogram features. Since the disparity min function uses a distance function, we use $d_{ij} = 1 - s_{ij}$. For Feature based functions, the feature-set $\mathcal F$ is a concatenation of the scene features $F_s$ and object features $F_o$. In order to define the Set Cover function, we define $U_i$ as the Scene and YOLO object labels corresponding to the Image. Recall that the labels for scenes and objects were chosen based on a pre-defined threshold (i.e. select scene and objects labels if the probability for the label is greater than a threshold). The Probabilistic Set Cover function is defined via a concatenation of the probabilities from the scene and object models. Query based summarization for keyframes is identical to extractive summarization, except that we first get a groundset $V_q$ which is related to the query. The queries, are either objects, scenes, faces/humans with age and gender, text in the video, as well as meta data like subtitles etc.

Finally, for entity or concept based summarization, we extract the entities from the videos. Entities we consider are objects, faces etc. For faces, we use the VGG Face model from~\cite{parkhi2015deep}, pretrained on Celeb Face data for Face recognition. The objects are localized using YOLO~\cite{redmon2016you}. We extract features from GoogLeNet~\cite{krizhevsky2012imagenet,szegedy2015going}, along with color histogram~\cite{swain1991color}. The similarity kernel we use here is $s_{ij} = \langle F_o^i, F_o^j \rangle + \mbox{corr}(H^i, H^j)$.

Next we discuss the choice of the submodular functions.
Facility Location, Disparity Min/Sum, Graph Cut, Saturated Coverage, and DPPs are instantiated using Similarity Kernels discussed above. Feature Based functions are defined directly via features, and we use the deep features as described above. In the case of the Set cover and probabilistic set cover functions, we use the labels and probabilities respectively from the deep models as the concepts.

\subsection{Optimization Algorithms}
The previous sections describe the models used in our system. We now investigate optimization algorithms which solve Problems 1 and 2. Variants of a greedy algorithm provide near optimal solutions with approximation guarantees for Problems 1-3~\cite{wolsey1982analysis,nemhauser1978analysis,sviridenko2004note}.

\noindent \textbf{Budget Constrained Submodular Maximization: } For the budget constrained version (Problem 1), the greedy algorithm is a slight variant, where at every iteration, we sequentially update $X^{t+1} = X^t \cup \mbox{argmax}_{j \in V \backslash X^t} \frac{f(j | X^t)}{c(j)}$. This algorithm has near optimal guarantees~\cite{sviridenko2004note}.

\noindent \textbf{Submodular Cover Problem: } For the Submodular Cover Problem (Problem 2), we again resort to a greedy procedure~\cite{wolsey1982analysis} which is near optimal. In this case, the update is similar to that of problem 1, i.e. choose $X^{t+1} = X^t \cup \mbox{argmax}_{j \in V \backslash X^t} f(j | X^t)$. We stop as soon as $f(X^t) = f(V)$, or in other words, we achieve a set which covers all the concepts.

\noindent \textbf{Lazy Greedy Implementations: } Each of the greedy algorithms above admit lazy versions which run much faster than the worst case complexity above~\cite{minoux1978accelerated}. The idea is that instead of recomputing $f(j | X^t), \forall j \notin ^t$, we maintain a priority queue of sorted gains $\rho(j), \forall j \in V$. Initially $\rho(j)$ is set to $f(j), \forall j \in V$. The algorithm selects an element $j \notin X^t$, if $\rho(j) \geq f(j | X^t)$, we add $j$ to $X^t$ (thanks to submodularity). If $\rho(j) \leq f(j | X^t)$, we update $\rho(j)$ to $f(j | X^t)$ and re-sort the priority queue. The complexity of this algorithm is roughly  $O(k n_R T_f)$, where $n_R$ is the average number of re-sorts in each iteration. Note that $n_R \leq n$, while in practice, it is a constant thus offering almost a factor $n$ speedup compared to the simple greedy algorithm.

\section{Implementational Tricks}
This section goes over implementation tricks via memoization. One of the parameters in the lazy greedy algorithms is $T_f$, which involves evaluating $f(X \cup j) - f(X)$. One option is to do a na\"{\i}ve implementation of computing $f(X \cup j)$ and then $f(X)$ and take the difference. However, due to the greedy nature of algorithms, we can use memoization and maintain a precompute statistics $p_f(X)$ at a set $X$, using which the gain can be evaluated much more efficiently. At every iteration, we evaluate $f(j | X)$ using $p_f(X)$, which we call $f(j | X, p_f)$.  We then update $p_f(X \cup j)$ after adding element $j$ to $X$. Table 1 provides the precompute statistics, as well as the computational gain for each choice of a submodular function $f$. Denote $T_f^o$ as the time taken to na\"{\i}vely compute $f(j | X) = f(X \cup j) - f(X)$. Denote $T_o^p$ as the time taken to evaluate this gain given the pre-compute statistics $p_X$. We see from Table 1, that evaluating the gains using memoization is often an order of magnitude faster. Moreover, notice that we also need to update the pre-compute statistics $p_X$ at every iteration. For the functions listed in Table 1, the cost of updating the pre-compute statistics is also $T_f^p$. Hence every iteration of the (lazy) greedy algorithm costs only $2T_f^p$ instead of $T_f^o$ which is an order of magnitude larger in every case. In our results section, we evaluate empirically the benefit of memoization in practice.

\begin{table}
    \centering
    \begin{tabular}{ |c|c|c|c|c|c|c|}
        \hline
         & \multicolumn{3}{c|}{\textbf{Memoization}} & \multicolumn{3}{c|}{\textbf{No Memoization}} \\
        \hline
        \textbf{Function} & \textbf{5\%} & \textbf{15\%} & \textbf{30\%} & \textbf{5\%} & \textbf{15\%} & \textbf{30\%}  \\
         \hline
        Fac Loc & 0.34 & 0.4 & 0.71 & 48 & 168 & 270  \\
         \hline
        Sat Cov & 0.36 & 0.64 & 0.92 & 55 & 177 & 301  \\
        \hline
         Gr Cut & 0.39 & 0.52 & 0.82 & 41 & 161 & 355  \\
        \hline
        Feat B & 0.16 & 0.21 & 0.32 & 9 & 16 & 21  \\
        \hline
        Set Cov & 0.21 & 0.31 & 0.41 & 5 & 16 & 31  \\
        \hline
        PSC & 0.11 & 0.37 & 0.42 & 7 & 19 & 35  \\
        \hline
        DPP & 32 & 107 & 411 & 171 & 1003 & 4908  \\
                \hline
        DM & 0.11 & 0.61 & 0.82 & 21 & 125 & 221  \\
                \hline
        DS & 0.21 & 0.63 & 0.89 & 41 & 134 & 246  \\
        \hline
    \end{tabular}
    \caption{Timing results in seconds for summarizing a two hour video for various submodular functions}
    \label{tab:my_label}
\end{table}

\begin{figure}
\includegraphics[width=0.5\textwidth, clip=true]{MultiFaceted_Summarization_Results.pdf}
\vspace{-3.3cm}
\caption{Illustration of the Results. The top figure shows the results from extractive summarization on TV shows, the second demonstrates entity summary on a TV show. The third figure shows the results of query based summarization on a query "SkyScraper" while the fourth one shows the results of extractive summarization on surveillance videos. In each case we compare Representation, Diversity and Coverage models. See the text for more details.}
\label{results}
\end{figure}

\begin{figure}
\centering{
\includegraphics[width=0.21\textwidth]{representation_new}
~
\includegraphics[width=0.21\textwidth]{coverage_new.png}

\includegraphics[width=0.21\textwidth]{outliers_new.png}
~
\includegraphics[width=0.21\textwidth]{importance_new}

\includegraphics[width=0.21\textwidth]{query_new}
~
\includegraphics[width=0.21\textwidth]{concept_new.png}}
\caption{Comparison of Diversity, Coverage and Representation Models for various domains and scenarios. See the text for more details.}
\label{quantres}
\end{figure}

\begin{figure*}
\includegraphics[width=\textwidth,height=4cm]{flowchartSnippet}
\caption{End-to-End Processing and Summarization of a Video}
\label{systemFlowchart}
\end{figure*}
\section{Results}
Our system is implemented in C++. We use Caffe~\cite{jia2014caffe} and DarkNet~\cite{redmon2016you} for deep CNNs and OpenCV for other computer vision tasks. A graphical representation of our system is depicted in Figure~\ref{systemFlowchart}.

Figure~\ref{results} shows the results for extractive summarization as keyframes, extractive summarization on concepts or entities and query based summarization on keyframes. We compare the different summarization models under various scenarios and evaluation measures. Instead of comparing all the submodular functions described above, we consider representatives from each class of functions. We use Facility Location as a representative function, Disparity Min for Diversity and Set Cover as a choice for Coverage functions.

We next create a dataset of videos from different categories. We select 10 videos from the following categories: Movies/TV shows, Surveillance camera footage, Travel videos and sports videos like Soccer. In the following sections, we annotate various events of interest (ground-truth) from these videos to define various evaluation criteria. The annotation mechanism and evaluation criteria is described in each of the sections below. The goal of this is to demonstrate the role of various summarization models.

\paragraph{Extractive Summarization: Representation} The top Figure in Fig.~\ref{results} demonstrates the results of extractive summarization on Movies and TV shows. Diversity Models tend to pick up outlier events, which in this case, include transition scenes and other outliers. In contrast, the Representation function (Facility Location) tends to pick the representative scenes. The coverage function does something in between. In the case of a TV show, representative shots are probably more important compared to the transition scenes. To quantify this, define an evaluation measure as follows. We divide a movie (TV Show) into a set of scenes $S_1, \cdots, S_k$ where each scene $S_i$ is a continuous shot of events. We do not include the outliers (we define outliers as shots less than a certain length -- for example transition scenes). Given a summary $X$, define $R(X) = \sum_{i = 1}^k \min(|X \cap S_i|, 1)/k$. A summary with a large value of $R(X)$ will not include the outliers and will pick only single representatives from each scene. We evaluate this on $10$ different TV show and movie videos. Figure~\ref{quantres} (top left) compares the representative, diversity, and coverage models and a random summary baseline. We see the representative model (Facility Location) tends to perform the best as expected, followed by the coverage model. The diversity model does poorly since it picks a lot of outliers.

\paragraph{Extractive Summarization: Coverage} Next, we define an evaluation criteria capturing coverage. For each frame in the video (sampled at 1FPS), define a set of concepts covered $\mathcal U$. Denote $\mathcal U(X)$ as the set of concepts covered by a set $X$. For each frame of the video, we hand pick a set of concepts (scenes and objects contained in the video). Define the coverage objective as $C(X) = \mathcal U(X)/\mathcal U(V)$. Figure~\ref{quantres} demonstrates the coverage objective for the different models. We obtain this by creating a set of 10 labeled videos of different categories (surveillance, TV shows/movies, and travel videos). As expected, the coverage function (set cover) achieves superior results compared to the other models.

\paragraph{Extractive Summarization: Outliers and Diversity} In the above paragraphs, we define two complementary evaluation criteria, one which captures representation and another which measures coverage. We argue how, for example, representation is important in Movies and TV shows. We now demonstrate how the diversity models tend to select outliers and anomalies. To demonstrate this, we select a set of surveillance videos. Most of our videos have repetitive events like no activity or people sitting/working. Given this, we mark all the different events (what we call outliers), including for example, people walking in the range of the camera or any other different activity. We create a dataset of 10 surveillance videos with different scenarios. Most of these videos have less activity. Given a set $S_1, S_2, \cdots S_k$ of these events marked in the video, define $D(X) = \sum_{i = 1}^k \min(|X \cap S_i|, 1)$. Note this measure is similar to the representative evaluation criteria ($R(X))$ except that it is defined w.r.t the outlier events. Figure~\ref{quantres} (middle left) shows the comparison of the performance of different models on this dataset. As expected, the Diversity measures outperforms the other models consistently.

\paragraph{Extractive Summarization: Importance} To demonstrate the benefit of having the right \emph{importance} or \emph{relevance} terms, we take a set of videos where intuitively the relevance term should matter a lot. Examples include sports videos like Soccer. To demonstrate this, we train a model to predict important events of the video (e.g. the goals, red card). We then define a simple Modular function where the score is the output of the classifier. We then test this out and compare the importance model to other summarization models. The results are shown in Figure~\ref{quantres} (middle right). As we expect, the model with the importance gets the highest scores.

\paragraph{Query Summarization: Diversity} We next look at query based summarization. The goal of query based summarization is to obtain a summary set of frames which satisfy a given query criteria. Figure~\ref{results} (third row) qualitatively shows the results for the query "Sky Scrapers". The Diversity measure is able to obtain a diversity of the different scenes. Even if there is an over-representation of a certain scene in the set of images satisfying the query, the diversity measure tends to pick a diverse set of frames. The representation measure however, tends to focus on the representative frames and can pick more than one image in the summary from scenes which have an over-representation in the query set. We see this Figure~\ref{results}. To quantify this, we define a measure $M(X)$ by dividing the video into a set of clusters of frames $S_1, \cdots, S_k$ where each cluster contains similar frames. These are often a set of continuous frames in the video. We evaluate this on a set of 10 travel videos, and compare the different models. We see that the diversity and representation models tend to perform the best (Figure~\ref{quantres}, bottom left), with the diversity model slightly outperforming the representative models. We also observe that there are generally very few outliers in the case of query based summarization, which is another reason why the diversity model tends to perform well.

\paragraph{Entity Summarization: } Lastly we look at Entity summarization. The goal here is to obtain a summary of the entities (faces, objects, humans) in the video. Figure~\ref{results} (second row) demonstrates the results for Entity summarization of Faces. We see the results for Diversity, Coverage and Representation Models. The diversity model tends to pick up outliers, many of which are false positives (i.e. not faces). The representation model skips all outliers and tends to pick representative faces. To quantitavely evaluate this, we define a representation measure as follows. We remove all the outliers, and cluster the set of entities (objects, faces) into a set of clusters $E_1, \cdots, E_k$ where $E_i$ is a cluster of similar entities. We evaluate this again on a set of 10 videos. Figure~\ref{quantres} (bottom right) shows the results for objects. The results for Faces is similar and in the interest of space, we do not include these. We see that the representation model tends to outperform the other models and skips all the outliers. The diversity model focuses on outliers and hence does not perform as well.

\paragraph{Scalability} Finally, we demonstrate the computational scalability of our framework. Table 2 shows the results of the time taken for Summarization for a two hour video (in seconds) with and without memoization. The groundset size is $|V| = 7200$. We see huge gains from using memoization compared to just computing the gains using the Oracle models of the functions. All our experiments were performed on Intel(R) Xeon(R) CPU E5-2603 v3 @1.6 GHz (Dual CPU) with 32 GB RAM. We used a NVIDIA 1080 GTX 8GB GPU for the Deep Learning. For the two hour video, the preprocessing took around 20 minutes on a single GPU. It would be much faster on multiple GPUs and moreover, this is typically done only once.

\section{Conclusion}
This paper presents a unified picture of multi-faceted video summarization for extractive, query based and entity based summarization. In each case, we take a closer look at the different summarization models and argue the benefits of these models in different domains. We qualitatively and quantitatively argue this by comparing the results on several domains. Finally, we discuss various implementation tricks to build applications around video and image summarization in production systems.

\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


Having a mermaid or seaside themed bachelorette or bridal party? Add sweet treats to these Mermaid Tail Favor Boxes to gift to your friends or use as a decoration. Adorned with a mermaid tail on top, these favor boxes feature a blue scale design with gold foil accents.
Measures 1.9" W x 1.9" D x 4.3" H.


Ahead of their Love Remains album being released on Friday (July 29th), Hillary Scott & The Scott Family made their Grand Ole Opry Debut last night (July 26th). While Hillary's performed at the Opry with Lady Antebellum, she couldn't have been more thrilled at the prospect of stepping on that famed stage with her parents, Linda Davis and Lang Scott, and sister Rylee.
'Love Remains' features the single, "Thy Will," which Hillary co-wrote about the aftermath of her miscarriage last fall. The track recently topped the Christian Songs Chart in the US.


text:
Mass Effect 3 story and beta gameplay leak
By Blake Ellison 11.08.2011 :: 10:46AM EST 11.08.2011
It's been a very leaky few days for BioWare: over the weekend, both the full story and a playable beta of Mass Effect 3 leaked to the public.
Internal story files were leaked, according to producer Jesse Howard in a post on BioWare's official forums. He claimed that the data was out of date and doesn't reflect the final experience, but obviously you shouldn't read them if you want an untainted ME3 experience.
The files may not be easy to come across, as discussion of spoiler-level detail is apparently attracting the wrong kind of attention. Gaming forum NeoGAF was sent a cease and desist letter from BioWare, for example.
As for the playable beta, the cat is totally out of the bag. Players beta testing the upcoming Xbox 360 dashboard refresh were invited to a private beta download of the game over the weekend. Weighing 1.8 GB, the demo covers roughly the first 10 minutes of gameplay, which has already been shown to press writing previews. The file has since been taken offline.
Videos of the beta are all over YouTube, and one can be found below. This is a true beta: there are plenty of missing textures, placeholder spaceships, dialogue sounds in rough states, and gameplay bugs including long load times and the inability to make some jumps. Fans of the series will probably be even more excited to see what the game looks like before it's fully baked.
While not spoiling any story elements – it's not hard to guess what happens in the game's start anyway – the biggest surprise is a gameplay change to allow the game to be played in one of three modes. Action Mode focuses on the combat scenes and skips much of the narrative guessing and calculations by automating conversation replies. Story Mode does the opposite: it reduces the combat difficulty and allows manual replies to conversations. Purists will lean toward RPG mode, which has the series' familiar combination of combat and conversation. Each setting can also be altered manually in the options menu.
Howard, still in damage-control mode, posted again on the forums to confirm the leak. The code is in a "rough, unfinished state" and was not meant for public release, he wrote.
Mass Effect 3 releases in March 2012 on Xbox 360 and PlayStation 3, unless the whole game leaks by then.
via BioWare Forums (1, 2)


text:
Home Cardinal Homilies 2009 4 April – 50th Anniversary of Our Lady of Lourdes Church, Drogheda
4 April – 50th Anniversary of Our Lady of Lourdes Church, Drogheda
50TH ANNIVERSARY OF OUR LADY OF LOURDES CHURCH, DROGHEDA
HOMILY GIVEN BY
SATURDAY 4 APRIL 2009
We are here today to celebrate the 50th anniversary of the official opening of this magnificent Church of Our Lady of Lourdes 50 years ago for on 5 April 1959 this Church was dedicated to the praise and glory of God by Cardinal John D'Alton. On that day the Mayor, Councillor Eugene Hughes, and the members of the Corporation processed in their beautiful robes to the opening ceremony. Cardinal D'Alton was escorted by canopy bearers and a Scouts Guard of Honour.
The architect, Simon Aloysius Leonard, of the firm W. H. Byrne, Architects, Dublin had been planning this new church along with Mgr Stokes for years. The tender of Creedon Construction for £132,000 had been accepted in 1956 and the length of the building was set as two years.
The decision to build a new church was taken for two reasons:
1. The condition of the original church – The Chapel of Ease at Hardman's Gardens had deteriorated seriously.
2. Expansion of the town, in this area, Bothar Brua, Pearse Park and Yellowbatter Ascal needed a larger church. People wanted a house of prayer and a place of worship to meet their needs
I reckon the choice of name was determined by the centenary, in 1958, of the Apparition of Our Lady to Saint Bernadette at Lourdes. So it was placed under the patronage of our Lady of Lourdes.
Some of you will remember the Sales of Work and the Carnivals to raise the funds. Others will recall the search for donors. Their donations are listed in the Commemoration booklet and are acknowledged on the plaques. The brochure says that Monsignor had a keen appreciation of the requirement for money to finance his capital projects. Donors were found for each of the 84 pews on which you sit and for the windows, rails and altars as well as the Mortuary Chapel and the Baptistery. Then there was the Golden Book which included a contribution from one Senator John F Kennedy of Boston, Massachusetts. It is no wonder Monsignor Stokes was conferred with the freedom of the Borough of Drogheda.
About 15,500 people gave a minimum of £1 each to the construction of this wonderful Church, dedicated to Mary, the Mother of Jesus and our Mother also. That in itself must have given to the people of this area, of this town, of this parish, a great sense of ownership – a great feeling of pride and satisfaction. Yes, it was the House of God – the House of Prayer – but it was also the House of the Faith of the People of Drogheda.
Today I congratulate you, and your parents and grandparents, on all of that. I congratulate everyone associated with producing the lovely booklet which captures so well, in words and photographs, the atmosphere of that wonderful weekend.
We are gathered here this evening to thank God for all of that. I am very happy to be joined in this celebration by so many priests – native sons of the parish, priests who formerly ministered here and by priests ministering here in Drogheda at present. I recall also the fact that Bishop Austin Quinn and Bishop James Lennon were here at the opening.
As we look to the past, we also contemplate the present and keep an eye on the future. The Catholic population of this parish is now estimated at 15,500, the same number as contributed £1 or more to the original church. Then there are almost 2,000 in the parish of Mell. There were big challenges facing Mgr Stokes and his companions. They faced them and they succeeded.
There are big challenges facing us today but challenges of a different kind. The challenge today is to build the faith of the Catholic community and to re-build it where it has deteriorated. These challenges must be addressed and they too can be managed. The challenge is for us to go and make disciples of each and every one of these people. Each and every one is made in the image and likeness of God. Each and every one has been purchased by the saving blood of Jesus Christ. Each and every one of us is to play our part.
We are celebrating the Year of St. Paul – a year called to mark the 2,000th anniversary of his birth. The Second Reading this evening is from St. Paul. He tells us "let this mind be in you which was in Christ, Jesus", for the attitude which we should have is the one which Jesus had. Jesus always had the nature of God but he did not think that he should try and remain equal to God. Jesus didn't jealously hold on to the glory that was his by right. Instead, he emptied himself; he made himself powerless as a slave is powerless. He gave up all he had and took the nature of a servant. He was humble and he walked a way of obedience -all the way to death on a cross. His attitude was that he put others first and thought others before himself.
If we are lead others to Christ, we ourselves must first find Christ in our own lives. We must try to be like him. We must have the same attitude. We normally think of saving our own skins first. This was not the way Jesus thought.
Here in this Church of Our Lady of Lourdes I am reminded of the example of St Bernadette. After Our Lady had appeared to Bernadette, she entered the convent and became a nun. She could have reasonably expected to have been be left in Lourdes to gives talks to pilgrims and to generally promote the shrine. But not at all, she was asked to transfer to a convent in Nevers which was a long distance from Lourdes. I am not sure if she ever came back. Bernadette obeyed because she believed that that was what God wanted her to do. She made a big sacrifice but today she is a great saint in Heaven because she did not try to seek out her own interests, but she obeyed what her superiors asked of her.
Today is the Foundation Day of the Medical Missionary of Mary. We rejoice in the news of the election of their new Congregational Leader, Sister Siobhan Corkery and we pray God's blessing upon her and her newly-elected Council.
The Medical Missionaries became great by having the attitude of Christ. They too became great by giving up all that they had and answering God's call to become medical missionaries in faraway Africa. They answered the call by, first of all, forgetting themselves and their own self-interest. They thought first of the good of the African people and of the advantages which the Good News of the Gospel could bring to Africa.
Today the mission territories are nearer home. All of those 15,500 people are part of the Body of Christ by baptism. Christ said: "Whatsoever you do to the least of my brethren you do to me". It is interesting to see the different ways the Body of Christ is treated in Mark's account of the passion.
First of all we have the fact that it was anointed by a woman with an alabaster jar full of a very expensive perfume made of pure nard. Some people ridiculed the idea and criticized her harshly. For them it was a waste of good perfume. "It could have been given to the poor" they said. But Jesus defends her action robustly. They could help the poor any time they liked. They could not always prepare her body for burial. She had done a fine and beautiful thing. She had shown great and devout respect for the body of Christ.
Mercy – compassion – this will be the quality on which we will all be judged at the final judgement. One of the earliest writers in the Church went so far as to say that "those who have no mercy will be condemned". Traditionally these demands of the Gospel have been handed down to us as the corporal and spiritual works of mercy. They are the kind of works that should characterize the life of every follower of Christ.
The corporal works of mercy require us to feed the hungry; give drink to the thirsty; clothe the naked; shelter the homeless; care for the sick; visit those in prison and bury the dead. Tomorrow in Armagh we will be remembering the 'Disappeared' whose bodies have not received Christian burial. Today's Gospel indicates the care taken to make sure that the dead body of Christ got a respectful burial.
I am sure that over the past 50 years many followers of Christ went out from this Church to carry out those same corporal works of mercy. They would have been inspired to do so by the words of God which they heard here. They would have been empowered by the Bread of Life which they received here.
Over the next fifty years may Mary help us all to continue to do so.
May Mary, Our Lady of Lourdes, Mother of Hope accompany us on that journey. May she teach us to proclaim the Living God,
Help us to bear witness to Jesus, the one Saviour.
May she make us kindly to our neighbours,
welcoming to the needy,
Concerned for justice,
impassioned builders of a more just world.
Our Lady of Lourdes, intercede for us
As we carry out our work in history,
Certain that the Father's plan will be fulfilled.


text:
The Women's 10 meter air pistol event at the 2020 Summer Olympics took place on 25 July 2021 at the Asaka Shooting Range.

Records
Prior to this competition, the existing world and Olympic records were as follows.

Schedule
All times are Japan Standard Time (UTC+9)

Results

Qualification

Final

References

Shooting at the 2020 Summer Olympics
Women's events at the 2020 Summer Olympics
Olympics

redpajama_set_name: RedPajamaWikipedia


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Exclusive: Mount Sinai to open Central Park Avenue location in Scarsdale
Allergy, cardiology, dermatology, family medicine, pediatric sports medicine and OBGYN services, to name a few, will be offered at the new facility.
Mario Marroquin
The 25,000-square-foot building that once housed Klaff's Home Design Store in Scarsdale will become a care facility for Mount Sinai Health System, The Journal News/lohud has learned.
The hospital network told The Journal News/lohud the property at 331 Central Park Ave. in Scarsdale is slated to open in September.
Mount Sinai President Dr. Arthur Klein said the new facility will enable the network to expand its service offerings. The health care network will employ 65 employees in the Scarsdale office.
A spokesperson for Mount Sinai said allergy, cardiology, dermatology, family medicine, pediatric sports medicine and OBGYN services, to name a few, will be offered at the new facility.
RELATED: Virginia healthcare developer makes two-property acquisition in Harrison
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SCARSDALE: A look at dueling development proposals near train station, what residents say
Mount Sinai said Dr. John Schirripa will head the new location.
The building at 331 Central Park Ave. is located 2.5 miles from the Mount Sinai Doctors Facility in Yonkers and less than 1.5 miles from its medical facility in Hartsdale.
The Central Park Avenue corridor from Yonkers to White Plains is home to two Montefiore Health System centers, physical therapy, psychotherapy and family medicine centers, to name only a few.
As reported by The Journal News/lohud, Simone Development, the owner of 331 Central Park Ave., began renovating 104 Corporate Park Drive in Harrison as part of a joint venture with Montefiore Health System.
Charles Schwab will continue to occupy 5,000 square feet at the building, according to Simone Development.
Simone Development and Mount Sinai opened a 100,000-square-foot primary care facility at 5 Cuba Hill Road in Greenlawn in November 2018.


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Religion, Rioja, and rain on the Camino de Santiago
7th May 2014 Redmond Traynor2 Comments on Religion, Rioja, and rain on the Camino de Santiago
A church bell rings through the dark village of Campiello, a hamlet in the north of Spain. Pilgrims quietly move from their bunks, stow away sleeping bags and sink into their boots. From the old bed-filled barn it is a short walk to the village bar where the hostel owner serves a breakfast of coffee and fried bread. The pilgrims eat in silence while farmers arrive from the black morning for coffee and a short brandy.
This ancient catholic pilgrimage to the burial site of Saint James in Santiago, begins in a number of locations across Europe. The "French Way" starts in Le Puy and runs over the Pyrenees and across the Spanish plains, while the "Northern Route" moves along the beaches of the Spanish coast. The Primitivo, or "original" way is the least walked and most challenging of the routes to Santiago, cutting across the snow-capped ridges of the Asturian mountains. It was made popular by King Alfonso of Spain in the eleventh century, when he moved off the French way, which lies to the south, in order to see the cathedral at Oviedo. The Primitivo runs over two hundred and fifty kilometres from Oviedo to the town of Lugo, before joining the Northern and French routes, and continuing to Santiago.
In the villages along the camino, pilgrims are met with a deep affection. Locals eagerly ask questions about previous towns and invite travellers to pray, before cheerily accompanying their departure with shouts of "arriva!". The bars serve the traditional pilgrim fare of pork and butter-bean stew with a carafe of the local Rioja. Along the high mountain paths, seventeenth century churches and small stone sanctuaries provide shelter and a place to pray. While the cultural and traditional infrastructure of the religious pilgrimage remains intact, the walk is no longer undertaken exclusively by the religious, as one French pilgrim who had walked the French route three times remarked: "I think now most people walk the camino not necessarily with any religion, but with a sort of spirituality".
From the large town of Oviedo, with its stone squares and magnificent cathedral, the Primitivo moves west across the wild green hills of the Asturian agricultural heartland. For sixty kilometres the route winds across a dark, grassy basin, passing through tiny untouched Medieval villages and densely wooded pine-forests. Soon the landscape folds in upon itself, the paths narrow and the ascent into the Asturian mountains begins. From Campiello the route ascends one thousand metres, following a mountain path for over one hundred kilometres until it moves into the province of Galicia and flattens out as it nears Santiago.
In the morning, thin wisps of cloud lie between the mountain ridges bringing heavy Spanish rain in the afternoon. Every evening pilgrims find accommodation in church-subsidised hostels named "Aubergues de Pergrinos", providing a bed and kitchen but usually no board. Pilgrims must present a "credential" to be stamped at each hostel, which certifies that they are on a pilgrimage. While the hostels on the French and Northern routes have to bring out extra mattresses to accommodate travellers, most of the aubergues on the Primitivo are empty, particularly in early Spring. Those pilgrims who are on the route tend to walk alone, waking early in the morning and going to sleep before eight each evening.
A surprising exception to this trend came at Grandas, a small town on the border of Galicia and Asturias. A young American mountain ranger and a German student, ten years his junior, had met and fallen in love on the one hundred and thirty kilometre stretch between Oviedo and Grandas. The arrival of other pilgrims in the hostel didn't deter them from lying together that night in the communal sleeping area and acting in a way dubiously becoming of a catholic pilgrimage.
An uncomfortable blend of untouched rusticity and large-scale industrialisation characterises the towns and villages along the route. In the rural hamlets old women still wear blue and white smocks and bonnets, carrying sticks for the herding of cattle, while the large towns of Tineo, Grandas and A Fonsagrada, are filled with manufacturing outlets and small factories. Yet, decay remains the striking feature of the country villages, with hollowed out, abandoned houses and plots of land for sale. The scars of the European crisis run deep in these struggling Northern towns, as one elderly lady put it: "The young have all had to leave. My three boys are in Madrid. These towns are old now, like me!"
At Lugo, the Primitivo ends and pilgrims join travellers from the Northern Route to complete the pilgrimage into Santiago. The land becomes flatter now and the towns more populous. Outside Lugo, a group of Spanish children cheer pilgrims on as they leave the city, shouting "Pergrinos! Pergrinos!". In Melida, the pilgrims from the French Way join the route. The approach is a long flat path through the suburbs of Santiago. Nearer the city, the architecture becomes older and the road begins to climb. Small, brass cockle-shells, the symbol of St. James, mark the way towards the cathedral, which slowly appears above the roofs of the town houses, looming large and mossy-brown in the vast central square of the town.
On the evening of arrival, pilgrims are invited to attend Mass in the cathedral. The names and nationalities of travellers are read out by the priest, and a short service is held. Then travellers climb behind the large golden altar to embrace and thank the jewell-studded statue of Saint James which looks out upon the pews.
Tagged pilgrim spain
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