What's the purpose of `if __name__ == '__main__'`?

Stackoverflow Stackexchange

Q: What does `if name == "__main__"` mean in Python?
Possible Duplicate:
What does <if name==“main”:> do?

I have wrote scripts in Python for quite a while now and I study more of Python as I need it. When reading other people's code I meet if name == "__main__": construct quite often.
What is it good for?

A: Basically,
There's a distinction between the "main" script file and external files which were imported or referenced in another way. If the script is the "main" script then the special variable __name__ will equal "__main__".
You can use this to protect the "execution" code from the classes and variables the script has. This enables you to import a script and use classes and variables without actually running that script's main code if it has any.
See also: What does if name == “main”: do?


Which test was devised to determine whether robots can think?

Turing test

The test was introduced by Turing in his 1950 paper "Computing Machinery and Intelligence" while working at the University of Manchester. It opens with the words: "I propose to consider the question, 'Can machines think? Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." Turing describes the new form of the problem in terms of a three-person game called the "imitation game", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: "Are there imaginable digital computers which would do well in the imitation game?" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that "machines can think".
Since Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a "mind", "understanding", or "consciousness", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.
History

Cognitive test

Cognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence.


Turing test

The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).


History of robots

It features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata.

Turing test

The test was introduced by Turing in his 1950 paper "Computing Machinery and Intelligence" while working at the University of Manchester. It opens with the words: "I propose to consider the question, 'Can machines think? Because "thinking" is difficult to define, Turing chooses to "replace the question by another, which is closely related to it and is expressed in relatively unambiguous words." Turing describes the new form of the problem in terms of a three-person game called the "imitation game", in which an interrogator asks questions of a man and a woman in another room in order to determine the correct sex of the two players. Turing's new question is: "Are there imaginable digital computers which would do well in the imitation game?" This question, Turing believed, was one that could actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that "machines can think".
Since Turing introduced his test, it has been both highly influential and widely criticized, and has become an important concept in the philosophy of artificial intelligence. Philosopher John Searle would comment on the Turing test in his Chinese room argument, a thought experiment that stipulates that a machine cannot have a "mind", "understanding", or "consciousness", regardless of how intelligently or human-like the program may make the computer behave. Searle criticizes Turing's test and claims it is insufficient to detect the presence of consciousness.
History


Turing test

The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give. Since the Turing test is a test of indistinguishability in performance capacity, the verbal version generalizes naturally to all of human performance capacity, verbal as well as nonverbal (robotic).

History of robots

It features a female humanoid automaton standing by a basin filled with water.When the user pulls the lever, the water drains and the female automaton refills the basin. Furthermore, he created a robotic musical band. According to Mark Rosheim, unlike Greek designs Arab automata worked with dramatic illusion and manipulated the human perception for practical application. The segmental gears described in The Book of Knowledge of Ingenious Mechanical Devices, published by Al-Jazari shortly before his death in 1206, appeared 100 years later in the most advanced European clocks. Al-Jazari also published instructions on the construction of humanoid automata. The first water clocks modeled on Arabic designs were constructed in Europe about 1000 CE, possibly on the basis of the information that was transmitted during Muslim-Christian contact in Sicily and Spain. Among the first recorded European water clocks is that of Gerbert of Aurillac, built in 985 CE. Hero's works on automata were translated into Latin amid the 12th century Renaissance. The early 13th-century artist-engineer Villard de Honnecourt sketched plans for several automata.


Cognitive test

Cognitive tests are assessments of the cognitive capabilities of humans and other animals. Tests administered to humans include various forms of IQ tests; those administered to animals include the mirror test (a test of visual self-awareness) and the T maze test (which tests learning ability). Such testing is used in psychology and psychometrics, as well as other fields studying human and animal intelligence.

Progress in artificial intelligence

Sub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)
Object recognition
Various robotics tasks that may require advances in robot hardware as well as AI, including:
Stable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)
Humanoid soccer
Speech recognition: "nearly equal to human performance" (2017)
Explainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.
Many tests of fluid intelligence (2020)
Bongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)
Visual Commonsense Reasoning (VCR) benchmark (as of 2020)
Stock market prediction: Financial data collection and processing using Machine Learning algorithms
Angry Birds video game, as of 2020
Various tasks that are difficult to solve without contextual knowledge, including:
Translation
Word-sense disambiguation
Proposed tests of artificial intelligence
In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.
The Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior.


Artificial intelligence

In the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI".
About 800,000 "AI"-related U.S. job openings existed in 2022.
Philosophy
Defining artificial intelligence
Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?" He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks."


Chinese room

Searle does not disagree that AI research can create machines that are capable of highly intelligent behavior. The Chinese room argument leaves open the possibility that a digital machine could be built that acts more intelligently than a person, but does not have a mind or intentionality in the same way that brains do.
Searle's "strong AI hypothesis" should not be confused with "strong AI" as defined by Ray Kurzweil and other futurists, who use the term to describe machine intelligence that rivals or exceeds human intelligence -- that is, artificial general intelligence, human level AI or superintelligence. Kurzweil is referring primarily to the amount of intelligence displayed by the machine, whereas Searle's argument sets no limit on this. Searle argues that a superintelligent machine would not necessarily have a mind and consciousness.
Turing test
The Chinese room implements a version of the Turing test. Alan Turing introduced the test in 1950 to help answer the question "can machines think?" In the standard version, a human judge engages in a natural language conversation with a human and a machine designed to generate performance indistinguishable from that of a human being. All participants are separated from one another. If the judge cannot reliably tell the machine from the human, the machine is said to have passed the test.


Progress in artificial intelligence

Sub-human Optical character recognition for printed text (nearing par-human for Latin-script typewritten text)
Object recognition
Various robotics tasks that may require advances in robot hardware as well as AI, including:
Stable bipedal locomotion: Bipedal robots can walk, but are less stable than human walkers (as of 2017)
Humanoid soccer
Speech recognition: "nearly equal to human performance" (2017)
Explainability. Current medical systems can diagnose certain medical conditions well, but cannot explain to users why they made the diagnosis.
Many tests of fluid intelligence (2020)
Bongard visual cognition problems, such as the Bongard-LOGO benchmark (2020)
Visual Commonsense Reasoning (VCR) benchmark (as of 2020)
Stock market prediction: Financial data collection and processing using Machine Learning algorithms
Angry Birds video game, as of 2020
Various tasks that are difficult to solve without contextual knowledge, including:
Translation
Word-sense disambiguation
Proposed tests of artificial intelligence
In his famous Turing test, Alan Turing picked language, the defining feature of human beings, for its basis. The Turing test is now considered too exploitable to be a meaningful benchmark.
The Feigenbaum test, proposed by the inventor of expert systems, tests a machine's knowledge and expertise about a specific subject. A paper by Jim Gray of Microsoft in 2003 suggested extending the Turing test to speech understanding, speaking and recognizing objects and behavior.

Artificial intelligence

In the late teens and early 2020s, AGI companies began to deliver programs that created enormous interest. In 2015, AlphaGo, developed by DeepMind, beat the world champion Go player. The program was taught only the rules of the game and developed strategy by itself. GPT-3 is a large language model that was released in 2020 by OpenAI and is capable of generating high-quality human-like text. These programs, and others, inspired an aggressive AI boom, where large companies began investing billions in AI research. According to AI Impacts, about $50 billion annually was invested in "AI" around 2022 in the U.S. alone and about 20% of the new U.S. Computer Science PhD graduates have specialized in "AI".
About 800,000 "AI"-related U.S. job openings existed in 2022.
Philosophy
Defining artificial intelligence
Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?" He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation. Since we can only observe the behavior of the machine, it does not matter if it is "actually" thinking or literally has a "mind". Turing notes that we can not determine these things about other people but "it is usual to have a polite convention that everyone thinks."


Meaning of life

To be fruitful and multiply. ()
To obtain freedom. ()
To fill the Earth and subdue it. ()
To serve humankind, to prepare to meet and become more like God, to choose good over evil, and have joy.
˹He is the One˺ Who created death and life in order to test which of you is best in deeds. And He is the Almighty, All-Forgiving. —
To worship God and enter heaven in afterlife.
To love, to feel, to enjoy the act of living
To love more.
To love those who mean the most. Every life you touch will touch you back.
To treasure every enjoyable sensation one has.
To seek beauty in all its forms.
To have fun or enjoy life.
To seek pleasure and avoid pain.
To be compassionate.
To be moved by the tears and pain of others, and try to help them out of love and compassion.
To love others as best we possibly can.
To eat, drink, and be merry.
To have power, to be better
To strive for power and superiority.
To rule the world.
To know and master the world.
To know and master nature.
To help life become as powerful as possible.

Meaning of life

The meaning of life pertains to the inherent significance or philosophical meaning of living (or existence in general). There is not a definitive answer, and thinking or discourse on the topic is sought in the English language through the question, "What is the meaning of life?" (or the related "Why are we here?" or "What is the purpose of existence?"). There have been many proposed answers to these questions from many different cultural and ideological backgrounds. The search for life's meaning has produced much philosophical, scientific, theological, and metaphysical speculation throughout history. Different people and cultures believe different things for the answer to this question. Opinions vary on the usefulness of using time and resources in the pursuit of an answer. Excessive pondering can be indicative of, or lead to, an existential crisis.
The meaning of life can be derived from philosophical and religious contemplation of, and scientific inquiries about, existence, social ties, consciousness, and happiness. Many other issues are also involved, such as symbolic meaning, ontology, value, purpose, ethics, good and evil, free will, the existence of one or multiple gods, conceptions of God, the soul, and the afterlife. Scientific contributions focus primarily on describing related empirical facts about the universe, exploring the context and parameters concerning the "how" of life. Science also studies and can provide recommendations for the pursuit of well-being and a related conception of morality. An alternative, humanistic approach poses the question, "What is the meaning of my life?"


Tex Stackexchange

Q: Missing abstract with amsart class I'm not very good at LaTeX but I've been using it to write my dissertation. Have just come to the end and trying to format everything correctly. Whenever I try to put my Abstract paragraph into the form \begin{abstract} (writing here) \end{abstract} and click play the whole abstract section just disappears. What am I doing wrong?
\documentclass[12pt]{amsart}

\begin{document}

\begin{abstract}
(my abstract here)
\end{abstract}
\end{document}

A: The amsart class does some Mysterious Stuff with abstracts: it stores the abstract in a box to be used in the title page.  To get it to appear, issue the magic command \maketitle after the abstract.
(It will then complain about a missing \title ... )
\documentclass[12pt]{amsart}

\begin{document}

\begin{abstract}
(my abstract here)
\end{abstract}

\maketitle
Some text
\end{document}

Mathoverflow Stackexchange

Q: How to write an abstract for a math paper? How would you go about writing an abstract for a Math paper? I know that an abstract is supposed to "advertise" the paper. However, I do not really know how to get started. Could someone tell me how they go about writing an abstract?

A: *

*Avoid notation if possible.  Notation makes it really hard to search electronically.

*Put the subject in context, e.g., "In a recent paper, T. Lehrer introduced the concept of left-bifurcled rectangles.  He conjectured no such rectangles exist when the number of bifurcles $n$ is odd."

*State your results, in non-technical language, if possible.  "In this paper we show the existence of left-bifurcled rectangles for all prime $n$."

*Mention a technique, if there is a new one:  "Our methods involve analytic and algebraic topology of locally euclidean metrizations of infinitely differentiable Riemannian manifolds".

*Never, ever, ever, cite papers in the bibliography by giving citation numbers; the abstract is an independent entity that should stand on its own.


Who are the authors of the original vae paper

Title: GAN and VAE from an Optimal Transport Point of View

Abstract: This short article revisits some of the ideas introduced in arXiv:1701.07875 and arXiv:1705.07642 in a simple setup. This sheds some lights on the connexions between Variational Autoencoders (VAE), Generative Adversarial Networks (GAN) and Minimum Kantorovitch Estimators (MKE).

Title: AVAE: Adversarial Variational Auto Encoder

Abstract: Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets.


What is xpool cross modal language video attention

Title: B\"acklund--Darboux transformations in Sato's Grassmannian

Abstract: We define B\"acklund--Darboux transformations in Sato's Grassmannian. They can be regarded as Darboux transformations on maximal algebras of commuting ordinary differential operators. We describe the action of these transformations on related objects: wave functions, tau-functions and spectral algebras. This paper is the second of a series of papers (hep-th/9510211, q-alg/9602011, q-alg/9602012) on the bispectral problem.

Title: X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

Abstract: In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However, videos inherently express a much wider gamut of information than texts. Instead, texts often capture sub-regions of entire videos and are most semantically similar to certain frames within videos. Therefore, for a given text, a retrieval model should focus on the text's most semantically similar video sub-regions to make a more relevant comparison. Yet, most existing works aggregate entire videos without directly considering text. Common text-agnostic aggregations schemes include mean-pooling or self-attention over the frames, but these are likely to encode misleading visual information not described in the given text. To address this, we propose a cross-modal attention model called X-Pool that reasons between a text and the frames of a video. Our core mechanism is a scaled dot product attention for a text to attend to its most semantically similar frames. We then generate an aggregated video representation conditioned on the text's attention weights over the frames. We evaluate our method on three benchmark datasets of MSR-VTT, MSVD and LSMDC, achieving new state-of-the-art results by up to 12% in relative improvement in Recall@1. Our findings thereby highlight the importance of joint text-video reasoning to extract important visual cues according to text. Full code and demo can be found at: https://layer6ai-labs.github.io/xpool/


what is the name of the chief justice of ghana

Supreme Court of Ghana

The following is a list of the judges of the Supreme Court. In July 2018, President Nana Akufo-Addo appointed four new judges to the Supreme Court. They were Samuel K. Marful-Sau and Agnes M.A Dordzie, both Justices of the Appeal Court, Nii Ashie Kotey, a former Dean of the Faculty of Law at the University of Ghana and Nene A. O. Amegatcher,  a lawyer in private practice who also a former president of the Ghana Bar Association. One of the longest-serving judges of the Court, William Atuguba retired in the same month. He had been on the Supreme Court after being nominated by Jerry Rawlings in November 1995 until July 2018.
The last female Chief Justice was Sophia Akuffo. She was the last Supreme Court Judge appointed by Jerry Rawlings to retire. She retired on 20 December 2019 and was replaced by Kwasi Anin-Yeboah on 7 January 2020.
In December 2019, President Akufo-Addo appointed three new judges to the Supreme Court. They were Mariama Owusu, Avril Lovelace-Johnson, and Gertrude Tokornoo. They were to replace Vida Akoto-Bamfo, Sophia Adinyira, and Sophia Akuffo who had either retired or were due to retire.
List of chief justices of the Supreme Court
Since its inception in 1876, the Supreme Court has had 27 chief justices.
Martyrs of the Rule of Law

Chief Justice of Ghana

Administer the oath of allegiance and the judicial oath to all justices of the superior courts or designate someone to act in his stead.
Chairman of the Rules of Court Committee which makes rules regulating the practice and procedure of all courts in Ghana.
To set up a tribunal to resolve grievances against the Electoral Commission of Ghana.
Convene and chair a tribunal to oversee proceedings relating to the removal of the president of Ghana from office.
Appoint judicial officers on the advice of the Judicial Council and subject to the approval of the president.
Current Chief Justice
The current chief justice is Gertrude Torkornoo. She succeeded Kwasi Anin-Yeboah as the 15th Chief Justice of Ghana and the third female to hold this position in the history of Ghana. Torkornoo was sworn in as Chief Justice by President Akufo-Addo on 12 June 2023.
Early Chief Justices (and Judicial Assessors)
1853–?1854 James Coleman Fitzpatrick
1854–1857 Henry Connor
1861–1866 William Hackett (acting 1861–1863)
1868 William Alexander Parker
Chief Justices of the Supreme Court
Since its inception in 1876, the Supreme Court has had 27 chief justices, including 13 in the Gold Coast era.
Justice Francis Yaonasu Kpegah, who was at the time the most senior of the Supreme Court judges acted as Chief Justice between March 2007 and June 2007.


Research on improving automatic image captioning by employing ChatGPT to ask questions to BLIP-2 for generating richer descriptions.

Title: ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions

Abstract: Asking insightful questions is crucial for acquiring knowledge and expanding our understanding of the world. However, the importance of questioning has been largely overlooked in AI research, where models have been primarily developed to answer questions. With the recent advancements of large language models (LLMs) like ChatGPT, we discover their capability to ask high-quality questions when provided with a suitable prompt. This discovery presents a new opportunity to develop an automatic questioning system. In this paper, we introduce ChatCaptioner, a novel automatic-questioning method deployed in image captioning. Here, ChatGPT is prompted to ask a series of informative questions about images to BLIP-2, a strong vision question-answering model. By keeping acquiring new visual information from BLIP-2's answers, ChatCaptioner is able to generate more enriched image descriptions. We conduct human-subject evaluations on common image caption datasets such as COCO, Conceptual Caption, and WikiArt, and compare ChatCaptioner with BLIP-2 as well as ground truth. Our results demonstrate that ChatCaptioner's captions are significantly more informative, receiving three times as many votes from human evaluators for providing the most image information. Besides, ChatCaptioner identifies 53% more objects within the image than BLIP-2 alone measured by WordNet synset matching. Code is available at https://github.com/Vision-CAIR/ChatCaptioner


Which positional encoding scheme improves Transformer performance on long sequences?

Title: PermuteFormer: Efficient Relative Position Encoding for Long Sequences

Abstract: A recent variation of Transformer, Performer, scales Transformer to longer sequences with a linear attention mechanism. However, it is not compatible with relative position encoding, which has advantages over absolute position encoding. In this paper, we discuss possible ways to add relative position encoding to Performer. Based on the analysis, we propose PermuteFormer, a Performer-based model with relative position encoding that scales linearly on long sequences. PermuteFormer applies position-dependent transformation on queries and keys to encode positional information into the attention module. This transformation is carefully crafted so that the final output of self-attention is not affected by absolute positions of tokens. PermuteFormer introduces negligible computational overhead by design that it runs as fast as Performer. We evaluate PermuteFormer on Long-Range Arena, a dataset for long sequences, as well as WikiText-103, a language modeling dataset. The experiments show that PermuteFormer uniformly improves the performance of Performer with almost no computational overhead and outperforms vanilla Transformer on most of the tasks.


who came up with the idea of the transcontinental railroad

First transcontinental railroad

The Big Four Four northern California businessmen formed the Central Pacific Railroad: Leland Stanford, (1824–1893), President; Collis Potter Huntington, (1821–1900), Vice President; Mark Hopkins, (1813–1878), Treasurer; Charles Crocker, (1822–1888), Construction Supervisor. All became substantially wealthy from their association with the railroad. Judah, Marsh, Strong, Stanford, Huntington, Hopkins and Crocker, along with James Bailey and Lucius Anson Booth, became the first board of directors of the Central Pacific Railroad.
Thomas Durant
Former ophthalmologist Dr. Thomas Clark "Doc" Durant was nominally only a vice president of Union Pacific, so he installed a series of respected men like John Adams Dix as president of the railroad. While serving as vice president of Union Pacific he would be a key figure in the Crédit Mobilier scandal which ultimately led to his removal from the company.
Grenville M. Dodge
Major General Grenville M. Dodge served as the chief engineer of Union Pacific during the construction of the First Transcontinental Railroad. In 1865 while fighting against Native-American tribes he would discover a pass in the Laramie Mountains, which would serve as a vital passage for the First Transcontinental Railroad. Dodge would serve in the United States House of Representatives for Iowa's 5th District from 1867 until 1869. During this time he would push for legislation to help the construction of the railroad.

First transcontinental railroad

America's first transcontinental railroad (known originally as the "Pacific Railroad" and later as the "Overland Route") was a  continuous railroad line built between 1863 and 1869 that connected the existing eastern U.S. rail network at Council Bluffs, Iowa, with the Pacific coast at the Oakland Long Wharf on San Francisco Bay. The rail line was built by three private companies over public lands provided by extensive U.S. land grants. Building was financed by both state and U.S. government subsidy bonds as well as by company-issued mortgage bonds. The Western Pacific Railroad Company built  of track from the road's western terminus at Alameda/Oakland to Sacramento, California. The Central Pacific Railroad Company of California (CPRR) constructed  east from Sacramento to Promontory Summit, Utah Territory. The Union Pacific Railroad (UPRR) built  from the road's eastern terminus at the Missouri River settlements of Council Bluffs and Omaha, Nebraska, westward to Promontory Summit.
The railroad opened for through traffic between Sacramento and Omaha on May 10, 1869, when CPRR President Leland Stanford ceremonially tapped the gold "Last Spike" (later often referred to as the "Golden Spike") with a silver hammer at Promontory Summit. In the following six months, the last leg from Sacramento to San Francisco Bay was completed. The resulting coast-to-coast railroad connection revolutionized the settlement and economy of the American West. It brought the western states and territories into alignment with the northern Union states and made transporting passengers and goods coast-to-coast considerably quicker, safer and less expensive.
The first transcontinental rail passengers arrived at the Pacific Railroad's original western terminus at the Alameda Terminal on September 6, 1869, where they transferred to the steamer Alameda for transport across the Bay to San Francisco. The road's rail terminus was moved two months later to the Oakland Long Wharf, about a mile to the north, when its expansion was completed and opened for passengers on November 8, 1869. Service between San Francisco and Oakland Pier continued to be provided by ferry.


Paper analyzing the impact of misinformation generated by AI on Open-Domain Question Answering systems

Title: Attacking Open-domain Question Answering by Injecting Misinformation

Abstract: With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the sensitivity of open-domain QA models to corpus pollution with misinformation documents. We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation, with large absolute performance drops on all models. Misinformation attack brings more threat when fake documents are produced at scale by neural models or the attacker targets hacking specific questions of interest. To defend against such a threat, we discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection in a joint fashion.


List of Orange Is the New Black characters

When Yvonne "Vee" Parker enters the prison and forms an African-American gang, Suzanne falls for Vee's charms and maternal influence, being exploited into becoming Vee's "muscle." While zealously loyal to Vee, Suzanne violently beat or threatened any inmates who crossed her, almost acting on command. Later, Vee attempts to coldly trick her into taking the fall for Red's severe beating, as a distraught Suzanne believes she may have done it unconsciously due to her violent history. During the third season, she is encouraged by the new counselor Berdie Rogers to be more creative, causing her to start writing several science fiction erotic stories that become a hit among the women in the prison. Suzanne reveals that she has no sexual experience and is completely naïve in regard to sex, having never actually had a girlfriend before, and that the stories are based on other sources. Eventually, the stories make their way to the staff, causing Rogers to get suspended. Meanwhile, Suzanne becomes nervous upon discovering that one of her fans, Maureen Kukudio, is interested in her romantically. Suzanne backs out of a possible sexual encounter, but is later seen forming a close bond with Maureen towards the end of the season.


how can I kill a process with a given name

Superuser Stackexchange

Q: How do I kill a process by its name in Linux? I have a process with the name bla, and I want to kill it by its name.
When I run ps aux | grep "bla", I get 2 entries:

*

*process bla and

*blablablab
How do I filter it out?

A: Take a look at pkill, if available.

Stackoverflow Stackexchange

Q: How to wrap content in html in a Rails helper I want to wrap some content in HTML in a Rails 3 helper so that in my view I can do this:
<%= rounded_box do-%>
  <%= raw target.text %>
<% end -%>

I have a helper method that looks like this:
def rounded_box(&block)
  str = "

  str << yield
  str << "

"
  raw str
end

The way I have it now returns the content properly wrapped in the HTML string, but not before rendering any erb in the rounded_box block (e.g. in this case the target.text is rendered twice, once wrapped, once not).
Is there a better way to do this? For simplicity, I'd like to avoid using content_tag, but if that's the only/best way I can do that.

A: Call capture on the block instead of yield:
def rounded_box(&block)
  str = "

"
  str << capture(&block)
  str << "

"
  raw str
end


Title: Bayesian Inference for Linear Dynamic Models with Dirichlet Process Mixtures

Abstract: Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on Dirichlet process mixtures is introduced. Efficient Markov chain Monte Carlo and Sequential Monte Carlo methods are then developed to perform optimal batch and sequential estimation in such contexts. The algorithms are applied to blind deconvolution and change point detection. Experimental results on synthetic and real data demonstrate the efficiency of this approach in various contexts.

Title: Vision Mamba: A Comprehensive Survey and Taxonomy

Abstract: State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy.


I am searching for a good and large-scale benchmark for testing the performance of text embeddings.

Title: MTEB: Massive Text Embedding Benchmark

Abstract: Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb.

Title: Gauge fixing, BRS invariance and Ward identities for randomly stirred flows

Abstract: The Galilean invariance of the Navier-Stokes equation is shown to be akin to a global gauge symmetry familiar from quantum field theory. This symmetry leads to a multiple counting of infinitely many inertial reference frames in the path integral approach to randomly stirred fluids. This problem is solved by fixing the gauge, i.e., singling out one reference frame. The gauge fixed theory has an underlying Becchi-Rouet-Stora (BRS) symmetry which leads to the Ward identity relating the exact inverse response and vertex functions. This identification of Galilean invariance as a gauge symmetry is explored in detail, for different gauge choices and by performing a rigorous examination of a discretized version of the theory. The Navier-Stokes equation is also invariant under arbitrary rectilinear frame accelerations, known as extended Galilean invariance (EGI). We gauge fix this extended symmetry and derive the generalized Ward identity that follows from the BRS invariance of the gauge-fixed theory. This new Ward identity reduces to the standard one in the limit of zero acceleration. This gauge-fixing approach unambiguously shows that Galilean invariance and EGI constrain only the zero mode of the vertex but none of the higher wavenumber modes.


Research on combining sentiment analysis and market data for volatility forecasting.

Title: Volatility forecasting using Deep Learning and sentiment analysis

Abstract: Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. To classify public sentiment, we use a Convolutional Neural Network, which obtained data from Reddit global news headlines. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day's volatility to make forecasts. We employed this method on the past volatility of the S&P500 and the major BRICS indices to corroborate its effectiveness. Our results demonstrate that including sentiment can improve Deep Learning volatility forecasting models. However, in contrast to return forecasting, the performance benefits of including sentiment appear for volatility forecasting appears to be market specific.

Title: A Sentiment Analysis Approach to the Prediction of Market Volatility

Abstract: Prediction and quantification of future volatility and returns play an important role in financial modelling, both in portfolio optimization and risk management. Natural language processing today allows to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. The findings suggest that there is evidence of correlation between sentiment and stock market movements: the sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. Also, in a surprising finding, for the sentiment found in Twitter comments we obtained a correlation coefficient of -0.7, and p-value below 0.05, which indicates a strong negative correlation between positive sentiment captured from the tweets on a given day and the volatility observed the next day. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modelling, based on Latent Dirichlet Allocation, to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modelling our classifier achieved a directional prediction accuracy for volatility of 63%.