Artificial Intelligence

1812 Submissions

[27] viXra:1812.0454 [pdf] submitted on 2018-12-29 01:37:51

Imageai Interaction with Imagej Via Jython Plugin/jikesrvm in the Context of Advanced Image Processing and Analysis – a Useful Insight Into the Promising World of Ai,python & Java Based Image Processing Informatics Framework.

Authors: Nirmal Tej Kumar
Comments: 3 Pages. Short Communication & Technical Notes

IMAGEAI Interaction with ImageJ via Jython Plugin/JikesRVM in the context of Advanced Image Processing and Analysis – A Useful Insight into the Promising World of AI,Python & Java Based Image Processing Informatics Framework.
Category: Artificial Intelligence

[26] viXra:1812.0452 [pdf] submitted on 2018-12-29 04:15:29

Electron Microscopy Deep Learning

Authors: George Rajna
Comments: 48 Pages.

MENNDL, an artificial intelligence system, automatically designed an optimal deep learning network to extract structural information from raw atomic-resolution microscopy data. [27] Researchers at Google have recently developed a new technique for synthesizing a motion blurred image, using a pair of un-blurred images captured in succession. [26] Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25] Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21]
Category: Artificial Intelligence

[25] viXra:1812.0451 [pdf] submitted on 2018-12-29 04:33:46

Structure of Artificial Neural Networks

Authors: George Rajna
Comments: 51 Pages.

A team of researchers at RWTH Aachen University's Institute of Information Management in Mechanical Engineering have recently explored the use of neuroscience techniques to determine how information is structured inside artificial neural networks (ANNs). [28] MENNDL, an artificial intelligence system, automatically designed an optimal deep learning network to extract structural information from raw atomic-resolution microscopy data. [27] Researchers at Google have recently developed a new technique for synthesizing a motion blurred image, using a pair of un-blurred images captured in succession. [26] Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25] Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22]
Category: Artificial Intelligence

[24] viXra:1812.0450 [pdf] submitted on 2018-12-29 04:51:45

Fourth Industrial Revolution

Authors: George Rajna
Comments: 53 Pages.

Indeed, what many are calling "the Fourth Industrial Revolution" is already here, disrupting jobs and labor markets, largely because of the rise and advance of artificial intelligence and robotics. [29] A team of researchers at RWTH Aachen University's Institute of Information Management in Mechanical Engineering have recently explored the use of neuroscience techniques to determine how information is structured inside artificial neural networks (ANNs). [28] MENNDL, an artificial intelligence system, automatically designed an optimal deep learning network to extract structural information from raw atomic-resolution microscopy data. [27] Researchers at Google have recently developed a new technique for synthesizing a motion blurred image, using a pair of un-blurred images captured in succession. [26] Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25] Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an a-MAZE-ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20]
Category: Artificial Intelligence

[23] viXra:1812.0443 [pdf] submitted on 2018-12-27 10:24:18

Review: Generic Multi-Objective Deep Reinforcement Learning(MODRL)

Authors: Norio Kosaka
Comments: 6 Pages. See the original paper as well. https://arxiv.org/ftp/arxiv/papers/1803/1803.02965.pdf

In this paper, the author reviewed the existing survey regarding MODRL and published in March 2018 by Thanh Thi Nguyen, and discussed the variety of reinforcement learning approaches in terms of multi-objective problem setting.
Category: Artificial Intelligence

[22] viXra:1812.0351 [pdf] submitted on 2018-12-19 07:47:39

NeuNetS for Broader Adoption of AI

Authors: George Rajna
Comments: 50 Pages.

On December 14, 2018, IBM released NeuNetS, a fundamentally new capability that addresses the skills gap for development of latest AI models for a wide range of business domains. [27] Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we're done typing. [26] Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25]
Category: Artificial Intelligence

[21] viXra:1812.0328 [pdf] submitted on 2018-12-20 05:04:18

(Eped Version 1.0 1.12.2018 6 Pages) the “Electronic Pediatrician” (Eped) – a Demo Software for Computer-Assisted Pediatric Diagnosis and Treatment Implemented Using Microsoft Visual Basic 6 (VB6), with Extended Applicability

Authors: Andrei Lucian Dragoi
Comments: 6 Pages.

This paper presents EPed (abbreviation for “Electronic Pediatrician”), which is a demo software for computer-assisted pediatric diagnosis and treatment built by the author in Microsoft Visual Basic 6 (VB6) (VB6), a software with extended applicability. Keywords: “Electronic Pediatrician” (EPed); computer-assisted pediatric diagnosis and treatment; Microsoft Visual Basic 6 (VB6);
Category: Artificial Intelligence

[20] viXra:1812.0306 [pdf] submitted on 2018-12-17 09:55:28

Power Law and Dimension of the Maximum Value for Belief Distribution with the Max Deng Entropy

Authors: Bingyi Kang
Comments: 13 Pages.

Deng entropy is a novel and efficient uncertainty measure to deal with imprecise phenomenon, which is an extension of Shannon entropy. In this paper, power law and dimension of the maximum value for belief distribution with the max Deng entropy are presented, which partially uncover the inherent physical meanings of Deng entropy from the perspective of statistics. This indicated some work related to power law or scale-free can be analyzed using Deng entropy. The results of some numerical simulations are used to support the new views.
Category: Artificial Intelligence

[19] viXra:1812.0286 [pdf] submitted on 2018-12-16 10:19:48

Trustworthy Artificial Intelligence

Authors: George Rajna
Comments: 48 Pages.

Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we're done typing. [26] Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25] Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22]
Category: Artificial Intelligence

[18] viXra:1812.0284 [pdf] submitted on 2018-12-16 11:31:38

Physiological Model of One Materialized Human Thought

Authors: Kondratenko Viktoria
Comments: 11 Pages.

ABSTRACT In this article, the creation in the second signal system of a correct reflex ring – physiological model of one of the materialized elementary, or compound human thoughts – is shown on a specific example. As tools, functionally full formal language and predicate logic language are used. The methodology is described in the Theory of axiomatic modeling of Kondratenko (1). As any other functional problem in any domain, according to the Theory, the problem is interpreted in mathematical logic as a theorem which is subject to proof. The reflex ring is physiological model of one of materialized elementary, or compound, thoughts of the specific person, as the ring represents a fragment of neural network of the person. The logical work of concepts of knowledge reflected in concepts No. 1 – 7, is guaranteed to provide the creation of the correct reflex ring having the property of "being physiological model of one of the materialized elementary, or compound thoughts of a person". At reflection on visual carriers of any concrete functionally complete sense received in the course of knowledge of the natural and man-made phenomena of the universe, only purely formular texts are an ideal format in terms of quantity of the symbols necessary for these purposes. Even the axiomatic format of reflection of the specified meanings demands one-two orders more of symbols, not to mention a verbal format from which the order of magnitude of formular symbols can exceed four in certain cases. Special importance is gained by this fact at reflection on visual carriers of biological and medical knowledge. Физиологическая модель одной из материализованных мыслей человека. Виктория Кондратенко Аннотация. В статье демонстрируется на конкретном примере построение во второй сигнальной системе корректного рефлекторного кольца – физиологической модели одной из материализованных элементарных, либо составных мыслей человека. В качестве инструментария используется функционально полный формальный язык, язык логики предикатов. Методология описана в Теории аксиоматического моделирования Кондратенко(1). Задача, как любая другая проблемная функциональная задача произвольной предметной области, согласно Теории, интерпретируется в математической логике в качестве подлежащей доказательству теоремы. Рефлекторное кольцо является физиологической моделью одной из материализованных элементарных, либо составных, мыслей конкретного человека, так как само кольцо представляет собой фрагмент нейронной сети человека. Логическое произведение концептов знаний, отраженных в концептах №№1 – 7, гарантировано обеспечит построение корректного рефлекторного кольца, обладающего свойством “являющегося физиологической моделью одной из материализованных элементарных, либо составных мыслей этого человека”. При отражении на визуальных носителях любого конкретного функционально завершенного смысла, полученного в процессе познания природных и рукотворных явлений в мироздании, только чисто формульные тексты являются идеальным форматом с точки зрения количества символов, необходимых для этих целей. Даже аксиоматический формат отражения указанных смыслов требует на один-два порядка больше необходимых символов, не говоря уже о вербальном формате, который может превысить в некоторых случаях и четыре прядка формульных символов. Особую важность приобретает этот факт при отражении на визуальных носителях биологических и медицинских знаний.
Category: Artificial Intelligence

[17] viXra:1812.0270 [pdf] submitted on 2018-12-15 06:06:03

Accuracy of Neural Network

Authors: George Rajna
Comments: 45 Pages.

Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25] Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an a-MAZE-ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20]
Category: Artificial Intelligence

[16] viXra:1812.0269 [pdf] submitted on 2018-12-15 08:19:41

Synthesizing Motion-Blurred Images

Authors: George Rajna
Comments: 47 Pages.

Researchers at Google have recently developed a new technique for synthesizing a motion blurred image, using a pair of un-blurred images captured in succession. [26] Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25] Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an a-MAZE-ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20]
Category: Artificial Intelligence

[15] viXra:1812.0268 [pdf] submitted on 2018-12-15 08:45:37

Approximate Computing Approach

Authors: George Rajna
Comments: 49 Pages.

Researchers at Fukuoka University, in Japan, have recently proposed a design methodology for configurable approximate arithmetic circuits. [27] Researchers at Google have recently developed a new technique for synthesizing a motion blurred image, using a pair of un-blurred images captured in succession. [26] Constructing a neural network model for each new dataset is the ultimate nightmare for every data scientist. [25] Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23]
Category: Artificial Intelligence

[14] viXra:1812.0265 [pdf] submitted on 2018-12-15 09:05:39

AI Control of Human Birth

Authors: George Rajna
Comments: 51 Pages.

A group of researchers from MIT have already developed an AI robot that can assist in a labour room. [28] Researchers at Fukuoka University, in Japan, have recently proposed a design methodology for configurable approximate arithmetic circuits. [27] Researchers at Google have recently developed a new technique for synthesizing a motion blurred image, using a pair of un-blurred images captured in succession. [26]
Category: Artificial Intelligence

[13] viXra:1812.0250 [pdf] submitted on 2018-12-14 11:59:51

Aspie96 at IronITA (EVALITA 2018): Irony Detection in Italian Tweets with Character-Level Convolutional RNN

Authors: Valentino Giudice
Comments: 6 Pages.

Irony is characterized by a strong contrast between what is said and what is meant: this makes its detection an important task in sentiment analysis. In recent years, neural networks have given promising results in different areas, including irony detection. In this report, I describe the system used by the Aspie96 team in the IronITA competition (part of EVALITA 2018) for irony and sarcasm detection in Italian tweets.
Category: Artificial Intelligence

[12] viXra:1812.0247 [pdf] submitted on 2018-12-15 05:30:08

Fair Computer-Aided Decisions

Authors: George Rajna
Comments: 44 Pages.

Algorithmic fairness is increasingly important because as more decisions of greater importance are made by computer programs, the potential for harm grows. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an a-MAZE-ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20]
Category: Artificial Intelligence

[11] viXra:1812.0185 [pdf] submitted on 2018-12-10 09:32:52

Machine Learning Design Peptides

Authors: George Rajna
Comments: 45 Pages.

Northwestern University researchers, teaming up with collaborators at Cornell University and the University of California, San Diego, have developed a new way of finding optimal peptide sequences: using a machine-learning algorithm as a collaborator. [28] A team of researchers led by Sanket Deshmukh, assistant professor of chemical engineering, has developed a method to investigate the structures of polymers that are sensitive to external stimuli. [27] In recent experiments studying analogous HYPERLINK "https://phys.org/tags/extreme+waves/" extreme waves of light, researchers have used artificial intelligence to study this problem, and have now determined a probability distribution that preferentially identifies the emergence of rogue waves. [26] Artificial intelligence's potential lies not only in its ability to help improve the educational performance of learners, but also in its capacity to foster respect and understanding between all humans. [25]
Category: Artificial Intelligence

[10] viXra:1812.0141 [pdf] submitted on 2018-12-07 07:58:14

AI Predict Rogue Waves of Light

Authors: George Rajna
Comments: 42 Pages.

In recent experiments studying analogous extreme waves of light, researchers have used artificial intelligence to study this problem, and have now determined a probability distribution that preferentially identifies the emergence of rogue waves. [26] Artificial intelligence's potential lies not only in its ability to help improve the educational performance of learners, but also in its capacity to foster respect and understanding between all humans. [25] The New York Times contacted IBM Research in late September asking for our help to use AI in a clever way to create art for the coming special section on AI. [24] Granting human rights to a computer would degrade human dignity. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17]
Category: Artificial Intelligence

[9] viXra:1812.0124 [pdf] submitted on 2018-12-08 04:29:44

Machine Learning in Biomedical Field

Authors: George Rajna
Comments: 43 Pages.

A team of researchers led by Sanket Deshmukh, assistant professor of chemical engineering, has developed a method to investigate the structures of polymers that are sensitive to external stimuli. [27] In recent experiments studying analogous extreme waves of light, researchers have used artificial intelligence to study this problem, and have now determined a probability distribution that preferentially identifies the emergence of rogue waves. [26] Artificial intelligence's potential lies not only in its ability to help improve the educational performance of learners, but also in its capacity to foster respect and understanding between all humans. [25] The New York Times contacted IBM Research in late September asking for our help to use AI in a clever way to create art for the coming special section on AI. [24] Granting human rights to a computer would degrade human dignity. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18]
Category: Artificial Intelligence

[8] viXra:1812.0094 [pdf] submitted on 2018-12-05 10:06:34

AI Transform Education

Authors: George Rajna
Comments: 40 Pages.

Artificial intelligence's potential lies not only in its ability to help improve the educational performance of learners, but also in its capacity to foster respect and understanding between all humans. [25] The New York Times contacted IBM Research in late September asking for our help to use AI in a clever way to create art for the coming special section on AI. [24] Granting human rights to a computer would degrade human dignity. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [21]
Category: Artificial Intelligence

[7] viXra:1812.0081 [pdf] submitted on 2018-12-04 09:41:03

AI Chip Radiation at CERN

Authors: George Rajna
Comments: 44 Pages.

An ESA-led team subjected Intel's new Myriad 2 artificial intelligence chip to one of the most energetic radiation beams available on Earth. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14]
Category: Artificial Intelligence

[6] viXra:1812.0080 [pdf] submitted on 2018-12-04 10:06:16

AI-Motorized Wheelchair

Authors: George Rajna
Comments: 34 Pages.

A new wheelchair may give people with severe mobility challenges another reason to smile about artificial intelligence—that grin might literally help them control their wheelchair. [22] Now, researchers at Stanford University have devised a new type of artificially intelligent camera system that can classify images faster and more energy efficiently, and that could one day be built small enough to be embedded in the devices themselves, something that is not possible today. [21] Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research. [20] A deep neural network running on an ordinary desktop computer is interpreting highly technical data related to national security as well as—and sometimes better than— today's best automated methods or even human experts. [19] Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. [18] Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13]
Category: Artificial Intelligence

[5] viXra:1812.0079 [pdf] submitted on 2018-12-04 10:20:56

On-Demand Video Dispatch Networks: A Scalable End-to-End Learning Approach

Authors: Damao Yang, Sihan Peng, He Huang, Hongliang Xue
Comments: 11 Pages.

We design a dispatch system to improve the peak service quality of video on demand (VOD). Our system predicts the hot videos during the peak hours of the next day based on the historical requests, and dispatches to the content delivery networks (CDNs) at the previous off-peak time. In order to scale to billions of videos, we build the system with two neural networks, one for video clustering and the other for dispatch policy developing. The clustering network employs autoencoder layers and reduces the video number to a fixed value. The policy network employs fully connected layers and ranks the clustered videos with dispatch probabilities. The two networks are coupled with weight-sharing temporal layers, which analyze the video request sequences with convolutional and recurrent modules. Therefore, the clustering and dispatch tasks are trained in an end-to-end mechanism. The real-world results show that our approach achieves an average prediction accuracy of 17%, compared with 3% from the present baseline method, for the same amount of dispatches.
Category: Artificial Intelligence

[4] viXra:1812.0069 [pdf] submitted on 2018-12-05 00:04:07

Divergence Measure of Intuitionistic Fuzzy Sets

Authors: Fuyuan Xiao
Comments: 9 Pages.

As a generation of fuzzy sets, the intuitionistic fuzzy sets (IFSs) have more powerful ability to represent and deal with the uncertainty of information. The distance measure between the IFSs is still an open question. In this paper, we propose a new distance measure between the IFSs on the basis of the Jensen{ Shannon divergence. The new distance measure of IFSs not only can satisfy the axiomatic de nition of distance measure, but also can discriminate the diference between the IFSs more better. As a result, the new distance measure can generate more reasonable results.
Category: Artificial Intelligence

[3] viXra:1812.0059 [pdf] submitted on 2018-12-03 07:53:03

Dual 8-bit Breakthrough in AI

Authors: George Rajna
Comments: 43 Pages.

IBM Research launched the reduced-precision approach to AI model training and inference with a landmark paper describing a novel dataflow approach for conventional CMOS technologies to rev up hardware platforms by dramatically reducing the bit precision of data and computations. [24] Recent successful applications of deep learning include medical image analysis, speech recognition, language translation, image classification, as well as addressing more specific tasks, such as solving inverse imaging problems. [23] Researchers at Caltech have developed an artificial neural network made out of DNA that can solve a classic machine learning problem: correctly identifying handwritten numbers. [22] Researchers have devised a magnetic control system to make tiny DNA-based robots move on demand—and much faster than recently possible. [21] Humans have 46 chromosomes, and each one is capped at either end by repetitive sequences called telomeres. [20] Just like any long polymer chain, DNA tends to form knots. Using technology that allows them to stretch DNA molecules and image the behavior of these knots, MIT researchers have discovered, for the first time, the factors that determine whether a knot moves along the strand or "jams" in place. [19] Researchers at Delft University of Technology, in collaboration with colleagues at the Autonomous University of Madrid, have created an artificial DNA blueprint for the replication of DNA in a cell-like structure. [18] An LMU team now reveals the inner workings of a molecular motor made of proteins which packs and unpacks DNA. [17] Chemist Ivan Huc finds the inspiration for his work in the molecular principles that underlie biological systems. [16] What makes particles self-assemble into complex biological structures? [15]
Category: Artificial Intelligence

[2] viXra:1812.0054 [pdf] submitted on 2018-12-03 11:32:36

Universal Forecasting Scheme By Professor Ramesh Chandra Bagadi

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has presented a Universal Forecasting Scheme.
Category: Artificial Intelligence

[1] viXra:1812.0053 [pdf] submitted on 2018-12-03 11:40:35

Theoretical Model For Holistic Non Unique Clustering By Professor Ramesh Chandra Bagadi

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has presented the analysis of Theoretical Model For Holistic Non Unique Clustering.
Category: Artificial Intelligence