Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely upto-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

Neural Networks And Learning Machines

1980 Preface * 1999 Preface * 1999 Acknowledgements * Introduction * 1 Circular Logic * 2 Phase Singularities (Screwy Results of Circular Logic) * 3 The Rules of the Ring * 4 Ring Populations * 5 Getting Off the Ring * 6 Attracting Cycles and Isochrons * 7 Measuring the Trajectories of a Circadian Clock * 8 Populations of Attractor Cycle Oscillators * 9 Excitable Kinetics and Excitable Media * 10 The Varieties of Phaseless Experience: In Which the Geometrical Orderliness of Rhythmic Organization Breaks Down in Diverse Ways * 11 The Firefly Machine 12 Energy Metabolism in Cells * 13 The Malonic Acid Reagent ('Sodium Geometrate') * 14 Electrical Rhythmicity and Excitability in Cell Membranes * 15 The Aggregation of Slime Mold Amoebae * 16 Numerical Organizing Centers * 17 Electrical Singular Filaments in the Heart Wall * 18 Pattern Formation in the Fungi * 19 Circadian Rhythms in General * 20 The Circadian Clocks of Insect Eclosion * 21 The Flower of Kalanchoe * 22 The Cell Mitotic Cycle * 23 The Female Cycle * References * Index of Names * Index of Subjects

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/596B270197FE27DE5FABB598B6210622/S0025557200150892a.pdf/div-class-title-span-class-italic-the-geometry-of-biological-time-span-by-a-t-winfree-pp-544-dm68-corrected-second-printing-1990-isbn-3-540-52528-9-springer-div.pdf

The geometry of biological time , by A. T. Winfree. Pp 544. DM68. Corrected Second Printing 1990. ISBN 3-540-52528-9 (Springer)

We propose a novel approach for image segmentation on the basis of a neural oscillator network. Unlike previous approaches, weight adaptation is introduced during segmentation for noise removal and feature preservation. Moreover, a logarithmic grouping rule is proposed to facilitate grouping of oscillators representing pixels with coherent properties. We show that our weight adaptation scheme is insensitive to termination times, and the resulting dynamic weights in a wide range of iterations lead to the same segmentation results. A computer algorithm derived from oscillatory dynamics is applied to synthetic and real images, and simulation results show that the algorithm yields favorable segmentation results.

Image segmentation by weight adaptation and oscillatory correlation

https://www.nature.com/articles/s41419-022-05204-x.pdf

Correction to: BET bromodomain inhibition rescues PD-1-mediated T-cell exhaustion in acute myeloid leukemia

Recent years have brought considerable advances to our ability to increase intelligibility through deep-learning-based noise reduction, especially for hearing-impaired (HI) listeners. In this study, intelligibility improvements resulting from a current algorithm are assessed. These benefits are compared to those resulting from the initial demonstration of deep-learning-based noise reduction for HI listeners ten years ago in Healy, Yoho, Wang, and Wang [(2013). J. Acoust. Soc. Am. 134, 3029-3038]. The stimuli and procedures were broadly similar across studies. However, whereas the initial study involved highly matched training and test conditions, as well as non-causal operation, preventing its ability to operate in the real world, the current attentive recurrent network employed different noise types, talkers, and speech corpora for training versus test, as required for generalization, and it was fully causal, as required for real-time operation. Significant intelligibility benefit was observed in every condition, which averaged 51% points across conditions for HI listeners. Further, benefit was comparable to that obtained in the initial demonstration, despite the considerable additional demands placed on the current algorithm. The retention of large benefit despite the systematic removal of various constraints as required for real-world operation reflects the substantial advances made to deep-learning-based noise reduction.

Progress made in the efficacy and viability of deep-learning-based noise reduction.

Acute myeloid leukemia (AML) is a clinically, morphologically, and genetically heterogeneous group of malignancies characterized by a wide range of genomic alterations responsible for defective regulation of the differentiation and self-renewal programs of hematopoietic stem cells. Here, we report a 4-month-old boy who had acute onset with leukocytosis and abdominal mass. The morphological analysis of bone marrow (BM) smear revealed extremely marrow hyperplasia, large quantities of immature cells, and primary and immature monocytic hyperplasia accounting for 57.5% of nucleated cells. The chromosome karyotype of the case was complex, representing 48, XY, +13, +19[12]/48, idem, del (p12)[8]. After RNAs sequencing, a mutation (c.346G > A, p.G116S) of the GNB1 gene was detected and localized to the mutational hotspot in Exon 7. Meanwhile, the other three mutations were identified by next-generation sequencing (NGS) and whole-exome sequencing (WES) of DNA from the BM aspirate and oral swab, including BCORL1 mutation [c.2632A > G, p.S878G, mutation allele frequency (VAF): 99.95%], SH2B3 mutation (c.1606G > A, p.A536T, VAF: 51.17%), and KMT2D mutation (c.11124C > G, p.S3708R, VAF: 48.95%). BCORL1 mutations have been associated with the pathogenesis of AML, whereas other mutations have rarely been previously reported in pediatric AML. The patient did not undergo the combination chemotherapy and eventually died of respiratory failure. In conclusion, the concurrence of BCORL1, GNB1, SH2B3, and KMT2D mutations may be a mutationally detrimental combination and contribute to disease progression.

/pdf/bcorl1-s878g-gnb1-g116s-sh2b3-a536t-and-kmt2d-s3708r-1gr2kooc.pdf

BCORL1 S878G, GNB1 G116S, SH2B3 A536T, and KMT2D S3708R tetramutation co-contribute to a pediatric acute myeloid leukemia: Case report and literature review

Monaural speech separation is a very challenging problem. Recent studies utilize supervised learning methods to estimate the ideal binary mask (IBM) to solve the problem. In a supervised learning framework, the issue of generalization to conditions different from those used in training is paramount. This paper describes methods that require only a small training corpus but can generalize to unseen conditions. The system utilizes support vector machines to learn classification cues and then employs a rethresholding method to estimate the IBM. A distribution fitting method is used to address unseen signal-to-noise ratio conditions and an iterative voice activity detection is used to address unseen noise conditions. Systematic evaluations show that the proposed approach produces high quality IBM estimates under unseen conditions.

DeLiang Wang

Papers

Image segmentation by weight adaptation and oscillatory correlation

Correction to: BET bromodomain inhibition rescues PD-1-mediated T-cell exhaustion in acute myeloid leukemia

Progress made in the efficacy and viability of deep-learning-based noise reduction.

BCORL1 S878G, GNB1 G116S, SH2B3 A536T, and KMT2D S3708R tetramutation co-contribute to a pediatric acute myeloid leukemia: Case report and literature review

On generalization of classification based speech separation