Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

https://link.springer.com/content/pdf/10.1007/s10462-020-09825-6.pdf

A survey of the recent architectures of deep convolutional neural networks

The rich and diverse phytoconstituents of wheatgrass have established it as a natural antioxidant and detoxifying agent. The anti-inflammatory potential of wheatgrass has been studied extensively. However, the neuroprotective potential of wheatgrass has not been studied in depth. In this study, we investigated the neuroprotective response of wheatgrass against age-related scopolamine-induced amnesia in mice. Scopolamine is an established anticholinergic drug that demonstrates the behavioural and molecular characteristics of Alzheimer's disease. In the current study, wheatgrass extracts (prepared from 5 and 7 day old plantlets) were administered to scopolamine-induced memory deficit mice. The Morris water maze (MWM) and Y-maze tests demonstrated that wheatgrass treatment improves the behavior and simultaneously enhances the memory of amnesic mice. We further evaluated the expression of neuroinflammation related genes and proteins in the hippocampal region of mice. Wheatgrass significantly upregulated the mRNA and protein expression of neuroprotective markers such as BDNF and CREB in scopolamine-induced mice. Simultaneously, wheatgrass also downregulated the expression of inflammatory markers such as TNF-α and tau genes in these mice. The treatment of scopolamine-induced memory impaired mice with wheatgrass resulted in an elevation in the level of the phosphorylated form of ERK and Akt proteins. Wheatgrass treatment of mice also regulated the phosphorylation of tau protein and simultaneously prevented its aggregation in the hippocampal region of the brain. Overall, this study suggests the therapeutic potential of wheatgrass in the treatment of age-related memory impairment, possibly through the involvement of ERK/Akt-CREB-BDNF pathway and concomitantly ameliorating the tau-related pathogenesis.

Wheatgrass extract imparts neuroprotective actions against scopolamine-induced amnesia in mice.

Modulation of aluminum sensing properties of a sulphone group containing chemosensor and its biological applications.

In recent years, Word-level Sign Language Recognition (WSLR) research has gained popularity in the computer vision community, and thus various approaches have been proposed. Among these approaches, the method using I3D network achieves the highest recognition accuracy on large public datasets for WSLR. However, the method with I3D only utilizes appearance information of the upper body of the signers to recognize sign language words. On the other hand, in WSLR, the information of local regions, such as the hand shape and facial expression, and the positional relationship among the body and both hands are important. Thus in this work, we utilized local region images of both hands and face, along with skeletal information to capture local information and the positions of both hands relative to the body, respectively. In other words, we propose a novel multi-stream WSLR framework, in which a stream with local region images and a stream with skeletal information are introduced by extending I3D network to improve the recognition accuracy of WSLR. From the experimental results on WLASL dataset, it is evident that the proposed method has achieved about 15% improvement in the Top-1 accuracy than the existing conventional methods.

Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions

Recognition of Bangia handwritten text is difficult due to its complex nature of having modifiers and headlines features. This paper presents a comparative study of different features namely LGH (Local Gradient of Histogram), PHOG (Pyramid Histogram of Oriented Gradient), GABOR, G-PHOG (Combined GABOR and PHOG) and profile feature by Marti­ Bunke when applied in middle zone recognition of Bangia words using Hidden Markov Model (HMM) based framework. For this purpose, a zone segmentation method is applied to extract the busy (middle) zones of handwritten words and features are extracted from the middle zone. The system has been tested on a sufficiently large and variation-rich dataset consisting of 11,253 training and 3,856 testing data. From the experiment, it has been noted that PHOG feature outperforms other features in middle zone recognition. Since PHOG feature outperform others, we use this feature for full word recognition, For this purpose initially upper and lower zone components are recognized by PHOG features and SVM classifier. Finally, the zone-wise results are combined by the context information of the corresponding components in each zone to obtain the word level recognition.

Partha Pratim Roy

Papers

Wheatgrass extract imparts neuroprotective actions against scopolamine-induced amnesia in mice.

Modulation of aluminum sensing properties of a sulphone group containing chemosensor and its biological applications.

Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions

Features for Handwritten

Antibacterial activity studies of ZnO nanostructures with different morphologies against E. coli and S. aureus