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

With the development of sensor technology, Electroencephalography (EEG) has been a popular area of interest in recent years. Also, a great degree of changes with age have been found in face, voice, fingerprint or other physiological based biometric identifier systems. The distinct characteristic of neuro-signals have focused the attention of research community towards building a user identification system which is resistant to vulnerable attacks. However, the permanence issue of the brain signals has been studied sporadically. In this paper, we investigate the robustness of EEG signals to address the longitudinal stability issue and its effectiveness in user identification systems. Discrete Wavelet Transform (DWT) signal decomposition technique has been applied to extract Alpha-band waves. Further, two statistical features, namely, Root Mean Square (RMS) and Integrated EEG (IEEG) have been calculated for the band waves. Person identification has been performed using three well-known classification techniques, namely, Random Forest (RF), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). EEG data from 10 users has been recorded in 6 different sessions within a period of 6 months. Finally, a decision fusion scheme, majority voting has been applied to boost the system performance. An average accuracy of 80% has been recorded using decision fusion. The results highlight a significant amount of variations across sessions, which shows various factors could effect the state of the mind with temporal With the development of sensor technology, Electroencephalography (EEG) has been a popular area of interest in recent years. Also, a great degree of changes with age have been found in face, voice, fingerprint or other physiological based biometric identifier systems. The distinct characteristic of neuro-signals have focused the attention of research community towards building a user identification system which is resistant to vulnerable attacks. However, the permanence issue of the brain signals has been studied sporadically. In this paper, we investigate the robustness of EEG signals to address the longitudinal stability issue and its effectiveness in user identification systems. Discrete Wavelet Transform (DWT) signal decomposition technique has been applied to extract Alpha-band waves. Further, two statistical features, namely, Root Mean Square (RMS) and Integrated EEG (IEEG) have been calculated for the band waves. Person identification has been performed using three well-known classification techniques, namely, Random Forest (RF), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). EEG data from 10 users has been recorded in 6 different sessions within a period of 6 months. Finally, a decision fusion scheme, majority voting has been applied to boost the system performance. An average accuracy of 80% has been recorded using decision fusion. The results highlight a significant amount of variations across sessions, which shows various factors could effect the state of the mind with temporal distance. Keywords: EEG, Biometrics, Ageing, Random Forest, Majority Voting.

Impact of Ageing on EEG Based Biometric Systems

The black pepper, most commonly used in Indian cuisines for ages, is considered as "king of spices." The present study evaluates the anticancer potential of black pepper and its main constituent, i.e. alkaloid piperine, against human leukemia cell line, K-562 cells. Gas chromatography-mass spectrometry (GC-MS) analysis confirmed the presence of piperine in black pepper extract. The methanolic extract of black pepper (BP-M) and pure piperine (PIP) showed a strong cytotoxic effect against this cell line. Both BP-M and PIP generated apoptotic bodies in K-562 cells and caused nuclear condensation as visualized by fluorescent microscopy, which was further confirmed by flow cytometry analysis. BP-M and PIP also generated reactive oxygen species in K-562 cells as established by flow cytometry. The translation of Bax, caspase-3 and caspase-9 genes was found to be upregulated with subsequent downregulation of Bcl-2 gene. The anti-proliferative effect of both BP-M and PIP was also observed by trypan blue staining and was further confirmed by the downregulated expression of proliferating cell nuclear antigen (PCNA). The molecular docking studies showed the binding of PIP with PCNA and Bcl-2 and supported the in vitro findings. The docking studies also proposed the binding of PIP to ADP binding pocket of Apaf-1 protein. Taken together, these findings signify the anticancer potential of both black pepper and PIP, thus proposing black pepper as a potent nutraceutical for preventing the progression of chronic myeloid leukemia.

Black pepper and piperine induce anticancer effects on leukemia cell line.

Mitochondria perform diverse functions in the cell and their roles during processes such as cell survival, differentiation, and migration are increasingly being appreciated. Mitochondrial and actin cytoskeletal networks not only interact with each other, but this multifaceted interaction shapes their functional dynamics. The interrelation between mitochondria and the actin cytoskeleton extends far beyond the requirement of mitochondrial ATP generation to power actin dynamics, and impinges upon several major aspects of cellular physiology. Being situated at the hub of cell signaling pathways, mitochondrial function can alter the activity of actin regulatory proteins and therefore modulate the processes downstream of actin dynamics such as cellular migration. As we will discuss, this regulation is highly nuanced and operates at multiple levels allowing mitochondria to occupy a strategic position in the regulation of migration, as well as pathological events that rely on aberrant cell motility such as cancer metastasis. In this review, we summarize the crosstalk that exists between mitochondria and actin regulatory proteins, and further emphasize on how this interaction holds importance in cell migration in normal as well as dysregulated scenarios as in cancer.

Mitochondria–actin cytoskeleton crosstalk in cell migration

Automatic extraction of date patterns from handwritten document involves difficult challenges due to writing styles of different individuals, touching characters and confusion among identification of alphabets and digits In this paper, we propose a framework for retrieval of date patterns from handwritten documents The method first classifies word components of each text line into month and non-month class using word level feature Next, non-month words are segmented into individual components and classified into one of alphabet, digit or punctuation Using this information of word and character level components, the date patterns are searched first using voting approach and then we detect the candidate lines for numeric and semi-numeric date using regular expression Gradient based features and Support Vector Machine (SVM) are used in our work for classification The experiment is performed on handwritten dataset and we have obtained encouraging results from it

Date field extraction in handwritten documents

This study investigated the protective effect of extracts from flowers of Brassica oleracea L. var. italica Plenck on type 2 diabetes mellitus and its associated disorders. Three different doses of each extract (petroleum ether, ethanol, and aqueous) were administered orally for 42 days. Biochemical parameters, behavioral studies, and histological studies were measured at different periods. Mortality was found to be nil up to 2,000 mg/kg. Statistically significant (P<0.001) improvement in serum glucose level was observed in the groups receiving 400 mg/kg of petroleum ether, aqueous, or ethanol extracts compared with the negative control group. Insulin level was decreased by aqueous extracts, whereas lipid profiles were improved by aqueous and ethanol extracts. A reduction in transfer latency was observed in treatments of all three extract types. Ethanol extract treatment (400 mg/kg) showed maximum percentage inhibition in a lipid peroxidation assay. Additionally, the aqueous and ethanol extract treatments markedly reduced tumor necrosis factor-α, interleukin-6, and glycosylated hemoglobin levels. Histological results showed that high doses of extracts alleviated the damages induced by type 2 diabetes mellitus in various organs and bones. Based on the results of this study, it can be concluded that B. oleracea has the potential to alleviate type 2 diabetes mellitus.

https://www.pnfs.or.kr/journal/download_pdf.php?doi=10.3746/pnf.2022.27.1.50

Partha Pratim Roy

Papers

Impact of Ageing on EEG Based Biometric Systems

Black pepper and piperine induce anticancer effects on leukemia cell line.

Mitochondria–actin cytoskeleton crosstalk in cell migration

Date field extraction in handwritten documents

Brassica oleracea Extracts Prevent Hyperglycemia in Type 2 Diabetes Mellitus