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

Hybrid nanoparticles combining the core of a solid lipid nanoparticle (SLN) system and chitosan (CS) shell were developed for the delivery of hydrophobic curcumin (cur). The curcumin loaded CS-SLNs nanoparticles were formulated by an amalgamation of double-emulsion evaporation and ionic gelation technique for stronger linkage between CS and SLN. The unmodified SLNs and surface modified core-shell CS-SLNs were characterized and compared for particle size, morphology, surface chemistry, and drug release. The CS-SLNs were found to be spherical nano-sized particles with the SLN core acting as a drug depot and outer chitosan shell providing stability and the controlled release of curcumin. Potential application in cancer drug delivery was evaluated by determination of in vitro cytotoxicity and apoptotic effect of encapsulated curcumin on MCF-7 cells. The results suggest that the prepared core-shell nanoparticles are promising delivery systems for poorly soluble drugs with low bioavailability.

Surface grafted core-shell chitosan-modified solid lipid nanoparticles: characterization and application in hydrophobic drug delivery

4D Printing in Biomedical Engineering: a State-of-the-Art Review of Technologies, Biomaterials, and Application

The feature fusion of the multi-scale features plays a significant role in localizing text instances of different sizes in the scene text detection (STD) paradigm. The existing approaches are not sufficient to tackle the issues of multi-scale text; consequently, their performance also varies with the text size. Here, we propose a gated multi-scale input feature fusion (GMIF) approach to overcome this issue in STD. The GMIF generates the local features from down-scaled input images and propagates these features from low resolution to the higher resolution global features through a gated recurrent unit-like mechanism. The consistent performance of the GMIF is validated with different text instance sizes of the test-set of the Total-text dataset. The GMIF obtained the performance in range (Precision 88.554-89.106, Recall 85.452-85.790, and f-measures 87.072 - 87.417) with marginal deviation, whereas the current state-of-the-art method, DBNet++, acquired in range (Precision 73.005-82.666, Recall 80.912-87.274, and f-measures 76.755 - 84.183) with significant deviation. Besides this, GMIF also achieved the best performance (f-measures) over ICDAR 2015 (as 88.0), Total-Text (as 87.4), and the second-best over the MSRA-TD500 (as 85.2) dataset. We have conducted an ablation study to show the impact of different components of the GMIF on the STD tasks, which shows the effectiveness of the overall GMIF approach.

GMIF: A Gated Multiscale Input Feature Fusion Scheme for Scene Text Detection

Leukocyte recruitment witnesses an orchestrated complex formation between the chemokines and their molecular partners. CCL2 chemokine that regulates monocyte trafficking is a worthwhile system from the pharmaceutical perspective. In the current study, four major catechins (EC/EGC/ECG/EGCG) were assessed for their inhibitory potential against CCL2-regulated monocyte/macrophage recruitment. Interestingly, catechins with the gallate moiety (ECG/EGCG) could only attenuate the CCL2-induced macrophage migration. These molecules specifically bound to CCL2 on a pocket comprising the N-terminal, β0-sheets, and β3-sheets, and the binding affinity of ECGC (Kd = 22 ± 4 μM) is ∼4 times higher than that of the ECG complex (Kd = 85 ± 6 μM). MD simulation analysis evidenced that the molecular specificity/stability of CCL2-catechin complexes is regulated by multiple factors, including stereospecificity, number of hydroxyl groups on the annular ring-B, the positioning of the carbonyl group, and the methylation of the galloyl ring. Further, a significant overlap on the binding surface of CCL2 for EGCG/ECG and receptor interactions as evidenced from NMR data provided the rationale for the observed inhibition of macrophage migration in response to EGCG/ECG binding. In summary, these galloylated epicatechins can be considered as potent protein-protein interaction (PPI) inhibitors that regulate CCL2-directed leukocyte recruitment for resolving inflammatory/immunomodulatory disorders.

Gallate Moiety of Catechin Is Essential for Inhibiting CCL2 Chemokine-Mediated Monocyte Recruitment.

The performance of text-to-speech (TTS) systems heavily depends on spectrogram to waveform generation, also known as the speech reconstruction phase. The time required for the same is known as synthesis delay. In this paper, an approach to reduce speech synthesis delay has been proposed. It aims to enhance the TTS systems for real-time applications such as digital assistants, mobile phones, embedded devices, etc. The proposed approach applies Fast Griffin Lim Algorithm (FGLA) instead Griffin Lim algorithm (GLA) as vocoder in the speech synthesis phase. GLA and FGLA are both iterative, but the convergence rate of FGLA is faster than GLA. The proposed approach is tested on LJSpeech, Blizzard and Tatoeba datasets and the results for FGLA are compared against GLA and neural Generative Adversarial Network (GAN) based vocoder. The performance is evaluated based on synthesis delay and speech quality. A 36.58% reduction in speech synthesis delay has been observed. The quality of the output speech has improved, which is advocated by higher Mean opinion scores (MOS) and faster convergence with FGLA as opposed to GLA.

Partha Pratim Roy

Papers

Surface grafted core-shell chitosan-modified solid lipid nanoparticles: characterization and application in hydrophobic drug delivery

4D Printing in Biomedical Engineering: a State-of-the-Art Review of Technologies, Biomaterials, and Application

GMIF: A Gated Multiscale Input Feature Fusion Scheme for Scene Text Detection

Gallate Moiety of Catechin Is Essential for Inhibiting CCL2 Chemokine-Mediated Monocyte Recruitment.

Fast Griffin Lim based Waveform Generation Strategy for Text-to-Speech Synthesis