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

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Macrophage Development Rewritten Macrophages provide protection against a wide variety of infections and critically shape the inflammatory environment in many tissues. These cells come in many flavors, as determined by differences in gene expression, cell surface phenotype and specific function. Schulz et al. (p. 86, published online 22 March) investigated whether adult macrophages all share a common developmental origin. Immune cells, including most macrophages, are widely thought to arise from hematopoietic stem cells (HSCs), which require the transcription factor Myb for their development. Analysis of Myb-deficient mice revealed that a population of yolk-sac–derived, tissue-resident macrophages was able to develop and persist in adult mice in the absence of HSCs. Importantly, yolk sac–derived macrophages also contributed substantially to the tissue macrophage pool even when HSCs were present. In mice, a population of tissue-resident macrophages arises independently of bone marrow–derived stem cells. Macrophages and dendritic cells (DCs) are key components of cellular immunity and are thought to originate and renew from hematopoietic stem cells (HSCs). However, some macrophages develop in the embryo before the appearance of definitive HSCs. We thus reinvestigated macrophage development. We found that the transcription factor Myb was required for development of HSCs and all CD11bhigh monocytes and macrophages, but was dispensable for yolk sac (YS) macrophages and for the development of YS-derived F4/80bright macrophages in several tissues, such as liver Kupffer cells, epidermal Langerhans cells, and microglia—cell populations that all can persist in adult mice independently of HSCs. These results define a lineage of tissue macrophages that derive from the YS and are genetically distinct from HSC progeny.

A lineage of myeloid cells independent of Myb and hematopoietic stem cells

Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

MicroRNAs are involved in many critical cellular activities through binding to their mRNA targets, for example, in cell proliferation, differentiation, death, growth control, and developmental timing. Prediction of microRNA targets can assist in efficient experimental investigations on the functional roles of these small noncoding RNAs. Their accurate prediction, however, remains a challenge due to the limited understanding of underlying processes in recognizing microRNA targets. In this article, we introduce an algorithm that aims at not only predicting microRNA targets accurately but also assisting in vivo experiments to understand the mechanisms of targeting. The algorithm learns a unique hypothesis for each possible mechanism of microRNA targeting. These hypotheses are utilized to build a superior target predictor and for biologically meaningful partitioning of the data set of microRNA-target duplexes. Experimentally verified features for recognizing targets that incorporated in the algorithm enable the establishment of hypotheses that can be correlated with target recognition mechanisms. Our results and analysis show that our algorithm outperforms state-of-the-art data-driven approaches such as deep learning models and machine learning algorithms and rule-based methods for instance miRanda and RNAhybrid. In addition, feature selection on the partitions, provided by our algorithm, confirms that the partitioning mechanism is closely related to biological mechanisms of microRNA targeting. The resulting data partitions can potentially be used for in vivo experiments to aid in the discovery of the targeting mechanisms.

/pdf/human-microrna-target-prediction-via-multi-hypotheses-h00ibklhuf.pdf

Human MicroRNA Target Prediction via Multi-Hypotheses Learning.

We report a novel evanescent-coupled germanium electro-absorption modulator with a small active area of 16 µm2 giving an extinction ratio of ∼ dB for a wavelength range of 1580–1610 nm. In addition, monolithic integration of both evanescent-coupled Ge electro-absorption modulator and Ge p-i-n photodetector is demonstrated for the first time.

High speed energy-efficient germanium electro-absorption modulator featuring monolithic integration with germanium p-i-n photodetector

We prove that, for many parameterized problems in the class FPT, the existence of polynomial kernels implies the collapse of the W-hierarchy (i.e., W[P] = FPT). The collapsing results are also extended to assumed exponential kernels for problems in the class FPT. In particular, we establish a close relationship between polynomial (and exponential) kernelizability and the existence of sub-exponential time algorithms for a spectrum of circuit satisfiability problems in FPT. To the best of our knowledge, this is the first work that connects hardness for polynomial kernelizability of FPT problems to parameterized intractability. Our work also offers some new insights into the class FPT.

Polynomial kernels collapse the W-hierarchy.

BSR (back-side via revealing) of TSV wafers after temporary bonding is very critical process within 3D integration. Adhesive performances of the temporary bonding were inspected step by step within BSR process by IR inspection system. We found out the delamination happened after backside dielectric deposition at specific pattern. The delamination area and structure were inspected and reported in this paper.

http://ieeexplore.ieee.org/iel7/7005556/7028249/07028277.pdf

The study of adhesive performance within backside-via revealing

Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT) models by reducing the complexity of the raw data with an autoregressive teacher model. In this study, we empirically show that as a side effect of this training, the lexical choice errors on low-frequency words are propagated to the NAT model from the teacher model. To alleviate this problem, we propose to expose the raw data to NAT models to restore the useful information of low-frequency words, which are missed in the distilled data. To this end, we introduce an extra Kullback-Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data. Experimental results across language pairs and model architectures demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by reducing the lexical choice errors on low-frequency words. Encouragingly, our approach pushes the SOTA NAT performance on the WMT14 English-German and WMT16 Romanian-English datasets up to 27.8 and 33.8 BLEU points, respectively. The source code will be released.

Liang Ding

Papers

Human MicroRNA Target Prediction via Multi-Hypotheses Learning.

High speed energy-efficient germanium electro-absorption modulator featuring monolithic integration with germanium p-i-n photodetector

Polynomial kernels collapse the W-hierarchy.

The study of adhesive performance within backside-via revealing

Understanding and Improving Lexical Choice in Non-Autoregressive Translation