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

We report a novel evanescent-coupled germanium electro-absorption modulator with a small active area of 16 µm 2 giving an extinction ratio of ∼10 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 This high speed low power electro-absorption modulator is extremely useful in developing CMOS-compatible integrated silicon photonics circuits for short reach optical communication links

Germanium electro-absorption modulator for power efficient optical links

In both SMT (statistical machine translation) and NMT (neural machine translation), training data often varies in source, theme and genre. It is less likely that the training data and texts in practical translation fall into a same domain, leading to a sub-optimal performance. Domain adaptation is to address such problems. Existing domain adaptive approach in machine translation employs topic model to obtain topic information. However, thus domain labels can be very much limited to in-domain and out-of-domain, when dividing topics into two types, without any more specific labels. We propose a novel domain adaptive approach to annotate Chinese sentences with CLCN (Chinese Library Classification Number) as the domain labels. We design a deep fusion model of neural network to combine two annotating models, including one applying a domain knowledge base built on thesis keywords and Chinese Scientific and Technical Vocabulary System, and the other applying deep learning method based on a CNN. Then, we have the fused domain annotator to filter the training data of NMT according to the test data. After running two predefined domain test sets on a NMT system trained by only partial of the original training data, we achieve an average 1.3 BLEU score improvement (5.4% relative), which demonstrates the feasibility and validity of proposed approach.

Combining Domain Knowledge and Deep Learning Makes NMT More Adaptive

In this work, Al/Al2O3 nanocomposite thin film is deposited on Si substrate by radio frequency sputtering to form a metal-insulator-semiconductor structure. It is found that the current conduction at low fields is greatly enhanced with temperature. The current increase can be attributed to the decrease in the tunneling resistance and/or the formation of some tunneling paths due to the release of some measurement-induced charges trapped in the thin film as a result of increase in the temperature. The current conduction evolves with a trend toward a three-dimensional transport as the temperature increases.

/pdf/temperature-dependence-of-current-transport-in-al-al2o3-2jje61ovq3.pdf

Temperature dependence of current transport in Al/Al2O3 nanocomposite thin films

The use of portable electronic devices like smart phones and tablets results in high demand for more function, smaller dimensions and reduced power consumption requirements. To meet these challenges, electronic package design uses thinner chips with fine pitch bumping. There has been active development in 2.5D and 3D IC packages with through silicon via (TSV). Tighter interconnection in addition to the increased density in the circuit in 2.5D and 3D IC systems provide higher performance with lower power consumption [1]. In addition, the down scaling trend of CMOS technology beyond 28nm node requires smaller chip size for a given input/output (I/O) count, pushing the interconnect pitch smaller and smaller. When the bump pitch is less than 50μm and the gap between the die and substrate is lesser than 25μm, traditional capillary underfill (CUF) material is likely to require a vacuum or pressure assisted process to pull the underfill and fill the gaps without any voids [2]. The narrower gap also makes flux cleaning after reflow more challenging. The development of wafer-level underfills can bring the financial benefits of wafer-level processing to flip chip assembly and packaging. The flip chip assembly is the application of underfill at the wafer level, eliminating the dispense, flow, and separate cure steps associated with assemblies utilizing capillary-flow underfills. In addition, the wafer-level material should include fluxing capabilities similar to no-flow underfills [3]. In this paper we describe the assembly process and challenges of the 100μm thin 2.5D TSV Si interposer to the test substrate and the assembly of three different test chips onto TSV Si interposer using capillary underfill and WL-UF (NST series from Nissan Chemical Industries, Ltd). The TSV Si interposer provides high density multilevel routing on the frontside of the wafers and through-silicon vias (TSVs) to connect the frontside metallization with the backside metallization for connection to the PCB [4].

https://ieeexplore.ieee.org/iel7/6732180/6745670/06745756.pdf

Process integration of solder bumps and Cu pillar microbumps on 2.5D fine pitch TSV interposer

Enhanced near-infrared photoluminescence (PL) from sulfur-related isoelectronic luminescent centers in silicon was observed from thermally quenched sulfur-implanted silicon in which additional copper or silver ions had been coimplanted. The PL from the sulfur and copper coimplanted silicon peaked between 70 and 100K and persisted to 260K. This result strongly supports the original conjecture from the optical detection of magnetic resonance studies that the strong PL from sulfur-doped silicon comes from S–Cu isoelectronic complexes [Frens et al., Phys. Rev. B 46, 12316 (1992); Mason et al., ibid. 58, 7007 (1998).].

/pdf/enhanced-near-infrared-photoluminescence-from-isoelectronic-dcn0qtbknf.pdf

Liang Ding

Papers

Germanium electro-absorption modulator for power efficient optical links

Combining Domain Knowledge and Deep Learning Makes NMT More Adaptive

Temperature dependence of current transport in Al/Al2O3 nanocomposite thin films

Process integration of solder bumps and Cu pillar microbumps on 2.5D fine pitch TSV interposer

Enhanced near-infrared photoluminescence from isoelectronic luminescent centers in sulfur-implanted silicon with copper or silver coimplantation