다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the “Squeeze-and-Excitation” (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251 percent, surpassing the winning entry of 2016 by a relative improvement of ${\sim }$ ∼ 25 percent. Models and code are available at https://github.com/hujie-frank/SENet .

/pdf/squeeze-and-excitation-networks-50icgwqgeu.pdf

Squeeze-and-Excitation Networks

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

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.

/pdf/image-to-image-translation-with-conditional-adversarial-5058t24et2.pdf

Image-to-Image Translation with Conditional Adversarial Networks

Humans and animals make predictions about the rewards they expect to receive in different situations. In formal models of behavior, these predictions are known as "value representations", and they play two very different roles. Firstly, they drive choice: expected values of available options are compared to one another, and the best is selected. Secondly, they support learning: expected values are compared to rewards actually received, and future expectations are updated accordingly. A fundamental unanswered question is whether these different functions are mediated by the same or different neural mechanisms. Here we employ a recently-developed multi-step task for rats that cleanly separates learning from choosing. We address the role of the orbitofrontal cortex (OFC), a key player in value-based cognition. Electrophysiological recordings and optogenetic perturbations indicate that, contrary to prominent theories, the OFC does not directly drive choices. Instead, it supplies value information to a learning process that updates choice mechanisms elsewhere in the brain. This result places important constraints on neural architectures for learning and choosing.

https://www.biorxiv.org/content/biorxiv/early/2018/03/22/245720.full.pdf

Value Representations in Orbitofrontal Cortex Drive Learning, not Choice

Previous research has shown that short-term memory for serial order can be influenced by background knowledge concerning regularities of sequential structure. Specifically, it has been shown that recall is superior for sequences that fit well with familiar sequencing constraints. The authors report a corresponding effect pertaining to serial recall errors. Undergraduate participants performed immediate serial recall on sequences of pseudowords generated on the basis of an artificial grammar. After extensive experience with this material, recall errors displayed a bias toward regularizing responses, response sequences more probable, with respect to the artificial grammar, than the originally presented stimulus sequence. This regularization effect squares well with recent trace redintegration and Bayesian models of serial recall, and appears to represent an analog of the schema-based error patterns observed in other domains of memory. In a classic study of immediate serial recall, Baddeley, Conrad, and Hull (1965) demonstrated that consonant strings were better recalled when they contained high-frequency letter transitions than when they contained low-frequency transitions. This bigram frequency effect (later replicated by Kantowitz, Ornstein, & Schwartz, 1972; Mayzner & Schoenberg, 1964) provides one of numerous demonstrations that short-term memory for serial order can be influenced by background knowledge concerning domain-specific regularities in sequential structure. Even before Baddeley et al.’s (1965) study, Miller and Selfridge (1951) had observed better recall for sequences of words that contained high-frequency word transitions than for sequences containing low-frequency transitions. More recently, a number of studies have shown better recall for nonwords containing high-frequency, versus low-frequency, phoneme sequences (a phonotactic frequency effect; Gathercole, 1995; Gathercole, Frankish, Pickering, & Peaker, 1999; Gathercole, Willis, Emslie, & Baddeley, 1991; Grant et al., 1997; Roodenrys & Hinton, 2002; van Bon & van der Pijl, 1997). In each of these instances, knowledge concerning regularities in sequential structure exerts a clear influence on short-term sequence memory, with highly probable sequences better recalled than less probable ones. In addition to being interesting in its own right, this finding may also have important implications for understanding language acquisition (Storkel, 2001) and for constraining models

OBSERVATIONS Regularization in Short-Term Memory for Serial Order

Conflict monitoring: computational and empirical studies

A sophisticated study of error-related brain potentials in patients with prefrontal lesions addresses how we monitor performance and adjust cognitive control based on task demands.

Anterior cingulate and prefrontal cortex: who's in

The question of whether deep neural networks are good at generalising beyond their immediate training experience is of critical importance for learning-based approaches to AI. Here, we consider tests of out-of-sample generalisation that require an agent to respond to never-seen-before instructions by manipulating and positioning objects in a 3D Unity simulated room. We first describe a comparatively generic agent architecture that exhibits strong performance on these tests. We then identify three aspects of the training regime and environment that make a significant difference to its performance: (a) the number of object/word experiences in the training set; (b) the visual invariances afforded by the agent's perspective, or frame of reference; and (c) the variety of visual input inherent in the perceptual aspect of the agent's perception. Our findings indicate that the degree of generalisation that networks exhibit can depend critically on particulars of the environment in which a given task is instantiated. They further suggest that the propensity for neural networks to generalise in systematic ways may increase if, like human children, those networks have access to many frames of richly varying, multi-modal observations as they learn.

Matthew Botvinick

Papers

Value Representations in Orbitofrontal Cortex Drive Learning, not Choice

OBSERVATIONS Regularization in Short-Term Memory for Serial Order

Conflict monitoring: computational and empirical studies

Anterior cingulate and prefrontal cortex: who's in

Environmental drivers of systematicity and generalization in a situated agent