There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.

/pdf/the-neural-basis-of-decision-making-2p5agsty56.pdf

The Neural Basis of Decision Making

Recipes for the linear analysis of EEG.

When does the brain know that a decision is difficult to make? How does decision difficulty affect the allocation of neural resources and timing of constituent cortical processing? Here, we use single-trial analysis of electroencephalography (EEG) to identify neural correlates of decision difficulty and relate these to neural correlates of decision accuracy. Using a cued paradigm, we show that we can identify a component in the EEG that reflects the inherent task difficulty and not simply a correlation with the stimulus. We find that this decision difficulty component arises ≈220 ms after stimulus presentation, between two EEG components that are predictive of decision accuracy [an “early” (170 ms) and a “late” (≈300 ms) component]. We use these results to develop a timing diagram for perceptual decision making and relate the component activities to parameters of a diffusion model for decision making.

/pdf/neural-representation-of-task-difficulty-and-decision-making-3rp1upm347.pdf

Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram.

Single and multi-unit recordings in primates have identified spatially localized neuronal activity correlating with an animal's behavioral performance. Due to the invasive nature of these experiments, it has been difficult to identify such correlates in humans. We report the first non-invasive neural measurements of perceptual decision making, via single-trial EEG analysis, that lead to neurometric functions predictive of psychophysical performance for a face versus car categorization task. We identified two major discriminating components. The earliest correlating with psychophysical performance was consistent with the well-known face-selective N170. The second component, which was a better match to the psychometric function, did not occur until at least 130 ms later. As evidence for faces versus cars decreased, onset of the later, but not the earlier, component systematically shifted forward in time. In addition, a choice probability analysis indicated strong correlation between the neural responses of the later component and our subjects' behavioral judgements. These findings demonstrate a temporal evolution of component activity indicative of an evidence accumulation process which begins after early visual perception and has a processing time that depends on the strength of the evidence.

/pdf/temporal-characterization-of-the-neural-correlates-of-25r8n6f5s1.pdf

Temporal Characterization of the Neural Correlates of Perceptual Decision Making in the Human Brain

Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. This review focuses on several advances in the state of the art that have shown promise in improving detection, diagnosis, and therapeutic monitoring of disease. Key in the advancement has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review describes recent developments in machine learning, focusing on supervised and unsupervised linear methods and Bayesian inference, which have made significant impacts in the detection and diagnosis of disease in biomedicine. We describe the different methodologies and, for each, provide examples of their application to specific domains in biomedical diagnostics.

https://www.annualreviews.org/doi/pdf/10.1146/annurev.bioeng.8.061505.095802

Machine Learning for Detection and Diagnosis of Disease

A fundamental feature of how we make decisions is that our responses are variable in the choices we make and the time it takes to make them. This makes it impossible to determine, for a single trial of an experiment, the quality of the evidence on which a decision is based. Even for stimuli from a single experimental condition, it is likely that stimulus and encoding differences lead to differences in the quality of evidence. In the research reported here, with a simple "face"/"car" perceptual discrimination task, we obtained late (decision-related) and early (stimulus-related) single-trial EEG component amplitudes that discriminated between faces and cars within and across conditions. We used the values of these amplitudes to sort the response time and choice within each experimental condition into more-face-like and less-face-like groups and then fit the diffusion model for simple decision making (a well-established model in cognitive psychology) to the data in each group separately. The results show that dividing the data on a trial-by-trial basis by using the late-component amplitude produces differences in the estimates of evidence used in the decision process. However, dividing the data on the basis of the early EEG component amplitude or the times of the peak amplitudes of either component did not index the information used in the decision process. The results we present show that a single-trial EEG neurophysiological measure for nominally identical stimuli can be used to sort behavioral response times and choices into those that index the quality of decision-relevant evidence.

https://www.pnas.org/content/pnas/106/16/6539.full.pdf

Paul Sajda

Papers

Recipes for the linear analysis of EEG.

Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram.

Temporal Characterization of the Neural Correlates of Perceptual Decision Making in the Human Brain

Machine Learning for Detection and Diagnosis of Disease

Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG