神经元膜电位噪声取值范围的评估

大脑神经元的活动是在复杂的生理学环境下工作的，而生理学环境中的噪声来源于多个方面．如何定量地正确评估神经系统中的噪声环境是神经信息处理的基本问题．本文通过神经能量的计算给出了噪声对神经元膜电位及对应的能量波形产生显著影响的临界值的估计范围，从而定义了神经元赖以活动的生理学意义上的噪声环境．     

Ellipsometry on sputter-deposited tin oxide films: optical constants versus stoichiometry, hydrogen content, and amount of electrochemically intercalated lithium

Tin oxide thin films were deposited by reactive radio-frequency magnetron sputtering onto In(2)O(3):Sn-coated and bare glass substrates. Optical constants in the 3002500-nm wavelength range were determined by a combination of variable-angle spectroscopic ellipsometry and spectrophotometric transmittance measurements. Surface roughness was modeled from optical measurements and compared with atomic-force microscopy. The two techniques gave consistent results. The fit between experimental optical data and model results could be significantly improved when it was assumed that the refractive index of the Sn oxide varied across the film thickness. Varying the oxygen partial pressure during deposition made it possible to obtain films whose complex refractive index changed at the transition from SnO to SnO(2). An addition of hydrogen gas during sputtering led to lower optical constants in the full spectral range in connection with a blueshift of the bandgap. Electrochemical intercalation of lithium ions into the Sn oxide films raised their refractive index and enhanced their refractive-index gradient.     

Shape "break-and-repair" strategy and its application to automated medical image segmentation

In three-dimensional medical imaging, segmentation of specific anatomy structure is often a preprocessing step for computer-aided detection/diagnosis (CAD) purposes, and its performance has a significant impact on diagnosis of diseases as well as objective quantitative assessment of therapeutic efficacy. However, the existence of various diseases, image noise or artifacts, and individual anatomical variety generally impose a challenge for accurate segmentation of specific structures. To address these problems, a shape analysis strategy termed "break-and-repair" is presented in this study to facilitate automated medical image segmentation. Similar to surface approximation using a limited number of control points, the basic idea is to remove problematic regions and then estimate a smooth and complete surface shape by representing the remaining regions with high fidelity as an implicit function. The innovation of this shape analysis strategy is the capability of solving challenging medical image segmentation problems in a unified framework, regardless of the variability of anatomical structures in question. In our implementation, principal curvature analysis is used to identify and remove the problematic regions and radial basis function (RBF) based implicit surface fitting is used to achieve a closed (or complete) surface boundary. The feasibility and performance of this strategy are demonstrated by applying it to automated segmentation of two completely different anatomical structures depicted on CT examinations, namely human lungs and pulmonary nodules. Our quantitative experiments on a large number of clinical CT examinations collected from different sources demonstrate the accuracy, robustness, and generality of the shape "break-and-repair" strategy in medical image segmentation.     

An Architecture for Human‐Guided Autonomy: Team TROOPER at the DARPA Robotics Challenge Finals

Recent robotics efforts have automated simple, repetitive tasks to increase execution speed and lessen an operator's cognitive load, allowing them to focus on higher‐level objectives. However, an autonomous system will eventually encounter something unexpected, and if this exceeds the tolerance of automated solutions, there must be a way to fall back to teleoperation. Our solution is a largely autonomous system with the ability to determine when it is necessary to ask a human operator for guidance. We call this approach human‐guided autonomy. Our design emphasizes human‐on‐the‐loop control where an operator expresses a desired high‐level goal for which the reasoning component assembles an appropriate chain of subtasks. We introduce our work in the context of the DARPA Robotics Challenge (DRC) Finals. We describe the software architecture Team TROOPER developed and used to control an Atlas humanoid robot. We employ perception, planning, and control automation for execution of subtasks. If subtasks fail, or if changing environmental conditions invalidate the planned subtasks, the system automatically generates a new task chain. The operator is able to intervene at any stage of execution, to provide input and adjustment to any control layer, enabling operator involvement to increase as confidence in automation decreases. We present our performance at the DRC Finals and a discussion about lessons learned.     

An Introduction to Using XML for the Management of Laboratory Data

Extensible Markup Language (XML) and the XML Path Language (XPath) are introduced with software examples demonstrating how one can use them to write laboratory data management programs. Topics explored include XML document creation, manipulation, and searching. Programming examples make use of the Microsoft® XML Parser library and the Visual Basic programming language. The problem of managing microplate screening data is used as an illustration. Source code for all examples can be downloaded from http://www.labprogrammer.net.     

New Research Results for Electronic Voting

Voting systems have become controversial in the years following the multiple election disasters that occurred in the United States during and after 2000. Of particular note were the electronic voting (e-voting) systems that were widely deployed to replace the pre-scored punch-card systems, systems that had have been frequently judged to be central to the problem in the 2000 Florida general election. The (premature) deployment of e-voting machines stimulated a new field of security and privacy research around the subject of elections, a subject that has turned out to be more complex and deeper than anyone would have predicted a decade ago. Here we present a sample of some of the finest recent work on election technology from authors on three continents.     

Translating the Foundational Model of Anatomy into OWL

The Foundational Model of Anatomy (FMA) represents the result of manual and disciplined modeling of the structural organization of the human body. It is a tremendous resource in bioinformatics that facilitates sharing of information among applications that use anatomy knowledge. The FMA was developed in Protégé and the Protégé frames language is the canonical representation language for the FMA. We present a translation of the original Protégé frame representation of the FMA into OWL. Our effort is complementary to the earlier efforts to represent FMA in OWL and is focused on two main goals: (1) representing only the information that is explicitly present in the frames representation of the FMA or that can be directly inferred from the semantics of Protégé frames; (2) representing all the information that is present in the frames representation of the FMA, thus producing an OWL representation for the complete FMA. Our complete representation of the FMA in OWL consists of two components: an OWL DL component that contains the FMA constructs that are compatible with OWL DL; and an OWL Full component that imports the OWL DL component and adds the FMA constructs that OWL DL does not allow.     

Statistical topic models for multi-label document classification

Machine learning approaches to multi-label document classification have to date largely relied on discriminative modeling techniques such as support vector machines. A?drawback of these approaches is that performance rapidly drops off as the total number of labels and the number of labels per document increase. This problem is amplified when the label frequencies exhibit the type of highly skewed distributions that are often observed in real-world datasets. In this paper we investigate a class of generative statistical topic models for multi-label documents that associate individual word tokens with different labels. We investigate the advantages of this approach relative to discriminative models, particularly with respect to classification problems involving large numbers of relatively rare labels. We compare the performance of generative and discriminative approaches on document labeling tasks ranging from datasets with several thousand labels to datasets with tens of labels. The experimental results indicate that probabilistic generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.     

Protypology: A new stage in the standardization of metal products

Standardization is analyzed as a scientific discipline. The science of standardization may be referred to as protypology. On that basis, the stages in the development of standards for various types of carbon-steel products with ultrafine-grain structure are discussed.