A novel sequence representation for unsupervised analysis of human activities

Formalizing computational models for everyday human activities remains an open challenge. Many previous approaches towards this end assume prior knowledge about the structure of activities, using which explicitly defined models are learned in a completely supervised manner. For a majority of everyday environments however, the structure of the in situ activities is generally not known a priori. In this paper we investigate knowledge representations and manipulation techniques that facilitate learning of human activities in a minimally supervised manner. The key contribution of this work is the idea that global structural information of human activities can be encoded using a subset of their local event subsequences, and that this encoding is sufficient for activity-class discovery and classification.In particular, we investigate modeling activity sequences in terms of their constituent subsequences that we call event n-grams. Exploiting this representation, we propose a computational framework to automatically discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding characterizations of these discovered classes from a holistic as well as a by-parts perspective. Using such characterizations, we present a method to classify a new activity to one of the discovered activity-classes, and to automatically detect whether it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our approach in a variety of everyday environments.     

A simple background elimination method for Raman spectra

In this paper, we consider a new background elimination method for Raman spectra. The proposed method is based on peak detection, smoothing, and interpolation. Since the background is usually slowly varying with respect to wavelength, we could estimate the background by eliminating significant peaks. For this purpose, we seek the peaks by inspecting the smoothed derivative of a given spectrum. After clipping out the corresponding peak regions, we estimate the background by applying a modified linear interpolation. Then the background is eliminated from the measured Raman spectrum by simple subtraction. The experimental results showed that the proposed method gave satisfactory results for real Raman spectra as well as synthetic data. As the proposed method requires no prior knowledge of spectrum, we expect that the method could be applied to other spectral data as well.     

Situvis: A sensor data analysis and abstraction tool for pervasive computing systems

Pervasive systems are large-scale systems consisting of many sensors capturing numerous types of information. As this data is highly voluminous and dimensional, data analysis tasks can be extremely cumbersome and time-consuming. Enabling computers to recognise real-world situations is an even more difficult problem, involving not only data analysis, but also consistency checking. Here we present Situvis, an interactive visualisation tool for representing sensor data and creating higher-level abstractions from the data. This paper builds on previous work, Clear et al. (2009) [8] through evolved tool functionality and an evaluation of Situvis. A user-trial consisting of 10 participants shows that Situvis can be used to complete the key tasks in the development process of situation specifications in over 50% less time than an improvised alternative toolset.     

BGA评估中使用的分析方法

球珊阵列（BGAs）在印刷电路板（PCB）组件上已经相当常见，进而带来了新的故障模式，要求新的分析工具或方法，以便正确评估这些元件。传统式SMT元件，如SOICs或OFPs，允许以可视方式检测焊点，查找异常情况；而BGAs则做不到这一点，因为大多数焊点隐藏在元件下面，人眼是看不到的。本文讨论了STI实验室遇到的部分故障模式，以及进行调查使用的工具和方法。     

NCSU's Virtual Computing Lab: A Cloud Computing Solution

North Carolina State University's Virtual Computing Lab cost-effectively and efficiently delivers diverse computing resources over the Internet to more than 30,000 students and faculty.     

A practical clinical trial of coordinated care management to treat substance use disorders among public assistance beneficiaries.

This study tested whether coordinated care management (CCM), a continuity of care intervention for substance use disorders (SUD), improved rates of abstinence when compared with usual welfare management for substance-using single adults and adults with dependent children applying for public assistance. The study was designed as a practical clinical trial and was implemented in partnership with a large city welfare agency. Participants were 421 welfare applicants identified via SUD screening and assigned via an unbiased computerized allocation program to a site that provided either CCM (n = 232) or usual care (UC; n = 189). Outcomes were assessed for 1 year postbaseline with self-reports and biological measures of substance use. As hypothesized, for participants not enrolled in methadone maintenance programs (n = 313), CCM clients received significantly more services than did UC clients. Nonmethadone CCM also showed significantly higher abstinence rates (odds ratio = 1.75; 95% confidence interval = 1.12, 2.76; d = 0.31) that emerged early in treatment and were sustained throughout follow-up. In contrast, no treatment services or outcome effects were found for methadone maintenance clients (n = 108). Findings suggest that CCM is promising as a wraparound to SUD treatment for welfare recipients. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Strain-dependent recovery of spontaneous hindlimb movement in spinal cord transected mice (CD1, C57BL/6, BALB/c).

Reorganization and plasticity after spinal cord injury have been recently shown to take place in sublesional neuronal networks, but the possibility of strain-dependent changes at that level has never been explored. The authors studied the spontaneous return of hindlimb movement in low-thoracic spinal cord transected (Tx) mice from 3 commonly used strains. Without intervention, most CD1, C57BL/6, and BALB/c mice displayed some hindlimb movement recovery after Tx. Although all assessment methods unanimously reported that CD1 displayed higher recovery levels than did the C57BL/6 and BALB/c, higher scores were generally found with the Antri-Orsal-Barthe (M. Antri, D. Orsal, & J. Y. Barthe, 2002) and the Average Combined Score (P. A. Guertin, 2005a) methods. Such spontaneous recovery in low-thoracic Tx mice is likely the result of neuronal plasticity at the lumbosacral spinal cord level, suggesting that these sublesional changes are strain dependent. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparative evolutionary psychology of sperm competition.

A comparative evolutionary psychological perspective predicts that species that recurrently faced similar adaptive problems may have evolved similar psychological mechanisms to solve these problems. Sperm competition provides an arena in which to assess the heuristic value of such a comparative evolutionary perspective. The sperm competition that results from female infidelity and polyandry presents a similar class of adaptive problems for individuals across many species. The authors first describe mechanisms of sperm competition in insects and in birds. They suggest that the adaptive problems and evolved solutions in these species provide insight into human anatomy, physiology, psychology, and behavior. The authors then review recent theoretical and empirical arguments for the existence of sperm competition in humans and discuss proposed adaptations in humans that have analogs in insects or birds. The authors conclude by highlighting the heuristic value of a comparative evolutionary psychological approach in this field. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Automation for High-throughput Identification and Picking of GFP Expressing Colonies

Green fluorescent protein (GFP) has many applications as a marker in living cells, and has become widely used as a reporter gene in microbial, plant and animal cells. Screening microbial colonies for GFP expression enables various types of assays (e.g. for secretory mutations). However, this is laborious, non-quantitative and potentially hazardous to the operator (due to UV illumination) when performed manually. In order to address this the GloPix robot was developed. The imaging system discriminates between colonies based on the level of fluorescence activity and the picking function automatically transfers cells to microplate wells. Measuring fluorescent activity allows quantitation of fluorescent tag concentration/expression.     

Statistical Learning for Humanoid Robots

The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.