Users manual for the Automated Performance Test System (APTS)

The characteristics of and the user information for the Essex Automated Performance Test System (APTS) computer-based portable performance assessment battery are given. The battery was developed to provide a menu of performance test tapping the widest possible variety of human cognitive and motor functions, implemented on a portable computer system suitable for use in both laboratory and field settings for studying the effects of toxic agents and other stressors. The manual gives guidance in selecting, administering and scoring tests from the battery, and reviews the data and studies underlying the development of the battery. Its main emphasis is on the users of the battery - the scientists, researchers and technicians who wish to examine changes in human performance across time or as a function of changes in the conditions under which test data are obtained. First the how to information needed to make decisions about where and how to use the battery is given, followed by the research background supporting the battery development. Further, the development history of the battery focuses largely on the logical framework within which tests were evaluated

Stability, reliability and cross-mode correlations of tests in a recommended 8-minute performance assessment battery

A need exists for an automated performance test system to study drugs, agents, treatments, and stresses of interest to the aviation, space, and environmental medical community. The purpose of this present study is to evaluate tests for inclusion in the NASA-sponsored Automated Performance Test System (APTS). Twenty-one subjects were tested over 10 replications with tests previously identified as good candidates for repeated-measure research. The tests were concurrently administered in paper-and-pencil and microcomputer modes. Performance scores for the two modes were compared. Data from trials 1 to 10 were examined for indications of test stability and reliability. Nine of the ten APT system tests achieved stability. Reliabilities were generally high. Cross-correlation of microbased tests with traditional paper-and-pencil versions revealed similarity of content within tests in the different modes, and implied at least three cognition and two motor factors. This protable, inexpensive, rugged, computerized battery of tests is recommended for use in repeated-measures studies of environmental and drug effects on performance. Identification of other tests compatible with microcomputer testing and potentially capable of tapping previously unidentified factors is recommended. Documentation of APTS sensitivity to environmental agents is available for more than a dozen facilities and is reported briefly. Continuation of such validation remains critical in establishing the efficacy of APTS tests

Algebras for parameterised monads

Parameterised monads have the same relationship to adjunctions with parameters as monads do to adjunctions. In this paper, we investigate algebras for parameterised monads. We identify the Eilenberg-Moore category of algebras for parameterised monads and prove a generalisation of Beck’s theorem characterising this category. We demonstrate an application of this theory to the semantics of type and effect systems

Overview of Planned Ultrasonic Imaging System with Automatic ALN Data Interpretation

This presentation discusses a new program designed to investigate the effectiveness with which adaptive learning network (ALNJ analysis can be combined with linear array, phase steered, ultrasonic imaging techniques to provide an enhanced means for automatic data interpretations. The DARPA-sponsored program is being performed as a team effort between Adaptronics, Inc. and Battelle-Northwest. Battelle, under a subcontract from Adaptronics, is adapting the linear array imaging system being developed for the Electric Power Research Institute of Palo Alto, California, for use on this project. A special ultrasonic array will be developed to operate with the high-speed imaging system to acquire and record both specular and nonspecular signal information in both the time and frequency domains. Signal information from a multitude of simple and complex reflectors and defects wilI be recorded on the PDP 11 disk pack incorporated into the ultrasonic imaging system. Adaptronics will utilize the time·domain and frequency spectral data recorded from several thousand data points to develop algorithms and train networks which may describe uniquely the pattern of the reflections. The objective of the program is to provide a high-speed and automatic means for detecting, locating, sizing and displaying flaws in solid materials

Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations

Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri’ or ‘Alexa’), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices. In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that speci cally targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio lter banks to further lower computations. We nd that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1⇥ reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.Microsoft Researc

Sub-basin and temporal variability of macroinvertebrate assemblages in Alpine streams: when and where to sample?

Can be viewed at https://rdcu.be/be8n

Layer by layer - Combining Monads

We develop a method to incrementally construct programming languages. Our approach is categorical: each layer of the language is described as a monad. Our method either (i) concretely builds a distributive law between two monads, i.e. layers of the language, which then provides a monad structure to the composition of layers, or (ii) identifies precisely the algebraic obstacles to the existence of a distributive law and gives a best approximant language. The running example will involve three layers: a basic imperative language enriched first by adding non-determinism and then probabilistic choice. The first extension works seamlessly, but the second encounters an obstacle, which results in a best approximant language structurally very similar to the probabilistic network specification language ProbNetKAT

FRuDA: Framework for Distributed Adversarial Domain Adaptation

Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is a lack of research on how uDA algorithms, particularly those based on adversarial learning, can work in distributed settings. In real-world applications, target domains are often distributed across thousands of devices, and existing adversarial uDA algorithms -- which are centralized in nature -- cannot be applied in these settings. To solve this important problem, we introduce FruDA: an end-to-end framework for distributed adversarial uDA. Through a careful analysis of the uDA literature, we identify the design goals for a distributed uDA system and propose two novel algorithms to increase adaptation accuracy and training efficiency of adversarial uDA in distributed settings. Our evaluation of FruDA with five image and speech datasets shows that it can boost target domain accuracy by up to 50% and improve the training efficiency of adversarial uDA by at least 11 times

Probing the N=14N = 14 subshell closure: gg factor of the 26^{26}Mg(21+^+_1) state

The first-excited state $g$~factor of $^{26}$Mg has been measured relative to the $g$ factor of the $^{24}$Mg($2^+_1$) state using the high-velocity transient-field technique, giving $g=+0.86\pm0.10$. This new measurement is in strong disagreement with the currently adopted value, but in agreement with the $sd$-shell model using the USDB interaction. The newly measured $g$ factor, along with $E(2^+_1)$ and $B(E2)$ systematics, signal the closure of the $\nu d_{5/2}$ subshell at $N=14$. The possibility that precise $g$-factor measurements may indicate the onset of neutron $pf$ admixtures in first-excited state even-even magnesium isotopes below $^{32}$Mg is discussed and the importance of precise excited-state $g$-factor measurements on $sd$~shell nuclei with $N\neq Z$ to test shell-model wavefunctions is noted.Comment: 8 pages, 5 figure