LLukas22/scidocs huggingface dataset:
Energy-Efficient Superconducting Computing—Power Budgets and Requirements

Large-scale computing system characteristics vary by application class, but power and energy use has become a major problem for all classes. Superconducting computing may be able to serve the needs of these systems significantly better than conventional technology. Recent developments in single flux quantum circuit technology for digital logic include variants with greatly improved energy efficiency. Concepts were investigated for computing systems capable of performance in the range from 1 to 1000 PFLOP/s. The concept systems were constrained to use existing commercial cryogenic refrigerators and Nb superconducting technology. In order to meet the performance goals, cache and main memory capable of operating at cryogenic temperatures will be required. Superconducting computing is shown to be potentially competitive on the basis of power and energy efficiency if key component technologies can meet specific goals. Potential advantages of superconducting computing are identified as well as areas requiring further development.
Evolution of order during vacuum-assisted self-assembly of graphene oxide paper and associated polymer nanocomposites.

Three mechanisms are proposed for the assembly of ordered, layered structures of graphene oxide, formed via the vacuum-assisted self-assembly of a dispersion of the two-dimensional nanosheets. These possible mechanisms for ordering at the filter-solution interface range from regular brick-and-mortar-like growth to complete disordered aggregation and compression. Through a series of experiments (thermal gravimetric analysis, UV-vis spectroscopy, and X-ray diffraction) a semi-ordered accumulation mechanism is identified as being dominant during paper fabrication. Additionally, a higher length-scale ordered structure (lamellae) is identified through the examination of water-swelled samples, indicating that further refinements are required to capture the complete formation mechanism. Identification of this mechanism and the resulting higher-order structure it produces provide insight into possibilities for creation of ordered graphene oxide-polymer nanocomposites, as well as the postfabrication modification of single-component graphene oxide papers.
Genetic influences on mechanically-assessed activity level in children.

BACKGROUND
Activity level is an important component of children's temperament, as well as being part of the core symptom domain of hyperactivity-impulsivity within attention deficit hyperactivity disorder (ADHD). Yet it is poorly understood, due partly to limitations on parent and teacher ratings, which are typically used as measurements of these symptoms.


METHODS
We aimed to study the etiology of objectively-measured activity level across two situations, using actigraphs. A population-based sample of 463 7-9-year-old twin pairs were assessed individually both when apart undergoing laboratory-based cognitive testing and when together during a break in testing.


RESULTS
Heritability of activity level was estimated as 24% during the test session and at 30% during the break in testing. Shared environmental influences accounted for 27% of the variance in activity level during the test session and 42% of activity level measured during the break. A genetic correlation of 1.0 indicated that the same genes influenced activity level across the two situations, justifying the use of a composite measure of the two situations. This produced a heritability estimate of 36%.


CONCLUSIONS
Objectively-measured activity level shows a moderate degree of genetic influence, with a common set of genes influencing activity level across situations. This supports the use of actigraphs as an additional source of information in studies that aim to improve phenotype definition for molecular genetic studies of activity level and ADHD.
Biometrics and Data Mining: Comparison of Data Mining-Based Keystroke Dynamics Methods for Identity Verification

Biometrics is the field that differentiates among various people based on their unique biological and physiological patterns such as retina, finger prints, DNA and keyboard typing patterns to name a few. Keystroke Dynamics is a physiological biometric that measures the unique typing rhythm and cadence of a computer keyboard user. This paper presents a Data Mining-based Keystroke Dynamics application for identity verification, and it reports the results of experiments comparing different approaches to Keystroke Dynamics. The methods compared were Decision Trees, a Näıve Bayesian Classifier, Memory Based Learning, and statistics-based Keystroke Dynamics.
Effective self-regulation of goal attainment

Self-regulatory strategies of goal setting and goal striving are analyzed in three experiments. Experiment 1 uses fantasy realization theory (Oettingen, in: J. Brandstätter, R.M. Lerner (Eds.), Action and Self Development: Theory and Research through the Life Span, Sage Publications Inc, Thousand Oaks, CA, 1999, pp. 315-342) to analyze the self-regulatory processes of turning free fantasies about a desired future into binding goals. School children 8-12 years of age who had to mentally elaborate a desired academic future as well as present reality standing in its way, formed stronger goal commitments than participants solely indulging in the desired future or merely dwelling on present reality (Experiment 1). Effective implementation of set goals is addressed in the second and third experiments (Gollwitzer, Am. Psychol. 54 (1999) 493-503). Adolescents who had to furnish a set educational goal with relevant implementation intentions (specifying where, when, and how they would start goal pursuit) were comparatively more successful in meeting the goal (Experiment 2). Linking anticipated si tuations with goal-directed behaviors (i.e., if-then plans) rather than the mere thinking about good opportunities to act makes implementation intentions facilitate action initiation (Experiment 3). ©2001 Elsevier Science Ltd. All rights reserved. _____________________________________________________________________________________ Successful goal attainment demands completing two different tasks. People have to first turn their desires into binding goals, and second they have to attain the set goal. Both tasks benefit from selfregulatory strategies. In this article we describe a series of experiments with children, adolescents, and young adults that investigate self-regulatory processes facilitating effective goal setting and successful goal striving. The experimental studies investigate (1) different routes to goal setting depending on how
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)

Introduction 1Motivation, diagnosis of features inside CNNs: In recent years, real applications usually propose new demands for deep learning beyond the accuracy. The CNN needs to earn trust from people for safety issues, because a high accuracy on testing images cannot always ensure that the CNN encodes correct features. Instead, the CNN sometimes uses unreliable reasons for prediction. Therefore, this study aim to provide a generic tool to examine middle-layer features of a CNN to ensure the safety in critical applications. Unlike previous visualization (Zeiler and Fergus 2014) and diagnosis (Bau et al. 2017; Ribeiro, Singh, and Guestrin 2016) of CNN representations, we focus on the following two new issues, which are of special values in feature diagnosis. • Disentanglement of interpretable and uninterpretable feature information is necessary for a rigorous and trustworthy examination of CNN features. Each filter of a conv-layer usually encodes a mixture of various semantics and noises (see Fig. 1). As discussed in (Bau et al. 2017), filters in high conv-layers mainly represent “object parts”2, and “material” and “color” information in high layers is not salient enough for trustworthy analysis. In particular, part features are usually more localized and thus is more helpful in feature diagnosis. Therefore, in this paper, we propose to disentangle part features from another signals and noises. For example, we may quantitatively disentangle 90% information of CNN features as object parts and interpret the rest 10% as textures and noises. • Semantic explanations: Given an input image, we aim to use clear visual concepts (here, object parts) to interpret chaotic CNN features. In comparisons, network visualization and diagnosis mainly illustrate the appearance corresponding to a network output/filter, without physically
Processing mode during repetitive thinking in socially anxious individuals: evidence for a maladaptive experiential mode.

BACKGROUND AND OBJECTIVES
Evidence from the depression literature suggests that an analytical processing mode adopted during repetitive thinking leads to maladaptive outcomes relative to an experiential processing mode. To date, in socially anxious individuals, the impact of processing mode during repetitive thinking related to an actual social-evaluative situation has not been investigated. We thus tested whether an analytical processing mode would be maladaptive relative to an experiential processing mode during anticipatory processing and post-event rumination.


METHODS
High and low socially anxious participants were induced to engage in either an analytical or experiential processing mode during: (a) anticipatory processing before performing a speech (Experiment 1; N = 94), or (b) post-event rumination after performing a speech (Experiment 2; N = 74). Mood, cognition, and behavioural measures were employed to examine the effects of processing mode.


RESULTS
For high socially anxious participants, the modes had a similar effect on self-reported anxiety during both anticipatory processing and post-event rumination. Unexpectedly, relative to the analytical mode, the experiential mode led to stronger high standard and conditional beliefs during anticipatory processing, and stronger unconditional beliefs during post-event rumination.


LIMITATIONS
These experiments are the first to investigate processing mode during anticipatory processing and post-event rumination. Hence, these results are novel and will need to be replicated.


CONCLUSIONS
These findings suggest that an experiential processing mode is maladaptive relative to an analytical processing mode during repetitive thinking characteristic of socially anxious individuals.
Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals.

OBJECTIVE
This paper aims to report on the accuracy of estimating sleep stages using a wrist-worn device that measures movement using a 3D accelerometer and an optical pulse photoplethysmograph (PPG).


APPROACH
Overnight recordings were obtained from 60 adult participants wearing these devices on their left and right wrist, simultaneously with a Type III home sleep testing device (Embletta MPR) which included EEG channels for sleep staging. The 60 participants were self-reported normal sleepers (36 M: 24 F, age  =  34  ±  10, BMI  =  28  ±  6). The Embletta recordings were scored for sleep stages using AASM guidelines and were used to develop and validate an automated sleep stage estimation algorithm, which labeled sleep stages as one of Wake, Light (N1 or N2), Deep (N3) and REM (REM). Features were extracted from the accelerometer and PPG sensors, which reflected movement, breathing and heart rate variability.


MAIN RESULTS
Based on leave-one-out validation, the overall per-epoch accuracy of the automated algorithm was 69%, with a Cohen's kappa of 0.52  ±  0.14. There was no observable bias to under- or over-estimate wake, light, or deep sleep durations. REM sleep duration was slightly over-estimated by the system. The most common misclassifications were light/REM and light/wake mislabeling.


SIGNIFICANCE
The results indicate that a reasonable degree of sleep staging accuracy can be achieved using a wrist-worn device, which may be of utility in longitudinal studies of sleep habits.
From Data to Actionable Knowledge: Big Data Challenges in the Web of Things

Extending the current Internet and providing connection, communication, and inter-networking between devices and physical objects, or "Things," is a growing trend that is often referred to as the Internet of Things. Integrating the real world data into the Web and providing Web-based interactions with the IoT resources is also often discussed under umbrella term of “Web of Things” (WoT). In this overview for the special issue on WoT, we describe the big data issues in the WoT, discuss the challenges of extracting actionable knowledge and insights from raw sensor data, and introduce the two papers in the special
PREY ADAPTATION AS A CAUSE OF PREDATOR-PREY CYCLES.

We analyze simple models of predator-prey systems in which there is adaptive change in a trait of the prey that determines the rate at which it is captured by searching predators. Two models of adaptive change are explored: (1) change within a single reproducing prey population that has genetic variation for vulnerability to capture by the predator; and (2) direct competition between two independently reproducing prey populations that differ in their vulnerability. When an individual predator's consumption increases at a decreasing rate with prey availability, prey adaptation via either of these mechanisms may produce sustained cycles in both species' population densities and in the prey's mean trait value. Sufficiently rapid adaptive change (e.g., behavioral adaptation or evolution of traits with a large additive genetic variance), or sufficiently low predator birth and death rates will produce sustained cycles or chaos, even when the predator-prey dynamics with fixed prey capture rates would have been stable. Adaptive dynamics can also stabilize a system that would exhibit limit cycles if traits were fixed at their equilibrium values. When evolution fails to stabilize inherently unstable population interactions, selection decreases the prey's escape ability, which further destabilizes population dynamics. When the predator has a linear functional response, evolution of prey vulnerability always promotes stability. The relevance of these results to observed predator-prey cycles is discussed.
Convex Channel Design for Improved Capacitorless DRAM Retention Time

A convex channel surface with Si0.8Ge0.2 is proposed to enhance the retention time of a capacitorless DRAM Generation 2 type of capacitorless DRAM cell. This structure provides a physical well together with an electrostatic barrier to more effectively store holes and thereby achieve larger sensing margin as well as retention time. The advantages of this new cell design as compared with the planar cell design are assessed via twodimensional device simulations. The results indicate that the convex heterojunction channel design is very promising for future capacitorless DRAM. Keywords-Capacitorless DRAM; Retention Time; Convex Channel; Silicon Germanium;
4A randomized trial of prolonged prone positioning in children with acute respiratory failure.

STUDY OBJECTIVE
To compare the effect of the prone position (PP) vs supine position (SP) on oxygenation in children with acute respiratory failure (ARF).


DESIGN
Prospective, randomized controlled trial.


SETTING
A 36-bed pediatric critical-care unit in a tertiary-care, university-based children's hospital.


PATIENTS
Ten children (mean [SD] age, 5 +/- 3.6 years) with ARF with a baseline oxygenation index (OI) of 22 +/- 8.5.


INTERVENTIONS
Following a period of stabilization in the SP, baseline data were collected and patients were randomized to one of two groups in a two-crossover study design: group 1, supine/prone sequence; group 2, prone/supine sequence. Each position was maintained for 12 h. Lung mechanics and acute response to inhaled nitric oxide were examined in each position.


MEASUREMENTS AND MAIN RESULTS
OI was significantly better in the PP compared to the SP over the 12-h period (analysis of variance, p = 0.0016). When patients were prone, a significant improvement in OI was detected (7.9 +/- 5.3; p = 0.002); this improvement occurred early (within 2 h in 9 of 10 patients) and was sustained over the 12-h study period. Static respiratory system compliance and resistance were not significantly affected by the position change. Inhaled nitric oxide had no effect on oxygenation in either position. Urine output increased while prone, resulting in a significantly improved fluid balance (+ 6.6 +/- 15.2 mL/kg/12 h in PP vs + 18.9 +/- 13.6 mL/kg/12 h in SP; p = 0.041). No serious adverse effects were detected in the PP.


CONCLUSION
In children with ARF, oxygenation is significantly superior in the PP than in the SP. This improvement occurs early, remains sustained for a 12-h period, and is independent of changes in lung mechanics.
Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss.

STUDY OBJECTIVES
The psychomotor vigilance test (PVT) is among the most widely used measures of behavioral alertness, but there is large variation among published studies in PVT performance outcomes and test durations. To promote standardization of the PVT and increase its sensitivity and specificity to sleep loss, we determined PVT metrics and task durations that optimally discriminated sleep deprived subjects from alert subjects.


DESIGN
Repeated-measures experiments involving 10-min PVT assessments every 2 h across both acute total sleep deprivation (TSD) and 5 days of chronic partial sleep deprivation (PSD).


SETTING
Controlled laboratory environment.


PARTICIPANTS
74 healthy subjects (34 female), aged 22-45 years.


INTERVENTIONS
TSD experiment involving 33 h awake (N = 31 subjects) and a PSD experiment involving 5 nights of 4 h time in bed (N = 43 subjects).


MEASUREMENTS AND RESULTS
In a paired t-test paradigm and for both TSD and PSD, effect sizes of 10 different PVT performance outcomes were calculated. Effect sizes were high for both TSD (1.59-1.94) and PSD (0.88-1.21) for PVT metrics related to lapses and to measures of psychomotor speed, i.e., mean 1/RT (response time) and mean slowest 10% 1/RT. In contrast, PVT mean and median RT outcomes scored low to moderate effect sizes influenced by extreme values. Analyses facilitating only portions of the full 10-min PVT indicated that for some outcomes, high effect sizes could be achieved with PVT durations considerably shorter than 10 min, although metrics involving lapses seemed to profit from longer test durations in TSD.


CONCLUSIONS
Due to their superior conceptual and statistical properties and high sensitivity to sleep deprivation, metrics involving response speed and lapses should be considered primary outcomes for the 10-min PVT. In contrast, PVT mean and median metrics, which are among the most widely used outcomes, should be avoided as primary measures of alertness. Our analyses also suggest that some shorter-duration PVT versions may be sensitive to sleep loss, depending on the outcome variable selected, although this will need to be confirmed in comparative analyses of separate duration versions of the PVT. Using both sensitive PVT metrics and optimal test durations maximizes the sensitivity of the PVT to sleep loss and therefore potentially decreases the sample size needed to detect the same neurobehavioral deficit. We propose criteria to better standardize the 10-min PVT and facilitate between-study comparisons and meta-analyses.
A Greedy-based Approach for Hiding Sensitive Itemsets by Transaction Insertion

Data mining technology is designed to derive useful knowledge from large database, which is used to aid decision making. The process of data collection and dissemination may, however, causes privacy concerns. Sensitive or personal information and knowledge of individuals, industries and organizations must be kept private before they are publicly shared or published. Thus, privacy-preserving data mining (PPDM) has become an important issue to efficiently hide sensitive information. In this paper, a greedy-based approach is proposed to hide sensitive itemsets by transaction insertion. The proposed approach first computes the maximal number of transactions to be inserted into the original database for totally hiding sensitive itemsets. The fake items of the transactions to be inserted are thus designed by the statistical approach, which can greatly reduce side effects in PPDM. Experiments are also conducted to evaluate the performance of the proposed approach.
Guest Editor's Introduction: Model-Driven Engineering

Model-driven engineering technologies offer a promising approach to address the inability of third-generation languages to alleviate the complexity of platforms and express domain concepts effectively.
Plastic deformation of GaAs single crystals as a function of electronic doping. I, Medium temperatures (150-650°C)

Abstract Plastic deformation of GaAs single crystals has been studied in the temperature range 20-650°C as a function of electronic doping, using different deformation techniques. In this paper, experiments conducted between 150°C and 650°C, using suitable pre-deformation conditions, are reported. The effects of electronic doping on macroscopic plasticity have been found to be consistent with dislocation velocity measurements for temperatures larger than 200°C. Activation Gibbs free energies were derived from activation volume measurements and found to be in reasonable agreement with activation energies of dislocation velocities. Observations of deformation substructures by transmission electron microscopy show that screw dislocations control the deformation at the lower temperatures.
Fast pedestrian detection with multi-scale orientation features and two-stage classifiers

In this paper, we propose an approach for fast pedestrian detection in images. Inspired by the histogram of oriented gradient (HOG) features, a set of multi-scale orientation (MSO) features are proposed as the feature representation. The features are extracted on square image blocks of various sizes (called units), containing coarse and fine features in which coarse ones are the unit orientations and fine ones are the pixel orientation histograms of the unit. A cascade of Adaboost is employed to train classifiers on the coarse features, aiming to high detection speed. A greedy searching algorithm is employed to select fine features, which are input into SVMs to train the fine classifiers, aiming to high detection accuracy. Experiments report that our approach obtains state-of-art results with 12.4 times faster than the SVM+HOG method.