nils-herrmann/clean_scirepeval huggingface dataset:
Erase and Retention Improvements in Charge Trap Flash Through Engineered Charge Storage Layer

The simultaneous improvement in the erase and retention characteristics in a TANOS (TaN-Al<sub>2</sub>O<sub>3</sub>-Si<sub>3</sub>N<sub>4</sub>-SiO<sub>2</sub>-Si) flash memory transistor by utilizing the band-engineered and compositionally graded SiN<sub>x</sub> trap layer is demonstrated. With the process optimizations, a > 4V memory window and excellent 150 degC 24-h retention (0.1-0.5 V charge loss) for a programmed DeltaV<sub>t</sub> = 4V with respect to the initial state are obtained. The band-engineered SiN<sub>x</sub> charge storage layer enables flash scaling beyond the floating-gate technology with a promise for improved erase speed, retention, lower supply voltages, and multilevel cell applications.
A single source k-shortest paths algorithm to infer regulatory pathways in a gene network

Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene.



In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines.


AVAILABILITY
All data and codes are freely available upon request.
A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3

To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v3 (You Only Look Once, v3) is constructed. The structural parameters of the YOLO v3 algorithm are modified to make it more suitable for lane detection. To improve the training efficiency, a method for automatic generation of the lane label images in a simple scenario, which provides label data for the training of the first-stage network, is proposed. Then, an adaptive edge detection algorithm based on the Canny operator is used to relocate the lane detected by the first-stage model. Furthermore, the unrecognized lanes are shielded to avoid interference in subsequent model training. Then, the images processed by the above method are used as label data for the training of the second-stage model. The experiment was carried out on the KITTI and Caltech datasets, and the results showed that the accuracy and speed of the second-stage model reached a high level.
Three-loop autopilot design and simulation

In order to realize missile real-time change flight trajectory, three-loop autopilot is setting up. The structure characteristics, autopilot model, and control parameters design method were researched. Firstly, this paper introduced the 11th order three-loop autopilot model. With the principle of systems reduce model order, the 5th order model was deduced. On that basis, open-loop frequency characteristic and closed-loop frequency characteristic were analyzed. The variables of velocity ratio, dynamic pressure ratio and elevator efficiency ratio were leading to correct system nonlinear. And then autopilot gains design method were induced. System flight simulations were done, and result shows that autopilot gains played a good job in the flight trajectory, autopilot satisfied the flight index.
Bacterial community dynamics and polycyclic aromatic hydrocarbon degradation during bioremediation of heavily creosote-contaminated soil.

Bacterial community dynamics and biodegradation processes were examined in a highly creosote-contaminated soil undergoing a range of laboratory-based bioremediation treatments. The dynamics of the eubacterial community, the number of heterotrophs and polycyclic aromatic hydrocarbon (PAH) degraders, and the total petroleum hydrocarbon (TPH) and PAH concentrations were monitored during the bioremediation process. TPH and PAHs were significantly degraded in all treatments (72 to 79% and 83 to 87%, respectively), and the biodegradation values were higher when nutrients were not added, especially for benzo(a)anthracene and chrysene. The moisture content and aeration were determined to be the key factors associated with PAH bioremediation. Neither biosurfactant addition, bioaugmentation, nor ferric octate addition led to differences in PAH or TPH biodegradation compared to biodegradation with nutrient treatment. All treatments resulted in a high first-order degradation rate during the first 45 days, which was markedly reduced after 90 days. A sharp increase in the size of the heterotrophic and PAH-degrading microbial populations was observed, which coincided with the highest rates of TPH and PAH biodegradation. At the end of the incubation period, PAH degraders were more prevalent in samples to which nutrients had not been added. Denaturing gradient gel electrophoresis analysis and principal-component analysis confirmed that there was a remarkable shift in the composition of the bacterial community due to both the biodegradation process and the addition of nutrients. At early stages of biodegradation, the alpha-Proteobacteria group (genera Sphingomonas and Azospirillum) was the dominant group in all treatments. At later stages, the gamma-Proteobacteria group (genus Xanthomonas), the alpha-Proteobacteria group (genus Sphingomonas), and the Cytophaga-Flexibacter-Bacteroides group (Bacteroidetes) were the dominant groups in the nonnutrient treatment, while the gamma-Proteobacteria group (genus Xathomonas), the beta-Proteobacteria group (genera Alcaligenes and Achromobacter), and the alpha-Proteobacteria group (genus Sphingomonas) were the dominant groups in the nutrient treatment. This study shows that specific bacterial phylotypes are associated both with different phases of PAH degradation and with nutrient addition in a preadapted PAH-contaminated soil. Our findings also suggest that there are complex interactions between bacterial species and medium conditions that influence the biodegradation capacity of the microbial communities involved in bioremediation processes.
Mindfulness meditation and physical activity: Evidence from 2012 National Health Interview Survey.

Physical activity is critical for health, yet only about half of the U.S. adult population meets basic aerobic physical activity recommendations and almost a third are inactive. Mindfulness meditation is gaining attention for its potential to facilitate health-promoting behavior and may address some limitations of existing interventions for physical activity. However, little evidence exists on mindfulness meditation and physical activity. This study assessed whether mindfulness meditation is uniquely associated with physical activity in a nationally representative sample.



Cross-sectional data from the adult sample (N = 34,525) of the 2012 National Health Interview Survey were analyzed. Logistic regression models tested whether past-year use of mindfulness meditation was associated with (a) inactivity and (b) meeting aerobic physical activity recommendations, after accounting for sociodemographics, another health-promoting behavior, and 2 other types of meditation. Data were weighted to represent the U.S. civilian, noninstitutionalized adult population.



Accounting for covariates, U.S. adults who practiced mindfulness meditation in the past year were less likely to be inactive and more likely to meet physical activity recommendations. Mindfulness meditation showed stronger associations with these indices of physical activity than the 2 other types of meditation.



These results suggest that mindfulness meditation specifically, beyond meditation in general, is associated with physical activity in U.S adults. Future research should test whether intervening with mindfulness meditation-either as an adjunctive component or on its own-helps to increase or maintain physical activity.
Predicting female physical attractiveness Waist-to-hip ratio versus thinness

Introductory psychology students (120 females and 120 males) rated attractiveness and fecundity of one of six computer-altered female  gures representing three body-weight categories (underweight, normal weight and overweight) and two levels of waist-to-hip ratio (WHR), one in the ideal range (0.72) and one in the non-ideal range (0.86). Both females and males judged underweight  gures to be more attractive than normal or overweight  gures, regardless of WHR. The female  gure with the high WHR (0.86) was judged to be more attractive than the  gure with the low WHR (0.72) across all body-weight conditions. Analyses of fecundity ratings revealed an interaction between weight and WHR such that the models did not differ in the normal weight category, but did differ in the underweight (model with WHR of 0.72 was less fecund) and overweight (model with WHR of 0.86 was more fecund) categories. These ndings lend stronger support to sociocultural rather than evolutionary hypotheses.
Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.
Find Duplicates among the PubMed, EMBASE, and Cochrane Library Databases in Systematic Review

Finding duplicates is an important phase of systematic review. However, no consensus regarding the methods to find duplicates has been provided. This study aims to describe a pragmatic strategy of combining auto- and hand-searching duplicates in systematic review and to evaluate the prevalence and characteristics of duplicates.



Literatures regarding portal vein thrombosis (PVT) and Budd-Chiari syndrome (BCS) were searched by the PubMed, EMBASE, and Cochrane library databases. Duplicates included one index paper and one or more redundant papers. They were divided into type-I (duplicates among different databases) and type-II (duplicate publications in different journals/issues) duplicates. For type-I duplicates, reference items were further compared between index and redundant papers. Of 10936 papers regarding PVT, 2399 and 1307 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 11.0% (1201/10936) and 6.1% (665/10936), respectively. They included 3431 type-I and 275 type-II duplicates. Of 11403 papers regarding BCS, 3275 and 2064 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 14.4% (1640/11403) and 9.1% (1039/11403), respectively. They included 5053 type-I and 286 type-II duplicates. Most of type-I duplicates were identified by auto-searching method (69.5%, 2385/3431 in PVT literatures; 64.6%, 3263/5053 in BCS literatures). Nearly all type-II duplicates were identified by hand-searching method (94.9%, 261/275 in PVT literatures; 95.8%, 274/286 in BCS literatures). Compared with those identified by auto-searching method, type-I duplicates identified by hand-searching method had a significantly higher prevalence of wrong items (47/2385 versus 498/1046, p<0.0001 in PVT literatures; 30/3263 versus 778/1790, p<0.0001 in BCS literatures). Most of wrong items originated from EMBASE database.



Given the inadequacy of a single strategy of auto-searching method, a combined strategy of auto- and hand-searching methods should be employed to find duplicates in systematic review.
Efficient Exploration for Dialog Policy Learning with Deep BBQ Networks \& Replay Buffer Spiking

When rewards are sparse and efficient exploration essential, deep Q-learning with -greedy exploration tends to fail. This poses problems for otherwise promising domains such as task-oriented dialog systems, where the primary reward signal, indicating successful completion, typically occurs only at the end of each episode but depends on the entire sequence of utterances. A poor agent encounters such successful dialogs rarely, and a random agent may never stumble upon a successful outcome in reasonable time. We present two techniques that significantly improve the efficiency of exploration for deep Q-learning agents in dialog systems. First, we demonstrate that exploration by Thompson sampling, using Monte Carlo samples from a Bayes-by-Backprop neural network, yields marked improvement over standard DQNs with Boltzmann or -greedy exploration. Second, we show that spiking the replay buffer with a small number of successes, as are easy to harvest for dialog tasks, can make Q-learning feasible when it might otherwise fail catastrophically.
A single layer wideband differential-fed patch antenna array with SIW feeding networks

A single layer wideband SIW-fed differential patch array is proposed in this paper. A SIW-CPS-CMS (substrate integrated waveguide - coupled lines - coupled microstrip line) transition is designed and has a bandwidth of about 50%, covering the E-band and W-band. The differential phase deviation between the coupled microstrip lines is less than 7.5° within the operation band. A 1×4 array and a 4×4 array are designed. The antenna is composed of SIW parallel power di vider network, SIW-CPS-CMS transition, and series differential-fed patch array. Simulated results show that the bandwidth of the 1×4 array and 4×4 array are 37% and 12%, and the realized gain are 10.5-12 dB and 17.2-20.2dB within the corresponding operation band, respectively. The features of single layer and wideband on impedance and gain of the proposed SIW-fed differential patch array make it a good candidate for automotive radar or other millimeter wave applications.
A survey of surface mount device placement machine optimisation: Machine classification

The optimisation of a printed circuit board assembly line is mainly influenced by the constraints of the surface mount device placement (SMD) machine and the characteristics of the production environment. Hence, this paper surveys the various machine technologies and characteristics and proposes five categories of machines based on their specifications and operational methods. These are dual-delivery, multi-station, turret-type, multi-head and sequential pick-and-place SMD placement machines. We attempt to associate the assembly machine technologies with heuristic methods; and address the scheduling issues of each category of machine. This grouping aims to guide future researchers in this field to have a better understanding of the various SMD placement machine specifications and operational methods, so that they can subsequently use them to apply, or even design heuristics, which are more appropriate to the machine characteristics and the operational methods. We also discuss our experiences in solving the pick-and-place sequencing problem of the theoretical and real machine problem, and highlight some of the important practical issues that should be considered in solving real SMD placement machine problems.
Optimizing Constrained-Environment Redirected Walking Instructions Using Search Techniques

A goal of redirected walking (RDW) is to allow large virtual worlds to be explored within small tracking areas. Generalized steering algorithms, such as steer-to-center, simply move the user toward locations that are considered to be collision free in most cases. The algorithm developed here, FORCE, identifies collision-free paths by using a map of the tracking area's shape and obstacles, in addition to a multistep, probabilistic prediction of the user's virtual path through a known virtual environment. In the present implementation, the path predictions describe a user's possible movements through a virtual store with aisles. Based on both the user's physical and virtual location / orientation, a search-based optimization technique identifies the optimal steering instruction given the possible user paths. Path prediction uses the map of the virtual world; consequently, the search may propose steering instructions that put the user close to walls if the user's future actions eventually lead away from the wall. Results from both simulated and real users are presented. FORCE identifies collision-free paths in 55.0 percent of the starting conditions compared to 46.1 percent for generalized methods. When considering only the conditions that result in different outcomes, redirection based on FORCE produces collision-free path 94.5 percent of the time.
Pornography and sexual aggression: are there reliable effects and can we understand them?

In response to some recent critiques, we (a) analyze the arguments and data presented in those commentaries, (b) integrate the findings of several metaanalytic summaries of experimental and naturalistic research, and (c) conduct statistical analyses on a large representative sample. All three steps support the existence of reliable associations between frequent pornography use and sexually aggressive behaviors, particularly for violent pornography and/or for men at high risk for sexual aggression. We suggest that the way relatively aggressive men interpret and react to the same pornography may differ from that of nonaggressive men, a perspective that helps integrate the current analyses with studies comparing rapists and nonrapists as well as with cross-cultural research.
ReveNAND: A Fast-Drift-Aware Resilient 3D NAND Flash Design

The paradigm shift from planar (two dimensional (2D)) to vertical (three-dimensional (3D)) models has placed the NAND flash technology on the verge of a design evolution that can handle the demands of next-generation storage applications. However, it also introduces challenges that may obstruct the realization of such 3D NAND flash. Specifically, we observed that the fast threshold drift (fast-drift) in a charge-trap flash-based 3D NAND cell can make it lose a critical fraction of the stored charge relatively soon after programming and generate errors.
 In this work, we first present an elastic read reference (VRef) scheme (ERR) for reducing such errors in ReveNAND—our fast-drift aware 3D NAND design. To address the inherent limitation of the adaptive VRef, we introduce a new intra-block page organization (hitch-hike) that can enable stronger error correction for the error-prone pages. In addition, we propose a novel reinforcement-learning-based smart data refill scheme (iRefill) to counter the impact of fast-drift with minimum performance and hardware overhead. Finally, we present the first analytic model to characterize fast-drift and evaluate its system-level impact. Our results show that, compared to conventional 3D NAND design, our ReveNAND can reduce fast-drift errors by 87%, on average, and can lower the ECC latency and energy overheads by 13× and 10×, respectively.
Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model. This ensemble model outputs a FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject to be clinically diagnosed with DAT based on their metabolism profile. A novel 7 group image stratification scheme is devised that groups images not only based on their associated clinical diagnosis but also on past and future trajectories of the clinical diagnoses, yielding a more continuous representation of the different stages of DAT spectrum that mimics a real-world clinical setting. The potential for using FPDS as a DAT biomarker was validated on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database taken across the proposed stratification, and a good classification AUC (area under the curve) of 0.78 was achieved in distinguishing between images belonging to subjects on a DAT trajectory and those images taken from subjects not progressing to a DAT diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on the mild cognitive impairment (MCI) to DAT conversion prediction task with an AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows respectively.
Analysis and Design of a Broadband Multifeed Tightly Coupled Patch Array Antenna

In this letter, a multifeed tightly coupled patch array antenna capable of broadband operation is analyzed and designed. First, an antenna array composed of infinite elements with each element excited by a feed is proposed. To produce specific polarized radiation efficiently, a new patch element is proposed, and its characteristics are studied based on a 2-port network model. Full-wave simulation results show that the infinite antenna array exhibits both a high efficiency and desirable radiation pattern in a wide frequency band (10 dB bandwidth) from 1.91 to 5.35 GHz (94.8%). Second, to validate its outstanding performance, a realistic finite 4 × 4 antenna prototype is designed, fabricated, and measured in our laboratory. The experimental results agree well with simulated ones, where the frequency bandwidth (VSWR < 2) is from 2.5 to 3.8 GHz (41.3%). The inherent compact size, light weight, broad bandwidth, and good radiation characteristics make this array antenna a promising candidate for future communication and advanced sensing systems.