nils-herrmann/clean_scirepeval huggingface dataset:
Ginkgo biloba extract (EGb 761) and CNS functions: basic studies and clinical applications.

The effects of EGb 761 on the CNS underlie one of its major therapeutic indications; i.e., individuals suffering from deteriorating cerebral mechanisms related to age-associated impairments of memory, attention and other cognitive functions. EGb 761 is currently used as symptomatic treatment for cerebral insufficiency that occurs during normal ageing or which may be due to degenerative dementia, vascular dementia or mixed forms of both, and for neurosensory disturbances. Depressive symptoms of patients with Alzheimer's disease (AD) and aged non-Alzheimer patients may also respond to treatment with EGb 761 since this extract has an "anti-stress" effect. Basic and clinical studies, conducted both in vitro and in vivo, support these beneficial neuroprotective effects of EGb 761. EGb 761 has several major actions; it enhances cognition, improves blood rheology and tissue metabolism, and opposes the detrimental effects of ischaemia. Several mechanisms of action are useful in explaining how EGb 761 benefits patients with AD and other age-related, neurodegenerative disorders. In animals, EGb 761 possesses antioxidant and free radical-scavenging activities, it reverses age-related losses in brain alpha 1-adrenergic, 5-HT1A and muscarinic receptors, protects against ischaemic neuronal death, preserves the function of the hippocampal mossy fiber system, increases hippocampal high-affinity choline uptake, inhibits the down-regulation of hippocampal glucocorticoid receptors, enhances neuronal plasticity, and counteracts the cognitive deficits that follow stress or traumatic brain injury. Identified chemical constituents of EGb 761 have been associated with certain actions. Both flavonoid and ginkgolide constituents are involved in the free radical-scavenging and antioxidant effects of EGb 761 which decrease tissue levels of reactive oxygen species (ROS) and inhibit membrane lipid peroxidation. Regarding EGb 761-induced regulation of cerebral glucose utilization, bilobalide increases the respiratory control ratio of mitochondria by protecting against uncoupling of oxidative phosphorylation, thereby increasing ATP levels, a result that is supported by the finding that bilobalide increases the expression of the mitochondrial DNA-encoded COX III subunit of cytochrome oxidase. With regard to its "anti-stress" effect, EGb 761 acts via its ginkgolide constituents to decrease the expression of the peripheral benzodiazepine receptor (PBR) of the adrenal cortex.
Walsh-Hadamard Transform and Cryptographic Applications in Bias Computing

Walsh-Hadamard transform is used in a wide variety of scientific and engineering applications, including bent functions and cryptanalytic optimization techniques in cryptography. In linear cryptanalysis, it is a key question to find a good linear approximation, which holds with probability (1 + d)/2 and the bias d is large in absolute value. Lu and Desmedt (2011) take a step toward answering this key question in a more generalized setting and initiate the work on the generalized bias problem with linearly-dependent inputs. In this paper, we give fully extended results. Deep insights on assumptions behind the problem are given. We take an information-theoretic approach to show that our bias problem assumes the setting of the maximum input entropy subject to the input constraint. By means of Walsh transform, the bias can be expressed in a simple form. It incorporates Piling-up lemma as a special case. Secondly, as application, we answer a long-standing open problem in correlation attacks on combiners with memory. We give a closed-form exact solution for the correlation involving the multiple polynomial of any weight for the first time. We also give Walsh analysis for numerical approximation. An interesting bias phenomenon is uncovered, i.e., for even and odd weight of the polynomial, the correlation behaves differently. Thirdly, we introduce the notion of weakly biased distribution, and study bias approximation for a more general case by Walsh analysis. We show that for weakly biased distribution, Piling-up lemma is still valid. Our work shows that Walsh analysis is useful and effective to a broad class of cryptanalysis problems.
Predicting Rapid Fire Growth (Flashover) Using Conditional Generative Adversarial Networks

A flashover occurs when a fire spreads very rapidly through crevices due to intense heat. Flashovers present one of the most frightening and challenging fire phenomena to those who regularly encounter them: firefighters. Firefighters’ safety and lives often depend on their ability to predict flashovers before they occur. Typical pre-flashover fire characteristics include dark smoke, high heat, and rollover (“angel fingers”) and can be quantified by color, size, and shape. Using a color video stream from a firefighter’s body camera, we applied generative adversarial neural networks for image enhancement. The neural networks were trained to enhance very dark fire and smoke patterns in videos and monitor dynamic changes in smoke and fire areas. Preliminary tests with limited flashover training videos showed that we predicted a flashover as early as 55 seconds before it occurred.
CNC-NOS: Class noise cleaning by ensemble filtering and noise scoring

Obtaining data in the real world is subject to imperfections and the appearance of noise is a common consequence of such flaws. In classification, class noise will deteriorate the performance of a classifier, as it may severely mislead the model building. Among the strategies emerged to deal with class noise, the most popular is that of filtering. However, instance filtering can be harmful as it may eliminate more examples than necessary or produce loss of information. An ideal option would be relabeling the noisy instances, avoiding losing data, but instance correcting is harder to achieve and may lead to wrong information being introduced in the dataset. For this reason, we advance a new proposal based on an ensemble of noise filters with the goal not only of accurately filtering the mislabeled instances, but also correcting them when possible. A noise score is also applied to support the filtering and relabeling process. The proposal, named CNC-NOS (Class Noise Cleaner with Noise Scoring), is compared against state-of-the-art noise filters and correctors, showing that it is able to deliver a quality training instance set that overcomes the limitations of such techniques, both in terms of classification accuracy and properly treated instances.
Nonoperatively treated burst fractures of the thoracic and lumbar spine in adults: a 23- to 41-year follow-up.

Several studies report a favorable short-term outcome after nonoperatively treated two-column thoracic or lumbar burst fractures in patients without neurological deficits. Few reports have described the long-term clinical and radiological outcome after these fractures, and none have, to our knowledge, specifically evaluated the long-term outcome of the discs adjacent to the fractured vertebra, often damaged at injury and possibly at an increased risk of height reduction and degeneration with subsequent chronic back pain.



To evaluate the long-term clinical and radiological outcome after nonoperatively treated thoracic or lumbar burst fractures in adults, with special attention to posttraumatic radiological disc height reduction.



Case series.



Sixteen men with a mean age of 31 years (range, 19-44) and 11 women with a mean age of 40 years (range, 23-61) had sustained a thoracic or lumbar burst fracture during the years 1965 to 1973. Four had sustained a burst fracture Denis type A, 18 a Denis type B, 1 a Denis type C, and 4 a Denis type E. Seven of these patients had neurological deficits at injury, all retrospectively classified as Frankel D.



The clinical outcome was evaluated subjectively with Oswestry score and questions regarding work capacity and objectively with the Frankel scale. The radiological outcome was evaluated with measurements of local kyphosis over the fractured segment, ratios of anterior and posterior vertebral body heights, adjacent disc heights, pedicle widths, sagittal width of the spinal canal, and lateral and anteroposterior displacement.



From the radiographical archives of an emergency hospital, all patients with a nonoperatively treated thoracic or lumbar burst fracture during the years 1965 to 1973 were registered. The fracture type, localization, primary treatment, and outcome were evaluated from the old radiographs, referrals, and reports. Twenty-seven individuals were clinically and radiologically evaluated a mean of 27 years (range, 23-41) after the injury.



At follow-up, 21 former patients reported no or minimal back pain or disability (Oswestry Score mean 4; range, 0-16), whereas 6 former patients (of whom 3 were classified as Frankel D at baseline) reported moderate or severe disability (Oswestry Score mean 39; range, 26-54). Six former patients were classified as Frankel D, and the rest as Frankel E. Local kyphosis had increased by a mean of 3 degrees (p<.05), whereas the discs adjacent to the fractured vertebrae remained unchanged in height during the follow-up.



Nonoperatively treated burst fractures of the thoracic or lumbar spine in adults with or without minor neurological deficits have a predominantly favorable long-term outcome, and there seems to be no increased risk for subsequent disc height reduction in the adjacent discs.
Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests

Network traffic monitoring and analysis-related research has struggled to scale for massive amounts of data in real time. Some of the vertical scaling solutions provide good implementation of signature based detection. Unfortunately these approaches treat network flows across different subnets and cannot apply anomaly-based classification if attacks originate from multiple machines at a lower speed, like the scenario of Peer-to-Peer Botnets. In this paper the authors build up on the progress of open source tools like Hadoop, Hive and Mahout to provide a scalable implementation of quasi-real-time intrusion detection system. The implementation is used to detect Peer-to-Peer Botnet attacks using machine learning approach. The contributions of this paper are as follows: (1) Building a distributed framework using Hive for sniffing and processing network traces enabling extraction of dynamic network features; (2) Using the parallel processing power of Mahout to build Random Forest based Decision Tree model which is applied to the problem of Peer-to-Peer Botnet detection in quasi-real-time. The implementation setup and performance metrics are presented as initial observations and future extensions are proposed.
Integration of image quality and motion cues for face anti-spoofing: A neural network approach

Many trait-specific countermeasures to face spoofing attacks have been developed for security of face authentication. However, there is no superior face anti-spoofing technique to deal with every kind of spoofing attack in varying scenarios. In order to improve the generalization ability of face anti-spoofing approaches, an extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection. Shearlet is utilized to develop an image quality-based liveness feature. Dense optical flow is utilized to extract motion-based liveness features. A bottleneck feature fusion strategy can integrate different liveness features effectively. The proposed approach was evaluated on three public face antispoofing databases. A half total error rate (HTER) of 0% and an equal error rate (EER) of 0% were achieved on both REPLAY-ATTACK database and 3D-MAD database. An EER of 5.83% was achieved on CASIA-FASD
Effects of Acute Smoked Marijuana on Complex Cognitive Performance

Although the ability to perform complex cognitive operations is assumed to be impaired following acute marijuana smoking, complex cognitive performance after acute marijuana use has not been adequately assessed under experimental conditions. In the present study, we used a within-participant double-blind design to evaluate the effects acute marijuana smoking on complex cognitive performance in experienced marijuana smokers. Eighteen healthy research volunteers (8 females, 10 males), averaging 24 marijuana cigarettes per week, completed this three-session outpatient study; sessions were separated by at least 72-hrs. During sessions, participants completed baseline computerized cognitive tasks, smoked a single marijuana cigarette (0%, 1.8%, or 3.9% Δ9-THC w/w), and completed additional cognitive tasks. Blood pressure, heart rate, and subjective effects were also assessed throughout sessions. Marijuana cigarettes were administered in a double-blind fashion and the sequence of Δ9-THC concentration order was balanced across participants. Although marijuana significantly increased the number of premature responses and the time participants required to complete several tasks, it had no effect on accuracy on measures of cognitive flexibility, mental calculation, and reasoning. Additionally, heart rate and several subjective-effect ratings (e.g., “Good Drug Effect,” “High,” “Mellow”) were significantly increased in a Δ9-THC concentration-dependent manner. These data demonstrate that acute marijuana smoking produced minimal effects on complex cognitive task performance in experienced marijuana users.
Psychological Vulnerability and Problem Gambling: An Application of Durand Jacobs’ General Theory of Addictions to Electronic Gaming Machine Playing in Australia

The aim of this study was to conduct an empirical investigation of the validity of Jacobs’ (in J Gambl Behav 2:15–31, 1986) general theory of addictions in relation to gambling problems associated with electronic gaming machines (EGM). Regular EGM gamblers (n = 190) completed a series of standardised measures relating to psychological and physiological vulnerability, substance use, dissociative experiences, early childhood trauma and abuse and problem gambling (the Problem Gambling Severity Index). Statistical analysis using structural equation modelling revealed clear relationships between childhood trauma and life stressors and psychological vulnerability, dissociative-like experiences and problem gambling. These findings confirm and extend a previous model validated by Gupta and Derevensky (in J Gambl Stud 14: 17–49, 1998) using an adolescent population. The significance of these findings are discussed for existing pathway models of problem gambling, for Jacobs’ theory, and for clinicians engaged in assessment and intervention.
Secure Transmission With Antenna Selection in MIMO Nakagami-  $m$ Fading Channels

This paper considers transmit antenna selection (TAS) and receive generalized selection combining (GSC) for secure communication in the multiple-input-multiple-output wiretap channel, where confidential messages transmitted from an NA-antenna transmitter to an NB-antenna legitimate receiver are overheard by an NE-antenna eavesdropper. We assume that the main channel and the eavesdropper's channel undergo Nakagami-m fading with fading parameters mB and mE, respectively. In order to assess the secrecy performance, we present a new unifying framework for the average secrecy rate and the secrecy outage probability. We first derive expressions for the probability density function and the cumulative distribution function of the signal-to-noise ratio with TAS/GSC, from which we derive exact expressions for the average secrecy rate and the secrecy outage probability. We then derive compact expressions for the asymptotic average secrecy rate and the asymptotic secrecy outage probability for two distinct scenarios: 1) the legitimate receiver is located close to the transmitter, and 2) the legitimate receiver and the eavesdropper are located close to the transmitter. For these scenarios, we present new closed-form expressions for several key performance indicators: 1) the capacity slope and the power offset of the asymptotic average secrecy rate, and 2) the secrecy diversity order and the secrecy array gain of the asymptotic secrecy outage probability. For the first scenario, we confirm that the capacity slope is one and the secrecy diversity order is mBNBNA. For the second scenario, we confirm that the capacity slope and the secrecy diversity order collapse to zero.
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.
Influence of leader and follower behavior on employee voice , team task responsibility , and team effectiveness

This study investigates the effect of leader and follower behavior on employee voice, team task responsibility and team effectiveness. This study distinguishes itself by including both leader and follower behavior as predictors of team effectiveness. In addition, employee voice and team task responsibility are tested as potential mediators of the relationship between task-oriented behaviors (informing, directing, verifying) and team effectiveness as well as the relationship between relation-oriented behaviors (positive feedback, intellectual stimulation, individual consideration) and team effectiveness. This cross-sectional exploratory study includes four methods: 1) inter-reliable coding of leader and follower behavior during staff meetings; 2) surveys of 57 leaders; 3) surveys of643 followers; 4) survey of 56 lean coaches. Regression analyses showed that both leaders and followers display more task-oriented behaviors opposed to relation-oriented behaviors during staff meetings. Contrary to the hypotheses, none of the observed leader behaviors positively influences employee voice, team task responsibility or team effectiveness. However, all three task-oriented follower behaviors indirectly influence team effectiveness. The findings from this research illustrate that follower behaviors has more influence on team effectiveness compared to leader behavior. , strengths and limitations of the research are discussed. Moreover, future research directions including the mediating role of culture and psychological safety are proposed as well.
Low Insertion Loss, Compact 4-bit Phase Shifter in 65 nm CMOS for 5G Applications

This letter presents a 28 GHz low insertion loss and compact size 4-bit phase shifter in 65 nm CMOS Technology. In order to get low insertion loss and compact size, this phase shifter is composed of two types of passive phase shifters, high-pass/low-pass type and switched filter type. Various techniques are used such as triple well body-floating, gate-floating, removal of capacitance of the off-state transistor using resonant inductor, and minimized interconnection line which is based on matching network. This phase shifter has 6.36 dB of average insertion loss over the 27.5-28.35 GHz, and loss variation is about 2 dB. The return loss is higher than 12 dB, RMS phase error is 8.98 °, OIP3 is 8.1 dBm at -10 dBm input power and its core size is 636 μm × 360 μm. To the authors' knowledge, these results are the state of the art in CMOS passive phase shifters in K-band frequency.
The Promise of Nanomagnetics and Spintronics for Future Logic and Universal Memory

This paper is both a review of some recent developments in the utilization of magnetism for applications to logic and memory and a description of some new innovations in nanomagnetics and spintronics. Nanomagnetics is primarily based on the magnetic interactions, while spintronics is primarily concerned with devices that utilize spin polarized currents. With the end of complementary metal-oxide-semiconductor (CMOS) in sight, nanomagnetics can provide a new paradigm for information process using the principles of magnetic quantum cellular automata (MQCA). This paper will review and describe these principles and then introduce a new nonlithographic method of producing reconfigurable arrays of MQCAs and/or storage bits that can be configured electrically. Furthermore, this paper will provide a brief description of magnetoresistive random access memory (MRAM), the first mainstream spintronic nonvolatile random access memory and project how far its successor spin transfer torque random access memory (STT-RAM) can go to provide a truly universal memory that can in principle replace most, if not all, semiconductor memories in the near future. For completeness, a description of an all-metal logic architecture based on magnetoresistive structures (transpinnor) will be described as well as some approaches to logic using magnetic tunnel junctions (MTJs).
Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns

In our study, we investigate the effectiveness of different models to the purchasing behaviour at YOOCHOOSE website. This paper provide a direct method in modeling the buying pattern in a clicking session by simply using the time-stamp of the clicks and show that the result is comparable to using more massive feature engineering that requires session summarizing. Our proposed method requires much lesser feature engineering and more natural modeling of the click events directly in a typical purchasing session in e-commerce.
Digital Nudging for Online Social Sharing: Evidence from A Randomized Field Experiment

This study investigates the effectiveness of digital nudging for users’ social sharing of online platform content. In collaboration with a leading career and education online platform, we conducted a large-scale randomized experiment of digital nudging using website popups. Grounding on the Social Capital Theory and the individual motivation mechanism, we proposed and tested four kinds of nudging messages: simple request, monetary incentive, relational capital, and cognitive capital. We find that nudging messages with monetary incentive, relational and cognitive capital framings lead to increase in social sharing behavior, while nudging message with simple request decreases social sharing, comparing to the control group without nudging. This study contributes to the prior research on digital nudging by providing causal evidence of effective nudging for online social sharing behavior. The findings of this study also provide valuable guidelines for the optimal design of online platforms to effectively nudge/encourage social sharing in practice.