nils-herrmann/clean_scirepeval huggingface dataset:
Science communication on YouTube: Factors that affect channel and video popularity.

YouTube has become one of the largest websites on the Internet. Among its many genres, both professional and amateur science communicators compete for audience attention. This article provides the first overview of science communication on YouTube and examines content factors that affect the popularity of science communication videos on the site. A content analysis of 390 videos from 39 YouTube channels was conducted. Although professionally generated content is superior in number, user-generated content was significantly more popular. Furthermore, videos that had consistent science communicators were more popular than those without a regular communicator. This study represents an important first step to understand content factors, which increases the channel and video popularity of science communication on YouTube.
Minimum jerk for human catching movements in 3D

To investigate fast human reaching movements in 3D, we asked 11 right-handed persons to catch a tennis ball while we tracked the movements of their arms. To ensure consistent trajectories of the ball, we used a catapult to throw the ball from three different positions. Tangential velocity profiles of the hand were in general bell-shaped and hand movements in 3D coincided with well known results for 2D point-to-point movements such as minimum jerk theory or the 2/3rd power law. Furthermore, two phases, consisting of fast reaching and slower fine movements at the end of hand placement could clearly be seen. The aim of this study was to find a way to generate human-like (catching) trajectories for a humanoid robot.
Domain Adaptation Using Adversarial Learning for Autonomous Navigation

Autonomous navigation has become an increasingly popular machine learning application. Recent advances in deep learning have also brought huge improvements to autonomous navigation. However, prior outdoor autonomous navigation methods depended on various expensive sensors or expensive and sometimes erroneously labeled real data. In this paper, we propose an autonomous navigation method that does not require expensive labeled real images and uses only a relatively inexpensive monocular camera. Our proposed method is based on (1) domain adaptation with an adversarial learning framework and (2) exploiting synthetic data from a simulator. To the best of the authors’ knowledge, this is the first work to apply domain adaptation with adversarial networks to autonomous navigation. We present empirical results on navigation in outdoor courses using an unmanned aerial vehicle. The performance of our method is comparable to that of a supervised model with labeled real data, although our method does not require any label information for the real data. Our proposal includes a theoretical analysis that supports the applicability of our approach.
The IEEE 802.11 universe

The introduction of IEEE's 802.11 standards has enabled a mass market, with a huge impact in the home, office, and public areas. Today, laptops, PCs, printers, cellular phones, VoIP phones, MP3 players, Blu-Ray players, and many more devices incorporate wireless LAN technology. With low-cost chipsets and support for high data rates, 802.11 has become a universal solution for an ever increasing application space. As a direct consequence of its high market penetration, several amendments to the basic 802.11 standard have been developed or are under development. They fix technology issues or add functionality expected to be required by future applications. In this article we overview the emerging 802.11 standard and address the technical context of its extensions. The article highlights its finalized amendments and those under development.
Leveraging multiple coflow attributes for information-agnostic coflow scheduling

Recently, designing information-agnostic coflow scheduling mechanisms attracts much attention since by leveraging priority queues, they could reduce coflow completion time in data-parallel clusters without a priori knowledge, such as flow size, coflow size. However, existing information-agnostic mechanisms generally schedule coflows only according to the sent data size of different coflows and ignore other useful coflow-level attributes like width, length and communication patterns. In this paper, we investigate that the coflow completion time could be further decreased by jointly leveraging multiple coflow-level attributes. Based on this investigation, we present a Multiple-attributes-based Coflow Scheduling (MCS) mechanism to reduce the coflow completion time. In MCS, a Shortest and Narrowest Coflow First (SNCF) algorithm is designed to separate coflows based on their widths and estimated lengths at the start of a coflow. During the transmission of coflows, one type of demotion thresholds employed in previous coflow scheduling mechanisms is too crude for various coflows. Therefore, we proposed a double-threshold scheme to adjust the priorities of narrow (small coflow width) and wide (large coflow width) coflows according to different thresholds. Trace-driven simulations with production workloads show that MCS outperforms the previous information-agnostic scheduler Aalo, and reduces the coflow completion time of small coflows.
Neurological manifestations of Ehlers-Danlos syndrome(s): A review

The term "Ehlers-Danlos syndrome" (EDS) groups together an increasing number of heritable connective tissue disorders mainly featuring joint hypermobility and related complications, dermal dysplasia with abnormal skin texture and repair, and variable range of the hollow organ and vascular dysfunctions. Although the nervous system is not considered a primary target of the underlying molecular defect, recently, increasing attention has been posed on neurological manifestations of EDSs, such as musculoskeletal pain, fatigue, headache, muscle weakness and paresthesias. Here, a comprehensive overview of neurological findings of these conditions is presented primarily intended for the clinical neurologist. Features are organized under various subheadings, including pain, fatigue, headache, stroke and cerebrovascular disease, brain and spine structural anomalies, epilepsy, muscular findings, neuropathy and developmental features. The emerging picture defines a wide spectrum of neurological manifestations that are unexpectedly common and potentially disabling. Their evaluation and correct interpretation by the clinical neurologist is crucial for avoiding superfluous investigations, wrong therapies, and inappropriate referral. A set of basic tools for patient's recognition is offered for raising awareness among neurologists on this underdiagnosed group of hereditary disorders.
URB-754: a new class of designer drug and 12 synthetic cannabinoids detected in illegal products.

URB-754 (6-methyl-2-[(4-methylphenyl)amino]-1-benzoxazin-4-one) was identified as a new type of designer drug in illegal products. Though many of the synthetic cannabinoids detected in illegal products are known to have affinities for cannabinoid CB1/CB2 receptors, URB-754 was reported to inhibit an endocannabinoid deactivating enzyme. Furthermore, an unknown compound (N,5-dimethyl-N-(1-oxo-1-(p-tolyl)butan-2-yl)-2-(N'-(p-tolyl)ureido)benzamide), which is deduced to be the product of a reaction between URB-754 and a cathinone derivative 4-methylbuphedrone (4-Me-MABP), was identified along with URB-754 and 4-Me-MABP in the same product. It is of interest that the product of a reaction between two different types of designer drugs, namely, a cannabinoid-related designer drug and a cathinone-type designer drug, was found in one illegal product. In addition, 12 cannabimimetic compounds, 5-fluoropentyl-3-pyridinoylindole, JWH-307, JWH-030, UR-144, 5FUR-144 (synonym: XLR11), (4-methylnaphtyl)-JWH-022 [synonym: N-(5-fluoropentyl)-JWH-122], AM-2232, (4-methylnaphtyl)-AM-2201 (MAM-2201), N-(4-pentenyl)-JWH-122, JWH-213, (4-ethylnaphtyl)-AM-2201 (EAM-2201) and AB-001, were also detected herein as newly distributed designer drugs in Japan. Furthermore, a tryptamine derivative, 4-hydroxy-diethyltryptamine (4-OH-DET), was detected together with a synthetic cannabinoid, APINACA, in the same product.
Therapeutic robocat for nursing home residents with dementia: preliminary inquiry.

Traditional pet therapy enhances individual well-being. However, there are situations where a substitute artificial companion (i.e., robotic pet) may serve as a better alternative because of insufficient available resources to care for a real pet, allergic responses to pets, or other difficulties. This pilot study, which compared the benefits of a robotic cat and a plush toy cat as interventions for elderly persons with dementia, was conducted at a special care unit of a large, not-for-profit nursing home. Various aspects of a person's engagement and affect were assessed through direct observations. Though not identical, similar trends were seen for the two cats. Interacting with the cats was linked with decreased agitation and increased pleasure and interest. The study is intended to pave the way for future research on robotherapy with nursing home residents.
Efficacy and safety of aripiprazole vs. haloperidol for long-term maintenance treatment following acute relapse of schizophrenia.

Aripiprazole is a novel atypical antipsychotic for the treatment of schizophrenia. It is a D2 receptor partial agonist with partial agonist activity at 5-HT1A receptors and antagonist activity at 5-HT2A receptors. The long-term efficacy and safety of aripiprazole (30 mg/d) relative to haloperidol (10 mg/d) were investigated in two 52-wk, randomized, double-blind, multicentre studies (using similar protocols which were prospectively identified to be pooled for analysis) in 1294 patients in acute relapse with a diagnosis of chronic schizophrenia and who had previously responded to antipsychotic medications. Aripiprazole demonstrated long-term efficacy that was comparable or superior to haloperidol across all symptoms measures, including significantly greater improvements for PANSS negative subscale scores and MADRS total score (p<0.05). The time to discontinuation for any reason was significantly greater with aripiprazole than with haloperidol (p=0.0001). Time to discontinuation due to adverse events or lack of efficacy was significantly greater with aripiprazole than with haloperidol (p=0.0001). Aripiprazole was associated with significantly lower scores on all extrapyramidal symptoms assessments than haloperidol (p<0.001). In summary, aripiprazole demonstrated efficacy equivalent or superior to haloperidol with associated benefits for safety and tolerability. Aripiprazole represents a promising new option for the long-term treatment of schizophrenia.
EEG Transient Event Detection and Classification Using Association Rules

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules
SDR-Based Transceiver of Digital Communication System Using USRP and GNU Radio

Transmission over wireless channel is challenging. As such, different application required different signal processing approach of radio system. So, a highly reconfigurable radio system is on great demand as the traditional fixed and embedded radio system are not viable to cater the needs for frequently change requirements of wireless communication. A software defined radio or better known as an SDR, is a software-based radio platform that offers flexibility to deliver the highly reconfigurable system requirements. This approach allows a different type of communication system requirements such as standard, protocol, or signal processing method, to be deployed by using the same set of hardware and software such as USRP and GNU Radio respectively. For researchers, this approach has opened the door to extend their studies in simulation domain into experimental domain. However, the realization of SDR concept is inherently limited by the analog components of the hardware being used. Despite that, the implementation of SDR is still new yet progressing, thus, this paper intends to provide an insight about its viability as a high re-configurable platform for communication system. This paper presents the SDR-based transceiver of common digital modulation system by means of GNU Radio and USRP.
Refactoring support for class library migration

As object-oriented class libraries evolve, classes are occasionally deprecated in favor of others with roughly the same functionality. In Java's standard libraries, for example, class Hashtable has been superseded by HashMap, and Iterator is now preferred over Enumeration. Migrating client applications to use the new idioms is often desirable, but making the required changes to declarations and allocation sites can be quite labor-intensive. Moreover, migration becomes complicated---and sometimes impossible---if an application interacts with external components, if a legacy class is not completely equivalent to its replacement, or if multiple interdependent classes must be migrated simultaneously. We present an approach in which mappings between legacy classes and their replacements are specified by the programmer. Then, an analysis based on type constraints determines where declarations and allocation sites can be updated. The method was implemented in Eclipse, and evaluated on a number of Java applications. On average, our tool could migrate more than 90% of the references to legacy classes.
The Automated Travel Agent : Hotel Recommendations Using Machine Learning

Expedia users who prefer the same types of hotels presumably share other commonalities (i.e., non-hotel commonalities) with each other. With this in mind, Kaggle challenged developers to recommend hotels to Expedia users. Armed with a training set containing data about 37 million Expedia users, we set out to do just that. Our machine-learning algorithms ranged from direct applications of material learned in class to multi-part algorithms with novel combinations of recommender system techniques. Kaggle’s benchmark for randomly guessing a user’s hotel cluster is 0.02260, and the mean average precision K = 5 value for näıve recommender systems is 0.05949. Our best combination of machine-learning algorithms achieved a figure just over 0.30. Our results provide insight into performing multi-class classification on data sets that lack linear structure.
Size Does Matter: Why Using Gadget-Chain Length to Prevent Code-Reuse Attacks is Hard

Code-reuse attacks based on return oriented programming are among the most popular exploitation techniques used by attackers today. Few practical defenses are able to stop such attacks on arbitrary binaries without access to source code. A notable exception are the techniques that employ new hardware, such as Intel’s Last Branch Record (LBR) registers, to track all indirect branches and raise an alert when a sensitive system call is reached by means of too many indirect branches to short gadgets—under the assumption that such gadget chains would be indicative of a ROP attack. In this paper, we evaluate the implications. What is “too many” and how short is “short”? Getting the thresholds wrong has serious consequences. In this paper, we show by means of an attack on Internet Explorer that while current defenses based on these techniques raise the bar for exploitation, they can be bypassed. Conversely, tuning the thresholds to make the defenses more aggressive, may flag legitimate program behavior as an attack. We analyze the problem in detail and show that determining the right values is difficult.
“My Religion Picked My Birth Control”: The Influence of Religion on Contraceptive Use

This research investigates the influence of religious preference and practice on the use of contraception. Much of earlier research examines the level of religiosity on sexual activity. This research extends this reasoning by suggesting that peer group effects create a willingness to mask the level of sexuality through the use of contraception. While it is understood that certain religions, that is, Catholicism does not condone the use of contraceptives, this research finds that Catholics are more likely to use certain methods of contraception than other religious groups. With data on contraceptive use from the Center for Disease Control’s Family Growth Survey, a likelihood probability model is employed to investigate the impact religious affiliation on contraception use. Findings suggest a preference for methods that ensure non-pregnancy while preventing feelings of shame and condemnation in their religious communities.
The unsteady eye: an information-processing stage, not a bug

How is space represented in the visual system? At first glance, the answer to this fundamental question appears straightforward: spatial information is directly encoded in the locations of neurons within maps. This concept has long dominated visual neuroscience, leading to mainstream theories of how neurons encode information. However, an accumulation of evidence indicates that this purely spatial view is incomplete and that, even for static images, the representation is fundamentally spatiotemporal. The evidence for this new understanding centers on recent experimental findings concerning the functional role of fixational eye movements, the tiny movements humans and other species continually perform, even when attending to a single point. We review some of these findings and discuss their functional implications.
A Deep Learning Approach for Motor Imagery EEG Signal Classification

Over the last few decades, the use of electroencephalography (EEG) signals for motor imagery based brain-computer interface (MI-BCI) has gained widespread attention. Deep learning have also gained widespread attention and used in various application such as natural language processing, computer vision and speech processing. However, deep learning has been rarely used for MI EEG signal classification. In this paper, we present a deep learning approach for classification of MI-BCI that uses adaptive method to determine the threshold. The widely used common spatial pattern (CSP) method is used to extract the variance based CSP features, which is then fed to the deep neural network for classification. Use of deep neural network (DNN) has been extensively explored for MI-BCI classification and the best framework obtained is presented. The effectiveness of the proposed framework has been evaluated using dataset IVa of the BCI Competition III. It is found that the proposed framework outperforms all other competing methods in terms of reducing the maximum error. The framework can be used for developing BCI systems using wearable devices as it is computationally less expensive and more reliable compared to the best competing methods.
Alumina ceramic-on-ceramic total hip replacement with a layered acetabular component.

A modular layered acetabular component (metal-polyethylene-ceramic) was developed in Japan for use in alumina ceramic-on-ceramic total hip replacement. Between May 1999 and July 2000, we performed 35 alumina ceramic-on-ceramic total hip replacements in 30 consecutive patients, using this layered component and evaluated the clinical and radiological results over a mean follow-up of 5.8 years (5 to 6.5). A total of six hips underwent revision, one for infection, two for dislocation with loosening of the acetabular component, two for alumina liner fractures and one for component dissociation with pelvic osteolysis. There were no fractures of the ceramic heads, and no loosening of the femoral or acetabular component in the unrevised hips was seen at final follow-up. Osteolysis was not observed in any of the unrevised hips. The survivorship analysis at six years after surgery was 83%. The layered acetabular component in our experience, has poor durability because of unexpected mechanical failures including alumina liner fracture and component dissociation.
Aim-mat: the auditory image model in MATLAB

This paper describes a version of the auditory image model (AIM)  implemented in MATLAB. It is referred to as “aim-mat” and it includes the basic modules that enable AIM to simulate the spectral analysis, neural encoding and temporal integration performed by the auditory system. The dynamic representations produced by non-static sounds can be viewed on a frame-by-frame basis or in movies with synchronized sound. The software has a sophisticated graphical user interface designed to facilitate the auditory modelling. It is also possible to add MATLAB code and complete modules to aim-mat. The software can be downloaded from http://www.mrccbu.cam.ac.uk/cnbh/aimmanual
Performance of closed loop SEPIC converter with DC-DC converter for solar energy system

The Non-Conventional sources such as solar energy has been replacement and best exploited electric source. The solar electric power required DC-DC converter for production, controllable and regulation of variable solar electric energy. The single ended boost converter has been replaced by SEPIC converter to overcome the problem associated with DC-DC converter. The problem associated with DC converter such as high amount of ripple, create harmonics, invert the voltage, create overheating and effective efficiency can be minimized and achieved best efficiency by SEPIC converters. This paper has been focused on design, comparison of DC-DC solar system with the SEPIC converter as using closed loop feedback control. In comparison DC-DC converter to SEPIC converter, it has highly efficient more than 1–5 %.
Clinical trial management of participant recruitment, enrollment, engagement, and retention in the SMART study using a Marketing and Information Technology (MARKIT) model.

Advances in information technology and near ubiquity of the Internet have spawned novel modes of communication and unprecedented insights into human behavior via the digital footprint. Health behavior randomized controlled trials (RCTs), especially technology-based, can leverage these advances to improve the overall clinical trials management process and benefit from improvements at every stage, from recruitment and enrollment to engagement and retention. In this paper, we report the results for recruitment and retention of participants in the SMART study and introduce a new model for clinical trials management that is a result of interdisciplinary team science. The MARKIT model brings together best practices from information technology, marketing, and clinical research into a single framework to maximize efforts for recruitment, enrollment, engagement, and retention of participants into a RCT. These practices may have contributed to the study's on-time recruitment that was within budget, 86% retention at 24 months, and a minimum of 57% engagement with the intervention over the 2-year RCT. Use of technology in combination with marketing practices may enable investigators to reach a larger and more diverse community of participants to take part in technology-based clinical trials, help maximize limited resources, and lead to more cost-effective and efficient clinical trial management of study participants as modes of communication evolve among the target population of participants.