nils-herrmann/clean_scirepeval huggingface dataset:
N-Desalkylquetiapine, a Potent Norepinephrine Reuptake Inhibitor and Partial 5-HT1A Agonist, as a Putative Mediator of Quetiapine's Antidepressant Activity

Quetiapine is an atypical antipsychotic drug that is also US FDA approved for treating bipolar depression, albeit by an unknown mechanism. To discover the potential mechanism for this apparently unique action, we screened quetiapine, its metabolite N-Desalkylquetiapine, and dibenzo[b,f][1,4]thiazepine-11(10-H)-one (DBTO) against a large panel of G-protein–coupled receptors, ion channels, and neurotransmitter transporters. DBTO was inactive at all tested molecular targets. N-Desalkylquetiapine had a high affinity (3.4 nM) for the histamine H1 receptor and moderate affinities (10–100 nM) for the norepinephrine reuptake transporter (NET), the serotonin 5-HT1A, 5-HT1E, 5-HT2A, 5-HT2B, 5-HT7 receptors, the α1B-adrenergic receptor, and the M1, M3, and M5 muscarinic receptors. The compound had low affinities (100–1000 nM) for the 5-HT1D, 5-HT2C, 5-HT3, 5-HT5, 5-HT6, α1A, α2A, α2B, α2C, H2, M2, M4, and dopamine D1, D2, D3, and D4 receptors. N-Desalkylquetiapine potently inhibited human NE transporter with a Ki of 12 nM, about 100-fold more potent than quetiapine itself. N-Desalkylquetiapine was also 10-fold more potent and more efficacious than quetiapine at the 5-HT1A receptor. N-Desalkylquetiapine was an antagonist at 5-HT2A, 5-HT2B, 5-HT2C, α1A, α1D, α2A, α2C, H1, M1, M3, and M5 receptors. In the mouse tail suspension test, N-Desalkylquetiapine displayed potent antidepressant-like activity in VMAT2 heterozygous mice at doses as low as 0.1 mg/kg. These data strongly suggest that the antidepressant activity of quetiapine is mediated, at least in part, by its metabolite N-Desalkylquetiapine through NET inhibition and partial 5-HT1A agonism. Possible contributions of this metabolite to the side effects of quetiapine are discussed.
Rotterdam computed tomography score as a prognosticator in head-injured patients undergoing decompressive craniectomy.

The Rotterdam computed tomography (CT) score was developed for prognostic purposes in traumatic brain injury (TBI).



To examine the prognostic discrimination and prediction of the Rotterdam CT score in the case of patients undergoing decompressive craniectomy (DC) for TBI.



The CT scans with the worst findings before DC were scored according to the Rotterdam CT classification. Mortality and Glasgow Outcome Scale score at the end of follow-up were used as outcome measures. Unfavorable and favorable outcomes were defined by a Glasgow Outcome Scale score of 1 to 3 and 4 to 5, respectively. We used binary logistic and proportional odds regression for prognostic analyses.



The relationship between the Rotterdam CT score and prognosis was quantified, and higher scores indicated worse patient outcomes. Univariate analysis showed that the Rotterdam CT score was significantly associated with mortality (odds ratio: 3.117, 95% confidence interval: 1.867-5.386; P < .001) and unfavorable outcomes (odds ratio: 2.612, 95% confidence interval: 1.733-3.939; P < .001). After adjustment for published outcome predictors of TBI in multivariate regression, the Rotterdam CT score remained an independent predictor of unfavorable outcomes (odds ratio: 1.830, 95% confidence interval: 1.043-3.212; P = .035).



For head-injured patients undergoing DC, the Rotterdam CT score provides great prognostic discrimination and is an independent predictor of unfavorable outcomes. We suggest that the Rotterdam CT score be included as a prognosticator in the overall assessment of clinical condition of TBI patients before DC.
Investigation of electrically gate-all-around hexagonal nanowire FET (HexFET) architecture for 5 nm node logic and SRAM applications

This work investigates, in detail, the electrically gate-all-around (eGAA) Hexagonal NW FET (HexFET) which combines the high current drive of FinFETs with the excellent electrostatic robustness of conventional Gate-All-Around Nanowire (GAA NW) FETs. We evaluate HexFET as a potential successor to FinFET for 5nm node logic and SRAM applications using first principles atomistic-based modeling, calibrated 3D numerical device simulations, and circuit-level benchmarking. From this, we conclude that the eGAA HexFET architecture offers superior performance to both FinFET and GAA NW FET for 5nm node applications.
On battery State of Charge estimation: A new mixed algorithm

A new SoC estimation algorithm is presented. The estimation is performed mixing the Coulomb-Counting and the Model-Based estimation approaches. The algorithm was tested on a brand new lithium-ion cell which use a nanoscale technology able to grant a more powerful and safer lithium-ion battery than the batteries currently on the market. The cell, called A123-M1, was first characterized and then identified. The cell was identified with a equivalent electrical circuits model which is usually called 2th Randle model: the used test-bench and the identification process are described. Then the SoC estimation problem is described. A briefly review of the state of art of SoC estimation is given and the drawbacks of the methods which are usually used are presented. Finally the new mixed estimation algorithm is described: the algorithm scheme and the results are shown.
Broadband Millimeter-Wave Power Combiner Using Compact SIW to Waveguide Transition

A novel broadband millimeter-wave passive power combiner based on substrate integrated waveguide (SIW) to waveguide transition is presented in this letter. Two transition circuits are symmetrically inserted into the E-plane of the rectangular waveguide and function as power-combining network. To satisfy the wideband request of the power combiner, a broadband and compact SIW-to-waveguide transition circuit has been developed using antisymmetric tapered probe. A Ka-band four-way power combiner was fabricated and the measured results agree well with the simulated ones. The measured results show that the proposed combiner achieved a bandwidth of 52% from 23.5 to 40 GHz with better than 15 dB return loss and an insertion loss of 0.75 to 1.4 dB. This millimeter-wave power combiner can be employed in high power-combining system for its simple structure and broadband performance.
Social Image Search with Diverse Relevance Ranking

Recent years have witnessed the success of many online social media websites, which allow users to create and share media information as well as describe the media content with tags. However, the existing ranking approaches for tag-based image search frequently return results that are irrelevant or lack of diversity. This paper proposes a diverse relevance ranking scheme which is able to simultaneously take relevance and diversity into account. It takes advantage of both the content of images and their associated tags. First, it estimates the relevance scores of images with respect to the query term based on both the visual information of images and the semantic information of associated tags. Then we mine the semantic similarities of social images based on their tags. With the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes Average Diverse Precision (ADP), a novel measure that is extended from the conventional Average Precision (AP). Comprehensive experiments and user studies demonstrate the effectiveness of the approach.
PreFix: Switch Failure Prediction in Datacenter Networks

In modern datacenter networks (DCNs), failures of network devices are the norm rather than the exception, and many research efforts have focused on dealing with failures after they happen. In this paper, we take a different approach by predicting failures, thus the operators can intervene and "fix" the potential failures before they happen. Specifically, in our proposed system, named PreFix, we aim to determine during runtime whether a switch failure will happen in the near future. The prediction is based on the measurements of the current switch system status and historical switch hardware failure cases that have been carefully labelled by network operators. Our key observation is that failures of the same switch model share some common syslog patterns before failures occur, and we can apply machine learning methods to extract the common patterns for predicting switch failures. Our novel set of features (message template sequence, frequency, seasonality and surge) for machine learning can efficiently deal with the challenges of noises, sample imbalance, and computation overhead. We evaluated PreFix on a data set collected from 9397 switches (3 different switch models) deployed in more than 20 datacenters owned by a top global search engine in a 2-year period. PreFix achieved an average of 61.81% recall and 1.84 * 10^-5 false positive ratio. It outperforms the other failure prediction methods for computers and ISP devices.
Platelet rich fibrin: a new paradigm in periodontal regeneration

Among the great challenges facing clinical research is the development of bioactive surgical additives regulating inflammation and increasing healing. Although the use of fibrin adhesives and platelet-rich plasma (PRP) is well documented, they have their own limitations. Hence, reconstructive dental surgeons are looking for an “edge” that jump starts the healing process to maximize predictability as well as the volume of regenerated bone. Overcoming the restrictions related to the reimplantation of blood-derived products, a new family of platelet concentrate, which is neither a fibrin glue nor a classical platelet concentrate, was developed in France. This second generation platelet concentrate called platelet-rich fibrin (PRF), has been widely used to accelerate soft and hard tissue healing. Its advantages over the better known PRP include ease of preparation/application, minimal expense, and lack of biochemical modification (no bovine thrombin or anticoagulant is required). This article serves as an introduction to the PRF “concept” and its potential clinical applications with emphasis on periodontal regeneration.
Detection Based on Structural Properties

Phishing attacks pose a serious threat to end-users and commercial institutions alike. Majority of the present day phishing attacks employ e-mail as their primary carrier, in order to allure unsuspecting victims to visit the masqueraded website. While the recent defense mechanisms focus on detection by validating the authenticity of the website, very few approaches have been proposed which concentrate on detecting e-mail based phishing attacks based on the structural properties inherently present in the phishing e-mail. Also, phishing attacks growing in ingenuity as well as sophistication render most of existing browser based solutions weak. In this paper, we propose a novel technique to discriminate phishing e-mails from the legitimate e-mails using the distinct structural features present in them. The derived features, together with oneclass Support Vector Machine (SVM), can be used to efficiently classify phishing e-mails before it reaches the users inbox, essentially reducing the human exposure. Our prototype implementation sits between a user's mail transfer agent (MTA) and mail user agent (MUA) and processes each arriving e-mail even before it reaches the inbox. Using live email data, we demonstrate that our approach is able to detect a wide range of phishing e-mails with minimal performance overhead
The Contextual Loss for Image Transformation with Non-aligned Data

Feed-forward CNNs trained for image transformation problems rely on loss functions that measure the similarity between the generated image and a target image. Most of the common loss functions assume that these images are spatially aligned and compare pixels at corresponding locations. However, for many tasks, aligned training pairs of images will not be available. We present an alternative loss function that does not require alignment, thus providing an effective and simple solution for a new space of problems. Our loss is based on both context and semantics – it compares regions with similar semantic meaning, while considering the context of the entire image. Hence, for example, when transferring the style of one face to another, it will translate eyes-to-eyes and mouth-to-mouth. Our code can be found at https://www.github.com/roimehrez/contextualLoss
Match performance of high-standard soccer players with special reference to development of fatigue.

The aim of this study was to assess physical fitness, match performance and development of fatigue during competitive matches at two high standards of professional soccer. Computerized time-motion analyses were performed 2-7 times during the competitive season on 18 top-class and 24 moderate professional soccer players. In addition, the players performed the Yo-Yo intermittent recovery test. The top-class players performed 28 and 58% more (P < 0.05) high-intensity running and sprinting, respectively, than the moderate players (2.43 +/- 0.14 vs 1.90 +/- 0.12 km and 0.65 +/- 0.06 vs 0.41 +/- 0.03 km, respectively). The top-class players were better (11%; P < 0.05) on the Yo-Yo intermittent recovery test than the moderate players (2.26 +/- 0.08 vs 2.04 +/- 0.06 km, respectively). The amount of high-intensity running, independent of competitive standard and playing position, was lower (35-45%; P < 0.05) in the last than in the first 15 min of the game. After the 5-min period during which the amount of high-intensity running peaked, performance was reduced (P < 0.05) by 12% in the following 5 min compared with the game average. Substitute players (n = 13) covered 25% more (P < 0.05) ground during the final 15 min of high-intensity running than the other players. The coefficient of variation in high-intensity running was 9.2% between successive matches, whereas it was 24.8% between different stages of the season. Total distance covered and the distance covered in high-intensity running were higher (P < 0.05) for midfield players, full-backs and attackers than for defenders. Attackers and full-backs covered a greater (P < 0.05) distance in sprinting than midfield players and defenders. The midfield players and full-backs covered a greater (P < 0.05) distance than attackers and defenders in the Yo-Yo intermittent recovery test (2.23 +/- 0.10 and 2.21 +/- 0.04 vs 1.99 +/- 0.11 and 1.91 +/- 0.12 km, respectively). The results show that: (1) top-class soccer players performed more high-intensity running during a game and were better at the Yo-Yo test than moderate professional players; (2) fatigue occurred towards the end of matches as well as temporarily during the game, independently of competitive standard and of team position; (3) defenders covered a shorter distance in high-intensity running than players in other playing positions; (4) defenders and attackers had a poorer Yo-Yo intermittent recovery test performance than midfielders and full-backs; and (5) large seasonal changes were observed in physical performance during matches.
Which Reward Should I Choose? Preliminary Evidence for the Middle-Option Bias in Reward-Based Crowdfunding

Crowdfunding has become an important research area, but we know little about how rewards influence fundraising success. This research-in-progress studies reward-based crowdfunding through a behavioraleconomics lens. We draw on dual-process theory and provide preliminary evidence for the middle-option bias in crowdfunding. Two empirical studies in a simple, controlled environment confirm the significance of the middle-option bias, both for varying numbers of donation options and for varying price ranges. Since our findings suggest that the positioning of rewards in a menu of rewards can influence support behavior and how much money project creators collect, they can inform the design of crowdfunding projects. Our future research will develop a mock crowdfunding website to study the middle-option bias in a more realistic environment.
Soft tissue cephalometric analysis: diagnosis and treatment planning of dentofacial deformity.

This article will present a new soft tissue cephalometric analysis tool. This analysis may be used by the orthodontist and surgeon as an aid in diagnosis and treatment planning. The analysis is a radiographic instrument that was developed directly from the philosophy expressed in Arnett and Bergman "Facial keys to orthodontic diagnosis and treatment planning, Parts I and II" . The novelty of this approach, as with the "Facial Keys" articles, is an emphasis on soft tissue facial measurement.
Pragmatics and Word Meaning

In this paper, we explore the interaction between lexical semantics and pragmatics. We argue that linguistic processing is informationally encapsulated and utilises relatively simple ‘taxonomic’ lexical semantic knowledge. On this basis, defeasible lexical generalisations deliver defeasible parts of logical form. In contrast, pragmatic inference is openended and involves arbitrary real-world knowledge. Two axioms specify when pragmatic defaults override lexical ones. We demonstrate that modeling this interaction allows us to achieve a more refined interpretation of words in a discourse context than either the lexicon or pragmatics could do on their own.
A Novel Broadband Even-Mode Matching Network for Marchand Baluns

In this paper, we present the development of an even-mode matching network for a Marchand balun to achieve 50-¿ match and high isolation at the balanced ports. A very wide matching bandwidth can be accomplished with the novel addition of an open-circuit stub to the network. Experimental results demonstrate that the even-mode matched balun achieves return losses that are greater than 15 dB at the balanced ports from 6 to 17 GHz. The measured isolation between the balanced ports is greater than 15 dB from 4 to 19 GHz. For the unbalanced port, the return loss is greater than 15 dB from 4 to 19 GHz. The measured insertion loss is less than 1 dB, and amplitude and phase imbalances are ±0.5 dB and ±5° from 4 to 18 GHz, respectively.
A Review of Window Query Processing for Data Streams

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control.  Ubiquitous computing
Tracking the student's performance in Web-based education using Scrum methodology

The Internet, communication and mobile technologies are emerging as predominant paradigm in the learning of students. Various technologies have created numerous opportunities for the learners to explore in the learning landscape. Even though traditional teaching and learning are still popular in India, the scope for Web based education is positive. It is commonly believed that the Web based education is poised to penetrate deep into the educational system in India in near future. While global web-based education system faces different subjects, ideas and different kind of talented student's, the biggest challenge is communication and tracking the performance of student's. To deal with this challenge, we can use scrum. Scrum is the essence and one of the powerful agile methodologies. This paper presents about scrum and scrum backlog.
Fingerprint Recognition for Person Identification and Verification Based on Minutiae Matching

There are various types of applications for fingerprint recognition which is used for different purposes. Fingerprint is one of the challenging pattern Recognition problem. The Fingerprint Recognition system is divided into four stages. First is Acquisition stage to capture the fingerprint image, The second is Pre-processing stage to enhancement, binarization, thinning fingerprint image. The Third stage is Feature Extraction Stage to extract the feature from the thinning image by use minutiae extractor methods to extract ridge ending and ridge bifurcation from thinning. The fourth stage is matching(Identification, Verification) to match two minutiae points by using minutiae matcher method in which similarity and distance measure are used. The algorithm is tested accurately and reliably by using fingerprint images from different databases. In this paper the fingerprint databases used are FVC2000 and FVC2002 Databases, we see that, the FVC2002 database perform better results compare with FVC2000 database. The recognition system evaluate with two factor FAR and FRR, In this system the result of FAR is 0.0154 and FRR is 0.0137 with Accuracy equal to 98.55%.
Improving Sub-Phone Modeling for Better Native Language Identification with Non-Native English Speech

Identifying a speaker’s native language with his speech in a second language is useful for many human-machine voice interface applications. In this paper, we use a sub-phone-based i-vector approach to identify non-native English speakers’ native languages by their English speech input. Time delay deep neural networks (TDNN) are trained on LVCSR corpora for improving the alignment of speech utterances with their corresponding sub-phonemic “senone” sequences. The phonetic variability caused by a speaker’s native language can be better modeled with the sub-phone models than the conventional phone model based approach. Experimental results on the database released for the 2016 Interspeech ComParE Native Language challenge with 11 different L1s show that our system outperforms the best system by a large margin (87.2% UAR compared to 81.3% UAR for the best system from the 2016 ComParE challenge).
Distributed Learning over Unreliable Networks

Most of today’s distributed machine learning systems assume reliable networks: whenever two machines exchange information (e.g., gradients or models), the network should guarantee the delivery of the message. At the same time, recent work exhibits the impressive tolerance of machine learning algorithms to errors or noise arising from relaxed communication or synchronization. In this paper, we connect these two trends, and consider the following question: Can we design machine learning systems that are tolerant to network unreliability during training? With this motivation, we focus on a theoretical problem of independent interest—given a standard distributed parameter server architecture, if every communication between the worker and the server has a non-zero probability p of being dropped, does there exist an algorithm that still converges, and at what speed? The technical contribution of this paper is a novel theoretical analysis proving that distributed learning over unreliable network can achieve comparable convergence rate to centralized or distributed learning over reliable networks. Further, we prove that the influence of the packet drop rate diminishes with the growth of the number of parameter servers. We map this theoretical result onto a real-world scenario, training deep neural networks over an unreliable network layer, and conduct network simulation to validate the system improvement by allowing the networks to be unreliable.