nils-herrmann/clean_scirepeval huggingface dataset:
Validating the theoretical structure of the Treatment Self-Regulation Questionnaire (TSRQ) across three different health behaviors.

Nearly 40% of mortality in the United States is linked to social and behavioral factors such as smoking, diet and sedentary lifestyle. Autonomous self-regulation of health-related behaviors is thus an important aspect of human behavior to assess. In 1997, the Behavior Change Consortium (BCC) was formed. Within the BCC, seven health behaviors, 18 theoretical models, five intervention settings and 26 mediating variables were studied across diverse populations. One of the measures included across settings and health behaviors was the Treatment Self-Regulation Questionnaire (TSRQ). The purpose of the present study was to examine the validity of the TSRQ across settings and health behaviors (tobacco, diet and exercise). The TSRQ is composed of subscales assessing different forms of motivation: amotivation, external, introjection, identification and integration. Data were obtained from four different sites and a total of 2731 participants completed the TSRQ. Invariance analyses support the validity of the TSRQ across all four sites and all three health behaviors. Overall, the internal consistency of each subscale was acceptable (most alpha values >0.73). The present study provides further evidence of the validity of the TSRQ and its usefulness as an assessment tool across various settings and for different health behaviors.
Unpacking the Black Box of Causality : Learning about Causal Mechanisms from Experimental and Observational Studies

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.
Probase: a probabilistic taxonomy for text understanding

Knowledge is indispensable to understanding. The ongoing information explosion highlights the need to enable machines to better understand electronic text in human language. Much work has been devoted to creating universal ontologies or taxonomies for this purpose. However, none of the existing ontologies has the needed depth and breadth for universal understanding. In this paper, we present a universal, probabilistic taxonomy that is more comprehensive than any existing ones. It contains 2.7 million concepts harnessed automatically from a corpus of 1.68 billion web pages. Unlike traditional taxonomies that treat knowledge as black and white, it uses probabilities to model inconsistent, ambiguous and uncertain information it contains. We present details of how the taxonomy is constructed, its probabilistic modeling, and its potential applications in text understanding.
i3PosNet: Instrument Pose Estimation from X-Ray

Performing delicate Minimally Invasive Surgeries (MIS) forces surgeons to accurately assess the position and orientation (pose) of surgical instruments. In current practice, this pose information is provided by conventional tracking systems (optical and electro-magnetic). Two challenges render these systems inadequate for minimally invasive bone surgery: the need for instrument positioning with high precision and occluding tissue blocking the line of sight. Fluoroscopic tracking is limited by the radiation exposure to patient and surgeon. A possible solution is constraining the acquisition of x-ray images. The distinct acquisitions at irregular intervals require a pose estimation solution instead of a tracking technique. We develop i3PosNet (Iterative Image Instrument Pose estimation Network), a patchbased modular Deep Learning method enhanced by geometric considerations, which estimates the pose of surgical instruments from single x-rays. For the evaluation of i3PosNet, we consider the scenario of drilling in the otobasis. i3PosNet generalizes well to different instruments, which we show by applying it to a screw, a drill and a robot. i3PosNet consistently estimates the pose of surgical instruments better than conventional image registration techniques by a factor of 5 and more achieving inplane position errors of 0.031mm±0.025mm and angle errors of 0.031◦ ± 1.126◦. Additional factors, such as depth are evaluated to 0.361mm± 8.98mm from single radiographs.
Individual and combined in vitro (anti)androgenic effects of certain food additives and cosmetic preservatives.

The individual and combined (binary mixtures) (anti)androgenic effect of butylparaben (BuPB), butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT) and propyl gallate (PG) was evaluated using the MDA-kb2 cell line. Exposing these cells to AR agonists results in the expression of the reporter gene (encoding for luciferase) and luminescence can be measured in order to monitor the activity of the reporter protein. In case of the evaluation of the anti-androgenic effect, the individual test compounds or binary mixtures were tested in the presence of a fixed concentration of a strong AR agonist (1000 pM 5-alpha-dihydrotestosterone; DHT). Cell viability was assessed using a resazurin based assay. For PG, this is the first report in the literature concerning its (anti)androgenic activity. In case of both individual and mixture testing none of the compounds or binary combinations showed androgenic activity. When tested in the presence of DHT, BuPB, BHA and BHT proved to be weak anti-androgens and this was confirmed during the evaluation of binary mixtures (BuPB+BHA, BuPB+BHT and BHA+BHT). Besides performing the in vitro testing of the binary combinations, two mathematical models (dose addition and response addition) were evaluated in terms of accuracy of prediction of the anti-androgenic effect of the selected binary mixtures. The dose addition model guaranteed a good correlation between the experimental and predicted data. However, no estimation was possible in case of mixtures containing PG, due to the lack of effect of the compound in case of the individual testing.
Dropout distillation

Dropout is a popular stochastic regularization technique for deep neural networks that works by randomly dropping (i.e. zeroing) units from the network during training. This randomization process allows to implicitly train an ensemble of exponentially many networks sharing the same parametrization, which should be averaged at test time to deliver the final prediction. A typical workaround for this intractable averaging operation consists in scaling the layers undergoing dropout randomization. This simple rule called “standard dropout” is efficient, but might degrade the accuracy of the prediction. In this work we introduce a novel approach, coined “dropout distillation”, that allows us to train a predictor in a way to better approximate the intractable, but preferable, averaging process, while keeping under control its computational efficiency. We are thus able to construct models that are as efficient as standard dropout, or even more efficient, while being more accurate. Experiments on standard benchmark datasets demonstrate the validity of our method, yielding consistent improvements over conventional dropout.
Speaker identification features extraction methods: A systematic review

Speaker Identification (SI) is the process of identifying the speaker from a given utterance by comparing the voice biometrics of the utterance with those utterance models stored beforehand. SI technologies are taken a new direction due to the advances in artificial intelligence and have been used widely in various domains. Feature extraction is one of the most important aspects of SI, which significantly influences the SI process and performance. This systematic review is conducted to identify, compare, and analyze various feature extraction approaches, methods, and algorithms of SI to provide a reference on feature extraction approaches for SI applications and future studies. The review was conducted according to Kitchenham systematic review methodology and guidelines, and provides an in-depth analysis on proposals and implementations of SI feature extraction methods discussed in the literature between year 2011 and 2106. Three research questions were determined and an initial set of 535 publications were identified to answer the questions. After applying exclusion criteria 160 related publications were shortlisted and reviewed in this paper; these papers were considered to answer the research questions. Results indicate that pure Mel-Frequency Cepstral Coefficients (MFCCs) based feature extraction approaches have been used more than any other approach. Furthermore, other MFCC variations, such as MFCC fusion and cleansing approaches, are proven to be very popular as well. This study identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness. The results presented in this research are based on past publications, citations, and number of implementations with citations being most relevant. This paper also presents the general process of SI.
Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial

Trastuzumab, a monoclonal antibody against human epidermal growth factor receptor 2 (HER2; also known as ERBB2), was investigated in combination with chemotherapy for first-line treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer.



ToGA (Trastuzumab for Gastric Cancer) was an open-label, international, phase 3, randomised controlled trial undertaken in 122 centres in 24 countries. Patients with gastric or gastro-oesophageal junction cancer were eligible for inclusion if their tumours showed overexpression of HER2 protein by immunohistochemistry or gene amplification by fluorescence in-situ hybridisation. Participants were randomly assigned in a 1:1 ratio to receive a chemotherapy regimen consisting of capecitabine plus cisplatin or fluorouracil plus cisplatin given every 3 weeks for six cycles or chemotherapy in combination with intravenous trastuzumab. Allocation was by block randomisation stratified by Eastern Cooperative Oncology Group performance status, chemotherapy regimen, extent of disease, primary cancer site, and measurability of disease, implemented with a central interactive voice recognition system. The primary endpoint was overall survival in all randomised patients who received study medication at least once. 
594 patients were randomly assigned to study treatment (trastuzumab plus chemotherapy, n=298; chemotherapy alone, n=296), of whom 584 were included in the primary analysis (n=294; n=290). Median follow-up was 18.6 months (IQR 11-25) in the trastuzumab plus chemotherapy group and 17.1 months (9-25) in the chemotherapy alone group. Median overall survival was 13.8 months (95% CI 12-16) in those assigned to trastuzumab plus chemotherapy compared with 11.1 months (10-13) in those assigned to chemotherapy alone (hazard ratio 0.74; 95% CI 0.60-0.91; p=0.0046). The most common adverse events in both groups were nausea (trastuzumab plus chemotherapy, 197 [67%] vs chemotherapy alone, 184 [63%]), vomiting (147 [50%] vs 134 [46%]), and neutropenia (157 [53%] vs 165 [57%]). Rates of overall grade 3 or 4 adverse events (201 [68%] vs 198 [68%]) and cardiac adverse events (17 [6%] vs 18 [6%]) did not differ between groups.



Trastuzumab in combination with chemotherapy can be considered as a new standard option for patients with HER2-positive advanced gastric or gastro-oesophageal junction cancer.



F Hoffmann-La Roche.
Parallelizing State Space Plans Online

Searching for parallel solutions in state space planners is a challenging problem, because it would require the planners to branch on all possible subsets of parallel actions, exponentially increasing their branching factor. We introduce a variant of our heuristic state search planner AltAlt, which generates parallel plans by using greedy online parallelization of partial plans. Empirical results show that our online approach outperforms postprocessing (offline) techniques in terms of the quality of the solutions returned.
Connectivity-Based k-Hop Clustering in Wireless Networks

In this paper we describe several new clustering algorithms for nodes in a mobile ad hoc network. The main contribution is to generalize the cluster definition and formation algorithm so that a cluster contains all nodes that are at distance at most k hops from the clusterhead. We also describe algorithms for modifying cluster structure in the presence of topological changes. We also proposed an unified framework for most existing and new clustering algorithm where a properly defined weight at each node is the only difference in otherwise the same algorithm. This paper studied node connectivity and node ID as two particular weights, for k = 1 and k = 2. Finally, we propose a framework for generating random unit graphs
Banshee: bandwidth-efficient DRAM caching via software/hardware cooperation

Placing the DRAM in the same package as a processor enables several times higher memory bandwidth than conventional off-package DRAM. Yet, the latency of in-package DRAM is not appreciably lower than that of off-package DRAM. A promising use of in-package DRAM is as a large cache. Unfortunately, most previous DRAM cache designs optimize mainly for cache hit latency and do not consider bandwidth efficiency as a first-class design constraint. Hence, as we show in this paper, these designs are suboptimal for use with in-package DRAM.
 We propose a new DRAM cache design, Banshee, that optimizes for both in-package and off-package DRAM bandwidth efficiency without degrading access latency. Banshee is based on two key ideas. First, it eliminates the tag lookup overhead by tracking the contents of the DRAM cache using TLBs and page table entries, which is efficiently enabled by a new lightweight TLB coherence protocol we introduce. Second, it reduces unnecessary DRAM cache replacement traffic with a new bandwidth-aware frequency-based replacement policy. Our evaluations show that Banshee significantly improves performance (15% on average) and reduces DRAM traffic (35.8% on average) over the best-previous latency-optimized DRAM cache design.
Prevalence of hypogonadism in males aged at least 45 years: the HIM study

The Hypogonadism in Males study estimated the prevalence of hypogonadism [total testosterone (TT) < 300 ng/dl] in men aged > or = 45 years visiting primary care practices in the United States. A blood sample was obtained between 8 am and noon and assayed for TT, free testosterone (FT) and bioavailable testosterone (BAT). Common symptoms of hypogonadism, comorbid conditions, demographics and reason for visit were recorded. Of 2162 patients, 836 were hypogonadal, with 80 receiving testosterone. Crude prevalence rate of hypogonadism was 38.7%. Similar trends were observed for FT and BAT. Among men not receiving testosterone, 756 (36.3%) were hypogonadal; odds ratios for having hypogonadism were significantly higher in men with hypertension (1.84), hyperlipidaemia (1.47), diabetes (2.09), obesity (2.38), prostate disease (1.29) and asthma or chronic obstructive pulmonary disease (1.40) than in men without these conditions. The prevalence of hypogonadism was 38.7% in men aged > or = 45 years presenting to primary care offices.
Exploiting the Semantic Web for Unsupervised Natural Language Semantic Parsing

In this paper, we propose to bring together the semantic web experience and statistical natural language semantic parsing modeling. The idea is that, the process for populating knowledgebases by semantically parsing structured web pages may provide very valuable implicit annotation for language understanding tasks. We mine search queries hitting to these web pages in order to semantically annotate them for building statistical unsupervised slot filling models, without even a need for a semantic annotation guideline. We present promising results demonstrating this idea for building an unsupervised slot filling model for the movies domain with some representative slots. Furthermore, we also employ unsupervised model adaptation for cases when there are some in-domain unannotated sentences available. Another key contribution of this work is using implicitly annotated natural-language-like queries for testing the performance of the models, in a totally unsupervised fashion. We believe, such an approach also ensures consistent semantic representation between the semantic parser and the backend knowledge-base.
Why Should I Trust You ? Predictors of Interpersonal Trust in a Knowledge Transfer Context

Prior research has demonstrated that interpersonal trust is critical to knowledge transfer in organizational settings. Yet there has been only limited systematic empirical work examining factors that promote a knowledge seekers trust in a knowledge source. We propose three categories of variables that affect interpersonal trust in this context: attributes of the relationship between the knowledge seeker and source; attributes of the knowledge source; and attributes of the knowledge seeker. We analyzed these multilevel data simultaneously with hierarchical linear modeling (HLM) using survey data from three companies in different industries and countries. We found that (1) variables in all three categories were statistically significant, with the biggest effect coming from more malleable features such as the cognitive dimension of social capital (i.e., shared vision and shared language), and little or no effect from more stable and visible features such as formal structure and demographic similarity; (2) benevolence-based trust was easier to predict than competence-based trust, both in terms of the number of significant predictors and the variance accounted for; and (3) knowledge seekers reliance on knowledgesource behaviors in determining how much to trust a sources competencethe so-called clues for competencewere relied on even more heavily by knowledge seekers with more division tenure, suggesting that certain attitudes in the trust realm may solidify over time.
Analysis of L-Band Digital Aeronautical Communication Systems: L-DACS1 and L-DACS2

New air-to-ground wireless datalinks are needed to supplement existing civil aviation technologies. The 960 – 1164 MHz part of the IEEE L band has been identified as a candidate spectrum. EUROCONTROL — the European organization for the Safety of Air Navigation, has funded two parallel projects and developed two proposals called L-DACS1 and L-DACS2. Although, there is a significant amount of literature available on each of the two technologies from the two teams that designed the respective proposals, there is very little independent comparison of the two proposals. The goal of this paper is to provide this comparison. We compare the two proposals in terms of their scalability, spectral efficiency, and interference resistance. Both the technologies have to co-exist with several other aeronautical technologies that use the same L-band.
Real-time fire and flame detection in video

The paper proposes a novel method to detect fire and/or flame by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker are detected by analyzing the video in the wavelet domain. Periodic behavior in flame boundaries is detected by performing a temporal wavelet transform. Color variations in fire are detected by computing the spatial wavelet transform of moving fire-colored regions. Other clues used in the fire detection algorithm include irregularity of the boundary of the fire-colored region and the growth of such regions in time. All of the above clues are combined to reach a final decision.
Portfolio optimization for heavy-tailed assets : Extreme Risk Index vs

Using daily returns of the S&P500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, our study applies extreme value techniques in portfolio management on a large scale. We compare the performance of this strategy with the Markowitz approach and investigate how the ERI method can be applied most effectively. Our results show that the annualized return of the ERI strategy is particularly high for assets with heavy tails. The comparison also includes maximal drawdown, transaction costs, portfolio concentration, and asset diversity in the portfolio. In addition to that we study the impact of an alternative tail index estimator.
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

Natural spatiotemporal processes can be highly nonstationary in many ways, e.g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting. From Cramér’s Decomposition , any non-stationary process can be decomposed into deterministic, time-variant polynomials, plus a zero-mean stochastic term. By applying differencing operations appropriately, we may turn time-variant polynomials into a constant, making the deterministic component predictable. However, most previous recurrent neural networks for spatiotemporal prediction do not use the differential signals effectively, and their relatively simple state transition functions prevent them from learning too complicated variations in spacetime. We propose the Memory In Memory (MIM) networks and corresponding recurrent blocks for this purpose. The MIM blocks exploit the differential signals between adjacent recurrent states to model the non-stationary and approximately stationary properties in spatiotemporal dynamics with two cascaded, self-renewed memory modules. By stacking multiple MIM blocks, we could potentially handle higher-order non-stationarity. The MIM networks achieve the state-of-the-art results on three spatiotemporal prediction tasks across both synthetic and real-world datasets. We believe that the general idea of this work can be potentially applied to other time-series forecasting tasks.
Botnet: Classification, Attacks, Detection, Tracing, and Preventive Measures

Botnets become widespread in wired and wireless networks, whereas the relevant research is still in the initial stage. In this paper, a survey of botnets is provided. We first discuss fundamental concepts of botnets, including formation and exploitation, lifecycle, and two major kinds of topologies. Several related attacks, detection, tracing, and countermeasures, are then introduced, followed by recent research work and possible future challenges.