nils-herrmann/clean_scirepeval huggingface dataset:
StreamRec: a real-time recommender system

Research and development of recommender systems has been a vibrant field for over a decade, having produced proven metho ds for “preference-aware” computing. Recommenders use commu nity opinion histories to help users identify interesting i tems from a considerably large search space (e.g., inventory from Amaz on [7], movies from Netflix ). Personalization, recommendation , a d the “human side" of data-centric applications are even beco ming important topics in the data management community . A popular recommendation method used heavily in practice is collaborative filtering, consisting of two phases: (1) An offline model-buildingphase that uses community opinions of items (e.g., movie ratings, “Diggs” ) to build a model storing meaning ful correlations between users and items. (2) An on-demandrecommendationphase that uses the model to produce a set of recommended items when requested from a user or application. To be effective, recommender systems must evolve with their content. In current update-intensive systems (e.g., socia l networks, online news sites), the restriction that a model be generate d offline is a significant drawback, as it hinders the system’s ability to evolve quickly. For instance, new users enter the system cha nging the collective opinions over items, or the system adds ne w items quickly (e.g., news posts, Facebook postings), which w dens the recommendation pool. These updates affect the recommen der model, that in turn affect the system’s recommendation qual ity in terms of providing accurate answers to recommender queries . In such systems, a completely real-time recommendation process is paramount. Unfortunately, most traditional state-of-the -art recommenders are “hand-built", implemented as custom software notbuilt for a real-time recommendation process . Further, for so me
Intra- and Inter-Brain Synchronization during Musical Improvisation on the Guitar

Humans interact with the environment through sensory and motor acts. Some of these interactions require synchronization among two or more individuals. Multiple-trial designs, which we have used in past work to study interbrain synchronization in the course of joint action, constrain the range of observable interactions. To overcome the limitations of multiple-trial designs, we conducted single-trial analyses of electroencephalography (EEG) signals recorded from eight pairs of guitarists engaged in musical improvisation. We identified hyper-brain networks based on a complex interplay of different frequencies. The intra-brain connections primarily involved higher frequencies (e.g., beta), whereas inter-brain connections primarily operated at lower frequencies (e.g., delta and theta). The topology of hyper-brain networks was frequency-dependent, with a tendency to become more regular at higher frequencies. We also found hyper-brain modules that included nodes (i.e., EEG electrodes) from both brains. Some of the observed network properties were related to musical roles during improvisation. Our findings replicate and extend earlier work and point to mechanisms that enable individuals to engage in temporally coordinated joint action.
KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest. The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E| 1 2 +o(1) with high probability, obtaining a significant speedup with respect to the Θ(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings, as well.
Robust real-time visual odometry for dense RGB-D mapping

This paper describes extensions to the Kintinuous  algorithm for spatially extended KinectFusion, incorporating the following additions: (i) the integration of multiple 6DOF camera odometry estimation methods for robust tracking; (ii) a novel GPU-based implementation of an existing dense RGB-D visual odometry algorithm; (iii) advanced fused realtime surface coloring. These extensions are validated with extensive experimental results, both quantitative and qualitative, demonstrating the ability to build dense fully colored models of spatially extended environments for robotics and virtual reality applications while remaining robust against scenes with challenging sets of geometric and visual features.
Privacy Preserving Social Network Data Publication

The introduction of online social networks (OSN) has transformed the way people connect and interact with each other as well as share information. OSN have led to a tremendous explosion of network-centric data that could be harvested for better understanding of interesting phenomena such as sociological and behavioural aspects of individuals or groups. As a result, online social network service operators are compelled to publish the social network data for use by third party consumers such as researchers and advertisers. As social network data publication is vulnerable to a wide variety of reidentification and disclosure attacks, developing privacy preserving mechanisms are an active research area. This paper presents a comprehensive survey of the recent developments in social networks data publishing privacy risks, attacks, and privacy-preserving techniques. We survey and present various types of privacy attacks and information exploited by adversaries to perpetrate privacy attacks on anonymized social network data. We present an in-depth survey of the state-of-the-art privacy preserving techniques for social network data publishing, metrics for quantifying the anonymity level provided, and information loss as well as challenges and new research directions. The survey helps readers understand the threats, various privacy preserving mechanisms, and their vulnerabilities to privacy breach attacks in social network data publishing as well as observe common themes and future directions.
The influence of cultural factors on obsessive compulsive disorder: religious symptoms in a religious society.

Judaism is one of many religions that demand cleanliness and exactness, inculcate the performance of rituals from childhood and view their non-performance as wrong or sinful. Rituals concerning cleanliness and exactness are the commonest presentations of OCD. In a sample of 34 psychiatric out-patients with OCD in north Jerusalem, religious symptoms were found in 13 of the 19 ultra-orthodox patients, and in one of the 15 non-ultra-orthodox patients. Nine of the 15 OCD patients with religious symptoms also had non-religious symptoms. Four main topics of religious symptomatology were found: prayer, dietary practices, menstrual practices and cleanliness before prayer. The dictates of religious codes regarding these topics are presented and the law is rigorous in its demands, in many cases encouraging repeating rituals. Nevertheless, repetitive performance of religious rituals is recognized by OCD sufferers and their rabbis as expressing psychopathology rather than heightened spirituality. The forms of the religious obsessions and the associated rituals in this sample were similar to the presentation of OCD in non-religious patients. Religion appears not to be a distinctive topic of OCD, rather it is the setting for the condition in very religious patients.
Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome. Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts,  provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods. The software is available at http://github.com/lamortenera/epicseg
Efficient coflow scheduling with Varys

Communication in data-parallel applications often involves a collection of parallel flows. Traditional techniques to optimize flow-level metrics do not perform well in optimizing such collections, because the network is largely agnostic to application-level requirements. The recently proposed coflow abstraction bridges this gap and creates new opportunities for network scheduling. In this paper, we address inter-coflow scheduling for two different objectives: decreasing communication time of data-intensive jobs and guaranteeing predictable communication time. We introduce the concurrent open shop scheduling with coupled resources problem, analyze its complexity, and propose effective heuristics to optimize either objective. We present Varys, a system that enables data-intensive frameworks to use coflows and the proposed algorithms while maintaining high network utilization and guaranteeing starvation freedom. EC2 deployments and trace-driven simulations show that communication stages complete up to 3.16X faster on average and up to 2X more coflows meet their deadlines using Varys in comparison to per-flow mechanisms. Moreover, Varys outperforms non-preemptive coflow schedulers by more than 5X.
Appearance learning for 3D tracking of robotic surgical tools

In this paper, we present an appearance learning approach which is used to detect and track surgical robotic tools in laparoscopic sequences. By training a robust visual feature descriptor on low-level landmark features, we build a framework for fusing robot kinematics and 3D visual observations to track surgical tools over long periods of time across various types of environment. We demonstrate 3D tracking on multiple types of tool (with different overall appearances) as well as multiple tools simultaneously. We present experimental results using the da Vinci® surgical robot using a combination of both ex-vivo and in-vivo environments.
On the Approximation Capability of Recurrent Neural Networks

The capability of recurrent neural networks to approximate functions from lists of real vectors to a real vector space is considered. We show the following results: From approximation results in the feedforward case follows that any measurable function can be approximated with a recurrent network arbitrarily well in probability. For lists with entries from a nite alphabet only one neuron is needed in the recurrent part of the network. On the other hand there exist computable functions that cannot be approximated in the maximum norm. This is valid even for functions on unary inputs if they have unlimited range. For limited range this is valid for inputs on an alphabet with at least two elements. But if only the length of a sequence is relevant i.e. sequences with entries from a unary alphabet are considered, any function with limited range can be approximated in the maximum norm. As a special case a sigmoidal recurrent network with one irrational weight can compute any function on unary inputs in linear time.
Distribution System Planning With Reliability

This paper presents a model for solving the multistage planning problem of a distribution network. The objective function to be minimized is the net present value of the investment cost to add, reinforce or replace feeders and substations, losses cost, and operation and maintenance cost. The model considers three levels of load in each node and two investment alternatives for each resource to be added, reinforced or replaced. The nonlinear objective function is approximated by a piecewise linear function, resulting in a mixed integer linear model that is solved using standard mathematical programming. The model allows us to find multiple solutions in addition to the optimal one, helping the decision maker to analyze and choose from a pool of solutions. In addition to the optimization problem, reliability indexes and associated costs are computed for each solution, based on the regulation model used in Brazil. Numerical results and discussion are presented for an illustrative 27-node test network.
Coupling Interactions and Performance: Predicting Team Performance from Thin Slices of Conflict

Do teams show stable conflict interaction patterns that predict their performance hours, weeks, or even months in advance? Two studies demonstrate that two of the same patterns of emotional interaction dynamics that distinguish functional from dysfunctional marriages also distinguish high from low-performance design teams in the field, up to 6 months in advance, with up to 91% accuracy, and based on just 15minutes of interaction data: Group Affective Balance, the balance of positive to negative affect during an interaction, and Hostile Affect, the expression of a set of specific negative behaviors were both found as predictors of team performance. The research also contributes a novel method to obtain a representative sample of a team's conflict interaction. Implications for our understanding of design work in teams and for the design of groupware and feedback intervention systems are discussed.
Online selection of discriminative tracking features

This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features according to how well they separate sample distributions of object and background pixels. This feature evaluation mechanism is embedded in a mean-shift tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented that demonstrate how this method adapts to changing appearances of both tracked object and scene background. We note susceptibility of the variance ratio feature selection method to distraction by spatially correlated background clutter and develop an additional approach that seeks to minimize the likelihood of distraction.
Distributed Agent Architecture for Port Automation

In the near future, container ports will no longer be able to expand into the surrounding land and will thus be unable to meet the storage requirements due to the boom in world trade. A solution to this problem is to increase the container throughput of the port by reducing the amount of time necessary to load and unload a ship. This paper presents distributed agent architecture to achieve this task. Under such architecture, an intelligent planning algorithm is continuously optimised by the dynamic and co-operative rescheduling of yard resources such as quay cranes and
Endometrial stromal sarcoma of the vagina.

Endometrial stromal sarcoma is a rare tumor and has unique histopathologic features. Most tumors of this kind occur in the uterus; thus, the vagina is an extremely rare site. A 34-year-old woman presented with endometrial stromal sarcoma arising in the vagina. No correlative endometriosis was found. Because of the uncommon location, this tumor was differentiated from other more common neoplasms of the vagina, particularly embryonal rhabdomyosarcoma and other smooth muscle tumors. Although the pathogenesis of endometrial stromal tumors remains controversial, the most common theory of its origin is heterotopic Müllerian tissue such as endometriosis tissue. Primitive cells of the pelvis and retroperitoneum are an alternative possible origin for the tumor if endometriosis is not present. According to the literature, the tumor has a fairly good prognosis compared with other vaginal sarcomas. Surgery combined with adjuvant radiotherapy appears to be an adequate treatment.
Androgen-deprivation therapy in prostate cancer and cardiovascular risk: a science advisory from the American Heart Association, American Cancer Society, and American Urological Association: endorsed by the American Society for Radiation Oncology.

Glenn N. Levine, MD, FAHA, Chair, Anthony V. D’Amico, MD, PhD, Peter Berger, MD, FAHA, Peter E. Clark, MD, Robert H. Eckel, MD, FAHA, Nancy L. Keating, MD, MPH, Richard V. Milani, MD, FAHA, Arthur I. Sagalowsky, MD, Matthew R. Smith, MD, PhD, Neil Zakai, MD on behalf of the American Heart Association Council on Clinical Cardiology and Council on Epidemiology and Prevention, the American Cancer Society, and the American Urological Association
Statistical properties of community structure in large social and information networks

A large body of work has been devoted to identifying community structure in networks. A community is often though of as a set of nodes that has more connections between its members than to the remainder of the network. In this paper, we characterize as a function of size the statistical and structural properties of such sets of nodes. We define the network community profile plot, which characterizes the "best" possible community - according to the conductance measure - over a wide range of size scales, and we study over 70 large sparse real-world networks taken from a wide range of application domains. Our results suggest a significantly more refined picture of community structure in large real-world networks than has been appreciated previously.
 Our most striking finding is that in nearly every network dataset we examined, we observe tight but almost trivial communities at very small scales, and at larger size scales, the best possible communities gradually "blend in" with the rest of the network and thus become less "community-like." This behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, this behavior is exactly the opposite of what one would expect based on experience with and intuition from expander graphs, from graphs that are well-embeddable in a low-dimensional structure, and from small social networks that have served as testbeds of community detection algorithms. We have found, however, that a generative model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community structure similar to our observations.
Nivolumab versus Docetaxel in Advanced Squamous-Cell Non-Small-Cell Lung Cancer.

Patients with advanced squamous-cell non-small-cell lung cancer (NSCLC) who have disease progression during or after first-line chemotherapy have limited treatment options. This randomized, open-label, international, phase 3 study evaluated the efficacy and safety of nivolumab, a fully human IgG4 programmed death 1 (PD-1) immune-checkpoint-inhibitor antibody, as compared with docetaxel in this patient population.



We randomly assigned 272 patients to receive nivolumab, at a dose of 3 mg per kilogram of body weight every 2 weeks, or docetaxel, at a dose of 75 mg per square meter of body-surface area every 3 weeks. The primary end point was overall survival.



The median overall survival was 9.2 months (95% confidence interval [CI], 7.3 to 13.3) with nivolumab versus 6.0 months (95% CI, 5.1 to 7.3) with docetaxel. The risk of death was 41% lower with nivolumab than with docetaxel (hazard ratio, 0.59; 95% CI, 0.44 to 0.79; P<0.001). At 1 year, the overall survival rate was 42% (95% CI, 34 to 50) with nivolumab versus 24% (95% CI, 17 to 31) with docetaxel. The response rate was 20% with nivolumab versus 9% with docetaxel (P=0.008). The median progression-free survival was 3.5 months with nivolumab versus 2.8 months with docetaxel (hazard ratio for death or disease progression, 0.62; 95% CI, 0.47 to 0.81; P<0.001). The expression of the PD-1 ligand (PD-L1) was neither prognostic nor predictive of benefit. Treatment-related adverse events of grade 3 or 4 were reported in 7% of the patients in the nivolumab group as compared with 55% of those in the docetaxel group.



Among patients with advanced, previously treated squamous-cell NSCLC, overall survival, response rate, and progression-free survival were significantly better with nivolumab than with docetaxel, regardless of PD-L1 expression level. (Funded by Bristol-Myers Squibb; CheckMate 017 ClinicalTrials.gov number, NCT01642004.).
Comparison and Reduction of Conducted EMI in SiC JFET and Si IGBT-Based Motor Drives

This paper compares the conducted electromagnetic interference (EMI) in Si insulated gate bipolar transistor (IGBT) and silicon carbide (SiC) junction field-effect transistor (JFET) based motor drives. Two inverters, with the SiC and Si transistors, respectively, are built using the same circuit layout and investigated accordingly. Their conducted EMI levels are compared under the conditions of without filter and with traditional common mode (CM) filters. Reasons of the exhibited different noise emissions are analyzed. To verify the discussions, two inverters are tested in the CM. This allows for the identification and analysis of their maximized CM and differential mode (DM) wave shapes. It is shown that the excited parasitic oscillations during the switching transients are magnified more in the SiC JFET inverter, which is the main cause of the noise difference. Lastly, improved filtering solutions are proposed, which effectively suppressed the increased high-frequency noise due to the faster SiC switching speed.
A lightweight, low-power electroadhesive clutch and spring for exoskeleton actuation

Clutches can be used to enhance the functionality of springs or actuators in robotic devices. Here we describe a lightweight, low-power clutch used to control spring engagement in an ankle exoskeleton. The clutch is based on electrostatic adhesion between thin electrode sheets coated with a dielectric material. Each electrode pair weighs 1.5 g, bears up to 100 N, and changes states in less than 30 ms. We placed clutches in series with elastomer springs to allow control of spring engagement, and placed several clutched springs in parallel to discretely adjust stiffness. By engaging different numbers of springs, the system produced six different levels of stiffness. Force at peak displacement ranged from 14 to 501 N, and the device returned 95% of stored mechanical energy. Each clutched spring element weighed 26 g. We attached one clutched spring to an ankle exoskeleton and used it to engage the spring only while the foot was on the ground during 150 consecutive walking steps. Peak torque was 7.3 N·m on an average step, and the device consumed 0.6 mW of electricity. Compared to other electrically-controllable clutches, this approach results in three times higher torque density and two orders of magnitude lower power consumption per unit torque. We anticipate this technology will be incorporated into exoskeletons that tune stiffness online and into new actuator designs that utilize many lightweight, low-power clutches acting in concert.