nils-herrmann/clean_scirepeval huggingface dataset:
Neurobiology of knowledge and misperception of lyrics

We conducted two functional magnetic resonance imaging (fMRI) experiments to investigate the neural underpinnings of knowledge and misperception of lyrics. In fMRI experiment 1, a linear relationship between familiarity with lyrics and activation was found in left-hemispheric speech-related as well as bilateral striatal areas which is in line with previous research on generation of lyrics. In fMRI experiment 2, we employed so called Mondegreens and Soramimi to induce misperceptions of lyrics revealing a bilateral network including middle temporal and inferior frontal areas as well as anterior cingulate cortex (ACC) and mediodorsal thalamus. ACC activation also correlated with the extent to which misperceptions were judged as amusing corroborating previous neuroimaging results on the role of this area in mediating the pleasant experience of chills during music perception. Finally, we examined the areas engaged during misperception of lyrics using diffusion-weighted imaging (DWI) to determine their structural connectivity. These combined fMRI/DWI results could serve as a neurobiological model for future studies on other types of misunderstanding which are events with potentially strong impact on our social life.
Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

For the purpose of enhancing the search ability of the cuckoo search (CS) algorithm, an improved robust approach, called HS/CS, is put forward to address the optimization problems. In HS/CS method, the pitch adjustment operation in harmony search (HS) that can be considered as a mutation operator is added to the process of the cuckoo updating so as to speed up convergence. Several benchmarks are applied to verify the proposed method and it is demonstrated that, in most cases, HS/CS performs better than the standard CS and other comparative methods. The parameters used in HS/CS are also investigated by various simulations.
Labia minora reconstruction using clitoral hood flaps, wedge excisions, and YV advancement flaps.

Labia minora reduction (labioplasty, labiaplasty) is the most common female genital aesthetic procedure. The majority of labia reductions are performed by trimming the labial edges. Many of these women present with (1) asymmetry; (2) scalloping of the labial edges with wide, occasionally painful scars; and (3) abrupt termination and distortion of the clitoral hood at its normal junctions with the clitoral frenula and the upper labium. Reconstruction can usually be performed with wedge excisions, labial YV advancement, and touch-up trimming. Reconstruction of a labial amputation, however, required the development of a new clitoral hood flap.



Twenty-four clitoral hood flaps were performed on 17 patients from June of 2006 through May of 2010. An island clitoral hood flap randomly based on the dartos fascia of the lower clitoral hood and medial labium majus is transposed to the ipsilateral labial defect to reconstruct a labium. Of the 10 patients with unilateral flaps, nine of the patients had previous bilateral labial reductions. Reconstruction of the opposite side in these nine women was performed using one or a combination of the following: wedge excisions, YV advancement flaps, or controlled touch-up trimming.



All 24 flaps survived, with four minor complications. Five patients underwent revision of a total of seven flaps, but only two were for complications. As experience increased, revisions for aesthetic improvement became less common.



Reconstruction of labia minora defects secondary to trimming labia reductions is very successful using a combination of clitoral hood flaps, wedge excisions, and YV advancements.
Pyramid Stereo Matching Network

Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in ill-posed regions. To tackle this problem, we propose PSMNet, a pyramid stereo matching network consisting of two main modules: spatial pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage of the capacity of global context information by aggregating context in different scales and locations to form a cost volume. The 3D CNN learns to regularize cost volume using stacked multiple hourglass networks in conjunction with intermediate supervision. The proposed approach was evaluated on several benchmark datasets. Our method ranked first in the KITTI 2012 and 2015 leaderboards before March 18, 2018. The codes of PSMNet are available at: https://github.com/JiaRenChang/PSMNet.
Strategic Object Oriented Reinforcement Learning

Humans learn to play video games significantly faster than state-of-the-art reinforcement learning (RL) algorithms. Inspired by this, we introduce strategic object oriented reinforcement learning (SOORL) to learn simple dynamics model through automatic model selection and perform efficient planning with strategic exploration. We compare different exploration strategies in a model-based setting in which exact planning is impossible. Additionally, we test our approach on perhaps the hardest Atari game Pitfall! and achieve significantly improved exploration and performance over prior methods.
Experimental exploration of the performance of binary networks

Binary neural networks for object recognition are desirable especially for small and embedded systems because of their arithmetic and memory efficiency coming from the restriction of the bit-depth of network weights and activations. Neural networks in general have a tradeoff between the accuracy and efficiency in choosing a model architecture, and this tradeoff matters more for binary networks because of the limited bit-depth. This paper then examines the performance of binary networks by modifying architecture parameters (depth and width parameters) and reports the best-performing settings for specific datasets. These findings will be useful for designing binary networks for practical uses.
Graphical models via univariate exponential family distributions

Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a general sub-class of graphical models where the node-wise conditional distributions arise from exponential families. This allows us to derive multivariate graphical model distributions from univariate exponential family distributions, such as the Poisson, negative binomial, and exponential distributions. Our key contributions include a class of M-estimators to fit these graphical model distributions; and rigorous statistical analysis showing that these M-estimators recover the true graphical model structure exactly, with high probability. We provide examples of genomic and proteomic networks learned via instances of our class of graphical models derived from Poisson and exponential distributions.
Citizens’ Perceptions of Data Protection and Privacy in Europe

Data protection and privacy gain social importance as technology and data flows play an ever greater role in shaping social structure. Despite this, understanding of public opinion on these issues is conspicuously lacking. This article is a meta-analysis of public opinion surveys on data protection and privacy focussed on EU citizens. The article firstly considers the understanding and awareness of the legal framework for protection as a solid manifestation of the complex concepts of data protection and privacy. This is followed by a consideration of perceptions of privacy and data protection in relation to other social goals, focussing on the most visible of these contexts the debate surrounding privacy, data protection and security. The article then considers how citizens perceive the ‘real world’ environment in which data processing takes place, before finally considering the public’s perception and evaluation of the operation of framework against environment. 1 Corresponding author Hallinan, Dara, Michael Friedewald, and Paul McCarthy, "Citizens' Perceptions of Data Protection and Privacy", Computer Law and Security Review,  2012, pp. 263-272. 1 CITIZENS’ PERCEPTIONS OF DATA PROTECTION AND PRIVACY IN EUROPE
Petrology of some oceanic island basalts: PRIMELT2.XLS software for primary magma calculation

PRIMELT2.XLS software is introduced for calculating primary magma composition and mantle potential temperature (TP) from an observed lava composition. It is an upgrade over a previous version in that it includes garnet peridotite melting and it detects complexities that can lead to overestimates in TP by >100 C. These are variations in source lithology, source volatile content, source oxidation state, and clinopyroxene fractionation. Nevertheless, application of PRIMELT2.XLS to lavas from a wide range of oceanic islands reveals no evidence that volatile-enrichment and source fertility are sufficient to produce them. All are associated with thermal anomalies, and this appears to be a prerequisite for their formation. For the ocean islands considered in this work, TP maxima are typically 1450–1500 C in the Atlantic and 1500–1600 C in the Pacific, substantially greater than 1350 C for ambient mantle. Lavas from the Galápagos Islands and Hawaii record in their geochemistry high TP maxima and large ranges in both TP and melt fraction over short horizontal distances, a result that is predicted by the mantle plume model.
Car Number Plate Recognition (CNPR) system using multiple template matching

Image processing algorithms are used for converting textual image to editable text. One application could be a car number plate recognition (CNPR) system where these type of algorithms automatically detect the car registration number by capturing an image. In this paper, we improve one of the existing CNPR algorithms. The contributions include: multiple template matching; considering light intensity and recognizing the car number plate even in low intense light and independence of distance of car number plate to camera. The involvement of different conditions and external factors actually improve the CNPR system efficiency. We run different experiments on different car number plates. Our proposed improvements yield better results in terms of false positive and false negative values for CNPR.
An efficient auction mechanism for service chains in the NFV market

Network Function Virtualization (NFV) is emerging as a new paradigm for providing elastic network functions through flexible virtual network function (VNF) instances executed on virtualized computing platforms exemplified by cloud datacenters. In the new NFV market, well defined VNF instances each realize an atomic function that can be chained to meet user demands in practice. This work studies the dynamic market mechanism design for the transaction of VNF service chains in the NFV market, to help relinquish the full power of NFV. Combining the techniques of primal-dual approximation algorithm design with Myerson's characterization of truthful mechanisms, we design a VNF chain auction that runs efficiently in polynomial time, guarantees truthfulness, and achieves near-optimal social welfare in the NFV eco-system. Extensive simulation studies verify the efficacy of our auction mechanism.
Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our implemented, open-source (https://github.com/hexiangnan/sigir16-eals) eALS consistently outperforms state-of-the-art implicit MF methods.
Operating characteristics of a rank correlation test for publication bias.

An adjusted rank correlation test is proposed as a technique for identifying publication bias in a meta-analysis, and its operating characteristics are evaluated via simulations. The test statistic is a direct statistical analogue of the popular "funnel-graph." The number of component studies in the meta-analysis, the nature of the selection mechanism, the range of variances of the effect size estimates, and the true underlying effect size are all observed to be influential in determining the power of the test. The test is fairly powerful for large meta-analyses with 75 component studies, but has only moderate power for meta-analyses with 25 component studies. However, in many of the configurations in which there is low power, there is also relatively little bias in the summary effect size estimate. Nonetheless, the test must be interpreted with caution in small meta-analyses. In particular, bias cannot be ruled out if the test is not significant. The proposed technique has potential utility as an exploratory tool for meta-analysts, as a formal procedure to complement the funnel-graph.
Crosstalk analysis for dual stripline with parallel and angled routing

Dual stripline routing is more and more widely used in the modern high speed PCB design due to its cost advantage of reduced overall layer count. However, the major challenge of a successful dual stripline design is to handle the additional interferences introduced by the signals on adjacent layers. This paper studies the crosstalk effect of the dual stripline with both parallel and angled routing, and proposes design solutions to tackle the challenge. Analytical and empirical algorithms are proposed to estimate the crosstalk waveforms from multiple aggressors, which provide quick design risk assessment, and the waveform is well correlated to the 3D full wave EM simulation results.
A study on scar revision

Scar evaluation and revision techniques are chief among the most important skills in the facial plastic and reconstructive surgeon’s armamentarium. Often minimized in importance, these techniques depend as much on a thorough understanding of facial anatomy and aesthetics, advanced principles of wound healing, and an appreciation of the overshadowing psychological trauma as they do on thorough technical analysis and execution . Scar revision is unique in the spectrum of facial plastic and reconstructive surgery because the initial traumatic event and its immediate treatment usually cannot be controlled. Patients who are candidates for scar revision procedures often present after significant loss of regional tissue, injury that crosses anatomically distinct facial aesthetic units, wound closure by personnel less experienced in plastic surgical technique, and poor post injury wound management [3,4]. While no scar can be removed completely, plastic surgeons can often improve the appearance of a scar, making it less obvious through the injection or application of certain steroid medications or through surgical procedures known as scar revisions.There are many variables affect the severity of scarring, including the size and depth of the wound, blood supply to the area, the thickness and color of your skin, and the direction of the scar [5,6].
Pictures of pain: their contribution to the neuroscience of empathy.

The study of empathy, a translation of the term 'Einfühlung', originated in 19th century Germany in the sphere of aesthetics, and was followed by studies in psychology and then neuroscience. During the past decade the links between empathy and art have started to be investigated, but now from the neuroscientific perspective, and two different approaches have emerged. Recently, the primacy of the mirror neuron system and its association with automaticity and imitative, simulated movement has been envisaged. But earlier, a number of eminent art historians had pointed to the importance of cognitive responses to art; these responses might plausibly be subserved by alternative neural networks. Focusing here mainly on pictures depicting pain and evoking empathy, both approaches are considered by summarizing the evidence that either supports the involvement of the mirror neuron system, or alternatively suggests other neural networks are likely to be implicated. The use of such pictures in experimental studies exploring the underlying neural processes, however, raises a number of concerns, and suggests caution is exercised in drawing conclusions concerning the networks that might be engaged. These various networks are discussed next, taking into account the affective and sensory components of the pain experience, before concluding that both mirror neuron and alternative neural networks are likelyto be enlisted in the empathetic response to images of pain. A somewhat similar duality of spontaneous and cognitive processes may perhaps also be paralleled in the creation of such images. While noting that some have repudiated the neuroscientific approach to the subject, pictures are nevertheless shown here to represent an unusual but invaluable tool in the study of pain and empathy.
Accelerated Path-Based Timing Analysis with MapReduce

Path-based timing analysis (PBA) is a pivotal step to achieve accurate timing signoff. A core primitive extracts a large set of paths subject to path-specific or less-pessimistic timing update. However, this process in nature demands a very high computational complexity and thus has been a major bottleneck in accelerating timing closure. Therefore, we introduce in this paper a fast and scalable PBA framework with MapReduce - a recent programming paradigm invented by Google for big-data processing. Inspired by the spirit of MapReduce, we formulate our problem into tasks that are associated with keys and values and perform massively-parallel map and reduce operations on a distributed system. Experimental results demonstrated that our approach can easily analyze million nodes in a single minute.
The complement of research and theory in practice: contact theory at work in nonfamilial intergenerational programs.

We assessed whether a shared site intergenerational care program informed by contact theory contributed to more desirable social behaviors of elders and children during intergenerational programming than a center with a more traditional programming approach that lacks some or all of the contact theory tenets.



We observed 59 elder and child participants from the two sites during intergenerational activities. Using the Intergenerational Observation Scale, we coded participants' predominant behavior in 15-s intervals through each activity's duration. We then calculated for each individual the percentage of time frames each behavior code was predominant.



Participants at the theory-based program demonstrated higher rates of intergenerational interaction, higher rates of solitary behavior, and lower rates of watching than at the traditional program.



Contact theory tenets were optimized when coupled with evidence-based practices. Intergenerational programs with stakeholder support that promotes equal group status, cooperation toward a common goal, and mechanisms of friendship among participants can achieve important objectives for elder and child participants in care settings.
Factors modifying the efficacy of transcranial magnetic stimulation in the treatment of depression: a review.

So far no convincing answer has emerged to the question of whether transcranial magnetic stimulation (TMS) can make a clinically useful contribution to the treatment of depression. Here we examine whether multiple sensitivity analyses can highlight parameters that predict a favorable treatment response.



Medline, Embase, and the Cochrane database for controlled trials were searched for relevant randomized controlled trials using the expression (transcranial magnetic stimulation or TMS) and depression.



Thirty-three studies were identified and included in the random-effects meta-analysis, and between 17 and 31 studies were included in the secondary analyses comparing outcome of studies with different parameters.



Study data were extracted with a standardized data sheet. A meta-analysis based on Cohen d effect size measure was done for all studies and various subsets. Regression analysis of effect sizes with study parameters was done in 24 studies.



Active TMS treatment was more effective than sham, but variability was too great to take any single study design as paradigmatic. No significant predictors of study effect size were found. Mean effect sizes were reduced, although still significant, in studies with stimulation intensity below 90% of motor threshold and new medication starting within 7 days before to 7 days after start of TMS.



The absence of significant outcome predictors in the presence of significant variability of outcome measures can be interpreted in 2 ways: either study sizes and numbers and designs are insufficient to afford the power necessary to detect such predictors or TMS has a nonspecific effect on depression that is not influenced by study parameters. Large-scale comparative trials are necessary to decide between these interpretations.
Performance Characterization of Gas-Solid Cyclone for Separation of Particle from Syngas Produced from Food Waste Gasifier Plant

A biofuel from any biodegradable formation process such as a food waste bio-digester plant is a mixture of several gases such as methane (CH4), carbon dioxide (CO2), hydrogen sulfide (H2S), ammonia (NH3) and impurities like water and dust particles. The results are reported of a parametric study of the process of separation of methane, which is the most important gas in the mixture and usable as a biofuel, from particles and H2S. A cyclone, which is a conventional, economic and simple device for gas-solid separation, is considered based on the modification of three Texas A&M cyclone designs (1D2D, 2D2D and 1D3D) by the inclusion of an air inlet tube. A parametric sizing is performed of the cyclone for biogas purification, accounting for the separation of hydrogen sulfide (H2S) and dust particles from the biofuel. The stochiometric oxidation of H2S to form elemental sulphur is considered a useful cyclone design criterion. The proposed design includes geometric parameters and several criteria for quantifying the performance of cyclone separators such as the Lapple Model for minimum particle diameter collected, collection efficiency and pressure drop. For biogas volumetric flow rates between 0 and 1 m/s and inlet flow velocities of 12 m/s, 15 m/s and 18 m/s for the 1D2D, 2D2D and 1D3D cyclones, respectively, it is observed that the 2D2D configuration is most economic in terms of sizing (total height and diameter of cyclone). The 1D2D configuration experiences the lowest pressure drop. A design algorithm coupled with a user-friendly graphics interface is developed on the MATLAB platform, providing a tool for sizing and designing suitable cyclones.