nils-herrmann/clean_scirepeval huggingface dataset:
Voice Puppetry

We introducea methodfor predictinga controlsignalfrom anotherrelatedsignal,and applyit to voice puppetry: Generatingfull facialanimationfrom expressi ve information in anaudiotrack. Thevoicepuppetlearnsa facialcontrolmodelfrom computervision of realfacialbehavior, automaticallyincorporatingvocalandfacialdynamicssuchascoarticulation. Animation is producedby usingaudioto drive themodel,which induces a probability distribution over the manifold of possiblefacial motions. We presenta linear-time closed-formsolution for the most probabletrajectoryover this manifold. The outputis a seriesof facial control parameters, suitablefor driving many different kindsof animationrangingfrom video-realisticimagewarpsto 3D cartooncharacters. This work may not be copiedor reproducedin whole or in part for any commercialpurpose.Permissionto copy in whole or in part without paymentof fee is grantedfor nonprofiteducationaland researchpurposesprovided that all suchwhole or partial copiesincludethe following: a noticethat suchcopying is by permissionof Mitsubishi Electric InformationTechnologyCenterAmerica;an acknowledgmentof the authorsandindividual contributionsto the work; andall applicableportionsof the copyright notice. Copying, reproduction,or republishingfor any otherpurposeshall requirea licensewith paymentof feeto MitsubishiElectricInformationTechnologyCenterAmerica.All rightsreserved.
Capacitive sensing of droplets for microfluidic devices based on thermocapillary actuation.

The design and performance of a miniaturized coplanar capacitive sensor is presented whose electrode arrays can also function as resistive microheaters for thermocapillary actuation of liquid films and droplets. Optimal compromise between large capacitive signal and high spatial resolution is obtained for electrode widths comparable to the liquid film thickness measured, in agreement with supporting numerical simulations which include mutual capacitance effects. An interdigitated, variable width design, allowing for wider central electrodes, increases the capacitive signal for liquid structures with non-uniform height profiles. The capacitive resolution and time response of the current design is approximately 0.03 pF and 10 ms, respectively, which makes possible a number of sensing functions for nanoliter droplets. These include detection of droplet position, size, composition or percentage water uptake for hygroscopic liquids. Its rapid response time allows measurements of the rate of mass loss in evaporating droplets.
Expectation propagation: a probabilistic view of Deep Feed Forward Networks

We present a statistical mechanics model of deep feed forward neural networks (FFN). Our energy-based approach naturally explains several known results and heuristics, providing a solid theoretical framework and new instruments for a systematic development of FFN. We infer that FFN can be understood as performing three basic steps: encoding, representation validation and propagation. We obtain a set of natural activations – such as sigmoid, tanh and ReLu – together with a state-of-the-art one, recently obtained by Ramachandran et al.  using an extensive search algorithm. We term this activation ESP (Expected Signal Propagation), explain its probabilistic meaning, and study the eigenvalue spectrum of the associated Hessian on classification tasks. We find that ESP allows for faster training and more consistent performances over a wide range of network architectures.
A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization

In evolutionary multiobjective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto-optimal solutions using a limited population size as the number of objectives increases. This paper proposes a reference vector-guided EA for many-objective optimization. The reference vectors can be used not only to decompose the original multiobjective optimization problem into a number of single-objective subproblems, but also to elucidate user preferences to target a preferred subset of the whole Pareto front (PF). In the proposed algorithm, a scalarization approach, termed angle-penalized distance, is adopted to balance convergence and diversity of the solutions in the high-dimensional objective space. An adaptation strategy is proposed to dynamically adjust the distribution of the reference vectors according to the scales of the objective functions. Our experimental results on a variety of benchmark test problems show that the proposed algorithm is highly competitive in comparison with five state-of-the-art EAs for many-objective optimization. In addition, we show that reference vectors are effective and cost-efficient for preference articulation, which is particularly desirable for many-objective optimization. Furthermore, a reference vector regeneration strategy is proposed for handling irregular PFs. Finally, the proposed algorithm is extended for solving constrained many-objective optimization problems.
Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval

Deep distance metric learning (DDML), which is proposed to learn image similarity metrics in an end-toend manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks. L2-normalization in the embedding space has been used to improve the performance of several DDML methods. However, the commonly used Euclidean distance is no longer an accurate metric for L2-normalized embedding space, i.e., a hyper-sphere. Another challenge of current DDML methods is that their loss functions are usually based on rigid data formats, such as the triplet tuple. Thus, an extra process is needed to prepare data in specific formats. In addition, their losses are obtained from a limited number of samples, which leads to a lack of the global view of the embedding space. In this paper, we replace the Euclidean distance with the cosine similarity to better utilize the L2-normalization, which is able to attenuate the curse of dimensionality. More specifically, a novel loss function based on the von Mises-Fisher distribution is proposed to learn a compact hyper-spherical embedding space. Moreover, a new efficient learning algorithm is developed to better capture the global structure of the embedding space. Experiments for both classification and retrieval tasks on several standard datasets show that our method achieves state-of-the-art performance with a simpler training procedure. Furthermore, we demonstrate that, even with a small number of convolutional layers, our model can still obtain significantly better classification performance than the widely used softmax loss.
Multi-Entity Bayesian Networks without Multi-Tears

An introduction is provided to Multi-Entity Bayesian Networks (MEBN), a logic system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures. Knowledge is encoded as a collection of Bayesian network fragments (MFrags) that can be instantiated and combined to form highly complex situation-specific Bayesian networks. A MEBN theory (MTheory) implicitly represents a joint probability distribution over possibly unbounded numbers of hypotheses, and uses Bayesian learning to refine a knowledge base as observations accrue. MEBN provides a logical foundation for the emerging collection of highly expressive probability-based languages. A running example illustrates the representation and reasoning power of the MEBN formalism.
Role of Dopamine Receptors in ADHD: A Systematic Meta-analysis

The dopaminergic system plays a pivotal role in the central nervous system via its five diverse receptors (D1–D5). Dysfunction of dopaminergic system is implicated in many neuropsychological diseases, including attention deficit hyperactivity disorder (ADHD), a common mental disorder that prevalent in childhood. Understanding the relationship of five different dopamine (DA) receptors with ADHD will help us to elucidate different roles of these receptors and to develop therapeutic approaches of ADHD. This review summarized the ongoing research of DA receptor genes in ADHD pathogenesis and gathered the past published data with meta-analysis and revealed the high risk of DRD5, DRD2, and DRD4 polymorphisms in ADHD.
Distinct developmental trends in sleep duration during early childhood.

Sleep is important to child development, but there is limited understanding of individual developmental patterns of sleep, their underlying determinants, and how these influence health and well-being. This article explores the presence of various sleep patterns in children and their implications for health-related quality of life.



Data were collected from the Longitudinal Study of Australian Children. Participants included 2926 young children followed from age 0 to 1 years to age 6 to 7 years. Data on sleep duration were collected every 2 years, and covariates (eg, child sleep problems, maternal education) were assessed at baseline. Growth mixture modeling was used to identify distinct longitudinal patterns of sleep duration and significant covariates. Linear regression examined whether the distinct sleep patterns were significantly associated with health-related quality of life.



The results identified 4 distinct sleep duration patterns: typical sleepers (40.6%), initially short sleepers (45.2%), poor sleepers (2.5%), and persistent short sleepers (11.6%). Factors such as child sleep problems, child irritability, maternal employment, household financial hardship, and household size distinguished between the trajectories. The results demonstrated that the trajectories had different implications for health-related quality of life. For instance, persistent short sleepers had poorer physical, emotional, and social health than typical sleepers.



The results provide a novel insight into the nature of child sleep and the implications of differing sleep patterns for health-related quality of life. The findings could inform the development of effective interventions to promote healthful sleep patterns in children.
The influence of organizational factors on successful ERP implementation

This study aims to examine organizational factors (i.e. top management support, training and education, enterprise-wide communication) that may influence the enterprise resource planning system implementation success in Iran.  Empirical data were collected via a survey questionnaire. The questionnaires were distributed to selected managers of companies adopting ERP systems in Iran.  The results indicate that the companies’ top management must provide full support and commitment to the project if the system is to be successful. In addition, management must also ensure the plans are communicated and understood by the entire company. Finally it is also illustrated that adequate training and education pertaining to the systems must be given to all users to ensure that they are able to use the system effectively and efficiently thus contributing to their satisfaction which will subsequently influence the implementation success.  The ERP implementation success dimensions were measured using subjective and perceptual measures. This was due to the difficulty in securing the related factual data from the participating organizations.  The findings may help companies planning to implement an ERP system to strategise their efforts and process to ensure successful implementation.  This study examines how organizational factors, namely top management support, training and education as well as enterprise wide communication among ERP users, affect ERP implementation success in Iran.
Characteristics of heart failure patients associated with good and poor response to cardiac resynchronization therapy: a PROSPECT (Predictors of Response to CRT) sub-analysis.

Predictors of Response to Cardiac Resynchronization Therapy (CRT) (PROSPECT) was the first large-scale, multicentre clinical trial that evaluated the ability of several echocardiographic measures of mechanical dyssynchrony to predict response to CRT. Since response to CRT may be defined as a spectrum and likely influenced by many factors, this sub-analysis aimed to investigate the relationship between baseline characteristics and measures of response to CRT.



A total of 286 patients were grouped according to relative reduction in left ventricular end-systolic volume (LVESV) after 6 months of CRT: super-responders (reduction in LVESV > or =30%), responders (reduction in LVESV 15-29%), non-responders (reduction in LVESV 0-14%), and negative responders (increase in LVESV). In addition, three subgroups were formed according to clinical and/or echocardiographic response: +/+ responders (clinical improvement and a reduction in LVESV > or =15%), +/- responders (clinical improvement or a reduction in LVESV > or =15%), and -/- responders (no clinical improvement and no reduction in LVESV > or =15%). Differences in clinical and echocardiographic baseline characteristics between these subgroups were analysed. Super-responders were more frequently females, had non-ischaemic heart failure (HF), and had a wider QRS complex and more extensive mechanical dyssynchrony at baseline. Conversely, negative responders were more frequently in New York Heart Association class IV and had a history of ventricular tachycardia (VT). Combined positive responders after CRT (+/+ responders) had more non-ischaemic aetiology, more extensive mechanical dyssynchrony at baseline, and no history of VT.



Sub-analysis of data from PROSPECT showed that gender, aetiology of HF, QRS duration, severity of HF, a history of VT, and the presence of baseline mechanical dyssynchrony influence clinical and/or LV reverse remodelling after CRT. Although integration of information about these characteristics would improve patient selection and counselling for CRT, further randomized controlled trials are necessary prior to changing the current guidelines regarding patient selection for CRT.
Reducing replication bandwidth for distributed document databases

With the rise of large-scale, Web-based applications, users are increasingly adopting a new class of document-oriented database management systems (DBMSs) that allow for rapid prototyping while also achieving scalable performance. Like for other distributed storage systems, replication is important for document DBMSs in order to guarantee availability. The network bandwidth required to keep replicas synchronized is expensive and is often a performance bottleneck. As such, there is a strong need to reduce the replication bandwidth, especially for geo-replication scenarios where wide-area network (WAN) bandwidth is limited.
 This paper presents a deduplication system called sDedup that reduces the amount of data transferred over the network for replicated document DBMSs. sDedup uses similarity-based deduplication to remove redundancy in replication data by delta encoding against similar documents selected from the entire database. It exploits key characteristics of document-oriented workloads, including small item sizes, temporal locality, and the incremental nature of document edits. Our experimental evaluation of sDedup with three real-world datasets shows that it is able to achieve up to 38X reduction in data sent over the network, significantly outperforming traditional chunk-based deduplication techniques while incurring negligible performance overhead.
Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization

In this paper we develop an adaptive dual free Stochastic Dual Coordinate Ascent (adfSDCA) algorithm for regularized empirical risk minimization problems. This is motivated by the recent work on dual free SDCA of Shalev-Shwartz (2016). The novelty of our approach is that the coordinates to update at each iteration are selected non-uniformly from an adaptive probability distribution, and this extends the previously mentioned work which only allowed for a uniform selection of “dual” coordinates from a fixed probability distribution. We describe an efficient iterative procedure for generating the non-uniform samples, where the scheme selects the coordinate with the greatest potential to decrease the sub-optimality of the current iterate. We also propose a heuristic variant of adfSDCA that is more aggressive than the standard approach. Furthermore, in order to utilize multi-core machines we consider a mini-batch adfSDCA algorithm and develop complexity results that guarantee the algorithm’s convergence. The work is concluded with several numerical experiments to demonstrate the practical benefits of the proposed approach.
Dominance of the right hemisphere and role of area 2 in human kinesthesia.

We have previously shown that motor areas are engaged when subjects experience illusory limb movements elicited by tendon vibration. However, traditionally cytoarchitectonic area 2 is held responsible for kinesthesia. Here we use functional magnetic resonance imaging and cytoarchitectural mapping to examine whether area 2 is engaged in kinesthesia, whether it is engaged bilaterally because area 2 in non-human primates has strong callosal connections, which other areas are active members of the network for kinesthesia, and if there is a dominance for the right hemisphere in kinesthesia as has been suggested. Ten right-handed blindfolded healthy subjects participated. The tendon of the extensor carpi ulnaris muscles of the right or left hand was vibrated at 80 Hz, which elicited illusory palmar flexion in an immobile hand (illusion). As control we applied identical stimuli to the skin over the processus styloideus ulnae, which did not elicit any illusions (vibration). We found robust activations in cortical motor areas [areas 4a, 4p, 6; dorsal premotor cortex (PMD) and bilateral supplementary motor area (SMA)] and ipsilateral cerebellum during kinesthetic illusions (illusion-vibration). The illusions also activated contralateral area 2 and right area 2 was active in common irrespective of illusions of right or left hand. Right areas 44, 45, anterior part of intraparietal region (IP1) and caudo-lateral part of parietal opercular region (OP1), cortex rostral to PMD, anterior insula and superior temporal gyrus were also activated in common during illusions of right or left hand. These right-sided areas were significantly more activated than the corresponding areas in the left hemisphere. The present data, together with our previous results, suggest that human kinesthesia is associated with a network of active brain areas that consists of motor areas, cerebellum, and the right fronto-parietal areas including high-order somatosensory areas. Furthermore, our results provide evidence for a right hemisphere dominance for perception of limb movement.
Global analytic solution of fully-observed variational Bayesian matrix factorization

The variational Bayesian (VB) approximation is known to be a promising approach to Bayesian estimation, when the rigorous calculation of the Bayes posterior is intractable. The VB approximation has been successfully applied to matrix factorization (MF), offering automatic dimensionality selection for principal component analysis. Generally, finding the VB solution is a non-convex problem, and most methods rely on a local search algorithm derived through a standard procedure for the VB approximation. In this paper, we show that a better option is available for fully-observed VBMF—the global solution can be analytically computed. More specifically, the global solution is a reweighted SVD of the observed matrix, and each weight can be obtained by solving a quartic equation with its coefficients being functions of the observed singular value. We further show that the global optimal solution of empirical VBMF (where hyperparameters are also learned from data) can also be analytically computed. We illustrate the usefulness of our results through experiments in multi-variate analysis.
ANALYSING ELECTRONIC WORD-OF-MOUTH ( eWOM ) IN SOCIAL MEDIA FOR CONSUMER INSIGHTS – A MULTIDISCIPLINARY APPROACH

Marketing has been criticised from all spheres today since the real worth of all the marketing efforts can hardly be precisely determined. Today consumers are better informed and also misinformed at times due to the bombardment of various pieces of information through a new type of interactive media, i.e., social media (SM). In SM, communication is through dialogue channels wherein consumers pay more attention to SM buzz rather than promotions of marketers. The various forms of SM create a complex set of online social networks (OSN), through which word-of-mouth (WOM) propagates and influence consumer decisions. With the growth of OSN and user generated contents (UGC), WOM metamorphoses to electronic word-of-mouth (eWOM), which spreads in astronomical proportions. Previous works study the effect of external and internal influences in affecting consumer behaviour. However, today the need is to resort to multidisciplinary approaches to find out how SM influence consumers with eWOM and online reviews. This paper reviews the emerging trend of how multiple disciplines viz. Statistics, Data Mining techniques, Network Analysis, etc. are being integrated by marketers today to analyse eWOM and derive actionable intelligence.
Design and Analysis of a Soft-Fingered Hand With Contact Feedback

This paper presents a novel approach to the fabrication of a soft robotic hand with contact feedback for grasping delicate objects. Each finger has a multilayered structure, consisting of a main structure and sensing elements. The main structure includes a softer layer much thicker than a stiffer layer. The gripping energy of the fingers is generated from the elastic energy of the prestretched softer layers, and controlled by simple tendon strings pulled/released by a single actuation. Due to the prestretching and the difference in moduli among layers, the shape/posture of the fingers in stable state is similar to that of soft fingers actuated by pressurization. Then, we were able to design a soft-fingered hand for different applications by changing the morphological shape of layers. In addition, the hand includes a soft, flexible, and stretchable sensing element for the detection of contact and applied force, which can also be used in other designs of soft fingers. We assessed the ability of the proposed soft hand to grasp food products with feedback of contact location. The experimental results showed that the proposed hand could safely grasp light and delicate objects, such as fruits, and that it could possibly distinguish among objects based on feedback from sensors. The design proposed in this paper may give rise to other soft hand designs, along with the possibility of using morphological imbalanced deformation of multilayered structures in soft robotics research.
Interactive visualization of streaming data with Kernel Density Estimation

In this paper, we discuss the extension and integration of the statistical concept of Kernel Density Estimation (KDE) in a scatterplot-like visualization for dynamic data at interactive rates. We present a line kernel for representing streaming data, we discuss how the concept of KDE can be adapted to enable a continuous representation of the distribution of a dependent variable of a 2D domain. We propose to automatically adapt the kernel bandwith of KDE to the viewport settings, in an interactive visualization environment that allows zooming and panning. We also present a GPU-based realization of KDE that leads to interactive frame rates, even for comparably large datasets. Finally, we demonstrate the usefulness of our approach in the context of three application scenarios - one studying streaming ship traffic data, another one from the oil & gas domain, where process data from the operation of an oil rig is streaming in to an on-shore operational center, and a third one studying commercial air traffic in the US spanning 1987 to 2008.