nils-herrmann/clean_scirepeval huggingface dataset:
Differences between effects of psychological versus pharmacological treatments on functional and morphological brain alterations in anxiety disorders and major depressive disorder: A systematic review

The most prevalent mental disorders, anxiety and mood disorders, are associated with both functional and morphological brain changes that commonly involve the 'fear network' including the (medial) prefrontal cortex, hippocampus and amygdala. Patients suffering from anxiety disorders and major depressive disorder often show excessive amygdala and reduced prefrontal cortex functioning. It is, however, still unclear whether these brain abnormalities disappear or diminish following effective treatment. This review aims to compare the effects of psychotherapy and pharmacotherapy on functional and morphological brain measures in these disorders. Sixty-three studies were included, 30 investigating psychotherapy effects and 33 investigating pharmacotherapy effects. Despite methodological differences, results suggest a functional normalization of the 'fear network'. Pharmacotherapy particularly decreases over-activity of limbic structures (bottom-up effect) while psychotherapy tends to increase activity and recruitment of frontal areas (top-down effect), especially the anterior cingulate cortex. Additionally, pharmacotherapy, but not psychotherapy, has been associated with morphological changes, depending on the disorder. These findings suggest that both types of treatments normalize (functional) brain abnormalities each in specific ways.
Materiality and Organizing Social Interaction in a Technological World

social structures such as roles, statuses, hierarchies, power relations, communication networks, and other similar constructs. Kallinikos (2011) calls such abstract social structures “institutional forces.” He suggests that institutions are temporally bound and, consequently, should not be simply seen as a way for researchers to vacillate between micro and macro levels of analysis, but that they are useful for moving from static to dynamic patterns of analysis such that each layer of sociomaterial imbrication becomes more substantial in that it shapes action in a path-dependent manner because of its history of accumulation. Defining and Interrelating Terms Early on, I argued that the goal of this chapter was to stimulate debate and discussion about popular terminology used in contemporary explanations of technology and organizing. In this spirit, I have reviewed the historical foundations of the terms “materiality,” “sociomateriality,” and “socio-technical systems” and I havemade some first, undoubtedly contentious, steps to define how these terms relate to one another. In doing so, I have placed certain boundaries around these concepts for the sake of definitional clarity. Below, I summarize the preceding discussions into a rough and entirely tentative glossary of terms: Materiality: The arrangement of an artifact’s physical and/or digital materials into particular forms that endure across differences in place and time and are important to users.  Enactment of a particular set of activities that meld materiality with institutions, norms, discourses, and all other phenomena we typically define as “social.” Sociomaterial Practice: The space in which multiple human (social) agencies andmaterial agencies are imbricated (also called a “technical subsystem”).  Coordinated human intentionality formed in partial response to perceptions of a technology’s material agency.  Ways in which a technology’s materiality acts. Material agency is activated as humans approach technology with particular intentions and decide which elements of its materiality to use at a given time.  Recognition of a recursive (not simultaneous) shaping of abstract social constructs and a technical infrastructure that includes technology’s materiality and people’s localized responses to it. OUP UNCORRECTED PROOF – REVISES, 17/8/2012, SPisocial constructs and a technical infrastructure that includes technology’s materiality and people’s localized responses to it. OUP UNCORRECTED PROOF – REVISES, 17/8/2012, SPi Materiality and Organizing
Automatic Dialogue Generation with Expressed Emotions

Despite myriad efforts in the literature designing neural dialogue generation systems in recent years, very few consider putting restrictions on the response itself. They learn from collections of past responses and generate one based on a given utterance without considering, speech act, desired style or emotion to be expressed. In this research, we address the problem of forcing the dialogue generation to express emotion. We present three models that either concatenate the desired emotion with the source input during the learning, or push the emotion in the decoder. The results, evaluated with an emotion tagger, are encouraging with all three models, but present better outcome and promise with our model that adds the emotion vector in the decoder.
A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression

Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.
Multi-domain spoken language understanding with transfer learning

This paper addresses the problem of multi-domain spoken language understanding (SLU) where domain detection and domaindependent semantic tagging problems are combined. We present a transfer learning approach to the multi-domain SLU problem in which multiple domain-specific data sources can be incorporated. To implement multi-domain SLU with transfer learning, we introduce a triangular-chain structured model. This model effectively learns multiple domains in parallel, and allows use of domain-independent patterns among domains to create a better model for the target domain. We demonstrate that the proposed method outperforms baseline models on dialog data for multi-domain SLU problems.
A low-cost wideband and high-gain slotted cavity antenna for millimeter-wave application

In this paper, a single-fed low-cost wideband and high-gain slotted cavity antenna based on substrate integrated waveguide (SIW) technology using high-order cavity modes is demonstrated. High-order resonant modes (TE130, TE310, TE330) inside the cavity are simply excited by a coaxial probe which is located at the centre of the antenna. Energy is coupled out of the cavity by a 3 &#x00D7; 3 slot array etched on the top surface of the cavity. Measured results show that the prototype achieves an impedance bandwidth (|S11| &lt;; -10 dB) of &gt; 26% (28 to 36.6 GHz), and a 1-dB gain bandwidth of 14.1% (30.3 to 34.9 GHz). In addition, a measured maximum gain of 13.8 dBi and efficiency of 92% are obtained.
Detection of high variability in gene expression from single-cell RNA-seq profiling

The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs). We have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases. In this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.
k-Shape: Efficient and Accurate Clustering of Time Series

The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we describe k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance measure, we develop a method to compute cluster centroids, which are used in every iteration to update the assignment of time series to clusters. An extensive experimental evaluation against partitional, hierarchical, and spectral clustering methods, with the most competitive distance measures, showed the robustness of k-Shape. Overall, k-Shape emerges as a domain-independent, highly accurate, and efficient clustering approach for time series with broad applications.
Multi-robot manipulation via caging in environments with obstacles

We present a decentralized approach to multi- robot manipulation where the team of robots surround and trap an object and transport it, by dragging or pushing, to the goal configuration in an environment with obstacles. The proposed feedback controllers are obtained by sequentially composing vector fields or behaviors and are decentralized in the sense that robots do not exchange each other's state information. Rather, cooperative manipulation is achieved by relying solely on each robot's local information and a global knowledge of the task. We present computer simulations and experimental results obtained using our multi-robot testbed.
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregards an opposite -- avoiding recommendations with completely irrelevant items. Due to that bias, standard algorithms, as well as commonly used evaluation metrics, become insensitive to negative feedback. In order to resolve this problem we propose to treat user feedback as a categorical variable and model it with users and items in a ternary way. We employ a third-order tensor factorization technique and implement a higher order folding-in method to support online recommendations. The method is equally sensitive to entire spectrum of user ratings and is able to accurately predict relevant items even from a negative only feedback. Our method may partially eliminate the need for complicated rating elicitation process as it provides means for personalized recommendations from the very beginning of an interaction with a recommender system. We also propose a modification of standard metrics which helps to reveal unwanted biases and account for sensitivity to a negative feedback. Our model achieves state-of-the-art quality in standard recommendation tasks while significantly outperforming other methods in the cold-start "no-positive-feedback" scenarios.
Weighted extreme learning machine for imbalance learning

Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are ‘‘generalized’’ single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users’ needs, the weighted ELM can be generalized to cost sensitive learning. & 2012 Elsevier B.V. All rights reserved.
Long-Lasting Novelty-Induced Neuronal Reverberation during Slow-Wave Sleep in Multiple Forebrain Areas

The discovery of experience-dependent brain reactivation during both slow-wave (SW) and rapid eye-movement (REM) sleep led to the notion that the consolidation of recently acquired memory traces requires neural replay during sleep. To date, however, several observations continue to undermine this hypothesis. To address some of these objections, we investigated the effects of a transient novel experience on the long-term evolution of ongoing neuronal activity in the rat forebrain. We observed that spatiotemporal patterns of neuronal ensemble activity originally produced by the tactile exploration of novel objects recurred for up to 48 h in the cerebral cortex, hippocampus, putamen, and thalamus. This novelty-induced recurrence was characterized by low but significant correlations values. Nearly identical results were found for neuronal activity sampled when animals were moving between objects without touching them. In contrast, negligible recurrence was observed for neuronal patterns obtained when animals explored a familiar environment. While the reverberation of past patterns of neuronal activity was strongest during SW sleep, waking was correlated with a decrease of neuronal reverberation. REM sleep showed more variable results across animals. In contrast with data from hippocampal place cells, we found no evidence of time compression or expansion of neuronal reverberation in any of the sampled forebrain areas. Our results indicate that persistent experience-dependent neuronal reverberation is a general property of multiple forebrain structures. It does not consist of an exact replay of previous activity, but instead it defines a mild and consistent bias towards salient neural ensemble firing patterns. These results are compatible with a slow and progressive process of memory consolidation, reflecting novelty-related neuronal ensemble relationships that seem to be context- rather than stimulus-specific. Based on our current and previous results, we propose that the two major phases of sleep play distinct and complementary roles in memory consolidation: pretranscriptional recall during SW sleep and transcriptional storage during REM sleep.
High-quality computational imaging through simple lenses

Modern imaging optics are highly complex systems consisting of up to two dozen individual optical elements. This complexity is required in order to compensate for the geometric and chromatic aberrations of a single lens, including geometric distortion, field curvature, wavelength-dependent blur, and color fringing.
 In this article, we propose a set of computational photography techniques that remove these artifacts, and thus allow for postcapture correction of images captured through uncompensated, simple optics which are lighter and significantly less expensive. Specifically, we estimate per-channel, spatially varying point spread functions, and perform nonblind deconvolution with a novel cross-channel term that is designed to specifically eliminate color fringing.
Exploring specialized near-memory processing for data intensive operations

Emerging 3D stacked memory systems provide significantly more bandwidth than current DDR modules. However, general purpose processors do not take full advantage of these resources offered by the memory modules. Taking advantage of the increased bandwidth requires the use of specialized processing units. In this paper, we evaluate the benefits of placing hardware accelerators at the bottom layer of a 3D stacked memory system compared to accelerators that are placed external to the memory stack. Our evaluation of the design using cycle-accurate simulation and RTL synthesis shows that, for important data intensive kernels, near-memory accelerators inside a single 3D memory package provide 3x-13x speedup over a Quad-core Xeon processor. Most of the benefits are from the application of accelerators, as the near-memory configurations provide marginal benefits compared to the same number of accelerators placed on a die external to the memory package. This comparable performance for external accelerators is due to the high bandwidth afforded by the high-speed off-chip links. On the other hand, near-memory accelerators consume 7%-39% less energy than the external accelerators.
The diagnosis and management of rhinitis: an updated practice parameter.

The American Academy of Allergy, Asthma & Immunology (AAAAI) and the American College of Allergy, Asthma and Immunology (ACAAI) have jointly accepted responsibility for establishing ‘‘The diagnosis and Management of Rhinitis: An Updated Practice Parameter.’’ This is a complete and comprehensive document at the current time. The medical environment is a changing environment, and not all recommendations will be appropriate for all patients. Because this document incorporated the efforts of many participants, no single individual, including those who served on the Joint Task Force, is authorized to provide an official AAAAI or ACAAI interpretation of these practice parameters. Any request for information about or an interpretation of these practice parameters by the AAAAI or ACAAI should be directed to the Executive Offices of the AAAAI, the ACAAI, and the Joint Council of Allergy, Asthma and Immunology. These parameters are not designed for use by pharmaceutical companies in drug promotion.
On Divergences and Informations in Statistics and Information Theory

The paper deals with the f-divergences of Csiszar generalizing the discrimination information of Kullback, the total variation distance, the Hellinger divergence, and the Pearson divergence. All basic properties of f-divergences including relations to the decision errors are proved in a new manner replacing the classical Jensen inequality by a new generalized Taylor expansion of convex functions. Some new properties are proved too, e.g., relations to the statistical sufficiency and deficiency. The generalized Taylor expansion also shows very easily that all f-divergences are average statistical informations (differences between prior and posterior Bayes errors) mutually differing only in the weights imposed on various prior distributions. The statistical information introduced by De Groot and the classical information of Shannon are shown to be extremal cases corresponding to alpha=0 and alpha=1 in the class of the so-called Arimoto alpha-informations introduced in this paper for 0<alpha<1 by means of the Arimoto alpha-entropies. Some new examples of f-divergences are introduced as well, namely, the Shannon divergences and the Arimoto alpha-divergences leading for alphauarr1 to the Shannon divergences. Square roots of all these divergences are shown to be metrics satisfying the triangle inequality. The last section introduces statistical tests and estimators based on the minimal f-divergence with the empirical distribution achieved in the families of hypothetic distributions. For the Kullback divergence this leads to the classical likelihood ratio test and estimator
Unconscious cerebral initiative and the role of conscious will in voluntary action

Voluntary acts are preceded by electrophysiological "readiness potentials" (RPs). With spontaneous acts involving no preplanning, the main negative RP shift begins at about -550 ms. Such RP's were used to indicate the minimum onset times for the cerebral activity that precedes a fully endogenous voluntary act. The time of conscious intention to act was obtained from the subject's recall of the spatial clock position of a revolving spot at the time of his initial awareness of intending or wanting to move (W). W occurred at about -200 ms. Control experiments, in which a skin stimulus was timed (S), helped evaluate each subject's error in reporting the clock times for awareness of any perceived event. For spontaneous voluntary acts, RP onset preceded the uncorrected Ws by about 350 ms and the Ws corrected for S by about 400 ms. The direction of this difference was consistent and significant throughout, regardless of which of several measures of RP onset or W were used. It was concluded that cerebral initiation of a spontaneous voluntary act begins unconsciously. However, it was found that the final decision to act could still be consciously controlled during the 150 ms or so remaining after the specific conscious intention appears. Subjects can in fact "veto" motor performance during a 100-200-ms period before a prearranged time to act. The role of conscious will would be not to initiate a specific voluntary act but rather to select and control volitional outcome. It is proposed that conscious will can function in a permissive fashion, either 1:o permit or to prevent the motor implementation of the intention to act that arises unconsciously. Alternatively, there may be the need for a conscious activation or triggering, without which the final motor output would not follow the unconscious cerebral initiating and preparatory processes.