Miracle-dz/UniFAR-FTUs huggingface dataset:
Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior.
A 3-Tier UWB-Based Indoor Localization Scheme for Ultra-Low-Power Sensor Nodes

In this paper, a hierarchical indoor localization scheme is proposed to enable the use of autonomously powered ultra-low-power tags attached to the objects with unknown position. Using the UWB characteristics and the 3-tier organization (consisting of a large number of cost-effective tags, a small number of cheap and low-power hubs and a few synchronized base stations), the proposed localization scheme can provide a wide geographical coverage and precise positioning. Localization is based on the arrival time of the UWB pulses at the reference nodes with known locations. We formalize the hub placement problem as an optimization problem. Simulations investigate the performance degradation due to the nonideal hub placement and hub localization error.
The ErbB4 Neuregulin Receptor Mediates Suppression of Oligodendrocyte Maturation

Neuregulin is required for proper oligodendrocyte development, but which receptors are involved and whether neuregulin promotes or inhibits maturation remain controversial. To assess the roles of the neuregulin receptor ErbB4 in oligodendrocyte development, we examined oligodendrocyte initiation and maturation in cultures derived from erbB4 knock-out mice and rat spinal cord in the presence of neutralizing erbB4 antibodies. No differences in the development of O4+ oligodendrocytes were detected in the presence or absence of erbB4 signaling. All four epidermal growth factor receptor family members were detected in the ventral neural tube at approximately the time of initial oligodendrocyte development, consistent with redundancy in neuregulin receptor signaling at the onset of oligodendrocyte development. In contrast, greater numbers of differentiated (monoclonal antibody O1+) oligodendrocytes developed in neural tube explants from erbB4-/- mice than either erbB4+/+ or erbB4+/- littermates as well as in cultures treated with anti-erbB4. These data indicate that ErbB4 is not required for oligodendrocyte development and, in fact, inhibits oligodendrocyte lineage maturation. Together with previous studies, these data suggest a model in which early oligodendrocyte lineage development is regulated by promiscuous neuregulin receptor signaling, but subsequent lineage progression occurs through a balance of receptor-specific promotion or inhibition of maturation.
Quality Prediction of Answers in Community of Questions and Answers of Zhihu

: The participation of Web2.0 in collaboration makes the Q&A social platform develop rapidly, but the problem that the answering quality is difficult to distinguish has gradually become apparent, even it hindered the healthy development of social platforms. In order to predict the Answers in Zhihu, which is the famous Community of Questions and Answers website, the quality prediction method based on Logistic Regression is proposed. Firstly, we collect dataset from Zhihu. Based on the factors, the training set is constructed. After that the two answer quality prediction models based on Logistic Regression is constructed. With the comparison of the two models, the final model is determined. It can be used to predict the answer quality of Zhihu.
Stereotypes in Video Games and How They Perpetuate Prejudice

Systemic research has only recently begun to examine the psychological effects of video games on users. To date, only two studies have examined how the exaggerated stereotypes of minorities in these games affect those who play them. During both studies, game play was found to bring about increases of prejudice, even in users who in screening initially rated low in prejudicial thinking. Taking the theories of how learning and stereotypes work, this paper integrates theoretical approaches into existing research on video games in order to bring about more awareness of the effect of racist images of minorities in the video game industry. LITERATURE REVIEW Society is becoming more electronically connected, thanks to advancements in communication technologies such as computers and the Internet. The Internet is used to augment nearly all forms of social interaction (e.g., shopping, dating, business meetings, entertainment and much more). These technologies have not only begun to permeate all aspects of traditional human interaction, but they have also spurred myriad new forms of socialization. The video gaming industry has produced revenues of $21 billion dollars in sales (Ortutay, 2009), making it more profitable than movies and television. Research shows that forty percent of Americans now regularly enjoy playing video games (Williams, Nicole, Consalvo, & Ivory, 2009). Video games offer many possibilities for cheap and easy entertainment, ranging from free, to fees of sixty dollars per game. However, as with any such new technology, one must examine the psychological repercussions on players. This paper aims to explore
Inbreeding depression and male fitness in black grouse

The male lifetime lekking performance was studied, and related to inbreeding‐outbreeding in a wild population of black grouse (Tetrao tetrix) in central Finland between 1989 and 1995. Inbreeding was measured as the mean heterozygosity and mean d2 of 15 microsatellite loci. We found a significantly positive relationship between mean d2 and lifetime copulation success (LCS), while the relationship between heterozygosity and LCS was close to significant. We also found that males that never obtained a lek territory had significantly lower mean heterozygosity than males that were observed on a territory at least during one mating season in their life. Furthermore, among males that were successful in obtaining a lek territory, LCS and mean d2 were highest for those males that held central territories. We suggest that inbred males have a disadvantage (or outbred males have an advantage) in the competition for territories that may explain the relationships with LCS and inbreeding. Furthermore, the fact that mean d2 was positively correlated with LCS whereas heterozygosity was not when we restricted the analysis to territorial males, suggests that mean d2 provides more information about levels of inbreeding‐outbreeding than heterozygosity alone, and potentially highlights the effects of heterosis. To our knowledge, this is the first time that measures of inbreeding and lifetime fitness have been linked in a non‐isolated population. This is important in establishing that the relationships found in previous studies are not artefacts of low gene flow created by limited dispersal but a general feature of wild vertebrate populations.
Semantic Summarization of Web Documents

Documents’ summarization techniques automatically extract relevant information from different sources with respect to a list of topics: they can be profitably used by a variety of applications and in particular for automatic indexing and categorization in order to facilitate the production and delivery of new multimedia contents. In this paper we propose a novel approach for summarizing documents retrieved from the Internet: we propose to capture the semantic nature of a document, expressed in natural language, in order to retrieve a number of RDF triplets and to clusterize these ones aggregating similar information. An overview of the system and some preliminary results are described.
Automatic video tagging using content redundancy

The analysis of the leading social video sharing platform YouTube reveals a high amount of redundancy, in the form of videos with overlapping or duplicated content. In this paper, we show that this redundancy can provide useful information about connections between videos. We reveal these links using robust content-based video analysis techniques and exploit them for generating new tag assignments. To this end, we propose different tag propagation methods for automatically obtaining richer video annotations. Our techniques provide the user with additional information about videos, and lead to enhanced feature representations for applications such as automatic data organization and search. Experiments on video clustering and classification as well as a user evaluation demonstrate the viability of our approach.
Recognition of Face Expression using Color

Face expression recognition can be stated as „identifying the expression of an individual from images of the face‟. Most of the existing systems of facial expression recognition focus on gray scale image features. This paper describes the novel approaches for effectively recognizing the facial expressions. In facial expression recognition (FER) framework, initially the face region of the image is detected using Ada boost learning algorithm. The RGB color image is transformed to another color space (YCbCr) to further improve face recognition performance. From this, the features are extracted by using Log-Gabor filter which results in a large amount of feature arrays. Furthermore, to improve the classification accuracy, the most desirable features are selected by using mutual information quotient (MIQ) technique. At last the multiclass linear discriminant analysis (LDA) classifier is used to classify these features. The experimental results evaluate the performance of using this framework under varying illumination and in low resolution images. Keywords— Log-Gabor filter, linear discriminant analysis, mutual information quotient, color space, facial expression recognition.
Radicalization to Violence: A Pathway Approach to Studying Extremism

ABSTRACT Prior research on extremism has identified a host of psychological, emotional, material, and group-based mechanisms that are potentially important drivers of individual radicalization. However, taken on their own, none of these factors have been shown to lead to extremist behaviors. Instead, radicalization is best understood as a set of complex causal processes in which multiple factors work together to produce extremist outcomes. This paper builds on prior research by showing how radicalization mechanisms drawn from five prominent research traditions combine to form multiple sufficient pathways to extremist violence. We identify these pathways by applying fuzzy-set/Qualitative Comparative Analysis (fs/QCA) to a sample of life-course narratives that includes violent and nonviolent extremists in the United States. We find that both a sense of community victimization and a fundamental shift in individuals’ cognitive frames are present in all pathways and act as necessary conditions for radicalization to violence. These conditions combine with a set of psychological, emotional, group, and material variables to produce eight pathways that are sufficient for explaining violent outcomes. Of these, the pathways that combine psychological rewards and group biases account for the radicalization processes of the majority of the cases in our sample.
Using Association Rules to Identify Similarities between Software Datasets

A number of V&amp;V datasets are publicly available. These datasets have software measurements and defectiveness information regarding the software modules. To facilitate V&amp;V, numerous defect prediction studies have used these datasets and have detected defective modules effectively. Software developers and managers can benefit from the existing studies to avoid analogous defects and mistakes if they are able to find similarity between their software and the software represented by the public datasets. This paper identifies the similar datasets by comparing association patterns in the datasets. The proposed approach finds association rules from each dataset and identifies the overlapping rules from the 100 strongest rules from each of the two datasets being compared. Afterwards, average support and average confidence of the overlap is calculated to determine the strength of the similarity between the datasets. This study compares eight public datasets and results show that KC2 and PC2 have the highest similarity 83% with 97% support and 100% confidence. Datasets with similar attributes and almost same number of attributes have shown higher similarity than the other datasets.
Rapid and Reliable Adaptation of Video Game AI

Current approaches to adaptive game AI typically require numerous trials to learn effective behavior (i.e., game adaptation is not rapid). In addition, game developers are concerned that applying adaptive game AI may result in uncontrollable and unpredictable behavior (i.e., game adaptation is not reliable). These characteristics hamper the incorporation of adaptive game AI in commercially available video games. In this paper, we discuss an alternative to these current approaches. Our alternative approach to adaptive game AI has as its goal adapting rapidly and reliably to game circumstances. Our approach can be classified in the area of case-based adaptive game AI. In the approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource-intensive learning) to evoke effective behavior in a controlled manner in online play. We performed experiments that test case-based adaptive game AI on three different maps in a commercial real-time strategy (RTS) game. From our results, we may conclude that case-based adaptive game AI provides a strong basis for effectively adapting game AI in video games.
Treatment with Activated Protein C (aPC) Is Protective during the Development of Myocardial Fibrosis: An Angiotensin II Infusion Model in Mice

Aims Myocardial fibrosis contributes to the development of heart failure. Activated Protein C (aPC) is a circulating anticoagulant with anti-inflammatory and cytoprotective properties. Using a model of myocardial fibrosis second to Angiotensin II (AngII) infusion, we investigated the novel therapeutic function aPC in the development of fibrosis. Methods and Results C57Bl/6 and Tie2-EPCR mice were infused with AngII (2.0 µg/kg/min), AngII and aPC (0.4 µg/kg/min) or saline for 3d. Hearts were harvested and processed for analysis or used for cellular isolation. Basic histology and collagen deposition were assessed using histologic stains. Transcript levels of molecular mediators were analyzed by quantitative RT-PCR. Mice infused with AngII exhibited multifocal areas of myocardial cellular infiltration associated with significant collagen deposition compared to saline control animals (p<0.01). AngII-aPC infusion inhibited this cellular infiltration and the corresponding collagen deposition. AngII-aPC infusion also inhibited significant expression of the pro-fibrotic cytokines TGF-β1, CTGF and PDGF found in AngII only infused animals (p<0.05). aPC signals through its receptor, EPCR. Using Tie2-EPCR animals, where endothelial cells over-express EPCR and exhibit enhanced aPC-EPCR signaling, no significant reduction in cellular infiltration or fibrosis was evident with AngII infusion suggesting aPC-mediate protection is endothelial cell independent. Isolated infiltrating cells expressed significant EPCR transcripts suggesting a direct effect on infiltrating cells. Conclusions This data indicates that aPC treatment abrogates the fibrogenic response to AngII. aPC does not appear to confer protection by stimulating the endothelium but by acting directly on the infiltrating cells, potentially inhibiting migration or activation.
Reliability Computing of Stochastic BEM Based on Grid Platform

Stochastic BEM provides a powerful tool for the reliability computing of complex or large scale structure. Though simple and applicable, AFOSM Method has shortcomings when distribution forms of random variable are concerned. In some cases, the distribution types of variables can be worked out through statistics analysis. Thus it is necessary to adopt appropriate methods to work out possible impacts of variable distribution. Stochastic BEM, based on Monte-Carlo numerical simulation, is capable to simulate such impacts effectively as a method of statistical approximation. But the coefficient matrix is asymmetric full-matrix in the solution of BEM matrix equation, the computational complexity increase by N3 with the increase of boundary node number. On solving the complex large-scale three dimensions structure problem, the large computational scale has been a barrier in the application of boundary element method, especially for the Stochastic BEM computation. Grid computing is a new style of distributed computing technology after the cluster computing. Grid is a integrated environment of computing and resource, which realize sharing resource and cooperative teamwork . It can absorb various computing resources and translate the min to convenient and reliable computer power. On the campus grid platform, the single or multiple large-scale BEM computing could carry out expediently and efficiently. This paper took the case of a campus grid in Hohai university, and introduced the characteristic of grid computing and the three-layer frame of the campus grid such as resource layer, grid layer and application service layer, and the campus grid platform is a system based on two-level scheduler, respectively grid layer scheduler and resource layer scheduler, thus a two-level parallel strategy in the above two layers can be adopted for the computing of stochastic BEM which has two parallel specialties in both BEM computing and Monte-Carlo method. Then the realization of stochastic BEM based on grid platform was put forward. Finally, there is a test which shows the high performance and efficiency of the two-level parallel strategy for the stochastic BEM computing on the grid platform.
Support Blob Machines The Sparsification of Linear Scale Space

A novel generalization of linear scale space is presented. The generalization allows for a sparse approximation of the function at a certain scale. To start with, we first consider the Tikhonov regularization viewpoint on scale space theory (15). The sparsification is then obtained using ideas from support vector machines (22) and based on the link between sparse approximation and support vector regression as described in (4) and (19). In regularization theory, an ill-posed problem is solved by searching for a solution having a certain differentiability while in some precise sense the final solution is close to the initial signal. To obtain scale space, a quadratic loss function is used to measure the closeness of the initial function to its scale σ image. We propose to alter this loss function thus obtaining our generalization of linear scale space. Comparable to the linear � -insensitive loss func- tion introduced in support vector regression (22), we use a quadratic � -insensitive loss function instead of the original quadratic measure. The � -insensitivity loss allows errors in the approximating function without actual increase in loss. It penalizes errors only when they become larger than the a priory specified constant � . The quadratic form is mainly maintained for consistency with linear scale space. Although the main concern of the article is the theoretical connection between the foregoing theories, the proposed approach is tested and ex- emplified in a small experiment on a single image.
On the convergence of Algebraic Optimizable Schwarz Methods with applications to elliptic problems

The Schwarz method can be used for the iterative solution of elliptic boundary value problems on a large domain Ω. One subdivides Ω into smaller, more manageable, subdomains and solves the differential equation in these subdomains using appropriate boundary conditions. Schwarz-Robin methods use Robin conditions on the artificial interfaces for information exchange at each iteration. Optimized Schwarz Methods (OSM) are those in which one optimizes the Robin parameters. While the convergence theory of classical Schwarz methods (with Dirichlet conditions on the artificial interface) is well understood, the overlapping Schwarz-Robin methods still lack a complete theory. In this paper, an abstract Hilbert space version of the OSM is presented, together with an analysis of conditions for its convergence. It is also shown that if the overlap is relatively uniform, these convergence conditions are met for Schwarz-Robin methods for two-dimensional elliptic problems, for any positive Robin parameter. In the discrete setting, we obtain that the convergence rate ω(h) varies like a polylogarithm of h. Numerical experiments show that the methods work well and that the convergence rate does not appear to depend on h.