Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud-Coffee: implementation of a parallel consistency-based multiple alignment algorithm in the T-Coffee package and its benchmarking on the Amazon Elastic-Cloud

Summary: We present the first parallel implementation of the T-Coffee consistency-based multiple aligner. We benchmark it on the Amazon Elastic Cloud (EC2) and show that the parallelization procedure is reasonably effective. We also conclude that for a web server with moderate usage (10K hits/month) the cloud provides a cost-effective alternative to in-house deployment. Availability: T-Coffee is a freeware open source package available from http://www.tcoffee. org/homepage.html Contact: cedric.notredame@crg.es
Clustering Amelioration and Optimization with Swarm Intelligence for Color Image Segmentation

Cluster examination is data mining task for the assignment of collection a set of items in such a path, to the point that questions in the same gathering (called a cluster) are more like one another than to those in different gatherings (clusters). K-means grouping is a technique for group investigation which intends to parcel n perceptions into k groups in which every perception fits in with the cluster with the closest mean. This paper, decided the aftereffect of standard parameter estimations of shading picture division with k-means and the modified k-means with ABC and ACO algorithms.The paper demonstrates that division of color picture with modified k-mean consolidated with swarm Intelligence calculations for color image segmentation gives preferable results over simple k-means and Modified k-means with Ant colony optimization gives better results than modified k-means with Artificial bee colony . Keywords— Data mining, clustering, k-means algorithm, swarm intelligence, artificial bee colony, ant colony.
Asynchronous haltere input drives specific wing and head movements in Drosophila

Halteres are multifunctional mechanosensory organs unique to the true flies (Diptera). A set of reduced hindwings, the halteres beat at the same frequency as the lift-generating forewings and sense inertial forces via mechanosensory campaniform sensilla. Though haltere ablation makes stable flight impossible, the specific role of wing-synchronous input has not been established. Using small iron filings attached to the halteres of tethered flies and an alternating electromagnetic field, we experimentally decoupled the wings and halteres of flying Drosophila and observed the resulting changes in wingbeat amplitude and head orientation. We find that asynchronous haltere input results in fast amplitude changes in the wing (“wing hitches”), but does not appreciably move the head. In multi-modal experiments, we find that wing and gaze optomotor responses are disrupted differently by asynchronous input. These effects of wing-asynchronous haltere input suggest that specific sensory information is necessary for maintaining wing amplitude stability and adaptive gaze control.
Multi-gradient Permutation Survival Analysis Identifies Mitosis and Immune Signatures Steadily Associated with Cancer Patient Prognosis

The inconsistency of the association between genes and cancer prognosis is often attributed to many variables that contribute to patient survival. Whether there exist the Genes Steadily Associated with Prognosis (GEARs) and what their functions are remain largely elusive. We have developed a novel method called “Multi-gradient Permutation Survival Analysis “ (MEMORY) to screen the GEARs using RNA-seq data from the TCGA database. Then we employed a network construction approach to identify hub genes from GEARs, and utilized them for cancer classification. In the case of LUAD, the GEARs were found to be related to mitosis. Our analysis suggested that LUAD cell lines carrying PIK3CA mutations exhibit increased drug resistance. For BRCA, the GEARs were related to immunity. The analysis revealed that CDH1 mutation might influence immune infiltration through the EMT process in BRCA. We further explored the prognostic relevance of mitosis and immunity through their respective scores. This study offers significant biological insights into GEARs and highlights their potential as robust prognostic indicators across diverse cancer types.
An extremely fast neural mechanism to detect emotional visual stimuli: A two-experiment study

Defining the brain mechanisms underlying initial emotional evaluation is a key but unexplored clue to understand affective processing. Event-related potentials (ERPs), especially suited for investigating this issue, were recorded in two experiments (n=36 and n=35). We presented emotionally negative (spiders) and neutral (wheels) silhouettes homogenized regarding their visual parameters. In Experiment 1, stimuli appeared at fixation or in the periphery (200 trials per condition and location), the former eliciting a N40 (39 milliseconds) and a P80 (or C1: 80 milliseconds) component, and the latter only a P80. In Experiment 2, stimuli were presented only at fixation (500 trials per condition). Again, a N40 (45 milliseconds) was observed, followed by a P100 (or P1: 105 milliseconds). Analyses revealed significantly greater N40-C1P1 peak-to-peak amplitudes for spiders in both experiments, and ANCOVAs showed that these effects were not explained by C1P1 alone, but that processes underlying N40 significantly contributed. Source analyses pointed to V1 as a N40 focus (more clearly in Experiment 2). Sources for C1P1 included V1 (P80) and V2/LOC (P80 and P100). These results and their timing point to low-order structures (such as visual thalamic nuclei or superior colliculi) or the visual cortex itself, as candidates for initial evaluation structures.
Behavioral fingerprinting of the naked mole-rat uncovers signatures of eusociality and social touch

The East African naked mole-rat (Heterocephalus glaber) lives in large and extremely cooperative subterranean colonies. However, both the biology that drives their social interactions and the behaviors that define their social hierarchy are poorly understood. Here, we study the spontaneous solitary and social behaviors of naked mole-rats using automated animal tracking coupled with unbiased behavior discovery and experimenter-determined behavior quantification. With this approach, we find that reproductive and non-reproductive castes engage in distinct spontaneous behaviors. Further, the relative usage frequencies of a specific animal’s spontaneous behavior can be used to estimate its rank in the colony’s dominance hierarchy. Strikingly, we discovered that face touch is a prominent form of social interaction—naked mole-rats actively engage in face-to-face contact hundreds of times in a single 10-minute social pairing. We speculate that face-to-face contact might be related to social recognition, as we observe it performed during interactions in which naked mole-rats need to identify each other. Lastly, to demonstrate the specific importance of face-to-face contact, we show that social housing conditions lead to widespread activation of the mechanosensory ion channel Piezo2 in neurons that innervate the face, but not the rest of the body. Together, these findings support the importance of both caste and rank for the organization of spontaneous behavior in naked mole-rats, and they show that face-to-face contact is a prominent social behavior in these animals.
Enhanced stimulus-induced and stimulus-free gamma in open-eye meditators

Visual stimuli induce “narrowband” gamma oscillations (30-70 Hz) that are linked to attention/binding and attenuate with aging and neurodegeneration. In contrast, meditation increases power in a broad frequency range (>25 Hz). However, the effect of meditation on stimulus-induced gamma is unknown. We recorded EEG from meditators and controls performing open-eye meditation while gamma-inducing stimuli were presented before, during and after meditation. We found that stimulus-induced gamma, like stimulus-free gamma, was stronger in meditators. Interestingly, both gamma signatures co-existed during meditation but were unrelated and prominent in occipital and fronto-temporal regions, respectively. Further, power spectral density (PSD) slope, which becomes shallower with aging, was steeper for meditators. Meditation could boost inhibitory mechanisms leading to stronger gamma and steeper PSDs, potentially providing protection against aging and neurodegeneration. One line summary Stimulus-induced and stimulus-free gamma are stronger in open-eye meditators.
Habit learning shapes activity dynamics in the central nucleus of the amygdala

As animals perform instrumental tasks, they may develop a habit response with extended experience. Habits are automatic, inflexible, outcome value insensitive behaviors that are regulated by a network of brain regions including the central nucleus of the amygdala (CeA). Prior work has demonstrated that the CeA governs motivational pursuit and is necessary for habit formation. However, the behavioral features that CeA neurons encode in habit formation remain relatively unknown. To address this, we first used male and female Long-Evans rats to quantify CeA cFos expression after performance of a maze task. There, we found that animals with extended training show elevated cFos expression. Then, we implanted animals with drivable silicon probes to record in-vivo single unit electrophysiological activity from the CeA as animals developed habit responding on the maze. We observed significant activity during outcome consumption late in training while also observing elevated unit activity when animals consumed outcomes of larger magnitudes. Outcome related activity did not persist during probe tests following outcome devaluation, despite animals continuing to perform the task. Together, these data add to growing evidence that suggests that the CeA is involved in motivational processes that contribute to the development of habit formation.
Salient region extraction based on global contrast enhancement and Saliency Cut

Saliency region extraction has become an important task and attracts much attention in numerous image processing application. Object of interest image segmentation from natural scene images for assisting the visually impaired people to access visual information is becoming more challenging research area for community. In this paper we propose a novel color contrast based saliency region extraction approach by adding pre-processing contrast enhancement technique and using saliency cut method to get a high quality foreground object mask, could be used for assistive technology system for visually impaired people. A lot of research has focused on high-quality segmentation, using color information and background or foreground prior, with surprisingly unnoticed image enhancement technique before region segmentation. Moreover, image enhancement is used to improve bad quality of picture into good picture, helpful for blind people’s daily life since all randomly taken images might not be good quality. We evaluate our proposed method on the most known benchmark databases. Keywords—Saliency region extraction; visual attention; visually impaired people; saliency map; saliency cut; image enhancement;
No Intentions in the Brain: A Wittgensteinian Perspective on the Science of Intention

In their paper “Why we may not find intentions in the brain,” Uithol et al. (2014) convincingly argue that “the processes underlying action initiation and control are considerably more dynamic and context sensitive than the concept of intention can allow for. ” Their paper could be seen as a critical note to the widespread tendency to search for identifiable neurocorrelates of mental concepts. Their more specific suggestion is that the absence of clear neural correlates undermines the traditional understanding of intention. In this paper I will try to take their argument a step further. First of all, I will argue that our folk psychology leaves room for various understandings of intentions, and that the concept of intention discussed by Uithol et al. is an academic concept that has its roots in the causal theory of action and in functionalist approaches to cognition. I will argue that both these paradigms are contested, and that there seems to be theoretical wiggle room for alternative understandings of intention. Subsequently I outline such an alternative perspective based on Wittgensteinian philosophy of psychology, emphasizing the regulative role of intention talk. However, the proposed understanding raises the question how to think about neural realization: is intention talk “just” talk, or do intentions really exist? I will propose that intention talk should be understood as a form of pattern recognition, and that the patterns involved are extended in both space and time. The conclusion outlines some important implications for the neuroscientific investigation of intentions.
An Automatic Matching Model for Chinese Test Questions and Knowledge Points Based on Text Classification

Computer assisted instruction system is a hot topic in the field of smart education. In the current research, the knowledge point relationships to which the questions belong are mostly matched by labor. Due to the large workload and the influence of expert experience, the quality and efficiency of manually matching test questions with knowledge points is difficult to guarantee. In this paper, we propose a model named AMMTC based on text classification in order to automatically match the test questions to the knowledge points. According to the model, firstly, we use the TF-IDF algorithm to extract the test text features and transform the test text into a vector space model. Secondly, we use the test text features to select a classification model with the highest accuracy from multiple classification models. Finally, the selected classification model is utilized to match the test questions to the knowledge points. The experimental result show that the AMMTC model can automatically match the test questions to the knowledge points, which not only reduces labor consumption, but also has high accuracy.
Depth Image Enhancement Algorithm Based on RGB Image Fusion

Considering the problem that large amount of structural defects in the depth image captured by Kinect2 decreased the accuracy of object detection and recognition. This paper proposed a depth image enhancement algorithm that fused RGB image information. Firstly, a synchronized RGB image was introduced and aligned with the depth image. Secondly, based on the preprocessing of the color image and the depth image, an effective supporting edge region was extracted, the Just Noticeable Blur (JNB) threshold was used as the weight information of the depth map hole filling to repair the invalid area in the depth image. Finally, in order to highlight the details of edge features, we used an improved Guided Filter algorithm to enhance the edge details. Experimental results show that the algorithm can accurately fill the holes of the depth image and can effectively keep the edge details of the object.
Faster Linear Transformations in $\textsf{HElib}$ , Revisited

HElib is a C++ library that implements fully homomorphic encryption (FHE). This supports homomorphic linear transformations on plaintext slots which consist of many rotation operations on plaintext slots, and currently, each rotation involves one or two automorphisms. In this paper, we describe a new method for linear transformations that perform only one automorphism for the rotation operation for many kinds of linear transformations. We then further investigate our new method to have algorithmic improvement, which can be understood as a generalization of the baby-step/giant-step strategy on HElib. Finally, we verify that our improvement works well by implementation, and see 2–4 times speedup than the current HElib implementation.
A Motion Detection Algorithm Integrating Foreground Matching into Gaussian Mixture Modeling

In conventional Gaussian Mixture Modeling (GMM), the risk that foreground models convert into background models increases with the accumulation of the foreground model's weight under a constant learning rate. That makes the conventional GMM unable to deal with slow moving objects. This paper proposes a motion detection algorithm which fuses foreground matching into the conventional GMM. The motion information contained in foreground models is obtained by checking in real-time the state of each pixel. Foreground matching enables the GMM to deal with indistinguishable moving objects and greatly improves its tolerance to slow moving objects. The quantitative evaluation and comparison show that the proposed algorithm outperforms the conventional GMM by detecting up to 23.3% of true positives with an acceptable cost of time consumption and the number of false detections.
A New Hybrid Seagull Optimization Algorithm for Feature Selection

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm’s heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.
Image Parsing with Superpixels and Gray Image Analysis

This paper presents an effective system with the aim of solving the problem of image parsing. In order to make full use of information and characteristics in the input image(colored), the gray image is calculated from the color image, and the superpixels of both the gray image and the color one are obtained. After feature extraction, the features abstracted from color image and gray image are fused together to construct the final features. Then, the likelihood ratio scores of the input image and the similar images in the retrieval set are calculated according to these final features and the final labeling result are obtained through MRF analysis. The effectiveness of the proposed system is tested on the public dataset of SIFT Flow and experimental result shows that the proposed method in this paper is efficient.
Memory Saving Method for Enhanced Convolution of Deep Neural Network

Convolution neural network (CNN) is a typical feedforward neural network, which gains extraordinary performance in image recognition. Features are extracted by convolution layers automatically for classification, but high in computation complexity. As an enhanced convolution algorithm, image to column (im2col) method accelerates the calculation with redundant memory overhead. In this work, we present Memory Saving Method (MSM) to improve convolution efficiency with lower memory consumption by elements rearrangement of input blocks. Block calculation can be executed both in serial and in parallel for their independence. It is demonstrated by the experimental results that MSM achieves the same acceleration effect yet sparing memory space for approximately two orders of magnitude with no drop of accuracy.
Obfuscation , Learning , and the Evolution of Investor Sophistication Citation

Investor sophistication has lagged behind the growing complexity of retail financial markets. To explore this, we develop a dynamic model to study the interaction between obfuscation and investor sophistication in mutual fund markets. Taking into account different learning mechanisms within the investor population, we characterize the optimal timing of obfuscation for financial institutions who offer retail products. We show that educational initiatives that are directed to facilitate learning by investors may induce providers to increase wasteful obfuscation, further disorienting investors and decreasing overall welfare. Obfuscation decreases with competition among firms, since the information rents from obfuscation dissipate as each institution attracts a smaller market share. We would like to thank Nittai Bergman, Tony Bernardo, Michael Brennan, Bhagwan Chowdry, Doug Diamond, Darrell Duffie, Paolo Fulghieri, Xavier Gabaix, Rick Green, Mark Grinblatt, Joel Hasbrouck, Leonid Kogan, David Laibson, Francis Longstaff, Holger Mueller, Lubos Pastor, Dick Roll, Andrei Shleifer, Eduardo Schwartz, Avanihdar Subrahmanyam, Sheridan Titman, Pietro Veronesi, Pierre-Olivier Weill, Bilge Yilmaz, and seminar participants at the University of Chicago, NYU, MIT, Columbia University, University of Washington, Northwestern University, University of Southern California, Yale University, UCLA, the Texas Finance Festival, and the annual Netspar Pension Management Conference at Tilburg University. Anderson School of Management, University of California, Los Angeles, 110 Westwood Plaza Suite C519, Los Angeles, CA 90095, bruce.carlin@anderson.ucla. edu, (310) 825-7246. MIT Sloan School of Management, 50 Memorial Drive E52-446, Cambridge, MA 02142-1347, manso@mit.edu, (617) 253-7218.
A New Approach for Classification of Epilepsy EEG Signals Based on Temporal Convolutional Neural Networks

Epilepsy, a chronic disease, whose symptoms are sudden onset of brain neurons by the abnormal discharge, leads to transient cerebral dysfunction. In recent years, the observation of EEG signals plays an important role in the diagnosis of epilepsy, as EEG signals contain much useful detailed information which helps doctors to diagnose epilepsy. However, artificial analyses rely heavily on doctors' experience and may produce erroneous and missed diagnosis. Thus many researchers focus on methods that can automate the process of EEG detection. According to recent studies, the mainstream approaches mainly consist of two steps: signal pre-processing and classification. In this paper, our method uses a deep convolutional network called Temporal Convolutional Neural Networks (TCN) to classify the EEG signals. The proposed approach can automatically learn feature representation from the raw EEG signals without any pre-processing. To evaluate the effectiveness of our approach, we design and train a classifier base on TCN to classify EEG data. The best performance of the 14 different combinations of two-class epilepsy detection gives an accuracy of 100.00%. Our results show that TCN is able to accurately distinguish epileptic data from non epileptic data. In terms of computation cost and accuracy, this novel approach proposed in this paper is more competitive than others, such as wavelet transform, Naïve Bayesian and KNN etc.
Stock Price Trend Prediction Model Based on Deep Residual Network and Stock Price Graph

Consider that people often use stock price graph to make decisions, this paper introduce a deep residual network (ResNet) model for prediction, using the stock price graph as input. The results show that the ResNet model has the average accuracy of 0.40, which is higher than the stochastic indicator of 0.33.
Favorable Random Gradients for Optimization of Deep Neural Networks

We present a technique to improve an optimization of deep neural networks by introducing favorable random gradients during an additional optimization sub-step that affects positively the training process. This technique allows training deep neural networks faster, resulting in smaller training loss. Only the random gradients that do not downgrade the network result on a training mini-batch are selected, and the additional optimization sub-step made by the chosen gradient-based optimizer can be applied. Otherwise, if the random gradients affect poorly, only the standard training step with backpropagation is performed on the given mini-batch.