Miracle-dz/UniFAR-FTUs huggingface dataset:
A feature extraction technique of EEG based on EMD-BP for motor imagery classification in BCI

The aim of this paper is to investigate a nonlinear approach for feature extraction of Electroencephalogram (EEG) signals in order to classify motor imagery for Brain Computer Interface (BCI). This approach consists of combining the Empirical Mode Decomposition (EMD) and band power (BP). Considering the non-stationary and nonlinear characteristics of the motor imagery EEG, the EMD method is proposed to decompose the EEG signal into set of stationary time series called Intrinsic Mode Functions (IMF). These IMFs are analyzed with the bandpower (BP) to detect the caracteristics of sensorimotor rhythms (mu and beta). Finally, the data were reconstructed with only with the specific IMFs and then the band power is employed on the new database. Once the new feature vector is reconstructed, the classification of motor imagery is applied using Hidden Markov Models (HMMs). The results obtained show that the EMD method allows the most reliable features to be extracted from EEG and that the classification rate obtained is higher and better than using only the direct BP approach. Such a system appears as a particularly promising communication channel for people suffering from severe paralysis, for instance for persons with myopatic diseases or muscular dystrophy (MD) to move a joystick to a desired direction corresponding to the specific motor imagery.
Discriminant Analysis for Unsupervised Feature Selection

Feature selection has been proven to be efficient in preparing high dimensional data for data mining and machine learning. As most data is unlabeled, unsupervised feature selection has attracted more and more attention in recent years. Discriminant analysis has been proven to be a powerful technique to select discriminative features for supervised feature selection. To apply discriminant analysis, we usually need label information which is absent for unlabeled data. This gap makes it challenging to apply discriminant analysis for unsupervised feature selection. In this paper, we investigate how to exploit discriminant analysis in unsupervised scenarios to select discriminative features. We introduce the concept of pseudo labels, which enable discriminant analysis on unlabeled data, propose a novel unsupervised feature selection framework DisUFS which incorporates learning discriminative features with generating pseudo labels, and develop an effective algorithm for DisUFS. Experimental results on different types of real-world data demonstrate the effectiveness of the proposed framework DisUFS.
On the Reduction of Verbose Queries in Text Retrieval Based Software Maintenance

We argue that verbose queries used for software retrieval contain many terms that follow specific discourse rules, yet hinder retrieval. We report the results of an empirical study on the effect of removing such terms from verbose queries in the context of Text Retrieval-based concept location. In the study, we remove terms from 424 queries, generated from bug reports of nine open source systems. Removing the terms leads to substantial improvement in retrieval: 73% of the queries are improved, leading to 21.8% and 13.4% gain in terms of MRR and MAP, respectively. Such improvement is larger than that of many more sophisticated state-of-the-art approaches. The results show promise and the future challenge lies with automatically identifying the terms to be removed from the verbose queries.
PW-MAC: An energy-efficient predictive-wakeup MAC protocol for wireless sensor networks

This paper presents PW-MAC (Predictive-Wakeup MAC), a new energy-efficient MAC protocol based on asynchronous duty cycling. In PW-MAC, nodes each wake up to receive at randomized, asynchronous times. PW-MAC minimizes sensor node energy consumption by enabling senders to predict receiver wakeup times; to enable accurate predictions, PW-MAC introduces an on-demand prediction error correction mechanism that effectively addresses timing challenges such as unpredictable hardware and operating system delays and clock drift. PW-MAC also introduces an efficient prediction-based retransmission mechanism to achieve high energy efficiency even when wireless collisions occur and packets must be retransmitted. We evaluate PW-MAC on a testbed of MICAz motes and compare it to X-MAC, WiseMAC, and RI-MAC, three previous energy-efficient MAC protocols, under multiple concurrent multihop traffic flows and under hidden-terminal scenarios and scenarios in which nodes have wakeup schedule conflicts. In all experiments, PW-MAC significantly outperformed these other protocols. For example, evaluated on scenarios with 15 concurrent transceivers in the network, the average sender duty cycle for X-MAC, WiseMAC, and RI-MAC were all over 66%, while PW-MAC's average sender duty cycle was only 11%; the delivery latency for PW-MAC in these scenarios was less than 5% that for WiseMAC and X-MAC. In all experiments, PW-MAC maintained a delivery ratio of 100%.
Modulation of NMDAR Subunit Expression by TRPM2 Channels Regulates Neuronal Vulnerability to Ischemic Cell Death

Neuronal vulnerability to ischemia is dependent on the balance between prosurvival and prodeath cellular signaling. In the latter, it is increasingly appreciated that toxic Ca2+ influx can occur not only via postsynaptic glutamate receptors, but also through other cation conductances. One such conductance, the Transient receptor potential melastatin type-2 (TRPM2) channel, is a nonspecific cation channel having homology to TRPM7, a conductance reported to play a key role in anoxic neuronal death. The role of TRPM2 conductances in ischemic Ca2+ influx has been difficult to study because of the lack of specific modulators. Here we used TRPM2-null mice (TRPM2−/−) to study how TRPM2 may modulate neuronal vulnerability to ischemia. TRPM2−/− mice subjected to transient middle cerebral artery occlusion exhibited smaller infarcts when compared with wild-type animals, suggesting that the absence of TRPM2 is neuroprotective. Surprisingly, field potentials (fEPSPs) recorded during redox modulation in brain slices taken from TRPM2−/− mice revealed increased excitability, a phenomenon normally associated with ischemic vulnerability, whereas wild-type fEPSPs were unaffected. The upregulation in fEPSP in TRPM2−/− neurons was blocked selectively by a GluN2A antagonist. This increase in excitability of TRPM2−/− fEPSPs during redox modulation depended on the upregulation and downregulation of GluN2A- and GluN2B-containing NMDARs, respectively, and on augmented prosurvival signaling via Akt and ERK pathways culminating in the inhibition of the proapoptotic factor GSK3β. Our results suggest that TRPM2 plays a role in downregulating prosurvival signals in central neurons and that TRPM2 channels may comprise a therapeutic target for preventing ischemic damage.
Evidence for non-hydrostatic gas from the cluster X-ray to lensing mass ratio

Using a uniform analysis procedure, we measure spatially resolved weak gravitational lensing and hydrostatic X-ray masses for a sample of 18 clusters of galaxies. We find a radial trend in the X-ray to lensing mass ratio: at r 2500 , we obtain a ratio M X /M L = 1.03 ± 0.07 which decreases to M X /M L = 0.78 ± 0.09 at r 500 . This difference is significant at 3a once we account for correlations between the measurements. We show that correcting the lensing mass for excess correlated structure outside the virial radius slightly reduces, but does not eliminate this trend. An X-ray mass underestimate, perhaps due to non-thermal pressure support, can explain the residual trend. The trend is not correlated with the presence or absence of a cool core. We also examine the cluster gas fraction f gas and find no correlation with M L , an important result for techniques that aim to determine cosmological parameters using f gas .
Molecular Cloning and Characterization of Violaxanthin De-Epoxidase (CsVDE) in Cucumber

Violaxanthin de-epoxidase (VDE) plays an important role in protecting the photosynthetic apparatus from photo-damage by dissipating excessively absorbed light energy as heat, via the conversion of violaxanthin (V) to intermediate product antheraxanthin (A) and final product zeaxanthin (Z) under high light stress. We have cloned a violaxanthin de-epoxidase gene (CsVDE) from cucumber. The amino acid sequence of CsVDE has high homology with VDEs in other plants. RT-PCR analysis and histochemical staining show that CsVDE is expressed in all green tissues in cucumber and Arabidopsis. Using GFP fusion protein and immunogold labeling methods, we show that CsVDE is mainly localized in chloroplasts in cucumber. Under high light stress, relative expression of CsVDE and the de-epoxidation ratio (A+Z)/(V+A+Z) is increased rapidly, and abundance of the gold particles was also increased. Furthermore, CsVDE is quickly induced by cold and drought stress, reaching maximum levels at the 2nd hour and the 9th day, respectively. The ratio of (A+Z)/(V+A+Z) and non-photochemical quenching (NPQ) is reduced in transgenic Arabidopsis down-regulated by the antisense fragment of CsVDE, compared to wild type (WT) Arabidopsis under high light stress. This indicates decreased functionality of the xanthophyll cycle and increased sensitivity to photoinhibition of photosystem II (PSII) in transgenic Arabidopsis under high light stress.
K-Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion

system that based on single biometric called uni- modal biometrics usually suffers from problems like imposter's attack or hacking, unacceptable error rate and low performance. So the need of using multimodal biometric system arises in such cases. The aim of this paper is to study the fusion at feature extraction level for face and fingerprint. The proposed system fuses the two traits at feature extraction level by first making the feature sets compatible for concatenation and then reducing the feature sets to handle the "problem of curse of dimensionality". After concatenation these features are classified. Features of both modalities are extracted using Gabor filter and Principal Component Analysis (PCA). K-Nearest Neighbour classifier is used to classify the different people in the database. The experimental results reveal that the fusion of more than one biometric trait at feature level fusion with the K-Nearest Neighbor technique exhibits robust performance and increases its performance with utmost level of accuracy.
Genome-Wide Analysis of Respiratory Burst Oxidase Homologs in Grape (Vitis vinifera L.)

Plant respiratory burst oxidase homolog (rboh) genes appear to play crucial roles in plant development, defense reactions and hormone signaling. In this study, a total of seven rboh genes from grape were identified and characterized. Genomic structure and predicted protein sequence analysis indicated that the sequences of plant rboh genes are highly conserved. Synteny analysis demonstrated that several Vvrboh genes were found in corresponding syntenic blocks of Arabidopsis, suggesting that these genes arose before the divergence of the respective lineages. The expression pattern of Vvrboh genes in different tissues was assessed by qRT-PCR and two were constitutively expressed in all tissues tested. The expression profiles were similarly analyzed following exposure to various stresses and hormone treatments. It was shown that the expression levels of VvrbohA, VvrbohB and VvrbohC1 were significantly increased by salt and drought treatments. VvrbohB, VvrbohC2, and VvrbohD exhibited a dramatic up-regulation after powdery mildew (Uncinula necator (Schw.) Burr. ) inoculation, while VvrbohH was down-regulated. Finally, salicylic acid treatment strongly stimulated the expression of VvrbohD and VvrbohH, while abscisic acid treatment induced the expression of VvrbohB and VvrbohH. These results demonstrate that the expression patterns of grape rboh genes exhibit diverse and complex stress-response expression signatures.
#HotForBots: Sex, the non-human and digitally mediated spaces of intimate encounter

Contemporary practices of sex and intimacy are increasingly digitally mediated. In this paper, we identify two distinctly spatial effects of these mediations. First, the digital extends the spaces of sex/uality beyond the immediately proximate, simultaneously expanding the potential for non-human object choice in intimate encounters. Second, the digital intensifies the experiential fidelity of intimate encounters by folding the remote into the spatially immediate, such that non-proximate intimate relations with human subjects as well as non-human objects may feel more proximate. We articulate these effects by building on and contributing to developments in the geographies of encounter, which allows us to bring together theories and conceptual framings of intimacy, digitality and sexuality in a uniquely spatial register. These effects of extension and intensification resonate in a selection of empirical examples of digitally mediated sex/uality that we place along continuums of more-and-less human and more-and-less proximate. These continuums comprise the conceptual axes of a heuristic framework that we advance to both (i) capture particular points at which configurations of spaces, practices and subject/object choices of sex crystallize given conditions of pervasive digital mediation, and (ii) provoke further interrogations of the multiple ways in which sex, sexuality and intimacy are recast by the digital.
The musical offers of children’s programming on «Televisión Española» as its hearing universe

The music that children are exposed to in their everyday lives plays an important role in shaping the way they interpret the world around them, and television soundtracks are, together with their direct experience of reality, one of the most significant sources of such input. This work is part of a broader research project that looks at what kind of music children listen to in a sample of Latin American and Spanish TV programmes. More specifically, this study focuses on children’s programmes in Spain, and was addressed using a semiotic theoretical framework with a quantitative and musical approach. The programme «Los Lunnis» was chosen as the subject of a preliminary study, which consisted in applying 90 templates and then analysing them in terms of the musical content. The results show that the programme uses music both as the leading figure and as a background element. The most common texture is the accompanied monody and the use of voice, and there is a predominance of electronic instrumental sounds, binary stress and major modes with modulations. Musical pieces are sometimes truncated and rhythmically the music is quite poor; the style used is predominantly that of foreign popular music, with a few allusions to the classical style and to incidental music. The data reveal the presence of music in cultural and patrimonial aspects, as well as in cognitive construction, which were not taken into account in studies on the influence of TV in Spain. Such aspects do emerge, however, when they are reviewed from the perspective of semiotics, musical representation, formal analysis and restructuring theories.
Forum Summarization Using Topic Models and Content-Metadata Sensitive Clustering

The advent of the Internet and improvements in data sharing and storage, have resulted in an explosion of textual data. But, complete assimilation of such massive amounts of data in its raw form is a daunting task. Automated text mining methods such as text summarization present the user with a condensed version of data containing only key information. This is especially useful in the case of online user forums that contain a large number of posts spread out across several threads. Document summarization methods have been extensively studied and several methods have been developed in the recent past. This paper aims at developing a new method for automatic summarization of online forums by using topic models and content/metadata sensitive clustering.
The impact of advertisements: how female magazine readers in China perceive fashion magazine advertisements and white skin

Most of the models in Chinese fashion magazine advertisements are Caucasian women today. White skin, as one of their Caucasian characteristics, is particularly emphasized. Millions of Chinese women are bombarded with the advertising’s message of “having white skin is beautiful” every day. My research suggests that this kind of adverting depicts white skin as a beauty ideal; the prevalence of Caucasian models in whitening products’ magazine advertisements constructs body-esteem, self-esteem, and purchase decision-making of Chinese women. The Social Judgment Theory is employed in this research as a theoretical framework. It is a way to explain when persuasive messages are most likely to succeed and how people make judgments about them. Understanding this phenomenon will ultimately provide insight into addressing the effects of the promotion of fashion magazine advertisements on Chinese women. It is further anticipated that future researchers will expand and improve the knowledge of the Chinese advertising market.
SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data

The Discrete Wavelet Transform is a proven tool for a wide range of database applications. However, despite broad acceptance, some of its properties have not been fully explored and thus not exploited, particularly for two common forms of multidimensional decomposition. We introduce two novel operations for wavelet transformed data, termed SHIFT and SPLIT, based on the properties of wavelet trees, which work directly in the wavelet domain. We demonstrate their significance and usefulness by analytically proving six important results in four common data maintenance scenarios, i.e., transformation of massive datasets, appending data, approximation of data streams and partial data reconstruction, leading to significant I/O cost reduction in all cases. Furthermore, we show how these operations can be further improved in combination with the optimal coefficient-to-disk-block allocation strategy. Our exhaustive set of empirical experiments with real-world datasets verifies our claims.
Control of Memory CD8+ T Cell Differentiation by CD80/CD86-CD28 Costimulation and Restoration by IL-2 during the Recall Response1

Memory CD8+ T cell responses have been considered to be independent of CD80/CD86-CD28 costimulation. However, recall responses are often severely blunted in CD28−/− mice. Whether this impairment represents a requirement for CD28 costimulation for proper memory CD8+ T cell development or a requirement during the recall response is unknown. Furthermore, how CD28 costimulation affects the phenotype and function of memory CD8+ T cells has not been characterized in detail. In this study, we investigate these questions by studying the role of the CD28 costimulatory pathway in memory CD8+ T cell responses to acute and persistent DNA virus infections. Memory CD8+ T cells against vaccinia virus (VV) infection which develop without CD28 costimulation exhibit lower expression of differentiation markers CD27 and CD122 (IL-15Rβ). These memory CD8+ T cells also fail to produce IL-2. Our data indicate that for an optimal recall response, CD28 costimulation is required both for T cell priming and also during the recall response. Similar requirements were observed for memory CD8+ T cell responses during persistent infection with murine gammaherpesvirus 68 (MHV-68) infection, indicating CD28 may play the same role in both acute and persistent infections. Finally, we show deficits in the recall response are restored by IL-2 signaling during recall, but not during priming. The data presented show that CD28 costimulation not only controls the magnitude of the primary response but also affects development of memory CD8+ T cells and is required during the recall response in addition to initial T cell priming.
Muti-Path Shift-Reduce Parsing with Online Training

: In this paper we describe an enhanced shift-reduce parsing method which differs from the traditional transition-based model in two ways: ﬁrst, we maintain multiple transition paths after each transition step, in order to alleviate the serious risk of going astray for the only-one-path transition; second, we adopt the online training algorithm rather than the classical training-after-extraction method, to obtain more robust discriminativity of the classiﬁer for transition decision. Experiments on the Tsinghua Chinese Treebank show that the enhanced model gains obvious improvement over the baseline. And in the CIPS evaluation task, a neat implementation without tricks could achieved nearly the state-of-the-art performance.
Bantam: a customizable, java-based, classroom compiler

This paper introduces the Bantam Java compiler project, a new language and compiler designed specifically for the classroom Bantam Java, the source programming language, is a small subset of the Java language, which is a commonly-used language in introductory programming courses. Because Bantam Java is similar to Java, it leverages the student's existing intuition and the student can automatically apply what they learn in the course directly to Java. The Bantam Java project is also customizable (it supports several tools and targets), which gives instructors flexibility in designing course assignments. Finally, the Bantam Java compiler project includes a free, comprehensive, student manual which can be used in conjunction with any compiler textbook.
Concept drift detection for online class imbalance learning

Concept drift detection methods are crucial components of many online learning approaches. Accurate drift detections allow prompt reaction to drifts and help to maintain high performance of online models over time. Although many methods have been proposed, no attention has been given to data streams with imbalanced class distributions, which commonly exist in real-world applications, such as fault diagnosis of control systems and intrusion detection in computer networks. This paper studies the concept drift problem for online class imbalance learning. We look into the impact of concept drift on single-class performance of online models based on three types of classifiers, under seven different scenarios with the presence of class imbalance. The analysis reveals that detecting drift in imbalanced data streams is a more difficult task than in balanced ones. Minority-class recall suffers from a significant drop after the drift involving the minority class. Overall accuracy is not suitable for drift detection. Based on the findings, we propose a new detection method DDM-OCI derived from the existing method DDM. DDM-OCI monitors minority-class recall online to capture the drift. The results show a quick response of the online model working with DDM-OCI to the new concept.
Evaluating Optimality Theory for Pronoun Resolution Algorithm Specification

A recent paper (Beaver, 2004) recasts the Centering algorithm for pronoun resolution (Brennan et al., 1987) in terms of optimality theory (OT). Although the limitations of centering for pronoun resolution are well known, the algorithm's restatement in OT highlights the strengths of centering and produces an algorithm whose behavior is more transparent to the developer. The authors' motivation for developing this algorithm was due to the software engineering characteristics of OT as a specification mechanism, rather than improved pronoun resolution performance. There has been little previous work marrying computational pronoun resolution work with OT. Therefore, this study reports our experience implementing the OT formulation of centering in order to evaluate its desirability as a pronoun resolution specification formalism. Although the expressive capability of OT has some limitations, the benefits of the OT formalism are worth some attention. This paper seeks to highlight those benefits, and indicate how they can be employed within pronoun resolution algorithms, whether or not an OT formalization is employed.