Miracle-dz/UniFAR-FTUs huggingface dataset:
Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function

This paper addresses the problem of speech separation and enhancement from multichannel convolutive and noisy mixtures, <italic>assuming known mixing filters</italic>. We propose to perform speech separation and enhancement in the short-time Fourier transform domain using the convolutive transfer function (CTF) approximation. Compared to time-domain filters, the CTF has much less taps. Consequently, it requires less computational cost and sometimes is more robust against the filter perturbations. We propose three methods: 1) for the multisource case, the multichannel inverse filtering method, i.e., the multiple input/output inverse theorem (MINT), is exploited in the CTF domain; 2) a beamforming-like multichannel inverse filtering method applying the single-source MINT and using power minimization, which is suitable whenever the source CTFs are not all known; and 3) a basis pursuit method, where the sources are recovered by minimizing their <inline-formula><tex-math notation="LaTeX">$\ell _1$</tex-math></inline-formula>-norm to impose spectral sparsity, while the <inline-formula><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula>-norm fitting cost between microphone signals and mixing model is constrained to be lower than a tolerance. The noise can be reduced by setting this tolerance at the noise power level. Experiments under various acoustic conditions are carried out to evaluate and compare the three proposed methods. Comparison with four baseline methods—beamforming-based, two time-domain inverse filters, and time-domain Lasso—shows the applicability of the proposed methods.
George Zhou University of Windsor Norma Nocente and

Over the last three decades, many studies have been conducted to identify students’ preconceptions on various science topics. It is time now for a synthetic study of preconceptions to enhance our understanding of students’ everyday cognition and to benefit our effort in developing effective instructional inventions for conceptual change. Through a classroom-based study, we collected quantitative and qualitative data about students’ preconceptions in physics. Data analysis produced an in-depth understanding of the features of students’ preconceptions and cognition. For example, we found that students thought analogically as scientists did, but they used analogies differently. We also found that students’ preconceptions were highly correlated and that some preconceptions were more fundamental than others. Having the core preconception probably means having many others. The pedagogical and research implications of these findings are highlighted.
Energy management and power control strategy at the low wind speed region of a wind generation microgrid

This paper investigates the problems of the energy management and power control in a wind generation microgrid at the low wind speed region. A control strategy based on fuzzy-logic control technique is presented that decides for the operational mode of each wind generator and the proper integration to the microgrid. In particular, the developed control algorithm online decides the best-in-case scenario according to the microgrid topology and wind speed conditions for `which, when and how long' each wind turbine of a microgrid should operate at maximum power point tracking (MPPT), remain at stand-still or shut down in order to provide maximum electric energy to the grid and preserve the lifespan of the wind turbines' mechanical and electrical parts. Thus, a multivariable and multi-objective problem is managed for the low wind speed region in order to provide the best in case operational planning of the wind turbines in a microgrid. The common DC-bus topology with permanent magnet synchronous generators (PMSG) has been adopted for the microgrid of this paper; however, it can be applied to any type of microgrid topology and electrical generators. Selective simulation results are presented in order to demonstrate the satisfactory performance of the developed control system.
Fine-grained news recommendation by fusing matrix factorization, topic analysis and knowledge graph representation

Most news recommendation methods focus on using textual information of news to solve data sparseness problem of collaborative filtering. While if the text is not informative enough, these methods can't work well. A collaborative model combining matrix factorization, topic analysis and knowledge graph representation is proposed by introducing the knowledge from external knowledge base to alleviate the deficiency of the text. The experiment conducted on real life news dataset shows that the joint model outperforms the state-of-the-art method by 14% in Recall@200 metric, and improves the recommendation performance on sparse items by 20%.
Stability in the linearized problem of quantitative elastography

The goal of quantitative elastography is to identify biomechanical parameters from interior displacement data, which are provided by other modalities, such as ultrasound or magnetic resonance imaging. In this paper, we analyze the stability of several linearized problems in quantitative elastography. Our method is based on the theory of redundant systems of linear partial differential equations. We analyze the ellipticity properties of the corresponding PDE systems augmented with the interior displacement data; we explicitly characterize the kernel of the forward operators and show injectivity for particular linearizations. Stability criteria can then be deduced. While joint reconstruction of all parameters suffers from non-ellipticity even for more measurements, our results show stability of the separate reconstruction of shear modulus, pressure and density; they indicate that singular strain fields should be avoided, and show how additional measurements can help in ensuring stability of particular linearized problems.
Functional specificity of Akt isoforms in cancer progression

Abstract Akt/PKB kinases are central mediators of cell homeostasis. There are three highly homologous Akt isoforms, Akt1/PKBα, Akt2/PKBβ and Akt3/PKBγ. Hyperactivation of Akt signaling is a key node in the progression of a variety of human cancer, by modulating tumor growth, chemoresistance and cancer cell migration, invasion and metastasis. It is now clear that, to understand the mechanisms on how Akt affects specific cancer cells, it is necessary to consider the relative importance of each of the three Akt isoforms in the altered cells. Akt1 is involved in tumor growth, cancer cell invasion and chemoresistance and is the predominant altered isoform found in various carcinomas. Akt2 is related to cancer cell invasion, metastasis and survival more than tumor induction. Most of the Akt2 alterations are observed in breast, ovarian, pancreatic and colorectal carcinomas. As Akt3 expression is limited to some tissues, its implication in tumor growth and resistance to drugs mostly occurs in melanomas, gliomas and some breast carcinomas. To explain how Akt isoforms can play different or even opposed roles, three mechanisms have been proposed: tissue-specificity expression/activation of Akt isoforms, distinct effect on same substrate as well as specific localization through the cyto-skeleton network. It is becoming clear that to develop an effective anticancer Akt inhibitor drug, it is necessary to target the specific Akt isoform which promotes the progression of the specific tumor.
Fast recognition of real objects by an optimized hetero-associative neural network

We have developed and realized a concept which is very well suited for a quick recognition of highly correlated patterns. For a hetero-associative memory we used a minimal optimized output code (index memory). We constructed a tree structure in which the assignment of indices has been optimized by simulated annealing. Thus the algorithm for optimal stability of the learned patterns works most effectively. Special care was taken of recognizing « real » objects, e.g. scanned letters. Here the characteristic noise is very anisotropic. We have slightly modified the minimal overlap strategy of Krauth and Mezard [1] by training with this specific noise, and could improve the performance of our network. In order to get insight into the network and its behaviour we used a measure called constructivity which shows clearly the anisotropic effects. We trained a network to recognize a scanned text and to produce the associated text file. Due to the architecture of the network many processes can be treated in parallel. Therefore we used transputers for the implementation.
Heuristics for the design of heterogeneous telecommunication networks with QoS

Network design is a key challenge in order to supply a large set of requirements from demanding customers which are looking for high quality telecommunications services. Such requirements include bandwidth capacity, fault tolerance, security, low levels of delay, among others, so that the fulfilment of these requirements will directly impact on quality of service and customer satisfaction. Considering the current network scenario, the existence of different classes of customers (according to their requirements) and the availability of many technologies (with different capacities and consequently proportional costs), it is possible to design topologies to meet the demanding requirements but also optimize costs implied by such infrastructure. In this paper, we propose two heuristics for the problem of resilient heterogeneous network design with quality of service. Computational results show the efficiency of the proposed methods compared with another method from the literature.
DEHEMS: creating a digital environment for large-scale energy management at homes

Located at the consumer-end of the Smart Grid, domestic energy monitoring and management systems aim to provide direct energy feedback whilst (or shortly after) consumption occurs, so as to persuade users to achieve energy saving and efficiency. However, existing solutions are challenged by the lack of large-scale practice and study on user behaviours and preferences. In this paper, we present a domestic energy management system (DEHEMS), which deploys electricity and gas monitoring in European-wide homes. The system has been developed in three cycles in order for households to participate and contribute. Results based on both qualitative and quantitative data analysis show that less energy has been consumed using the system. Additionally, positive behavioural changes have been achieved among households.
IDE D EEP N EURAL N ETWORKS

Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relationship between Gaussian processes with a recursive kernel definition and random wide fully connected feedforward networks with more than one hidden layer. We exhibit limiting procedures under which finite deep networks will converge in distribution to the corresponding Gaussian process. To evaluate convergence rates empirically, we use maximum mean discrepancy. We then exhibit situations where existing Bayesian deep networks are close to Gaussian processes in terms of the key quantities of interest. Any Gaussian process has a flat representation. Since this behaviour may be undesirable in certain situations we discuss ways in which it might be prevented. 1
Multilevel support vector regression analysis to identify condition-specific regulatory networks

MOTIVATION The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification. RESULTS We present a new approach, namely multilevel support vector regression (ml-SVR), to systematically identify condition-specific regulatory modules. The approach is built upon a multilevel analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. To evaluate the effectiveness of the proposed strategy, we first compared the ml-SVR approach with other existing methods on simulation data and yeast cell cycle data. The resulting performance shows that the ml-SVR approach outperforms other methods in the identification of both regulators and their target genes. We then applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. AVAILABILITY AND IMPLEMENTATION The ml-SVR MATLAB package can be downloaded at http://www.cbil. ece.vt. edu/software.htm.
A framework for evaluating the impact of compression on registration algorithms without gold standard

The impact of lossy compression has often been discussed in the medical area. In this study, an evaluation of the impact of lossy compression on the performance of rigid registration algorithms for medical images is proposed. The robustness, repeatability and accuracy of these algorithms is estimated through a statistical procedure for each compression ratio. Results are obtained thanks to a grid technology handling the computation cost of the method. Experiments reveal that the impact of compression is quite negligible below a significant compression ratio if the registration algorithm has a good multi-scale handling. Beyond this threshold, feature-based methods are highly penalized.
Neuroinflammation in Response to Intracerebral Injections of Different HMGB1 Redox Isoforms

Background: Neuroinflammation triggered by infection or trauma is the cause of central nervous system dysfunction. High-mobility group box 1 protein (HMGB1), released from stressed and dying brain cells, is a potent neuroinflammatory mediator. The proinflammatory functions of HMGB1 are tightly regulated by post-translational redox modifications, and we here investigated detailed neuroinflammatory responses induced by the individual redox isoforms. Methods: Male Dark Agouti rats received a stereotactic injection of saline, lipopolysaccharide, disulfide HMGB1, or fully reduced HMGB1, and were accessed for blood-brain barrier modifications using magnetic resonance imaging (MRI) and inflammatory responses by immunohistochemistry. Results and Conclusions: Significant blood-brain barrier disruption appeared 24 h after injection of lipopolysaccharide, disulfide HMGB1, or fully reduced HMGB1 compared to controls, as assessed in post-gadolinium T1-weighted MRI images and confirmed by increased uptake of FITC-conjugated dextran. Immunohistochemistry revealed that both HMGB1 isoforms also induced a local production of IL-1β. Additionally, disulfide HMGB1 increased major histocompatibility complex class II expression and apoptosis. Together, the results demonstrate that extracellular, cerebral HMGB1 causes significant blood-brain barrier disruption in a redox-independent manner and activates several components of neuroinflammation. Blocking HMGB1 might potentially improve clinical outcome in conditions such as stroke and traumatic brain injury.
Cancer Gene Prioritization for Targeted Resequencing Using FitSNP Scores

Background Although the throughput of next generation sequencing is increasing and at the same time the cost is substantially reduced, for the majority of laboratories whole genome sequencing of large cohorts of cancer samples is still not feasible. In addition, the low number of genomes that are being sequenced is often problematic for the downstream interpretation of the significance of the variants. Targeted resequencing can partially circumvent this problem; by focusing on a limited number of candidate cancer genes to sequence, more samples can be included in the screening, hence resulting in substantial improvement of the statistical power. In this study, a successful strategy for prioritizing candidate genes for targeted resequencing of cancer genomes is presented. Results Four prioritization strategies were evaluated on six different cancer types: genes were ranked using these strategies, and the positive predictive value (PPV) or mutation rate within the top-ranked genes was compared to the baseline mutation rate in each tumor type. Successful strategies generate gene lists in which the top is enriched for known mutated genes, as evidenced by an increase in PPV. A clear example of such an improvement is seen in colon cancer, where the PPV is increased by 2.3 fold compared to the baseline level when 100 top fitSNP genes are sequenced. Conclusions A gene prioritization strategy based on the fitSNP scores appears to be most successful in identifying mutated cancer genes across different tumor entities, with variance of gene expression levels as a good second best.
Apache flink: Stream and batch processing in a single engine

Apache Flink 1 is an open-source system for processing streaming and batch data. Flink is built on the philosophy that many classes of data processing applications, including real-time analytics, continuous data pipelines, historic data processing (batch), and iterative algorithms (machine learning, graph analysis) can be expressed and executed as pipelined fault-tolerant dataﬂows. In this paper, we present Flink’s architecture and expand on how a (seemingly diverse) set of use cases can be uniﬁed under a single execution model.
Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain

The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop a structural feature based collaborative reconstruction (SFCR) method for QSM including both magnitude and susceptibility based information. The SFCR algorithm is composed of two consecutive steps corresponding to complementary reconstruction models, each with a structural feature based l 1 norm constraint and a voxel fidelity based l 2 norm constraint, which allows both the structure edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the M-step, the initial susceptibility map is reconstructed by employing a k-space based compressed sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in spatial domain using weighted constraints derived from the initial susceptibility map from the M-step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine position most approximate to the gold standard COSMOS result.
Circulating Progenitor Cell Count for Cardiovascular Risk Stratification: A Pooled Analysis

Background Circulating progenitor cells (CPC) contribute to the homeostasis of the vessel wall, and a reduced CPC count predicts cardiovascular morbidity and mortality. We tested the hypothesis that CPC count improves cardiovascular risk stratification and that this is modulated by low-grade inflammation. Methodology/Principal Findings We pooled data from 4 longitudinal studies, including a total of 1,057 patients having CPC determined and major adverse cardiovascular events (MACE) collected. We recorded cardiovascular risk factors and high-sensitive C-reactive protein (hsCRP) level. Risk estimates were derived from Cox proportional hazard analyses. CPC count and/or hsCRP level were added to a reference model including age, sex, cardiovascular risk factors, prevalent CVD, chronic renal failure (CRF) and medications. The sample was composed of high-risk individuals, as 76.3% had prevalent CVD and 31.6% had CRF. There were 331 (31.3%) incident MACE during an average 1.7±1.1 year follow-up time. CPC count was independently associated with incident MACE even after correction for hsCRP. According to C-statistics, models including CPC yielded a non-significant improvement in accuracy of MACE prediction. However, the integrated discrimination improvement index (IDI) showed better performance of models including CPC compared to the reference model and models including hsCRP in identifying MACE. CPC count also yielded significant net reclassification improvements (NRI) for CV death, non-fatal AMI and other CV events. The effect of CPC was independent of hsCRP, but there was a significant more-than-additive interaction between low CPC count and raised hsCRP level in predicting incident MACE. Conclusions/Significance In high risk individuals, a reduced CPC count helps identifying more patients at higher risk of MACE over the short term, especially in combination with a raised hsCRP level.
Tetraspanin 8 is an interactor of the metalloprotease meprin β within tetraspanin-enriched microdomains

Abstract Meprin β is a dimeric type I transmembrane protein and acts as an ectodomain sheddase at the cell surface. It has been shown that meprin β cleaves the amyloid precursor protein (APP), thereby releasing neurotoxic amyloid β peptides and implicating a role of meprin β in Alzheimer’s disease. In order to identify non-proteolytic regulators of meprin β, we performed a split ubiquitin yeast two-hybrid screen using a small intestinal cDNA library. In this screen we identified tetraspanin 8 (TSPAN8) as interaction partner for meprin β. As several members of the tetraspanin family were described to interact with metalloproteases thereby affecting their localization and/or activity, we hypothesized similar functions of TSPAN8 in the regulation of meprin β. We employed cell biological methods to confirm direct binding of TSPAN8 to meprin β. Surprisingly, we did not observe an effect of TSPAN8 on the catalytic activity of meprin β nor on the specific cleavage of its substrate APP. However, both proteins were identified as present in tetraspanin-enriched microdomains. Therefore we hypothesize that TSPAN8 might be important for the orchestration of meprin β at the cell surface with impact on certain proteolytic processes that have to be further identified.