Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the benefit of neural network based speech separation algorithms with hearing impaired listeners

Source separation is a useful technology for improving the benefit from hearing aids. However, most of the existing approaches to evaluating source separation rely on computational methods, and do not consider the effect of the algorithm on the end user. We seek to address this mismatch by quantifying the benefit of two state-of-the-art deep neural network (DNN) based source separation techniques, in terms of actual speech intelligibility benefits evaluated via subjective listening tests with 15 hearing impaired (HI) listeners, as well as more established computational metrics by which most source separation algorithms are currently compared. We present here our proposed source separation approach which is a novel application of the ’Convolutional Recurrent Neural Network’ (CRNN) deep learning architecture, and compare it with feedforward deep neural network (FDNN) approach. We evaluate these approaches on two talker mixtures from Danish hearing in noise test (HINT) database. We are particularly interested in speech separation in this work as the hearing-impaired listeners have problems understanding speech in the presence of one or more competing voices.
Knockdown of JARID 2 inhibits the proliferation and invasion of ovarian cancer through the PI 3 K / Akt signaling pathway

Protein Jumonji (JARID2), a member of the family of JmjC domain-containing proteins, has been reported to serve an important role in tumor growth and metastasis. However, the expression pattern and role of JARID2 in ovarian cancer remains unclear. Therefore, in the present study, the role of JARID2 in ovarian cancer was investigated, as well as the underlying mechanisms. The results of the present study demonstrated that the expression of JARID2 is upregulated in human ovarian cancer cell lines. Furthermore, downregulation of JARID2 significantly suppressed proliferation, migration, invasion and epithelial-mesenchymal transition in human ovarian cancer cells. Mechanistically, downregulation of JARID2 decreased the protein expression levels of phosphorylated phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt) in ovarian cancer cells. In conclusion the observations suggested that knockdown of JARID2 inhibited proliferation, migration and invasion in vitro through the inactivation of the PI3K/Akt signaling pathway. Therefore, JARID2 may represent a potential therapeutic target for the treatment of ovarian cancer.
Drivers of freshwater fi sh colonisations and extirpations under climate change

2011). In this context, species colonisations and extirpations result in so-called niche tracking (i.e. the process by which species follow favourable environmental conditions through geographical space, Graham et al. 1996, Tingley et al. 2009) fundamentally driven by the contraction and expansion of species ranges (Th omas 2010). In order to quantify modifi cations of species assemblages along geographical or environmental gradients (i.e. beta diversity), species turnover is among the most common metrics used, as well as its temporal equivalent, to estimate compositional changes over time (Albouy et al. 2012). Numerically, species turnover considers the number of colonisation (species gain) and extirpation (species loss) events within each spatio-temporal unit and compares the net change with the baseline species richness (Peterson et al. 2002, Th uiller et al. 2005). A vast number of studies and meta-analyses have addressed the issue of spatial variability in species composition (see Heino 2011, Leprieur et al. 2011, Oberdorff et al. 2011 for a review on the freshwater realm), demonstrating that beta diversity could be driven by functional traits, geographical gradients and ecosystem properties (Soininen et al. 2007). By contrast, assessing temporal Ecography 37: 001–010, 2014 doi: 10.1111/ecog.00753 © 2014 Th e Authors. Ecography © 2014 Nordic Society Oikos Subject Editor: Th ierry Oberdorff . Editor-in-Chief: Carsten Rahbek. Accepted 14 July 2014
Diet quality of nursing mothers using the Healthy Eating Index

Goal: To evaluate the overall diet quality of the nursing mothers using the Healthy Eating Index as an instrument. Methods: We carried out a cross-sectional-observational-epidemiological study using as data collection instrument a semi-structured questionnaire to characterize the sample and the habitual intake reminder. The diet quality was evaluated through the Healthy Eating Index (HEI) validated to the Brazilian population. For the analysis, we estimated the means and standard deviations, the medians and interquartile ranges of the HEI total and of each component. We used the Kolmogorov-Sminorv test to verify the normality of the data. HEI total score averages by groups according to socio-demographic, nutritional and obstetric variables were assessed using Student's t-tests, ANOVA and Tukey's “post hoc” test. Results: A total of 106 nursing mothers with a mean HEI score of 64.36 ± 10.68 were studied. Foods from the group of total fruit, whole fruit, total cereal, whole cereal and milk and dairy products were the components of HEI of the nursing mothers with lower scores, and with a higher frequency of minimum score. It was possible to observe a low intake of total fruits, whole fruits, total cereals, whole cereal and milk and dairy products. Comparing the means and medians of the total HEI score with the demographic, socioeconomic, nutritional and obstetric variables of the nursing mothers, we observed that women with 12 or more years of completed study had a significantly higher mean in relation to the less educated mothers.
Modifying existing applications for

This research investigates the use of DPDK to saturate a 100GbE link. This is first done by looking at Pktgen, an already existing DPDK packet generator. After that iperf3 is modified to use DPDK, as it normally uses the Linux networking stack. The performance of this new iperf3 is then compared to that of the original iperf3. It is shown that the necessary modifications to iperf3 for this were quite straightforward. However, these modifications resulted in less throughput than before. The cause for which is probably some missing features in the DPDK network stack that was used.
recent years in the Brazilian Amazonia Interactions between rainfall , deforestation and fires during

References http://rstb.royalsocietypublishing. org/content/363/1498/1779.full. html#related-urls Article cited in: http://rstb.royalsocietypublishing. org/content/363/1498/1779.full. html#ref-list-1 This article cites 36 articles, 16 of which can be accessed free This article is free to access Email alerting service here right-hand corner of the article or click Receive free email alerts when new articles cite this article sign up in the box at the top
Surface Network Construction from Non-parallel Cross-sections ( sap 0378 )

Surface construction from incomplete data is a topic of wide interest in geometry processing. A common class of incomplete inputs that arises in several application domains, including bio-medicine and geology, is a stack of curves representing the planar crosssections of a complete surface. In medical imaging, for example, these curves are typically hand-drawn by physicians to delineate contours of anatomical structures on 2D cross-sections of a 3D image volume generated by MRI, CT or ultrasound. Given a stack of planar cross-sections, a complete 3D surface is desired that connects the curves on each cross-section plane. In particular, such surface needs to be both topologically correct (i.e., closed and interpolating input curves) and geometrically smooth.
Justifying the Use of an Adjective of a Notion

To communicate sensibly with humans, it is important to provide a sensible answer with a justification. In this paper, we tackle the problem of finding the correct answer to a decision question that involves an adjective of a notion. We provide a simple solution to find the answer to this classification problem and provide the reasons to justify the answer. Since these answers are subjective, the answer is more convincing if a reason can be provided. In addition, providing a justifiable answer allows the program to make its own decision and becomes more autonomous. The solution depends on the program’s ability to acquire all the necessary knowledge, to locate the appropriate knowledge when needed, and to infer relationships among categories.
METAMODEL-ASSISTED RISK ANALYSIS FOR STOCHASTIC SIMULATION WITH INPUT UNCERTAINTY

For complex stochastic systems, simulation can be used to study the system inherent risk behaviors characterized by a sequence of percentiles. In this paper, we develop a Bayesian framework to quantify the overall estimation uncertainty of percentile responses. Suppose that the input parametric families are known. The input model estimation uncertainty is quantified by posterior samples of input parameters. Then, a distributional metamodel is introduced to simultaneously model the percentile response surfaces, which can efficiently propagate the input uncertainty to outputs. Our Bayesian framework can deliver credible intervals for percentiles, and a variance decomposition is further derived to estimate the contributions of input and simulation uncertainties. The empirical studies indicate that our approach has promising performance for system risk analysis.
A Temporal Model of Learner Behaviors in OELEs using Process Mining

Open-ended learning environments (OELEs) present learners with complex problems and a set of tools for solving these problems. Developing logging mechanisms that capture learners’ interactions with the system provide a wealth of trace data that can be employed for studying relations between their behaviors and performance. Such analyses provide a framework for making the OELE intelligent in that it can adapt its feedback to meet the needs of individual learners. In our previous research, we have developed learner modeling schemes that are based on sequential pattern mining (SPM) and Hidden Markov models (HMMs) to represent and track the temporal sequence of learners’ interactions with the OELE. We briefly discuss the pros and cons of these models, and then propose a process modeling approach to capture the temporal nature of learners’ behaviors. We apply the process modeling method to data collected from students working with the Betty’ Brain OELE, where students learn about scientific processes by building causal models.
Consistent Reconstruction of 3 D Cloth Surface

We propose a multiview method for reconstructing a folded cloth surface on which regularly-textured color patches are printed. These patches provide not only easy pixel-correspondence between multiviews but also the following two new functions. 1) Error recovery: errors in 3D surface reconstruction (e.g. errors in occlusion boundaries and shaded regions) can be recovered based on the spatio-temporal consistency of the patches. 2) Single-view hole filling: patches that are visible only from a single view can be extrapolated from the reconstructed ones based on the regularity of the patches. Using these functions for improving 3D reconstruction also produces the patch configuration on the reconstructed surface, showing how the cloth is deformed from its reference shape. Experimental results demonstrate the above improvements and the accurate patch configurations produced by our method.
Silencing Rac 1 protects H 9 c 2 cells against hypoxia-reoxygenation injury by inactivation of notch pathway

This study was aimed to determine the regulatory landscape of ectopic Ras-related C3 botulinum toxin substrate 1 (Rac1) in cardiomyocytes with hypoxia-reoxygenation (H/R) injury. We constructed a cell model with H/R injury in H9c2 cardiomyocytes to simulate myocardial Ischemia reperfusion injury (MIRI). When Rac1 was overexpressed or silenced, 3-(4,5-dimethylthiazol -2-yl)-2,5-diphenyltetrazolium bromide (MTT) colorimetric assay and flow cytometry were used to detect cell viability, reactive oxygen species (ROS) content and apoptosis in transformed cells. Key proteins Notch 1, Notch 2 and Jagged 1 (JAG1) of the Notch signaling pathway and apoptosis mediators B-cell lymphoma-2 (Bcl-2) and Caspase-3 were observed. Reoxygenation treatment provoked decline in the H9c2 cell viability in a time-dependent manner and about 54.67% viability was identified for 12 h. Thereby, 12 h reoxygenation duration was determined in further studies. Silencing Rac1 could remarkably raise cell viability (P < 0.05), suppress apoptosis (P < 0.05), scavenge ROS (P < 0.01) and inhibit expressions of key proteins of Notch signaling pathway, while overexpressing Rac1 had opposite effects on H/R-treated H9c2 cardiomyocytes (P < 0.05). Furthermore, silencing Rac1 suppressed H9c2 cell apoptosis by increasing Bcl-2 and decreasing cleaved Caspase-3. Collectively, our findings suggest that Rac1 acts as a key mediator of cardiomyocytes survival and responding oxidant stress after H/R injury. This study provides novel insight into the MIRI suppressive capacity of silencing Rac1 by inactivation of Notch signaling pathway, which holds great clinical potential in the cardioprotection against MIRI.
Study on PV diagram and motion curve of suction and discharge valve in reciprocating compressor

In this paper, through the establishment of the experiment test platform, the synchronous test of crankshaft rotation angle, cylinder pressure and valve motion of reciprocating refrigeration compressor is realized. And the factors affecting the performance of reciprocating refrigeration compressor and the performance of compressor are studied. The rotary encoder is used for monitoring transient pressure variation of suction and discharge valve port and internal cylinder during compressor running. And it can measure precisely the displacement of valve movement by optical fiber sensor. Through the study analysis on P-V diagrams under different conditions, compressor suction and discharge process of work and work loss are given to present relation between work and the refrigeration compressor condensing temperature. And also analyzing suction and discharge valve movement curve is to study the delay phenomenon while valve opening and closing, and present the most optimal proposal for valve plate structure design.
Exact Quantitative Model Checking Through Rational Search

Model checking systems formalized using probabilistic models such as discrete time Markov chains (DTMCs) and Markov decision processes (MDPs) can be reduced to computing constrained reachability properties. Linear programming methods to compute reachability probabilities for DTMCs and MDPs do not scale to large models. Thus, model checking tools often employ iterative methods to approximate reachability probabilities. These approximations can be far from the actual probabilities, leading to inaccurate model checking results. In this article, we present a new algorithm and its implementation that improves approximate results obtained by scalable techniques like value iteration to compute exact reachability probabilities.
Supervised Hyperbolic Representation Learning for Real-World Networks

Graphs are inherently complex structures, and learning suitable representations from these dynamic networks for downstream prediction tasks is a canonical problem with great practical implications. Indeed, the generation of node embeddings that accurately capture the structure and high dimensionality of a graph may prove incredibly useful in the development of models for traditional prediction and classification tasks across nodes and edges. In this work, we focus on two critical components of representation learning on graphs: the efficient and effective generation of node embeddings, and the use of such embeddings for node classification and link prediction. In particular, our work aims to leverage recent developments of representation learning in hyperbolic space to provide two significant contributions to the field: 1. The development and empirical evaluation of a supervised algorithm to embed graphs in hyperbolic space, providing significant improvements over recent unsupervised methods. 2. The development of a comprehensive multimodal framework to combine both Euclidean and hyperbolic embeddings for increased representational power on node classification and link prediction tasks, further improving results beyond current state-of-the-art performance. We provide an open-source implementation of our findings, embedding frameworks, and visualization methods at www.github. com/mananshah99/hyperbolic.
Combinatorial Analysis of the Burrows-Wheeler Transform ∗

The Burrows-Wheeler Transform is a well known transformation widely used in Data Compression: important competitive compression software, such as Bzip (cf. [16]) and Szip (cf, [15]) and some indexing software, like the FM-index (cf. [4]), are deeply based on the Burrows Wheeler Transform. The main advantage of using BWT for data compression consists in its feature of “clustering” together equal characters. In this paper we show the existence of fixed points of BWT, i.e., words on which BWT has no effect. We show a characterization of the permutations associated to BWT of fixed points and we give the explicit form of fixed points on a binary ordered alphabet {a, b} having at most four b’s and those having at most four a’s.
Conformism and Public News Prepared by Gabriel Desgranges and Céline Rochon

We study a model where investment decisions are based on investors’ information about the unknown and endogenous return of the investment. The information of investors consists of endogenously determined messages sold by financial analysts who have access to both public and private information on the return of the investment. We assume that the return of the investment is correlated with the aggregate investment. This results into a beauty contest among analysts (or a “conformism” effect). In equilibrium, analysts sell all the information they have to all the investors. A striking result is that there are sometimes multiple equilibria. There are equilibria where the beauty contest is exacerbated. Because of the correlation across analysts' information sources, not all the information available in the economy is transmitted to investors. JEL Classification Numbers: D82, D84, G14
A Survey Paper on Crop Disease Identification and Classification Using Pattern Recognition and Digital Image Processing Techniques

Agricultural scientists play an important role in detecting and finding cure for plant diseases. Sometimes manual identification of disease is time consuming and laborious process. One of the most important factors contributing to low yield is disease attack. Many studies show that quality of agricultural products may be reduced due to various factors of plant diseases. In banana plant, diseases which are commonly observed are panama wilt, yellow sigatoka, black sigatoka, banana streak virus and banana bunchy top virus [10]. The banana plant leaf diseases not only restrict the growth of the plant but also destroy the crop. Banana plant leaf diseases must be identified early and accurately as it can prove detrimental to the yield. Hence, a machine learning method is required to identify the affected leaf images in timely manner. The images required for this work are captured from the fields using digital camera. The captured images are then processed on computer using pattern recognition and digital image processing techniques. These techniques will help in identifying banana plant diseases thereby increasing the yield of banana [10]. This a survey paper on disease identification and classification of banana crops. A summary of various techniques for disease identification and classification is also done.
Ju n 20 15 Coordinate Descent with Arbitrary Sampling I : Algorithms and Complexity ∗

We study the problem of minimizing the sum of a smooth convex function and a convex blockseparable regularizer and propose a new randomized coordinate descent method, which we call ALPHA. Our method at every iteration updates a random subset of coordinates, following an arbitrary distribution. No coordinate descent methods capable to handle an arbitrary sampling have been studied in the literature before for this problem. ALPHA is a remarkably flexible algorithm: in special cases, it reduces to deterministic and randomized methods such as gradient descent, coordinate descent, parallel coordinate descent and distributed coordinate descent – both in nonaccelerated and accelerated variants. The variants with arbitrary (or importance) sampling are new. We provide a complexity analysis of ALPHA, from which we deduce as a direct corollary complexity bounds for its many variants, all matching or improving best known bounds.
Network Science : SI Dynamics of Disease Spread

Imagine you are walking down a crowded hallway. You aren’t in contact with everyone all at once. You talk to or simply pass by different people at different times as you walk down the hall. These connections would best be represented using a temporal network. In this work, we examine temporal networks to determine the behavior of disease spread across these networks and how it differs from the behavior of static networks. We use differential equations for mean field approximations to theoretically model how infection spreads throughout a temporal network. We extend our model to incorporate network structure by deriving a degree-based mean field theory. We then validate our theories with simulations in Mathematica. We also look into including multiple rounds of infections to see how it affects the spreading behavior. From our results we are able to determine how the temporal aspect affects the rate of spread of the disease and the overall size of the infected population. Acknowledgements: We would like to thank Carthage College for funding our research and Dr. Yaple for being our mentor. RHIT Undergrad. Math. J., Vol. 17, No. 1 Page 181
Non-thermal images of SN 1006: from radio to gamma-rays

Supernova remnants (SNRs) are believed to be the main sources of galactic cosmic rays. Discovery of the non-thermal component in X-ray spectrum of SN 1006 in 1995 and detection of a number of SNRs by H.E. S.S. strengthen the investigation of SNRs. SN 1006 remains to be one of the most interesting objects for high-energy astrophysics. Electrons accelerated by the shock are the source of the non-thermal radiation in radio, X-rays (via synchrotron emission) and \gamma-rays (via inverse-Compton process). Experimental images of SN 1006 are known in all these bands, including the very-high energy \gamma-ray range. An important task is therefore to model the distribution of the surface brightness in SNRs. We develop a method for synthesis of SNR maps due to the non-thermal radiation of electrons in radio, X-rays and \gamma-rays. In particular, the method takes into account the injection of particles and the behaviour of magnetic field at shocks with different obliquities as well as the radiation losses of electrons downstream of the shock. The method is used to model images of SN 1006. The images well correlate with the observations in radio and X-ray but not in \gamma-ray range. For coincide in \gamma-ray range it is needed to increases parameters \eta and E_{f}.
The effects of a community-based pulmonary rehabilitation programme on exercise tolerance anmd quelity of life: a renadomized controlled trial

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