Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity

Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.
Maximum Entropy Based Normalization Of Word Posteriors For Phonetic And Lvcsr Lattice Search

In many key word-spotting systems, the word posterior probability is an elementary quantity. In theory, the posterior of a keyword match denotes the probability of the match being correct. However, posteriors estimated on lattices, in particular phoneme lattices, are often off by orders of magnitude. This paper investigates the problem of providing "correct" posteriors, in the context of lattice-based word spotting. Unlike other work on word posteriors that focusses on relative ranking of posteriors, we emphasize relevance of the absolute value of the posterior in our user scenario. We stipulate that the posteriors should approach empirical precisions in a limit sense. Using this as a constraint, we estimate a mapping function based on maximum entropy. We find that for posteriors generated from phonetic lattices, mapped posteriors are satisfyingly consistent with empirical precision. In a joint search task, where different words are ranked together by posterior, FOM (figure of merit) improved from 11.2% to 57.8%, which demonstrated the effectiveness of the method. Applied to searching LVCSR-based word lattices, the improvement is neglectable, but it is still effective when combining phonetic and word-lattice search in a hybrid mode, yielding an improvement from 46.7% to 65.8%
Generation of Monoclonal Antibodies against Dengue Virus Type 4 and Identification of Enhancing Epitopes on Envelope Protein

The four serotypes of dengue virus (DENV1-4) pose a serious threat to global health. Cross-reactive and non-neutralizing antibodies enhance viral infection, thereby exacerbating the disease via antibody-dependent enhancement (ADE). Studying the epitopes targeted by these enhancing antibodies would improve the immune responses against DENV infection. In order to investigate the roles of antibodies in the pathogenesis of dengue, we generated a panel of 16 new monoclonal antibodies (mAbs) against DENV4. Using plaque reduction neutralization test (PRNT), we examined the neutralizing activity of these mAbs. Furthermore, we used the in vitro and in vivo ADE assay to evaluate the enhancement of DENV infection by mAbs. The results indicate that the cross-reactive and poorly neutralizing mAbs, DD11-4 and DD18-5, strongly enhance DENV1-4 infection of K562 cells and increase mortality in AG129 mice. The epitope residues of these enhancing mAbs were identified using virus-like particle (VLP) mutants. W212 and E26 are the epitope residues of DD11-4 and DD18-5, respectively. In conclusion, we generated and characterized 16 new mAbs against DENV4. DD11-4 and D18-5 possessed non-neutralizing activities and enhanced viral infection. Moreover, we identified the epitope residues of enhancing mAbs on envelope protein. These results may provide useful information for development of safe dengue vaccine.
(B)ELF in multicultural student teamwork

Abstract Many researchers agree that multicultural teams are a “double-edged sword” with the potential for high levels of creativity and production, but also conflict. This paper argues that effective communication is vital for developing “virtuous,” rather than vicious, circles and that research into (B)ELF offers an insight into what effective communication in multicultural and multilingual teams can look like. The conceptual frame develops research into ELF and BELF by also drawing on organisational and management research to examine team processes and the role of language within them. The second part of the paper presents illustrative examples from data collected in an ethnographic study from an English-medium marketing master’s programme at WU Vienna. The students’ teamwork project comprises an international market entry simulation and can be seen as a training ground for managing both business content and team processes. The findings indicate that both the ELF context and the ELF talk furthered the development of rapport, and that the students’ “casual talk” supported their “work talk. ” The paper finishes with a call for more empirical research into language use among recent business graduates and how to prepare students better for a globalised workplace.
A Non-parametric "Trim and Fill" Method of Assessing Publication Bias in Meta-analysis

Meta-analysis collects and synthesizes results from individual studies to estimate an overall eeect size. If published studies are chosen, say through a literature review, an inherent selection bias may arise, since for example, studies may tend to be published more readily if they are statistically signiicant, or deemed to be morèinteresting' in terms of the impact of their outcomes. We develop a simple rank-based data augmentation technique, formalizing the use of funnel plots, to estimate and adjust for the numbers and outcomes of missing studies. Several non-parametric estimators are proposed for the number of missing studies, and their properties are developed analytically and through simulations. We apply the method to simulated and epidemiological data sets, and show it is both eeective and consistent with other criteria in the literature.
Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records

Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.
SPAWC 131 Main Menu Performance Analysis of Redundant Filterbank Precoders with Blind Channel Estimation Capabilities

The aim of this paper is to provide performance analysis of a deterministic method proposed recently for blind identication of linear time-invariant channels, in the presence of additive noise, for block transmission systems using redundant precoding. Robustness of the blind estimation method is also analyzed in the presence of slow channel variations arising due to Doppler shifts or due to phase noise at the transmit/receive oscillators. The theoretical analysis is validated by simulations.
Duality and Sum Capacity of Gaussian MIMO Broadcast Channels

In this report, we survey the landmark papers on the sum capacity of Gaussian multiple-input multiple-output (MIMO) broadcast channels (BC). Three different groups have reached the same conclusion that the sum rate capacity of Gaussian MIMO BC is achieved by the dirty paper coding (DPC). In particular, we thoroughly investigate the approach which utilized the duality relation between the DPC achievable region of Gaussian MIMO BC and the capacity region of Gaussian MIMO multiple access channels (MAC). This report starts with the explanation of the DPC in Gaussian MIMO BC and then establishes the duality between Gaussian scalar MAC and BC. The duality result is extended to the duality between the capacity region of Gaussian MIMO MAC and the DPC achievable region of Gaussian MIMO BC. Using this extended duality as well as the notion of Sato’s upper bound, we finally show that the DPC region achieves the sum capacity of Gaussian MIMO BC.
Factor analysis based semantic variability compensation for automatic conversation representation

The main objective of this paper is to identify themes from dialogues of telephone conversations in a real-life customer care service. In this task, the word semantic variability contained in these conversations may impact the classification performance by retaining the noise in their vectorial representation. In this article, we propose an original method to compensate this semantic variability using the Factor Analysis (FA) paradigm, initially designed for speech processing tasks to compensate the acoustic variability, mainly in Speaker Verification (SV) and Automatic Speech Recognition (ASR). In our proposal, we used the FA paradigm to estimate the semantic variability as an additive component located in a subspace of low dimension (with respect to the super-vector space). This additive semantic variability is estimated in Factor Analysis model space. From this estimation, a specific vector transformation is obtained and is applied to vectors of dialogue representation. Experiments are reported using a corpus collected in the call center of the Paris Transportation Service. Results show the effectiveness of the proposed representation paradigm with a theme identification accuracy of 80.0%, showing a significant improvement with respect to previous results on the same corpus. Index Terms: Human/Human conversation representation, Semantic variability, Factor analysis, Variability compensation, Automatic classification, Latent Dirichlet Allocation.
Microtubule-Destabilizing Agents Induce Focal Adhesion Structure Disorganization and Anoikis in Cancer Cells

Microtubule disruption provokes cytoskeleton and cell adhesion changes whose importance for apoptosis induction remains unclear. The present study focuses on the functional and the molecular adhesion kinetics that are induced by microtubule disruption-mediated apoptosis. We showed that antimicrotubules induce a biphasic sequence of adhesion response that precedes the onset of apoptosis and focal adhesion kinase hydrolysis. Antimicrotubules first induced an increase of the cellular adhesion paralleled by the raise of focal adhesion sites and actin contractility, which was followed by a sharp decrease of cell adhesion and disorganization of focal adhesion and actin stress fibers. The latter sequence of events ends by cell rounding, detachment from the extracellular matrix, and cell death. Microtubule-disrupting agents induced a sustained paxillin phosphorylation, before the activation of apoptosis, that requires the prior activation of extracellular signal-regulated kinase and p38 but not c-Jun NH2-terminal kinase. Interestingly, integrin-linked kinase overexpression rescued the antimicrotubule-mediated loss of cell viability. Altogether, these results propound that antimicrotubule agents induce anoikis through the loss of focal adhesion structure integrity.
Efficacy of α‐glucosidase inhibitors combined with dipeptidyl‐peptidase‐4 inhibitor (alogliptin) for glucose fluctuation in patients with type 2 diabetes mellitus by continuous glucose monitoring

The combination therapy of dipeptidyl‐peptidase (DPP)‐4 inhibitor and α‐glucosidase inhibitors (α‐GIs) is highly effective in suppressing postprandial hyperglycemia. The aim of the present study was to compare the effects of voglibose and miglitol on glucose fluctuation, when used in combination with DPP‐4 inhibitor by using continuous glucose monitoring (CGM).
Evaluating the Impact of Human Resources on Firm Performance: A Literature Review

Research has been controversial regarding theorized findings of High Performing Work System (HPWS)/firm performance research due to several reasons. First, operationalization of variables differs drastically from one study to the next. Second, theoretical frameworks also vary greatly as does the methods researchers use to test their hypotheses. In order to significantly theorize findings regarding the HPWS/firm performance relationship, researchers must come to a consensus on how to conduct such studies. Only then, when similar methodology is implemented among studies and across industries, can researchers actually theorize the HPWS/firm performance relationship accurately. Therefore, in present study, 33 HPWS/firm performance studies from 1994 to 2013 based on a Proquest and Google Scholar Database were analyzed. First It has been determined how the major variables of an HPWS and firm performance should be operationalized, then a method for better development of theoretical frameworks has been suggested. A new model to measure an HPWS called the Total Strategic Resource Approach, which is based on the universalistic, contingent and resource-based view was suggested. We also recommend using the balanced scorecard approach to firm performance measurement, and finally, we recommend the use of structural equation modeling to build more robust models that test multiple mediating and moderating variables as related to the HPWS/firm performance relationship.
Analyzing network protocol binary software with joint symbolic execution

Network protocol binary software is threaten by vulnerabilities. Current analyzing methods do not make full use of the interaction information in network protocol and do not treat both sides of network protocol as a whole system. This paper proposes a novel method based on joint symbolic execution to test synchronously both server and client of the whole network protocol binary software stacks. Moreover, this paper presents a prototype system, S2EProtocol-joint, upon Selective Symbolic Execution (S2E) platform to automatically test network protocol binary software. The experiment results validate that this approach is effective and efficient in detecting vulnerabilities.
From Joyous to Clinically Depressed: Mood Detection Using Multimodal Analysis of a Person's Appearance and Speech

Clinical depression is a critical public health problem, with high costs associated to a person's functioning, mortality, and social relationships, as well as the economy overall. Currently, there is no dedicated objective method to diagnose depression. Rather, its diagnosis depends on patient self-report and the clinician's observation, risking a range of subjective biases. Our aim is to develop an objective affective sensing system that supports clinicians in their diagnosis and monitoring of clinical depression. In this PhD work, my approach is based on multimodal analysis, i.e. combinations of vocal affect, head pose and eye movement from a video-audio real-world clinically validated data. In addition, this work will investigate the cross-cultural generalization of depression characteristics from different languages and countries.
Component-Based Modeling and Processing of Medical Ultrasound Signals

Component-based modeling and processing can be found in many signal and image processing applications. In this paper, we apply this approach to cardiac medical ultrasound imaging. We show how the raw data generated in the ultrasound scanning process can be modeled as consisting of two distinct components. By decomposing these signals into the two components, we are able to apply separate processing pipelines tailored to each component. The potential benefits of this approach to medical ultrasound imaging are demonstrated in two central issues of the commonly used processing: 1) the very large amounts of data generated by the scanning process, and 2) artifacts in the images and videos generated by standard processing, commonly referred to as side lobe artifacts. We manage to compress the data by a factor of over 20 as well as remove the side lobe artifacts. These benefits are demonstrated on both simulated data and real cardiac scanning data.
The Graph Minor Algorithm with Parity Conditions

We generalize the seminal Graph Minor algorithm of Robertson and Seymour to the parity version. We give polynomial time algorithms for the following problems:\begin{enumerate}\itemthe parity $H$-minor (Odd $K_k$-minor) containment problem, and\itemthe disjoint paths problem with $k$ terminals and the parity condition for each path, \end{enumerate}as well as several other related problems. We present an $O(m \alpha(m,n) n)$ time algorithm for these problems for any fixed $k$, where $n,m$ are the number of vertices and the number of edges, respectively, and the function $\alpha(m,n)$ is the inverse of the Ackermann function (see Tarjan \cite{tarjan}). Note that the first problem includes the problem of testing whether or not a given graph contains $k$ disjoint odd cycles (which was recently solved in \cite{tony, oddstoc}), if we fix $H$ to be equal to the graph of $k$ disjoint triangles. The algorithm for the second problem generalizes the Robertson Seymour algorithm for the $k$-disjoint paths problem. As with the Robertson-Seymour algorithm for the $k$-disjoint paths problem for any fixed $k$, in each iteration, we would like to either use the presence of a huge clique minor, or alternatively exploit the structure of graphs in which we cannot find such a minor. Here, however, we must maintain the parity of the paths and can only use an ``odd clique minor & quot;. This requires new techniques to describe the structure of the graph when we cannot find such a minor. We emphasize that our proof for the correctness of the above algorithms does not depend on the full power of the Graph Minor structure theorem \cite{RS16}. Although the original Graph Minor algorithm of Robertson and Seymour does depend on it and our proof does have similarities to their arguments, we can avoid the structure theorem by building on the shorter proof for the correctness of the graph minor algorithm in \cite{kw}. Consequently, we are able to avoid the much of the heavy machinery of the Graph Minor structure theory. Utilizing some results of \cite{kw} and \cite{lex1, lex2}, our proof is less than 50 pages.
Misclassiication Minimization

The problem of minimizing the number of misclassiied points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that \counts" misclassiications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualiication assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a nite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassiication minimization problem.
On demand elastic capacity planning for service auto-scaling

Cloud computing allows on demand elastic service scaling. The capability of a service to predict resource requirements for the next operational period defines how well it will exploit the elasticity of cloud computing in order to reduce operational costs. In this work, we consider a capacity planning process for service scale-out as an online pricing model. In particular, we study the impact of buffering service requests on revenues in various settings with allocation and maintenance costs. In addition, we analyze the incurred latency implied by buffering service requests. We believe that our insights will allow to significantly simplify predictions and mitigate the unknowns of future demands on resources.
Two Phases Outlier Detection in Different Subspaces

Mining high dimensional outlier is not fully resolved for its dimensional particularity. The existing full space based methods can find distinct outliers and neglect those hidden in some subspaces. Subspace based approaches can detect most outliers that are apparent in low dimensional spaces, while missing the invisible outliers in subspaces. This paper proposes a novel two-phase inspection model. The first phase measures neighbor's density in subspaces to find low dimensional outliers. The second phase evaluates deviation degree of neighbors in connected subspaces. The undiscovered outliers appear a fast dispersion and scatter more than its neighbors. We analysis two-phase results statistically, and merge into one score for each object. The outliers are expressed with top score objects. The evaluation on synthetic and real data sets shows that our proposal outperform state of the art algorithms in high dimensional outlier issue.
Image interpolation using across-scale pixel correlation

A novel method is proposed for image interpolation. It is assumed that the pixel correlation between local regions across scales would remain similar. In addition, this a priori similarity could be extracted from a set of available image data that have the same content but different resolutions. A simple architecture is devised to estimate the correlation efficiently, which is then used to predict the unknown pixel values in a high-resolution image. Evaluation shows a promising performance of the proposed algorithm.
The Spectral Flow and the Maslov Index

exist and have no zero eigenvalue. A typical example for A(t) is the div-grad-curl operator on a 3-manifold twisted by a connection which depends on t. Atiyah et al proved that the Fredholm index of such an operator DA is equal to minus the “spectral flow” of the family {A(t)}t∈R. This spectral flow represents the net change in the number of negative eigenvalues of A(t) as t runs from −∞ to ∞. This “Fredholm index = spectral flow” theorem holds for rather general families {A(t)}t∈R of self-adjoint operators on Hilbert spaces. This is a folk theorem that has been used many times in the literature, but no adequate exposition has yet appeared. We give such an exposition here as well as several applications. More precisely, we shall prove the following theorem.