Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic detection and tracking of target singer in multi-singer music recordings

In this paper, we investigate the problem of automatically detecting and tracking a specified person's singing portions within a music recording with multiple simultaneous or nonsimultaneous singers. This problem reflects an important issue in multimedia applications which require transcription and indexing of music data in response to the increasing demand nowadays for content-based information retrieval. The major challenges of this study arise from the fact that the singer's voices are inextricably intertwined with the signal of the background accompaniment. To determine whether or when an accompanied voice is present and from a sought singer, methods are presented for separating vocal from non-vocal regions, for extracting and modeling singers' vocal characteristics, and for distinguishing among vocal regions performed by the target singer and other simultaneous or non-simultaneous singers.
Sequential Convolutional Architectures for Multi-Sentence Text Classification CS 224 N-Final Project Report

We describe a natural extension of deep learning to the multi-sentence text analysis domain – in particular, we construct a novel recurrent convolutional architecture by drawing inspiration from video analysis to exploit the hierarchical and sequential nature of text. We provide not only a highperforming and general model, but also a more linguistically plausible text classification scheme than bag-of-n-grams or averaged word-vector based approaches.
Boundary dynamics and multiple reflection expansion for Robin boundary conditions

In the presence of a boundary interaction, Neumann boundary conditions should be modified to contain a function S of the boundary fields: $({\ensuremath{\nabla}}_{N}+S)\ensuremath{\varphi}=0. $ Information on quantum boundary dynamics is then encoded in the S-dependent part of the effective action. In the present paper we extend the multiple reflection expansion method to the Robin boundary conditions mentioned above, and calculate the heat kernel and the effective action (i) for constant S, (ii) to the order ${S}^{2}$ with an arbitrary number of tangential derivatives. Some applications to symmetry breaking effects, tachyon condensation and a brane world are briefly discussed.
Towards a gender perspective in qualitative research on voluntary medical male circumcision in east and southern Africa

The World Health Organization endorsed voluntary medical male circumcision (VMMC) in 2007 as an effective method to provide partial protection against heterosexual female-to-male transmission of HIV in regions with high rates of such transmission, and where uptake of VMMC is low. Qualitative research conducted in east and southern Africa has focused on assessing acceptability, barriers to uptake of VMMC and the likelihood of VMMC increasing men's adoption of risky sexual behaviours. Less researched, however, have been the perceptions of women and sexual minorities towards VMMC, even though they are more vulnerable to HIV/AIDS transmission than are heterosexual men. The purpose of this paper is to identify core areas in which a gendered perspective in qualitative research might improve the understanding and framing of VMMC in east and southern Africa. Issues explored in this analysis are risk compensation, the post-circumcision appearance of the penis, inclusion of men who have sex with men as study respondents and the antagonistic relation between VMMC and female genital cutting. If biomedical and social science researchers explore these issues in future qualitative inquiry utilising a gendered perspective, a more thorough understanding of VMMC can be achieved, which could ultimately inform policy and implementation.
Variational Bayesian inference with Automatic Relevance Determination for Generative Topographic Mapping

Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced by Bishop et al. as a data visualization technique. In this paper, we focus on variational Bayesian inference for the GTM. The variational Bayesian GTM was first proposed by Olier et al. However, the GTM of Olier et al. uses a single regularization term and regularization parameter to avoid overfitting and therefore cannot locally control the degree of regularization. To overcome the problem, we propose the variational Bayesian inference with Automatic Relevance Determination (ARD) hierarchical prior for the GTM. The proposed model uses multiple regularization parameters and therefore can individually control the degree of regularization in each local area of the data space. Several experiments show that the proposed GTM provides better visualization than the conventional GTM approaches.
Demosaicing of Color Images Using Steerable Wavelets

In some types of digital color cameras only a single value is provided for each pixel: either, red, green, or blue. The reconstruction of the three color values for each pixel is known as the “demosaicing” problem. This report suggests a solution to this problem based on the steerable wavelet decomposition. The reconstructed images present a faithful interpolation of missing data while preserving edges and textures.
Exploiting Multi-Word Similarity for Retrieval in Medical Document Collections: the TSRM Approach

In this paper, we investigate on potential improve- ments to Information Retrieval (IR) models related to document representation and conceptual, topic retrieval, in medical docu- ment collections. We propose the TSRM 1 (Term Similarity and Retrieval Model) approach, where document representations are based on multi-word domain terms, rather than mere single key-words, typically applied in traditional IR. The proposed representation is semantically compact and more efficient, being reduced to a limited number of meaningful multi-word terms (phrases), rather than large vectors of single-words, part of which may be void of distinctive content semantics. In computing document similarity, contrary to other state-of-the- art methods examined in this work, TSRM adopts a knowledge poor solution, namely an approach which does not require any existing knowledge resources, such as ontologies, or thesauri. The evaluation of TSRM is based on OHSUMED, a standard TREC collection of medical documents and illustrated the ef- ficiency of TSRM over other well established general purpose IR models.
Solution of Linear Electrical Circuit Problem Using Neural Networks

paper, Neural network algorithm is introduced to study the singular system of a linear electrical circuit for time invariant and time varying cases. The discrete solutions obtained using neural network are compared with Runge-Kutta(RK) method and exact solutions of the electrical circuit problem and are found to be very accurate. Error graphs for inductor currents and capacitor voltages are presented in a graphical form to show the efficiency of neural network algorithm. This neural network algorithm can be easily implemented in a digital computer for any singular system of electrical circuits.
Sparse Network Lasso for Local High-dimensional Regression

We introduce the sparse network lasso, which is suited for interpreting models in addition to having high predicting power, for high dimensionality d and small sample size n types of problems. More specifically, we consider a function that consists of local models, where each local model is sparse. We introduce sample-wise network regularization and sample-wise exclusive group sparsity (a.k.a., l1,2 norm) to introduce diversity into the local models, with different chosen feature sets interpreted as different local models. This would help to interpret not only features but also local models (i.e., samples) in practical problems. The proposed method is a convex method, and thus, it can find a globally optimal solution. Moreover, we propose a simple yet efficient iterative least-squares based optimization procedure for the sparse network lasso, which does not need a tuning parameter, and is guaranteed to converge to a globally optimal solution. The solution is empirically shown to outperform straightforward alternatives in predicting outputs for both simulated and molecular biological personalized medicine data.
A 3V 890μA 13.56MHz OOK receiver with sensitivity improvement by base-band amplifier for short-range wireless biotelemetry application

This paper presents an On-Off Keying (OOK) receiver with sensitivity improvement approach. The RF frontend amplifier of the receiver is only a single-stage amplifier with front-end band-pass resonant network which can achieve gain around 30dB at 13.56MHz. The baseband amplifier is composed of two parallel amplification paths and the amplified signals from each path are combined at output node. This baseband amplifier enables the receiver to achieve sensitivity of −52dBm without any additional RF amplifier stage which is approximately 36dB improvement from the previous work. The receiver is operating under a CR2032 battery with current consumption of 890μA.
Viewing Semantic Networks as Hypermedia

The analogy of a semantic network to hypertext has long been recognized, and a semantic network has been considered as a logical model of hypertext – especially for those hypertexts with typed nodes and links. Moreover, wordnets form the most representative type of semantic networks in the field of Natural Language Processing and semantics in particular. It is obvious that hypertext and wordnets share many common points regarding their fundamental principles and the objectives towards which they both aim. In particular, they are both targeted towards capturing relations that possibly exist between objects and thus providing information of the underlying objects via various types of links used for describing the relations. In this respect, we strongly believe that if semantic networks are viewed beyond strictly linguistically constraints and applications, the results could only be beneficial.
Sickle Cell Disease: New Opportunities and Challenges in Africa

Sickle cell disease (SCD) is one of the most common genetic causes of illness and death in the world. This is a review of SCD in Africa, which bears the highest burden of disease. The first section provides an introduction to the molecular basis of SCD and the pathophysiological mechanism of selected clinical events. The second section discusses the epidemiology of the disease (prevalence, morbidity, and mortality), at global level and within Africa. The third section discusses the laboratory diagnosis and management of SCD, emphasizing strategies that been have proven to be effective in areas with limited resources. Throughout the review, specific activities that require evidence to guide healthcare in Africa, as well as strategic areas for further research, will be highlighted.
Preferences and bipolarity in query languages

The integration of user preferences into queries addressed to a relational database management system allows to obtain more relevant answers. This paper is a short review of the different languages (PreferenceSQL and SQLf) proposed to integrate users' preferences inside queries. A particular attention is put on the expression of preferences, and the impact it has on the result provided to the user. Two particular concepts are dealt with : commensurability and bipolarity. This framework can be the base for an extended SQLf language.
Testosterone disrupts human collaboration by increasing egocentric choices

Collaboration can provide benefits to the individual and the group across a variety of contexts. Even in simple perceptual tasks, the aggregation of individuals' personal information can enable enhanced group decision-making. However, in certain circumstances such collaboration can worsen performance, or even expose an individual to exploitation in economic tasks, and therefore a balance needs to be struck between a collaborative and a more egocentric disposition. Neurohumoral agents such as oxytocin are known to promote collaborative behaviours in economic tasks, but whether there are opponent agents, and whether these might even affect information aggregation without an economic component, is unknown. Here, we show that an androgen hormone, testosterone, acts as such an agent. Testosterone causally disrupted collaborative decision-making in a perceptual decision task, markedly reducing performance benefit individuals accrued from collaboration while leaving individual decision-making ability unaffected. This effect emerged because testosterone engendered more egocentric choices, manifest in an overweighting of one's own relative to others' judgements during joint decision-making. Our findings show that the biological control of social behaviour is dynamically regulated not only by modulators promoting, but also by those diminishing a propensity to collaborate.
15 e-Negotiation Systems and Software Agents : Methods , Models , and Applications

Negotiation is a decentralized decision-making process that seeks to find an agreement which will satisfy the requirements of two or more parties in the presence of limited common knowledge and conflicting preferences. Negotiation participants are agents who negotiate on their own behalf or represent the interests of their principals. When electronic negotiations enter the stage, these agents could be intelligent software entities that take part in the process of searching for an acceptable agreement. The degree of involvement of these “intelligent agents” in negotiations can range from supporting human negotiators (e.g. information search, offer evaluation) to fully automating the conduct of negotiations. Choosing the degree of involvement depends upon the characteristics of the problem in the negotiation. In this chapter, we review electronic negotiation systems and intelligent agents for negotiations. Different types of negotiation agents, their roles and requirements, and various methods for effective support or conduct of negotiations are discussed. Selected applications of intelligent negotiation agents are presented.
An Indian strain of hepatitis E virus (HEV): cloning, sequence, and expression of structural region and antibody responses in sera from individuals from an area of high-level HEV endemicity

Hepatitis E virus (HEV) is responsible for a majority of sporadic and epidemic viral hepatitides in India and other developing countries. Even though the genomes of four geographically distinct strains of HEV have been cloned and sequenced, the Indian strain of HEV remains largely uncharacterized. We have cloned and sequenced about 2.2 kb of the HEV genome constituting the structural region from an Indian strain of HEV. The nucleotide and amino acid sequences show a high degree of conservation with sequences from other HEV strains. Open reading frames (ORF) 2 and 3 were expressed in Escherichia coli as N-terminal hexahistidine epitope fusions. The purified proteins were then used in an immunoblot assay to evaluate the antibody status in sera from individuals from an area of high-level HEV endemicity. The anti-ORF2 antibodies were found to be nonspecific and could not be correlated to clinical disease. The immunoglobulin M anti-ORF3 was found to be specific for the presence of acute disease. The implications of these findings in HEV diagnosis and vaccine development are discussed.
Ensuring Value Liveness of Distributed Software Through Changeling

This paper extends the Changeling methodology, which ensures safety of concurrent and distributed software, to provide responsiveness properties. Changeling employs formal methods to generate executable assertions which ensure that the physical state, in the actual run time environment, is consistent with the logical state specified in the assertion. The focus, here, is on the generation of executable assertions from value liveliness properties established in the temporal specifications. Specifically, a translation process was developed to generate run-time-assured programs. The verification tool adopted by Changeling is Interleaving Set Temporal Logic (ISTL) since the logic is based on partial order semantics and capable of expressing the intermediate assertions(behavior) of distributed programs. The translation is shown to be sound and relatively complete; an example of ensuring value liveliness properties is presented for distributed Branch and Bound type problems.
Population-Based Metaheuristics: A Comparative Analysis

To optimally solve hard optimization problems in real life, many methods were designed and tested. The metaheuristics proved to be the generally adequate techniques, while the exact traditional optimization mathematical methods are prohibitively expensive in computational time. The population-based metaheuristics, which manipulate a set of candidate solutions at a time, have advantages over the single- state methods and therefore are preferred techniques when hard problems are to be solved. Such metaheuristics include Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Scatter Search and many more methods. In this survey a comparative analysis of the main population-based metaheuristics was accomplished; the focus is on the fundamental properties regarding operational principle, on the adequate problems, the advantages and disadvantages in use.
An Evaluation of Integrated Curriculum Based on Students' Perspective

Methods: Questionnaires were used to evaluate the differences in the student perception of IC. The content of the questionnaire dealt with the appropriateness of time management, coherence of the content, teachinglearning method, and IC evaluation. Results: There were positive responses from the second implementation period than from the first, for almost all sub-items. However, the results showed that IC has to be reinforced by instructors teaching a coherentcontent, by using a variety of teaching-learning methods, by having the students participate actively, and by offering holistic and integrated assessments. Conclusion: The following three steps are recommended for the improvement of IC: facilitate an IC committee, develop teaching-learning methods, and guide student participation.
Ligand-induced conformational dynamics of the Escherichia coli Na+/H+ antiporter NhaA revealed by hydrogen/deuterium exchange mass spectrometry

Significance Sodium ion/proton antiporters exchange sodium ions against protons across the membrane and are thereby essential for ion homeostasis inside living cells. The bacterial sodium ion/proton antiporter NhaA is the most important working model for this class of proteins. However, the structure of NhaA has only been determined at low pH where it is inactive. Nonetheless, different models were proposed for the mechanism of antiport. Here, we analyzed the conformational changes of unmodified NhaA upon ligand binding at physiological pH. We utilized hydrogen/deuterium exchange mass spectrometry to monitor conformational changes of the entire protein. We observed that, upon ligand binding, NhaA undergoes a conformational change consisting of two movements, and propose a model that reconciles apparently conflicting data in the field. Na+/H+ antiporters comprise a family of membrane proteins evolutionarily conserved in all kingdoms of life and play an essential role in cellular ion homeostasis. The NhaA crystal structure of Escherichia coli has become the paradigm for this class of secondary active transporters. However, structural data are only available at low pH, where NhaA is inactive. Here, we adapted hydrogen/deuterium-exchange mass spectrometry (HDX-MS) to analyze conformational changes in NhaA upon Li+ binding at physiological pH. Our analysis revealed a global conformational change in NhaA with two sets of movements around an immobile binding site. Based on these results, we propose a model for the ion translocation mechanism that explains previously controversial data for this antiporter. Furthermore, these findings contribute to our understanding of related human transporters that have been linked to various diseases.
Quantum information processing , operational quantum logic , convexity , and the foundations of physics Los Alamos Technical Report LAUR 03-1199

1 Quantum information processing, operational quantum logic, convexity, and the foundations of physics Quantum information science is a source of task-related axioms whose consequences can be explored in general settings encompassing quantum mechanics, classical theory, and more. Quantum states are compendia of probabilities for the outcomes of possible operations we may perform a system: " operational states. " I discuss general frameworks for " operational theories " (sets of possible operational states of a system), in which convexity plays key role. The main technical content of the paper is in a theorem that any such theory naturally gives rise to a " weak effect algebra " when outcomes having the same probability in all states are identified, and in the introduction of a notion of " operation algebra " that also takes account of sequential and conditional operations. Such frameworks are appropriate for investigating what things look like from an " inside view, " i.e. for describing perspectival information that one subsystem of the world can have about another. Understanding how such views can combine, and whether an overall " geometric " picture (" outside view ") coordinating them all can be had, even if this picture is very different in structure from the perspectives within it, is the key to whether we may be able to achieve a unified, " objective " physical view in which quantum mechanics is the appropriate description for certain perspectives, or whether quantum mechanics is truly telling us we must go beyond this " geometric " conception of physics.
A Survey of Recent View-based 3D Model Retrieval Methods

Extensive research efforts have been dedicated to 3D model retrieval in recent decades. Recently, view-based methods have attracted much research attention due to the high discriminative property of multi-views for 3D object representation. In this report, we summarize the view-based 3D model methods and provide the further research trends. This paper focuses on the scheme for matching between multiple views of 3D models and the application of bag-of-visual-words method in 3D model retrieval. For matching between multiple views, the many-to-many matching, probabilistic matching and semisupervised learning methods are introduced. For bag-of-visual-words application in 3D model retrieval, we first briefly review the bag-of-visual-words works on multimedia and computer vision tasks, where the visual dictionary has been detailed introduced. Then a series of 3D model retrieval methods by using bag-of-visual-words description are surveyed in this paper. At last, we summarize the further research content in view-based 3D model retrieval.