Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent neural network language model training with noise contrastive estimation for speech recognition

In recent years recurrent neural network language models (RNNLMs) have been successfully applied to a range of tasks including speech recognition. However, an important issue that limits the quantity of data used, and their possible application areas, is the computational cost in training. A signi? cant part of this cost is associated with the softmax function at the output layer, as this requires a normalization term to be explicitly calculated. This impacts both the training and testing speed, especially when a large output vocabulary is used. To address this problem, noise contrastive estimation (NCE) is explored in RNNLM training. NCE does not require the above normalization during both training and testing. It is insensitive to the output layer size. On a large vocabulary conversational telephone speech recognition task, a doubling in training speed on a GPU and a 56 times speed up in test time evaluation on a CPU were obtained.
MACROECONOMIC RELATIONSHIP IN INDIA: ARDL EVIDENCE ON COINTEGRATION AND CAUSALITY

The main objective of the paper is to estimate the dynamic interrelation among the macroeconomic variables viz., real output, money, price, interest rate and exchange rate using monthly data for India covering the period from 1991:1 to 2007:12 using ARDL approach to cointegration. The bounds test revealed that there exists a long-run relation between real output, money supply, interest rate and exchange rate when the price variable was the dependent variable. Also, a long-run relationship between real output, money supply, price and interest rate was found when exchange rate was the dependent variable. However, reverse cointegration relationships were not noticed when the real output, money supply and interest rate were the dependent variables. The short-run causality found no evidence of causality between real output and money and a unidirectional causality running from price and interest rate to real output was found. The exchange rate was found independent to the changes in real output. The exchange rate and price were found to be independent to changes in money. Further, it was noticed that the price is caused by output, money, interest rate and exchange rate. The causality was found to be neutral from output, money, price, and exchange rate on interest rate. Finally, it was found that output, money, price and interest rate has no effect on exchange rate in the short-run.
The SMN Tudor SIM-like domain is key to SmD1 and coilin interactions and to Cajal body biogenesis

ABSTRACT Cajal bodies (CBs) are nuclear organelles involved in the maturation of spliceosomal small nuclear ribonucleoproteins (snRNPs). They concentrate coilin, snRNPs and the survival motor neuron protein (SMN). Dysfunction of CB assembly occurs in spinal muscular atrophy (SMA). Here, we demonstrate that SMN is a SUMO1 target that has a small ubiquitin-related modifier (SUMO)-interacting motif (SIM)-like motif in the Tudor domain. The expression of SIM-like mutant constructs abolishes the interaction of SMN with the spliceosomal SmD1 (also known as SNRPD1), severely decreases SMN–coilin interaction and prevents CB assembly. Accordingly, the SMN SIM-like-mediated interactions are important for CB biogenesis and their dysfunction can be involved in SMA pathophysiology.
A Privacy Preference Manager for the Social Semantic Web

Current Social Web applications provide users with means to easily publish their personal information on the Web. However, once published, users cannot control how their data can be accessed apart from applying generic preferences (such as "friends" or "family"). In this paper, we describe how we enable finer-grained privacy preferences using the Privacy Preference Ontology (PPO); a light-weight vocabulary for defining privacy settings on the Social Web. In particular, we describe the formal semantic model of PPO and also present My Privacy Manager, a privacy preference manager that let users (1) create privacy preferences using the aforementioned ontology and (2) restrict access to their data to third-party users based on profile features such as interests, relationships and common attributes.
Transcriptional Interference by Independently Regulated Genes Occurs in Any Relative Arrangement of the Genes and Is Influenced by Chromosomal Integration Position

ABSTRACT Transcriptional interference is the influence, generally suppressive, of one active transcriptional unit on another unit linked in cis. Its wide occurrence in experimental systems suggests that it may also influence transcription in many loci, but little is known about its precise nature or underlying mechanisms. Here we report a study of the interaction of two nearly identical transcription units juxtaposed in various arrangements. Each reporter gene in the constructs has its own promoter and enhancer and a strong polyadenylation signal. We used recombinase-mediated cassette exchange (RMCE) to insert the constructs into previously tagged genomic sites in cultured cells. This strategy also allows the constructs to be assessed in both orientations with respect to flanking chromatin. In each of the possible arrangements (tandem, divergent, and convergent), the presence of two genes strongly suppresses expression of both genes compared to that of an identical single gene at the same integration site. The suppression is most severe with the convergent arrangement and least severe in total with the divergent arrangement, while the tandem arrangement is most strongly influenced by the integration site and the genes’ orientation within the site. These results suggest that transcriptional interference could underlie some position effects and contribute to the regulation of genes in complex loci.
Enhancing Fuzzy Rule Based Systems in Multi-Classification Using Pairwise Coupling with Preference Relations

This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multi-classifier approach using fuzzy preference relations for dealing with multi-class classification. The idea is to decompose the original data-set into binary classification problems using a pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each one of them. Along the inference process, each FRBCS generates an association degree for its two classes, and these values are encoded into a fuzzy preference relation. The final class of the whole FRBCS will be obtained by decision making following a non-dominance criterium. We show the goodness of our proposal in contrast with the base fuzzy model with an extensive experimental study following a statistical study for analysing the differences in performance among the algorithms. We will also contrast our results versus the well-known C4.5 decision tree.
Facial expression representation and classification using 2DPCA

This paper's purpose is to compare the performance between the two feature extraction methods PCA and 2DPCA by applying them to feature extraction stage in the facial expression classification model. To test and evaluate their performance, we performed experiments on two face image databases: JAFFE and YALE. The experimental results also indicated that the performance and speed of 2DPCA are better than PCA in feature extraction capabilities.
Traceless Bioresponsive Shielding of Adenovirus Hexon with HPMA Copolymers Maintains Transduction Capacity In Vitro and In Vivo

Capsid surface shielding of adenovirus vectors with synthetic polymers is an emerging technology to reduce unwanted interactions of the vector particles with cellular and non-cellular host components. While it has been shown that attachment of shielding polymers allows prevention of undesired interactions, it has become evident that a shield which is covalently attached to the vector surface can negatively affect gene transfer efficiency. Reasons are not only a limited receptor-binding ability of the shielded vectors but also a disturbance of intracellular trafficking processes, the latter depending on the interaction of the vector surface with the cellular transport machinery. A solution might be the development of bioresponsive shields that are stably maintained outside the host cell but released upon cell entry to allow for efficient gene delivery to the nucleus. Here we provide a systematic comparison of irreversible versus bioresponsive shields based on synthetic N-(2-hydroxypropyl)methacrylamide (HPMA) copolymers. In addition, the chemical strategy used for generation of the shield allowed for a traceless bioresponsive shielding, i.e., polymers could be released from the vector particles without leaving residual linker residues. Our data demonstrated that only a bioresponsive shield maintained the high gene transfer efficiency of adenovirus vectors both in vitro and in vivo. As an example for bioresponsive HPMA copolymer release, we analyzed the in vivo gene transfer in the liver. We demonstrated that both the copolymer's charge and the mode of shielding (irreversible versus traceless bioresponsive) profoundly affected liver gene transfer and that traceless bioresponsive shielding with positively charged HPMA copolymers mediated FX independent transduction of hepatocytes. In addition, we demonstrated that shielding with HPMA copolymers can mediate a prolonged blood circulation of vector particles in mice. Our results have significant implications for the future design of polymer-shielded Ad and provide a deeper insight into the interaction of shielded adenovirus vector particles with the host after systemic delivery.
Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design

NASA's vision for space exploration stresses the cultivation of human-robotic systems. Similar systems are also envisaged for a variety of hazardous earthbound applications such as urban search and rescue. Recent research has pointed out that to reduce human workload, costs, fatigue driven error and risk, intelligent robotic systems will need to be a significant part of mission design. However, little attention has been paid to joint human-robot teams. Making human-robot collaboration natural and efficient is crucial. In particular, grounding, situational awareness, a common frame of reference and spatial referencing are vital in effective communication and collaboration. Augmented Reality (AR), the overlaying of computer graphics onto the real worldview, can provide the necessary means for a human-robotic system to fulfill these requirements for effective collaboration. This article reviews the field of human-robot interaction and augmented reality, investigates the potential avenues for creating natural human-robot collaboration through spatial dialogue utilizing AR and proposes a holistic architectural design for human-robot collaboration.
Matching product titles using web-based enrichment

Matching product titles from different data feeds that refer to the same underlying product entity is a key problem in online shopping. This matching problem is challenging because titles across the feeds have diverse representations with some missing important keywords like brand and others containing extraneous keywords related to product specifications. In this paper, we propose a novel unsupervised matching algorithm that leverages web earch engines to (1) enrich product titles by adding important missing tokens that occur frequently in search results, and (2) compute importance scores for tokens based on their ability to retrieve other (enriched title) tokens in search results. Our matching scheme calculates the Cosine similarity between enriched title pairs with tokens weighted by their importance scores. We propose an optimization that exploits the templatized structure of product titles to reduce the number of search queries. In experiments with real-life shopping datasets, we found that our matching algorithm has superior F1 scores compared to IDF-based cosine similarity.
FT-AVS: a Fault-tolerant Architecture for Real-Time Active Vision

The nature of most problems addressed by robotics requires that robotic systems possess real-time properties. Additionally, as a result of steady increases in power and decreases in the cost of technology, it has become feasible to integrate sophisticated vision systems into robotic tasks. This can be seen by the recent interest in active vision.The purpose of this paper is two-fold: we first present a novel architecture for real-time active vision systems, and then enhance the architecture with a unified approach to fault tolerance. Our system is designed modularly in order to enable the flexible addition of hardware and software redundancy and also to allow reconfiguration when and where needed. This gives us the ability to handle faults in the context of active vision. Further, the distribution of software on the available hardware is such that users can utilize a dual-model of execution (simulation and rapid prototyping). Lastly, the tests we ran on the implemented architecture in order to validate the results of the experimentation and simulations show a good correlation of parameters.
Extensibility via a meta-level architecture

Meta-level architectures are recognized as a means to achieve run-time extensibility, and have been applied as such in existing hypermedia systems. Yet, designing a good meta-level architecture is notoriously hard and remains an art rather than a science. This paper shows how to derive a meta-level architecture for hypermedia navigation, thereby providing a way to control how third-party components interact with the linking engine. This extra level of control allows for a better and safer integration between an extensible system and the third-party components extending it.
Continuous probabilistic transform for voice conversion

Voice conversion, as considered in this paper, is defined as modifying the speech signal of one speaker (source speaker) so that it sounds as if it had been pronounced by a different speaker (target speaker). Our contribution includes the design of a new methodology for representing the relationship between two sets of spectral envelopes. The proposed method is based on the use of a Gaussian mixture model of the source speaker spectral envelopes. The conversion itself is represented by a continuous parametric function which takes into account the probabilistic classification provided by the mixture model. The parameters of the conversion function are estimated by least squares optimization on the training data. This conversion method is implemented in the context of the HNM (harmonic+noise model) system, which allows high-quality modifications of speech signals. Compared to earlier methods based on vector quantization, the proposed conversion scheme results in a much better match between the converted envelopes and the target envelopes. Evaluation by objective tests and formal listening tests shows that the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods.
Social sciences via network analysis and computation - Preamble

Web clipping deals with retrieving text and graphic components from web pages. The projects are conducted in collaboration with company Nevtron & Company, d.o. o. that manages the leading IT portal in Slovenia and produces news media, whose role involves experimental testing of proposed solutions. We describe an approach that automates fast and effective collection of data which is crucial for future editorial and business decisions. We have developed solutions that provide a better flexibility to users in selecting, retrieving, extracting and tracking information in publicly available publications on the web. This paper presents retrieval, data pre-processing, sentiment classification, and empirical evaluation of Slovenian news articles enriched with political, business, economic and finance content between September 1st 2007 and December 31st 2013.
Involvement of the Thalamocortical Loop in the Spontaneous Switching of Percepts in Auditory Streaming

Perceptual grouping of successive frequency components, namely, auditory streaming, is essential for auditory scene analysis. Prolonged listening to an unchanging triplet-tone sequence produces a series of illusory switches between a single coherent stream (S1) and two distinct streams (S2). The predominant percept depends on the frequency difference (Δf) between high and low tones. Here, we combined the use of different Δfs with an event-related fMRI design to identify whether the temporal dynamics of brain activity differs depending on the direction of perceptual switches. The results demonstrated that the activity of the medial geniculate body (MGB) in the thalamus occurred earlier during switching from nonpredominant to predominant percepts, whereas that of the auditory cortex (AC) occurred earlier during switching from predominant to nonpredominant percepts, regardless of Δf. The asymmetry of temporal precedence indicates that the MGB and AC activations play different roles in perceptual switching and depend on perceptual predominance rather than on S1 and S2 percepts per se. Our results suggest that feedforward and feedback processes in the thalamocortical loop are involved in the formation of percepts in auditory streaming.
Experimental Setup for Human Aware Navigation

Validation of algorithms developed by assistance robotics research on real platforms is essential. Producing robotic architectures that promote scientiﬁc advances while regarding usability for the ﬁnal user is a challenging issue where an appropriate trade-off between both requirements must be found. This paper proposes a new framework for the development of mobility assistance techniques to improve the quality of life of elder population using a robotic wheelchair. An example of improvement of the wheelchair navigation is presented. The control of the wheelchair is done using a Bayesian semi-autonomous approach that estimates the user intended destination from input commands given through a Kinect sensor. Safe and comfortable autonomous navigation is performed using a probabilistic navigation method that takes into account the dynamics of the environment and human social conventions.
Sungrazing comets: Properties of nuclei and in-situ detectability of cometary ions at 1 AU

Abstract A one-dimensional sublimation model for cometary nuclei is used to derive size limits for the nuclei of sungrazing comets and to estimate oxygen ion fluxes at 1 AU from their evaporation. Given that none of the ≈300 sungrazers detected by the SOlar and Heliospheric Observatory (SOHO) was observed after disappearing behind the sun, and that small nuclei with a radius of ≈3.5 m could be observed, it is assumed that all SOHO sungrazers were completely destroyed. For the case that sublimation alone is sufficient for destruction, the model yields an upper size limit as a function of nuclear density ϱ, albedo A , and perihelion distance q. If the density of the nuclei is that typical of porous ice (600 kg m −3 ), the maximum size is 63 m. These results confirm similar model calculations by Weissman (1983). An analytical expression is derived that approximates the model results well. We discuss possible modifications of our results by different disruption mechanisms. While disruption by thermal stress does not change the upper size limits significantly, they may be somewhat increased by tidal disruption (up to 100 m for a density of 600 kg m −3 ), dependent on the isotropy of the sublimation process and the tensile strength of the comet. Implications for the Kreutz family of sungrazers are discussed. Oxygen ions from the sublimation of sungrazing comets form a tail. Fluxes from this tail are sufficiently high to be measured at 1 AU by particle detectors on spacecraft, but the duration of a tail crossing is only about half an hour. Therefore, the probability of a spacecraft actually encountering a tail of an evaporating sungrazer is only of the order of 2% per year.
Validation of planar partitions using constrained triangulations

Planar partitions—full tessellations of the plane into non-overlapping polygons—are frequently used in GIS to model concepts such as land cover, cadastral parcels or administrative boundaries. Since in practice planar partitions are often stored as a set of individual objects (polygons) to which attributes are attached (e.g. stored with a shapefile), and since different errors/mistakes can be introduced during their construction, manipulation or exchange, several inconsistencies will often arise in practice. The inconsistencies are for instance overlapping polygons, gaps and unconnected polygons. We present in this paper a novel algorithm to validate such planar partitions. It uses a constrained triangulation as a support for the validation, and permits us to avoid different problems that arise with existing solutions based on the construction of a planar graph. We describe in the paper the details of our algorithm, our implementation, how inconsistencies can be detected, and the experiments we have made with real-world data (the CORINE2000 dataset).
Therapeutic options for severe asthma

As the overall prevalence of asthma has escalated in the past decades, so has the population of patients with severe asthma. This condition is often difficult to manage due to the relative limitation of effective therapeutic options for the physician and the social and economic burden of the disease on the patient. Management should include an evaluation and elimination of modifiable risk factors such as smoking, allergen exposure, obesity and non-adherence, as well as therapy for co-morbidities like gastro-esophageal reflux disease and obstructive sleep apnea. Current treatment options include conventional agents such as inhalational corticosteroids, long acting β2 agonists, leukotriene antagonists, and oral corticosteroids. Less conventional treatment options include immunotherapy with methotrexate, cyclosporine and tacrolimus, biological drugs like monoclonal antibodies, tumor necrosis factor-α blockers and oligonucleotides, phosphodiesterase inhibitors, antimicrobials and bronchial thermoplasty.
CD-NP: A Novel Engineered Dual Guanylyl Cyclase Activator with Anti-Fibrotic Actions in the Heart

Natriuretic peptides (NPs) are cardioprotective through the activation of guanylyl cyclase (GC) receptors A and B. CD-NP, also known as cenderitide, is a novel engineered NP that was designed to uniquely serve as a first-in-class dual GC receptor agonist. Recognizing the aldosterone suppressing actions of GC-A activation and the potent inhibitory actions on collagen synthesis and fibroblast proliferation through GC-B activation, the current study was designed to establish the anti-fibrotic actions of CD-NP, administered subcutaneously, in an experimental rat model of early cardiac fibrosis induced by unilateral nephrectomy (UNX). Our results demonstrate that a two week subcutaneous infusion of CD-NP significantly suppresses left ventricular fibrosis and circulating aldosterone, while preserving both systolic and diastolic function, in UNX rats compared to vehicle treated UNX rats. Additionally we also confirmed, in vitro, that CD-NP significantly generates the second messenger, cGMP, through both the GC-A and GC-B receptors. Taken together, this novel dual GC receptor activator may represent an innovative anti-fibrotic therapeutic agent.
Assessment of neuropathic pain

Neuropathic pain is initiated or caused by a primary lesion or dysfunction of the nervous system. A consensus regarding the classification and assessment of neuropathic pain has yet to be reached. The diseases responsible for neuropathic pain are diverse; the clinical presentation of pain can vary widely between individuals despite the same underlying aetiology. Common features of neuropathic pain include spontaneous and evoked pains. Investigations may help to identify evidence of nerve dysfunction but it cannot be assumed that it is necessarily the cause of their pain.