Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence and Predictors of Antiretroviral Treatment Modification in HIV-Infected Adults: A Brazilian Historical Cohort from 2001 to 2010

This study estimated the incidence of and time to first antiretroviral therapy (ART) modification. This longitudinal analysis comprised a sample of 236 patients from three HIV/AIDS referral centers in Belo Horizonte, Brazil—part of a major historical cohort. Inclusion criteria were as follows: having been treatment-naive patient ≥18 years old who initiated ART between 2001 and 2005 in these three referral centers. The main endpoint was time to first ART modification. Patients were followed up for five years, covering the period 2001–2010, during which time Pearson's chi-square test was performed to compare ART modification between groups. Kaplan-Meier inverse survival curves were employed to describe the probability of ART modification and Cox proportional hazard regression was used to estimate the adjusted hazard ratio (aHR) of ART modification. Among 247 patients from the major cohort, 236 were eligible. Median follow-up time was 37.2 months and the contribution in person-months was 7,615.4 months. A total of 108 (45.8%) patients had their ART regimen modified at least once (incidence rate: 1.42 per 100 person-months). Adverse drug reactions were the main reason for ART modification. Women (aHR = 1.62; p = 0.022) and patients on protease inhibitor- (PI-) based regimens (aHR = 2.70; p < 0.001) were at higher risk of ART modification.
Mean Shape Models for Polyp Detection in CT Colonography

A global representation of polyp and non polyp shapes are constructed following a point distribution model (PDM) as an alternative to current methods which only inspect local shape characteristics at a point on the surface. The decision on whether or not a candidate lesion is a polyp can then be made by comparing the minimum Euclidean distance of the candidate lesion to the constructed mean shapes. The model closer in distance to the candidate lesion is selected to represent that particular lesion - polyp or non polyp. This shape model can also be used to investigate the shape variability of the different lesions detected by constructing an allowable shape domain for each of these lesions.
Distributed under Creative Commons Cc-by 4.0 the Vibrating Reed Frequency Meter: Digital Investigation of an Early Cochlear Model

The vibrating reed frequency meter, originally employed by Békésy and later by Wilson as a cochlear model, uses a set of tuned reeds to represent the cochlea's graded bank of resonant elements and an elastic band threaded between them to provide nearest-neighbour coupling. Here the system, constructed of 21 reeds progressively tuned from 45 to 55 Hz, is simulated numerically as an elastically coupled bank of passive harmonic oscillators driven simultaneously by an external sinusoidal force. To uncover more detail, simulations were extended to 201 oscillators covering the range 1–2 kHz. Calculations mirror the results reported by Wilson and show expected characteristics such as traveling waves, phase plateaus, and a response with a broad peak at a forcing frequency just above the natural frequency. The system also displays additional fine-grain features that resemble those which have only recently been recognised in the cochlea. Thus, detailed analysis brings to light a secondary peak beyond the main peak, a set of closely spaced low-amplitude ripples, rapid rotation of phase as the driving frequency is swept, frequency plateaus, clustering, and waxing and waning of impulse responses. Further investigation shows that each reed's vibrations are strongly localised, with small energy flow along the chain. The distinctive set of equally spaced ripples is an inherent feature which is found to be largely independent of boundary conditions. Although the vibrating reed model is functionally different to the standard transmission line, its cochlea-like properties make it an intriguing local oscillator model whose relevance to cochlear mechanics needs further investigation.
On Throughput Scaling of Wireless Networks: Effect of Node Density and Propagation Model

This paper derives a lower bound to the per-node throughput achievable by a wireless network when n source-destination pairs are randomly distributed throughout a disk of radius $n^\gamma$, $ \gamma \geq 0$, propagation is modeled by attenuation of the form $1/(1+d)^\alpha$, $\alpha >2$, and successful transmission occurs at a fixed rate W when received signal to noise and interference ratio is greater than some threshold $\beta$, and at rate 0 otherwise. The lower bound has the form $n^{1-\gamma}$ when $\gamma < 1/2$, and $(n \ln n)^{-1/2}$ when $\gamma \geq 1/2$. The methods are similar to, but somewhat simpler than, those in the seminal paper by Gupta and Kumar.
Intrusion Detection System Using Data Mining Technique : Support Vector Machine

— Security and privacy of a system is compromised, when an intrusion happens. Intrusion Detection System (IDS) plays vital role in network security as it detects various types of attacks in network. So here, we are going to propose Intrusion Detection System using data mining technique: SVM (Support Vector Machine). Here, Classification will be done by using SVM and verification regarding the effectiveness of the proposed system will be done by conducting some experiments using NSL-KDD Cup’99 dataset which is improved version of KDD Cup’99 data set. The SVM is one of the most prominent classification algorithms in the data mining area, but its drawback is its extensive training time. In this proposed system, we have carried out some experiments using NSL-KDD Cup’99 data set. The experimental results show that we can reduce extensive time required to build SVM model by performing proper data set pre-processing. Also when we do proper selection of SVM kernel function such as Gaussian Radial Basis Function, attack detection rate of SVM is increased and False Positive Rate (FPR) is decrease.
Use of fuzzy min-max neural network for speaker identification

This paper presents the use of fuzzy min-max neural network for the text independent speaker identification. The fuzzy min-max neural network utilizes fuzzy sets as pattern classes. It is a three layer feedforward network that grows adaptively to meet the demands of the problem. The database containing speech utterances recorded from fifty speakers in Marathi language is used for experimentation. Mel frequency cepstral coefficients that represent short time spectrum are used as features for identification. The results obtained with fuzzy min-max neural network are compared with Gaussian mixture model. It is observed that fuzzy neural network outperforms the Gaussian mixture model and attains the identification accuracy of 99.99 % with 15 second speech utterance.
Sparse Generalized Kernel Modeling for Nonlinear Systems

A generalized kernel modeling approach is proposed for identification of discrete-time nonlinear systems. Each kernel regressor in the generalized kernel model has an individually fitted diagonal covariance matrix which is determined by maximizing the correlation between the regressor and training data. A state-of-the-art construction algorithm based on orthogonal least squares regression with leave-one-out test statistic and local regularization is applied to select a parsimonious generalized kernel model from the full regression matrix. The effectiveness of the proposed nonlinear modeling approach is demonstrated by the experimental results involving one simulated system and two real data sets.
Foundations of Multidimensional Network Analysis

Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.
Sentiment Diffusion of Public Opinions about Hot Events: Based on Complex Network

To study the sentiment diffusion of online public opinions about hot events, we collected people’s posts through web data mining techniques. We calculated the sentiment value of each post based on a sentiment dictionary. Next, we divided those posts into five different orientations of sentiments: strongly positive (P), weakly positive (p), neutral (o), weakly negative (n), and strongly negative (N). These sentiments are combined into modes through coarse graining. We constructed sentiment mode complex network of online public opinions (SMCOP) with modes as nodes and the conversion relation in chronological order between different types of modes as edges. We calculated the strength, k-plex clique, clustering coefficient and betweenness centrality of the SMCOP. The results show that the strength distribution obeys power law. Most posts’ sentiments are weakly positive and neutral, whereas few are strongly negative. There are weakly positive subgroups and neutral subgroups with ppppp and ooooo as the core mode, respectively. Few modes have larger betweenness centrality values and most modes convert to each other with these higher betweenness centrality modes as mediums. Therefore, the relevant person or institutes can take measures to lead people’s sentiments regarding online hot events according to the sentiment diffusion mechanism.
Creating 3D/Mid-air gestures

Researchers and developers have continually proposed various forms of gestures for computing applications. Real problems arise when choosing the best set of gestures for a given application, and there is a strong debate in the literature on whether to include users in the design process of the gestures. This paper elaborates on this debate by synthesizing the ideas and theories put forth by previous work, and describes the emergence of user-centered approach amongst the primary domination of developer-based approach in this particular research area. Three influential methods were summarized to represents the essence of the user-centered approach; and recent works that applied these methods were reviewed to consider the various ways they were adopted and adapted in creating gesture languages for computing systems. By presenting the overview of our observation and findings, we hope to provide another perspective on the user-centered design approach that would be of assistance to other researchers with similar interests in this area.
Polymerase Discordance in Novel Swine Influenza H3N2v Constellations Is Tolerated in Swine but Not Human Respiratory Epithelial Cells

Swine-origin H3N2v, a variant of H3N2 influenza virus, is a concern for novel reassortment with circulating pandemic H1N1 influenza virus (H1N1pdm09) in swine because this can lead to the emergence of a novel pandemic virus. In this study, the reassortment prevalence of H3N2v with H1N1pdm09 was determined in swine cells. Reassortants evaluated showed that the H1N1pdm09 polymerase (PA) segment occurred within swine H3N2 with ∼80% frequency. The swine H3N2-human H1N1pdm09 PA reassortant (swH3N2-huPA) showed enhanced replication in swine cells, and was the dominant gene constellation. Ferrets infected with swH3N2-huPA had increased lung pathogenicity compared to parent viruses; however, swH3N2-huPA replication in normal human bronchoepithelial cells was attenuated - a feature linked to expression of IFN-β and IFN-λ genes in human but not swine cells. These findings indicate that emergence of novel H3N2v influenza constellations require more than changes in the viral polymerase complex to overcome barriers to cross-species transmission. Additionally, these findings reveal that while the ferret model is highly informative for influenza studies, slight differences in pathogenicity may not necessarily be indicative of human outcomes after infection.
MODELING AND ANALYSIS OF SIMULTANEOUS MULTITHREADING

In simultaneous multithreading, several threads can issue instructions in each processor cycle. A simple and versatile timed Petri net model of simultaneous multithreading is proposed and is used to compare the performance of architectures with and without simultaneous multithreading. Performance results are obtained by event-driven simulation of net models and are verified by state‐space‐based analysis using combinations of modeling parameters for which the state space remains reasonably small.
Bandwidth adaptation for scalable videos over wireless networks

Multicast/broadcast services (MBS) are able to provide video services for many users simultaneously. Fixed amount of bandwidth allocation for all of the MBS videos is not effective in terms of bandwidth utilization, overall forced call termination probability, and handover call dropping probability. Therefore, variable bandwidth allocation for the MBS videos can efficiently improve the system performance. In this paper, we propose a bandwidth allocation scheme that efficiently allocates bandwidth among the MBS sessions and the non-MBS traffic calls (e.g., voice, unicast, internet, and other background traffic). The proposed scheme reduces the bandwidth allocation for the MBS sessions during the congested traffic condition only to accommodate more number of calls in the system. Our scheme allocates variable amount of bandwidths for the BMS sessions and the non-MBS traffic calls. The performance analyses show that the proposed bandwidth adaptation scheme maximizes the bandwidth utilization and significantly reduces the handover call dropping probability and overall forced call termination probability.
Semi-Supervised Spectral Mapping for Enhancing Separation between Classes

We present a spectral mapping technique for semisupervised pattern classification. Importance scores of features are firstly evaluated with a semi-supervised feature selection algorithm by Zhao et al. Training data are then embedded into a low-dimensional space with a spectral mapping derived from the selected and weighted feature vectors with which test data are classified by the nearest neighbor rule. The performance of the proposed pattern classification algorithm is examined with synthetic and real datasets.
A novel inverter frequency dividing droop controller based on voltage-controlled mode

With large amount of power electronic devices applied in distributed generations or micro-grid, harmonics pollution is becoming a more and more serious problem which limits their widely application. Frequency dividing droop controller is proposed to eliminate voltage harmonics, according droop control theory. But it failed to give an expected index to represent its harmonic suppression ability. In this paper, via setting a harmonics reference value which usually can be selected as 5% of rated fundamental frequency voltage amplitude, a novel frequency dividing droop controller for inverter's control is presented. This setting reference value can be viewed as the upper limit of harmonic component. Controller framework and every module are also discussed. Through comparison of simulation results, its improvement effects are introduced. Better performance is obtained.
Spontaneous Activity in Developing Turtle Retinal Ganglion Cells: Pharmacological Studies

Extracellular recordings were obtained from the ganglion cell (GC) layer during correlated spontaneous bursting activity (SBA) in the immature turtle retina. Pharmacological agents were bath-applied, and their effects on burst and correlation parameters were determined. SBA requires synaptic transmission. It was blocked in the presence of curare and mecamylamine, two cholinergic nicotinic antagonists, and enhanced with neostigmine, a cholinesterase inhibitor. SBA was profoundly inhibited during blockade of glutamatergic receptors with the broad spectrum antagonist kynurenate and it vanished with 6,7-dinitroquinoxaline-2–3-dione (DNQX) and 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX), two AMPA/kainate receptor antagonists. Blockade of NMDA receptors withd(−)-2-amino-5-phosphonopentanoic acid (d-AP-5) led only to a modest reduction in SBA. Blockade of GABAA receptors with bicuculline prolonged the duration of the bursts. Inhibition of GABA uptake with nipecotic acid led to a decrease in burst rate. Blockade of K+ channels with cesium (Cs+) and tetraethylammonium (TEA) led to a dramatic decrease in excitability. Burst propagation between neighboring GCs was reduced by K+ channel blockade. Gap junction blockade had no consistent effect on bursts or correlation parameters. None of these drugs had a strong effect on the refractory period between bursts. We conclude that correlated SBA in immature turtle GCs requires both cholinergic nicotinic and glutamatergic (mainly through AMPA/kainate receptors) synaptic transmission. GABAergic activity modulates the intensity and the duration of the bursts. Extracellular K+ is involved in lateral activity propagation and increases retinal excitability, which may be required for burst generation.
Dihedral angle-based maps of tetrahedral meshes

We present a geometric representation of a tetrahedral mesh that is solely based on dihedral angles. We first show that the shape of a tetrahedral mesh is completely defined by its dihedral angles. This proof leads to a set of angular constraints that must be satisfied for an immersion to exist in R3. This formulation lets us easily specify conditions to avoid inverted tetrahedra and multiply-covered vertices, thus leading to locally injective maps. We then present a constrained optimization method that modifies input angles when they do not satisfy constraints. Additionally, we develop a fast spectral reconstruction method to robustly recover positions from dihedral angles. We demonstrate the applicability of our representation with examples of volume parameterization, shape interpolation, mesh optimization, connectivity shapes, and mesh compression.
Words, numbers and all that: The lexicon in sentence understanding

1 Cross-disciplinary Issues in Lexical Theories It is hardly a controversial statement that the acquisition and processing of language require knowledge of its words. Yet, the type and use of information encoded in a lexical entry, the relation of words to each other in the lexicon, and the relationship of the lexicon to the grammar, are complex and unsettled issues, on which researchers hold very diierent views. While there is a recent consensus that mechanisms operating in the lexicon are not substantially diierent from those operating in the syntax, there are diierences on whether the syntax is in the lexicon, or the lexicon is in the syntax. On the one view, the lexicon is a static repository of very rich representations, which regulate the composition of words to an extent that goes beyond the phrase, while on the other view the lexicon is dynamically generated as a result of composition and competition mechanisms, largely syntactic in nature. Roughly speaking, computational linguistics and psycholinguistics in general follow the rst view, and the two elds are converging on some similar lexicalised, probabilistic models of grammars. Theoretical linguistics has recently proposed models of the latter type. In computational linguistics, work on the lexicon has stemmed from two diierent areas of research: parsing and grammar formalisms, and construction of electronic databases (lexicography). In the area of parsing, the interest in probabilistic models and lexicalised grammars did not develop simultaneously. Parsers based on probabilistic context-free grammars were motivated by the diiculties in building robust, large-scale systems using the explicit representation of linguistic knowledge. Large corpus annotation eeorts and the creation of tree-banks (text corpora annotated with syntactic structures) enabled researchers to develop and automatically train probabilistic models of syntactic disambiguation (Mar-cus, Santorini, and Marcinkiewicz 1993). In an attempt to take advantage of the insights gained in the area of statistical speech processing, computational linguists initially adopted very simpliied statistical models of grammar and parsing, abandoning the more sophisticated lexicalised feature-based formalisms However, it soon became apparent that the success of probabilistic context-free grammars was limited by the strong (and incorrect) assumption of probabilistic independence of
Information Cascades on Arbitrary Networks

Among various fields of complex networks, information diffusion has recently attracted huge attention due to the growing popularity social network services and increasing accessibility of their data. Indeed, analysis of information diffusion patterns not only can be widely applicable from epidemiology to viral marketing, but also can greatly benefit to society by offering predictions of information flows. To shed light on how new ideas, technologies, and epidemics diffuse through networks, numerous models have been established [5, 9, 4]. Kempe, Kleinberg, and Tardos (2003) integrated those previous models to propose two fundamental models, (the linear threshold model and the independent cascade model) and their generalizations (the general threshold model and the general cascade model) [6]. In particular, the threshold model assumes that an individual needs to have enough proportion (or threshold) of his neighbors who have previously influenced by the same information. This mechanism is based on utility maximization of individuals in game theoretic consideration. Despite its significance, because of its difficulties, analysis of the threshold models have entirely focused on limited conditions such as the submodular influence (by Mossel-Roch (STOC 07)), homogeneous thresholds (by Whitney(Phys. Rev. E. 10)), and locally tree-like networks (by Watts(PNAS 02)) [7, 11, 10]. Under the general threshold model and arbitrary networks, we proved that only if all nodes are of degrees ω(logn), the final cascade size is highly concentrated around its mean with high probability. In other words, for any ε > 0 and δ > 0,