Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards The Automated Inference Of Queueing Network Models From High-Precision Location Tracking Data

Traditional methods for deriving performance models of customer flow in real-life systems are manual, timeconsuming and prone to human error. This paper proposes an automated four-stage data processing pipeline which takes as input raw high-precision location tracking data and which outputs a queueing network model of customer flow. The pipeline estimates both the structure of the network and the underlying interarrival and service time distributions of its component service centres. We evaluate our method’s effectiveness and accuracy in four experimental case studies.
Predicting query difficulty on the web by learning visual clues

We describe a method for predicting query difficulty in a precision-oriented web search task. Our approach uses visual features from retrieved surrogate document representations (titles, snippets, etc.) to predict retrieval effectiveness for a query. By training a supervised machine learning algorithm with manually evaluated queries, visual clues indicative of relevance are discovered. We show that this approach has a moderate correlation of 0.57 with precision at 10 scores from manual relevance judgments of the top ten documents retrieved by ten web search engines over 896 queries. Our findings indicate that difficulty predictors which have been successful in recall-oriented ad-hoc search, such as clarity metrics, are not nearly as correlated with engine performance in precision-oriented tasks such as this, yielding a maximum correlation of 0.3. Additionally, relying only on visual clues avoids the need for collection statistics that are required by these prior approaches. This enables our approach to be employed in environments where these statistics are unavailable or costly to retrieve, such as metasearch.
Observational evidence that soil moisture variations affect precipitation

Land‐atmosphere feedback, by which precipitation‐induced soil moisture anomalies affect subsequent precipitation, may be an important element of Earth's climate system, but its very existence has never been demonstrated conclusively at regional to continental scales. Evidence for the feedback is sought in a 50‐yearobservational precipitation dataset covering the United States. The precipitation variance and autocorrelation fields are characterized by features that agree (in structure, though not in magnitude) with those produced by an atmospheric general circulation model (AGCM). Because the model‐generated features are known to result from land‐atmosphere feedback alone, the observed features are suggestive of the existence of feedback in nature.
Dissipation of energy in steel frames with PR connections

The major sources of energy dissipation in steel frames with partially restrained (PR) connections are evaluated. Available experimental results are used to verify the mathematical model used in this study. The verified model is then used to quantify the energy dissipation in PR connections due to hysteretic behavior, due to viscous damping and at plastic hinges if they are formed. Observations are made for two load conditions: a sinusoidal load applied at the top of the frame, and a sinusoidal ground acceleration applied at the base of the frame representing a seismic loading condition. This analytical study confirms the general behavior, observed during experimental investigations, that PR connections reduce the overall stiffness of frames, but add a major source of energy dissipation. As the connections become stiffer, the contribution of PR connections in dissipating energy becomes less significant. A connection with a T ratio (representing its stiffness) of at least 0.9 should not be considered as fully restrained as is commonly assumed, since the energy dissipation characteristics are different. The flexibility of PR connections alters the fundamental frequency of the frame. Depending on the situation, it may bring the frame closer to or further from the resonance condition. If the frame approaches the resonance condition, the effect of damping is expected to be very important. However, if the frame moves away from the resonance condition, the energy dissipation at the PR connections is expected to be significant with an increase in the deformation of the frame, particularly for low damping values. For low damping values, the dissipation of energy at plastic hinges is comparable to that due to viscous damping, and increases as the frame approaches failure. For the range of parameters considered in this study, the energy dissipations at the PR connections and at the plastic hinges are of the same order of magnitude. The study quantitatively confirms the general observations made in experimental investigations for steel frames with PR connections; however, proper consideration of the stiffness of PR connections and other dynamic properties is essential in predicting the dynamic behavior.
The Hopcroft-Tarjan Planarity Algorithm

This is an expository article on the Hopcroft-Tarjan planarity algorithm. A graph-theoretic analysis of a version of the algorithm is presented. An explicit formula is given for determining which vertex to place first in the adjacency list of any vertex. The intention is to make the Hopcroft-Tarjan algorithm more accessible and intuitive than it currently is. Let G be a simple 2-connected graph with vertex set V (G) and edge set E(G). The number of vertices of G is denoted by n. If u,v 2 V (G), then A(u) denotes the set of all vertices adjacent to u. v ! u means that v is adjacent to u, so that v 2 A(u) (and since G is undirected, also u ! v). Refer to the book by Bondy and Murty [3] for other graph-theoretic terminology. Hopcroft and Tarjan [13] gave a linear-time algorithm to determine whether G is planar, using a depth-first search. The depth-first search computes two low-point arrays for the vertices, L1(u) and L2(u), and assigns a weight to the edges of G, where the weight is computed from the low-points. The edges incident on each u 2 V (G) are then ordered according to their weights. The algorithm then uses the revised graph in which incident edges have been ordered, and performs another depth-first search, the so-called PathFinder to embed the graph in the plane. The purpose of this article is to explain exactly why the particular weighting scheme used for the edges is necessary, to elucidate why it works, and to suggest another, more intuitive way of embedding the graph in the plane, once the low-point depth-first search has been executed. The Hopcroft-Tarjan algorithm is complicated and subtle. For example, see the description of it in the paper of Williamson [20]. In [2], Di Battista, Eades, Tamassia, and Tollis state that “The known planarity algorithms * This work was supported by an operating grant from the Natural Sciences and Engineering Research Council of Canada. 1
Loss of hippocampal neuronal nitric oxide synthase contributes to the stress‐related deficit in learning and memory

Nitric oxide (NO) has been involved in many pathophysiological brain processes. However, the exact role of NO in the cognitive deficit associated to chronic stress exposure has not been elucidated. In this study, we investigated the participation of hippocampal NO production and their regulation by protein kinase C (PKC) in the memory impairment induced in mice subjected to chronic mild stress model (CMS). CMS mice showed a poor learning performance in both open field and passive avoidance inhibitory task respect to control mice. Histological studies showed a morphological alteration in the hippocampus of CMS mice. On the other hand, chronic stress induced a diminished NO production by neuronal nitric oxide synthase (nNOS) correlated with an increment in gamma and zeta PKC isoenzymes. Partial restoration of nNOS activity was obtained after PKC activity blockade. NO production by inducible nictric oxide synthase isoform was not detected. The magnitude of oxidative stress, evaluated by reactive oxygen species production, after excitotoxic levels of NMDA was increased in hippocampus of CMS mice. Moreover, ROS formation was higher in the presence of nNOS inhibitor in both control and CMS mice. Finally, treatment of mice with nNOS inhibitors results in behavioural alterations similar to those observed in CMS animals. These findings suggest a novel role for nNOS showing protective activity against insults that trigger tissue toxicity leading to memory impairments.
iNewsBox: modeling and exploiting implicit feedback for building personalized news radio

Online news reading has become the major method to know about the world as web provide more information than other media like TV and radio. However, traditional online news reading interface is inconvenient for many types of people, especially for those who are disabled or taking a bus. This paper presents a mobile application iNewsBox enabling users to listen to news collected from the Internet. In order to simplify necessary interactions of getting valuable news, we also propose a framework for using implicit feedback to recommend news in this paper. Experiment shows our algorithms in iNewsBox are effective.
Real-time Translation of Nonverbal Communication in Cross-Cultural Online Encounters

Nonverbal behavior plays a crucial role in human communication, and often leads to misinterpretations and misunderstandings between people from different cultural backgrounds; even if they speak the same language fluently. While translation systems are available for verbal communication, real-time translators for nonverbal communication (NVC) do not exist yet. We present the conceptual design and an early prototype of a real-time NVC translator using body tracking and gesture recognition systems for avatarmediated interactions. It contributes to the ambitious goal of bridging between cultures by facilitating cross-cultural online meetings. Possible applications of the NVC translator are discussed as a facilitating tool for global teamwork and conflict resolution.
Boosting naïve Bayesian learning on a large subset of MEDLINE

We are concerned with the rating of new documents that appear in a large database (MEDLINE) and are candidates for inclusion in a small specialty database (REBASE). The requirement is to rank the new documents as nearly in order of decreasing potential to be added to the smaller database as possible, so as to improve the coverage of the smaller database without increasing the effort of those who manage this specialty database. To perform this ranking task we have considered several machine learning approaches based on the naï ve Bayesian algorithm. We find that adaptive boosting outperforms naï ve Bayes, but that a new form of boosting which we term staged Bayesian retrieval outperforms adaptive boosting. Staged Bayesian retrieval involves two stages of Bayesian retrieval and we further find that if the second stage is replaced by a support vector machine we again obtain a significant improvement over the strictly Bayesian approach.
Interaction Trees: Optimizing Ensembles of Decision Trees for Gene-Gene Interaction Detections

One of the main goals of genome wide association studies (GWAS) has been detecting gene-gene interactions, also known as epistasis in a broad sense, underlying complex diseases. However, high dimensionalities of genotype data and exponential complexity of the search space with respect to the order of targeted interactions make most of existing interaction detection strategies practically inapplicable. Because they are capable of capturing interactions among input variables in addition to the nonlinear effects, decision trees and their ensembles have been recently shown to be resourceful strategies in detecting interactions in GWAS data. However, unlike other nodes, selection of root nodes for decision trees is merely based on marginal effects of candidate variables over the training data, which can greatly limit their epistasis detection performance, especially when disease genotypes have low marginal effects. In this study, we show that modifying the selection criterion of the root node of each new tree joining the ensemble, in a way that captures the interaction with the best variable ranked by the ensemble at the time, leads to a higher power in epistasis detection by decision tree ensembles. We demonstrate the efficacy of this idea using the three most popular decision tree ensemble algorithms: Bagging, Random Forest and Adaboost. Our simulation studies using five two-locus epistasis models with low marginal effects show a considerable enhancement of interaction detection power of all mentioned ensemble strategies after applying the proposed modification.
Performance Analysis of Licensed-Assisted Access Using LTE with Adaptive Carrier Selection

Licensed-Assisted Access (LAA) using Long Term Evolution (LTE) is a promising technique to extend the available bandwidth by aggregating the unlicensed spectrum. To guarantee fair co-existence of nodes deployed by different operators and different RATs, listen-before-talk (LBT) mechanism and carrier selection can be adopted by LAA as efficient candidates. In this paper, we provide insight into the LBT based LAA networks deployed by multiple operators using a hard core point process (HCPP) model. Moreover, an adaptive carrier selection scheme is developed where neighboring small cells select different carriers to avoid blocking each other so that larger number of active small cells can be retained after LBT channel sensing. The analytical results are developed in terms of active probability and coverage probability for single carrier, random carrier selection and the adaptive carrier selection scheme. It is revealed by numerical results that the number of active small cells can be significantly increased by employing adaptive carrier selection especially for large number of carriers, which further improves the network throughput.
Algorithmique et géométrie discrète pour la caractérisation des courbes et des surfaces. (Algorithmic and digital geometry for curve and surface characterization)

Cette these se situe dans le cadre de la geometrie discrete qui constitue l'une des grandes familles de methodes dediees a l'analyse automatisee des formes dans les images numeriques 2D et 3D. Tous les systemes d'acquisition d'images fournissent des donnees organisees sur une grille reguliere, appelees donnees discretes. Les methodes que nous nous proposons d'explorer et detendre conservent aux donnees ce caractere discret, par opposition aux techniques qui construisent prealablement un modele continu approximant les objets a analyser. Nous nous interessons plus particulierement a l'etude des courbes et des surfaces discretes. Dans un premier temps, nous analysons les objets de base que sont les droites, les plans et les cercles discrets. Nous presentons des algorithmes qui permettent de les caracteriser et proposons des extensions a ces methodes. Ensuite, nous etudions des metriques sur les objets discrets comme la transformation en distance euclidiennne ou la notion de geodesique discrete. Une approche basee sur la visibilite dans les domaines discrets est introduite. La troisieme partie est consacree a la definition et a l'evaluation d'estimateurs de mesures euclidiennes telles que la longueur, la courbure ou l'aire. Des resultats de convergence de ces estimateurs sont etablis. Dans une derniere partie, nous presentons les applications dans lesquelles ces recherches ont ete utilisees : classification automatisee d'objets archeologiques et analyse des microstructures d'echantillon de neige.
Scale dependence of sex ratio in wild plant populations: implications for social selection

Abstract Social context refers to the composition of an individual's social interactants, including potential mates. In spatially structured populations, social context can vary among individuals within populations, generating the opportunity for social selection to drive differences in fitness functions among individuals at a fine spatial scale. In sexually polymorphic plants, the local sex ratio varies at a fine scale and thus has the potential to generate this opportunity. We measured the spatial distribution of two wild populations of the gynodioecious plant Silene vulgaris and show that there is fine‐scale heterogeneity in the local distribution of the sexes within these populations. We demonstrate that the largest variance in sex ratio is among nearest neighbors. This variance is greatly reduced as the spatial scale of social interactions increases. These patterns suggest the sex of neighbors has the potential to generate fine‐scale differences in selection differentials among individuals. One of the most important determinants of social interactions in plants is the behavior of pollinators. These results suggest that the potential for selection arising from sex ratio will be greatest when pollen is shared among nearest neighbors. Future studies incorporating the movement of pollinators may reveal whether and how this fine‐scale variance in sex ratio affects the fitness of individuals in these populations.
Kernel Basis Pursuit

Estimating a non-uniformly sampled function from a set of learning points is a classical regression problem. Kernel methods have been widely used in this context, but every problem leads to two major tasks: optimizing the kernel and setting the fitness-regularization compromise. This article presents a new method to estimate a function from noisy learning points in the context of RKHS (Reproducing Kernel Hilbert Space). We introduce the Kernel Basis Pursuit algorithm, which enables us to build a L 1 -regularized-multiple-kernel estimator. The general idea is to decompose the function to learn on a sparse-optimal set of spanning functions. Our implementation relies on the Least Absolute Shrinkage and Selection Operator (LASSO) formulation and on the Least Angle Regression Stepwise (LARS) solver. The computation ofthe full regularization path, through the LARS, will enable us to propose new adaptive criteria to find an optimal fitness-regularization compromise. Finally, we aim at proposing a fast parameter-free method to estimate non-uniform-sampled functions.
An iterative beamforming optimization algorithm for generalized MIMO Y channels

We consider a K-user multiple-input multiple-output (MIMO) relay channel, where each user sends K - 1 independent messages to the other K - 1 users via a common relay. All users and the relay are assumed to have multiple antennas. The communication happens in two time slots, the multiple access (MA) stage and the broadcast (BC) stage. A linear transmit beamforming and receive combining scheme is proposed based on signal subspace alignment and physical layer network coding. The optimization of the beamforming and combining vectors is addressed with an iterative suboptimal algorithm based on orthogonal projection optimization in the signal subspaces. Suboptimal power allocation is also considered to maximize the effective signal-to-noise ratios (SNRs). The bit error rate (BER) performance of the proposed scheme in various channel configurations is verified by simulation, which shows that the proposed scheme produces significant improvement over existing one.
Generic membership function and its application to fuzzy modeling

This paper proposes a generalized form of spline-based fuzzy membership function to improve accuracy in linguistic fuzzy modeling. The extra parameters defining the left side slope and right side slope of the membership function (as well as other membership function parameters) are learned by gradient descent. The comparison to some competing approaches shows that the proposed technique is able to soften the conflict between intepretability and accuracy in fuzzy modeling.
Rheological Properties of Middle Ear Effusions from Children with Otitis Media with Effusion

Freshly harvested middle ear effusions (MEE) were collected from children with otitis media with effusion. Both elasticity (G′) and viscosity (η′) of the MEE were determined by an oscillating sphere magnetic rheometer and compared with transportability of the MEE on mucus-depleted frog palates. A maximum value of transport was obtained at G′ of about 20 dyne/cm2 (at 1 Hz), and below this value there was a significant positive correlation between the transport rate and log G′. Above 20 dyne/cm2, the negative correlation between the transport rate and log G′ was significant. This shows there is a sharp increase in transport with increase in G′ up to 20 dyne/cm2, and at the higher value of G′ there is a slow decrease in transport with increasing G′. A similar significant correlation between the transport rate and log η′ was observed.
Compatibility between shared variable valuations in timed automaton network model-checking

Many real-time systems are modelled as timed automaton networks, which are parallel compositions of timed automata. Those timed automata interact with each other through shared variables and/or communication channels. In the literate, symbolic states with different shared variable valuations are treated as distinct ones. This paper presents a compatibility relation between shared variable valuations. Using this relation, the reachability analysis algorithm can avoid exploring all the states with different shared variable valuations. An algorithm is presented in this paper to detect compatible shared variable valuations. A compact data structure is also proposed to record the shared variable valuation sets found by this algorithm. Based on these results, an optimized reachability analysis algorithm is presented. Case studies show that our technique improves the space-efficiency of reachability analysis significantly.
Cerebellar and frontal hypometabolism in alcoholic cerebellar degeneration studied with positron emission tomography

Local cerebral metabolic rate for glucose was studied utilizing 18F‐2‐fluoro‐2‐deoxy‐D‐glucose and positron emission tomography (PET) in 14 chronically alcohol‐dependent patients and 8 normal control subjects of similar age and sex. Nine of the 14 patients (Group A) had clinical signs of alcoholic cerebellar degeneration, and the remaining 5 (Group B) did not have signs of alcoholic cerebellar degeneration. PET studies of Group A revealed significantly decreased local cerebral metabolic rates for glucose in the superior cerebellar vermis in comparison with the normal control subjects. Group B did not show decreased rates in the cerebellum. Both Groups A and B showed decreased local cerebral metabolic rates for glucose bilaterally in the medial frontal area of the cerebral cortex in comparison with the normal control subjects. The severity of the clinical neurological impairment was significantly correlated with the degree of hypometabolism in both the superior cerebellar vermis and the medial frontal region of the cerebral cortex. The degree of atrophy detected in computed tomography scans was significantly correlated with local cerebral metabolic rates in the medial frontal area of the cerebral cortex, but not in the cerebellum. The data indicate that hypometabolism in the superior cerebellar vermis closely follows clinical symptomatology in patients with alcoholic cerebellar degeneration, and does not occur in alcohol‐dependent patients without clinical evidence of cerebellar dysfunction. Hypometabolism in the medial frontal region of the cerebral cortex is a prominent finding in alcohol‐dependent patients with or without alcoholic cerebellar degeneration.