Miracle-dz/UniFAR-FTUs huggingface dataset:
Awareness of presence, instant messaging and WebWho

We report from a study of how awareness of presence can affect instant messaging behavior. WebWho is a web based awareness system that visualizes where people are located in a large university computer lab. It allows students to virtually locate one another and, among other functions, to communicate via an instant messaging system. Typically, instant messages are signed with the sender's name, but messages can also be sent anonymously. The students use the messaging system to support collaborative work and coordinate social activities, as well as for playful behavior. We have performed analyses of messages logs with respect to sender location, anonymous or not, and message content. Results show that awareness of both physical presence, i.e. when both when sender and recipient share the same room, and virtual presence, mediated via WebWho, affect the message contents.
Slippage Sensory Feedback and Nonlinear Force Control System for a Low- Cost Prosthetic Hand

The low acceptability of current prosthetic devices can be attributed to the extensive psychological effort and the high cost associated with them. To address these concerns, an on-line slippage detector was developed using only inexpensive force sensors placed at the tips of a prototype hand. The prototype consists of a five fingered prosthetic hand consisting of active digits driven independently by DC motors. Force sensor resistors (FSR) are placed at the tip of each active finger and potentiometers are attached at the proximal and middle joints. Using the information from the FSR, not only can we detect the level of normal force exerted but also slippage between the fingers and the object by calculating the fluctuations of the exerted force. An on-line algorithm is developed to calculate the derivative of the force and determine when slippage is produced. Nonlinear model predictive control (NMPC) is used to provide feedback control to the prosthetic device. It utilizes a neural network to model the dynamics of each finger. Using this model, it is possible to predict future plant performance (the amount of force exerted by the prosthetic hand). Consequently, the controller uses this prediction to calculate the best input (current needed to drive the actuators) for the system to obtain the desired output over a specific time horizon. In order to calculate the future control inputs, the optimization system minimizes the cost function associated with the difference between the measured force and the reference / target output. Experimental protocols involve grasping various objects and inducing slippage. Data was collected using the NI DAQ cards and LabVIEW software. Experiments showed promising results using this strategy in which the force exerted on an object can be modulated without additional efforts from the users.
A new bivariate model with log-normal density prior for local variance estimation in AWGN

This paper concerns wavelet-based image denoising using Bayesian technique. The conventional probability density function (PDF) used in the denoising process usually has parameters that are calculated from the first few moments only (for example, mean and variance). In this work, a new image denoising algorithm based on bivariate Pearson Type VII distribution is presented. This PDF is used in view of the fact that it allows higher order moments to be incorporated in the probabilistic modeling of the wavelet coefficients. One of the cruxes of the Bayesian image denoising methods is to estimate statistical parameters for shrinkage function. In this paper, maximum a posteriori (MAP) is used for local variances with Log-normal density prior for local observed variances and Gaussian distribution for noisy wavelet coefficients. The experimental results show that the proposed method gives good denoising results.
A Multi-flow-Driven Mechanism to Support Live Video Streaming on VANETs

Vehicular Ad Hoc Networks (VANETs) promise a wide scope of real-time multimedia services, which need to deal with strict quality level requirements and highly dynamic network topology. To handle these challenges, multipath routing approaches have been applied to bring improvements in Quality of Experience (QoE) levels and support on-road real-time video delivery. Though, in real situations, there will likely be multiple streams simultaneously, which can cause more congestion periods and packet loss rates. This paper proposes the Multi-flow-driven VIdeo Delivery (MVIDE) mechanism to select best routes for live video sequences in VANETs. MVIDE can be integrated with routing protocols to define routes considering the characteristics of multiple paths, vehicle mobility, and application requirements. This cross-layer mechanism ranks the quality of candidate paths based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method and, subsequently, assigns paths to different sub streams. MVIDE was added to the GPSR-MA protocol, being called GPSR-MA-MVIDE. Simulating results show that GPSR-MA-MVIDE outperforms GPSR-MA in terms of multi-flow handling and received video quality.
The scalarized multi-objective multi-armed bandit problem: An empirical study of its exploration vs. exploitation tradeoff

The multi-armed bandit (MAB) problem is the simplest sequential decision process with stochastic rewards where an agent chooses repeatedly from different arms to identify as soon as possible the optimal arm, i.e. the one of the highest mean reward. Both the knowledge gradient (KG) policy and the upper confidence bound (UCB) policy work well in practice for the MAB-problem because of a good balance between exploitation and exploration while choosing arms. In case of the multi-objective MAB (or MOMAB)-problem, arms generate a vector of rewards, one per arm, instead of a single scalar reward. In this paper, we extend the KG-policy to address multi-objective problems using scalarization functions that transform reward vectors into single scalar reward. We consider different scalarization functions and we call the corresponding class of algorithms scalarized KG. We compare the resulting algorithms with the corresponding variants of the multi-objective UCBl-policy (MO-UCB1) on a number of MOMAB-problems where the reward vectors are drawn from a multivariate normal distribution. We compare experimentally the exploration versus exploitation trade-off and we conclude that scalarized-KG outperforms MO-UCB1 on these test problems.
Assessment on the Efficacy and Safety of Aidi Injection Combined with Vinorelbine and Cisplatin for Treatment of Advanced Nonsmall Cell Lung Cancer

Background: The aim of this study was to assess the efficacy and safety of vinorelbine and cisplatin (NP chemotherapy) alone or in combination with Aidi injection for the treatment of advanced nonsmall cell lung cancer (NSCLC). Methods: Pertinent publications were identified in PubMed, EMBASE, Cochrane Library, CNKI, CQVIP, and Wanfang databases, up to December 8, 2015. After quality assessment of all included randomized controlled trials evaluating Aidi injection combined with NP chemotherapy for the treatment of advanced NSCLC, a meta-analysis was performed by Review Manager 5.2 and STATA 12.0 for statistical analyses. Results: Twelve studies including 509 and 503 cases in the experimental and control groups, respectively, were finally analyzed. The meta-analysis revealed that when cisplatin dose ranging from 20 to 40 mg/m2, combination of Aidi injection and NP chemotherapy was statistically different compared with NP chemotherapy alone in enhancing efficiency (relative risk [RR] = 1.24, 95% confidence interval [CI] [1.05–1.47], P = 0.010) and reducing the incidence of Grade II or above nausea and vomiting (RR = 0.49, 95% CI [0.30–0.80], P = 0.005). Meanwhile, with cisplatin ranging from 80 to 120 mg/m2, no significant differences in efficiency (RR = 1.11, 95% CI [0.87–1.42], P = 0.390) and Grade II or above nausea and vomiting (RR = 0.88, 95% CI [0.71–1.10], P = 0.260) were obtained. In addition, Aidi injection combined with NP chemotherapy was superior to NP chemotherapy alone in improving the quality of life, alleviating Grade II or above leukopenia and thrombocytopenia. Conclusions: Aidi injection combined with NP chemotherapy can enhance efficiency, improve the quality of life, and decrease adverse effects in patients with advanced NSCLC.
When Family Gets in the Way of Recovery

Alcohol and substance use often results in the individual experiencing social and interpersonal problems. Among many of the factors that impact alcohol and substance use, relationships can be part of the problem or the solution. Support systems are considered to be a key ingredient of effective treatment. Unfortunately, the individual’s social system may not be, either intentionally or unintentionally, supportive of the individual’s recovery. In an effort to address this challenge to an individual’s recovery, motivational interviewing principles and practices can be applied to families and other social systems that are ambivalent to changing problematic behaviors that may be negatively impacting the individual’s recovery or substance use cessation goals.
Nonnegative shared subspace learning and its application to social media retrieval

Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.
Diapedesis of monocytes is associated with MMP‐ mediated occludin disappearance in brain endothelial cells

The blood‐brain barrier (BBB), a selective barrier formed by endothelial cells and dependent on the presence of tight junctions, is compromised during neuroinflammation. A detailed study of tight junction dynamics during transendothelial migration of leukocytes has been lacking. Therefore, we retrovirally expressed green fluorescent protein (GFP) fused to the N‐terminus of the tight junction protein occludin in the rat brain endothelial cell line GP8/3.9. Confocal microscopy analyses revealed that GFP‐occludin colocalized with the intracellular tight junction protein, ZO‐1, localized at intercellular connections, and was absent at cell borders lacking apposing cells. Using live cell imaging we found that monocytes scroll over the brain endothelial cell surface toward cell‐cell contacts, induce gap formation, which is associated with local disappearance of GFP‐occludin, and subsequently traverse the endothelium paracellularly. Immunoblot analyses indicated that loss of occludin was due to protein degradation. The broad spectrum matrix metalloproteinase (MMP) inhibitor BB‐3103 significantly inhibited endothelial gap formation, occludin loss, and the ability of monocytes to pass the endothelium. Our results provide a novel insight into the mechanism by which leukocytes traverse the BBB and illustrate that therapeutics aimed at the stabilization of the tight junction may be beneficial to resist a neuroinflammatory attack. —Reijerkerk, A. , Kooij, G. , van der Pol, S. M. A. , Khazen, S. , Dijkstra, C. D. , de Vries, H. E. Diapedesis of monocytes is associated with MMP‐mediated occludin disappearance in brain endothelial cells. FASEB J. 20, E1901–E1909 (2006)
Computational Analysis of Natural Ventilation Flows in Geodesic Dome Building in Hot Climates

For centuries, dome roofs were used in traditional houses in hot regions such as the Middle East and Mediterranean basin due to its thermal advantages, structural benefits and availability of construction materials. This article presents the computational modelling of the wind- and buoyancy-induced ventilation in a geodesic dome building in a hot climate. The airflow and temperature distributions and ventilation flow rates were predicted using Computational Fluid Dynamics (CFD). The three-dimensional Reynolds-Averaged Navier-Stokes (RANS) equations were solved using the CFD tool ANSYS FLUENT15. The standard k-epsilon was used as turbulence model. The modelling was verified using grid sensitivity and flux balance analysis. In order to validate the modelling method used in the current study, additional simulation of a similar domed-roof building was conducted for comparison. For wind-induced ventilation, the dome building was modelled with upper roof vents. For buoyancy-induced ventilation, the geometry was modelled with roof vents and also with two windows open in the lower level. The results showed that using the upper roof openings as a natural ventilation strategy during winter periods is advantageous and could reduce the indoor temperature and also introduce fresh air. The results also revealed that natural ventilation using roof vents cannot satisfy thermal requirements during hot summer periods and complementary cooling solutions should be considered. The analysis showed that buoyancy-induced ventilation model can still generate air movement inside the building during periods with no or very low wind.
Fast Fading Channel Estimation for UMTS Long Term Evolution

In this paper, we present an Approximate Linear Minimum Mean Square Error (ALMMSE) fast fading channel estimator for Orthogonal Frequency Division Multiplexing (OFDM). The ALMMSE channel estimator utilizes the knowledge of the structure of the autocorrelation matrix given by the Kronecker product between the time correlation matrix and the frequency correlation matrix. Simulation results show that the proposed ALMMSE channel estimator allows to move with about 25 km/h higher velocity than the Least Squares (LS) channel estimator in realistic scenarios.
Non rigid image registration by modeling deformations as elastic waves

In this paper we propose an inter-subject non-rigid image registration method that is derived from the concept of elastodynamics. Non-rigid warps are modeled as elastic waves which tend to recover extensive deformations. The deformable transformation model is given by Navier PDE which is solved iteratively using finite difference approximations. The dynamical displacements so computed represent the warp field which is then used to bring the subject image into spatial correspondence with the atlas. The results were validated using Jaccard coefficient (J), Dice coefficient (D), Overlap coefficient (O) and Normalized Cross Correlation (NCC) with mean ± std values of 0.81 ± . 04, 0.89 ± . 03, 0.99 ± . 01 and 0.97 ± . 01 respectively. This quantitative assessment confirmed that the proposed registration method performs very well.
Quality of service profiling Citation

Many computations exhibit a trade off between execution time and quality of service. A video encoder, for example, can often encode frames more quickly if it is given the freedom to produce slightly lower quality video. A developer attempting to optimize such computations must navigate a complex trade-off space to find optimizations that appropriately balance quality of service and performance. We present a new quality of service profiler that is designed to help developers identify promising optimization opportunities in such computations. In contrast to standard profilers, which simply identify time-consuming parts of the computation, a quality of service profiler is designed to identify subcomputations that can be replaced with new (and potentially less accurate) subcomputations that deliver significantly increased performance in return for acceptably small quality of service losses. Our quality of service profiler uses loop perforation (which transforms loops to perform fewer iterations than the original loop) to obtain implementations that occupy different points in the performance/quality of service trade-off space. The rationale is that optimizable computations often contain loops that perform extra iterations, and that removing iterations, then observing the resulting effect on the quality of service, is an effective way to identify such optimizable subcomputations. Our experimental results from applying our implemented quality of service profiler to a challenging set of benchmark applications show that it can enable developers to identify promising optimization opportunities and deliver successful optimizations that substantially increase the performance with only small quality of service losses.
Exploring the range of impedance conditioning by virtual inductance for grid connected voltage source converters

Conditioning of network impedance by insertion of a virtual inductance as an inherent feature of the converter control system has the potential to extend the range of stable operation of the overall system. This paper presents a scenario in which such potential is explored and its range of application is identified. It shows first that implementation of a virtual inductance is naturally suited for combination with Virtual Flux-based control systems, and that it can be extended to the more general case of a virtual reactance. The paper then presents the structure of a control system for operation under unbalanced conditions, where the virtual inductance directly influences the current reference calculation. Compared with the standard approach of unity power factor control of grid integrated converters, the suggested technique allows for improving the stability range of converters operating in high-impedance grids, and for remote point control of reactive power or power factor.
SURE Guided Gaussian Mixture Image Denoising

The Gaussian mixture is a patch prior that has enjoyed tremendous success in image processing. In this work, by using Gaussian factor modeling, its dedicated expectation maximization (EM) inference, and a statistical filter selection and algorithm stopping rule, we develop SURE (Stein's unbiased risk estimator) guided piecewise linear estimation (S-PLE), a patch-based prior learning algorithm capable of delivering state-of-the-art performance at image denoising. In light of this algorithm's features and its results, we also seek to address the number of components to be included when setting up a Gaussian mixture for image patch modeling. By juxtaposing both options, we show that a simple learned prior can perform as well as, if not better than, a much richer yet fixed prior.
ORAS: Opportunistic routing with asynchronous sleep in Wireless Sensor Networks

Opportunistic routing and asynchronous sleep are both recent techniques those change the traditional way of routing and MAC layer implementation. The existing opportunistic routing schemes, designed for increasing throughput of connection rather than saving energy, are not appropriate for Wireless Sensor Networks due to low energy efficiency caused by overhearing and scheduling. In this paper, we propose opportunistic routing with asynchronous sleep (ORAS), a robust integrated routing and MAC protocol that combines opportunistic routing and asynchronous sleep. ORAS makes a sender rendezvous with just enough asynchronous sleeping neighbors to ensure the packet received by a potential forwarder with a high probability. ORAS is opportunistic in nature as it exploits the spatial and temporal diversity of data traffic to do transmission. We evaluate the efficiency of ORAS through extensive experiments on testbed of TelosB motes. Our results show that ORAS can achieve high performance in terms of packet delivery ratio, latency and power efficiency.
Adaptable Learning Pathway Generation with Ant Colony Optimization

One of the new major directions in research on web-based educational systems is the notion of adaptability: the educational system adapts itself to the learning profile, preferences and ability of the student. In this paper, we look into the issues of providing adaptability with respect to learning pathways. We explore the state of the art with respective to deriving the most apt learning pathway to recommend to the learner. Our proposal suggests a novel way of modeling learning pathways that combines rule-based prescriptive planning, which could be found in many of the classic Intelligent Tutoring Systems, and Ant Colony Optimization-based inductive planning, for recommending learning paths by stochastically computing past learners' traveled paths and their performances. A web-based prototype has been developed using C# and . NET technologies.
Nonconsensus Protein Binding to Repetitive DNA Sequence Elements Significantly Affects Eukaryotic Genomes

Recent genome-wide experiments in different eukaryotic genomes provide an unprecedented view of transcription factor (TF) binding locations and of nucleosome occupancy. These experiments revealed that a large fraction of TF binding events occur in regions where only a small number of specific TF binding sites (TFBSs) have been detected. Furthermore, in vitro protein-DNA binding measurements performed for hundreds of TFs indicate that TFs are bound with wide range of affinities to different DNA sequences that lack known consensus motifs. These observations have thus challenged the classical picture of specific protein-DNA binding and strongly suggest the existence of additional recognition mechanisms that affect protein-DNA binding preferences. We have previously demonstrated that repetitive DNA sequence elements characterized by certain symmetries statistically affect protein-DNA binding preferences. We call this binding mechanism nonconsensus protein-DNA binding in order to emphasize the point that specific consensus TFBSs do not contribute to this effect. In this paper, using the simple statistical mechanics model developed previously, we calculate the nonconsensus protein-DNA binding free energy for the entire C. elegans and D. melanogaster genomes. Using the available chromatin immunoprecipitation followed by sequencing (ChIP-seq) results on TF-DNA binding preferences for ~100 TFs, we show that DNA sequences characterized by low predicted free energy of nonconsensus binding have statistically higher experimental TF occupancy and lower nucleosome occupancy than sequences characterized by high free energy of nonconsensus binding. This is in agreement with our previous analysis performed for the yeast genome. We suggest therefore that nonconsensus protein-DNA binding assists the formation of nucleosome-free regions, as TFs outcompete nucleosomes at genomic locations with enhanced nonconsensus binding. In addition, here we perform a new, large-scale analysis using in vitro TF-DNA preferences obtained from the universal protein binding microarrays (PBM) for ~90 eukaryotic TFs belonging to 22 different DNA-binding domain types. As a result of this new analysis, we conclude that nonconsensus protein-DNA binding is a widespread phenomenon that significantly affects protein-DNA binding preferences and need not require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding specificity.