Miracle-dz/UniFAR-FTUs huggingface dataset:
Walking the tightrope: responsive yet stable traffic engineering

Current intra-domain Traffic Engineering (TE) relies on offline methods, which use long term average traffic demands. It cannot react to realtime traffic changes caused by BGP reroutes, diurnal traffic variations, attacks, or flash crowds. Further, current TE deals with network failures by pre-computing alternative routings for a limited set of failures. It may fail to prevent congestion when unanticipated or combination failures occur, even though the network has enough capacity to handle the failure.This paper presents TeXCP, an online distributed TE protocol that balances load in realtime, responding to actual traffic demands and failures. TeXCP uses multiple paths to deliver demands from an ingress to an egress router, adaptively moving traffic from over-utilized to under-utilized paths. These adaptations are carefully designed such that, though done independently by each edge router based on local information, they balance load in the whole network without oscillations. We model TeXCP, prove the stability of the model, and show that it is easy to implement. Our extensive simulations show that, for the same traffic demands, a network using TeXCP supports the same utilization and failure resilience as a network that uses traditional offline TE, but with half or third the capacity.
Best relays selection method for error-resilient 3-D video transmission

In color-plus-depth map three-dimensional (3-D) video representation, color and depth map sequences exhibit different error sensitivities to the overall quality. Recognizing that, in this paper, we propose to exploit the relay cooperation benefits to achieve unequal error protection (UEP) for better quality 3-D video transmission. Specifically, we propose to apply the first best amplify-and-forward (AF) relay for transmitting color information with high priority (HP) while the second best AF relay is employed for the depth information with low priority (LP). In particular, we drive closed-form expressions for the bit error probability (BEP). Simulation results of the BEP are presented to demonstrate the proposed UEP scheme in terms of the received 3-D video quality.
SocialSensor at MediaEval Placing Task 2014

We describe the participation of the SocialSensor team in the Placing Task of MediaEval 2014. We submitted three runs based on tag information for the full test set, using extensions over an existing language modelling approach, and two runs (one based on the full test set and the other on the 25,500 subset) based on visual content, using geospatial clustering and supervised-learning. Our best performance (median error 230km, 23% at 1km) was achieved with the use of tag features, using only internal training data.
ADAR2-mediated editing of RNA substrates in the nucleolus is inhibited by C/D small nucleolar RNAs

Posttranscriptional, site-specific adenosine to inosine (A-to-I) base conversions, designated as RNA editing, play significant roles in generating diversity of gene expression. However, little is known about how and in which cellular compartments RNA editing is controlled. Interestingly, the two enzymes that catalyze RNA editing, adenosine deaminases that act on RNA (ADAR) 1 and 2, have recently been demonstrated to dynamically associate with the nucleolus. Moreover, we have identified a brain-specific small RNA, termed MBII-52, which was predicted to function as a nucleolar C/D RNA, thereby targeting an A-to-I editing site (C-site) within the 5-HT2C serotonin receptor pre-mRNA for 2′-O-methylation. Through the subcellular targeting of minigenes that contain natural editing sites, we show that ADAR2- but not ADAR1-mediated RNA editing occurs in the nucleolus. We also demonstrate that MBII-52 forms a bona fide small nucleolar ribonucleoprotein particle that specifically decreases the efficiency of RNA editing by ADAR2 at the targeted C-site. Our data are consistent with a model in which C/D small nucleolar RNA might play a role in the regulation of RNA editing.
Effect of connector design on the fracture resistance of all-ceramic fixed partial dentures.

STATEMENT OF PROBLEM Fracture of all-ceramic fixed partial dentures (FPDs) tends to occur in the connector area. PURPOSE The objective of this study was to test the hypothesis that the radii of curvature at the connector affects the fracture resistance of 3-unit FPDs. MATERIAL AND METHODS With the use of a standardized silicone mold, 40 three-unit FPD wax patterns were fabricated with the same dimensions and divided into 4 groups of 10 specimens per group. Each pattern was modified at the connector areas of the occlusal embrasure (OE) and the gingival embrasure (GE); 2 wax carvers with radii of curvature at their tips of 0.90 mm and 0.25 mm were used. The dimensions of the connectors were standardized with an electronic caliper to 4 +/- 0.12 mm in height and 5 +/- 0.13 mm in width. Connector designs were as follows: Design I: OE and GE 0.90 mm; Design II: OE 0.90 mm and GE 0.25 mm; Design III: OE 0.25 mm and GE 0.90 mm; and Design IV (control): OE and GE 0.25 mm. An experimental hot-pressed core ceramic was used to make the FPD frameworks, which were consequently cemented on epoxy dies with dual-polymerizing composite (Variolink II) and loaded to fracture in a universal testing machine at a crosshead speed of 0.5 mm/min. The failure load data were analyzed with analysis of variance (ANOVA; P=.05) and Duncan's test (alpha=.01). RESULTS The mean failure loads and standard deviations were as follows: 943 +/- 151 N for Design I; 746 +/- 106 N for Design II; 944 +/- 144 N for Design III; and 673 +/- 55 N for Design IV. ANOVA revealed a significant difference (P< or = .0001) between the mean failure loads of different connector designs. The mean loads to failure for Designs I and III were significantly higher than those for Designs II and IV (Duncan's test). CONCLUSION Within the limitations of this study and for the experimental ceramic tested, as the radius at the gingival embrasure increased from 0.25 to 0.90 mm, the mean failure load increased by 140%. The radius of curvature at the occlusal embrasure had only a minor effect on the fracture susceptibility of 3-unit FPDs.
Dynamic selection of composite Web services based on a new structured TCNN

In order to realize a high quality and good performance service composition, we propose new QoS driven dynamic selection of composite Web services, which takes account of both the QoS properties and interface parameter matching degree. When doing the selection, we are aware that the task is more or less a multistage decision making process. Motivated by transiently chaotic neural network high parallel performance and powerful computation ability, a new structured transiently chaotic neural network algorithm is proposed. In order to make this algorithm more adaptable for the multistage decision making problem, we propose a new structured neural network to express the composed service instead of using traditional neural networks, which minimizes the neurons involved and shows higher performance. Finally, through experimentation one can find that the method proposed in this paper is more practical and effective than others.
The effect of meaningfulness and integrative processing in expressive writing on positive and negative affect and life satisfaction

Meaningfulness and integrative processing of expressive writing may influence the effect of expressive writing. Participants completed measures of positive affect, negative affect and life satisfaction before and after an expressive writing intervention. Participants were randomly assigned to one of four expressive writing instruction conditions, which combined higher and lower levels of meaning and integrative processing instructions. Meaningfulness and integrative processing instructions had significant effects in increasing positive affect and there was a significant interaction between meaningfulness instructions and integrative processing instructions; participants in the high meaningfulness and high integrative processing instruction condition showed the greatest increase in positive affect. Meaningfulness had a significant effect in decreasing negative affect. The intervention did not influence life satisfaction. Both meaningfulness and integrative processing instructions led to more self-reported personal meaningfulness of the writing and more cognitive, emotional, behavioural and situational changes. More self-reported meaningfulness of the writing and more cognitive, emotional, behavioural and situational changes made as a result of the writing were in turn associated with greater increases in positive affect. The results of the study affirm the importance of meaningfulness and processing in expressive writing and potentially provide information regarding how to increase the effectiveness of expressive writing.
Energy-Efficient Adaptive Video Transmission: Exploiting Rate Predictions in Wireless Networks

The unprecedented growth of mobile video traffic is adding significant pressure to the energy drain at both the network and the end user. Energy-efficient video transmission techniques are thus imperative to cope with the challenge of satisfying user demand at sustainable costs. In this paper, we investigate how predicted user rates can be exploited for energy-efficient video streaming with the popular Hypertext Transfer Protocol (HTTP)-based adaptive streaming (AS) protocols [e.g., dynamic adaptive streaming over HTTP (DASH)]. To this end, we develop an energy-efficient predictive green streaming (PGS) optimization framework that leverages predictions of wireless data rates to achieve the following objectives: 1) Minimize the required transmission airtime without causing streaming interruptions; 2) minimize total downlink base station (BS) power consumption for cases where BSs can be switched off in deep sleep; and 3) enable a tradeoff between AS quality and energy consumption. Our framework is first formulated as mixed-integer linear programming (MILP) where decisions on multiuser rate allocation, video segment quality, and BS transmit power are jointly optimized. Then, to provide an online solution, we present a polynomial-time heuristic algorithm that decouples the PGS problem into multiple stages. We provide a performance analysis of the proposed methods by simulations, and numerical results demonstrate that the PGS framework yields significant energy savings.
Combined Ranking and Selection with Control Variates

Nelson and Staum derived R&S procedures that employ control-variate estimators (CVs) instead of sample means to obtain more statistical efficiency. However, control-variate estimators require more computational effort than sample means, and effective controls must be identified. We present a new CV screening procedure to avoid much of the computation cost. We also present a two-stage CV combined procedure which captures the ability to eliminate inferior systems in the first stage and the statistical efficiency of control variates for selection in the second stage. An empirical evaluation is provided
Evaluating Answer Validation in Multi-stream Question Answering

We follow the opinion that Question Answering (QA) performance can be improved by combining different systems. Thus, we planned an evaluation oriented to promote the specialization and further collaboration between QA systems. This multistream QA requires to develop the modules able to select the proper stream according to the question and the candidate answers provided. We describe here the evaluation framework we have developed with special focus on the evaluation measures and the study of their behavior in a comparative evaluation.
Fault detection for quantized feedback systems with actuator faults

This paper investigates the fault detection problem of quantized feedback systems with actuator faults, and system outputs are respectively quantized by a uniform quantizer and a non-uniform quantizer. A new method is proposed to deal with quantization errors, that is, the quantization errors are considered as the unknown inputs of the residual generator. In order to detect the faults, an optimal residual generator is first designed to guarantee a suitable compromise between the sensitivity to faults and the robustness against disturbances as well as quantization errors, and then the residual signal is evaluated; finally, two different thresholds on the basis of the stochastic properties of quantization errors are designed for the two types of quantizers. A simulated example is given to illustrate the effectiveness of the proposed method.
Covariate Adjustment in Estimating the Area Under ROC Curve with Partially Missing Gold Standard

In ROC analysis, covariate adjustment is advocated when the covariates impact the magnitude or accuracy of the test under study. Meanwhile, for many large scale screening tests, the true condition status may be subject to missingness because it is expensive and/or invasive to ascertain the disease status. The complete‐case analysis may end up with a biased inference, also known as “verification bias. ” To address the issue of covariate adjustment with verification bias in ROC analysis, we propose several estimators for the area under the covariate‐specific and covariate‐adjusted ROC curves (AUCx and AAUC). The AUCx is directly modeled in the form of binary regression, and the estimating equations are based on the U statistics. The AAUC is estimated from the weighted average of AUCx over the covariate distribution of the diseased subjects. We employ reweighting and imputation techniques to overcome the verification bias problem. Our proposed estimators are initially derived assuming that the true disease status is missing at random (MAR), and then with some modification, the estimators can be extended to the not missing at random (NMAR) situation. The asymptotic distributions are derived for the proposed estimators. The finite sample performance is evaluated by a series of simulation studies. Our method is applied to a data set in Alzheimer's disease research.
Priority control using multi-buffer for DTN

DTN (delay/disruption-tolerant networking) is a network enabling data to be carried end-to-end without continuous connection. DTN uses a store and forward method, in which a node stores the data when no other nodes exist in a communicable range and forwards a copy of the data when they do. Since the buffer has a limited buffering capacity, the data often need to be dropped. When there are data that have different priorities, the high priority data may be pushed out from the buffer by the low priority data. Thus the high priority data arrival rate lowered by pushing data out needs to be increased. In this paper, we propose and evaluate a buffer control system to raise the high priority data arrival rate without decreasing the total arrival rate when the buffer has a limit. The priority control system avoids the high priority data dropping caused by low priority data by choosing the dropped data in reference to the data priority when the new data are sent to the full node. We show that the priority control system increases the high priority data arrival rate and maintains the high priority data drop rate at no more than the desired value.
Image segmentation and selective smoothing by using Mumford-Shah model

Recently, Chan and Vese developed an active contour model for image segmentation and smoothing by using piecewise constant and smooth representation of an image. Tsai et al. also independently developed a segmentation and smoothing method similar to the Chan and Vese piecewise smooth approach. These models are active contours based on the Mumford-Shah variational approach and the level-set method. In this paper, we develop a new hierarchical method which has many advantages compared to the Chan and Vese multiphase active contour models. First, unlike previous works, the curve evolution partial differential equations (PDEs) for different level-set functions are decoupled. Each curve evolution PDE is the equation of motion of just one level-set function, and different level-set equations of motion are solved in a hierarchy. This decoupling of the motion equations of the level-set functions speeds up the segmentation process significantly. Second, because of the coupling of the curve evolution equations associated with different level-set functions, the initialization of the level sets in Chan and Vese's method is difficult to handle. In fact, different initial conditions may produce completely different results. The hierarchical method proposed in this paper can avoid the problem due to the choice of initial conditions. Third, in this paper, we use the diffusion equation for denoising. This method, therefore, can deal with very noisy images. In general, our method is fast, flexible, not sensitive to the choice of initial conditions, and produces very good results.
Perceived discrimination, psychological distress, and current smoking status: results from the Behavioral Risk Factor Surveillance System Reactions to Race module, 2004-2008.

OBJECTIVES We examined the association between perceived discrimination and smoking status and whether psychological distress mediated this relationship in a large, multiethnic sample. METHODS We used 2004 through 2008 data from the Behavioral Risk Factor Surveillance System Reactions to Race module to conduct multivariate logistic regression analyses and tests of mediation examining associations between perceived discrimination in health care and workplace settings, psychological distress, and current smoking status. RESULTS Regardless of race/ethnicity, perceived discrimination was associated with increased odds of current smoking. Psychological distress was also a significant mediator of the discrimination-smoking association. CONCLUSIONS Our results indicate that individuals who report discriminatory treatment in multiple domains may be more likely to smoke, in part, because of the psychological distress associated with such treatment.
SIFT-ing through features with ViPR

Recent advances in computer vision have given rise to a robust and invariant visual pattern recognition technology that is based on extracting a set of characteristic features from an image. Such features are obtained with the scale invariant feature transform (SIFT) which represents the variations in brightness of the image around the point of interest. Recognition performed with these features has been shown to be quite robust in realistic settings. This paper describes the application of this particular visual pattern recognition (ViPR) technology to a variety of robotics applications: object recognition, navigation, manipulation, and human-machine interaction. The paper also describes the technology in more detail and presents a business case for visual pattern recognition in the field of robotics and automation
In aged men, central vessel transmural pressure is reduced by brief Valsalva manoeuvre during strength exercise

A brief Valsalva manoeuvre, lasting 2–3 s, performed by young healthy men during strength exercise reduces transmural pressure acting on intrathoracic arteries. In this study, we sought to verify this finding in older men. Twenty normotensive, prehypertensive and moderately hypertensive otherwise healthy men 46–69 years old performed knee extensions combined with inspiration or with brief Valsalva manoeuvre performed at 10, 20 and 40 mmHg mouth pressure. Same respiratory manoeuvres were also performed at rest. Non‐invasively measured blood pressure, knee angle, respiratory airflow and mouth pressure were continuously registered. In comparison to inspiration, estimated transmural pressure acting on thoracic arteries changed slightly and insignificantly during brief Valsalva manoeuvre at 10 and 20 mmHg mouth pressure. At 40 mmHg mouth pressure, transmural pressure declined at rest (−8·8 ± 11·4 mmHg) and during knee extension (−12·1 ± 11·9 mmHg). This decline ensued, as peak systolic pressure increase caused by this manoeuvre, was distinctly <40 mmHg. Only a main effect of mouth pressure was revealed (P<0·001) and neither exercise nor interaction between these factors, what suggests that transmural pressure decline, depended mainly on intrathoracic pressure developed during brief Valsalva manoeuvre. Resting blood pressure did not influence the effect of brief Valsalva manoeuvre on transmural pressure.
Parallel scalability in speech recognition

We propose four application-level implementation alternatives called algorithm styles and construct highly optimized implementations on two parallel platforms: an Intel Core i7 multicore processor and a NVIDIA GTX280 manycore processor. The highest performing algorithm style varies with the implementation platform. On a 44-min speech data set, we demonstrate substantial speedups of 3.4 X on Core i7 and 10.5 X on GTX280 compared to a highly optimized sequential implementation on Core i7 without sacrificing accuracy. The parallel implementations contain less than 2.5% sequential overhead, promising scalability and significant potential for further speedup on future platforms.
A hybrid method of molecular dynamics and harmonic dynamics for docking of flexible ligand to flexible receptor

We have developed a new docking method to consider receptor flexibility, a hybrid method of molecular dynamics and harmonic dynamics. The global motions of the whole receptor were approximately introduced into those of the receptor in the docking simulation as harmonic dynamics. On the other hand, the local flexibility of the side chains was also considered by conventional molecular dynamics. We confirmed that this new method can reproduce the fluctuations of the whole receptor by making a comparison of the directions and amplitudes of the global fluctuations. Then this method was applied to the docking of HIV‐1 protease and its ligand. As a result, we observed a docking process where the ligand enters into the binding pocket well, which implies that this method is effective enough to reproduce a molecular complex formation. © 2004 Wiley Periodicals, Inc. J Comput Chem 25: 1995–2005, 2004
Universal Joint Feature Extraction for P300 EEG Classification Using Multi-Task Autoencoder

The process of recording electroencephalography (EEG) signals is onerous and requires massive storage to store signals at an applicable frequency rate. In this paper, we propose the event-related potential encoder network (ERPENet), a multi-task autoencoder-based model, that can be applied to any ERP-related tasks. The strength of ERPENet lies in its capability to handle various kinds of ERP datasets and its robustness across multiple recording setups, enabling joint training across datasets. The ERPENet incorporates convolutional neural networks (CNNs) and long short-term memory (LSTM), in an autoencoder setup, which tries to simultaneously compress the input EEG signal and extract related P300 features into a latent vector. Here, we can infer the process for generating the latent vector as universal joint feature extraction. The network also includes a classification part for attended and unattended events classification as an auxiliary task. We experimented on six different P300 datasets. The results show that the latent vector exhibits better compression capability than the previous state-of-the-art semi-supervised autoencoder model. For attended and unattended events classification, pre-trained weights are adopted as initial weights and tested on unseen P300 datasets to evaluate the adaptability of the model, which shortens the training process as compared to using random Xavier weight initialization. At the compression rate of 6.84, the classification accuracy outperforms conventional P300 classification models: the XdawnLDA, DeepConvNet, and EEGNet achieving 79.37%– 88.52% classification accuracy depending on the dataset.