Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of the user model on simulation-based learning of dialogue strategies

Over the past decade, a variety of user models have been proposed for user simulation-based reinforcement-learning of dialogue strategies. However, the strategies learned with these models are rarely evaluated in actual user trials and it remains unclear how the choice of user model affects the quality of the learned strategy. In particular, the degree to which strategies learned with a user model generalise to real user populations has not be investigated. This paper presents a series of experiments that qualitatively and quantitatively examine the effect of the user model on the learned strategy. Our results show that the performance and characteristics of the strategy are in fact highly dependent on the user model. Furthermore, a policy trained with a poor user model may appear to perform well when tested with the same model, but fail when tested with a more sophisticated user model. This raises significant doubts about the current practice of learning and evaluating strategies with the same user model. The paper further investigates a new technique for testing and comparing strategies directly on real human-machine dialogues, thereby avoiding any evaluation bias introduced by the user model
Learning to Move

Locomotion—moving the body from place to place—is one of infants' greatest achievements. In addition to conquering gravity, infants must cope with variable and novel constraints on balance and propulsion. At the same time that they are learning to move, changes in infants' bodies, skills, and environments change the biomechanical constraints on movement. Recent work highlights both flexibility and specificity in infants' responses to novel and variable situations, demonstrating that infants are learning to learn as they master locomotion. Within sitting, crawling, cruising, and walking postures, experienced infants adapt their locomotor responses to the current biomechanical constraints on movement. However, what infants have learned about coping with variability and novelty in earlier-developing postures does not transfer to later-developing postures.
A Fractional-Order Primal-Dual Denoising Algorithm

Objective: By combining fractional calculus and duality theory, a novel fractional-order primal-dual model which is equivalent with the fractional ROF model is proposed. We theoretically analyze its structural similarity with the saddle-point optimization model. So the algorithms for solving the saddle-point problem can be used for solving the model. Methods: The primal-dual algorithm based on resolvent for solving the saddle-point problem is used for solving the proposed model. The adaptive variable step size iterative optimization strategy is used, which can improve the optimizing efficiency, and remedy the step size limitation of the traditional numerical algorithms. In order to guarantee the convergence of the algorithm, the range of the parameter is given. Results: The experiment results show that the proposed fractional-order primal-dual model is effective in avoiding the staircase effect and preserving texture and detail information, and the adoptive numerical algorithm has faster convergence speed. Conclusion: This paper proposes a fractional-order primal-dual denoising model, which can be solved by a primal-dual algorithm based on resolvent. The experiment results show that the proposed model can improve the image visual effect effectively, and the adoptive numerical algorithm has faster convergence speed. Keywordsimage denoising; fractional-order; primaldual; saddle-point problem; variation method
Examining Predictors of Depression Among Older Incarcerated Women

For older women in prison, adjustments to prison life are often affected by a variety of mental and physical health symptoms. Many come to prison with specific histories of traumatic life experiences that place them at risk for high levels of depression. Using data gathered from 327 older women (mean age = 56.5), we examined the relationship between a variety of preprison conditions and prison deprivation factors as possible predictors of depression. Stepwise regression results indicated that depression was greater for those with prior abuse histories, functional health problems along with poorer perceptions of mental and physical health, safety and other prison environment concerns. Although race was not a significant predictor in the regression model, a t-test did find that whites were more depressed than African Americans in our sample. Other correlates of depression are also identified that lend insights into the high rates of depression found among elderly women in prison. With such high rates of psychiatric disorders among women offenders, more treatment interventions are needed to adequately serve this population.
Improving tourist experience by Big Data tools

Due to the emerging Big Data applications traditional data management techniques result inadequate in many real life scenarios. In particular, OLAP techniques require substantial changes in order to offer useful analysis due to huge amount of data to be analyzed and their velocity and variety. In this paper, we describe an approach for dynamic Big Data searching that based on data collected by a suitable storage system, enrich data in order to guide users through data exploration in a efficient and effective way.
Implementing Robots in Defence Through Motion Capture with Mixed Reality

Our soldiers are fighting for us, risking their lives and people working in mines spoiling their health. In this paper we will see how we will implement the technology of mixed reality and motion capture will give solutions for replacing humans with robots. We can save a lot of lot of human lives and it will be more cost efficient. As on today we are implementing motion capture in analyzing the responses of military soldiers to test their capabilities and doing animations in movies. So let us extend the existing features to implement a remote robot control system that allows us to replace humans with robots.
Initial condition sensitivity of global quantities in magnetohydrodynamic turbulence

The effect of subtle changes in initial conditions on the evolution of global quantities in two-dimensional magnetohydrodynamic (MHD) turbulence is studied. It is found that a change in the initial phases of complex Fourier modes of the Elsasser variables, while keeping the initial values of total energy, cross helicity, and Alfven ratio unchanged, has a significant effect on the evolution of cross helicity. On the contrary, the total energy and Alfven ratio are insensitive to the initial phases. The simulations are based on direct numerical simulation using the pseudospectral method.
Using sdn technology to enable cost-effective bandwidth-on-demand for cloud services [Invited]

We describe bandwidth-on-demand in an evolved multilayer, software-defined networking (SDN) based cloud services model. We present motivation for using a multilayer architecture for the wide-area network (WAN). Our laboratory testbed is described, and both the hardware and management architecture are presented. We also show an initial proof-of-concept demonstration of this capability.
The Target Discounted-Sum Problem

The target discounted-sum problem is the following: Given a rational discount factor 0 <; λ <; 1 and three rational values a, b, and t, does there exist a finite or an infinite sequence w ∈ {a, b}* or w ∈ {a, b}ω, such that Σi=0|w| w(i)λi equals t? The problem turns out to relate to many fields of mathematics and computer science, and its decidability question is surprisingly hard to solve. We solve the finite version of the problem, and show the hardness of the infinite version, linking it to various areas and open problems in mathematics and computer science: β-expansions, discounted-sum automata, piecewise affine maps, and generalizations of the Cantor set. We provide some partial results to the infinite version, among which are solutions to its restriction to eventually-periodic sequences and to the cases that λ ≥ 1/2 or λ =1/n, for every n ∈ N. We use our results for solving some open problems on discounted-sum automata, among which are the exact-value problem for nondeterministic automata over finite words and the universality and inclusion problems for functional automata.
Drive-by-Microwave technologies for isolated direct gate drivers

The wireless power transmission technology using an electro-magnetic resonant coupler (EMRC) has been applied to an isolated direct gate driver for GaN power switching devices. This direct gate driver with the Drive-by-Microwave technologies dose not needs an additional isolated voltage source and a photo-coupler because it can supply an isolated gate signal and signal power all together. The wireless power transmission capability in the driver is crucial for its performances, especially, regarding a switching speed and power consumption. This paper presents the potential of GaN/Sapphire direct gate driver using 5.8GHz wireless power transmission with a compact butterfly-shaped EMRC. Since the fabricated direct gate driver with the integrated EMRC drove a GaN power switching device with a fast turn on/off time, it is proved that the GaN/Sapphire HFETs is best suitable for the direct gate driver.
Findings of the 2014 Workshop on Statistical Machine Translation

This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries
SN 2005cs in M51 – I. The first month of evolution of a subluminous SN II plateau

Early-time optical observations of supernova (SN) 2005cs in the Whirlpool Galaxy (M51) are reported. Photometric data suggest that SN 2005cs is a moderately underluminous Type II plateau SN (SN IIP). The SN was unusually blue at early epochs (U − B ≈− 0.9 about three days after explosion) which indicates very high continuum temperatures. The spectra show relatively narrow P Cygni features, suggesting ejecta velocities lower than observed in more typical SNe IIP. The earliest spectra show weak absorption features in the blue wing of the He I 5876-A absorption component and, less clearly, of Hβ and Hα. Based on spectral modelling, two different interpretations can be proposed: these features may either be due to high-velocity H and He I components, or (more likely) be produced by different ions (N II ,S iII). Analogies with the low-luminosity, 56 Ni-poor, low-velocity SNe IIP are also discussed. While a more extended spectral coverage is necessary in order to determine accurately the properties of the progenitor star, published estimates of the progenitor mass seem not to be consistent with stellar evolution models.
Aligned Image Sets Under Channel Uncertainty: Settling Conjectures on the Collapse of Degrees of Freedom Under Finite Precision CSIT

A conjecture made by Lapidoth et al. at Allerton 2005 (also an open problem presented at ITA 2006) states that the degrees of freedom (DoF) of a two user broadcast channel, where the transmitter is equipped with two antennas and each user is equipped with one antenna, must collapse under finite precision channel state information at the transmitter (CSIT). That this conjecture, which predates interference alignment, has remained unresolved, is emblematic of a pervasive lack of understanding of the DoF of wireless networks-including interference and X networks-under channel uncertainty at the transmitter(s). In this paper, we prove that the conjecture is true in all non-degenerate settings (e.g., where the probability density function of unknown channel coefficients exists and is bounded). The DoF collapse even when perfect channel knowledge for one user is available to the transmitter. This also settles a related recent conjecture by Tandon et al. The key to our proof is a bound on the number of codewords that can cast the same image (within noise distortion) at the undesired receiver whose channel is subject to finite precision CSIT, while remaining resolvable at the desired receiver whose channel is precisely known by the transmitter. We are also able to generalize the result along two directions. First, if the peak of the probability density function is √ allowed to scale as O(( √P)α), representing the concentration of probability density (improving CSIT) due to, e.g., quantized feedback at rate (α/2) log(P), then the DoF is bounded above by 1+α, which is also achievable under quantized feedback. Second, we generalize the result to arbitrary number of antennas at the transmitter, arbitrary number of single-antenna users, and complex channels. The generalization directly implies a collapse of DoF to unity under non-degenerate channel uncertainty for the general K-user interference and M × N user X networks as well.
Schnorr Signatures in the Multi-User Setting

A theorem by Galbraith, Malone-Lee, and Smart (GMLS) from 2002 showed that, for Schnorr signatures, single-user security tightly implies multi-user security. Recently, Bernstein pointed to an error in the above theorem and promoted a key-prefixing variant of Schnorr signatures for which he proved a tight implication from single to multi-user security. Even worse, he identified an “apparently insurmountable obstacle to the claimed [GMLS] theorem”. This paper shows that, without key prefixing, single-user security of Schnorr signatures tightly implies multi-user security of the same scheme.
Using Graphics Processor Units (GPUs) for Automatic Video Structuring

The rapid pace of development of graphic processor units (GPUs) in recent years in terms of performance and programmability has attracted the attention of those seeking to leverage alternative architectures for better performance than that which commodity CPUs can provide. In this paper, the potential of the GPU in automatically structuring video is examined, specifically in shot boundary detection and representative keyframe selection techniques. We first introduce the programming model of the GPU and outline the implementation of techniques for shot boundary detection and representative keyframe selection on both the CPU and GPU, using histogram comparisons. We compare the approaches and present performance results for both the CPU and GPU. Overall these results demonstrate the significant potential for the GPU in this domain.
Information Sharing in VANETs

ABSTRACT This chapter looks at a vehicular ad hoc network (VANET) as a peer-to-peer network, where mobile users may request information contents as well as provide them to other nodes, and addresses the major technical issues that emerge when dealing with information sharing in VANETs. After briefly reviewing the main proposals appeared in the literature on application and network protocols for data exchange in VANETs, the chapter focuses on a possible application for data sharing between vehicular users. It then highlights the main challenges in such a scenario and introduces some mechanisms that can be applied to solve two major issues in content sharing: content query propagation and content caching. A comparison among the schemes presented for query propagation, as well as between the mechanisms introduced for data caching, is shown through simulation results derived using the network simulator ns2. Finally, future challenges and emerging research topics for content sharing and dissemination in VANETs are outlined.
Dynamic Probabilistic Approach for Improving the Route Discovery in MANETs

The pure flooding is considered the common mechanism have been proposed to distribute the Route Request Packet in DSR protocol. This strategy is simple and easy to implement but it suffers from a serious problem lead to the broadcast storm problem. There are several probabilistic approaches are used to overcome this problem. In this study we developed a mathematical model that can be used locally by each node within the network to calculate a retransmission probability (pt) as a function of number of first-hop neighbors (k). In order to evaluate the performance of the proposed algorithm, a number of scenarios were simulated using the MANET Simulator (MANSim). The performance of the new model was evaluated and compared with pure and other probabilistic algorithms to estimate the enhancement it achieves over the current probabilistic algorithms. The model enhances the performance of probabilistic broadcast by increasing network reachability with an acceptable increase in the number of transmissions especially in the low and moderate node density.
The Arithmetic of Emotion: Integration of Incidental and Integral Affect in Judgments and Decisions

Research has demonstrated that two types of affect have an influence on judgment and decision making: incidental affect (affect unrelated to a judgment or decision such as a mood) and integral affect (affect that is part of the perceiver’s internal representation of the option or target under consideration). So far, these two lines of research have seldom crossed so that knowledge concerning their combined effects is largely missing. To fill this gap, the present review highlights differences and similarities between integral and incidental affect. Further, common and unique mechanisms that enable these two types of affect to influence judgment and choices are identified. Finally, some basic principles for affect integration when the two sources co-occur are outlined. These mechanisms are discussed in relation to existing work that has focused on incidental or integral affect but not both.
A risk calculator to predict adult attention-deficit/hyperactivity disorder: generation and external validation in three birth cohorts and one clinical sample

Abstract Aim Few personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD). Methods Using logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC – UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old). Results The overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79–0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71–0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower –0.57 (95% CI 0.54–0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73–0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/. Conclusions The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution.
Reverberation robust TDOA estimation using convex region prior

The Time Difference of Arrival (TDOA) of an acoustic source with respect to a given pair of microphones is typically estimated as the maximizer of a Measure of Synchrony (MoS) between the pair of microphone signals, within the interval [-D/v, D/v], where D is the inter-microphone distance and v is the speed of sound. In practical enclosures, phantom sources are created due to reverberation, which can cause false peaks in the MoS, leading to erroneous TDOA estimates. In several practical enclosures such as meeting rooms, conference halls, lecture halls, etc., the regions which can admit an acoustic source are restricted, because of furniture and other fixtures. Consequently, it is possible to acquire prior knowledge of the region which can accommodate an acoustic source. In this paper, we propose an approach to utilize this prior knowledge of source region for accurate TDOA estimation. We transform the prior knowledge of source region into an interval of possible TDOAs using a newly developed concept of family of hyperboloids. This interval, referred to as Region Constrained TDOA Interval (RCTI) is shown to be smaller than [-D/v, D/v]. TDOA is then estimated as the maximizer of a suitable MoS within the RCTI. We demonstrate that estimating TDOA within RCTI, with generalized cross correlation (GCC) class of functions as MoS, is more robust to reverberation than estimating TDOA within [-D/v, D/v].
SELF-SIMILARITY SYMMETRY AND FRACTAL DISTRIBUTIONS IN ITERATIVE DYNAMICS OF DISSIPATIVE MAPPINGS

We consider transformations of deterministic and random signals governed by simple dynamical mappings. It is shown that the resulting signal can be a random process described in terms of fractal distributions and fractal domain integrals. In typical cases a steady state satisfies a dilatation equation, relating an unknown function f(x) to f(�x) (for example, f(x) = g(x)f(�x)). We discuss simple linear models as well as nonlinear systems with chaotic be- havior including dissipative circuits with delayed feedback. 2000 Mathematical Subject Classification 37F25; 28A80; 37H50; 70K55; 37D45 Keywords Scaling; Fractals; Stochastic difference equations; Nonlinear differ- ence equations; Chaotic behavior
Adapting evolutionary dynamics of variation for multi-objective optimization

Many real-world applications involve complex optimization problem with various competing specifications and constraints that are often difficult, if not impossible, to be solved without the aid of powerful and efficient optimization algorithms. Although evolutionary algorithms have proven to be successful with respect to the optimization goals of proximity and diversity, their capability is bottlenecked by the evolutionary operators' abilities to deal with the complicated search spaces. Furthermore, it is well known that the algorithm's performances in different problems are sensitive to the parameter setting of the operators. In an effort to adapt the evolutionary search ability along the different regions of the search space, this paper proposes a dynamic variation operator whose parameter value is deterministically adapted during the algorithm run so as to maintain a balance between the extensive exploration in the early phase and local fine-tuning in the end phase. Comparative studies with some representative variation operators are performed on different benchmark problems to illustrate the effectiveness and efficiency of the proposed operator.