Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Method of Dynamic Texture Segmentation Based Level Set

Dynamic texture is a part of the natural scene. Currently dynamic texture segmentation issue is research hotspot in the field of computer vision and even robot research area. This paper presents a efficient dynamic texture segmentation method based on level set strategy. We define the evolution energy function according the characteristics of intensity constancy and conservation in the dynamic texture movement. We use the re- initialization condition to punish in the evolution process and use the optical flow based terminal condition to limit the evolution and make the level set more efficient. It overcomes the shortcoming of reinitializing and slow evolution speed in the existing methods. The experimental results show that the method can effectively achieve the dynamic texture segmentation of moving objects and it is a very useful method.
Mismatch in Credit Markets

This paper studies a decentralized credit market where borrowers and lenders engage in costly search to establish credit relationships. We endogenize the market participation decision by borrowers to capture the entry and exit of small and medium-sized entreprises who depend on access to credit for survival. We also allow incentive frictions in the form of moral hazard to interact with search frictions in setting up incentive compatible loan contracts. We show that free entry can in the limit eliminate information-based credit rationing in equilibrium, with search frictions determining whether or not credit markets break down. More generally, we find that entry and incentive frictions are important in determining the extent of credit rationing, while entry and search frictions are important for determining the likelihood of credit market breakdown. JEL Classification: C7, D5, D8, G1.
Software Pipelining with Reduced Register Requirement (cid:3)

Although it is well-known that a strong interaction exists between software pipelining and register allocation, simultaneous software pipelining and register allocation is still less understood and remains an open problem. In this paper, we (cid:12)rst analyze the in(cid:13)uence of the re-use of registers on the loop data dependence graph and model the problem of software pipelining to reduce register requirement based on the concepts of row-number and column-number of decomposed software pipelining. Given the row-numbers of all operations in the loop, generating the column-numbers to obtain the minimum register requirement can be modelled as the integer programming problem which can be solved in polynomial time. Then, a solution is presented to the special case without resource constraints and an approximate solution is presented to the general case with resource constraints. Register spilling and register coloring are considered to further reduce the number of actually needed registers. The preliminary experimental results are presented to indicate the e(cid:14)ciency of our new approach.
A GEOMETRIC APPROACH TO REACHABILITY COMPUTATIONS FOR CONSTRAINED DISCRETE-TIME SYSTEMS

Abstract The problem of reachability computations for systems with simultaneous constraints on the states, control inputs and disturbances is treated. A problem formulation is provided for the general case and, for the special case where the disturbance constraints are independent of the states and inputs, it is discussed how the solution can be approached using standard geometric concepts. It is also discussed how the procedure can be implemented for a class of piecewise affine systems with polyhedral constraints using computational geometry software.
FIXED-ORDER CONTROL OF ACTIVE SUSPENSION: A HYBRID APPROACH

Abstract This paper considers the design of a controller for an active suspension benchmark problem. For a plant model of 27th order, a 5th order controller that meets performance specifications in terms of sensitivity and control sensitivity is designed, using a novel Hybrid Evolutionary-Algebraic approach to low-order mixed-sensitivity design. This method combines evolutionary techniques with a Riccati approach by splitting the problem into a convex and a non-convex sub-problem. Simulation results demonstrate that a performance superior to that of previously published results can be achieved.
Food Marketing towards Children: Brand Logo Recognition, Food-Related Behavior and BMI among 3–13-Year-Olds in a South Indian Town

Objectives To assess exposure to marketing of unhealthy food products and its relation to food related behavior and BMI in children aged 3–13, from different socioeconomic backgrounds in a south Indian town. Methods Child-parent pairs (n = 306) were recruited at pediatric clinics. Exposure to food marketing was assessed by a digital logo recognition test. Children matched 18 logos of unhealthy food (high in fat/sugar/salt) featured in promotion material from the food industry to pictures of corresponding products. Children's nutritional knowledge, food preferences, purchase requests, eating behavior and socioeconomic characteristics were assessed by a digital game and parental questionnaires. Anthropometric measurements were recorded. Results Recognition rates for the brand logos ranged from 30% to 80%. Logo recognition ability increased with age (p<0.001) and socioeconomic level (p<0.001 comparing children in the highest and lowest of three socioeconomic groups). Adjusted for gender, age and socioeconomic group, logo recognition was associated with higher BMI (p = 0.022) and nutritional knowledge (p<0.001) but not to unhealthy food preferences or purchase requests. Conclusions Children from higher socioeconomic groups in the region had higher brand logo recognition ability and are possibly exposed to more food marketing. The study did not lend support to a link between exposure to marketing and poor eating behavior, distorted nutritional knowledge or increased purchase requests. The correlation between logo recognition and BMI warrants further investigation on food marketing towards children and its potential role in the increasing burden of non-communicable diseases in this part of India.
Self-Directed Movements of Droplets on Radially Patterned Surfaces Based on Self-Assembled Monolayers

This paper demonstrates macroscopic (millimetre-scale), self-directed transport of droplets on radially patterned silicon surfaces to induce wettability gradients based on self-assembled monolayers (SAMs). A difference in wettability between the opposite sides of the droplet edge creates a Laplace pressure gradient inside the droplet, forcing the droplet to move forward. Circulating wedge-shaped features with alternate hydrophobic and hydrophilic regions were photographically patterned and combined with selective coating of silanol- and thiol-based SAMs to transport the droplets right to the centre passively. Positions and velocities of the droplets were measured using high speed CCD camera
A High Performance Cluster File System with Standard Network File System Interface

The Cluster technology can organize many cheap servers to cluster, and obtain the high performance. The traditional cluster file systems although can achieve high performance, but these systems all require to modify the software on client. This requirement make these systems can not be widely applied. This paper provides a novel high performance cluster file system with standard network file system access interface: CFS-SI. CFS-SI has following characters: (1) CFS-SI unitizes the advantage of cluster file system, and can adequately use the network bandwidth and the storage capability; (2) CFS-SI use the load balance to improve the performance based on IP spoof technology; (3) CFS-SI can give standard network file system access interface to client and do no require any software modification to client. The experiment in this paper shows that CFS-SI can not only inherit the excellence of normal cluster file system, but also can provide high performance for file operation.
Retrieval from Image Datasets with Repetitive Structures

(cid:151)This work aims to enhance the matching and retrieval performance over image datasets which have similar spatial structures that occur very frequently. Instead of treating images as bags of features, we try to encode the spatial relationships in the representation. This process would help to resolve the ambiguity when two classes of images have similar sets of features although in different spatial arrangements. To demonstrate the fact a sizeable dataset of license plate images is used. We have proposed a method to use graphs to encode the spatial relationships among features. The problem of image matching thus turns to (cid:2)nding the maximum similarity between labelled graphs. It is shown that the precision of the retrieved results increases with this matching scheme since most of the false matches are eliminated.
Specialized Architectures for Optical Flow Computation: A Performance Comparison of ASIC, DSP, and Multi-DSP

In this paper we present three specialized architectures for optical flow computation based on: i) an ASIC in CMOS standard cell technology, ii) a single DSP TMS320C40, and ii) a TMS320C40-based multiprocessor. The pros and cons of each architecture are discussed. keywords: optical flow estimation, ASIC, multi-DSP
MIREX Grand Challenge 2014 User Experience: Qualitative Analysis of User Feedback

Evaluation has always been fundamental to the Music Information Retrieval (MIR) community, as evidenced by the popularity of the Music Information Retrieval Evaluation eXchange (MIREX). However, prior MIREX tasks have primarily focused on testing specialized MIR algorithms that sit on the back end of systems. Not until the Grand Challenge 2014 User Experience (GC14UX) task had the users ’ overall interaction and experience with complete systems been formally evaluated. Three systems were evaluated based on five criteria. This paper reports the results of GC14UX, with a special focus on the qualitative analysis of 99 free text responses collected from evaluators. The analysis revealed additional user opinions, not fully captured by score ratings on the given criteria, and demonstrated the challenge of evaluating a variety of systems with different user goals. We conclude with a discussion on the implications of findings and recommendations for future UX evaluation tasks, including adding new criteria: Aesthetics, Performance, and Utility.
Estimations of the natural rate of interest in Colombia

Three methodologies to estimate the natural interest rate, NIR, are implemented for the Colombian economy. Two methods are statistical filters and the third involves some economic theory. The first method is based on unobserved components decomposition of the real interest rate and explores the statistical characteristics of the data. The second is a multivariate version of the Hodrick-Prescott filter augmented by an economic relationship, HPMV. The NIR in both cases is defined as the trend component of the market real interest rate; then, the NIR may be considered as a long-run real interest rate anchor for monetary policy. The third method consists in estimating a semi-structural model for a small open economy. In this case the NIR is defined as the interest rate that does not affect the output dynamics in the short run and assures output and inflation convergence to their long run equilibriums. This implies that the NIR is a medium-run anchor for monetary policy. Three features are observed in the dynamics of the NIR estimates for the period 2000-2009. The first part of the sample (2000-2003) shows a downward trend, followed by a period of stabilization and upward trend (2004-2008) and at the end of the sample the NIR start decreasing again. The NIR in the last quarter of the sample, 2009Q2, is around 3.1 in average.
Competence progress intrinsic motivation

One important role of an agent's motivational system is to choose, at any given moment, which of a number of skills the agent should attempt to improve. Many researchers have suggested “intrinsically motivated” systems that receive internal reward for model learning progress, but for the most part this notion has not been applied with respect to skill competence, or to choose between skills. In this paper we propose an agent motivated to gain competence in its environment by learning a number of skills, addressing head-on the mechanism of competence progress motivation for the purpose of governing the efficient learning of skills. We demonstrate this new approach in a simple illustrative domain and show that it outperforms a naïve agent, achieving higher competence faster by focusing attention and learning effort on skills for which progress can be made while ignoring those skills that are already learned or are at the moment too difficult.
Modeling and Analysis of Mobility Management in Mobile Communication Networks

Many strategies have been proposed to reduce the mobility management cost in mobile communication networks. This paper studies the zone-based registration methods that have been adopted by most mobile communication networks. We focus on two special zone-based registration methods, called two-zone registration (2Z) and two-zone registration with implicit registration by outgoing calls (2Zi). We provide a new mathematical model to analyze the exact performance of 2Z and 2Zi. We also present various numerical results, to compare the performance of 2Zi with those of 2Z and one-zone registration (1Z), and show that 2Zi is superior to 2Z as well as 1Z in most cases.
Micro-Datacenter Management Architecture for Mobile Wellness Information

With the cloud computing, storage and computing resources are moving to remote resources such as virtual servers and storage systems in large datacenters, which raise many performance and management challenges. We consider distributed cloud systems, which deploy micro DC that are geographically distributed over a large number of locations in a wide-area network. In this article, we also argue for a micro DC-based network model that provides higher-level connectivity and logical network abstraction that are integral parts of wellness applications. We revisit our previously proposed logical network models that are used to configure the logical wellness network.
Analysis of a sensor fusion hybrid solution for indoor/outdoor robot navigation

Autonomous mobile robots need robust, flexible and accurate navigation algorithms. One approach consists in fusing as many information sources as possible, integrating measures from internal sensors with data obtained from external sensing entities. This work presents a solution for combined indoor/outdoor robot navigation, and analyzes some preliminary results in an outdoor environment using a Particle Filter for GPS/INS sensor fusion. Experiments are based in predesigned trajectories which have been simulated in first place and then reproduced using a robotic platform. As a concluding remark, some considerations about the use of Particle Filters and the differences between simulated and real data are presented.
Deductive Error Reconstruction and Classification in a Logic Programming Framework

Although diagnosis is a prerequisite for dynamic student modeling, we are far from having an applicable and transferable inventory of sound diagnostic methods. There is some evidence, that a substantial part of such a diagnostic methodology could be realized in the framework of logic programming. The focus of this paper is on using meta-interpretation for implementing diagnostic algorithms in a transparent and reproducible way and also for enhancing their computational efficiency. A specific method is presented which generates bug hypotheses from failure patterns in incomplete derivation trees, given a correct domain model and an incorrect student solution. It is easily shown that this process of error classificationwhich separates the (partial) reconstruction of the student’s solution from the inference of bugs is computationally less costly than the classical use of a mal-rule library. It will also be discussed how this approach could be enhanced by a learning component for generating classification rules.
Reversibility and switching options values in the geological disposal of radioactive waste

This article offers some economic insights for the debate on the reversible geological disposal of radioactive waste. Irreversibility due to large sunk costs, an important degree of fiexibility and several sources of uncertainty are taken into account in the decision process relative to the radioactive waste disposal. We draw up a stochastic model in a continuous time framework to study the decision problem of a reversible repository project for the radioactive waste, with multiple disposal stages. We consider that the value of reversibility, related to the radioactive waste packages, is jointly affected by economic and technological uncertainty. These uncertainties are modeled, first, by a 2-Dimensional Geometric Brownian Motion, and, second, by a Geometric Brownian Motion with a Poisson jump process. A numerical analysis and a sensitivity study of various parameters are also proposed.
IMF Working Paper Monetary and Capital Markets Department The Measurement of Central Bank Autonomy : Survey of Models , Indicators , and Empirical Evidence

This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. This paper presents a survey of the literature on the measurement of central bank autonomy. We distinguish inputs that constitute the building blocks in the literature, and the literature that builds on them. Issues including sensitivity analysis, robustness, and endogeneity are discussed. The review shows that empirical evidence regarding the beneficial effects of central bank autonomy is substantial, although some technical issues still remain for further research. In particular, central bank autonomy raises the issue of subjecting the monetary authorities to democratic control; this calls for additional research on the linkages between central bank autonomy and accountability and transparency. Additional empirical analysis on the relationship between the financial strength of the central bank and its de facto autonomy, and between its autonomy and financial stability, would also be desirable. JEL Classification: E58, E52
Ensuring Cache Freshness in On-Demand Routing Protocols for Mobile Ad Hoc Network: A Cross-layer Framework

One of the big challenges in ad hoc network design is packet routing. Studies have shown that on-demand routing protocols perform better than table-driven routing protocols. In order to avoid route discovery for each packet, on-demand routing protocols cache routes previously learnt. A node in ad hoc network learns routing information by overhearing or forwarding packets to other nodes and keep learned routes in its route cache. However, node movement results broken links and therefore increases risk of cache pollution. Ensuring cache freshness in on-demand routing protocols, therefore, presents a serious challenge. A lot of research has been done in route cache organization, however, little effort has been done for route cache timeout policy to prevent stale routes from being used. In this paper we propose a new cross-layer framework to improve route cache performance in on-demand routing protocols. The proposed framework presents novel use of Received Signal Strength Indicator (RSSI) information to choose cache timeout of individual links in route cache.
Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis

This paper presents an algorithm, for use with a Portable Powered Ankle-Foot Orthosis (i.e., PPAFO) that can automatically detect changes in gait modes (level ground, ascent and descent of stairs or ramps), thus allowing for appropriate ankle actuation control during swing phase. An artificial neural network (ANN) algorithm used input signals from an inertial measurement unit and foot switches, that is, vertical velocity and segment angle of the foot. Output from the ANN was filtered and adjusted to generate a final data set used to classify different gait modes. Five healthy male subjects walked with the PPAFO on the right leg for two test scenarios (walking over level ground and up and down stairs or a ramp; three trials per scenario). Success rate was quantified by the number of correctly classified steps with respect to the total number of steps. The results indicated that the proposed algorithm's success rate was high (99.3%, 100%, and 98.3% for level, ascent, and descent modes in the stairs scenario, respectively; 98.9%, 97.8%, and 100% in the ramp scenario). The proposed algorithm continuously detected each step's gait mode with faster timing and higher accuracy compared to a previous algorithm that used a decision tree based on maximizing the reliability of the mode recognition.
A Spatiotemporal Framework for Estimating Trial-to-Trial Amplitude Variation in Event-Related MEG/EEG

A spatiotemporal framework for estimating trial-to-trial variability in evoked response (ER) data is presented. Spatial and temporal bases capture the aspects of the response that are consistent across trials, while the basis expansion coefficients represent the variable components of the response. We focus on the simplest case of constant spatiotemporal response shape and varying amplitude across trials. Two different constraints on the amplitude evolution are employed to effectively integrate the individual responses and improve robustness at low SNR. The linear dynamical system response constraint estimates the current trial amplitude as an unknown constant scaling of the estimate in the previous trial plus zero-mean Gaussian noise with unknown variance. The independent response constraint estimates response amplitudes across trials as independent Gaussian random variables having unknown mean and variance. We develop a generalized expectation-maximization algorithm to obtain the maximum-likelihood (ML) estimates of the signal waveform, noise covariance matrix, and unknown constraint parameters. ML source localization is achieved by scanning the likelihood over different sets of spatial bases. We demonstrate the variability estimation and source localization effectiveness of the proposed algorithms using both real and simulated ER data.
Position estimation algorithm based on natural landmark and fish-eyes' lens for indoor mobile robot

Many generic position estimation algorithms are vulnerable to ambiguity introduced by no unique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. In this paper, we consider the problem of mobile robot indoor position estimation using only visual information from a single camera which has a fisheye lens. The position estimation algorithm based on natural landmark identification and barrel distortion calibration. When mobile robot is indoor environment, usual behavior consists of memorizing some key points along the performed path to use these compared with references map.