Miracle-dz/UniFAR-FTUs huggingface dataset:
Surrounding View for Enhancing Safety on Vehicles

This paper presents a vision system based on several wide angle cameras and robust methods to compose a view of the vehicle perimeter in only one image. Our proposal can be split into two stages: firstly, an offline calibration step and secondly, an online panorama composition given the acquired images from the cameras. The images are horizontally undistorted, warped and stitched together according to lookup tables (LUTs) generated during the calibration process. The offline part of the method calibrates every camera individually and computes the pixel mapping required for the images transformation into the surrounding view. This mapping encodes a special undistortion process and the image registration between adjacent cameras. In particular, the proposed method does not fully undistort the images but it projects them onto a cylindrical surface to reduce the amount of radial distortion and facilitate the panoramic stitching of the images. Our approach includes a feathering algorithm for seams removal and a polar conversion of the panoramic view. This method has been successfully tested on a small wheeled robot and it is fully scalable to larger platforms such as an armored patrol. Vehicles safety can be enhanced thanks to perimeter monitoring.
Efficient multi-scale curvature and crease estimation

We consider the problem of multi-scale estimation of principal cur-vatures and crease lines on a surface represented with a mesh of triangles and affected by noise. We show that curvature at different scales can be efﬁciently and accurately estimated by modifying a ﬁtting technique and applying it to neighborhoods of various size, depending on scale: we discard bending portions of surfaces during ﬁtting, and we apply Monte-Carlo sampling to speed up computation. Next we propose a new technique for extracting crease lines and we show how such lines can summarize shape features at the various scales. This is a ﬁrst step towards building a scale-space of surface features.
In-group marker going out: Meaning-making in a community of practice

Abstract This study examines the linguistic aspects of the formation of a community of practice (CofP) and analyses one aspect of the common repertoire that is being actively developed within the community. On the one hand, it looks at the community itself, placing emphasis on the exact practices, beliefs, and resources that can identify a group brought together at times against their will, as a CofP. On the other, this research takes under scrutiny one specific linguistic variable (Japanese negation) to show how involvement in a CofP can bring about rapid and unexpected changes in the meaning and use of a linguistic variant. (Community of practice, indexical meaning, Japanese, negation)*
Real-time video stylization using spatial-temporal gabor filtering

This paper describes a new video stylization approach that achieves non-photorealistic rendering effects by using highly efficient spatial-temporal Gabor filtering. An edge extraction algorithm is developed to detect long coherent edges, to which the human visual system is sensitive. A nonlinear diffusion is then applied to remove unimportant details. Our approach extends the optical flow computation for constructing the Gabor flow to represent pixel similarity, and to preserve the temporal coherence when applied to video sequences. In particular, our video stylization is designed in a spatiotemporal manner to achieve temporal coherence in resulting animations. Real-time performance is achieved through the highly parallel implementation on modern graphics hardware (GPU). Therefore, our video stylization can be naturally applied to real-time video communication and interactive video-based rendering. The experimental results have demonstrated the high-quality production of our real-time video stylization.
Blind identification and DOA estimation for array sources in presence of scattering

A novel identification method for point source, coherently distributed (CD) source and incoherently distributed (ICD) source is proposed. The differences among the point source, CD source and ICD source are studied. According to the different characters of covariance matrix and general steering vector of the array received source, a second order blind identification method is used to separate the sources, the mixing matrix could be obtained. From the mixing matrix, the type of the source is identified by using an amplitude criterion. And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix. Computer simulations validate the efficiency of the method.
Multiple description video coding approach based on analytical study of distributed source coding

The deployment of distributed devices and their applications in number of fields, has motivated the research in Distributed Source Coding (DSC). The research in this field has been branched into lossless and lossy DSC. Many approaches have been devised to achieve the theoretical bound of minimum sending rate to achieve lossless coding. In this paper, an analytical survey of theorems, techniques and applications of DSC has been performed. Based on this analysis, an efficient multiple description video coding approach has been developed which can be deployed over multi-channel mesh networks to achieve reliable video streaming over community mesh based Internet.
Adaptive Sharing for Image Classification

In this paper, we formulate the image classification problem in a multi-task learning framework. We propose a novel method to adaptively share information among tasks (classes). Different from imposing strong assumptions or discovering specific structures, the key insight in our method is to selectively extract and exploit the shared information among classes while capturing respective disparities simultaneously. It is achieved by estimating a composite of two sets of parameters with different regularization. Besides applying it for learning classifiers on pre-computed features, we also integrate the adaptive sharing with deep neural networks, whose discriminative power can be augmented by encoding class relationship. We further develop two strategies for solving the optimization problems in the two scenarios. Empirical results demonstrate that our method can significantly improve the classification performance by transferring knowledge appropriately.
Selective Activation Induced Cleavage of the NR2B Subunit by Calpain

Although activation of calcium-activated neutral protease (calpain) by the NMDA receptor has been suggested to play critical roles in synaptic modulation and neurologic disease, the nature of its substrates has not been completely defined. In this study, we examined the ability of calpain to cleave the NMDA receptor in cultured hippocampal neurons. Activation of the NMDA receptor by agonist application led to rapid calpain-specific proteolysis of spectrin and decreased levels of NR2A/2B subunits. Cleavage of the NR2A/2B subunit created a 115 kDa product that retained the ability to bind 125I-MK-801 and is predicted to be active. Increases in levels of this product appeared within 5 min of NMDA receptor activation and were stable for periods of >30 min. Subtype-specific antibodies demonstrated that the NR2B subunit was cleaved in these primary cultures, but the NR2A subunit was not. An inhibitor of calpain blocked both the decrease of intact NR2B and the increase of the low molecular weight form, whereas neither caspase nor cathepsin inhibitors had an effect on these events. Cell surface biotinylation experiments demonstrated that the 115 kDa fragment remained on the cell surface. This NR2B fragment was also found in the rat hippocampus after transient forebrain ischemia, showing that this process also occurs in vivo. This suggests that calpain-mediated cleavage of the NR2B subunit occurs in neurons and gives rise to active NMDA receptor forms present on the cell surface after excitotoxic glutamatergic stimulation. Such forms could contribute to excitotoxicity and synaptic remodeling.
Risperidone, quetiapine, and olanzapine adjunctive treatments in major depression with psychotic features: a comparative study

Objectives The purpose of this study was to compare the effectiveness of novel antipsychotics in the treatment of psychotic depression. Method Consecutive patients who were admitted (n = 51) with a confirmed diagnosis of major depression with psychotic features (delusions or hallucinations or both) participated in this open-label, naturalistic study. All patients were treated with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) (citalopram or venlafaxine extended release [XR]), and atypical antipsychotic agents were added, as tolerated, during the first week of initiating the citalopram or venlafaxine. There were patients (n = 16) who received risperidone, who received quetiapine (n = 20), and who received olanzapine (n = 15), as an adjunctive treatment to either citalopram or venlafaxine for at least 8 weeks. Outcome measures included the Clinical Global Impression-Severity subscale (CGI-S), as the primary outcome measure, as well as the Hamilton Rating Scale for Depression-21 item (HAM-D21) and the Brief Psychiatric Rating Scale (BPRS). Tolerance to treatments and weight changes were monitored over the period of the trial. Results All patients completed the trial with no drop outs. At 8 weeks, there was a statistically significant (P < 0.001) clinical improvement in all outcome measures for both the depressive and psychotic symptoms, for all three groups of atypical adjunctive treatments. Utilizing analysis of variance (ANOVA), there were no significant differences between the three adjunctive treatment groups in outcome measures. The three antipsychotic agents were equally tolerated. At 8 weeks there was slight increase in weight in the three treatment groups, which was statistically significant (P > .01) in the olanzapine group. Conclusion Quetiapine, risperidone, and olanzapine, given as adjunctive treatment with SSRIS or SNRIs can significantly and equally improve depressive and psychotic symptoms, in the short-term treatment of major depression with psychotic features. The author recommends that large controlled trials be conducted to examine the differences in long-term efficacy and tolerance between the atypical antipsychotic agents, in the treatment of major depression with or without psychotic features.
PERFORMANCE AUDITS AND MANAGERIAL CONTROL SYSTEM IN ITALIAN LOCAL GOVERNMENTS : DO THEY HAVE A FUTURE ?

the paper deals with the introduction of management control and performance audit in Italian local governments. The first part provides a concise description of the local governments’ reform. As in other countries where performance oriented reforms have been adopted, particular emphasis was given to efficiency and effectiveness. In order to control the most important «value drivers», legislators recommended the adoption of tools and procedures for the measurement of the performance on costs, quality and quantity of services delivered. The expected impact of the reform was: improved accountability, rationalization of public spending, and a higher outcome/costs ratio. More than a decade since the reform was adopted, it is still difficult to recognize improvements in terms of effective performance measurement and accountability. According to recent surveys, only a few local govermentss have developed a first stage of managerial control system and performance audit. The «cause and effect» relation between the managerial approach of the reform act and an improvement in performance has not worked properly. Is this due to “technical difficulties” in the implementation of managerial tools within the public context, or is the assumed relationship between control, audit and performance simply overestimated by the reform? The paper argues that only some of the factors that hinder the effectiveness of managerial control system in this context depend on technical problems, while others are due to the reform itself.
A novel approach to the computation of polyhedral invariant sets for constrained systems

This paper presents two algorithms for the computation of polyhedral positively invariant sets for constrained discrete-time certain and uncertain linear systems, extended from [11]. The approach is based on the novel use of auxiliary constant state feedback laws that give distinct, stable, real-valued closed loop eigenvalues. The algorithms are simple and non-iterative, and include only linear programming (LP). Illustrative examples show the benefits of the new approach.
Virtual world affordances for people with lifelong disability

One article © 2014 IEEE. Reprinted, with permission, from : Stendal, K. , Molka-Danielsen, J., Munkvold, B.E. , & Balandin, S. (2013, 7-10 Jan. 2013). Social Affordances for People with Lifelong Disability through Using Virtual Worlds. Paper presented at the System Sciences (HICSS), 2013 46th Hawaii International Conference on. http://dx.doi. org/10.1109/HICSS.2013. 485
Model predictive control for visual servo steering of nonholonomic mobile robots

In this paper, model predictive controller for the visual servo steering of a mobile robot in polar coordinate is presented. Firstly, a kinematic predictive steering controller utilized to generate the command of velocity is introduced. Then, a dynamic predictive controller is designed to steer the system. The model predictive control (MPC) can deal with the constraints easily and it can be iteratively formulated as a quadratic programming (QP) problem, which can be solved using neurodynamic optimization, for example, primal-dual neural network (PDNN), over a finite receding horizon. The applied neurodynamics are globally convergent to the exact optimal solutions of reformulated convex programming problems. Finally, experiment results are provided to illustrate the effectiveness of the MPC scheme.
Effects of Age on Gait Parameters and Muscle Activity During Adjustment, and the Relationship of Fear of Falling

Previous research has shown that with advancing age, there are increasing incidences of slip and fall injuries. Understanding mechanisms associated with gait adjustments across a known slippery surface may help in proactively avoiding slips and falls. The primary goal of this study involved examination of gait parameters and muscle activity characteristics of the lower extremities during two different walking conditions. Research has shown that both physical and mental changes accompany the aging process in humans. Moreover, research has shown that emotions and physiological responses are related. A secondary goal of this study was to examine the relationships of fear of falling with gait parameters and muscle activity. This study consisted of exposing 14 younger and 14 older participants to controlled slippery conditions safely, while studying normal and adjusted gait characteristics (friction requirement, heel contact velocity, and step length) and muscle activity characteristics (Integrated EMG). First, a baseline measure was done to study normal gait prior to any exposure to slipping. A second measure was done following a slip from a contaminated floor surface, but before the initiation of a second slip. The results indicate that there were significant gait parameter differences between younger and older participants for both walking conditions. Results also indicate that there were differences in muscle activity between to the two age groups for the adjusted condition. Findings suggest that older individuals require an additional step to properly adjust gait for a contaminated walking surface.
Stable control of a simulated one-legged running robot with hip and leg compliance

We present a control strategy for a simplified model of a one-legged running robot which features compliant elements in series with hip and leg actuators. For this model, proper spring selection and initial conditions result in "passive dynamic" operation close to the desired motion, without any actuation. However, this motion is not stable. Our controller is based on online calculations of the desired passive dynamic motion which is then parametrized in terms of a normalized "locomotion time". We show in simulation that the proposed controller stabilizes a wide range of velocities and is robust to modeling errors. It also tracks changes in desired robot velocity and remains largely passive despite a fixed set of springs, masses, and inertias. Comparisons of simulated runs with direct hip actuation show 95% hip actuation energy savings at 3 m/s. Such energy savings are critical for the power autonomy of electrically actuated legged robots.
Ionophore-resistant mutant of Toxoplasma gondii reveals involvement of a sodium/hydrogen exchanger in calcium regulation

Calcium is a critical mediator of many intracellular processes in eukaryotic cells. In the obligate intracellular parasite Toxoplasma gondii, for example, a rise in [Ca2+] is associated with significant morphological changes and rapid egress from host cells. To understand the mechanisms behind such dramatic effects, we isolated a mutant that is altered in its responses to the Ca2+ ionophore A23187 and found the affected gene encodes a homologue of Na+/H+ exchangers (NHEs) located on the parasite's plasma membrane. We show that in the absence of TgNHE1, Toxoplasma is resistant to ionophore-induced egress and extracellular death and amiloride-induced proton efflux inhibition. In addition, the mutant has increased levels of intracellular Ca2+, which explains its decreased sensitivity to A23187. These results provide direct genetic evidence of a role for NHE1 in Ca2+ homeostasis and important insight into how this ubiquitous pathogen senses and responds to changes in its environment.
A Steady Network Coordinate System for Network Distance Estimating

A lot of large distributed system can benefit from the implement of network coordinate system, which can estimate latencies among Internet hosts. In this paper, we focus on problems in building network coordinate system. Firstly, we analyze the disadvantages of some algorithms that based on fixed reference nodes and algorithms based on unfixed reference nodes. Then we propose a new architecture of network coordinate system, in which, network delay space is divided into several global clustering firstly, and a new way to select reference nodes and acquire coordinates is applied. According to the experiments on PlanetLab, our method proved to be accurate and steady.
Optimizing shared cache behavior of chip multiprocessors

One of the critical problems associated with emerging chip multiprocessors (CMPs) is the management of on-chip shared cache space. Unfortunately, single processor centric data locality optimization schemes may not work well in the CMP case as data accesses from multiple cores can create conflicts in the shared cache space. The main contribution of this paper is a compiler directed code restructuring scheme for enhancing locality of shared data in CMPs. The proposed scheme targets the last level shared cache that exist in many commercial CMPs and has two components, namely, allocation, which determines the set of loop iterations assigned to each core, and scheduling, which determines the order in which the iterations assigned to a core are executed. Our scheme restructures the application code such that the different cores operate on shared data blocks at the same time, to the extent allowed by data dependencies. This helps to reduce reuse distances for the shared data and improves on-chip cache performance. We evaluated our approach using the Splash-2 and Parsec applications through both simulations and experiments on two commercial multi-core machines. Our experimental evaluation indicates that the proposed data locality optimization scheme improves inter-core conflict misses in the shared cache by 67% on average when both allocation and scheduling are used. Also, the execution time improvements we achieve (29% on average) are very close to the optimal savings that could be achieved using a hypothetical scheme.
Sonar based Simultaneous Localization and Mapping using a Neuro Evolutionary Optimization

This paper addresses a solution of Simultaneous Localization and Mapping (SLAM) using a Neuro Evolutionary Optimization algorithm. The proposed algorithm solves the global optimization problem of the SLAM using the cost function which represents the quality of a robot's trajectory in a world coordinate frame. In our algorithm, the neural network helps to estimate the difference of two consecutive positions accurately using the sensor inputs at each position. The Evolutionary Programming (EP) is used to find the most suitable neural network. The proposed neural network based SLAM is applied to the robot which has sonar sensors. The various experimental results demonstrate that the neural network based SLAM guarantees a consistent environmental map under the sonar sensor measurements.
Wavelet Signs: A New Tool for Signal Analysis

—We propose a new analysis tool for signals, called signature, that is based on complex wavelet signs. The complex-valued signature of a signal at some spatial location is deﬁned as the ﬁne-scale limit of the signs of its complex wavelet coefﬁcients. We show that the signature equals zero at sufﬁciently regular points of a signal whereas at salient features, such as jumps or cusps, it is non-zero. We establish that signature is invariant under fractional differentiation and rotates in the complex plane under fractional Hilbert transforms. We derive an appropriate discretization, which shows that wavelet signatures can be computed explicitly. This allows an immediate application to signal analysis.
SUMMAC: a text summarization evaluation

The TIPSTER Text Summarization Evaluation (SUMMAC) has developed several new extrinsic and intrinsic methods for evaluating summaries. It has established definitively that automatic text summarization is very effective in relevance assessment tasks on news articles. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in accuracy. Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high. Systems that performed most accurately in the production of indicative and informative topic-related summaries used term frequency and co-occurrence statistics, and vocabulary overlap comparisons between text passages. However, in the absence of a topic, these statistical methods do not appear to provide any additional leverage: in the case of generic summaries, the systems were indistinguishable in accuracy. The paper discusses some of the tradeoffs and challenges faced by the evaluation, and also lists some of the lessons learned, impacts, and possible future directions. The evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as well as evaluation of other ‘output-related’ NLP technologies, where there may be many potentially acceptable outputs, with no automatic way to compare them.