Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear Cooperative Detection for Alarm Messages in Cluster-Based Vehicular Ad Hoc Networks

Vehicular ad hoc network (VANET) is a special class of mobile ad hoc Networks (MANET), and is an important component of Intelligent Transportation System (ITS). VANET has a future potential in terms of a rich set of applications that it can provide to its customer. Accident detection is a key functionality of ITS. Due to the effects of channel fading and shadowing, individual vehicles may not be able to reliably detect the existence of an accident. In this paper, we propose a linear cooperation framework for accident detection in a cluster-based VANET. In this framework, accident detection is based on the linear combination of local statistics gathered by individual vehicles. Our objectives are minimizing the probability of false alarm and avoiding broadcast storm. Based on the problem formulation and derivation, we proposed two efficient algorithms to do alarm detection: local detection and global detection. The numerical results show that our heuristic strategies perform well on false alarm detection.
Teaching Social Work Research Through the Lens of Social Justice, Human Rights, and Diversity

ABSTRACT In response to the official charge of the Council on Social Work Education’s Commission on Research to promote quality in research curricula across methods and paradigms in BSW, MSW, and doctoral education, this article offers ideas, as well as an annotated bibliography, on how to apply a distinct lens of social justice, human rights, and diversity in teaching research methods in social work. We hope it will play a part in advancing the science of social work by more widely disseminating knowledge about effectively and intentionally focusing our research methods courses in ways that are more reflective of the values and purposes of the profession. Toward that end, we explore key themes for course topics, content, and strategies for inclusion.
Fundamental Features of Quantum Dynamics Studied in Matter-Wave Interferometry—Spin Weak Values and the Quantum Cheshire-Cat

The validity of quantum-mechanical predictions has been confirmed with a high degree of accuracy in a wide range of experiments. Although the statistics of the outcomes of a measuring apparatus have been studied intensively, little has been explored and is known regarding the accessibility of quantum dynamics. For these sorts of fundamental studies of quantum mechanics, interferometry using neutron matter-waves in particular, provides almost ideal experimental circumstances. In this device quantum interference between spatially separated beams occurs on a macroscopic scale. Recently, the full determination of weak-values of neutrons 1 2 - spin adds a new aspect to the study of quantum dynamics. Moreover, a new counter-intuitive phenomenon, called quantum Cheshire Cat, is observed in an interference experiment. In this article, we present an overview of these experiments.
Photonic Band-Gap Structures with Periodicity Interruptions: Theory and Applications

The introduction of defects in a photonic band-gap (PBG) material breaks its periodicity, and produces interface states of the electromagnetic field, at frequencies within the stop-bands, manifested by sharp peaks in the transmission response of the structure. We study PBGs properties in the presence of multiple periodicity interruptions, by using a full-wave approach. Results are presented, both for TE and TM polarizations, showing the transmission efficiencies as a function of the involved parameters. We discuss how the selectivity and the position of the transmission peaks inside the stop-band can be controlled. We show that a pass-band filtering behavior can also be obtained.
LandHouse: A Network Storage Structure Combining Regular Graph with Distributed Hash Table and Its Data Accessing Methods

It generally utilized either peer-to-peer overlay network or server clusters to offer wide area network storage service. These network storage systems suffered from performance penalties or single-point failure. This paper proposed a network storage structure which combines regular graph with distributed hash table, and presented corresponding data accessing methods. The results of simulation demonstrated the improvement of the proposal in both accessing delay and searching hops compared to distributed hash table.
Capital Structure Effects on the Prices of Individual Equity Call Options

We examine whether values of equity options traded on individual firms are sensitive to the firm's capital structure. Specifically, we estimate the compound option (CO) model, which views equity as an option on the firm. Compared to the Black-Scholes (BS) model, the CO model reduces pricing errors by 20% on average, and pricing improvements monotonically increase up to 70% with both leverage and expiration. We show that the CO model implies a market value of firm leverage and allows imputation of the firm's implied volatility, both of which have potential applications in corporate finance.
Active control of underwater propulsor noise using polypyrrole conducting polymer actuators

The field of biomimetics seeks to distill biological principles from nature and implement them in engineering systems in an effort to improve various performance metrics. In this paper, a biology-based approach is used to address the problem of radiated propulsor noise in underwater vehicles using active control. This approach is one of "tail articulation" of a stator blade, which is carried out using a suitable strategy that effectively alters the flow field impinging on a rotor downstream and in turn changes the radiated noise characteristics of the rotor blades. This articulation is accomplished by attaching an actuator at the trailing edge of an upstream stator blade and modulating it using a control strategy. Two actuation methods are used to articulate the stator-tail: a conducting polymer actuator and a stepper motor. An encapsulated conducting polymer (CP) actuator based on polypyrrole is designed, fabricated, and tested for operation in water. This CP actuator is shown to alter the flow field in an open channel water tunnel. Flow measurements are also conducted using a motor controlled articulation to yield greater actuator authority. Wake deficits are reduced up to 60% with trailing edge articulation, and the corresponding radiated noise from the propulsor is predicted to drop by 5-10 dB. Wake deficit reduction occurs most effectively with Strouhal numbers of 0.25 to 0.35, the range previously reported by others to be the operating regime of propulsion efficiency in swimming fish. Thesis Supervisor: Dr. Anuradha Annaswamy Title: Principal Research Scientist
The thalamus interrupts top-down attentional control for permitting exploratory shiftings to sensory signals

When attention is involuntarily drawn in a direction different to that of the target, slower motor response times are observed (i.e. the meridian effect). Previous data suggested that the thalamus might participate in the generation of visual salience. What may be the role of the thalamus in the capture by luminance transients when attentional control is in action? A single experiment was administrated in a group of ten healthy volunteers as well as in a group of three patients with unilateral thalamic infarcts. Subjects participated in a task where attentional control was interrupted by a distractor. The meridian effect was present only in the performance of the healthy volunteers and when distractors occurred in the ipsilesional (intact) hemifield of the thalamic patients. These results suggest that when an important signal appears during attentional focalization, the thalamus interrupts current focalization and permits the compilation of an attentional program in the midbrain aiming at generating an orienting response towards the source of this signal.
Visualizing Case Studies

Case studies contain valuable information about development records. They are, however, usually presented as textual documents. Here we begin an initial design for viewing case study data. A prototype for viewing case study data is created using information visualization techniques for the purpose of testing against existing techniques. The evaluations show that the new design is just as usable as existing techniques, making it suitable for future development. Future work may show our design can accomplish tasks both faster and more efficiently.
Debunking the myths about Fortran

Debunking the Myths About Fortran. Over the last few decades, there has been a lot of misinformation about the Fortran programming language. Very often, various people, many of whom should know better, state these myths as absolute truth, in spite of obvious evidence to the contrary. These myths and others like them are very widespread. I have heard each of these myths at least once in my personal experience. This article will list and demolish these myths. If you are wondering if this effort is even necessary, please see the last myth in this article. References to the ANSI and International Standards are by section number unless otherwise indicated. References to all other published works are by page number. References to private e-mail are by the sender’s name. A list of references is at the end. The information in this document is on a best effort basis. I actively solicit additions, corrections, and clarifications. Thanks from the author to all who contributed. Contributors are listed at the end. A shorter version of this article appeared in the August 1999 issue of the Fortran Forum.
Reliable Autonomous Navigation in Semi-Structured Environments using the Three-Dimensional Normal Distributions Transform ( 3 D-NDT )

This thesis is concerned with autonomous navigation of a robotic vehicle in a semi-structured environment. Starting from the low-level problem of range sensing and moving on to more complex tasks like spatial representation, registration and mapping, path planning and following, this dissertation aims at providing a consistent picture of an autonomous navigation system. A unifying concept throughout this work is the use of the Three-Dimensional Normal Distributions Transform (3D-NDT) as a base for modeling space. The 3D-NDT is a compact, yet expressive means of modeling the environment, using a set of Gaussian probability density functions. Closely related to Gaussian Mixture Models (GMMs), the 3D-NDT can adequately model semi-structured indoor or natural environments. Unlike GMMs however, the 3D-NDT can be constructed very rapidly from 3D sensor data, making it ideal for use in timecritical algorithms, often necessary for successful autonomous navigation. One of the contributions of this dissertation is a method for comparison of spatial representation models. The proposed method is used on a large benchmarking data set, demonstrating the accuracy and expressive power of the 3DNDT. The proposed methodology is modified and used to compare the accuracy of four 3D range sensors in complex operational scenarios. A central task in robotics is that of registering range measurements in a consistent model of the environment. In an important contribution, we propose a registration algorithm which operates directly on the 3D-NDT representations of the raw point cloud data. Using the compactness and expressive power of the 3D-NDT, accurate registration results are obtained at run times, an order of magnitude faster then those of current algorithms. Algorithms to choose a good starting position for registration and to estimate the match covariance are also presented, paving the way to future 3D-NDT-based simultaneous localization and mapping (SLAM) solutions. This dissertation also makes a contribution to another essential component of an autonomous navigation system — namely the planning and safe execution of a drivable path. A traversability analysis technique, based on the 3D-NDT, is proposed and used in a modified wavefront path planner to produce safe paths in complex 3D environments. Finally, the proposed traversability analysis algorithm is used for obstacle detection and avoidance.
Regression Using Multikernel and Semiparametric Support Vector Algorithms

In this letter, we propose a sequential training scheme for multikernel support vector regression (SVR). Unlike the multistage backfitting technique; our method re-tunes, at every stage, all previously trained weights using a semiparametric algorithm in the presence of one more kernel function. In this way, local minima are avoided and any combination of arbitrary kernel functions is acceptable. By experimenting on some synthetic and real data sets, we demonstrate that our method yields a better trade-off between sparsity and accuracy in comparison with the conventional single-kernel SVR and the multikernel backfitting SVR.
Harmonic optimization for the multi-stage saturable magnetically controlled reactor using particle swarm optimization algorithm

Magnetically controlled reactors (MCRs) as shunt reactors have offered flexible ways regulating the reactive power in the power system. MCRs can be implemented in ultra high voltage power systems and are much more economical and operational than the thyristor controlled reactors (TCRs). However, one remarkable defect of the MCRs is that they will inject great harmonics into the power system. In this paper, the structure and the mathematical model of a harmonic free multi-stage saturable MCR (MSMCR) are proposed. This paper illustrated the use of the particle swarm optimization (PSO) algorithm to decide the areas of the stages based on the harmonics mathematical model of the MSMCR. Since the 3rd harmonic will not be injected to the power systems if delta connected symmetrically, the current harmonics injected to the power system can be limited to as low as 0.8% of the rated output current of the MSMCR, theoretically. A simulation is finally produced to verify the research in PSCAD/EMTDC.
Effects of inflationary bubbles on the polarization and temperature anisotropies of the cosmic microwave background

We predict the imprint of linear bubbly perturbations on the polarization and temperature anisotropies of the cosmic microwave background (CMB). We model analytically a bubbly density perturbation at the beginning of the radiation-dominated era and we apply the linear theory of cosmological perturbations to compute its time evolution. At decoupling, it uniquely marks the CMB polarization and temperature anisotropy sky. As predicted by recent general work regarding spatially limited cosmological seeds, during evolution the perturbation propagates beyond the size of the bubble and it reaches the CMB sound horizon in the time considered. Therefore, its signal appears as a series of concentric rings, each characterized by its own amplitude and sign, on the scale of the sound horizon at decoupling (1° on the sky). Polarization and temperature rings are strictly correlated; photons coming from the centre of the bubble are not polarized, because of the spherical symmetry of the present problem. As expected for linear perturbations with size L and density contrast δ at decoupling, δTT is roughly δ(LH−1)2; the polarization is about 10 per cent of the temperature anisotropy. We predict the impact of a distribution of bubbles on the CMB polarization and temperature power spectra. Considering models containing both cold dark matter (CDM) Gaussian and bubbly non-Gaussian fluctuations, we simulate and analyse 10°×10° sky patches with angular resolution of about 3.5 arcmin. The CMB power associated with the bubbles is entirely on subdegree angular scales (200l1000), which will be explored by the forthcoming high-resolution CMB experiments with per cent precision. Depending on the parameters of the bubbly distribution, we find extra power with respect to the ordinary CDM Gaussian fluctuations; we infer simple analytical scalings of the power induced by bubbly perturbations and we constrain our parameters with the existing data.
Cooperative Control of Distributed Agents with Nonlinear Dynamics and Delayed Information Exchange: a Stabilizing Receding-Horizon Approach

This paper addresses the problem of cooperative control of a team of distributed agents with nonlinear discrete-time dynamics. Each agent is assumed to evolve in discrete-time based on locally computed control laws and by exchanging delayed state information with a subset of neighboring cooperating agents. The cooperative control problem is formulated in a receding-horizon (RH) framework, where the control laws depend on the local state variables (feedback action) and on delayed information gathered from cooperating neighboring agents (feedforward action). A rigorous stability analysis is carried out exploiting the stabilizing properties of the RH local control laws on one hand and input-to-state stability (ISS) arguments on the other hand. In particular, it is shown that, under suitable assumptions, each controlled agent is ISS under the action of the local control law. The stability of the team of agents is then proved by utilizing small-gain theorem results.
Frequency-Selective Channel State Feedback in Multiuser MIMO Downlink

In this paper we derive a novel method for encoding the MIMO channel state information (CSI) for frequency selective channels and limited feedback. The technique is based on the decomposition of the directional information of a MIMO channel in a two-coordinate system formed by the tangent space of a Grassmann manifold and the tangent space of a unitary group. The first component provides a non-redundant description of the subspace spanned by a set of channel eigenvectors, while the second component provides a non-redundant addendum of information to identify the strongest channel vector directions within that subspace. One advantage of this feedback representation is that it allows to exploit the correlation of MIMO channels by applying standard source coding techniques to the two coordinate components. We also provide simulation results of an LTE downlink system, which show that the proposed CSI feedback description offers dramatic throughput improvement over the existing feedback scheme for MU-MIMO transmission.
Packet dispersion techniques and capacity estimationConstantinos

The packet pair technique estimates the capacity of a path (bottleneck bandwidth) from the dispersion (spacing) experienced by two back-to-back packets 5]]19]]13]. It has also been claimed that the dispersion of longer packet bursts (`packet trains') can measure the available bandwidth of a path 5]]9]]3]. This paper examines such packet pair and packet train dispersion techniques. We rst demonstrate that the dispersion of packet pairs in loaded paths follows a multimodal distribution , and discuss the queueing eeects that cause multiple local modes. The path capacity is not necessarily the global mode, and so it cannot be estimated using statistical procedures for the most common bandwidth range. The eeect of the probing packet size is also investigated, showing that the conventional wisdom of using maximum sized packet pairs is not optimal. On the contrary, if the probing packets of diierent packet pairs are of variable size, the sub-capacity local modes become wider and weaker. We then focus on the dispersion of long packet trains. Increasing the length of the packet train reduces the measurement variance, but the estimates converge to a value, referred to as Asymptotic Dispersion Rate (ADR), that is lower than the capacity, and that is not related with the available bandwidth. We show the eeect of cross traac on the dispersion of long packet trains, and derive analytic expressions for the ADR in certain path conngurations. Putting all pieces together, we present a capacity estimation methodology that has been implemented in a measurement tool called pathrate. We present capacity measurements of several Internet paths using pathrate, and evaluate its accuracy and its robustness to cross traac eeects.
A fast algorithm for distributed optimization over directed networks

In this paper, we consider the distributed optimization problem over a directed network. In such practical network case, the double-stochasticity of mixing matrices and consensus may generally be violated. To deal with these challenges, we use non-doubly stochastic matrices and develop a new algorithm called ExtraPush via combining two well-known distributed algorithms, i.e., Extra and subgradient-push. The proposed algorithm uses a fixed step size like in Extra and accepts the column-stochastic mixing matrices like in subgradient-push. We present preliminary convergence analysis for ExtraPush under a bounded sequence assumption. Moreover, we provide a series of experiments to show the effectiveness, particularly, the fast convergence and consensus of the proposed algorithm.
Hemorrhagic Fever with Renal Syndrome Complicated with Pregnancy: A Case Report

Hantaviruses cause two forms of human disease: hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome. Hantavirus infection can occur in pregnant women and it can have an influence on the maternal and fetal outcomes, although this is a rare finding even in endemic areas. We describe here a recent case of HFRS complicating pregnancy.
Neuroinvasion of the Highly Pathogenic Influenza Virus H7N1 Is Caused by Disruption of the Blood Brain Barrier in an Avian Model

Influenza A virus (IAV) causes central nervous system (CNS) lesions in avian and mammalian species, including humans. However, the mechanism used by IAV to invade the brain has not been determined. In the current work, we used chickens infected with a highly pathogenic avian influenza (HPAI) virus as a model to elucidate the mechanism of entry of IAV into the brain. The permeability of the BBB was evaluated in fifteen-day-old H7N1-infected and non-infected chickens using three different methods: (i) detecting Evans blue (EB) extravasation into the brain, (ii) determining the leakage of the serum protein immunoglobulin Y (IgY) into the brain and (iii) assessing the stability of the tight-junction (TJ) proteins zonula occludens-1 and claudin-1 in the chicken brain at 6, 12, 18, 24, 36 and 48 hours post-inoculation (hpi). The onset of the induced viremia was evaluated by quantitative real time RT-PCR (RT-qPCR) at the same time points. Viral RNA was detected from 18 hpi onward in blood samples, whereas IAV antigen was detected at 24 hpi in brain tissue samples. EB and IgY extravasation and loss of integrity of the TJs associated with the presence of viral antigen was first observed at 36 and 48 hpi in the telencephalic pallium and cerebellum. Our data suggest that the mechanism of entry of the H7N1 HPAI into the brain includes infection of the endothelial cells at early stages (24 hpi) with subsequent disruption of the TJs of the BBB and leakage of virus and serum proteins into the adjacent neuroparenchyma.
An instruction-level fine-grained recovery approach for soft errors

With the continuously progress of integrated circuits, the dependability of computing, caused by soft errors, has become a growing design concern. For mitigating the effects of soft errors, software-based fault tolerance techniques are attractive because of their low costs and flexibility. But current researches mostly focus on error detection, and available error recovery methods generally provoke significant memory overhead and performance degradation, which will be prohibitively high for low-end systems. In this paper, we present an instruction-level fine-grained recovery approach named FASER, to detect and correct soft errors with minimal costs. The liveness of registers are firstly analyzed through the assembly code of the target program. Based on SWIFT, an instruction-level error detecting method, FASER presents the concrete implementation of checkpoint and recovery process at the granularity of storeless basic blocks. And we also propose several optimization measures for improving performance. The experimental results indicate that averagely 99.41% detected errors are successfully recovered by FASER. And on the basis of error detection, only additional 5.38% performance overhead is introduced, which is very low comparing with available methods.