Miracle-dz/UniFAR-FTUs huggingface dataset:
Team Halmstad Approach to Cooperative Driving in the Grand Cooperative Driving Challenge 2016

This paper is an experience report of team Halmstad from the participation in a competition organised by the i-GAME project, the Grand Cooperative Driving Challenge 2016. The competition was held in Helmond, The Netherlands, during the last weekend of May 2016. We give an overview of our car’s control and communication system that was developed for the competition following the requirements and specifications of the i-GAME project. In particular, we describe our implementation of cooperative adaptive cruise control, our solution to the communication and logging requirements, as well as the high level decision making support. For the actual competition we did not manage to completely reach all of the goals set out by the organizers as well as ourselves. However, this did not prevent us from outperforming the competition. Moreover, the competition allowed us to collect data for further evaluation of our solutions to cooperative driving. Thus, we discuss what we believe were the strong points of our system, and discuss post-competition evaluation of the developments that were not fully integrated into our system during competition time.
Fast and Accurate Estimation of Angle-of-Arrival for Satellite-Borne Wideband Communication System

Accurate estimation of angle-of-arrival (AoA) is critical to wideband satellite communications, but is susceptible to receive noises and can be ambiguous due to space/cost-effective hybrid antenna array designs with localized analog phased subarrays. As a matter of fact, there has yet to be an unambiguous estimator even for narrow-band systems. This paper proposes a new design of subarray-specific time-varying phase shifts, which enables unambiguous and noise-tolerant estimation of AoA in localized hybrid arrays. Particularly, the new phase shifts deliver deterministic phase changes in the cross-correlations of receive signals between subarrays, and enable the cross-correlations to be coherently accumulated across subarrays and sub-carriers to eliminate ambiguities and tolerate noises. Another important contribution of the paper is that we optimize the frequency interval for coherent accumulation across sub-carriers, leveraging between estimation errors, and accumulation gains. Evident from simulations, our approach is able to dramatically improve the estimation accuracy by orders of magnitudes with significantly reduced requirements of complexities and training symbols, as compared with the state of the art. The approach is robust against noises, with estimation errors asymptotically achieving a rigorously developed lower bound.
Detection of high rate DDoS attack from flash events using information metrics in software defined networks

The OpenFlow based Software Defined networks (SDN) is a new network architecture has gained much popularity in these days. Although the centralized control of SDN provides an enormous benefit, there are still a lot of security challenges are in control plane. As Distributed Denial of Services (DDoS) attack is one of the main security threat to the Internet, the goal of this paper is to detect the attack at the control layer by using the flow table information of the OpenFlow switches. The controller is the separate entity of SDN if it is made unreachable by a DDoS attack the entire architecture become defunct. In the current high-speed network scenario, discriminating a high-rate DDoS traffic from the flash events(FE) is a relatively more challenging task. The characteristics of the high-rate DDoS traffic are nearly similar to the legitimate FE traffic. Hence, in this work for detection purpose, we have used information theory based metrics such as General Entropy(GE) and Generalized Information Distance (GID). We evaluate the effectiveness of these metrics with Shannon entropy and Kullberg-Leibler divergence. The extensive simulation result shows that the considered metrics outperforms the other metrics with reduced false positives.
Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks

This paper introduces a very challenging dataset of historic German documents and evaluates Fully Convolutional Neural Network (FCNN) based methods to locate handwritten annotations of any kind in these documents. The handwritten annotations can appear in form of underlines and text by using various writing instruments, e.g., the use of pencils makes the data more challenging. We train and evaluate various end-to-end semantic segmentation approaches and report the results. The task is to classify the pixels of documents into two classes: background and handwritten annotation. The best model achieves a mean Intersection over Union (IOU) score of 95.6% on the test documents of the presented dataset. We also present a comparison of different strategies used for data augmentation and training on our presented dataset. For evaluation, we use the Layout Analysis Evaluator for the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts.
Stabilizers: Safe Lightweight Check- pointing for Concurrent Programs

A checkpoint is a mechanism that allows program execution to be restarted from a previously saved state. Checkpoints can be used in conjunction with exception handling abstractions to recover from exceptional or erroneous events, to support debugging or replay mechanisms, or to facilitate algorithms that rely on speculative evaluation. While relatively straightforward in a sequential setting, for example through the capture and application of continuations, it is less clear how 6 ascribea meaningful semantics for lightweight and safe check~oints in the Dresence of concurrency. For a thread to correctly resume execution fmm a saved checkpoint, it must ensure that all other threads which have witnessed its unwanted effects after the checkpoint was established are also reverted to a meaningful earlier state. If this is not done, data inconsistencies and other undesirable behavior may result. However, automatically determining what constitutes a consistent global state is not straightforward since thread interactions are a dynamic property of the program; requiring applications to specify such states explicitly is not pragmatic if interactions are complex. In this paper, we present a safe and efficient on-the-fly checkpointing mechanism for concurrent programs. We introduce a new abstraction called stabilizers that permits the specification and restoration of globally consistent checkpoints. This state is computed through lightweight monitoring of communication events among threads (e.g., message-passing operations or updates to shared variables). Our implementation results show that the memory and computation overheads for using stabilizers on highlyconcurrent server applications is small, averaging roughly 4 to 6%, leading us to conclude that stabilizers are a viable abstraction for defining restorable checkpoint state in complex concurrent programs.
A Learning Automata Based Dynamic Resource Provisioning in Cloud Computing Environments

Cloud computing provides more reliable and flexible access to IT resources, on-demand and self-service service request are some key advantages of it. Managing up-layer cloud services efficiently, while promising those advantages and SLA, motivates the challenge of provisioning and allocating resource on-demand in infrastructure layer, in response to dynamic workloads. Studies mostly have been focused on managing these demands in the physical layer and few in the application layer. This paper focuses on resource allocation method in application level that allocates an appropriate number of virtual machines to an application which requires a dynamic amount of resources. A Learning Automata based approach has been chosen to implement the method. Experimental results demonstrate that the proposed technique offers more cost effective resource provisioning approach while provisions enough resource for applications.
Studies on the Constrained Group-Wise K-Means Algorithm

In this paper, the constrained group-wise k-means algorithm is proposed to select groups of features during clustering. The idea of the algorithm is it not only constrains the weight of individual features via the lasso penalty, but also restrains group-wise weight of features by the group lasso penalty. Under the effect of these two penalties, this algorithm not only performs better in clustering, but is also comparable to other approaches in its ability to obtain the correct features with costing less computational time than the sparse k-means algorithm.
A DETAILED MODEL ATMOSPHERE ANALYSIS OF COOL WHITE DWARFS IN THE SLOAN DIGITAL SKY SURVEY

We present optical spectroscopy and near-infrared photometry of 126 cool white dwarfs (WDs) in the Sloan Digital Sky Survey (SDSS). Our sample includes high proper motion targets selected using the SDSS and USNO-B astrometry and a dozen previously known ultracool WD candidates. Our optical spectroscopic observations demonstrate that a clean selection of large samples of cool WDs in the SDSS (and the SkyMapper, Pan-STARRS, and the Large Synoptic Survey Telescope data sets) is possible using a reduced proper motion diagram and a tangential velocity cut-off (depending on the proper motion accuracy) of 30 km s−1. Our near-infrared observations reveal eight new stars with significant absorption. We use the optical and near-infrared photometry to perform a detailed model atmosphere analysis. More than 80% of the stars in our sample are consistent with either pure hydrogen or pure helium atmospheres. However, the eight stars with significant infrared absorption and the majority of the previously known ultracool WD candidates are best explained with mixed hydrogen and helium atmosphere models. The age distribution of our sample is consistent with a Galactic disk age of 8 Gyr. A few ultracool WDs may be as old as 12–13 Gyr, but our models have problems matching the spectral energy distributions of these objects. There are only two halo WD candidates in our sample. However, trigonometric parallax observations are required for accurate mass and age determinations and to confirm their membership in the halo.
Electrical Consumption Pattern Base on Meter Data Management System Using Big Data Techniques

The present work warns of the potential performance of the mapreduce technique for handling big data from the intelligent measurement of electrical energy, this information is managed by the measurement data management system and is used to find the energy consumption pattern of the users. The large amount of information from the intelligent measurement of electrical energy requires a short-term analysis for a subsequent decision-making required to obtain the answer to the demand of the energy in each electric distribution company. In this way, this work presents a technique to handle a large amount of information in a short time to generate trends and statistics of electric energy consumption information from the information coming from several years of information storage.
Regulation and Migratory Role of P-Selectin Ligands during Intestinal Inflammation

Dendritic cells from mesenteric lymph nodes (MLN) can convert retinal to retinoic acid (RA), which promotes induction of the gut-specific homing receptor α4β7. In contrast, priming within peripheral lymph nodes leads to upregulation of E- and P-selectin ligands (E- and P-lig). Apart from its α4β7 promoting effect, RA was shown to suppress E- and P-lig induction in vitro. However, enhanced frequencies of P-lig+ CD4+ T cells were reported during intestinal inflammation. To understand this contradiction, we first determined whether location of intestinal inflammation, that is, ileitis or colitis, affects P-lig induction. Both conditions promoted P-lig expression on CD4+ T cells; however, P-lig expressed on T cells facilitated Th1 cell recruitment only into the inflamed colon but not into inflamed small intestine induced by oral Toxoplasma gondii infection. A majority of P-lig+CD4+ T cells found within MLN during intestinal inflammation co-expressed α4β7 confirming their activation in the presence of RA. Mesenteric P-lig+CD4+ cells co-expressed the 130 kDa isoform of CD43 which requires activity of core 2 (beta)1,6-N-acetyl-glycosaminyltransferase-I (C2GlcNAcT-I) suggesting that C2GlcNAcT-I contributes to P-lig expression under these conditions. To test whether inflammatory mediators can indeed overrule the inhibitory effect of RA on P-lig expression we stimulated CD4+ T cells either polyclonal in the presence of IL-12 and IFNγ or by LPS-activated MLN-derived dendritic cells. Both conditions promoted P-lig induction even in the presence of RA. While RA impeded the induction of fucosyltransferase-VII it did not affect IL-12-dependent C2GlcNAcT-I induction suggesting that C2GlcNAcT-I can support P-lig expression even if fucosyltransferase-VII mRNA upregulation is dampened.
Elevated levels of antibodies against xenobiotics in a subgroup of healthy subjects

In spite of numerous research efforts, the exact etiology of autoimmune diseases remains largely unknown. Genetics and environmental factors, including xenobiotics, are believed to be involved in the induction of autoimmune disease. Some environmental chemicals, acting as haptens, can bind to a high‐molecular‐weight carrier protein such as human serum albumin (HSA), causing the immune system to misidentify self‐tissue as an invader and launch an immune response against it, leading to autoimmunity. This study aimed to examine the percentage of blood samples from healthy donors in which chemical agents mounted immune challenges and produced antibodies against HSA‐bound chemicals. The levels of specific antibodies against 12 different chemicals bound to HSA were measured by ELISA in serum from 400 blood donors. We found that 10% (IgG) and 17% (IgM) of tested individuals showed significant antibody elevation against aflatoxin‐HSA adduct. The percentage of elevation against the other 11 chemicals ranged from 8% to 22% (IgG) and 13% to 18% (IgM). Performance of serial dilution and inhibition of the chemical–antibody reaction by specific antigens but not by non‐specific antigens were indicative of the specificity of these antibodies. Although we lack information about chemical exposure in the tested individuals, detection of antibodies against various protein adducts may indicate chronic exposure to these chemical haptens in about 20% of the tested individuals. Currently the pathological significance of these antibodies in human blood is still unclear, and this protein adduct formation could be one of the mechanisms by which environmental chemicals induce autoimmune reactivity in a significant percentage of the population. Copyright © 2014. The Authors. Journal of Applied Toxicology Published by John Wiley & Sons Ltd.
Precipitation of ScAl3 Phase during Solutionizing and Ageing of Hypoeutectic Al-Sc Alloys and its Impact to Mechanical Properties

In this paper, precipitation of ScAl3 phase during solutionizing and ageing and its impact to mechanical properties of hypoeutectic Al-Sc alloy and effect of Sc addition amount were investigated by TEM and SEM microstructure observation and mechanical property testing. During solutionizing at 640oC for 24h, for Al-0.2wt. %Sc and Al-0.4wt. %Sc alloy, ScAl3 particles formed in the course of solidification become smaller, indicating, as a whole, Sc is partially dissolved into Al solution. Simultaneously, precipitation of two types of ScAl3 particles are observed by TEM. One has a size of 200~300nm, and the other is much small, 5~20nm in size. Though re-dissolution of Sc solute into Al solution and precipitation of large and fine ScAl3 particles occur in the solutionizing course, but the strength and hardness are decreased. The key reason for it is thought to be the softening effect of high level of vacancies in matrix lattice from the high solutionizing temperature. After further aging at 300oC for 3h, a great number of fine ScAl3 particles are precipitated in the Al matrix, which leads to a considerable precipitation hardening effect, thus the strength and hardness are increased obviously. Increasing the Sc content in the alloy results in a considerable rise in as-cast and as-aged strength and hardness, due to the solution strengthening and precipitation hardening respectively.
A Vehicle Detection Approach using Deep Learning Methodologies

The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading the data set, the design of the convolutional neural network, configuration of training options, training of the Faster R-CNN object detector and evaluation of trained detector. In addition, in the scope of the study, Faster R-CNN, R-CNN deep learning methods were mentioned and experimental analysis comparisons were made with the results obtained from vehicle detection.
Interactive Hand Pose Estimation: Boosting accuracy in localizing extended finger joints

Accurate 3D hand pose estimation plays an important role in Human Machine Interaction (HMI). In the reality of HMI, joints in fingers stretching out, especially corresponding fingertips, are much more important than other joints. We propose a novel method to refine stretching-out finger joint locations after obtaining rough hand pose estimation. It first detects which fingers are stretching out, then neighbor pixels of certain joint vote for its new location based on random forests. The algorithm is tested on two public datasets: MSRA15 and ICVL. After the refinement stage of stretching-out fingers, errors of predicted HMI finger joint locations are significantly reduced. Mean error of all fingertips reduces around 5mm (relatively more than 20%). Stretching-out fingertip locations are even more precise, which in MSRA15 reduces 10.51mm (relatively 41.4%).
Insights from a Recent Meeting: Current Status and Future Directions in Magnesium Corrosion Research

The United States Department of Energy Vehicle Technologies Office held a Technical Review Meeting entitled “Current Status and Future Directions in Magnesium Corrosion Research” at Oak Ridge National Laboratory in April 2016. Current funded Mg corrosion projects presented their latest results, along with invited presentations from academic and industrial researchers in the area of Mg corrosion. This paper overviews select topics from the meeting, with an emphasis on relaying meeting discussion points regarding future needs in understanding and mitigating Mg corrosion, particularly for automotive applications. Topics highlighted include the need for pretreatments and coatings suitable for mass vehicle production of Mg components, particularly for dissimilar metal joints involving Mg, Al, or steel, and corrosion evaluation methods more relevant to automotive components and operating conditions. From a scientific perspective, the need for better fundamental understanding of Mg dissolution, hydrogen evolutio.
A survey of techniques for dynamic branch prediction

Branch predictor (BP) is an essential component in modern processors since high BP accuracy can improve performance and reduce energy by decreasing the number of instructions executed on wrong‐path. However, reducing the latency and storage overhead of BP while maintaining high accuracy presents significant challenges. In this paper, we present a survey of dynamic branch prediction techniques. We classify the works based on key features to underscore their differences and similarities. We believe this paper will spark further research in this area and will be useful for computer architects, processor designers, and researchers.
Transplantation of Cold Stored Porcine Kidneys After Controlled Oxygenated Rewarming

The concept of "controlled oxygenated rewarming" (COR) using ex vivo machine perfusion after cold storage was evaluated as tool to improve renal graft function after transplantation. Renal function after 20 min warm ischemia and 21 h cold storage was studied in an auto-transplant model in pigs (25-30 kg, n = 6 per group). In the study group, preimplant ex vivo machine perfusion for 90 min was added after cold storage, including gentle warming up of the graft to 20°C (COR). Kidneys that were only cold stored for 21 h served as controls. In vivo follow up was one week; the remaining native kidney was removed during transplantation. COR significantly improved cortical microcirculation upon early reperfusion and reduced free radical mediated injury and cellular apoptosis. Post-transplant kidney function (peak levels in serum) was also largely and significantly improved in comparison to the control group. A weak inverse correlation was found between renal flow during COR and later peak creatinine after transplantation (r2 = 0.5), better values were seen for oxygen consumption, measured during machine perfusion at 20°C (r2 = 0.81). Gentle graft rewarming prior to transplantation by COR improves post-transplant graft outcome and may also be a valuable adjunct in pretransplant graft assessment.
SELF-ASSEMBLY OF MAGNESIUM ALUMINATE DUE TO DEWETTING OF OVERLAID GOLD THIN FILM

The self-assembly of magnesium aluminate spinel as a result of dewetting an overlaid thin film of (chiefly) gold was investigated. Thin films of gold were deposited on single-crystalline spinel substrates and were heat-treated to dewet gold film which led to self-assembly of intricate structures consisting of faceted spherical particles atop of frustums. The current work was conducted in continuation of previous studies which reported formation of such intricate structures. The most recent studies had evidently overruled a pure gold self-assembly scenario as was pointed out in preliminary investigations. It was in fact proven that these structures consist of three distinct parts: (i) a single or polycrystalline gold faceted sphere, (ii) quasi-phase interfacial bilayer, and (iii) a crystalline MgAl2O4 necking structure spontaneously risen from spinel substrate. In the current work, samples were produced through different film deposition methods of sputter, thermal evaporation, and e-beam evaporation coating which underwent thermal annealing to induce dewetting of gold film and subsequent self-assembly of intricate structures. Several characterization methods such as electron microscopy, X-ray energy dispersive spectroscopy, electron energy loss spectroscopy, and atom probe tomography were utilized to survey the different features of the intricate structures with focus on chemical analysis of the gold-spinel interface. The results rejected the previous findings about formation of interface complexion at the boundary of gold-spinel. It was found out that gold-spinel interface is in fact an ordinary metal-oxide boundary with sharp atomic distinction and no inter-diffusion or formation of interfacial complexion. It was further discovered that dewetting pure gold thin films does not result in formation of spinel self-assembly and existence of elemental impurities of copper (Cu) in the initial film is vital in development of such structures. Finally, it was concluded that chemical composition of metallic overlayer and the heat treatment parameters most fundamentally influence formation and physical characteristics of those self-assembled structures.