Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of information flow topology on closed-loop stability of vehicle platoon with rigid formation

Besides automated controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the closed-loop stability of homogeneous vehicular platoon moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and V2V communications. Under linear feedback controllers, a unified closed-loop stability theorem is proved by using the algebraic graph theory and Routh-Hurwitz stability criterion. The theorem explicitly establishes the stabilization threshold of linear controller gains for platoons with a large class of different information flow topologies. Numerical simulations are used to illustrate the results.
CroVeWA: Crosslingual Vector-Based Writing Assistance

We present an interactive web-based writing assistance system that is based on recent advances in crosslingual compositional distributed semantics. Given queries in Japanese or English, our system can retrieve semantically related sentences from high quality English corpora. By employing crosslingually constrained vector space models to represent phrases, our system naturally sidesteps several difficulties that would arise from direct word-to-text matching, and is able to provide novel functionality like the visualization of semantic relationships between phrases interlingually and intralingually.
A collaborative peer-to-peer architecture to defend against DDoS attacks

Nowadays, we are witnessing an important increase in attacks among which distributed denial-of-service (DDoS) that easily flood the victims using multiple paths. Intrusion detection and filtering are necessary mechanisms to combat against these attacks and secure networks. However, the existing detection techniques for DDoS attacks have their entities work in isolation. In this paper, we propose an efficient and distributed collaborative architecture that allows the placement and the cooperation of the defense entities to better address the main security challenges. The use of content based DHT (distributed hash table) algorithm permits also to improve the scalability and the load balancing of the whole system. This modular architecture has been implemented on IDS (intrusion detection system) entities with the DHT Pastry protocol and has shown a promising performance.
Multi-band study of NGC 7424 and its two newly-discovered ULXs

We have studied the face-on, barred spiral NGC 7424 (site of the rare Type IIb SN 2001ig) with Chandra, Gemini and the Australia Telescope Compact Array. After giving revised X-ray colours and luminosity of the supernova, here we focus on some other interesting sources in the galaxy: in particular, our serendipitous discovery of two ultraluminous X-ray sources (ULXs). The brighter one (~ 10^{40} erg/s) has a power-law-like spectrum with photon index Gamma ~ 1.8. The other ULX shows a spectral state transition or outburst between the two Chandra observations, 20 days apart. Optical data show that this ULX is located in a young (age ~ 7-10 Myr), bright complex rich with OB stars and clusters. An exceptionally bright, unresolved radio source (0.14 mJy at 4.79 GHz, implying a radio luminosity twice as high as Cas A) is found slightly offset from the ULX (~ 80 pc). Its radio spectral index alpha ~ -0.7 suggests optically-thin synchrotron emission, either from a young supernova remnant or from a radio lobe powered by a ULX jet. An even brighter, unresolved radio source (0.22 mJy at 4.79 GHz) is found in another young, massive stellar complex, not associated with any X-ray sources: based on its flatter radio spectral index (alpha ~ -0.3), we suggest that it is a young pulsar wind nebula, a factor of 10 more radio luminous than the Crab.
Online Object Tracking : A Benchmark Supplemental Material

We present more evaluation results in this document. Tracking Speed. Table 1 shows the statistics of the tracking speed of each algorithm in OPE running on a PC with Intel i7 3770 CPU (3.4GHz). The speed of L1APG is slower than [4] as we set the parameters of L1APG to be the default ones of MTT, where the canonical size of template is larger than the default one of L1APG. The implementation of ASLA and SCM are based on IVT and set their parameters for particle filter to be the same. Spatial Shifts. Figure 1 illustrates eight spatial shifts used in SRE. The amount of shift is 10% of width or height of the ground-truth bounding box. Sequence Ranking. We rank the difficulty of sequences based on the AUC scores of trackers in SRE. The average of top 5 AUC scores of trackers for each sequence is used for ranking. The performance plots for top 4 most difficult sequences are shown in Figure 2 and Figure 3 (cf. Figure 1 of the manuscript). Attribute Distribution. The attribute distribution for each subset is shown in Figure 4. The attribute distributions of the whole dataset and OCC subset are shown in Figure 2 of manuscript. Overall Performance. Figure 5 shows the plots for the overall performance of all the evaluated trackers (cf. Figure 3 of the manuscript). Attribute-based Performance. The plots for the performance of all the evaluated trackers on the attribute subsets are shown from Figure 6 to Figure 11 (cf. Section 5.2 and Figure 4 of the manuscript). Initialization with Different Scale. Figure 12 and Figure 13 show the plots of SRE of the trackers initialized with different bounding box sizes (cf. Figure 5 of the manuscript). Randomness. Numerous tracking algorithms contain components that may be initialized with random seeds, e.g., feature extraction (Haar-like features in MIL), learning algorithm (random forests in CXT), and search mechanism (particle filter). We use the Struck to illustrate this point as it is one of the top performing algorithms. The randomness of Struck lies in its learning algorithm where it needs to Method FPS-A F-MAX S-MAX F-MIN S-MIN CPF [19] 109 284 woman 12.4 fleetface LOT [18] 0.70 2.0 girl 0.1 fleetface IVT [20] 33.4 34 woman 32 dudek ASLA [13] 8.5 9.9 doll 6.4 matrix SCM [26] 0.51 0.6 carDark 0.4 singer2 L1APG [4] 2.0 3.2 faceocc1 1.1 jogging MTT [25] 1 1.9 bolt 0.3 basketball VTD [15] 5.7 9.9 freeman4 3.5 faceocc1 VTS [16] 5.7 9.8 freeman4 3.5 faceocc1 LSK [17] 5.5 18 car4 1.5 football1 ORIA [23] 9.0 27 freeman4 3.2 soccer DFT [21] 13.2 27 girl 2.8 dudek KMS [7] 3,159 11,337 freeman3 423 singer1 SMS [5] 19.2 187 crossing 0.3 fleetface VR-V [6] 109 354 girl 18 singer1 Frag [1] 6.3 15 girl 2.7 walking OAB [9] 22.4 76 freeman3 4.0 fleetface SemiT [10] 11.2 46 freeman3 1.9 shaking BSBT [22] 7.0 39 freeman3 1.6 fleetface MIL [2] 38.1 53 car4 31 walking CT [24] 64.4 96 carDark 43 walking TLD [14] 28.1 138 woman 10 dudek Struck [11] 20.2 23 singer1 8.3 basketball CSK [12] 362 1889 freeman3 119 singer2 CXT [8] 15.3 33 subway 3.1 doll
ASYMPTOTIC PERFORMANCE FOR DELAYED EXPONENTIAL FITTING

The Damped and Delayed Sinusoidal (DDS) model is suitable for modeling compactly short time events (as the audio transient for instance). This property is closely related to its ability to reduce the time-support of each sinusoidal component. In this work, we derive an analytical and numerical conditional CRB for this model. In particular, we show that this model has, in general and at least, similar theoretical performance than the well-known Exponentially Damped Sinusoidal model. Moreover, the performances in the DDS case can be improved, compared to those of the EDS case for closely spaced sinusoids. Consequently, we can exploit the advantageous properties of the DDS model and in the same time, we can keep high theoretical model parameter estimation accuracy. Key-Words: Sinusoidal model, time-delay, Cramer-Rao Bound
Searching and Ranking in Entity-Relation Graphs Dual Degree Project Report

Ranking results to queries over information spaces where the data is represented in a directed graph has been the focus of much recent research work. In this report, we study three ranking paradigms addressing di erent issues in this regard. Entity-Relation(ER) graphs have entities as nodes, with relations between them being represented as edges. The PageRank system for ranking relies on the link-structure of the graph alone, and does not incorporate the query into search results explicitly. The HubRank system developed at IITB presented a viable way to dynamically personalize PageRank at query-time on ER graphs, by utilizing clever hubset selection strategies and early termination bounds. In this work, we rst look at extending the HubRank system to allow for personalization based on recent user-browsing history. Queries with hard predicates, such as Find papers written before 1990 near cryptography require the ranking to be carried out only on a subset of the graph (papers before 1990). We present a 'reverse push' algorithm to handle such queries, and evaluate its performance. Finally, we shift to looking for novel ways to combine link-structure information with local text-matches on nodes on a graph. The problem is rst formulated as an optimization problem for a single query. We then propose methods to extend it to learn to rank from multiple queries at once, and discuss issues faced in the same.
Testing a contact intervention based on intergroup friendship between Roma and non-Roma Hungarians: reducing bias through institutional support in a non-supportive societal context

Although intergroup friendships have been shown to reduce prejudice, little research has considered whether interventions fostering intergroup friendship would be effective in highly prejudicial contexts. We conducted a quasi-experiment (N = 61) to test whether a contact-based intervention based on intergroup friendship could reduce bias against Roma people among non-Roma Hungarians. Participants in the contact condition engaged in a face-to-face interaction with a Roma person, and responded to questions involving mutual self-disclosure. Through pre- and post-test questionnaires, we observed significant positive change in attitudes and contact intentions among participants in the contact condition, while these effects were not observed among participants in the control condition. Positive change was moderated by perceived institutional norms, which corroborates the potential of contact-based interventions.
Impedance Matching and Power Division Using Impedance Inverter for Wireless Power Transfer via Magnetic Resonant Coupling

Future applications of wireless power transfer will include powering various devices in a room, charging electric vehicles in a parking area, charging moving robots, and so on. Therefore, practical wireless power transfer must be able to support complicated configurations, for example, combination of multireceiver and repeaters. Many past works have discussed methods for improving efficiency and, more recently, extended the methods to multireceiver systems. However, controllable power division among receivers is also an important feature as receivers nearer to the transmitter tend to absorb more power compared to further ones. In this paper, a new impedance matching and power division method utilizing impedance inverters only at the receiver sides is proposed. The mathematical equations in the proposed method are then generalized for arbitrary number of receivers and arbitrary number of repeaters.
Tractable Triangles and Cross-Free Convexity in Discrete Optimisation

The minimisation problem of a sum of unary and pairwise functions of discrete variables is a general NP-hard problem with wide applications such as computing MAP configurations in Markov Random Fields (MRF), minimising Gibbs energy, or solving binary Valued Constraint Satisfaction Problems (VCSPs). We study the computational complexity of classes of discrete optimisation problems given by allowing only certain types of costs in every triangle of variable-value assignments to three distinct variables. We show that for several computational problems, the only non- trivial tractable classes are the well known maximum matching problem and the recently discovered joint-winner property. Our results, apart from giving complete classifications in the studied cases, provide guidance in the search for hybrid tractable classes; that is, classes of problems that are not captured by restrictions on the functions (such as submodularity) or the structure of the problem graph (such as bounded treewidth). Furthermore, we introduce a class of problems with convex cardinality functions on cross-free sets of assignments. We prove that while imposing only one of the two conditions renders the problem NP-hard, the conjunction of the two gives rise to a novel tractable class satisfying the cross-free convexity property, which generalises the joint-winner property to problems of unbounded arity.
Clustering Using Hybrid Models for Semantic Similarity

Different document representation models have been proposed to measure semantic similarity between documents using corpus statistics. Some of these models explicitly estimate semantic similarity based on measures of correlations between terms, while others apply dimension reduction techniques to obtain latent representation of concepts. This paper proposes new hybrid models that combine explicit and latent analysis to estimate semantic similarity between documents. The proposed models have been used to enhance the performance of document clustering algorithms. Experiments on thirteen benchmark data sets show that hybrid models achieve significant improvement in clustering performance when used with clustering algorithms that are sensitive to errors in estimating document similarity.
A Novel and Robust Wavelet based Super Resolution Reconstruction of Low Resolution Images using Efficient Denoising and Adaptive Interpolation

High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap is composed of pure translation and rotation, the blur is space invariant and the noise is additive white Gaussian noise. Super resolution reconstruction consists of registration, restoration and interpolation phases. Once the Low resolution image are registered with respect to a reference frame then wavelet based restoration is performed to remove the blur and noise from the images, finally the images are interpolated using adaptive interpolation. We are proposing an efficient wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the low resolution images are decomposed into many levels to obtain different frequency bands. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution we have proposed an adaptive interpolation technique. Our proposed wavelet based denoising with adaptive interpolation for super resolution reconstruction preserves the edges as well as smoothens the image without introducing artifacts. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution image with a high PSNR, ISNR ratio and a good visual quality.
Feedback and the fundamental line of low-luminosity low-surface-brightness/dwarf galaxies

We study in simple terms the role of feedback in establishing the scaling relations of low-surface-brightness (LSB) and dwarf galaxies with stellar masses in the range 6 × 10 5 ≤ M * ≤ 3 x 10 1 0 M O . These galaxies - as measured, for example, from the Sloan Digital Sky Survey (SDSS) and in the Local Group - show tight correlations of internal velocity, metallicity and surface brightness (or radius) with M * . They define a fundamental line which distinguishes them from the brighter galaxies of high surface brightness and metallicity. The idealized model assumes spherical collapse of cold dark matter (CDM) haloes to virial equilibrium and angular momentum conservation. The relations for bright galaxies are reproduced by assuming that M * is a constant fraction of the halo mass M. The upper bound to the low-luminosity LSBs coincides with the virial velocity of haloes in which supernova feedback could significantly suppress star formation, V 30 km s - 1 , retain gas-rich discs with feedback-regulated star formation.
Security Issues in Cloud Computing: A survey

Cloud computing is attracting great attention nowadays. The elastic nature of cloud makes it suitable for almost any type of organization. The major challenge faced by cloud users and providers are security concerns towards cloud services. These security issues acts as a barrier in the growth of cloud computing. The trust between provider and users is the most important factor to be considered for a cloud service and application. The notion of trust among the various cloud users is also essential to promote the reputation of various cloud providers and their offered services. Here we present an analysis of security issues in a cloud environment. Solution exists to a certain extent for various issues. There are trust based solutions available to provide security in various collaborative environments. Analysis of these solutions can be used to think about a trust based solution in a cloud computing environment. General Terms Cloud Computing, Security, Trust.
Optimizing Dendritic Cell-Based Immunotherapy: Tackling the Complexity of Different Arms of the Immune System

Earlier investigations have revealed a surprising complexity and variety in the range of interaction between cells of the innate and adaptive immune system. Our understanding of the specialized roles of dendritic cell (DC) subsets in innate and adaptive immune responses has been significantly advanced over the years. Because of their immunoregulatory capacities and because very small numbers of activated DC are highly efficient at generating immune responses against antigens, DCs have been vigorously used in clinical trials in order to elicit or amplify immune responses against cancer and chronic infectious diseases. A better insight in DC immunobiology and function has stimulated many new ideas regarding the potential ways forward to improve DC therapy in a more fundamental way. Here, we discuss the continuous search for optimal in vitro conditions in order to generate clinical-grade DC with a potent immunogenic potential. For this, we explore the molecular and cellular mechanisms underlying adequate immune responses and focus on most favourable DC culture regimens and activation stimuli in humans. We envisage that by combining each of the features outlined in the current paper into a unified strategy, DC-based vaccines may advance to a higher level of effectiveness.
Tracing the Path: New Model and Algorithms for Collaborative Filtering

Automated recommendation systems have emerged in the past decade as a useful tool to reduce the information overload faced by users at e-commerce sites. Recently Drineas et al. Kleinberg and Sandler, and others have introduced algorithms with pivvable performance guarantees. In this work we expand the mixture model introduced by Hoffman and Puzicha to include extra information often readily available to the algorithm designer. We show how this additional information leads to fast and simple algorithms with recommendation guarantees. We then begin the study of algorithms that work when the sampling step in the mixture model is done without repetition. This version of the problem often serves as a better-model for situations occurring in practice (e.g. few of us own multiple copies of the same book), but has not been rigorously analyzed in the context of recommendation systems.
Flood forecasting using medium-range probabilistic weather prediction

Abstract. Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.
On the Effects of Competing Advertisements in Keyword Auctions

The strength of the competition plays a significant role in the eciency of an online advertising campaign, as well as in traditional ad campaigns: the presence of a competitor’s ad makes the customer less likely to buy one’s own product. Models for keyword auctions usually assume that, for the advertiser, the value of a click is fixed and independent of the other sponsored ads in the auctions result. However in reality, all clicks are not created equal — the ones leading to a conversion or a purchase are definitely more important. How is the customer’s decision aected by the set of competing advertisements presented to him? We propose a new valuation model for keyword auctions that is competitionaware and takes into account the entire set of sponsored results present in the auction. We study properties of our model under the two most interesting mechanisms (GSP, VCG), both analytically and in simulations.
Perception of ultrasonic haptic feedback on the hand: localisation and apparent motion

Ultrasonic haptic feedback is a promising means of providing tactile sensations in mid-air without encumbering the user with an actuator. However, controlled and rigorous HCI research is needed to understand the basic characteristics of perception of this new feedback medium, and so how best to utilise ultrasonic haptics in an interface. This paper describes two experiments conducted into two fundamental aspects of ultrasonic haptic perception: 1) localisation of a static point and 2) the perception of motion. Understanding these would provide insight into 1) the spatial resolution of an ultrasonic interface and 2) what forms of feedback give the most convincing illusion of movement. Results show an average localisation error of 8.5mm, with higher error along the longitudinal axis. Convincing sensations of motion were produced when travelling longer distances, using longer stimulus durations and stimulating multiple points along the trajectory. Guidelines for feedback design are given.
Using the Divergence Framework for Randomness: CHiC 2013 Lab Report

For this first participation to the CHiC Lab, we focused on understanding the challenges of working with a collection of cultural heritage objects with short textual descriptions and on how to fine-tune a set of weighting models from the probability models based on Divergence From Randomness to perform uniformly in monolingual and multilingual scenarios. The official runs submitted used PL2 as the retrieval model and query expansion for four monolingual runs for English and Italian, and two multilingual runs against an English-Italian collection. Our best results were obtained in the unofficial runs using DLH13 with stemming and stopwords removal.