Miracle-dz/UniFAR-FTUs huggingface dataset:
A robust novel method for face recognition from 2D depth images using DWT and DFT score fusion

Face recognition has been an area of interest among researchers in pattern recognition for the past few decades. Researches in face recognition are basically concentrated on texture based and geometry based features. The main advantage of Face recognition systems utilizing depth information is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person identification using 2D Face Depth data. Most of the face recognition systems are based on reconstruction of 3D shapes from the 2D depth data. But the reconstruction requires much more computation time. Further the use of unregistered 2D Face depth data significantly increases the operational speed of the system with huge database enrollment. In this work, the unregistered. 2D Face Depth data is fed to a classifier in multiple spectral representations. Discrete Wavelet Transform (DWT) and Discrete Fourier Transform (DFT) are used for the spectral representations. The face recognition accuracy obtained when the feature extractors are used individually, is evaluated. Fusion of the matching scores proves that the recognition accuracy can be improved significantly by fusion of scores of multiple representations. Robustness analysis which covers the FAR (False Acceptance Rate) and TRR (True Rejection Rate) is also done. FRAV3D database provided by Face recognition and artificial vision group of Universidad Rey Juan Carlos, Madrid Spain is used for testing the algorithm.
Thyroid Hormone Enhances Nitric Oxide-Mediated Bacterial Clearance and Promotes Survival after Meningococcal Infection

Euthyroid sick syndrome characterized by reduced levels of thyroid hormones (THs) is observed in patients with meningococcal shock. It has been found that the level of THs reflects disease severity and is predictive for mortality. The present study was conducted to investigate the impact of THs on host defense during meningococcal infection. We found that supplementation of thyroxine to mice infected with Neisseria meningitidis enhanced bacterial clearance, attenuated the inflammatory responses and promoted survival. In vitro studies with macrophages revealed that THs enhanced bacteria-cell interaction and intracellular killing of meningococci by stimulating inducible nitric oxide synthase (iNos)-mediated NO production. TH treatment did not activate expression of TH receptors in macrophages. Instead, the observed TH-directed actions were mediated through nongenomic pathways involving the protein kinases PI3K and ERK1/2 and initiated at the membrane receptor integrin αvβ3. Inhibition of nongenomic TH signaling prevented iNos induction, NO production and subsequent intracellular bacterial killing by macrophages. These data demonstrate a beneficial role of THs in macrophage-mediated N. meningitidis clearance. TH replacement might be a novel option to control meningococcal septicemia.
Active-Vision-based Traffic Surveillance and Control

This paper discusses research-in-progress to develop a mobile, bus-mounted machine vision system for transit and traffic monitoring in urban corridors, as required by Intelligent Transportation Systems. In contrast to earlier machine vision technologies used for traffic management, which mainly rely on simple algorithms to detect certain traffic characteristics, the new proposed approach makes use of a recent trend in computer vision research; namely the active vision paradigm. Active vision systems have mechanisms that can actively control camera parameters such as orientation, focus, zoom, and vergence in response to the requirements of the task and external stimuli. Mounting active vision systems on buses will have the advantage of providing real-time feedback of the current traffic conditions while possessing the intelligence and visual skills which allow them to interact with a rapidly changing dynamic environment such
A Coordinated Communication Scheme for Distributed Fault Tolerant Control

In this paper, we present a distributed fault tolerant control methodology for interconnected nonlinear uncertain systems. Linearly parameterized neural networks are used to adaptively approximate the unknown interconnections and fault functions, based on local state information, as well as communicated information from other subsystems. The exchange of state information between subsystems is based on a coordinated communication scheme. The stability of the distributed fault tolerant control scheme is established through a rigorous Lyapunov analysis.
A scheduling algorithm for relative delay differentiation

An important issue in the design of multi-service IP networks is to find appropriate mechanisms to provide different levels of quality of service (QoS) for different types of applications. In this paper, scheduling mechanisms for real-time traffic in a differentiated services IP network are studied. A new scheduling algorithm called weighted earliest due date (WEDD) is proposed that provides tunable delay differentiation for real-time applications like voice over IP. WEDD is an enhancement of the earliest due date (EDD) scheduler in the sense that not only different deadlines but also different deadline violation probabilities are provided. The ratios of the violation probabilities are directly defined by a set of parameters. Simulation studies are performed to show that the scheduler is able to maintain the specified ratios under various load conditions.
A Bayesian Method for Detecting and Characterizing Allelic Heterogeneity and Boosting Signals in Genome-Wide Association Studies

The standard paradigm for the analysis of genome-wide association studies involves carrying out association tests at both typed and imputed SNPs. These methods will not be optimal for detecting the signal of association at SNPs that are not currently known or in regions where allelic heterogeneity occurs. We propose a novel association test, complementary to the SNP-based approaches, that attempts to extract further signals of association by explicitly modeling and estimating both unknown SNPs and allelic heterogeneity at a locus. At each site we estimate the genealogy of the case-control sample by taking advantage of the HapMap haplotypes across the genome. Allelic heterogeneity is modeled by allowing more than one mutation on the branches of the genealogy. Our use of Bayesian methods allows us to assess directly the evidence for a causative SNP not well correlated with known SNPs and for allelic heterogeneity at each locus. Using simulated data and real data from the WTCCC project, we show that our method (i) produces a significant boost in signal and accurately identifies the form of the allelic heterogeneity in regions where it is known to exist, (ii) can suggest new signals that are not found by testing typed or imputed SNPs and (iii) can provide more accurate estimates of effect sizes in regions of association.
The analysis and design of the job recommendation model based on GBRT and time factors

In this paper, we first give a comprehensive summary of models and algorithms applied in three online job recommender systems and point out the advantages and disadvantages of these models. Then we introduce a job recommendation model based on Gradient Boosting Regression Tree and time factors (T-GBRT). The T-GBRT model aggregates the time factors into the GBRT to predict personal preferences and adds time factor weight to topK rankings, with a neighbor based filtering trick in reducing the amount of calculation. At the end of the paper, the model performs the best in the experiment with four criterions, comparing to other three models, which proves the efficiency of the new model.
Effectiveness study of cooperative sensor systems for VRU-safety

The performance of environment perception sensors plays an important roll for the effectiveness of advanced driver assistance systems (ADAS). This paper analyzes different methods for sensor model generation for effectiveness simulations and derives a new sensor model unifying the main advantages of common deterministic and stochastic approaches. As an example, the paper utilizes the new sensor model to investigate the benefits of cooperative sensor systems for pedestrian safety. Cooperative sensors are a relatively new technology which allows for unique object classification and localization regardless of a sight obstruction. To generate a model, real world measurements are collected with a cooperative sensor system operating at 2.4 GHz. Finally, effectiveness simulations are performed to analyze the benefit of cooperative sensor systems in comparison to a single sensor system based on a monocular camera and a high-end fusion system with respect to a set of pedestrian accidents.
A Note on Backward Inductionl

In 1987, I wrote a paper (Binmore [14]) that questioned the rationality of the backward induction principle in finite games of perfect information. Since that time, a small literature has grown up in which Antonelli and Bicchieri [1], BenPorath [9], Bicchieri [10,11], Bonanno [21,22], Pettit and Sugden [28], Reny [30], Samet [32], Stalnaker [37,36] and numerous others have attempted with varying success to treat the issues formally. I believe my claim that rational players would not necessarily use their backward-induction strategies if there were to be a deviation from the backwardinduction path is now generally accepted. But Aumann’s [3] has recently offered a formal defense of the proposition that prior common knowledge of the players’ rationality implies that play will nevertheless necessarily follow the backwardinduction path. He argues that the conclusion is counter-intuitive in certain games, but attributes our discomfort with the result to a failure to appreciate how strong his assumptions are. However, although Aumann’s deep and thought-provoking contributions to the foundations of game theory provide the chief inspiration for this note, my purpose is not to comment specifically on his recent article. Its purpose is to question the significance of this and other results of the formalist genre.
Markerless Augmented Reality for Panoramic Sequences

This thesis describes the development and implementation of a system for inserting virtual objects into a panoramic image sequence. Virtual objects are inserted into the panoramic scene without the use of physical fiducial markers in the scene; placement is done through the detection of natural feature points within the panoramic imagery. An author inserts a planar image, text, or a three-dimensional model in one panorama, and the system automatically places these augmentations in a perspective-correct manner in other panoramic images near the insertion location. In the case of planar augmentations, the system also corrects occlusion of the plane by foreground objects. By applying transparency to the planar augmentation, it’s possible to give the occlusion the appearance of lying behind foreground objects. This enhances the realism of the augmentation, by giving a correct representation of relative depth.
Towards an understanding classification of well-being for care of older people

As the ageing population increases, more and more people require care and support. To date there has been limited understanding of older people's needs and the general well-being when they are in care. In this paper we investigated the relationships between the indices of independence in daily activities of the people who are in care and the behaviour rating scale [2] defined in the nursing assessment in connection with the indices to predetermine individuals' well-being during their stay. For this study, we focus on three well-being attributes, Cognition, Social Relations and Communications which are important for understanding the people in care. The study is based on a set of datasets consists of 40,000 records. The datasets were trained, tested and analysed using four Machine Learning (ML) classifications algorithms that are Bayesian Networks (BN), Naïve Bayesian (NB), Naïve Bayesian Tree (NBTree) and Instance Based Learner (IBL). The results show that Bayesian based algorithms performs very good with well-being data with respect to determining the relationship between the daily activities and the behavior rating scale, to predetermine the needs and well-being of the people who are in care.
Resisting Flooding Attacks on AODV

AODV is a reactive MANET routing protocol that is vulnerable to a dramatic collapse of throughput when malicious intruders flood the network with bogus route requests. We introduce a simple mechanism to resist such attacks that can be incorporated into any reactive routing protocol. It does not require expensive cryptography or authentication mechanisms, but relies on locally applied timers and thresholds to classify nodes as malicious. No modifications to the packet formats are needed, so the overhead is a small amount of calculation at nodes, and no extra communication. Using NS2 simulation, we compare the performance of networks using AODV under flooding attacks with and without our mechanism, showing that it significantly reduces the effect of a flooding attack. Keywords–MANET, Routing, AODV, Security, Attack, Flooding
PolyS: Network-based Signature Generation for Zero-day Polymorphic Worms

With growing sophistication of computer worms, it is very important to detect and prevent the worms quickly and accurately at their early phase of infection. Traditional signature based IDS, though effective for known attacks but failed to handle the zero-day attack promptly. Recent works on polymorphic worms does not guarantee accurate signature in presence of noise in suspicious flow samples. In this paper we propose PolyS, an improved version of Hamsa, a network based automated signature generation scheme to thwart zeroday polymorphic worms. We contribute a novel architecture that reduces the noise in suspicious traffic pool, thus enhancing the accuracy of worm’s signature. Also we propose a signature generation algorithm for successfully matching polymorphic worm payload with higher speed and memory efficiency. Analysis shows that our system is fast, accurate, attackresilient and capable of generating quality signature with low false positive and false negative.
Building the state-of-the-art in POS tagging of Italian Tweets

English. In this paper we describe our approach to EVALITA 2016 POS tagging for Italian Social Media Texts (PoSTWITA). We developed a two-branch bidirectional Long Short Term Memory recurrent neural network, where the first bi-LSTM uses a typical vector representation for the input words, while the second one uses a newly introduced word-vector representation able to encode information about the characters in the words avoiding the increasing of computational costs due to the hierarchical LSTM introduced by the character–based LSTM architectures. The vector representations calculated by the two LSTM are then merged by the sum operation. Even if participants were allowed to use other annotated resources in their systems, we used only the distributed data set to train our system. When evaluated on the official test set, our system outperformed all the other systems achieving the highest accuracy score in EVALITA 2016 PoSTWITA, with a tagging accuracy of 93.19%. Further experiments carried out after the official evaluation period allowed us to develop a system able to achieve a higher accuracy. These experiments showed the central role played by the handcrafted features even when machine learning algorithms based on neural networks are used. Italiano. In questo articolo descriviamo il sistema che abbiamo utilizzato per partecipare al task POS tagging for Italian Social Media Texts (PoSTWITA) della conferenza EVALITA 2016. Per questa partecipazione abbiamo sviluppato un sistema basato su due reti neurali parallele entrambi bidirezionali e ricorrenti di tipo Long Short Term Memory (LSTM). Mentre la prima rete neurale è una LSTM bidirezionale che prende in input vettori che rappresentano le parole in maniera tipica rispetto a precedenti lavori, la seconda prende in input una nuova rappresentazione vettoriale delle parole che contiene informazioni sui caratteri contenuti evitando un incremento del costo computazionale del sistema rispetto a LSTM che prendono in input rappresentazioni vettoriali delle sequenze di caratteri. Le rappresentazioni vettoriali ottenute dalle due LSTM vengono in fine combinate attraverso l’operatore di somma. Il nostro sistema, utilizzando come dati annotati solo quelli distribuiti dagli organizzatori del task, quando valutato sul test set uffciale ha ottenuto il miglior risultato nella competizione EVALITA 2016 PoSTWITA, riportando una accuratezza di 93.19%. Ulteriori esperimenti condotti dopo il periodo ufficiale di valutazione ci hanno permesso di sviluppare un sistema capace di raggiungre una accuratezza ancora maggiore, mostrandoci l’importanza dell’ingegnerizzazione manuale delle features anche quando vengono utilizzati algoritmi di apprendimento basati su reti
The Relationship of Clinical, Cognitive and Social Measures in Schizophrenia: A Preliminary Finding Combining Measures in Probands and Relatives

This study examines performance of schizophrenia patients, unaffected relatives and controls in social cognition, cognitive and psychiatric scales looking for possible markers of vulnerability in schizophrenia. Performance of schizophrenia patients from multiplex families, first-degree relatives, and matched controls was compared and, subsequently, discriminant analysis method was used for identifying the best predictors for group membership. By using Multigroup Discriminant Analyses on the three groups, the best predictors were PANSS, Premorbid Adjustment Scale, Faux Pas test, and a face/emotion categorizing task. This model obtained 82% correct global classification, suggesting that the combination of psychiatric scales and neuropsychological/social cognition tesks are the best approach for characterizing this disease. Although preliminary, our results suggest that social cognition tasks are robust markers of schizophrenia family impairments, and that combining clinical, social and neuropsychological measures is the best approach to asses patients and relatives vulnerability.
A new genetic-algorithm-based technique for low noise amplifier synthesis

A new technique for automated synthesis of linear and low noise amplifiers (LNAs) based on genetic algorithm and morphological approach is presented. It accounts for a simultaneous set of LNA performances; also, it allows the precise control of amplifier structure and element values leading to practical solutions. The technique is implemented in the software tool GENEAMP. Example designs of 2-10 GHz one-stage LNA and Ka-band two-stage MMIC LNA are demonstrated.
Reweighted Nuclear Norm Minimization for Matrix Completion

It is now well understood that low rank matrix completion problem can be solved by convex relaxtion from NP-hard rank minimization problem to nuclear norm minimization problem. However, there is still big gap between rank minimization and nuclear norm minimization. This paper steps forward to close the gap using reweighted nuclear norm minimization. Although the reweighted nuclear norm minimization turns out to be nonconvex problem, we propose a new NESTA algorithm to solve it with great performance. We present two experiments showing the power of reweighted nuclear norm minimization. Along the way, we also propose the NESTA algorithm to solve quadratic constrained nuclear norm minimizaiton problem. Through theoretical and numerical analysis, the NESTA algorithm is shown to be both accurate and time efficient. We believe that the NESTA algorithm is the most accurate one among all present first order gradient descent algorithms for matrix completion.
Working Paper No . 2010 / 22 Infrastructure and City Competitiveness in India

Do local improvements in infrastructure provision improve city competitiveness? What public finance mechanisms stimulate local infrastructure supply? And how do local efforts compare with national decisions of placing inter-regional trunk infrastructure? In this paper, we examine how the combination of local and national infrastructure supply improve city competitiveness, measured as the city’s share of national private investment. For the empirical analysis, we collect city-level data for India, and link private investment decisions to infrastructure provision. We find that a city’s proximity to international ports and highways connecting large domestic markets has the largest effect on its attractiveness for private investment. In comparison, the supply of local infrastructure services – such as municipal roads, street lighting, water supply, and drainage – enhance competitiveness, but their impacts are much smaller. Thus, while local efforts are important for competitiveness, they are less likely to be successful in cities distant from the country’s main trunk infrastructure. In terms of financing local infrastructure, we find that a city’s ability to raise its own source revenues by means of local taxes and user fees increases infrastructure supply, whereas as inter governmental transfers do not have statistically significant effects.
Multi-period hedge ratios for a multi-asset portfolio when accounting for returns comovement

This article presents a model to select the optimal hedge ratios of a portfolio comprised of an arbitrary number of commodities. In particular, returns dependency and heterogeneous investment horizons are accounted for by copulas and wavelets, respectively. We analyze a portfolio of London Metal Exchange metals for the period July 1993-December 2005, and conclude that neglecting cross correlations leads to biased estimates of the optimal hedge ratios and the degree of hedge effectiveness. Furthermore, when compared with a multivariate-GARCH specification, our methodology yields higher hedge effectiveness for the raw returns and their short-term components.
PROPELLER‐EPI with parallel imaging using a circularly symmetric phased‐array RF coil at 3.0 T: Application to high‐resolution diffusion tensor imaging

A technique integrating multishot periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and parallel imaging is presented for diffusion echo‐planar imaging (EPI) at high spatial resolution. The method combines the advantages of parallel imaging to achieve accelerated sampling along the phase‐encoding direction, and PROPELLER acquisition to further decrease the echo train length (ETL) in EPI. With an eight‐element circularly symmetric RF coil, a parallel acceleration factor of 4 was applied such that, when combined with PROPELLER acquisition, a reduction of geometric distortions by a factor substantially greater than 4 was achieved. The resulting phantom and human brain images acquired with a 256 × 256 matrix and an ETL of only 16 were visually identical in shape to those acquired using the fast spin‐echo (FSE) technique, even without field‐map corrections. It is concluded that parallel PROPELLER‐EPI is an effective technique that can substantially reduce susceptibility‐induced geometric distortions at high field strength. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.