Miracle-dz/UniFAR-FTUs huggingface dataset:
Narrow-band interference mitigation using compressive sensing in AF-OFDM systems

In this paper, narrow-band interference (NBI) mitigation is addressed in amplify-and-forward orthogonal-frequency-division-multiplexing (AF-OFDM) cooperative communication systems. Based on the channel gains between the interferer, destination and the relay nodes, three copies of the NBI are received at the destination node in addition to the desired signal. Hence, NBI degrades the performance of AF-OFDM systems which motivates the need for mitigation techniques to reduce its effect. NBI is a sparse signal in the frequency-domain, hence, compressive sensing (CS) framework can be used to estimate and cancel the NBI before detecting the transmitted signal. However, frequency-grid-mismatch destroys the sparsity of NBI in the frequency domain at the destination terminal. A structured-dictionary-mismatch formulation is proposed to approximate the received NBI vector by two sparse vectors. An £2,1 norm minimization problem is solved to recover the sparse vectors. The recovered NBI is then canceled from the received signal before detection. Simulation results demonstrate the merits of the proposed approach.
A user study on the Snap-To-Feature interaction method

Recent advances in mobile computing and augmented reality (AR) technology have lead to popularization of mobile AR applications. Touch screen input is common in mobile devices, and also widely used in mobile AR applications. However, due to unsteady camera view movement, it can be hard to carry out precise interactions in handheld AR environments, for tasks such as tracing physical objects. In this research, we investigate a Snap-To-Feature interaction method that helps users to perform more accurate touch screen interactions by attracting user input points to image features in the AR scene. A user experiment is performed using the method to trace a physical object, which is typical for modeling real objects within the AR scene. The results shows that the Snap-To-Feature method makes a significant difference in the accuracy of touch screen based AR interaction.
LIP6@CLEF2017: Multi-Modal Spatial Role Labeling using Word Embeddings

We report our participation to the multi-modal Spatial Role Labeling (mSpRL) lab at CLEF 2017. The task consists in extracting and classifying spatial relationships from textual data and associated images. Our approach focuses on the classification part as we use a base-line system for the extraction of the relations: we train a linear Support Vector Machine (SVM) model to classify hand-crafted vectors representing spatial relations. We present the obtained experiments and discuss also the effect of model parameters. Finally, we conclude the paper and introduce ideas for future developments.
Ant colony optimization for community detection in large-scale complex networks

In this paper we present a new ant colony optimization for community detection in large networks, which takes modularity Q as objective function. An important difference that distinguishes our algorithm from the former ant algorithms is the manner in which the ants are used in the algorithm. Unlike those existing methods in which each ant searches for a candidate solution, each ant in our algorithm only decides whether its current vertex joins the community of its previous vertex with the aid of a simulated annealing idea, whose purpose is to locally optimize function Q. In each iteration, the ants work collectively so as to uncover the community structure of the network. Moreover, we introduce a thought of “layer and rule” into this method for further improving its performance. Our algorithm doesn't employ the pheromone, which reduces its running time and makes it well suitable for large-scale networks. Meanwhile, it still performs very well on both computer-generated benchmark and some widely used real-world networks compared with a set of competing algorithm in terms of clustering quality.
Mobile ad-hoc network key management with certificateless cryptography

In this paper, we present an idea of adopting certificateless public key encryption (CL-PKE) schemes over mobile ad hoc network (MANET), which has not been explored before. In current literature, essentially there exists two main approaches, namely the public key cryptography and identity-based (ID-based) cryptography. Unfortunately, they both have some inherent drawbacks. In the public key cryptography system, a certificate authority (CA) is required to issue certificates between users' public keys and private keys to ensure their authenticity, whilst in an ID-based cryptography system, users' private keys are generated by a key generation center (KGC), which means the KGC knows every users' keys (the key escrow problem). To avoid these obstacles, Al-Riyami and Paterson proposed certificateless cryptography systems where the public keys do not need to be certified and the KGC does not know users' keys. Essentially, certificateless cryptography relies between the public key cryptography and ID-based cryptography. In this work, we adopt this system's advantage over MANET. To implement CL-PKE over MANET and to make it practical, we incorporate the idea of Shamir's secret sharing scheme. The master secret keys are shared among some or all the MANET nodes. This makes the system self-organized once the network has been initiated. In order to provide more flexibility, we consider both a full distribution system and a partial distribution system. Furthermore, we carry out two simulations to support our schemes. We firstly simulate our scheme to calculate our encryption, decryption and key distribution efficiency. Then we also simulate our scheme with AODV to test the network efficiency. The simulations are performed over OPNET.
GENERALIZED EVENT-RELATED POTENTIALS : SYSTEM MODELS FOR CONTINUOUS EEG WITH TASK VARIABLES 1

If we adopt the simplified view that the brain is a deterministic system having EEG as output, with isolated task events as input, then we can use average eventrelated potentials (ERPs) to approximate impulse response functions. In an actual experiment, however, task events are not isolated. Rather, they interact via brain memory systems, and their associated electrophysiological responses often overlap in time. Consequently, an average ERP may obscure the response dynamics. Moreover, an average does not capture physiological interactions between events that may be of interest in a cognitive experiment. As an alternative to averaging, we demonstrate the feasibility of using linear/nonlinear system identification methods to characterize quasi-deterministic event-related dynamics. The method requires continuous EEG data with task variables, and it produces estimates of both linear responses and nonlinear interactions, which characterize a Volterra system model. Each task variable has an associated linear impulse response waveform, that is, a temporally deconvolved ERP. Matrix-like kernels represent nonlinear interactions for each pair of task variables. In the context of the general linear model, Friston et al. (1998, Magn Reson Med 39:41-52; 2000, NeuroImage 12:466-77) have applied a similar approach to fMRI time series. Thus, dynamic system parameter estimation provides a common framework for processing both ERP and event-related fMRI experiments. We illustrate the method using data from a visual event-related potential experiment.
Wavelet Based Semi-blind Channel Estimation For Multiband OFDM

This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding ``unsignificant'' wavelet coefficients from the estimation process. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error and bit error rate and enhances the estimation accuracy with less computational complexity than traditional semi-blind methods.
Optical flow image analysis of facial expressions of human emotion: forensic applications

The objective of this study was to induce emotions in individuals to determine if specific facial movements could be detected and analysed by the optical flow technique. This analysis is in the form of motion vector plots. The procedure ascertains if specific emotions can be defined as a set of facial movements which are common to most people when they experience a particular emotion. 'Emotion vector maps' would then be established for specific emotions. These vector sets could then be applied to automated facial image analysis for security/forensic purposes. Individuals were videotaped while watching emotion-inducing short films. After the viewing of each short film, volunteers completed a brief self-reporting questionnaire to establish the emotions they experienced. The image sequences were then analysed according to emotion, by using optical flow analysis. Results were statistically analysed. Trends and analyses are presented in relation to security and video surveillance. Issues and the development of pattern recognition systems applied to human facial images for security purposes are briefly discussed.
AdS black holes and thermal Yang-Mills correlators

We study the real time correlators of scalar glueball operators for Yang-Mills theory at finite temperature in flat space. The analytic structure of the frequency space propagator in perturbative field theory is seen to be qualitatively different to the strong coupling results that may be obtained from perturbations about AdS black hole spacetimes: we find branch cuts rather than poles. This difference appears to persist away from the strict zero and infinite coupling limits, possibly suggesting a phase transition in large-N thermal = 4 SYM theory as a function of the 't Hooft coupling.
Assessment of the Taxonomic Status of Some Species Included in the Shannoni Complex, with the Description of a New Species of Psathyromyia (Diptera: Psychodidae: Phlebotominae)

ABSTRACT The Shannoni complex, comprising sand flies belonging to the subgenus Psathyromyia (Psathyromyia) Barretto, consists of several species, of which the females have banana-shaped spermathecae and the males have digitiform parameres on the terminalia. Among these species, Psathyromyia shannoni (Dyar) and two taxa considered as its junior synonyms (Phlebotomus limai Fonseca and Phlebotomus bigeniculatus Floch & Abonnenc), Psathyromyia pestanai (Barretto & Coutinho), and a new species are the focus of this study. On the basis of morphological and morphometric characters, Psathyromyia bigeniculata (Floch and Abonnenc) stat. rev.; comb. n. and Psathyromyia limai (Fonseca) stat. rev.; comb. n. are resurrected from the synonymy of Pa. shannoni. Pa. pestanai is proposed as a new junior synonym of Pa. limai. Psathyromyia ribeirensis sp. n. , occurring in the middle and upper Ribeira Valley in the state of São Paulo, Brazil is described. Pa. limai, described from the Serra da Cantareira (São Paulo municipality) and also found in the lower Ribeira Valley and on the adjacent coastal plain, is morphologically very close to the new species.
Chronic cough – assessment of treatment efficacy based on two questionnaires

Introduction Efficacy of chronic cough treatment is ambiguous. The aim of the study was to analyze chronic cough alleviation after specific treatment and the relationship between cough etiology and treatment efficacy. Material and methods A stepwise diagnostic approach was used to diagnose cough etiology in non-smoking adults with chronic cough. In all patients specific treatment was applied. Two different questionnaires – a visual analog scale and a 5-degree scale – were used to assess cough severity before and after 4-6 months of treatment. Results A significant correlation between pre-treatment and post-treatment results of both questionnaires was found (Spearman coefficient 0.43, p = 0.0003 and 0.73, p < 0.0001, respectively). Baseline questionnaire analysis revealed no differences in cough severity between patients with different cough causes or multiple cough causes. Although specific treatment resulted in a significant decrease of cough severity in the entire group, only partial improvement was noted. According to the visual analogue scale, a decrease of cough severity by at least 50% was achieved only in 54.4% of patients (37/68). Similarly, satisfactory improvement was noted in only 54.4% (37/68) of patients when using the 5-point scale. There were three sub-groups of patients, in whom no relevant decrease of cough severity was observed despite treatment: patients with 1. three coexisting cough causes, 2. non-asthmatic eosinophilic bronchitis, and 3. chronic idiopathic cough. Conclusions Cough severity does not depend on its etiology. Efficacy of chronic cough treatment in non-smoking patients is only moderate.
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form

We combine logical and distributional representations of natural language meaning by transforming distributional similarity judgments into weighted inference rules using Markov Logic Networks (MLNs). We show that this framework supports both judging sentence similarity and recognizing textual entailment by appropriately adapting the MLN implementation of logical connectives. We also show that distributional phrase similarity, used as textual inference rules created on the fly, improves its performance.
Focal Lesions in the Rat Central Nervous System Induced by Endothelin‐1

Axon injury following cerebral ischemia has received little scientific attention compared to the abundance of information dealing with the pathophysiology of grey matter ischemia. There are differences in the initial response of grey and white matter to ischemia in vitro. In this study we investigate whether the vasoactive peptide, endothelin-1, can generate a focal ischemic lesion in the white matter and compare the findings with endothelin-1-induced lesions in the grey matter. Using a minimally invasive technique to microinject endothelin-1 into selected brain regions, we observed an acute reduction in local MRI perfusion in the injected hemisphere after 1 hour. Twenty-four hours after microinjection of 10 pmoles of endothelin-1, we observed a loss of neurons in the grey matter. At 72 hours, neutrophils were absent and a macrophage/microglia response and astrocyte gliosis were detected. No breakdown in the blood-brain barrier was detected. After injection of 10 pmoles endothelin-1 into the cortical white matter, we observed prolific amyloid precursor protein-positive immunostaining (indicative of axonal disruption) and an increase in tau-1 immunostaining in oligodendrocytes at 6 hours. Similar to the grey matter lesions, no neutrophils were present, a macrophage/microglia response did not occur until 72 hours and there was no disruption in the blood-brain barrier. Focal injections of endothelin-1 into specific areas of the rat CNS represent a model to investigate therapeutic approaches to white matter ischemia.
Towards GPU-Accelerated Large-Scale Graph Processing in the Cloud

Recently, we have witnessed that cloud providers start to offer heterogeneous computing environments. There have been wide interests in both clusters and cloud of adopting graphics processors (GPUs) as accelerators for various applications. On the other hand, large-scale graph processing is important for many data-intensive applications in the cloud. In this paper, we propose to leverage GPUs to accelerate large-scale graph processing in the cloud. Specifically, we develop an in-memory graph processing engine G2 with three non-trivial GPU-specific optimizations. Firstly, we adopt fine-grained APIs to take advantage of the massive thread parallelism of the GPU. Secondly, G2 embraces a graph partition based approach for load balancing on heterogeneous CPU/GPU architectures. Thirdly, a runtime system is developed to perform transparent memory management on the GPU, and to perform scheduling for an improved throughput of concurrent kernel executions from graph tasks. We have conducted experiments on an Amazon EC2 virtual cluster of eight nodes. Our preliminary results demonstrate that 1) GPU is a viable accelerator for cloud-based graph processing, and 2) the proposed optimizations improve the performance of GPU-based graph processing engine. We further present the lessons learnt and open problems towards large-scale graph processing with GPU accelerations.
Score standardization for inter-collection comparison of retrieval systems

The goal of system evaluation in information retrieval has always been to determine which of a set of systems is superior on a given collection. The tool used to determine system ordering is an evaluation metric such as average precision, which computes relative, collection-specific scores. We argue that a broader goal is achievable. In this paper we demonstrate that, by use of standardization, scores can be substantially independent of a particular collection, allowing systems to be compared even when they have been tested on different collections. Compared to current methods, our techniques provide richer information about system performance, improved clarity in outcome reporting, and greater simplicity in reviewing results from disparate sources.
Inflammation, insulin resistance, and adiposity: a study of first-degree relatives of type 2 diabetic subjects.

OBJECTIVE Inflammatory markers such as C-reactive protein (CRP) are associated with insulin resistance, adiposity, and type 2 diabetes. Whether inflammation causes insulin resistance or is an epiphenomenon of obesity remains unresolved. We aimed to determine whether first-degree relatives of type 2 diabetic subjects differ in insulin sensitivity from control subjects without a family history of diabetes, whether first-degree relatives of type 2 diabetic subjects and control subjects differ in CRP, adiponectin, and complement levels, and whether CRP is related to insulin sensitivity independently of adiposity. RESEARCH DESIGN AND METHODS We studied 19 young normoglycemic nonobese first-degree relatives of type 2 diabetic subjects and 22 control subjects who were similar for age, sex, and BMI. Insulin sensitivity (glucose infusion rate [GIR]) was measured by the euglycemic-hyperinsulinemic clamp. Dual-energy X-ray absorptiometry determined total and abdominal adiposity. Magnetic resonance imaging measured abdominal adipose tissue volumes. RESULTS First-degree relatives of type 2 diabetic subjects had a 20% lower GIR than the control group (51.8 +/- 3.9 vs. 64.9 +/- 4.6 micromol x min(-1) x kg fat-free mass(-1), P = 0.04). However, first-degree relatives of subjects with type 2 diabetes and those without a family history of diabetes had normal and comparable levels of CRP, adiponectin, and complement proteins. When the cohort was examined as a whole, CRP was inversely related to GIR (r = -0.33, P = 0.04) and adiponectin (r = -0.34, P = 0.03) and positively related to adiposity (P < 0.04). However, CRP was not related to GIR independently of fat mass. In contrast to C3 (r = 0.41, P = 0.009) and factor B (r = 0.43, P = 0.005), CRP was unrelated to factor D. CONCLUSIONS The insulin-resistant state is not associated with changes in inflammatory markers or complement proteins in subjects at high risk of type 2 diabetes. Our study confirms a strong relationship between CRP and fat mass. Increasing adiposity and insulin resistance may interact to raise CRP levels.
Overdependence on Degraded Gist Memory in Alzheimer ’ s Disease

Alzheimer’s disease (AD) reduces associative effects on false recognition in the Deese–Roediger– McDermott task, either due to impaired memory for gist or impaired use of gist in memory decisions. Gist processes were manipulated by blocking or mixing studied words according to their associations and by varying the associative strength between studied and nonstudied words at test. Both associative blocking and associative strength had smaller effects on false recognition in AD patients than in control participants, consistent with gist memory impairments. However, unlike the case with control participants, blocking influenced true and false recognition equally in AD patients, demonstrating an overdependence on gist when making memory decisions. AD also impaired item-specific recollections, relative to control participants, as true recognition of studied words was reduced even when the two groups were equated on gist-based false recognition. We propose that the overdependence on degraded gist memory in AD is caused by even larger impairments in item-specific recollections.
SOM based methods in early fault detection of nuclear industry

Early fault detection in nuclear industry is studied. Tools have been developed for control room operators and experts in an industrial project. Self- Organizing Map (SOM) method has been used in combination with other methods. Decision support visualizations are introduced. The usability of methods have been tested and verified by constructing prototype systems. The use of SOM method in dynamic systems is discussed. Applications for industrial domain are presented. Data sets from a Finnish nuclear power plant have been analyzed. Promising results in failure management are achieved.
DDoS detection based on feature space modeling

This work tries to use a feature space modeling methodology to identify DDoS attacks. Compared with the existing approaches, the proposed feature space presents a more general model in DDoS detection. It changes the non-separable attacks into separable cases and more importantly, it also allows the unknown attacks potentially being identified by their own features. To validate these claims, a classification algorithm is defined under this feature space. We use a subset in KDD Cup 1999 data in the experiments. The KDD Cup 1999 training dataset contains 6 different types of DDoS attacks and the testing dataset contains more 4 novel DDoS attacks. In detecting these 6 already known DDoS attacks and 4 novel DDoS attacks from the normal, we get a high detection rate under this feature space by using the proposed classification algorithm, which shows the discriminative abilities of the feature space.
Blind spectrum sensing in cognitive radio over fading channels and frequency offsets

This paper deals with the problem of spectrum sensing in cognitive radio. We consider a stochastic system model where the Primary User (PU) transmits a periodic signal over fading channels. The effect of frequency offsets due to oscillator mismatch, and Doppler offset is studied. We show that for this case the Likelihood Ratio Test (LRT) cannot be evaluated pointwise. We present a novel approach to approximate the marginilisation of the frequency offset using a single point estimate. This is obtained via a low complexity Constrained Adaptive Notch Filter (CANF) to estimate the frequency offset. Performance is evaluated via numerical simulations and it is shown that the proposed spectrum sensing scheme can achieve the same performance as the “near-optimal” scheme, that is based on a bank of matched filters, using only a fraction of the complexity required.
A non-parametric scale-based corner detector

This paper introduces a new Harris-affine corner detector algorithm, that does not need parameters to locate corners in images, given an observation scale. Standard detectors require to fine tune the values of parameters which strictly depend on the particular input image. A quantitative comparison between our implementation and a standard Harris-affine implementation provides good results, showing that the proposed methodology is robust and accurate. The benchmark consists of public images used in literature for feature detection.