Miracle-dz/UniFAR-FTUs huggingface dataset:
A quorum-based framework for establishing control channels in dynamic spectrum access networks

Establishing a control channel for medium access control is a challenging problem in multi-channel and dynamic spectrum access (DSA) networks. In the design of multi-channel MAC protocols, the use of channel (or frequency) hopping techniques (a.k.a. parallel rendezvous) have been proposed to avoid the bottleneck of a single control channel. In DSA networks, the dynamic and opportunistic use of the available spectrum requires that the radios are able to "rendezvous" -- i.e., find each other to establish a link. The use of a dedicated global control channel simplifies the rendezvous process but may not be feasible in many opportunistic spectrum sharing scenarios due to the dynamically changing availability of all the channels, including the control channel. To address this problem, researchers have proposed the use of channel hopping protocols for enabling rendezvous in DSA networks. This paper presents a systematic approach, based on quorum systems, for designing and analyzing channel hopping protocols for the purpose of control channel establishment. The proposed approach, called Quorum-based Channel Hopping (QCH) system, can be used for implementing rendezvous protocols in DSA networks that are robust against link breakage caused by the appearance of incumbent user signals. We describe two optimal QCH systems under the assumption of global clock synchronization: the first system is optimal in the sense that it minimizes the time-to-rendezvous between any two channel hopping sequences; the second system is optimal in the sense that it guarantees the even distribution of the rendezvous points in terms of both time and channel, thus solving the \emph{rendezvous convergence} problem. We also propose an asynchronous QCH system that does not require global clock synchronization. Our analytical and simulation results show that the channel hopping schemes designed using our framework outperform existing schemes under various network conditions.
Acute-Phase-HDL Remodeling by Heparan Sulfate Generates a Novel Lipoprotein with Exceptional Cholesterol Efflux Activity from Macrophages

During episodes of acute-inflammation high-density lipoproteins (HDL), the carrier of so-called good cholesterol, experiences a major change in apolipoprotein composition and becomes acute-phase HDL (AP-HDL). This altered, but physiologically important, HDL has an increased binding affinity for macrophages that is dependent on cell surface heparan sulfate (HS). While exploring the properties of AP-HDL∶HS interactions we discovered that HS caused significant remodeling of AP-HDL. The physical nature of this change in structure and its potential importance for cholesterol efflux from cholesterol-loaded macrophages was therefore investigated. In the presence of heparin, or HS, AP-HDL solutions at pH 5.2 became turbid within minutes. Analysis by centrifugation and gel electrophoresis indicated that AP-HDL was remodeled generating novel lipid poor particles composed only of apolipoprotein AI, which we designate β2. This remodeling is dependent on pH, glycosaminoglycan type, is promoted by Ca2+ and is independent of protease or lipase activity. Compared to HDL and AP-HDL, remodeled AP-HDL (S-HDL-SAA), containing β2 particles, demonstrated a 3-fold greater cholesterol efflux activity from cholesterol-loaded macrophage. Because the identified conditions causing this change in AP-HDL structure and function can exist physiologically at the surface of the macrophage, or in its endosomes, we postulate that AP-HDL contains latent functionalities that become apparent and active when it associates with macrophage cell surface/endosomal HS. In this way initial steps in the reverse cholesterol transport pathway are focused at sites of injury to mobilize cholesterol from macrophages that are actively participating in the phagocytosis of damaged membranes rich in cholesterol. The mechanism may also be of relevance to aspects of atherogenesis.
On the performance of Maestro2 high performance network equipment, using new improvement techniques

Cluster computers have become the vehicle of choice to build high performance computing environments. To fully exploit the computing power of these environments, one must utilize high performance network and protocol technologies, since the communication patterns of parallel applications running on clusters require low latency and high throughput, not achievable by using off-the-shell network technologies. We have developed a technology to build high performance network equipment, called MaestroS. This paper describes the novel techniques used by Maestro2 to extract maximum performance from the physical medium and studies the impact of software-level parameters. The results obtained clearly show that Maestro2 is a promising technology, presenting very good results both in terms of latency and throughput. The results also show the large impact of software overhead in the overall performance of the system and validate the need for optimized communication libraries for high performance computing.
COTS FPGA/SRAM irradiations using a dedicated testing infrastructure for characterization of large component batches

This paper introduces a new testing platform for irradiation of large batches of COTS FPGA and SRAMs. The main objective is measurement of component radiation response and assessment of component-to-component variability within one batch. The first validation and test results using the testing platform are presented for 150nm TFT SRAM (Renesas) and different sizes of the 130nm ProASIC3 FPGA (Microsemi).
Skin image retrieval using Gabor wavelet texture feature

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images.
Метод автоматической классификации текстов по тональности, основанный на словаре эмоциональной лексики (The Automatic Sentiment Text Classification Method Based on Emotional Vocabulary)

В статье представлен метод автоматической классификации текстов по тональности, основанный на словаре эмоциональной лексики. Приводится описание процесса создания словаря: выделение оценочных слов, назначение им весов, определение влияния слов-модификаторов и слов, выражающих отрицание. Для оценки метода используются коллекции семинара РОМИП. Результаты сравниваются с результатами других методов.
Sleep Stage Classification Using Unsupervised Feature Learning

Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.
Structural Run Based Feature Vector to Classify Printed Tamil Characters Using Neural Network

Feature Extraction plays most crucial and important role in character recognition. The selection of stable and representative set of features is the main problem in pattern recognition. Because of font characteristics and style variation of machine printed Tamil characters, feature extraction remains a problem. Feature extraction involves reducing the amount of resources required to describe a set of data. In this paper, new method has been proposed to extract structural features from Machine printed Tamil characters using horizontal and vertical projections. Based on the structural properties of upper and lower modifiers, characters are divided into various categories and features are extracted accordingly. The extracted features from the real life degraded documents are classified to identify the characters. The system has been tested with printed Tamil characters and achieves 99.67% character recognition accuracy on average. Experimental results show that structure and category of the characters are identified by the proposed method for the regular characters of various sizes.
Shadowing: And Other Techniques for Doing Fieldwork in Modern Societies

This book offers an array of techniques to study people on the move, and is addressed to all social scientists interested in fieldwork as a way of grasping phenomena typical of the late modernity. Its starting point is that present times require different metaphors than static "cultures", "organizations" or even "societies". It is time to start constructing a mobile ethnology, that is knowledge about people, objects and ideas that circulate globally. The present text offers suggestions concerning the ways such construction may take.
Knowledge Integration in Creative Problem Solving

Most psychological theories of problem solving have focused on modeling explicit processes that gradually bring the solver closer to the solution in a mostly explicit and deliberative way. This approach to problem solving is typically inefficient when the problem is too complex, ill-understood, or ambiguous. In such a case, a ‘creative’ approach to problem solving might be more appropriate. In the present paper, we propose a computational psychological model implementing the Explicit-Implicit Interaction theory of creative problem solving that involves integrating the results of implicit and explicit processing. In this paper, the new model is used to simulate insight in creative problem solving and the overshadowing effect.
A Secure Routing Protocol Based on RPL for Internet of Things

Data transportation and routing in Internet of Things (IoT) is a challenging issue where massive data collection and gathering are predictable. The Routing Protocol for Low- power and Lossy Networks (RPL) is one of the best candidates to ensure routing in 6LoWPAN networks. However, RPL is vulnerable to a number of attacks related to exchanged control messages. In this paper, we propose a new secure routing protocol based on RPL referred to as Secure-RPL (SRPL). The main aim of SRPL is to prevent misbehaving nodes from maliciously changing control message values such as the rank of a node that may disturb a network by creating a fake topology. We introduce the concept of rank threshold along with hash chain authentication technique to deal with internal attacks like sinkhole, black hole, selective forwarding attacks etc. Simulation results show that SRPL is robust and resistant to this kind of attacks based on malicious manipulation of RPL metrics.
Planning of Ballistic Movement following Stroke: Insights from the Startle Reflex

Following stroke, reaching movements are slow, segmented, and variable. It is unclear if these deficits result from a poorly constructed movement plan or an inability to voluntarily execute an appropriate plan. The acoustic startle reflex provides a means to initiate a motor plan involuntarily. In the presence of a movement plan, startling acoustic stimulus triggers non-voluntary early execution of planned movement, a phenomenon known as the startReact response. In unimpaired individuals, the startReact response is identical to a voluntarily initiated movement, except that it is elicited 30–40 ms. As the startReact response is thought to be mediated by brainstem pathways, we hypothesized that the startReact response is intact in stroke subjects. If startReact is intact, it may be possible to elicit more task-appropriate patterns of muscle activation than can be elicited voluntarily. We found that startReact responses were intact following stroke. Responses were initiated as rapidly as those in unimpaired subjects, and with muscle coordination patterns resembling those seen during unimpaired volitional movements. Results were striking for elbow flexion movements, which demonstrated no significant differences between the startReact responses elicited in our stroke and unimpaired subject groups. The results during planned extension movements were less straightforward for stroke subjects, since the startReact response exhibited task inappropriate activity in the flexors. This inappropriate activity diminished over time. This adaptation suggests that the inappropriate activity was transient in nature and not related to the underlying movement plan. We hypothesize that the task-inappropriate flexor activity during extension results from an inability to suppress the classic startle reflex, which primarily influences flexor muscles and adapts rapidly with successive stimuli. These results indicate that stroke subjects are capable of planning ballistic elbow movements, and that when these planned movements are involuntarily executed they can be as rapid and appropriate as those in unimpaired individuals.
Temperature-inducible amber suppressor: construction of plasmids containing the Escherichia coli serU- (supD-) gene under control of the bacteriophage lambda pL promoter

An Escherichia coli DNA fragment containing the structural gene serU132 for the nonsense suppressor tRNASer2am was identified and purified by being cloned into a plasmid vector. Information obtained from DNA sequence analysis was used to select a serU132 fragment for insertion downstream from the bacteriophage lambda pL promoter in two pBR322-lambda derivatives. In nonsense mutant strains bearing the resulting serU132 hybrid plasmids, the presence of the lambda cI857 repressor gene carried on the same plasmid or in a prophage genome permits thermal regulation of suppressor synthesis.
Multiscale annotation of still images with GAT

This paper presents GAT, a Graphical Annotation Tool for still images that works both at the global and local scales. This interface has been designed to assist users in the annotation of images with relation to the semantic classes described in an ontology. Positive, negative and neutral labels can be assigned to both the whole images or parts of them. The user interface is capable of exploiting segmentation data to assist in the selection of objects. Moreover, the annotation capabilities are complemented with additional functionalities that allow the creation and evaluation of an image classifier. The implemented Java source code is published under a free software license.
Recent advances in carbocupration of α-heterosubstituted alkynes

Carbocupration of α-heterosubstituted alkynes leads to the formation of stereodefined functionalized vinyl copper species as single isomer. Recent advances in the field show that a simple pre-association of the organometallic derivative with an additional polar functional group in the vicinity of the reaction center may completely change the stereochemical outcome of the reaction. Representative examples are given in this mini-review.
On nearly optimal controls of hybrid LQG problems

The paper is concerned with hybrid control of a class of linear quadratic Gaussian (LQG) systems modulated by a finite-state Markov chain. It develops approximation schemes for systems involving singularly perturbed Markov chains with weak and strong interactions, which are useful for natural time-scale separation and for large-scale Markovian systems. The presentation encompasses three cases: the recurrent Markov chains, inclusion of transient states, and inclusion of absorbing states. Near optimality of the approximation schemes is obtained, and a numerical example is presented. Computational results indicate our approximation schemes perform well.
Significant Closure of the Human Immunodeficiency Virus Type 1 and Hepatitis C Virus Preseroconversion Detection Windows with a Transcription-Mediated-Amplification-Driven Assay

ABSTRACT While the present generation of serology-based assays has significantly decreased the number of human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV) infections acquired by transfusion, the possibility of infected donations escaping detection still exists. The average seronegative viremic window duration during which immunological assays are unable to detect the virus is estimated to be between 16 and 22 days for HIV-1 and approximately 70 days for HCV. Significant reduction of detection window duration was demonstrated using a nucleic acid amplification assay, the Procleix HIV-1/HCV Assay, which utilizes transcription-mediated amplification technology to simultaneously detect HIV-1 and HCV RNAs. For 26 commercially available HIV-1 seroconversion panels tested, specimens were reactive in the HIV-1/HCV assay at the same time as or earlier than in serological assays. Overall, the HIV-1/HCV assay was able to reduce the detection window duration by an average of 14 days and 6 days compared to tests relying on recognition of HIV-1 antibody and p24 antigen, respectively. For 24 commercially available HCV seroconversion panels tested, the specimens were reactive in the HIV-1/HCV assay at an earlier blood sampling date than in serological assays, reducing the detection window duration by an average of 26 days. Similar results were obtained in testing the HIV-1 and HCV seroconversion panels in the virus-specific HIV-1- and HCV-discriminatory assays, respectively. In conclusion, the HIV-1/HCV assay and corresponding discriminatory assays significantly reduced detection window durations compared to immunoassays.
Gath-geva approach to forecast electric energy consumption

Short-term load forecasting is necessary for adequate scheduling and operation of power systems. It's generally made by developing models in relations to climate and previous load data. In this paper, we discuss in detail how Fuzzy clustering based on Gath and Geva algorithm is successfully applied to electric load forecasting. Results and discussions from real-world case studies based on data from RTE France of electricity consumption in 2010 are presented. The variance accounted for (VAF) and the RMSE indices used in modeling process are respectively 99.5059 and 0.0545 for training and 85.8950, 0.1872 for validation showing the good prediction performance and the suitability of the proposed approach for short-term electric load forecasting.
Determining web pages similarity using distributed learning automata and graph partitioning

Determining similarity between web pages is a key factor for the success of many web mining applications such as recommendation systems and adaptive web sites. In this paper, we propose a new hybrid method of distributed learning automata and graph partitioning to determine similarity between web pages using the web usage data. The idea of the proposed method is that if different users request a couple of pages together, then these pages are likely to correspond to the same information needs therefore can be considered similar. In the proposed method, a learning automaton is assigned to each web page and tries to find the similarities between that page and other pages of a web site utilizing the results of a graph partitioning algorithm performed on the graph of the web site. Computer experiments show that the proposed method outperforms Hebbian algorithm and the only learning automata based method reported in the literature.
Exploratory analysis of predictors of patient adherence to subcutaneous interferon beta-1a in multiple sclerosis: TRACER study

ABSTRACT Objective: The TRACER multicenter retrospective study aimed to collect data on treatment adherence in a real-life setting, in order to identify predictors of adherence at baseline. Methods: We recruited 384 relapsing-remitting (RR) multiple sclerosis patients with at least 12 months of use of RebiSmart®. This electronic device records the performed injections and assesses adherence as the percentage of ‘not missing doses’, through the connection to the iMed database. Subjects with at least 80% of completed doses at the 12 month of therapy were defined ‘treatment adherents’. Results: After 12 months, 89.3% of patients were adherent; 93.2% of patients aged 26–40 years at baseline were adherent (vs 79% of the ≤25 and 87.5% of the ≥41 year olds; p = 0.006). Furthermore, 90.5% of patients with a baseline Expanded Disability Status Scale (EDSS) score <4 showed ≥80% adherence (vs 71.4% in those with EDSS score ≥4; p = 0.016). Fifty-four percent of the patients who were not adherent after 3 months were also not adherent after 12 months (OR 16.8; CI 95%:7.1–39.8). Conclusions: Patients aged 26–40 years and with an EDSS score <4 at baseline were the most adherent. The status of ‘treatment adherent’ in the first 3 months was predictive of higher adherence in the long term.
Determination of Tissue Volumes

Purpose: To evaluate MR imaging for tissue volume measurements. Material and Methods: Imaging was done with a spoiled gradient echo technique using the human lower leg as a simple model and CT as the reference technique. Areas of adipose tissue, muscle plus skin and bone tissue were evaluated in 5 volunteers using signal intervals (MR) and attenuation intervals (CT) defined by histograms. Volumes were determined from 7 images (MR-7, CT-7), from 70 contiguous images (MR-70), and also from a commercially-available 3D program based on a seeding technique applied according to the operators' judgment (MR-op). Results: the sum of bone tissue, muscle plus skin and adipose tissue were similar for CT and MR. Compared to CT-7, MR-7 and MR-70 overestimated adipose tissue (0–8%) and muscle plus skin (11–15%), while bone tissue was underestimated (61–78%). Determinations based on the contiguous images or the 7 images were similar (standard error 2–5%). MR-op resulted in pronounced underestimation of muscle plus skin and bone tissue and a minor underestimation of adipose tissue. Conclusion: Valid determinations of adipose tissue volume are possible with MR but further technical development is required for other tissue volumes. the technique applied according to the operators' judgement, as used for 3D surface rendition, is unreliable for tissue volume determinations.
The Potential of a Hybrid Fixed/User Relay Architecture - A Performance Analysis

Future wireless systems are likely to comprise a dense grid of fixed relay nodes (FRNs) and contain a large number of user terminals (UTs) that can act as user relay nodes (URNs) under certain circumstances. In this paper, we argue that significant gains can be attained if FRNs and URNs act together under the umbrella of a hybrid architecture. To this end, we perform an exact and asymptotic outage probability (OP) analysis of a dual hop system with single relay selection under Rayleigh fading. Furthermore, we derive an ergodic capacity upper bound as a function of the number of FRNs and URNs. We show that in low signal-to-noise ratio (SNR), FRNs are much more effective in reducing OP than URNs. Although it is always more beneficial to employ FRNs instead of URNs, in high SNR URNs are almost equally as effective as FRNs in reducing OP. In terms of ergodic capacity, FRNs achieve higher performance than URNs in all SNR regimes. We conclude that from a system design viewpoint, if the aim is to meet certain quality-of-service constraints, FRNs can be reserved to serve low SNR users, while URNs could be employed when channel conditions to destination UTs are more favorable.