Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing accuracy of multi-label classification by applying one-vs-one support vector machine

Multi-label classification is a supervised learning, where one example can belong to several classes. In the case of Support Vector Machine (SVM), One-versus-All (OVA) is the most common approach to tackle this problem. However, the accuracy is very limited due to extremely imbalanced training set. It is interesting that there have only very few works that applied One-versus-One (OVO) in the multi-label domain even though it has been shown to provide better accuracy than OVA in the multiclass domain. In this paper, we propose a multi-label classification framework that employs OVO incorporating with the undersampling technique to alleviate the imbalanced issue. In the experiment, there are five standard benchmarks. The results show that our proposed algorithm outperforms OVA and traditional OVO in all data sets in terms of accuracy and F1.
Management Practices and Their Potential Influence on Johne's Disease Transmission on Canadian Organic Dairy Farms—A Conceptual Analysis

Johne’s disease (JD) is a chronic, production-limiting disease of ruminants. Control programs aiming to minimize the effects of the disease on the dairy industry have been launched in many countries, including Canada. Those programs commonly focus on strict hygiene and management improvement, often combined with various testing methods. Concurrently, organic dairy farming has been increasing in popularity. Because organic farming promotes traditional management practices, it has been proposed that organic dairy production regulations might interfere with implementation of JD control strategies. However, it is currently unclear how organic farming would change the risk for JD control. This review presents a brief introduction to organic dairy farming in Canada, JD, and the Canadian JD control programs. Subsequently, organic practices are described and hypotheses of their effects on JD transmission are developed. Empirical research is needed, not only to provide scientific evidence for organic producers, but also for smaller conventional farms employing organic-like management practices.
Why are patients with progressive nonfluent aphasia nonfluent?

Objective: To investigate the cognitive and neural basis for nonfluent speech in progressive nonfluent aphasia (PNFA). Background: Nonfluent speech is the hallmark feature of PNFA, and this has been attributed to impairments in syntactic processing, motor-speech planning, and executive functioning that also occur in these patients. Patients with PNFA have left inferior frontal atrophy. Methods: A large semi-structured speech sample and neuropsychological measures of language and executive functioning were examined in 16 patients with PNFA, 12 patients with behavioral-variant frontotemporal dementia (bvFTD), and 13 age-matched controls. Speech fluency was quantified as words per minute (WPM) in the semi-structured speech sample. Stepwise linear regression analyses were used to relate WPM to grammatic, motor-speech planning, and executive aspects of patient functioning. These measures were then related to cortical thickness in 8 patients with PNFA and 7 patients with bvFTD using structural MRI. Results: WPM was significantly reduced in patients with PNFA relative to controls and patients with bvFTD. Regression analyses revealed that only grammatic measures predicted WPM in PNFA, whereas executive measures were the only significant predictor of WPM in bvFTD. Cortical thinning was significant in PNFA relative to controls in left inferior frontal and anterior-superior temporal regions, and a regression analysis related this area to reduced WPM in PNFA. Significant cortical thinning associated with limited grammatic processing also was seen in the left inferior frontal-superior temporal region in PNFA, and this overlapped with the area of frontal-temporal thinning related to reduced WPM. Conclusion: Nonfluent speech in PNFA may be due in part to difficulty with grammatic processing associated with left inferior frontal and anterior-superior temporal disease.
An interval-based algorithm to represent conformational states of experimentally determined polypeptide templates and fast prediction of approximated 3D protein structures

Predicting the three-dimensional (3-D) structure of a protein that has no templates in the Protein Data Bank (PDB) is a very hard, still an impossible task. Computational prediction methods have been developed during the last years, but the problem still remains challenging. In this paper we present a new strategy based on Interval Arithmetic to store structural information obtained from experimental protein templates and predict native-like approximate three-dimensional structures of proteins. Our objective is to perform the prediction in a very fast manner and predict native-like structures that can be used as starting point structures to ab initio methods. We illustrate the efficacy of our method in five case studies of polypeptides.
Large-Scale In Vitro Expansion of Polyclonal Human Switched-Memory B Lymphocytes

Polyclonal preparations of therapeutic immunoglobulins, namely intravenous immunoglobulins (IVIg), are essential in the treatment of immunodeficiency and are increasingly used for the treatment of autoimmune and inflammatory diseases. Currently, patients’ accessibility to IVIg depends exclusively upon volunteer blood donations followed by the fractionation of pooled human plasma obtained from thousands of individuals. Presently, there are no in vitro cell culture procedures allowing the preparation of polyclonal human antibodies. All in vitro human therapeutic antibodies that are currently generated are based on monoclonal antibodies, which are mostly issued from genetic engineering or single cell antibody technologies. Here, we describe an in vitro cell culture system, using CD40-CD154 interactions, that leads to a 1×106-fold expansion of switched memory B lymphocytes in approximately 50 days. These expanded cells secrete polyclonal IgG, which distribution into IgG1, IgG2, IgG3 and IgG4 is similar to that of normal human serum. Such in vitro generated IgG showed relatively low self-reactivity since they interacted moderately with only 24 human antigens among a total of 9484 targets. Furthermore, up to one liter of IgG secreting cells can be produced in about 40 days. This experimental model, providing large-scale expansion of human B lymphocytes, represents a critical step toward the in vitro production of polyclonal human IgG and a new method for the ex vivo expansion of B cells for therapeutic purposes.
Enhanced Locality Preserving Projections

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data. In this paper, a new manifold learning algorithm, called Enhanced Locality Preserving Projections, to identify the underlying manifold structure of a data set. ELPP considers both the between-class scatter and the within-class scatter in the processing of manifold learning. Equivalently, the goal of ELPP is to preserve the within-class geometric structure, while maximizing the between-class distance. Different from Principal component analysis (PCA) that aims to find a linear mapping which preserves total variance by maximizing the trace of feature variance and the optimal mapping is the leading eigenvectors of the total variance matrix associated with the leading eigenvalues, While locality preserving projections(LPP)that is in favor of preserving the local structure of the data set. We choose proper dimension of subspace that detects the intrinsic manifold structure for classification tasks. Experimental results on UMIST face database showed ELPP can represent class separability and clustering performance better than LDA and MMC. Extensive experiments on face recognition show the effectiveness of the proposed ELPP method.
A Heterogeneous MPSoC with Hardware Supported Dynamic Task Scheduling for Software De(cid:2)ned Radio

In this paper, we present a fully programmable, heterogeneous single chip SDR platform with multimedia support. Running at 175 MHz, a peak performance of 40 GOPS is delivered while dissipating 1.5 W. The chip contains a hardware unit called CoreManager for run-time scheduling of tasks. The CoreManager solves the typical MPSoC pro-grammability problem, improves energy e–ciency and makes the platform scalable.
Printed Antenna Arrays with High Side Lobe Suppression

The problem of side lobe suppression (SLS) in printed antenna arrays has been investigated in the paper. Influence of several factors that make difficult design and realization of antenna arrays with relatively high SLS has been analyzed. We introduced a new type of printed antenna array with symmetrical pentagonal dipoles and symmetrical tapered feed network with Chebyshev distribution enabling SLS better than 34 dB in E-plane. Agreement between simulated and measured results is very good. The antenna is suitable for integration with other microwave circuits. Presented antenna is low cost and very simple for realization.
Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously—elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding ‘feeder’ cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.
Performance of low-complexity RAKE reception in a realistic UWB channel

We evaluate the link performance of an ultra-wide band (UWB) system using reduced-complexity RAKE receivers, which are based on either partial combining (called PRAKE) or selective combining (called SRAKE). The first is suboptimum and combines the first arriving multipath components, while the second combines the strongest multipath components. We use a statistical tapped-delay-line channel model that is based on extensive measurement campaigns, and reflects both small-scale and large-scale variations of the channel. Through semi-analytical evaluations of the bit error probability, we show that the simpler PRAKE receiver is almost as good as the SRAKE even for a small number of fingers. We show how this behavior can be related to the channel characteristics.
Dynamic flow control using packet loss rate and round trip time

This paper proposes a dynamic end-to-end flow control algorithm that is more effective than previous methods considering either the Rate of Packet Loss (RPL) or Round Trip Time (RTT). According to our quantitative analysis, the proposed method performs 1.6 to 6 times better than the previous method in terms of the RPL. At the same time, the total number of transmitted packets is increased, which indicates that the proposed method can provide greater bandwidth capacity than previous methods.
A HYBRID MODELLING APPROACH FOR INFLUENZA EPIDEMICS BASED ON CELLULAR AUTOMATA AND AGENT BASED TECHNIQUES

The classical tools for modeling epidemics are ODEs and PDEs, but they have some shortcomings. It is extremely complex to expand the model onto heterogeneous populations (age-groups, sex, infection risk, etc.) or to add time dependend effects (e.g. daily routines or vaccination after infection). Thanks to electronic data processing there is also much more data available nowadays than 100 or even 30 years ago, making it possible to create new kinds of models. Cellular automata (CA) and agent-based (AB) computing allow a different approach of the problem. CA by definition introduce spatial structures to a model. AB systems even take it a step further, they are based on agents that may hold various characteristics (age, sex, membership of a certain family or neighborhood, etc.). Because of their distinct strengths and drawbacks a combination of both methods seems promising and therefore was implemented for the simulation of influenza outbreaks in urban populations. An AB framework was created allowing the population to be heterogeneous, within it CA are introduced for timeconsuming computation of small units such as schools or neighborhoods. The framework allows it to steer the daily routine of the population and control its movement from one unit to another. The results of the model are fairly interesting as they show a behavior similar to real influenza patterns. Result for population of one million agents
Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges

Social influence analysis has become one of the most important technologies in modern information and service industries. It will definitely become an essential mechanism to perform complex analysis in social networking big data. It is attracting an increasing amount of research ranging from popular topics extraction to social influence analysis, including analysis and processing of big data, social influence evaluation, influential users identification, and information diffusion modeling. We provide a comprehensive investigation of social influence analysis, and discuss the characteristics of social influence and the architecture of social influence analysis based on social networking big data. The relationship between big data and social influence analysis is also discussed. In addition, research challenges relevant to real-world issues based on social networking big data in social influence analysis are discussed, focusing on research issues such as scalability, data collection, dynamic evolution, causal relationships, network heterogeneity, evaluation metrics, and effective mechanisms. Our goal is to provide a broad research guideline of existing and ongoing efforts via social influence analysis in large-scale social networks, and to help researchers better understand the existing work, and design new algorithms and methods for social influence analysis.
Latent infection of human bocavirus accompanied by flare of chronic cough, fatigue and episodes of viral replication in an immunocompetent adult patient, Cologne, Germany

Introduction: The human bocavirus (HBoV) is a parvovirus and is associated with mild to life-threatening acute or persisting respiratory infections, frequently accompanied by further pathogens. So far, there is limited knowledge on the mechanisms of persistence, and no reports on chronic infections or latency have been published so far. Case presentation: An immunocompetent male patient suffers from a chronic HBoV1 infection, i.e. viral DNA was detected in both serum and bronchoalveolar lavage (BAL) for >5 months without co-infections and with respiratory symptoms resolved spontaneously while receiving symptomatic treatment with montelukast and corticosteroids. Following the symptomatic medication of a chronic infection with HBoV1 viraemia indicating active viral replication lasting over 5 months, the patient cleared the viraemia and no further viral DNA was detectable in the BAL. However, by fluorescence in situ hybridization analyses of mucosal biopsies, it was shown that the virus genome still persisted in the absence of viral shedding but in a more compact manner possibly representing a supercoiled episomal form of this otherwise linear single-stranded DNA genome. This indicated the entry into a latency phase. Moreover, the cytokine profile and the IP-10/TARC ratio, a marker for fibrotization, seem to have been altered by HBoV1 replication. Although specific IgG antibodies were detectable during the whole observation period, they showed an apparently insufficient neutralising activity. Conclusion: On the one hand, these findings suggest that the symptomatic medication may have led to clearance of the virus from blood and airways and, moreover, that the viral DNA persists in the tissue as an altered episomal form favoured by lacking neutralising antibodies. This appears to be important in order to reduce possible long-term effects such as lung fibrosis.
Some Theorem Proving

Theorem proving can be a very useful formal method. However it currently takes a lot of time and study to learn how to use a theorem prover, and proving even apparently simple theorems can be tedious. Theorem proving, and its beneets in software and hardware development , should be accepted more readily and widely if new users can do larger proofs of more complete models earlier in their training and with less work. We present some generally applicable tools which we found helpful in formally verifying a secure web server. The rst is a program to check goals for common mistakes arising indirectly from type inference. We also give tactics, or proof advancing routines, to simplify goals and handle assumptions. Finally we give tactics which prove goals by selecting assumptions to establish the goal or nd a contradiction. These are another step to making theorem proving easier, increasing productive, and reducing unnecessary complication.
Global Management of Coherent Shared Memoryon an SCI

| The I/O-based implementations of the SCI standard allow cost-eecient use of shared memory on a wide range of cluster architectures. These implementations have typically been used for message-passing interfaces, but we are exploiting the use of I/O based SCI as a way to create NUMA architectures with commodity components. A major issue is that data placement and especially data consistency becomes the responsibility of the programmer. We propose an interface, called SciOS, that provides a coherent shared memory abstraction where all the physical memory in the cluster is treated like in a NUMA architecture. We use a relaxed memory consistency model, dynamic page migration , page replication, and use remote memory instead of swap to disk to lower the average memory access times. We describe our prototype based on Dolphin's PCI-SCI adapter, with focus on the global management of the shared memory.
High efficiency X-band AlGaN/GaN MMICs for space applications with lifetimes above 105 hours

We report on technology, performance and reliability of state-of-the-art AlGaN/GaN MMICs for space applications. Our quarter-micron gate length HEMTs have breakdown voltages beyond 150 V and deliver 5 W/mm output power density at 30 V drain bias with 50% PAE at 10 GHz operating frequency. Packaged two-stage MMICs with 8 W output power for telemetry applications have a PAE above 40% with a lifetime above 105 h at a channel temperature of 200°C. Initial space evaluation tests indicate a suitable stability of our technology in space.
Face recognition by optimal band selection in contourlet transform domain

Face Recognition (FR) under varying pose, illumination and expression (PIE) conditions is challenging, and extracting PIE-invariant features is an effective approach to solve this problem. For enhancing the performance of an FR system, this paper proposes a unique combination of Contourlet Transform (CT), Discrete Cosine Transform (DCT) and Binary Particle Swarm Optimization (BPSO). CT and DCT are used for efficient feature extraction and a BPSO-based feature selection algorithm searches the feature space for an optimal feature subset. The optimal band selection in multiresolution and multidirectional subspaces of CT enables extraction of PIE-invariant facial features. DCT reduces the feature-vector size, lays a ground for BPSO-based feature selection, and also aids in better classification. Experimental results show the promising performance of the proposed algorithm for enhanced face recognition on eight benchmark face databases, namely Color FERET, Pointing Head Pose and UMIST (pose); Extended Yale B and CMU-PIE (illumination); JAFFE and ORL (expression); CAS-PEAL (expression and lighting). A significant increase in the recognition rate and a substantial reduction in the number of features selected are observed as compared to other FR systems.
Image Compression Using SVD Technique and Measurement of Quality Parameters

Singular value decomposition (SVD) based algorithm is designed to compress the black and white images to evaluate the effect of information content of the image. Inorder to evaluate the effect second algorithm is developed to calculate the SNR, PSNR, MSE, and RMSE value of the compressed images with respect to reference image. It is interpreted from the results that high level of compression the information content of the image is degraded to higher level.
Improved multi-scale principal component analysis based OFDM channel estimation

High data rate wireless communication system introduces the unavoidable inter symbol interference (ISI), for which orthogonal frequency division multiplexing (OFDM) gives a best solution by utilizing the property of orthogonality among the sub carriers. In addition, estimating the channel characteristics under non-linear time varying random channel environment, is always a tough task for a successful communication of wireless data. By combining the merits of wavelet transform (WT) with principal component analysis (PCA), a signal processing method called improved multi-scale principal component analysis (I-MSPCA) was utilized here to solve the problem of channel estimation in OFDM communication system. Multi level decomposition nature of WT until the frequency range of interest gives a clear way to eliminate the correlated noise component, whereas PCA enables to identify the principal components of the received signal, which in turn make the process of channel estimation easier. General comparative measure, the bit error rate (BER) was calculated and compared to establish the efficacy of the proposed I-MSPCA method.
Vitality detection in personal authentication systems using fingerprints

Fingerprints are considered as the sign of each human being, and this has contributed the development of biometric applications based on such features. Since 2002, an important vulnerability has been shown: it is possible to deceive fingerprint scanners through artificial replicas of fingertips. In order to address this shortcoming it is need to recognize a spoofing attempt with artificial fingers looking for some “life signs” each time an user submit a fingerprint (vitality detection problem). Although this research field is still in its infancy, several methods have been proposed so far, based on additional hardware to the existing capture device and also on fingerprint image processing for extracting those “life sign” from the image captured by the sensor. The first goal of this Ph.D. thesis has been to investigate the current state-of-the-art in fingerprint vitality detection. We compared the performance of each fingerprint vitality detection approach. The second contribution of this Ph.D. thesis is the development of two different new approaches (“power spectrum” and “ridge-width”). The former (in the frequency domain) is based on 2D-Fourier Transform of the fingerprint image. The latter (in the space domain) is based on some morphological considerations (intra-distance ridges and ridge width). Experiments have been carried out on a dataset of images of “live” fingers and fake stamps collected at the DIEE laboratory in Cagliari (82 live fingers and 72 fake stamps, 20 acquisition for each finger/stamp).