Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Compression, Encryption and Fault-tolerant Coding for Distributed Storage

Storing data in distributed systems aims to offer higher bandwidth and scalability than storing locally. But, a couple of disadvantageous issues must be taken into account such as unreliability caused by faults, temporal downtimes and malicious attacks. To improve dependability, redundancy codes like parity can be used as well as more sophisticated codes such as Reed/Solomon. Another issue-security requirements-arise when data is kept in untrusted units in a network. To encrypt data, it is common to use security algorithms like AES. For efficient transfer and storage, the amount of data can be reduced by compression algorithms. All these techniques-data distribution, fault-tolerant coding, encryption and compression-can be employed together using independent algorithms, but in a proper combination. A superposition of these techniques exploiting synergies is still an issue for research. Thus, in this paper we study proper technique combinations applied to distributed storage. The combinations are classified and examined with respect to their potential benefit and limitations. For our model, performance parameters from the distributed storage system NetRAID are used.
Draft Genome Sequence for Ralstonia sp. Strain OR214, a Bacterium with Potential for Bioremediation

ABSTRACT Ralstonia sp. strain OR214 belongs to the class Betaproteobacteria and was isolated from subsurface sediments in Oak Ridge, TN. A member of this genus has been described as a potential bioremediation agent. Strain OR214 is tolerant to various heavy metals, such as uranium, nickel, cobalt, and cadmium. We present its draft genome sequence here.
Stability and transient analyses of temporal Talbot-effect-based repetition-rate multiplication mode-locked laser systems

We demonstrate four-times repetition rate multiplication on 10-Gb/s pulse train generated from the active harmonically mode-locked fiber ring laser, hence, achieving 40-Gb/s pulse train by using fractional temporal Talbot effect. The stability and the transient response of the multiplied pulses are studied using the phase-plane technique of nonlinear control engineering. It has been found that the stability of the Talbot-based multiplied pulse train, based on the phase-plane analysis, is hardly achievable even under the perfect multiplication conditions. Furthermore, uneven pulse amplitude distribution is observed in the multiplied pulse train.
Synthesis of CdIn2S4 Microsphere and Its Photocatalytic Activity for Azo Dye Degradation

CdIn2S4 was prepared by ultrasonic spray pyrolysis. The prepared CdIn2S4 was characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FSEM), transmission electron microscopy (TEM), UV-visible diffuse reflectance spectroscopy (UV-Vis DRS), and N2-sorption techniques. Aqueous photocatalytic activity was evaluated by the decomposition of methyl orange under visible light irradiation. The results indicate that the prepared CdIn2S4 has spherical morphology with mesoporous structure which can efficiently degrade methyl orange in water. The sample prepared at 500°C exhibits the optimized photocatalytic activity.
Sulphonylureas-Induced Increase in Insulin Binding and Glucose Metabolism by Isolated Rat Adipocytes

The effects of oral hypoglycaemic drugs, SPC-703 (n-/p-toluenesulphonyl/-5-methyl-2-pirazoline-1-carbonami de) and tolbutamide on insulin binding and glucose metabolism by isolated adipocytes were studied. After 10 days of administration of both sulphonylurea derivatives, no differences were observed in insulin concentration between both experimental and the control groups of animals, despite a significant fall in blood glucose level. SPC-703 and tolbutamide in concentrations of 1 mM added in vitro to the suspension of adipocytes had no effect on insulin binding or on basal and insulin simulated glucose metabolism. Daily administration of 300 mg/kg body weight of SPC-703 or tolbutamide for 10 days resulted in 48% and 34% increase of specific binding of insulin by adipocytes, respectively. From the Scatchard plot analysis we noted that the increase of binding resulted from increased affinity of insulin receptors for hormone. Simultaneous increase in basal and insulin stimulated glucose metabolism by adipocytes, as measured by 14CO2 production and 14C incorporation into cellular lipids, was observed. The results indicate that hypoglycaemic action of sulphonylureas may be explained by increased affinity of insulin receptors and the stimulating action of these compounds on peripheral glucose metabolism.
Exploiting Social Capabilities in the Minority Game

The minority game (MG) is an inductive binary-decision model that is able to study emergent behaviors in a population of agents who compete, through adaptation, for scarce resources. The original formulation of the game was inspired by the W.B. Arthur’s El Farol Bar problem: a fixed number of people have to independently decide, each week, whether to go to a bar having a limited capacity. People’s choices are only affected by the information about the number of visitors who attended the bar in the past weeks. Basic MG assumes that the information about the past game outcomes is publicly available, and it does not contemplate any communication among players. This article proposes the Dynamic Sociality Minority Game (DSMG). DSMG is an original variant of the classic MG where (1) information about the outcome of the previously played game step is only known to agents that really attended the bar the previous week, and (2) a dynamically established acquaintance network is introduced to propagate such information to nonattendant players. Specific settings of the game are identified in which DSMG is able to show a better coordination level among players with respect to the standard MG. Emergent properties of the DSMG along with players’ wellness are thoroughly analyzed through agent-based simulation of a simple road-traffic model.
A Parallel Computational Model for Multichannel Phase Unwrapping Problem

In this paper, a parallel model for the solution of the computationally intensive multichannel phase unwrapping (MCh-PhU) problem is proposed. Firstly, the Extended Minimum Cost Flow (EMCF) algorithm for solving MCh-PhU problem is revised within the rigorous mathematical framework of the discrete calculus; thus permitting to capture its topological structure in terms of meaningful discrete differential operators. Secondly, emphasis is placed on those methodological and practical aspects, which lead to a parallel reformulation of the EMCF algorithm. Thus, a novel dual-level parallel computational model, in which the parallelism is hierarchically implemented at two different (i.e., process and thread) levels, is presented. The validity of our approach has been demonstrated through a series of experiments that have revealed a significant speedup. Therefore, the attained highperformance prototype is suitable for the solution of large-scale phase unwrapping problems in reasonable time frames, with a significant impact on the systematic exploitation of the existing, and rapidly growing, large archives of SAR data.
Middle Cerebral Artery Infarcts Encompassing the Insula Are More Prone to Growth

Background and Purpose— Based on previous observations that infarcts encompassing the insula were linked to unfavorable clinical outcome, we hypothesized that insular damage was directly associated with worsened infarction in ischemic but potentially viable neighboring brain tissue. Methods— Using acute diffusion- and perfusion-weighted MRI within the first 12 hours of symptom onset and a follow-up MRI on day 5 or later, we calculated the percentage of mismatch lost (PML) in 61 consecutive patients with ischemic stroke within the middle cerebral artery territory. PML denoted the percentage of mismatch tissue between diffusion-weighted imaging and mean transit time maps that eventually underwent infarction. We explored the relationship between PML and insular location using a regression model. Results— The median PML was 17.7% (interquartile range, 3.5% to 54.2%) in insular and 2.5% (0.0% to 12.7%) in noninsular infarcts (P<0.01). The PML correlated with the volume of abnormal regions on diffusion-weighted imaging (P<0.01), mean transit time (P<0.01), cerebral blood flow maps (P<0.01), and cerebral blood volume maps (P<0.01). A linear regression model with PML as response and with acute MRI volumes, age, and the site of vascular occlusion as covariates showed that insular involvement was an independent predictor of PML (P=0.01). The regression model predicted an approximately 3.2-fold increase in PML with insular involvement. Conclusions— Infarction of the insula is associated with increased conversion of ischemic but potentially viable neighboring tissues into infarction. The unfavorable tissue outcome in insular infarcts may not be a mere bystander effect from proximal middle cerebral artery occlusions.
Distributed non-smooth resource allocation over a network

Networked systems are common and crucial. One of the canonical problems in such systems is distributed resource allocation. From this rather broad class of problems, we consider a convex non-smooth resource allocation problem with a global resource constraint. Specifically, the objective function is separable and consists of a sum of convex functions, each associated with a node in a given network. Each component of the objective depends on a single variable local to the associated node and the sum of all local variables must remain constant at all times. For scalability, we constrain the nodes to only communicate and exchange resources with their immediate neighbors. We propose an algorithm that combines subgradient optimization with distributed averaging. Starting the algorithm from a feasible point, the nodes iteratively exchange resources with their neighbors to get close to the optimal set while satisfying the total resource constraint at all times. We show that under mild technical conditions the algorithm converges in an epsilon-sense, as long as the stepsize is chosen sufficiently small and the distributed averaging process is sufficiently accurate. We derive expressions for how the stepsize and the number of consensus iterations affect the accuracy of the final result.
Segmentation of dance movement: effects of expertise, visual familiarity, motor experience and music

According to event segmentation theory, action perception depends on sensory cues and prior knowledge, and the segmentation of observed actions is crucial for understanding and memorizing these actions. While most activities in everyday life are characterized by external goals and interaction with objects or persons, this does not necessarily apply to dance-like actions. We investigated to what extent visual familiarity of the observed movement and accompanying music influence the segmentation of a dance phrase in dancers of different skill level and non-dancers. In Experiment 1, dancers and non-dancers repeatedly watched a video clip showing a dancer performing a choreographed dance phrase and indicated segment boundaries by key press. Dancers generally defined less segment boundaries than non-dancers, specifically in the first trials in which visual familiarity with the phrase was low. Music increased the number of segment boundaries in the non-dancers and decreased it in the dancers. The results suggest that dance expertise reduces the number of perceived segment boundaries in an observed dance phrase, and that the ways visual familiarity and music affect movement segmentation are modulated by dance expertise. In a second experiment, motor experience was added as factor, based on empirical evidence suggesting that action perception is modified by visual and motor expertise in different ways. In Experiment 2, the same task as in Experiment 1 was performed by dance amateurs, and was repeated by the same participants after they had learned to dance the presented dance phrase. Less segment boundaries were defined in the middle trials after participants had learned to dance the phrase, and music reduced the number of segment boundaries before learning. The results suggest that specific motor experience of the observed movement influences its perception and anticipation and makes segmentation broader, but not to the same degree as dance expertise on a professional level.
Design of a FPGA based position PI servo controller for a DC motor with dry friction

In this paper we present the design, implementation and experimental validation of a FPGA based position servo controller for a DC motor with dry friction. VHDL and block diagram modules for trajectory generation, encoder signal decoding, PI controller and PWM control signal generation are described. The control system is implemented in the DE3 board of Terasic Technologies Inc using Quartus II environment of Altera Corporation. Servo system simulation was made using Simulink. The analytical non linear model for the anti-windup saturation on the integral control action, saturation on the actuator and dry friction is validated through experiments and simulations.
Transactions Adaptables pour les Environnements Mobiles

Dans cette these,nous nous interessons aux transactions dans les environnements mobiles. La problematique visee est l'influence du contexte sur l'execution des transactions. En effet, les environnements mobiles se caracterisent par une grande variabilite au niveau reseau sans fil et par des unites mobiles a ressources limitees. Ces caracteristiques affectent la gestion des donnees, en particulier, elles entrainent un nombre important de defaillances transactionnelles et des couts d'execution imprevus. Nous proposons un modeles de Transactions Mobiles Adaptables (AMT) permettant de definir des transactions avec plusieurs alternatives d'execution. Ce modele offre la possibilite d'adapter les transactions au contexte et de maitriser le cout de leur execution selon des criteres de qualite acceptables pour l'application. Le modele AMT a ete specifie avec le formalisme ACTA. Nous proposons une etude analytique qui montre que (1) les transactions de type AMT augmentent la probabilite de validation, et que (2) les couts deviennent previsibles grâce a la possibilite de choisir le type d'execution en fonction du contexte. Pour valider notre approche, nous avons concu l'intergiciel TransMobi qui gere la perception de l'environnement et qui implante le modele AMT a l'aide de protocoles appropries. En particulier, nous proposons le protocole de validation CO2PC qui assure l'atomicite semantique. Nous avons developpe un prototype de TransMobi en PersonalJava qui utilise des bases de donnees relationnelles, un reseau WLAN et des assistants personnels (PDA).
CloudSimNFV: Modeling and Simulation of Energy-Efficient NFV in Cloud Data Centers

Network Function Virtualization (NFV) takes advantage of hardware virtualization to undertake software processing for various functions, and complements the drawbacks of traditional network technology. To speed up NFV related research, we need a user friendly and easy to use research tool, which could support data center simulation, scheduling algorithms implementation and extension, and provide energy consumption simulation. As a cloud simulation toolkit, CloudSim has strong extendibility that could be extended to simulate NFV environment. This paper introduces a NFV cloud framework based on CloudSim and an energy consumption model based on multi-dimensional extension, implementing a toolkit named ClousimNFV to simulate the NFV scenario, proposing several scheduling algorithm based on for NFV applications. The toolkit validation and algorithm performance comparison are also given.
Hardware image assessment for wireless endoscopy capsules

Wireless capsule endoscopy is a new technology in the realm of telemedicine that has many advantages over the traditional endoscopy systems. Transmitted images should help diagnosis of diseases of the gastrointestinal tract. Two important technical challenges for the manufacturers of these capsules are power consumption and size of the circuitry. Also, the system must be fast enough for real-time processing of image or video data. To solve this problem, many hardware designs have been proposed for implementation of the image processing unit. In this paper we propose an architecture that could be used for the assessment of endoscopy images. The assessment allows avoidance of transmission of medically useless images. Hence, volume of data is reduced for more efficient transmission of images by the endoscopy capsule. This is done by color space conversion and moment calculation of images captured by the capsule. The inputs of the proposed architecture are RGB image frames and the outputs are images with converted colors and calculated image moments. Experimental results indicate that the proposed architecture has low complexity and is appropriate for a real-time application.
A novel approach to examining compositional heterogeneity of detergent‐resistant lipid rafts

Lipid rafts play an important role in cell signalling, cell adhesion and other cellular functions. Compositional heterogeneity of lipid rafts provides one mechanism of how lipid rafts provide the spatial and temporal regulation of cell signalling and cell adhesion. The constitutive presence of some signalling receptors/molecules and accumulation of others in the lipid raft allows them to interact with each other and thereby facilitate relay of signals from the plasma membrane to the cell interior. Devising a method that can analyze these lipid microdomains for the presence of signalling receptors/molecules on an individual raft basis is required to address the issue of lipid raft heterogeneity. SDS–PAGE analysis, currently used for analyses of detergent‐resistant lipid rafts, does not address this question. We have designed a cell‐free assay that captures detergent‐resistant lipid rafts with an antibody against a raft‐resident molecule and detects the presence of another lipid raft molecule. Our results suggest that detergent‐resistant lipid rafts, also known as detergent‐resistant membranes, are heterogeneous populations on an immortalized mouse T‐cell plasma membrane with respect to antigen receptor/signalling complex and other signalling/adhesion proteins. This cell‐free assay provides a simple and quick way to examine the simultaneous presence of two proteins in the lipid rafts and has the potential to estimate trafficking of molecules in and out of the lipid microdomains during cell signalling on a single detergent‐resistant lipid raft basis.
Classification of Internet traffic using artificial neural networks

Artificial Neural Networks have for long been used for nonlinear pattern recognition and other signal processing applications. In this work we use them to classify Internet traffic based on the application to which the traffic packets belong. The classification of Internet traffic is an active research topic due to its applicability in the areas like differentiated services and network security. Traditionally, such a classification is done using the packet header field of ‘port number’, which is a unique number associated with the application that generated the packet. However, certain recent developments in networking techniques have rendered the port numbers unreliable for this purpose. This motivates our scheme of classification that uses statistical information and thus, it does not involve reading any packet headers to determine the application. The classification with artificial neural networks is done using a conventional feed-forward backpropagation network with three layers. The number of nodes in the hidden layer is empirically selected such that the performance function, i.e. the mean square error in case of feed-forward networks, is minimized. A comparison is also made with a similar classification done using the classical statistical method of clustering. It is observed that a highly accurate classification is obtained using either approach, with the artificial neural networks giving slightly better results. The neural net approach is also more time-efficient because the training is done offline and the process of classification involves only simulating the trained network for one input. One more forte of the neural network approach is the capability to adapt with the system dynamics. The classification results mentioned in this paper indicate that artificial neural networks exhibit a strong potential for use in applications involving classification of Internet traffic flows.
A high-throughput media design approach for high performance mammalian fed-batch cultures

An innovative high-throughput medium development method based on media blending was successfully used to improve the performance of a Chinese hamster ovary fed-batch medium in shaking 96-deepwell plates. Starting from a proprietary chemically-defined medium, 16 formulations testing 43 of 47 components at 3 different levels were designed. Media blending was performed following a custom-made mixture design of experiments considering binary blends, resulting in 376 different blends that were tested during both cell expansion and fed-batch production phases in one single experiment. Three approaches were chosen to provide the best output of the large amount of data obtained. A simple ranking of conditions was first used as a quick approach to select new formulations with promising features. Then, prediction of the best mixes was done to maximize both growth and titer using the Design Expert software. Finally, a multivariate analysis enabled identification of individual potential critical components for further optimization. Applying this high-throughput method on a fed-batch, rather than on a simple batch, process opens new perspectives for medium and feed development that enables identification of an optimized process in a short time frame.
Evaluation of the genetic overlap between osteoarthritis with body mass index and height using genome-wide association scan data

Objectives Obesity as measured by body mass index (BMI) is one of the major risk factors for osteoarthritis. In addition, genetic overlap has been reported between osteoarthritis and normal adult height variation. We investigated whether this relationship is due to a shared genetic aetiology on a genome-wide scale. Methods We compared genetic association summary statistics (effect size, p value) for BMI and height from the GIANT consortium genome-wide association study (GWAS) with genetic association summary statistics from the arcOGEN consortium osteoarthritis GWAS. Significance was evaluated by permutation. Replication of osteoarthritis association of the highlighted signals was investigated in an independent dataset. Phenotypic information of height and BMI was accounted for in a separate analysis using osteoarthritis-free controls. Results We found significant overlap between osteoarthritis and height (p=3.3×10−5 for signals with p≤0.05) when the GIANT and arcOGEN GWAS were compared. For signals with p≤0.001 we found 17 shared signals between osteoarthritis and height and four between osteoarthritis and BMI. However, only one of the height or BMI signals that had shown evidence of association with osteoarthritis in the arcOGEN GWAS was also associated with osteoarthritis in the independent dataset: rs12149832, within the FTO gene (combined p=2.3×10−5). As expected, this signal was attenuated when we adjusted for BMI. Conclusions We found a significant excess of shared signals between both osteoarthritis and height and osteoarthritis and BMI, suggestive of a common genetic aetiology. However, only one signal showed association with osteoarthritis when followed up in a new dataset.