Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Non-Stationary Data Retrieval in Wireless Sensor Networks

Wireless sensor networks provide an effective tool for acquiring sensor measurements across a large area. Devices in a sensor ﬁeld can automatically conﬁgure themselves into a working network, ready to collect data. The challenge is to collect that data in a timely and power-efﬁcient manner. In some environments, a data collection device (sink) may need to travel periodically through the ﬁeld to gather data because keeping a local storage system is impractical. The goal is for this moving sink to gather the most data in the shortest amount of time, using the least amount of inter-node communication. Network reconﬁgure time, memory limitations, and brief radio contact time challenge this goal. In this work, we explore three aspects of data retrieval. First, we explore with what nodes the sink should communicate. We present a selection technique for picking target nodes to increase data retrieval rates. We then optimize data collection through message forwarding and exclusion. These two techniques improve data retrieval by 35% on average. Finally, we consider the effect and efﬁciency of various sink paths.
Reusability at the Early Development Stages of Mobile Wireless Communication Systems

Development productivity and quality improvements, as well as cost reduction, are the main factors for the need and popularity of software reuse. In addition, the increasing demand for large and complex software systems, and the difficulty of supplying them on time and within the budget make the reusability concept attractive. In this context, this work provides a framework for mobile wireless communication systems that allows designers to apply reusability to the early stages of the development process. In order to achieve this, firstly, existing systems are investigated and common network entities and their respective functionalities are identified. Secondly, the framework is generated based on these commonalities with a technique called Use Case Maps.
Prototype filter design for filter bank based multicarrier transmission

This paper concentrates on an efficient prototype filter design in the context of filter bank based multicarrier (FBMC) transmission. An advantage of the chosen method, frequency sampling technique, is that near perfect reconstruction prototype filters can be expressed using a closed-form representation with only a few adjustable parameters. The performance of various designs are analyzed using the offset-QAM based FBMC system. Numerical results are provided to characterize different optimization criteria in terms of frequency selectivity of resulting prototype filters and total interference level of the filter bank structure. Furthermore, it is shown what kind of performance trade-offs can be obtained by adjusting those free parameters. In this sense, the presented results offer useful information to a system designer.
Application of the U.S. EPA Mode of Action Framework for Purposes of Guiding Future Research: A Case Study Involving the Oral Carcinogenicity of Hexavalent Chromium

Mode of action (MOA) analysis provides a systematic description of key events leading to adverse health effects in animal bioassays for the purpose of informing human health risk assessment. Uncertainties and data gaps identified in the MOA analysis may also be used to guide future research to improve understanding of the MOAs underlying a specific toxic response and foster development of toxicokinetic and toxicodynamic models. An MOA analysis, consistent with approaches outlined in the MOA Framework as described in the Guidelines for Carcinogen Risk Assessment, was conducted to evaluate small intestinal tumors observed in mice chronically exposed to relatively high concentrations of hexavalent chromium (Cr(VI)) in drinking water. Based on review of the literature, key events in the MOA are hypothesized to include saturation of the reductive capacity of the upper gastrointestinal tract, absorption of Cr(VI) into the intestinal epithelium, oxidative stress and inflammation, cell proliferation, direct and/or indirect DNA modification, and mutagenesis. Although available data generally support the plausibility of these key events, several unresolved questions and data gaps were identified, highlighting the need for obtaining critical toxicokinetic and toxicodynamic data in the target tissue and in the low-dose range. Experimental assays that can address these data gaps are discussed along with strategies for comparisons between responsive and nonresponsive tissues and species. This analysis provides a practical application of MOA Framework guidance and is instructive for the design of studies to improve upon the information available for quantitative risk assessment.
Projecte de suport escolar "Enxaneta"

Com una confrontació entre, d’una part, les característiques i les disposicions sociocognitives i sociolin-güístiques dels alumnes, que estan lligades als seus models de socialització no escolars (sobretot família i grup d’iguals) i que els preparen i els disposen de manera desigual a l’hora de fer front als requisits de l’aprenentatge escolar. I de l’altra part, l’opacitat i el caràcter implícit d’aquests requisits, els modes de funcionament del sistema educatiu, les pràctiques professionals i les formes de treball que s’exigeixen als alumnes.
Preferential Attachment Graphs Are All But Asymmetric ∗ July 6 , 2016

Recent years have seen a resurgence of interest in graph asymmetry and many of its novel applications ranging from graph compression to discovery of graph dynamics (e.g., age of a node). It has been known for some time that Erdős-Rényi graphs are asymmetric with high probability, but it is also known that real world graphs (web, biological networks) have a significant amount of symmetry. So the first natural question to ask is whether preferential attachment graphs in which in each step a new node with m edges is added exhibit any symmetry. It turns out that the problem is much harder to crack than in the Erdős-Rényi case and reveals unexpected results. First of all, in recent work it was proved that attachment graphs are symmetric for m = 1 (as expected) but surprisingly there is some non-negligible probability of symmetry for m = 2. The question was open for m ≥ 3. Based on empirical results it was conjectured, however, that the preferential attachment graphs are asymmetric with high probability when more than two edges are added each time a new node arrives. In this paper we settle this conjecture in the positive. This result has implications for other questions of interest, such as structural entropy of preferential attachment graphs, their structural compression, and even recovering node arrival times given an unlabeled preferential attachment graph.
State of the Art in Negotiation Ontologies for Enhancing Business Intelligence

Negotiation is defined as two or more agents making agreement on the common interests, which provides an efficient approach for companies to gain business intelligence from suppliers and customers. Current negotiation research focuses on automatically stimulating negotiation behaviors among agents. Ontology, as a modeling technology in semantic web, is regarded as an effective tool to improve the negotiation ability of agents, considering that ontology can provide richer semantic expressions for agents in negotiation process. In this paper, we make a brief review on the current negotiation ontology researches. In addition, by means of a unique ontology notation system, we unify the notations used in these ontologies, to maximally promote knowledge sharing outcome in this field. By comparing and contrasting the ontologies from five perspectives, we draw a brief conclusion to the present status of negotiation ontology researches, and make our suggestions to the further research in this field.
Folic acid supplementation in pregnancy: are there devils in the detail?

Maternal folic acid (FA) supplementation is well recognised to protect against neural tube defects. Folate is a critical cofactor in one-carbon metabolism involved in the epigenetic regulation of transcription that underpins development. Thus, it is possible that maternal FA supplementation may have additional, unforeseen persistent effects in the offspring. This is supported by the modification by maternal supplementation with one-carbon donors and FA of the epigenetic regulation of offspring phenotype in mutant mice. The present article reviews studies in human subjects and experimental animals of the effect of maternal FA intake and phenotypic outcomes in the offspring. Maternal FA intake was associated with a short-term increased incidence of allergy-related respiratory impairment in children and multigenerational respiratory impairment in rats. Higher maternal folate status during pregnancy was associated positively with insulin resistance in 6-year-olds. In rats, maternal FA supplementation modified hepatic metabolism and vascular function through altered transcription, in some cases underpinned by epigenetic changes. FA supplementation in pregnant rats increased mammary tumorigenesis, but decreased colorectal cancer in the offspring. Maternal FA supplementation decreased a range of congenital cardiac defects in children. These findings support the view that maternal FA supplementation induces persistent changes in a number of phenotypic outcomes in the offspring. However, the number of studies is limited and insufficient to indicate a need to change current recommendations for FA intake in pregnancy. Nevertheless, such effects should be investigated thoroughly in order to support firm conclusions about the risk of unanticipated long-term negative effects of maternal FA supplementation in humans.
Anti-Hemagglutinin Antibody Derived Lead Peptides for Inhibitors of Influenza Virus Binding

Antibodies against spike proteins of influenza are used as a tool for characterization of viruses and therapeutic approaches. However, development, production and quality control of antibodies is expensive and time consuming. To circumvent these difficulties, three peptides were derived from complementarity determining regions of an antibody heavy chain against influenza A spike glycoprotein. Their binding properties were studied experimentally, and by molecular dynamics simulations. Two peptide candidates showed binding to influenza A/Aichi/2/68 H3N2. One of them, termed PeB, with the highest affinity prevented binding to and infection of target cells in the micromolar region without any cytotoxic effect. PeB matches best the conserved receptor binding site of hemagglutinin. PeB bound also to other medical relevant influenza strains, such as human-pathogenic A/California/7/2009 H1N1, and avian-pathogenic A/Mute Swan/Rostock/R901/2006 H7N1. Strategies to improve the affinity and to adapt specificity are discussed and exemplified by a double amino acid substituted peptide, obtained by substitutional analysis. The peptides and their derivatives are of great potential for drug development as well as biosensing.
Ergodic Diffusion Control of Multiclass Multi-Pool Networks in the Halfin-Whitt Regime

We consider Markovian multiclass multi-pool networks with heterogeneous server pools, each consisting of many statistically identical parallel servers, where the bipartite graph of customer classes and server pools forms a tree. Customers form their own queue and are served in the first-come first-served discipline, and can abandon while waiting in queue. Service rates are both class and pool dependent. The objective is to study the limiting diffusion control problems under the long run average (ergodic) cost criteria in the Halfin--Whitt regime. Two formulations of ergodic diffusion control problems are considered: (i) both queueing and idleness costs are minimized, and (ii) only the queueing cost is minimized while a constraint is imposed upon the idleness of all server pools. We develop a recursive leaf elimination algorithm that enables us to obtain an explicit representation of the drift for the controlled diffusions. Consequently, we show that for the limiting controlled diffusions, there always exists a stationary Markov control under which the diffusion process is geometrically ergodic. The framework developed in our earlier work is extended to address a broad class of ergodic diffusion control problems with constraints. We show that that the unconstrained and constrained problems are well posed, and we characterize the optimal stationary Markov controls via HJB equations.
Enhancing Binary Feature Vector Similarity Measures

Similarity and dissimilarity measures play an important role in pattern classification and clustering. For a century, researchers have searched for a good measure. Here, we review, categorize, and evaluate various binary vector similarity / dissimilarity measures. One of the most contentious disputes in the similarity measure selection problem is whether the measure includes or excludes negative matches. While inner-product based similarity measures consider only positive matches, other conventional measures credit both positive and negative matches equally. Hence, we propose an enhanced similarity measure that gives variable credits and show that it is superior to conventional measures in IRIS biometric authentication and offline handwritten character recognition applications. Finally, the proposed similarity measure can be further boosted by applying weights and we demonstrate that it outperforms the weighted Hamming distance.
Image Similarity Based on Animate Vision: Information Path Matching

In this paper we show how, taking into account basic characteristics of biological vision, namely saccadic eye movements, a more effective image retrieval processing can be achieved. In particular, given two fixation sequences, extracted, respectively, from a target image and a test image, the sequences can be compared in order to provide a measure of similarity between the two images, and we propose two novel matching algorithms.
Coordinated local reactive power control in smart distribution grids for voltage regulation using sensitivity method to maximize active power

Distributed generation (DG) integrated into Smart grid is the future of distribution systems. The expected impact is wide in terms of control, management, supply and use. In particular, in order to integrate DG based on renewable sources in distribution networks without compromising the integrity of the grid, it needs to develop proper control techniques to allow power delivery to customers in compliance with power quality and reliability standards. This paper proposes a coordinate local control approach based on a mixed distribution network (DN) sensitivity analysis to maintain voltage levels within regulatory limits. The proposed control is based on a reactive/active power regulation able to offer voltage regulation as ancillary services to the DN, and to maximize the produced active power of the DGs. The validation of the proposed control technique has been conducted through a several number of simulations on a real MV Italian distribution system.
Correlation tracking algorithm based on adaptive template update

Aiming at the common problem of target changing and partial occlusion in the process of infrared dynamic tracking, a weighted function based on the distance of each pixel to the template center is put forward. And the traditional mean absolute difference (MAD) algorithm is improved to solve the interference. For template update we introduce an adaptive template update strategy which can adaptively correct template through the analysis of current frame matching coefficient and the gray scale information of the template edge. The experimental results show that our algorithm not only maintains the low computational complexity of MAD algorithm but also has higher stability and accuracy during continuous tracking.
Transcranial magnetic stimulation (TMS) for major depression: a multisite, naturalistic, observational study of quality of life outcome measures in clinical practice

Background Transcranial magnetic stimulation (TMS) is an effective and safe therapy for major depressive disorder (MDD). This study assessed quality of life (QOL) and functional status outcomes for depressed patients after an acute course of TMS. Methods Forty-two, U.S.-based, clinical TMS practice sites treated 307 outpatients with a primary diagnosis of MDD and persistent symptoms despite prior adequate antidepressant pharmacotherapy. Treatment parameters were based on individual clinical considerations and followed the labeled procedures for use of the approved TMS device. Patient self-reported QOL outcomes included change in the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) and the EuroQol 5-Dimensions (EQ-5D) ratings from baseline to end of the acute treatment phase. Results Statistically significant improvement in functional status on a broad range of mental health and physical health domains was observed on the SF-36 following acute TMS treatment. Similarly, statistically significant improvement in patient-reported QOL was observed on all domains of the EQ-5D and on the General Health Perception and Health Index scores. Improvement on these measures was observed across the entire range of baseline depression symptom severity. Conclusion These data confirm that TMS is effective in the acute treatment of MDD in routine clinical practice settings. This symptom benefit is accompanied by statistically and clinically meaningful improvements in patient-reported QOL and functional status outcomes.
Ability and Hurdle to Provide Banking Online Services: A Case Study of Banking Employees in Nigeria

Identifying the employee version regarding the necessities of customers and the status of the technological initiatives is one of the latest strategies of the online banking companies. As many of the studies are limited to collect the opinion of the customer, the other side of the coin is not coming to lime light. So, the present study intended to capture the opinion of the employees regarding two vital aspects -- the ability of the organization to follow the pioneers of the industry with respect to adapting online banking practices and the hurdles that the online banking organizations are facing nationally and internationally. Two research questions are framed accordingly and the opinion of the employee regarding the questions is collected. A total of 90 employees of three reputed banks are selected at random and data is collected from them using the study tool, questionnaire. The collected is analyzed using simple statistical techniques and portrayed for better understanding. For the first question, answer is taken as yes and no and for the second research question, the average of the opinions regarding the social, economic, political and legal environments is considered. Thus, the opinion of the employees about Nigerian technology market is portrayed using pictorial representation. From the figures, it is affirmed that strategies have been being implemented in Nigeria by following the forerunners of the online banking industry. At the same time, it is also opined by majority of employees that the compartmentalized working of the sectors has become a big problem for the development of the online banking. On the whole, it is proved that synchronized services of all the sectors, aimed at customer oriented collaborative strategies can convince the customers to use online banking ventures and hence to upgrade themselves
A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking

This paper presents a sensorial-cooperative architecture to detect, track and classify entities in semi-structured outdoor scenarios for intelligent vehicles. In order to accomplish this task, information provided by in-vehicle Lidar and monocular vision is used. The detection and tracking phases are performed in the laser space, and the object classification methods work both in laser space (using a Gaussian Mixture Model classifier) and in vision spaces (AdaBoost classifier). A Bayesian-sum decision rule is used in order to combine the results of both classification techniques, and hence a more reliable object classification is achieved. Experiments confirm the effectiveness of the proposed architecture.
The circuit design of current-mode image sensor embedded smooth spatial filter with flash A/D converter

The paper presents a current-mode CMOS image sensor embedded smooth spatial filter algorithm with a flash analog-to-digital (A/D) converter. The sensor includes a 66×66 pixel array with an on-chip 6-bit A/D converter that can identify the output value of pixels in gray level resolution. The last row of pixel cells (1 ×66) based on the double sampling is used for reducing fixed pattern noise (FPN). Processing circuits are dedicated for the spatial filter algorithm and support reusability to increase the image processing speed and to reduce the design complexity and chip area. The sensor chip has been designed and implemented in TSMC 0. 35µm 2P4M CMOS mixed-mode process. Experimental results show that each pixel occupies a area of 15. 8µm×10. 6µm with a fill factor of 37.2%. The power consumption is 35.15mW when the sensor operates at 175 frames/second.
Using an integrated toolset for program understanding

This paper demonstrates the use of an integrated toolset for program understanding. By leveraging the unique capabilities of individual tools, and exploiting their power in combination, the resultant toolset is able to facilitate specific reverse engineering tasks that would otherwise be difficult or impossible. This is illustrated by applying the integrated toolset to several typical reverse engineering scenarios, including code localization, data flow analysis, pattern matching, system clustering, and visualization, using a mid-size production program as the reference system.
OR Forum - Modeling the Impacts of Electricity Tariffs on Plug-In Hybrid Electric Vehicle Charging, Costs, and Emissions

Plug-in hybrid electric vehicles (PHEVs) have been touted as a transportation technology with lower fuel costs and emissions impacts than other vehicle types. Most analyses of PHEVs assume that the power system operator can either directly or indirectly control PHEV charging to coordinate it with power system operations. This paper examines the incentives of individual drivers making charging decisions with different electricity tariffs, and it compares the cost and emissions impacts of these charging patterns to the ideal case of charging controlled by the system operator. Our results show that real-time pricing performs worst among all of the tariffs we consider, because linear prices are inherently limited in signaling efficient use of resources in a system with nonconvexities. We also show that controlling overnight PHEV charging is significantly more important than limiting midday vehicle charging.