Miracle-dz/UniFAR-FTUs huggingface dataset:
A new method for correcting time and soft errors in combinational circuits

In this paper a simple method for fault tolerance with respect to transient or soft errors in the combinational part of sequential circuits is investigated. The memory elements of the sequential circuits are fault-tolerant master-slave-flip-flops. For correcting errors in the combinational part instead of error-correcting codes (ECCs) much simpler error-detecting codes can be used. An error detection signal indicating an error in a combinational part of the circuit blocks the clock signal in the second half of the clock cycle with the result that the previous correct state values of all the slave latches are preserved as long as the transient error (of any duration) stays in the combinational circuit. As soon as the transient error disappears, the system can continue to work from a correct state immediately; no complicated restart of the system is necessary. The system has only to stop for the duration of the transient error. The necessary time delay of the error signal determined by the error-detecting code limits the length of the clock cycle for which the proposed method is applicable. Therefore simple error-detecting codes such as parity codes, split-parity codes and duplication codes should be used. For the mentioned codes the time delay and the error detection probabilities which correspond to the error correction probabilities of this method are determined for benchmark circuits experimentally.
Consumable Credentials in Linear-Logic-Based Access-Control Systems

We present a framework to support consumable credentials in a logic-based distributed authorization system. Such credentials convey use-limited authority (e.g., to open a door once) or authority to utilize resources that are themselves limited (e.g., to spend money). We design a framework based on linear logic to enforce the consumption of credentials in a distributed system, and to protect credentials from nonproductive consumption as might result from misbehavior or failure. Finally, we give several usage examples in the framework, and evaluate the performance of our implementation for use in a ubiquitous computing deployment at our institution.
The impact of personal relevance on emotion processing: evidence from event-related potentials and pupillary responses

Abstract Emotional stimuli attract attention and lead to increased activity in the visual cortex. The present study investigated the impact of personal relevance on emotion processing by presenting emotional words within sentences that referred to participants’ significant others or to unknown agents. In event-related potentials, personal relevance increased visual cortex activity within 100 ms after stimulus onset and the amplitudes of the Late Positive Complex (LPC). Moreover, personally relevant contexts gave rise to augmented pupillary responses and higher arousal ratings, suggesting a general boost of attention and arousal. Finally, personal relevance increased emotion-related ERP effects starting around 200 ms after word onset; effects for negative words compared to neutral words were prolonged in duration. Source localizations of these interactions revealed activations in prefrontal regions, in the visual cortex and in the fusiform gyrus. Taken together, these results demonstrate the high impact of personal relevance on reading in general and on emotion processing in particular.
The Relationships of Metals in River Sediments (Nahr-Ibrahim, Lebanon) and Adjacent Floodplain Soils

Metals in bed-load sediments are multi-source, namely: weathered rocks, metals discharged directly into river, and eroded riparian soils. Contaminated flood plains represent storage system for releasing pollutants to rivers. An essential first step towards modeling this storage is to investigate the relationship. The objective of this study is compare the composition (CaCO3, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) of bed sediments and adjacent flood soils to evaluate compositional relationships for a river (Nahr-Ibrahim, Lebanon) characterized by marked differences in discharge between winter and summer. The comparison would draw first preliminary conclusion of floodplain storage for future pollution and second would approximate sources of metals in bed sediments. The bedrock of the river is mainly limestone, the floodplain soils are mostly Mediterranean type, and water column is saturated with carbonate species. Bed-load sediments and soil samples pairs were collected from the mouth of the river up to 13 km, during the dry season. The sediment and soil samples were digested using aqua regia to extract metals, and quantified by ICP-MS techniques. Inorganic carbon was determined by back titration and organic carbon by LOI method. The data revealed that compositional CaCO3 in sediments is higher than soils, explained by carbonate precipitation from Ca-saturated water column. The metal content of Fe and Mn are derived naturally in soils and sediments. Down stream, near coastal urban zones, as river opens to floodplain soils, the pollutant metals are highly enriched in soils than sediments. Therefore, sources of these metals are due to eroded soils during wet season and precipitating carbonates in dry season reduced original metal pollutants metal concentration. However, upstream (in inland) the concentration of the metals Cd, Cr, Cu, Pb and Cd were higher in sediments than soils; this suggested that sources of metals in bed sediments are due to direct discharge of these metals into river channel.
Learning to Walk in Every Direction with the TBR-Learning algorithm

Legged robots are versatile machines that can outperform wheeled robots on rough terrain (Raibert, 1986), for instance in exploration or rescue missions. Their versatility is, however, tempered by their mechanical and control complexity, which makes them prone to mechanical damages and difficult to control robustly (Raibert, 1986; Bongard et al., 2006; Koos et al., 2013a). A promising way to compensate for these two weaknesses is to let robots discover on their own the best way to move in the current situation. A legged robot can thus cope with an unexpected terrain or with mechanical damages by learning a new walking gait (Bongard et al., 2006; Koos et al., 2013a), in the same way as animals can learn to limp with a sprained ankle. Reinforcement learning (Kohl and Stone, 2004; Tedrake et al., 2005) and evolutionary algorithms (Zykov et al., 2004; Chernova and Veloso, 2004; Hornby et al., 2005) have been investigated to discover walking gaits for physical robots. Nevertheless, most of these investigations are limited to straight, forward walking, whereas a robot that only walks along a straight line is obviously unable to accomplish any mission. Only a handful of works deal with controllers able to turn or to change the walking speed. In these cases, controllers are successively evaluated on each possible direction (Mouret et al., 2006), or learned with an incremental process (Kodjabachian and Meyer, 1998). Compared to learning a simple controller, these two approaches significantly increase the learning time and the complexity of the search process. In the present paper, we describe the Transferabilitybased Behavioral Repertoire Evolution (TBR-Evolution), a new learning algorithm that allows a robot to learn to walk in every direction in a single run of evolutionary algorithm. This algorithm combines the BR-Evolution algorithm (Cully and Mouret, 2013), which creates a behavioral repertoire in a single run, with the transferability approach (Koos et al., 2013b), which minimizes the number of evaluations on a physical robot when evolving controllers thanks to a simulator. A behavioral repertoire is a collection of simple controllers, where each of them reaches one position. An exter1 2 3 4 5
A Systematic Review of the Experience of Using Second Life in the Education of Undergraduate Nurses.

BACKGROUND The virtual world of Second Life(®) is an emerging technology that is being considered as a simulation methodology for the education of professionals. Particularly for nursing, the adoption of simulation, although a response to technological advancement, is occurring during changes in population health care needs, the resultant impact on the workforce, and also the changing profile of students. METHOD This systematic review aimed to establish the current applications of Second Life in the education of undergraduate nursing students. Databases searched were CINAHL(®), Medline(®), Education Research Complete(™), ERIC(™), Computers and Applied Sciences Complete(™), and Library, Information Sciences and Technology(™). RESULTS Fourteen studies met the inclusion and exclusion criteria. Evidence identified included the themes of transferability from theory to practice, focus on learner centeredness, and evaluative processes. CONCLUSION This review demonstrates that positive learning outcomes are achievable in Second Life. Evaluative research is in an early stage, and further investigation is warranted.
Distributed Formation Control with Omnidirectional Vision-Based Motion Segmentation and Visual Servoing

We consider the problem of distributed leader-follower formation control for nonholonomic mobile robots equipped with central-panoramic cameras. By specifying the desired formation in the image plane, we translate the control problem into a separate visual servoing task for each follower. We use a rank constraint on the omnidirectional optical flows across multiple frames to estimate the position and velocities of the leaders in the image plane of each follower. We show that the direct feedback-linearization of the leader-follower dynamics suffers from degenerate configurations due to the nonholonomic constraints on the robot kinematics and the nonlinearity of the omnidirectional projection model. We therefore design a nonlinear tracking controller that avoids such degenerate configurations, while preserving the formation input-to-state stability. Our control law naturally incorporates collision avoidance by exploiting the geometry of omnidirectional cameras. We present simulations and experiments evaluating our omnidirectional vision-based formation control scheme.
Assessment of Regional Floods Using Cluster Analysis and Region of Influence ( ROI ) Method

In this study, the Region of Influence, ROI, and clustering analysis models are applied to data available for the Nile River. Data analysis was completed for annual extreme flows of a network of 14 stream gauging stations. The methodology consists of: selection of a set of attributes to be included in the distance metric measure among the array of possible advance postulated attribute sets, using a screening process; determination of appropriate weighting values for each selected attribute; performing cluster analysis in attribute-space for 1 to K clusters; computation and plotting of error criterion and F-statistic for the clusters; selecting an appropriate number of clusters; incorporating the ROI approach by determining a threshold value to define a cutoff for the inclusion of stations into ROI for a site; the determination of a weighting function that reflects the relative closeness to the site of each station in a site’s ROI, corresponding to options 1, 2, and 3 of the ROI approach; having defined the ROI regions, performing the R-statistic homogeneity test for both clustering and ROI optional regions; partitioning the normalized regional range in the coefficient of variation values for each region; identification of the fixed regions that form the most homogeneous sites in the region; and finally, estimation of their dimensionless growth curves. The performance of the models is summarized in terms of the variability of the at-site values about the regional curve, root mean squared error (RMSE), and bias. Results indicate that ROI option 1 performs much better than ROI options 2 and 3, and the clustering options.
Prediction of Happy Endings in German Novels based on Sentiment Information

Identifying plot structure in novels is a valuable step towards automatic processing of literary corpora. We present an approach to classify novels as either having a happy ending or not. To achieve this, we use features based on different sentiment lexica as input for an SVMclassifier, which yields an average F1-score of about 73%.
Biopolymers from lactic acid bacteria. Novel applications in foods and beverages

Lactic acid bacteria (LAB) are microorganisms widely used in the fermented food industry worldwide. Certain LAB are able to produce exopolysaccharides (EPS) either attached to the cell wall (capsular EPS) or released to the extracellular environment (EPS). According to their composition, LAB may synthesize heteropolysaccharides or homopolysaccharides. A wide diversity of EPS are produced by LAB concerning their monomer composition, molecular mass, and structure. Although EPS-producing LAB strains have been traditionally applied in the manufacture of dairy products such as fermented milks and yogurts, their use in the elaboration of low-fat cheeses, diverse type of sourdough breads, and certain beverages are some of the novel applications of these polymers. This work aims to collect the most relevant issues of the former reviews concerning the monomer composition, structure, and yields and biosynthetic enzymes of EPS from LAB; to describe the recently characterized EPS and to present the application of both EPS-producing strains and their polymers in the fermented (specifically beverages and cereal-based) food industry.
A Multimodal and Multistage Face Recognition Method for Simulated Portrait

Recognition of simulated portrait obtained through face composition technique is a challenging task in public security area. An innovative method for simulated portrait recognition is presented in this paper. This method can be used to perform recognition of simulated portraits from anonymous cadaver, from the description of a witness, and from surveillance video. Based on principal component analysis (PCA), we have constructed eigenface, eigen-brow+eye, eigeneye, eigennose, and eigenmouth. Altogether 31 recognition modes can be formed through different weighted combinations of these eigen-parts. Face recognition of simulated portrait is first performed using this multimodal part based PCA (MMP-PCA) technique, results of which are then used as inputs for further recognition based on modified line segment Hausdorff distance (LHD). Experiment results show that this innovative recognition method has achieved good results for the recognition of simulated portraits in a database of 100,000 face images
Branch-and-Price for a Survivable Network Design Problem

We consider a specific variant of the survivable network design problem suitable to model real world scenarios occurring in the extension of fiber optic networks. In this article, two mixed integer programming models, which can be solved by branch-and-price, are discussed and compared to existing approaches theoretically as well as by a computational study. We further discuss the usage of alternative dual-optimal solutions to stabilize our approaches and significantly reduce the computational times needed to solve the linear relaxations of our models. The obtained computational results show that both branch-and-price approaches are suitable for solving small to medium sized problem instances to proven optimality.
Single- and Multistep Resistance Selection Studies on the Activity of Retapamulin Compared to Other Agents against Staphylococcus aureus and Streptococcus pyogenes

ABSTRACT Retapamulin had the lowest rate of spontaneous mutations by single-step passaging and the lowest parent and selected mutant MICs by multistep passaging among all drugs tested for all Staphylococcus aureus strains and three Streptococcus pyogenes strains which yielded resistant clones. Retapamulin has a low potential for resistance selection in S. pyogenes, with a slow and gradual propensity for resistance development in S. aureus.
Extraction of investment strategies based on moving averages: A genetic algorithm approach

Investment strategies as rules for buy and sell are introduced as conditional statements involving inequalities of various moving averages. Different conditional statements on moving averages are represented as strings, encodable as chromosomes in an approach based on genetic algorithm. The data mining of good investment strategies corresponds to the extraction of rules that are fit in the sense of evolutionary computation. Three different kinds of moving averages (simple moving average, exponential moving average and adaptive moving average) of various durations have been used in the conditional statement involving the closing price of a given stock. The performance of a given investment strategy is evaluated using the rate of overall return in both the training set and the test set, thereby converting the problem of discovering good investment strategies to an optimization problem in combinatorics, which is solved with a genetic algorithm approach. Stock data from NASDAQ, including Microsoft, Intel, Oracle, and Dell are tested and comparisons of genetic algorithms with benchmark methods such as random walk, buy and hold, and exhaustive search are performed. Results show evidence of superior performance in genetic algorithm in term of the rate of overall return for the test set. Within the confine of limited data, all three moving averages show similar results for the four technology stock investigated.
Improving Design Pattern Instance Recognition by Dynamic Analysis

Design pattern instance recognition is often done by static analysis, thus approaches are limited to the recognition of static parts of design patterns. The dynamic behavior of patterns is disregarded and leads to lots of false positives during recognition. This paper presents an approach to combine the advantages of static and dynamic analyses to overcome this problem and improve the design pattern instance recognition.
Extending persistent monitoring by combining ocean models and Markov Decision Processes

Ocean processes are complex and have a high variability in both time and space. Thus, ocean scientists must collect data over long time periods to obtain synoptic views and resolve multidimensional spatiotemporal variability. In this paper, we present a methodology for incorporating time-varying currents into a Markov Decision Process for persistent path execution by underwater gliders. The application of an hybrid Gaussian distribution of ocean currents and a modified Markov Decision Process technique enables the incorporation of uncertainty from a deterministic ocean model. The proposed approach achieves improved navigational accuracy, and can extend the distance travelled over the duration of a mission. We present a derivation of our methodology, an outline of the proposed algorithms, and simulation predictions that are validated through experimental field trials.
Study of high temperature piezoelectric scandium aluminum nitride thin films

AlN has weak piezoelectric property (piezoelectric coefficient d33=5.5pCN-1) and a high Curie temperature (>1150°C). By Sc-doping in AlN thin films, it is possible to synthesize ScxAl1-xN alloy with high piezoelectric coefficient and high temperature stability. In this study, c-axis oriented AlN thin films have been successfully grown on Si (100) substrates by DC magnetron reactive sputtering method. First-principles calculations are also performed to investigate the structure of ScxAl1-xN.
High Density Linkage Map Construction and Mapping of Yield Trait QTLs in Maize (Zea mays) Using the Genotyping-by-Sequencing (GBS) Technology

Increasing grain yield is the ultimate goal for maize breeding. High resolution quantitative trait loci (QTL) mapping can help us understand the molecular basis of phenotypic variation of yield and thus facilitate marker assisted breeding. The aim of this study is to use genotyping-by-sequencing (GBS) for large-scale SNP discovery and simultaneous genotyping of all F2 individuals from a cross between two varieties of maize that are in clear contrast in yield and related traits. A set of 199 F2 progeny derived from the cross of varieties SG-5 and SG-7 were generated and genotyped by GBS. A total of 1,046,524,604 reads with an average of 5,258,918 reads per F2 individual were generated. This number of reads represents an approximately 0.36-fold coverage of the maize reference genome Zea_mays. AGPv3.29 for each F2 individual. A total of 68,882 raw SNPs were discovered in the F2 population, which, after stringent filtering, led to a total of 29,927 high quality SNPs. Comparative analysis using these physically mapped marker loci revealed a higher degree of synteny with the reference genome. The SNP genotype data were utilized to construct an intra-specific genetic linkage map of maize consisting of 3,305 bins on 10 linkage groups spanning 2,236.66 cM at an average distance of 0.68 cM between consecutive markers. From this map, we identified 28 QTLs associated with yield traits (100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, corn grains per row, ear weight, and grain weight per plant) using the composite interval mapping (CIM) method and 29 QTLs using the least absolute shrinkage selection operator (LASSO) method. QTLs identified by the CIM method account for 6.4% to 19.7% of the phenotypic variation. Small intervals of three QTLs (qCGR-1, qKW-2, and qGWP-4) contain several genes, including one gene (GRMZM2G139872) encoding the F-box protein, three genes (GRMZM2G180811, GRMZM5G828139, and GRMZM5G873194) encoding the WD40-repeat protein, and one gene (GRMZM2G019183) encoding the UDP-Glycosyltransferase. The work will not only help to understand the mechanisms that control yield traits of maize, but also provide a basis for marker-assisted selection and map-based cloning in further studies.
Featuring Functional Component Groups of Sustainable Solid Waste Material on Carbon Dioxide Capture

The abundant biomass from palm oils can be converted into valuable product via pyrolysis process. In this research, the pyrolysis of sustainable oil palm solid waste, empty fruit bunch (EFB), was performed using a static batch reactor in a tubular furnace. The bio-solid waste material obtained was treated with hydrophobic solution (methyl ester) and then impregnated with bio-oil substrate obtained from the pyrolysis. The modified bio-solid waste material was then tested for carbon dioxide capture. SEM results indicated presence uniform and sticky-like material on the surface when further treated with bio-oil substrate. Though, TGA results indicated that addition of hydrophobic solution and bio-oil reduced the thermal stability of the EFB bio-solid. At higher temperature of 150 – 3500C bio-oil impregnated sample indicated little amount of volatile components with carbon residue of 85.39%. The FTIR results of the hydrphobic impregnated bio-solid waste indicated a unique broad peak among all the samples at 620.05cm-1 which shows the presence of bending stretch carbon dioxide. This shows that bio-oil modified bio-solid char obtained from EFB has the capabity of capturing CO2.