Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-effective fabrication of nanoscale electrode memristors with reproducible electrical response

This Letter aims to promote basic research into memristors, which will help provide theorists with much-needed reliable benchmarks and will also aid the technology progress. It addresses an information gap presently in the literature on simple microfabrication techniques for the realisation of such devices. Consequently, a fabrication method is reported for implementing the full active-material stack, requiring a single lithography and evaporation step. A cost-effective technique that can reliably shrink device lateral dimensions towards the nanoscale is also demonstrated. Experimental results confirm the suitability of the proposed methods for fabricating memristors of varying dimensions that exhibit consistent electrical characteristics.
Evidence-Based Word Alignment

In this paper we describe word alignment experiments using an approach based on a disjunctive combination of alignment evidence. A wide range of statistical, orthographic and positional clues can be combined in this way. Their weights can easily be learned from small amounts of hand-aligned training data. We can show that this "evidence-based" approach can be used to improve the baseline of statistical alignment and also outperforms a discriminative approach based on a maximum entropy classifier.
Anaphase A Chromosome Movement and Poleward Spindle Microtubule Flux Occur At Similar Rates in Xenopus Extract Spindles

We have used local fluorescence photoactivation to mark the lattice of spindle microtubules during anaphase A in Xenopus extract spindles. We find that both poleward spindle microtubule flux and anaphase A chromosome movement occur at similar rates (∼2 μm/min). This result suggests that poleward microtubule flux, coupled to microtubule depolymerization near the spindle poles, is the predominant mechanism for anaphase A in Xenopus egg extracts. In contrast, in vertebrate somatic cells a “Pacman” kinetochore mechanism, coupled to microtubule depolymerization near the kinetochore, predominates during anaphase A. Consistent with the conclusion from fluorescence photoactivation analysis, both anaphase A chromosome movement and poleward spindle microtubule flux respond similarly to pharmacological perturbations in Xenopus extracts. Furthermore, the pharmacological profile of anaphase A in Xenopus extracts differs from the previously established profile for anaphase A in vertebrate somatic cells. The difference between these profiles is consistent with poleward microtubule flux playing the predominant role in anaphase chromosome movement in Xenopus extracts, but not in vertebrate somatic cells. We discuss the possible biological implications of the existence of two distinct anaphase A mechanisms and their differential contributions to poleward chromosome movement in different cell types.
Word count : 10 , 391 Medical Innovation Revisited : Social Contagion versus Marketing Effort 1

This paper shows that Medical Innovation (Coleman, Katz and Menzel 1966) and several subsequent studies analyzing the diffusion of the drug tetracycline have confounded social contagion with marketing effects. First, we describe the medical community’s understanding of tetracycline and how the drug was marketed at the time the Medical Innovation data were collected. We find little reason to expect social contagion to have been important in physicians’ decision to adopt, but find that aggressive marketing efforts may have played an important role. Next, we re-analyze the Medical Innovation data set, supplemented with newly collected data on advertising volume (a measure of marketing efforts). Contagion effects disappear once we control for marketing efforts. Given the prominence of Medical Innovation and subsequent reanalyses, our results underscore the importance of controlling for potential confounds when studying the role of social contagion in innovation diffusion.
Incorporating User Defined Data Types into a Web-based Data Visualization Tool

Many organizations have incorporated data collection into their software processes. However, interpreting the data is just as important as the collection of data. The Web Measurement Environment (WebME), a Web-based data visualization tool, is being developed to facilitate the understanding of collected data. The WebME system will extend and improve the functionality provided by an existing system, the Software Management Environment (SME), which was developed to execute on a single system and view data from a specific domain. SME was designed to graphically display software development data, such as staff hours used, errors found, or lines of code developed, from one particular development environment. WebME will permit the analysis of development data from multiple development domains. These enhancements and the capability of adding user defined data types to permit multiple views into the data are the focus of this paper. In particular, the data definition language for creating and using new data types is described. In addition, the issues of type compatibility and system architecture are discussed.
A SUMMARY OF THE VARIOUS STATISTICAL METHODS CURRENTLY IN USE FOR THE ANALYSIS OF GENE EXPRESSION MICROARRAY DATA

Microarrays generate a large volume of experimental data and many methods, such as clustering and classification, can be used to analyze this data. Initial investigation through exploratory analysis provides great insight into the data by detecting structures and patterns. After patterns have been identified, they can be used for both discovery and prediction of genes through various methods. Although pattern recognition methods incorporate some prior information, their use is limited because they do not provide an explanation for the underlying mechanism of genetic networks. Recently, many attempts have been made to use microarray data for uncovering these mechanisms. From initial investigative studies to developing genetic networks, this paper discusses an assortment of statistical methods that are currently being used to analyze gene expression microarray data.
Performance Driven Facial Animation Course Notes Example : Motion Retargeting

When done correctly, a digitally recorded facial performance is an accurate measurement of the performer’s motions. As such it reflects all the idiosyncrasies of the performer. However, often the digital character that needs to be animated is not a digital replica of the performer. In this case, the decision to use performance capture might be motivated by cost issues, the desire to use a favorite actor regardless of the intended character, or the desire to portray an older, younger, or otherwise altered version of the actor. The many incarnations of Tom Hanks in Polar Express illustrate several of these scenarios. In this scenario, the recorded (source) performance has to be adapted to the target character. In this section of the course, we examine different techniques for retargeting or cross-mapping a recorded facial performance onto a digital face. We have grouped these techniques in several categories mostly as a function of whether they use a blendshape system for the source and/or the target face.
Stiffened springback reflectors

The recently developed Spring Back Reflector has low stiffness in the deployed configuration. A general method of stiffening this type of reflector without changing its folding properties is presented. The stiffening is achieved by adding a conical skirt around the edge of the dish; this skirt snaps elastically when the dish is folded. The paper concludes that it is possible to design spring back reflectors that are up to 80 times stiffer and with a deployed fundamental frequency up to 8 times higher than in the original design. INTRODUCTION The newly developed Spring Back Reflector [1], shown schematically in Fig. 1, consists of a thin-walled graphite mesh dish with an integral lattice of ribs and connecting elements. These components, together with a further stiffening edge beam along the rim, are made from triaxial plies of carbon-fibre reinforced plastic (CFRP). The whole structure is made as a single piece, without any expensive and potentially unreliable joints, and typically has a diameter of 6 m, thickness varying between 0.3 mm and 3.2 mm, and a total mass of around 20 kg. The folding concept is both simple and effective: since there are no joints or hinges, opposite edges of the reflector are pulled towards each other by about half of their original distance, and thus the reflector becomes folded elastically as shown in Fig. 1. It can then be stowed in the normally unused space in the nose cone of a rocket launcher. Once in orbit, the tie cables that hold the reflector in its packaged configuration are released, and the reflector deploys dynamically by releasing its stored elastic strain energy. Figure 1: Schematic views of spring-back reflector, folded and deployed. In order to be folded as described, Spring Back Reflectors need to have low stiffness but it is precisely their low stiffness that makes it difficult for them to achieve and retain a high shape accuracy. Furthermore, shape distortions that occur during the manufacturing of thin CFRP structures —of the order D/1000 in the present case—make it even more difficult to meet the stringent shape accuracy requirements imposed on communications antennas. This is a significant problem, and potentially a severe limitation on the applicability of this type of reflector. The surface accuracy of the reflector could be adjusted with mechanical devices, but this would defy the simplicity of the concept and reduce the reliability of the system. This paper proposes a modification of the original concept, based on the idea of adding a thin-walled stiffening element around the edge of the dish. This element significantly increases the overall stiffness of the dish in the deployed configuration, and yet its configuration is such that the stiffened dish can still be folded elastically. A detailed finite element study is presented of the effects of changing the various parameters of the stiffening element from which optimal configurations of a 1/10th scale model —i.e. about 0.5 m diameter— reflector are
Mining Significant Statistically Blowout Patterns

One blowout pattern is a zone on multiple data streams in which the data points are dense but highly unbalanced. It can be applied into a variety of fields. for example, it may imply a successful sales promotion especially for a few areas. the difficulties of exploring blowout patterns are: 1). It is not periodic, 2). the distribution is unknown, and 3). How to distinguish it from outliers or clusters. We have proposed a novel method for it. First, we employ a density based clustering algorithm to detect dense data points. Second, we use a novel concept (Normal Standard Deviation: NSD) for the evaluation of the data distribution in the zones. the zones that include dense but unbalanced data points are highlighted as significant blowout patterns. the results demonstrate that our method can find and locate blowout patterns efficiently and effectively compared to kernel framework and slide window.
Mobility Friendly Publish/Subscribe

This paper describes an event dissemination algorithm that implements a topic-based publish/subscribe abstraction in mobile ad-hoc networks (MANETs). Our algorithm relies on (1) the mobility of the processes and (2) the validity period of the events to ensure the reliability of the dissemination (under reasonable conditions) with a thrifty usage of the memory. The algorithm is inherently portable and does not assume any specific routing protocol. Old events are collected to save the memory and the energy consumption is, in some sense, related to the size of the event scope a subscriber is interested in. We give simulation results in different mobility models and highlight the advantages/drawbacks of our approach as well as we expose some interesting relations between validity periods and reliability.
Estimation of articulatory movements from speech acoustics using an HMM-based speech production model

We present a method that determines articulatory movements from speech acoustics using a Hidden Markov Model (HMM)-based speech production model. The model statistically generates speech spectrum and articulatory parameters from a given phonemic string. It consists of HMMs of articulatory parameters for each phoneme and an articulatory-to-acoustic mapping for each HMM state. For a given speech spectrum, maximum a posteriori estimation of the articulatory parameters of the statistical model is presented. The performance on sentences was evaluated by comparing the estimated articulatory parameters with the observed parameters. The average RMS errors of the estimated articulatory parameters were 1.50 mm from the speech acoustics and the phonemic information in an utterance and 1.73 mm from the speech acoustics only.
Comparative Genomics of Isolates of a Pseudomonas aeruginosa Epidemic Strain Associated with Chronic Lung Infections of Cystic Fibrosis Patients

Pseudomonas aeruginosa is the main cause of fatal chronic lung infections among individuals suffering from cystic fibrosis (CF). During the past 15 years, particularly aggressive strains transmitted among CF patients have been identified, initially in Europe and more recently in Canada. The aim of this study was to generate high-quality genome sequences for 7 isolates of the Liverpool epidemic strain (LES) from the United Kingdom and Canada representing different virulence characteristics in order to: (1) associate comparative genomics results with virulence factor variability and (2) identify genomic and/or phenotypic divergence between the two geographical locations. We performed phenotypic characterization of pyoverdine, pyocyanin, motility, biofilm formation, and proteolytic activity. We also assessed the degree of virulence using the Dictyostelium discoideum amoeba model. Comparative genomics analysis revealed at least one large deletion (40–50 kb) in 6 out of the 7 isolates compared to the reference genome of LESB58. These deletions correspond to prophages, which are known to increase the competitiveness of LESB58 in chronic lung infection. We also identified 308 non-synonymous polymorphisms, of which 28 were associated with virulence determinants and 52 with regulatory proteins. At the phenotypic level, isolates showed extensive variability in production of pyocyanin, pyoverdine, proteases and biofilm as well as in swimming motility, while being predominantly avirulent in the amoeba model. Isolates from the two continents were phylogenetically and phenotypically undistinguishable. Most regulatory mutations were isolate-specific and 29% of them were predicted to have high functional impact. Therefore, polymorphism in regulatory genes is likely to be an important basis for phenotypic diversity among LES isolates, which in turn might contribute to this strain’s adaptability to varying conditions in the CF lung.
Increasing Realism of Human Agents by Modeling Individual Differences : Methodology , Architecture , and Testbed

A key challenge in creating simulated human agents is to produce sufficiently realistic behavior. A critical component of such realism is the range of variations in behaviors exhibited by humans. These variations are due to a variety of factors, including varying levels of intelligence and skill, differences in cognitive and decision making styles, personality differences, and different affective states: collectively termed individual differences. In this paper we describe an approach for increasing the realism of simulated human agents by explicitly modeling these individual differences. The core component of this approach is a generic methodology for modeling individual differences within symbolic cognitive architectures, via parametric manipulations of the architectural structures and processes. Individual differences profiles are first translated into specific architecture parameters, which then bias the architecture output in corresponding directions, causing variations in performance. The architecture is embedded within a modeling and analysis testbed environment, which supports the simulation of multiple agent interactions within the task simulation; modeling of a variety of agent ypes by specifying their distinct individual differences profiles; and modeling and analysis of alternative mappings of distinct individual differences onto the cognitive architecture parameters. Prototype versions of the cognitive architecture and testbed have been implemented and evaluated in the context of a military simulation training scenario, representing the interaction of a number of commanders in the context of a specific mission. The simulated mission demonstrated istinct differences in individual commander behavior, as a function of their individual profile, and consequent significant differences in the final mission outcome. The testbed environment was effective in facilitating the rapid selection of alternative individual differences profiles and observation of resulting individual behavior variations and subsequent mission outcome. These preliminary results indicate the ability of the modeling methodology to effectively represent individual differences, and the ability of the testbed to support flexible exploration in the broad research area of individual differences modeling.
Visual tracking via an ensemble of random classifiers

One largest problem for tracking-by-detection methods is the incomplete and noisy training set. Occlusion, illumination and many other problems could lead to this problem. Models that are not adaptive enough would fail to track the target when drastic appearance change takes place. Adaptive ones, although keep tracking at first, could lose the target because of learning too many incorrect samples. In this paper, we present an ensemble model of random classifiers updated on different dataset. When the appearance of the target changes drastically and some sub-models are confused, the others could help correct the tracking result. A latent variable is added for choosing sub-models and it naturally leads to predicting the new samples with a weighted sum of sub-models. To calculate the weight, we add a generative model to each random classifier. Experiments show that our method could track the target robustly and accurately.
A Magnetic Thrust Chamber Design For A Laser Fusion Rocket Based on Impact Fast Ignition Scheme

The laser fusion rocket, which is expected as a rocket that it replaces one of the chemical propulsions, can be efficiently operated by using a shaped target. The shaped target is made of fusion pellet and the pellet is surrounded by a moderator (propellant). We here examine the applicability of the impact fast ignition scheme to the shaped target. It was found that it is difficult to increase the thrust efficiency by using a simple spherical moderator.
CCLA-based robust energy-to-peak control of Networked Control Systems

This paper is concerned with the problem of robust l2-l∞ control for Networked Control systems. Dynamic feedback controller for Networked Control Systems with convex polyhedral uncertainties is given by the linear matrix inequality(LMI) method and cone complementary linear algorithm(CCLA). And the controller can guarantee a prescribed energy-to-peak disturbance attenuation level for all possible uncertainties and time delay. A numerical example shows the validity of the proposed design procedure.
A Community-Structure Based Adaptively Optimized Link Prediction Algorithm

Since link prediction helps improve our understandings about the structure, functions, and evolution of networks, it has drawn much attention from both computer science and physical communities. Among many mainstream proposed algorithms, the common-neighbor based ones show prominent efficiency but neglect the influence of community structure. Based on the assumption that in the same communities common neighbors show closer relations with endpoints than in different communities, we hold that using community structure in link prediction can further distinguish the contributions of common neighbors, thus improving the prediction accuracy. Accordingly, we propose Community-Structure based model (CS), which controls the contributions of the common neighbors in different communities with endpoints. Experiments on twelve real-world networks show that compared with three typical common-neighbor based baselines, the CS model provides more accurate predictions.
A low-complexity cooperative algorithm for robust localization in wireless sensor networks

Location awareness for wireless sensor networks has attracted significant research interests in recent years. In hostile environments, there may be malicious attacks to mislead the location estimation of target nodes. In this paper, we discuss the benefit brought by cooperation in the context of robust localization against malicious anchors. Cooperation provides improved detection about the existence of malicious anchors, as well as the ability to estimate their true locations. We also investigate various loss functions and propose an accelerated cooperative robust localization algorithm based on Huber loss function. The proposed algorithm offers accuracy comparable to existing cooperative robust localization methods but at significantly reduced computational complexity.
CROFlux — Passive DNS method for detecting fast-flux domains

In this paper we present our approach to fast flux detection called CROFlux that relies on the passive DNS replication method. The presented model can significantly reduce the number of false positive detections, and can detect other suspicious domains that are used for fast flux. This algorithm is used and implemented in Advanced Cyber Defense Centre - a European project co-funded by the European Commission.
Insights on a Scalable and Dynamic Traffic Management System

Complex Event Processing (CEP) systems process large streams of data trying to detect events of interest. Traditional CEP systems, such as Esper, lack the required scalability and processing capability to cope with the constantly increasing amount of data that needs to be processed. Furthermore, user defined rules are static so changes in the monitored environment cannot be easily detected. In this paper we investigate the development of a scalable and dynamic traffic management system. Our work makes several contributions: We propose a novel system that combines Esper with a stream processing framework, Storm, in order to parallelize the processing of larger amounts of data. We propose a novel rules’ assignment algorithm for distributing Esper rules to the available CEP engines, in a way that maximizes the overall system’s throughput. Finally, our system adapts to changes of the environment by processing historical data via Hadoop and dynamically updating the Esper rules based on the generated results. Our work has been evaluated using real data, in several traffic monitoring scenarios for the city of Dublin. Our detailed experimental results indicate the benefits in the working of our approach and the significant increase in the system’s throughput when a large number of Esper rules were examined concurrently.