Miracle-dz/UniFAR-FTUs huggingface dataset:
State Estimation for Gene Regulatory Networks with Sampled Output Measurements and Time-varying Delays

This paper investigates the H∞ state estimation problem for gene regulatory networks (GRNs) with timevarying delays and Markovian jumping parameters. The system output, which is used by the estimator, is assumed to be sampled and held during a sampling period. To deal with the time-varying delays, each delay function is limited to certain lower and upper bounds. By using Lyapunov-Krasovskii functionals, sufficient conditions for the stochastic stability of augmented GRN/Estimator networks are derived. Then, the estimator gains are synthesized from the derived stability conditions. Furthermore, we investigate how to design the estimator in presence of disturbance. All stability conditions are formulated in the form of linear matrix inequalities which can be easily solved by numerical methods. The obtained conditions are dependent on both the lower and upper bounds of delays. At the end, some simulation results are presented to demonstrate the effectiveness of the proposed method.
Semantic Cartography in Information Retrieval Systems

In this paper we examine the state of research into the domain of information visualization. We start by describing the spectrum of current paradigms of information visualization. We address the problem of their integration in the web applications and precisely in information search systems. Then, we deal about the challenges and directives for a good conception of semantic visualization. Then, we discuss about the existing methods of information visualization evaluation and present the most relevant challenges facing both information visualization construction and evaluation tasks.
The Immune Epitope Database 2.0

The Immune Epitope Database (IEDB, www.iedb.org) provides a catalog of experimentally characterized B and T cell epitopes, as well as data on Major Histocompatibility Complex (MHC) binding and MHC ligand elution experiments. The database represents the molecular structures recognized by adaptive immune receptors and the experimental contexts in which these molecules were determined to be immune epitopes. Epitopes recognized in humans, nonhuman primates, rodents, pigs, cats and all other tested species are included. Both positive and negative experimental results are captured. Over the course of 4 years, the data from 180 978 experiments were curated manually from the literature, which covers ∼99% of all publicly available information on peptide epitopes mapped in infectious agents (excluding HIV) and 93% of those mapped in allergens. In addition, data that would otherwise be unavailable to the public from 129 186 experiments were submitted directly by investigators. The curation of epitopes related to autoimmunity is expected to be completed by the end of 2010. The database can be queried by epitope structure, source organism, MHC restriction, assay type or host organism, among other criteria. The database structure, as well as its querying, browsing and reporting interfaces, was completely redesigned for the IEDB 2.0 release, which became publicly available in early 2009.
Simulating large-scale aggregate MASs with alchemist and scala

Recent works in the context of large-scale adaptive systems, such as those based on opportunistic IoT-based applications, promote aggregate programming, a development approach for distributed systems in which the collectivity of devices is directly targeted, instead of individual ones. This makes the resulting behaviour highly insensitive to network size, density, and topology, and as such, intrinsically robust to failures and changes to working conditions (e.g., location of computational load, communication technology, and computational infrastructure). Most specifically, we argue that aggregate programming is particularly suitable for building models and simulations of complex large-scale reactive MASs. Accordingly, in this paper we describe SCAFI (Scala Fields), a Scala-based API and DSL for aggregate programming, and its integration with the ALCHEMIST simulator, and usage scenarios in the context of smart mobility.
Convergence of stochastic gene networks to hybrid piecewise deterministic processes

We study the asymptotic behavior of multiscale stochastic gene networks using weak limits of Markov jump processes. Depending on the time and concentration scales of the system we distinguish four types of limits: continuous piecewise deterministic processes (PDP) with switching, PDP with jumps in the continuous variables, averaged PDP, and PDP with singular switching. We justify rigorously the convergence for the four types of limits. The convergence results can be used to simplify the stochastic dynamics of gene network models arising in molecular biology.
Design and Evaluation of an Efficient Reliable Manycast protocol for Ad-hoc Networks

The protocol presented here guarantees that at least a specified number, k, of destinations receive a given message. The guarantees are deterministic and the failure probability is zero. Such a strong delivery assurance provides a more powerful primitive than protocols which attempt to minimise failure probability, and is useful for meeting many user-level requirements. Simulations under a wide range of settings confirm that the overhead is low even k is high, and that the protocol is scalable. These desirable performance results are due to many design features, in particular, the decentralised delivery management whereby any node manycast initiator or destinationcan take responsibility in a cost-effective manner for achieving the specified delivery ratio. The goal of attaining total reliability is comprehensively addressed: the design considers even the rare, but not impossible, scenarios of the network behaving as an adversary.
Fault diagnosis for nonlinear hydraulic-mechanical drilling pipe handling system

Leakage and increased friction are common faults in hydraulic cylinders that can have serious consequences if they are not detected at early stage. In this paper, the design of a fault detector for a nonlinear hydraulic mechanical system is presented. By considering the system in steady state, two residual signals are generated and analysed with a composite hypothesis test which accommodates for unknown parameters. The resulting detector is able to detect abrupt changes in leakage or friction given the noisy pressure and position measurements. Test rig measurements validate the properties of residuals and high fidelity simulation and experimental results demonstrate the performance and feasibility of the proposed method.
Reconfigurable consumer direct logistics systems

The goals of the interdisciplinary project HORN were as well to improve competitiveness of service providers that deliver grocery items as to develop new hardware and software needed for its realisation. In the first part, we introduce this home replenishment project, and analyse the needs and challenges of mobile services with similar constraints. In the second part, we introduce our modular software engineering approach and especially present the adopted reconfiguration mechanism for autonomous mobile applications, and its profiling concept.
General Disclaimer One or More of the following Statements May Affect This Document

The objective of this work is to provide a broad assessment of Thematic Mapper data quality in addition to specific investigations of particular topics including band to band registration, scene to scene registration, geodetic rectification, interdetector responses, and spatial resolution. Preliminary results for investigations of band to band registration and interdetector responses are prese.ted. Based on the Thematic Mapper images analyzed so far, the band to band registration accuracy of this complex instrument is very good. For bands within the same focal plane, the mean misregistrations are well within the specification, 0.2 pixels. For bands between the cooled and uncooled focal planes, there is a consistent mean misregistration of 0.5 pixels along-scan and 0.2-0.3 pixels across-scan. It exceeds the permitted 0.3 pixels for registration of bands between focal planes. If the mean misregistrations were removed by the data processing software, an analysis of the standard deviation of the misregistration indicates all band combinations would meet the registration specifications except for those including the thermal band. The large registration error for the latter may be due to conceptual problems in registering data of different spatial resolutions. Analysis of the periodic noise in one image indicates a noise component in band 1 with a spatial frequency equivalent to 3.2 pixels in the along-scan direction.
The white coat trap: pharmacy as an ethnic-dominated occupation in Israel

ABSTRACT Health professions are perceived by the Arab minority in Israel as a desired path of education and employment. Arabs in Israel are thus highly represented in these professions, especially pharmacy. This study analyses the unique ethnic and gender composition of pharmacists in Israel and explores their career experiences and perceptions. Both quantitative and qualitative methodologies are employed. The findings reflect dual marginality in the pharmacy profession in Israel, that is currently both female and ethnic-dominated. This situation is described in the Arab pharmacists’ stories, that delineate the disparity between their aspirations when starting their careers and their hardships after working for several years as pharmacists. Arabs in Israel face unique difficulties in the labour market. Many choose the health professions, including pharmacy, as a path of mobility. Pharmacy, however, is perceived as a niche (ethnic and female-dominated) profession in Israel. Hence, its practitioners suffer from devaluation in wage and prestige.
Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a general model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We additionally provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than 30% error-reduction over a competitive baseline that considers pairs of characters in isolation.
A live video imaging method for capturing presentation information in distance learning

We propose a novel approach of video imaging method suitable to support distance learning. Automatic active camera control and video selection system visualizes presentation information of a lecturer and active students. We consider the dynamic situation of the lecture to determine the appropriate target object to be shot which gives remote students live lecture information. We have implemented our method and used it in several distance learning courses held between Kyoto University and UCLA.
The other side of the coin: Analyzing software encoding schemes against fault injection attacks

The versatility and cost of embedded systems have made it ubiquitous. Such wide-application exposes an embedded system to a variety of physical threats like side-channel attacks (SCA) and fault attacks (FA). Recently, a couple of software encoding schemes were proposed as a protection against SCA. These protection schemes are based on dual-rail precharge logic (DPL), previously shown resistant to both SCA and FA. In this paper, we analyze the previously proposed software encoding schemes against FA. Our results show that software encoding offers only limited resistance to FA. Finally, improvement to software-encoding schemes is improved. With this improvement, software encoding can serve as a common SCA and FA counter-measure with an exploitable fault probability as low as 0.0048.
Covariance of dark energy parameters and sound speed constraints from large H i surveys

A B ST R A C T An interesting probe ofthe nature ofdark energy isthe m easure ofitssound speed, cs.W e review the signicance for constraining sound speed m odels ofdark energy using large neutralhydrogen (HI)surveyswith the Square Kilom etre Array (SKA). Our analysis considers the eect on the sound speed m easurem ent that arises from thecovarianceof cs with thedark energy density, de,and a tim e-varyingequation of state, w(a)= w0 + (1 a)wa.W end thattheapproxim atedegeneracy between dark energy param etersthatarisesin powerspectrum observationsis lifted through red- shifttom ography ofthe HI-galaxy angularpowerspectrum ,resulting in sound speed constraints that are not severely degraded.The cross-correlation ofthe galaxy and theintegrated Sachs-W olfe(ISW )eectspectra contributesapproxim ately 10percent oftheinform ation thatisneeded to distinguish variationsin thedark energy param - eters,and m ostofthe discrim inating signalcom esfrom the galaxy auto-correlation spectrum . W ealsond thatthesound speed constraintsareweakly sensitivetotheHI biasm odel.These constraintsdo notim provesubstantially fora signicantly deeper HIsurvey sincem ostoftheclusteringsensitivity tosound speed variationsarisesfrom z �1:5.A detection ofm odelswith sound speedsclose to zero, cs�0:01; ispossible fordark energy m odelswith w � 0:9. K ey w ords: cosm ologicalparam eters-large-scalestructureoftheuniverse-cosm ic m icrowavebackground -radio lines:galaxies
Breadth-First Search on Heterogeneous Platforms: A Case of Study on Social Networks

Breadth-First Search (BFS) is the core of many graph analysis algorithms and it is used in many problems, such as social network, computer network analysis, and data organization. BFS is an iterative algorithm that due to its irregular behavior is quite challenging to parallelize. Several approaches implement efficient algorithms for BFS for multicore architectures and for Graphics Processors, but it is still an open problem how to distribute the work among the main cores and the accelerators. In this paper, we assess several approaches to perform BFS on different heterogenous architectures (highend and embedded mobile processors composed of a multi-core CPU and an integrated GPU) with a focus on social network graphs. In particular, we propose two heterogenous approaches to exploit both devices. The first one, called Selective, selects on which device to execute each iteration. It is based on a previous approach, but we have adapted it to take advantage of the features of social network graphs (fewer iterations but more unbalanced). The second approach, referred as Concurrent, allows the execution of specific iterations concurrently in both devices. Our heterogenous implementations can be up to 1.56x faster and 1.32x more energy efficient with respect to the best of only-CPU or only-GPU baselines. We have also found that for a highly memory bound problem like BFS, the CPU-GPU collaborative execution is limited by the shared-memory bus bandwidth.
Recommendation strategies for e-learning: preliminary effects of a personal recommender system for lifelong learners

Drachsler, H. , Hummel, H. G. K. , Van den Berg, B. , Eshuis, J., Berlanga, A. J., Nadolski, R. J., Waterink, W. , Boers, N. , & Koper, R. (2007). Recommendation strategies for e-learning: preliminary effects of personal recommender system for lifelong learners. Unpublished manuscript.
Developing an Artificial Intelligence Engine

As computer games become more complex and consumers demand more sophisticated computer controlled agents, developers are required to place a greater emphasis on the artificial intelligence aspects of their games. One source of sophisticated AI techniques is the artificial intelligence research community. This paper discusses recent efforts by our group at the University of Michigan Artificial Intelligence Lab to apply state of the art artificial intelligence techniques to computer games. Our experience developing intelligent air combat agents for DARPA training exercises, described in John Laird's lecture at the 1998 Computer Game Developer's Conference, suggested that many principles and techniques from the research community are applicable to games. A more recent project, called the Soar/Games project, has followed up on this by developing agents for computer games, including Quake II and Descent 3. The result of these two research efforts is a partially implemented design of an artificial intelligence engine for games based on well established AI systems and techniques.
How to Breathe Life into Cultural Heritage 3D Reconstructions

Virtual 3D reconstructions of destroyed or disappeared cultural heritage enable viewers to effectively travel back through time and visualize monuments whose fragments they can see in museums or archaeological sites. A powerful way to convey information through three-dimensional geometry is to add interactive digital storytelling to virtual models. In this paper we present our work on interactive virtual cultural heritage applications with storytelling and show how users appreciate this presentation form, considering it as breathing life into 3D geometry. We describe the Tašlihan project, which consists of a documentary, interactive digital story and serious game about this valuable cultural monument from Sarajevo, Bosnia and Herzegovina, of which only one wall remains as a memento to its existence.
Noise Reduction for Cone-Beam Micro-CT by Fuzzy-Logic Based Non-Linear Filters

In Micro-CT, the smaller voxel requires much higher dose to achieve an adequate signal-to-noise ratio compared to clinical CT. Thus, it necessitates a method that reduces statistical noise in order to improve dose efficiency. We have developed a fuzzy-logic based non-linear Gaussian (NLG) filtering for smoothing projection data before reconstructed. We propose a fuzzy-logic based edge detector to determining the degree of smoothing. Both the fuzzy membership function and fuzzy rules are defined in such a way that the performance of the edge detector is optimized in terms of its robustness to noise. Additionally, we use NLG filter to obtain the filtering weights depending not only on difference in pixel locations but also on difference in density values. Several filtering steps with different NLG parameters can effectively reduce noise without losing structure information. Results with phantom and animal data suggested that our method is useful for the image quality improvement and radiation dose reduction of the micro-CT.
Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity

Triggered by recent experimental results, temporally asymmetric Hebbian (TAH) plasticity is considered as a candidate model for the biological implementation of competitive synaptic learning, a key concept for the experience-based development of cortical circuitry. However, because of the well known positive feedback instability of correlation-based plasticity, the stability of the resulting learning process has remained a central problem. Plagued by either a runaway of the synaptic efficacies or a greatly reduced sensitivity to input correlations, the learning performance of current models is limited. Here we introduce a novel generalized nonlinear TAH learning rule that allows a balance between stability and sensitivity of learning. Using this rule, we study the capacity of the system to learn patterns of correlations between afferent spike trains. Specifically, we address the question of under which conditions learning induces spontaneous symmetry breaking and leads to inhomogeneous synaptic distributions that capture the structure of the input correlations. To study the efficiency of learning temporal relationships between afferent spike trains through TAH plasticity, we introduce a novel sensitivity measure that quantifies the amount of information about the correlation structure in the input, a learning rule capable of storing in the synaptic weights. We demonstrate that by adjusting the weight dependence of the synaptic changes in TAH plasticity, it is possible to enhance the synaptic representation of temporal input correlations while maintaining the system in a stable learning regime. Indeed, for a given distribution of inputs, the learning efficiency can be optimized.
Deblurring and Sparse Unmixing of Hyperspectral Images Using Multiple Point Spread Functions

This paper is concerned with deblurring and spectral analysis of ground-based astronomical images of space objects. A numerical approach is provided for deblurring and sparse unmixing of ground-based hyperspectral images (HSIs) of objects taken through atmospheric turbulence. Hyperspectral imaging systems capture a three-dimensional (3D) datacube (tensor) containing two-dimensional (2D) spatial information and one-dimensional (1D) spectral information at each spatial location. Pixel intensities vary with wavelength bands, providing a spectral trace of intensity values and generating a spatial map of spectral variation (spectral signatures of materials). The deblurring and spectral unmixing problem is quite challenging since the point spread function (PSF) depends on the imaging system as well as the seeing conditions and is wavelength varying. We show how to efficiently construct an optimal Kronecker product--based preconditioner, and provide numerical methods for estimating the multiple PSFs using spectr.
Academic and Social Adjustment of High School Refugee Youth in Newfoundland

This article addresses the complex academic and social adjustment issues of newcomer youth of refugee background at a high school in Newfoundland and Labrador, a province where the newcomer population is small but the percentage of refugees in relation to all newcomers is high. Data for this qualitative study include documents from educational authorities and ESL teachers, field notes of classroom observations, qualitative survey questionnaires from 15 newcomer students, and interviews with 6 students of refugee background and 3 teachers. We found that these refugee youth were challenged due not only to language difficulties and educational gaps, but also to differences in educational systems, school cultures, and student-teacher dynamics between their previous school- ing and what they encountered in Newfoundland. They had to cope with social isolation and different practices of body language, dress code, personal hygiene, and sexual orientation. The study also identified inadequacies in the current curriculum, teacher in-service education, and diversity initiatives in the school system. Cet article porte sur les enjeux complexes lies a l’adaptation academique et sociale que vivent de jeunes refugies dans une ecole secondaire a Terre-Neuve-et-Labrador, une province ou les nouveaux arrivants sont peu nombreux mais ou le pourcentage de refugies parmi les nouveaux arrivants est eleve. Les donnees pour cette etude qualitative incluent des documents provenant d’autorites scolaires et d’enseignants d’ALS, des notes decoulant d’observations en classe, des questionnaires qualitatifs aupres de 15 eleves nouvellement arrives, et des entrevues aupres de 6 eleves refugies et de 3 enseignants. Les resultats indiquent que les jeunes refugies sont confrontes non seulement a des difficultes linguistiques et des lacunes d’education, mais egalement a des differences dans les systemes educatifs, dans les cultures scolaires et dans la dynamique entre les eleves et les enseignants qui distinguent leurs experiences pedagogiques precedentes de celles qu’ils vivent a Terre-Neuve. Ils devaient faire face a l’isolement social et aux pratiques diffe rentes relatives au langage corporel, au code vestimentaire, a l’hygiene personnel et a l’orientation sexuelle. L’etude a egalement identifie des lacunes dans le pro gramme d’etudes actuel, dans le perfectionnement professionnel des enseignants et dans les initiatives scolaires portant sur la diversite.