Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressive sensing under matrix uncertainties: An Approximate Message Passing approach

In this work, we consider a general form of noisy compressive sensing (CS) when there is uncertainty in the measurement matrix as well as in the measurements. Matrix uncertainty is motivated by practical cases in which there are imperfections or unknown calibration parameters in the signal acquisition hardware. While previous work has focused on analyzing and extending classical CS algorithms like the LASSO and Dantzig selector for this problem setting, we propose a new algorithm whose goal is either minimization of mean-squared error or maximization of posterior probability in the presence of these uncertainties. In particular, we extend the Approximate Message Passing (AMP) approach originally proposed by Donoho, Maleki, and Montanari, and recently generalized by Rangan, to the case of probabilistic uncertainties in the elements of the measurement matrix. Empirically, we show that our approach performs near oracle bounds. We then show that our matrix-uncertain AMP can be applied in an alternating fashion to learn both the unknown measurement matrix and signal vector. We also present a simple analysis showing that, for suitably large systems, it suffices to treat uniform matrix uncertainty as additive white Gaussian noise.
Reusing a System Testing Process Using with a Model

In this paper, we proposed an approach to permit the development of a testing platform in parallel with application development by accumulating knowledge of a testing platform developed in the past. First, we define a meta-model called the system testing development platform, and describe how to accumulate a testing platform. Then, we show how to find an appropriate testing platform. This helps system architects or application developers get information about testing platforms they are working on. Last, we explain the creation of template testing codes that help application developers concentrate on developing test data.
Text island spotting in large speech databases

Automatic transcript aligned and corrected This competition is arbitrated by a matching score Wi. Experimental context :-First experiments assessed on 3 hours of radio ESTER (with exact transcript and a 10% WER transcripts)-Second experiments assessed on 11 hours of RTBF on wich time stamps where manually added. -All words available in database are added to the language model-Language model : about 67000 words trained on « lemonde »-Speech recognition system : SPEERAL, an asynchronous decoder based on the A* algorithm. Results : Conclusions :-On ESTER tests approximative transcripts bring a WER gain of about 14% relative, while exact ones allows a WER gain close to 24% relative. -Spotting performance is good; more than 95.3% of segments have been found, with a precision of about 96.7%. -On RTBF tests, spotting performance is good; more than 95.3% of segments have been found, with a precision of about 96.7%. Fast-match to transcript island-The principle of the proposed method is close to approaches used in the field of information retrieval. -In our case, the hypothesis is a query which may be answered by one of the transcript island. -The lexicon is represented by a lexical space Ls where each dimension is associated to a word. The coefficients of these vectors represent the frequencies of words in the document. -As the current hypothesis is developed, a set of word clusters Ci is built and updated. -These clusters result from the intersection of hc and the transcript island Ii. -For each new word added to the hypothesis hc, transcript islands are considered as candidates for guiding the search.
Updating Geodata by Automatic Object Extraction-Modelling Uncertainties

In this paper we describe a method, which allows to model the positional and abstraction uncertainty of object’s borders stochastically and leads to a probabilitybased decision of topological relations between area objects. The method was applied to evaluate the results of automatic extracted objects. By means of examples we demonstrate that the method is useful for update and quality description of geodata.
Endophyte-infection reduces leaf dehydration of ryegrass and tall fescue plants under moderate water deficit

Drought stress is one of the limiting factors in perennial ryegrass growth and development. In order to investigate the drought responses among different cultivars and evaluate the role of fungal endophyte in modifying drought responses, a glasshouse experiment was conducted using ryegrass cultivars ‘Alto’ and ‘Banquet II’, and a Mediterranean tall fescue cultivar, ‘Grasslands Flecha’ as contrast with the two ryegrass cultivars. Under drought stress ‘Flecha’ had a higher stomatal conductance and transpiration rate than the two ryegrass cultivars, and also significantly greater deeproot allocation. Endophyte-infected plants exhibited reduced leaf dehydration under stress compared with endophyte-free plants. Under stress, endophyte-free plants displayed significantly lower relative water content and a more negative leaf water potential and osmotic potential than well-watered endophyte-free plants, whereas endophyte-infected plants displayed smaller and non-significant changes when subjected to water deficit stress. Endophyte-infected plants also had less deep-root allocation than endophyte-free plants. The results of this study increase understanding of the relationship between endophytes and their ryegrass or tall fescue hosts under drought stress.
Specification and verification of system-level hardware designs using time diagrams

An approach to the specification and verification of system-level hardware designs is presented. It is based on Timing Diagrams, a graphical specification language with an intuitive semantics, which is especially appropriate for the description of asynchronous distributed systems such as hardware designs. Timing Diagrams and their semantics are formally defined based on a translation to temporal logic. It is shown that for the resulting type of formulas there is an efficient model checking procedure, thus allowing fully automatic verification of hardware designs. <<ETX>>
Performance Analysis of Glazed Liquid Photovoltaic-thermal Collector with Use of Detail Model

The current trend of using solar energy is focused on maximum utilization of area available on the bui lding envelope. Therefore the interest in multifunctional technology in solar col lector field increases. The photovoltaic-thermal (P VT) collector converts solar energy into both electric and thermal energy. The p aper describes the mathematical model of glazed liq uid PVT solar collector based on detailed construction parameters and energ y balance. The model can be used for design analysi s and optimization of glazed PVT collector construction. The mathematical model was experimentally validated for thermal and electric energy performance under steady-state conditions. The model has been implemented as a new component in TRNSYS simula tion environment and has been used for evaluation of the PVT collect r performance in different climate and operation c ditions. Influence of glazed PVT collector design on annual performance o f domestic hot water system was analysed with use o f TRNSYS. The results of the annual simulations were used for decision on the design of final glazed PVT collector prototype .
Consumption Taxes , Redistribution and Informality

We study how the presence of large informal sectors in developing countries impacts the distributional properties of consumption taxes. We assemble a dataset of household expenditure using micro-data from 20 countries at different levels of economic development. Using the place of purchase to proxy for informal consumption, we show a large negative relation between informal consumption shares and households’ total expenditure, which is robust to product and geography controls. This implies that consumption taxes are de-facto progressive: households in the top decile pay 70% more taxes as a share of expenditure than households in the bottom decile. Finally, we build a model of optimal commodity taxation in the presence of informal consumption, which we calibrate to our data. We find that optimal tax rates are less differentiated across products with an informal sector. Tax exempting necessities, such as food, is rarely optimal as it leads to only a marginal gain in progressivity. JEL Classification: H21, H23, H32, O23 ∗Pierre Bachas: World Bank Research, pbachas@worldbank.org. Lucie Gadenne: University of Warwick, Institute for Fiscal Studies and CEPR, l.gadenne@warwick.ac. uk. Anders Jensen: Harvard Kennedy School and NBER, Anders Jensen@hks.harvard. edu. The findings and conclusions are those of the authors and do not represent the views of the World Bank. We thank Elie Gerschel, Mariana Racimo, Roxanne Rahnama and Alvaro Zuniga for excellent research assistance. Financial support from the World Bank Development Economics’ Research Support Budget is gratefully acknowledged.
A multifaceted approach to modeling the immune response in tuberculosis

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). No available vaccine is reliable and, although treatment exists, approximately 2 million people still die each year. The hallmark of TB infection is the granuloma, a self‐organizing structure of immune cells forming in the lung and lymph nodes in response to bacterial invasion. Protective immune mechanisms play a role in granuloma formation and maintenance; these act over different time/length scales (e.g., molecular, cellular, and tissue scales). The significance of specific immune factors in determining disease outcome is still poorly understood, despite incredible efforts to establish several animal systems to track infection progression and granuloma formation. Mathematical and computational modeling approaches have recently been applied to address open questions regarding host–pathogen interaction dynamics, including the immune response to Mtb infection and TB granuloma formation. This provides a unique opportunity to identify factors that are crucial to a successful outcome of infection in humans. These modeling tools not only offer an additional avenue for exploring immune dynamics at multiple biological scales but also complement and extend knowledge gained via experimental tools. We review recent modeling efforts in capturing the immune response to Mtb, emphasizing the importance of a multiorgan and multiscale approach that has tuneable resolution. Together with experimentation, systems biology has begun to unravel key factors driving granuloma formation and protective immune response in TB. WIREs Syst Biol Med 2011 3 479–489 DOI: 10.1002/wsbm.131
RESONANT ROTATION STATES OF THE JOVIAN AND SATURNIAN SATELLITES

Introduction: Many of the moons of the giant planets are trapped in a 1:1 spin-orbit resonance. Up to now this special rotation state is confirmed by observations for 8 satellites of Jupiter, 15 satellites of Saturn, 15 satellites of Uranus, and 7 satellites of Neptune [1]. These rotation states are a result from the strong tidal torque exerted on the satellites by their central body. However, slight deviations from the resonant state, e.g., physical librations, can provide hints to the interior structure of the satellites. By deriving the resonant rotation states for the satellites, we provide a critical reference to which the actual measurements can be compared. By that the resonant rotation states can contribute to the knowledge on the formation and evolution of moons around the giant planets in our Solar System. Method: We derive the resonant rotation states from the ephemerides of the satellites. First we obtain the osculating orbital elements of a satellite in an inertial reference frame, i.e., International Celestial Reference Frame (ICRF). Thereby we apply a sampling time step of about 1% of the orbital period and use the complete time interval available in the satellite ephemeris. Then we decompose the signal (time series of one osculating orbital element) in a secular part and a sum of periodic terms
A Data Model and an XQuery Extension for Concurrent XML Structures

An XML document is mainly hierarhical, but some applications need to simultaneously associate more than one hierarchy to the same data. In general, concurrent hierarchies cannot be merged in order to get a well-formed XML document. This work stands in this context: it aims at describing and querying hierarchical XML structures defined over the same textual data in a concurrent way. Our proposal called MSXD is composed of a data model (which can be seen as an index over the data and between all the structures) and a dedicated query language defined as an extension of XQuery. The key idea is to proposea method for a compact description of multiple tree-like structures over a single text based on segmentation. Segmentation encoding allows querying overlap/containment relations of markups belonging to different structures. This paper also tackles a solution to define a multistructured schema in order to describe the relationships (as weak constraints) between parts of concurrent structures. Finally, this paper focuses on the architecture of the XML environment implementing our proposals. Povzetek: Sistem omogoˇ ca uveljavljanje veˇ c hierarhij v dokumentih XML.
Exploiting Interest Locality for Peer-Assisted Search in UGC Video Systems

While there are several ways for video finding in UGC (user generated content) video systems, video search is still the number one source of video views in aggregation. In this paper, we propose to use peer-assisted search to alleviate the server burden caused by video search. To this end, we have measured and analyzed YouKu, the largest UGC video system in China. With a large dataset, we have found non-power law distribution of video popularity, low replication level for popular videos, skewed user activity and interest locality. Based on the findings, we design two-layer hierarchical semantic overlay structures to implement peer-assisted search for UGC video systems. A novel search algorithm called WISE is proposed to guide queries quickly to the semantically relevant clusters by visiting a very small fraction of nodes. Simulations using the YouKu trace demonstrate that WISE is effective and helpful to assist the search in UGC video systems. To the best of our knowledge, this is the first work to study peer-assisted search in UGC.
Misuse of Prescription and Illicit Drugs Among High-risk Young Adults in Los Angeles and New York

Background Prescription drug misuse among young adults is increasingly viewed as a public health concern, yet most research has focused on student populations and excluded high-risk groups. Furthermore, research on populations who report recent prescription drug misuse is limited. This study examined patterns of prescription drug misuse among high-risk young adults in Los Angeles (LA) and New York (NY), which represent different local markets for illicit and prescription drugs. Design and Methods Between 2009 and 2011, 596 young adults (16 to 25 years old) who had misused prescription drugs within the past 90 days were interviewed in Los Angeles and New York. Sampling was stratified to enroll three groups of high-risk young adults: injection drug users (IDUs); homeless persons; and polydrug users. Results In both sites, lifetime history of receiving a prescription for an opioid, tranquilizer, or stimulant was high and commonly preceded misuse. Moreover, initiation of opioids occurred before heroin and initiation of prescription stimulants happened prior to illicit stimulants. NY participants more frequently misused oxycodone, heroin, and cocaine, and LA participants more frequently misused codeine, marijuana, and methamphetamine. Combining prescription and illicit drugs during drug using events was commonly reported in both sites. Opioids and tranquilizers were used as substitutes for other drugs, e.g., heroin, when these drugs were not available. Conclusion Patterns of drug use among high-risk young adults in Los Angeles and New York appear to be linked to differences in local markets in each city for illicit drugs and diverted prescription drugs.
Learning Morphological Data of Tomato Fruits

Three methods for attribute reduction in conjunction with Neural Networks, Naive Bayes, and k-Nearest Neighbor classifiers are investigated here when classifying a particularly challenging data set. The difficulty encountered with this data set is mainly due to the high dimensionality and to some inbalance between classes. As a result of this research, a subset of only 8 attributes (out of 34) is identified leading to a 92.7% classification accuracy. The confusion matrix analysis identifies class 7 as the one poorly learned across all combinations of attributes and classifiers. This information can be further used to upsample this underrepresented class or to investigate a classifier less sensitive to imbalance.
RECENT ADVANCEMENT IN CARBON NANOTUBES AND THEIR COMPOSITES

Since their observation in 1991 by Ijima [1], carbon nanotubes have been the focus of considerable research. The extensive basic studies performed by physicists and chemists in the processing of carbon nanotubes during the past decade have established a sound foundation for exploring their technological application. The exceptional mechanical properties of carbon nanotubes can be exploited in the development of nanotube-based composite materials that may far exceed the properties of existing fiber-reinforced composites. Nanocomposites of carbon tubes that have extraordinary specific stiffness and specific strength represent tremendous opportunity for application in the 21 century. This paper provides a concise review of the recent advancement in carbon nanotubes and their composites. We examine the research work reported in the literature on the structure and processing of carbon nanotubes, as well as the characterization and modeling of the properties of carbon nanotubes and their composites.
An Investigation of Emotion Recognition and Theory of Mind in People with Chronic Heart Failure

Objectives Cognitive deficits are common in patients with chronic heart failure (CHF), but no study has investigated whether these deficits extend to social cognition. The present study provided the first empirical assessment of emotion recognition and theory of mind (ToM) in patients with CHF. In addition, it assessed whether each of these social cognitive constructs was associated with more general cognitive impairment. Methods A group comparison design was used, with 31 CHF patients compared to 38 demographically matched controls. The Ekman Faces test was used to assess emotion recognition, and the Mind in the Eyes test to measure ToM. Measures assessing global cognition, executive functions, and verbal memory were also administered. Results There were no differences between groups on emotion recognition or ToM. The CHF group’s performance was poorer on some executive measures, but memory was relatively preserved. In the CHF group, both emotion recognition performance and ToM ability correlated moderately with global cognition (r = .38, p = .034; r = .49, p = .005, respectively), but not with executive function or verbal memory. Conclusion CHF patients with lower cognitive ability were more likely to have difficulty recognizing emotions and inferring the mental states of others. Clinical implications of these findings are discussed.
A NEW NONSMOOTH EQUATIONS APPROACH TO NONLINEAR COMPLEMENTARITY PROBLEMS∗

Based on Fischer’s function, a new nonsmooth equations approach is presented for solving nonlinear complementarity problems. Under some suitable assumptions, a local and Q-quadratic convergence result is established for the generalized Newton method applied to the system of nonsmooth equations, which is a reformulation of nonlinear complementarity problems. To globalize the generalized Newton method, a hybrid method combining the generalized Newton method with the steepest descent method is proposed. Global and Q-quadratic convergence is established for this hybrid method. Some numerical results are also reported.
On The Ridiculousness of Notice and Consent: Contradictions in App Privacy Policies

The dominant privacy framework of the information age relies on notions of “notice and consent. ” That is, service providers will disclose, often through privacy policies, their data collection practices, and users can then consent to their terms. However, it is unlikely that most users comprehend these disclosures, which is due in no small part to ambiguous, deceptive, and misleading statements. By comparing actual collection and sharing practices to disclosures in privacy policies, we demonstrate the scope of the problem. Through analysis of 68,051 apps from the Google Play Store, their corresponding privacy policies, and observed data transmissions, we investigated the potential misrepresentations of apps in the Designed For Families (DFF) program, inconsistencies in disclosures regarding third-party data sharing, as well as contradictory disclosures about secure data transmissions. We find that of the 8,030 DFF apps (i.e., apps directed at children), 9.1% claim that their apps are not directed at children, while 30.6% claim to have no knowledge that the received data comes from children. In addition, we observe that 10.5% of 68,051 apps share personal identifiers with third-party service providers, yet do not declare any in their privacy policies, and only 22.2% of the apps explicitly name third parties. This ultimately makes it not only difficult, but in most cases impossible, for users to establish where their personal data is being processed. Furthermore, we find that 9,424 apps do not use TLS when transmitting personal identifiers, yet 28.4% of these apps claim to take measures to secure data transfer. Ultimately, these divergences between disclosures and actual app behaviors illustrate the ridiculousness of the notice and consent framework.