Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularization Parameter Selection Method for Sign LMS with Reweighted L1-Norm Constriant Algorithm

Broadband frequency-selective fading channels usually have the inherent sparse nature. By exploiting the sparsity, adaptive sparse channel estimation (ASCE) algorithms, e.g., least mean square with reweighted L1-norm constraint (LMS-RL1) algorithm, could bring a considerable performance gain under assumption of additive white Gaussian noise (AWGN). In practical scenario of wireless systems, however, channel estimation performance is often deteriorated by unexpected non-Gaussian mixture noises which include AWGN and impulsive noises. To design stable communication systems, sign LMS-RL1 (SLMS-RL1) algorithm is proposed to remove the impulsive noise and to exploit channel sparsity simultaneously. It is well known that regularization parameter (REPA) selection of SLMS-RL1 is a very challenging issue. In the worst case, inappropriate REPA may even result in unexpected instable convergence of SLMS-RL1 algorithm. In this paper, Monte Carlo based selection method is proposed to select suitable REPA so that SLMS-RL1 can achieve two goals: stable convergence as well as usage sparsity information. Simulation results are provided to corroborate our studies.
SDNs, Clouds, and Big Data: New Opportunities

We are at the cusp of a technological revolution driven mainly by advances in hardware technology, network architectural support, and the ability to process big data. The hardware industry, driven by Moore's law, continues to provide steadily increasing computing capability with diminishing costs. With the support of hardware advances, platforms for distributed storage and processing of big data, such as Apache Hadoop, provide the ability to scalably and reliably process massive amounts of data using a cluster of commodity servers. In parallel, a revolution is ongoing in the networking world. The emergence of the software defined networking (SDN) architecture promises to rectify the ossified architecture of the Internet allowing network managers to flexibly program the network. In this paper, we argue that SDN and big data, two technological trends that promise to revolutionize all aspects of modern life, can leverage the freedom afforded by each other to jointly increase their value proposition. In particular, SDN can utilize the large amounts of operational data to optimize the network behavior, while big data platforms can benefit from the flexible architectural support provided by SDN. In this paper, we will provide a brief self-contained exposition on opportunities for SDN and big data to synergize and jointly optimize. We will also point out open research issues and will identify future directions of work.
Ambient multi-perceptive system for residential health monitoring based on electronic mailings Experimentation within the AILISA project

This paper is a reflexion on information systems for residential healthcare. Based on several years of experiments, it is proposed a model of information system, and technical hints to select available hard and software technologies. An implementation is described, based on Emails. Some experimental results, collected within the framework of the French national project AILISA, are presented.
Corneal hysteresis and visual field asymmetry in open angle glaucoma.

PURPOSE To investigate the association between corneal biomechanical parameters and asymmetric primary open angle glaucoma (POAG) using the Ocular Response Analyzer (ORA). METHODS In a prospective cross-sectional study, ORA parameters were measured in 117 POAG patients with asymmetric visual fields (VF). The asymmetry in VF was defined as a five point difference between the eyes using the Advanced Glaucoma Intervention Study (AGIS) scoring system. Subjects with previous intraocular or refractive surgery, ocular comorbidities and diabetes were excluded. RESULTS In worse eyes, mean AGIS scores were significantly higher (8.1 ± 4.3 vs. 1.0 ± 1.6; P < 0.001) and mean corneal hysteresis (CH) was significantly lower (8.2 ± 1.9 vs. 8.9 ± 1.9 mm Hg; P < 0.001). Median ORA-corrected intraocular pressure was higher in the worse eyes (IOP(cc), 17.4 mm Hg vs. 16.9 mm Hg; P < 0.001). Worse eyes had a slightly lower mean corneal resistance factor (P = 0.04) and more myopic mean spherical equivalent (P = 0.02). No difference was seen in the central corneal thickness (CCT; P = 0.63) and Goldmann applanation tonometry (GAT; P = 0.32). On multivariate analysis, only CH retained an association with the worse eye (odds ratio, 25.9; 95% confidence interval, 10.1-66.5). ROC curves showed that only CH and IOP(cc) had a discriminative ability for the eye with worse VF (AUC, 0.82 and 0.70, respectively). CONCLUSIONS Asymmetric POAG was associated with asymmetry in ORA parameters but not in CCT and GAT. Lower CH was associated with worse eyes independently of its effect on IOP measurement and had the best discriminability for the eye with the worse VF.
Representing Higher-Order Singularities in Vector Fields on Piecewise Linear Surfaces

Accurately representing higher-order singularities of vector fields defined on piecewise linear surfaces is a non-trivial problem. In this work, we introduce a concise yet complete interpolation scheme of vector fields on arbitrary triangulated surfaces. The scheme enables arbitrary singularities to be represented at vertices. The representation can be considered as a facet-based "encoding" of vector fields on piecewise linear surfaces. The vector field is described in polar coordinates over each facet, with a facet edge being chosen as the reference to define the angle. An integer called the period jump is associated to each edge of the triangulation to remove the ambiguity when interpolating the direction of the vector field between two facets that share an edge. To interpolate the vector field, we first linearly interpolate the angle of rotation of the vectors along the edges of the facet graph. Then, we use a variant of Nielson's side-vertex scheme to interpolate the vector field over the entire surface. With our representation, we remove the bound imposed on the complexity of singularities that a vertex can represent by its connectivity. This bound is a limitation generally exists in vertex-based linear schemes. Furthermore, using our data structure, the index of a vertex of a vector field can be combinatorily determined
General state space Markov chains and MCMC algorithms

This paper surveys various results about Markov chains on gen- eral (non-countable) state spaces. It begins with an introduction to Markov chain Monte Carlo (MCMC) algorithms, which provide the motivation and context for the theory which follows. Then, sucient conditions for geomet- ric and uniform ergodicity are presented, along with quantitative bounds on the rate of convergence to stationarity. Many of these results are proved using direct coupling constructions based on minorisation and drift con- ditions. Necessary and sucient conditions for Central Limit Theorems (CLTs) are also presented, in some cases proved via the Poisson Equa- tion or direct regeneration constructions. Finally, optimal scaling and weak convergence results for Metropolis-Hastings algorithms are discussed. None of the results presented is new, though many of the proofs are. We also describe some Open Problems.
A Replication of Comparative Study of Moving Windows on Linear Regression and Estimation by Analogy

Context: Recent studies have shown that estimation accuracy can be affected by only using a window of recent projects as training data for building an effort estimation model. The effect and its extent can be affected by effort estimation methods (e.g. linear regression (LR) or estimation by analogy (EbA)), windowing policies (fixed-size or fixed-duration), and between organizations. However, different effects between organizations have only been explored with LR as the estimation method, and different effects between estimation methods and windowing policies have mainly been explored with data from only one organization. Objective: To further investigate the effect on estimation accuracy of using windows, with different windowing policies, when using EbA as the estimation method. Also, to compare the effect of LR with EbA as an estimation method, when using windows. Method: Using a data set studied with LR in previous research, we examine the effects of using windows on the accuracy of effort estimates, using EbA with both fixed-size and fixed-duration windowing policies. Results: With this data set, fixed-size windows, no matter their size, do not improve the accuracy of estimates obtained using EbA. This reinforces previous research with this data set, which used LR as the estimation approach. However, fixed-duration windows can improve the accuracy of estimates obtained with EbA. This contradicts previous research with this data set, which used LR as the estimation approach. Variations in the settings for EbA can change the sizes at which windows are helpful. Conclusions: This study reinforces that the effect of using windows can be affected by the effort estimation approach, and by the windowing policy. Contrary to previous research, fixed-duration windows are found to be more helpful than fixed-size windows, and significant improvements are found with EbA that were not found with LR. Further research is needed to understand these differences.
A Simple Scheme for Security and Access Control in Cyber-Physical Systems

In a time when technology changes continuously, where things you need today to run a certain system, might not be needed tomorrow anymore, security is a constant requirement. No matter what systems we have, or how we structure them, no matter what means of digital communication we use, we are always interested in aspects like security, safety, privacy. An example of the ever-advancing technology are cyber-physical systems. We propose a complex security architecture that integrates several consecrated methods such as cryptography, steganography and digital signatures. This architecture is designed to not only ensure security of communication by transforming data into secret code, it is also designed to control access to the system and detect and prevent cyber attacks.
Impacts of projected climate change on sediment yield and dredging costs

Changes in climate may significantly affect how sediment moves through watersheds into harbours and channels that are dredged for navigation or flood control. Here, we applied a hydrologic model driven by a large suite of climate change scenarios to simulate both historical and future sediment yield and transport in two large, adjacent watersheds in the Great Lakes region. Using historical dredging expenditure data from the U.S. Army Corps of Engineers, we then developed a pair of statistical models that link sediment discharge from each river to dredging costs at the watershed outlet. Although both watersheds show similar slight decreases in streamflow and sediment yield in the near‐term, by Mid‐Century, they diverge substantially. Dredging costs are projected to change in opposite directions for the two watersheds; we estimate that future dredging costs will decline in the St. Joseph River by 8–16% by Mid‐Century but increase by 1–6% in the Maumee River. Our results show that the impacts of climate change on sediment yield and dredging may vary significantly by watershed even within a region and that agricultural practices will play a large role in determining future streamflow and sediment loads. We also show that there are large variations in responses across climate projections that cause significant uncertainty in sediment and dredging projections.
Acoustic-Phonetic Characteristics of Clear Speech in Bilinguals

This study examined the language-dependency of clear speech modifications by comparing the clear speech strategies of late bilinguals in both their L1 (Finnish) and L2 (English). Results generally supported the hypothesis of language-independent enhancement of global clear speech modifications, but language-dependent segmental enhancement. The global clear speech strategies produced by Finnish-English bilinguals in their L2 (English) were similar in the extent of the modifications to those of native English speakers, indicating a surprising flexibility of the non-native speech production system.
Response of plants to water stress

Water stress adversely impacts many aspects of the physiology of plants, especially photosynthetic capacity. If the stress is prolonged, plant growth, and productivity are severely diminished. Plants have evolved complex physiological and biochemical adaptations to adjust and adapt to a variety of environmental stresses. The molecular and physiological mechanisms associated with water-stress tolerance and water-use efficiency have been extensively studied. The systems that regulate plant adaptation to water stress through a sophisticated regulatory network are the subject of the current review. Molecular mechanisms that plants use to increase stress tolerance, maintain appropriate hormone homeostasis and responses and prevent excess light damage, are also discussed. An understanding of how these systems are regulated and ameliorate the impact of water stress on plant productivity will provide the information needed to improve plant stress tolerance using biotechnology, while maintaining the yield and quality of crops.
Scale-Adjusted Metrics for Predicting the Evolution of Urban Indicators and Quantifying the Performance of Cities

More than a half of world population is now living in cities and this number is expected to be two-thirds by 2050. Fostered by the relevancy of a scientific characterization of cities and for the availability of an unprecedented amount of data, academics have recently immersed in this topic and one of the most striking and universal finding was the discovery of robust allometric scaling laws between several urban indicators and the population size. Despite that, most governmental reports and several academic works still ignore these nonlinearities by often analyzing the raw or the per capita value of urban indicators, a practice that actually makes the urban metrics biased towards small or large cities depending on whether we have super or sublinear allometries. By following the ideas of Bettencourt et al. [PLoS ONE 5 (2010) e13541], we account for this bias by evaluating the difference between the actual value of an urban indicator and the value expected by the allometry with the population size. We show that this scale-adjusted metric provides a more appropriate/informative summary of the evolution of urban indicators and reveals patterns that do not appear in the evolution of per capita values of indicators obtained from Brazilian cities. We also show that these scale-adjusted metrics are strongly correlated with their past values by a linear correspondence and that they also display crosscorrelations among themselves. Simple linear models account for 31%–97% of the observed variance in data and correctly reproduce the average of the scale-adjusted metric when grouping the cities in above and below the allometric laws. We further employ these models to forecast future values of urban indicators and, by visualizing the predicted changes, we verify the emergence of spatial clusters characterized by regions of the Brazilian territory where we expect an increase or a decrease in the values of urban indicators.
Score lists in multipartite hypertournaments

Given non-negative integers $n_{i}$ and $\alpha_{i}$ with $0 \leq \alpha_{i} \leq n_i$ $(i=1,2,.,k)$, an $[\alpha_{1},\alpha_{2},. ,\alpha_{k}]$-$k$-partite hypertournament on $\sum_{1}^{k}n_{i}$ vertices is a $(k+1)$-tuple $(U_{1},U_{2},.,U_{k},E)$, where $U_{i}$ are $k$ vertex sets with $|U_{i}|=n_{i}$, and $E$ is a set of $\sum_{1}^{k}\alpha_{i}$-tuples of vertices, called arcs, with exactly $\alpha_{i}$ vertices from $U_{i}$, such that any $\sum_{1}^{k}\alpha_{i}$ subset $\cup_{1}^{k}U_{i}^{\prime}$ of $\cup_{1}^{k}U_{i}$, $E$ contains exactly one of the $(\sum_{1}^{k} \alpha_{i})! $ $\sum_{1}^{k}\alpha_{i}$-tuples whose entries belong to $\cup_{1}^{k}U_{i}^{\prime}$. We obtain necessary and sufficient conditions for $k$ lists of non-negative integers in non-decreasing order to be the losing score lists and to be the score lists of some $k$-partite hypertournament.
Cooperative Transportation by Multiple Robots with Machine Learning

This paper investigates the development of a physical multi-robot system, where a group of intelligent robots work cooperatively to transport an object to a goal location and orientation in an unknown dynamic environment. Multi-agent technology and machine learning are integrated into the same physical platform to provide innovative capabilities for carrying out the task. First, a new multi-agent architecture is developed. Second, two methods that facilitate optimized machine learning, Reinforcement learning (RL) and genetic algorithms (GA), are integrated into the decision-making agent in the architecture. An arbitrator is incorporated into a probabilistic switching scheme for selecting the optimal strategy in a given state of the task. Finally, the robot force/motion control and local modeling are integrated into the architecture to implement a physical multi-robot system. Simulation and experimental studies are carried out to demonstrate the feasibility of the system.
Modeling and Analysis of Fluid Driven Fracture Propagation Under the Plane Strain Condition

The process of fracture propagation driven by the pressure of the fluid flow between the fracture surfaces has been of considerable interest for understanding natural geological phenomena such as the formation of volcanic dikes and developing hydraulic fracturing technologies for industrial applications. Man-made hydraulic fracturing has been most commonly used for stimulation of oil and gas reservoirs to increase hydrocarbon production, stimulation of geothermal reservoirs, remediation of soil and groundwater aquifers, injection of wastes, goafing and fault reactivation in mining, and measurement of underground in situ stresses.Computational modeling and simulation of fluid driven fracture propagation in realistic geological formation has been a challenging problem because of various complexities including formation heterogeneities and the use of highly nonlinear engineered fluids. At present, one of the main obstacles for the robust industrial application of the simulating technology is the computational efficiency and stability. The objective of this study is to investigate the numerical efficiency and stability of various algorithms that can be potentially used in modeling of fluid driven fracture propagation. For simplicity, we have focused on fracture propagation in the plane strain condition. The fracture is assumed to in homogeneous linearelastic medium and modeled using displacement discontinuity boundary element method (DDBEM). The nonlinear power-law fluid flow is modeled using conventional lubrication theory. The coupled equations are then discretized using zero order elements for its efficiency. The coupled equations become increasingly stiff and difficulty to solve when the power law indices become smaller. Various numerical algorithms such as Newton iterationwith line search, trust-region and quasi-Newton method are investigated and compared. We have also extended the model to the fluid driven non-planar fracture propagation. A numerical crack propagation criterion based on the minimum local shear stress under mixed loading condition is proposed and compared with conventional theoretical and numerical criteria. The new crack propagation criterion provides more accurate and smooth crack initiation paths. Finally we have studied the geomechanics interaction between two simultaneous fluid driven fractures. The results provided some useful inputs for optimal design of multiple stage and multiple fracturing treatments along horizontal wells currently adoptedby the oil and gas industry for the economical recovery of unconventional resources such as shale gas and oil.
Silicone gel breast implant failure and frequency of additional surgeries: analysis of 35 studies reporting examination of more than 8,000 explants.

Although it is well known that silicone gel breast implants (SGBIs) produce many "local" complications (i.e., pain, hard fibrous capsules, disfigurement, chronic inflammation, implant shell failure) and necessitate frequent surgical revisions, no large cohort retrospective quantitative analysis of clinical data has been reported to date, especially for the prevalence of failures and additional surgeries. Data from 35 different studies that encompass more than 8000 explanted SGBIs have now been analyzed and are reported here. Because examination of a prosthesis when explanted is the definitive method for determining shell integrity, the only studies that were used were ones that reported implant duration, the total number of SGBIs explanted, and the number of SGBIs for which shell rupture or failure ("not intact") was confirmed upon surgical removal. An exponential regression plot of data indicated a direct correlation of implant duration with percent shell failure (r2 = 0. 63 and r = 0.79 ). SGBI failure was found to be 30% at 5 years, 50% at 10 years, and 70% at 17 years. The failure rate was 6% per year during the first 5 years following primary implant surgery. ANOVA comparison of three implant age groups (mean implant durations of 3. 9, 10.2, and 18.9 years) indicated a highly significant statistical correlation of percent failure with implant duration (p < 0.001). Complications necessitating at least one additional surgery occurred for 33% of implants within 6 years following primary implant surgery. Shell failure was found to be an order of magnitude greater than the 4 to 6% rupture prevalence suggested by the AMA Council on Scientific Affairs in 1993, the 0.2 to 1.1% cited by manufacturers at that time, and the 5% rupture that was stated to be "not a safety standard that the FDA can accept. "
Long-term effects of a community-wide alcohol server training intervention.

OBJECTIVE The Rhode Island Community Alcohol Abuse and Injury Prevention Project (CAAIPP), implemented from 1984 through 1989, employed the "community gatekeeper" approach to reduce alcohol-related injuries and deaths. Targeting alcohol servers rather than drinkers, community-wide interventions were designed and implemented to encourage responsible serving behaviors through the adoption of techniques of responsible service. The primary goal of the CAAIPP server intervention evaluation was to assess both short-term and long-term changes in behavior of alcohol beverage servers who were recipients of CAAIPP training. METHOD A 5-hour training curriculum on "Responsible Alcohol Service" was offered to all alcohol servers in a randomly selected study community. A prospective study design was used to evaluate long-term changes in the self-reported behavior of 321 trainees using three time-points over 5 years. A cross-sectional survey was conducted 4 years posttraining to compare rates of self-reported server behaviors in the intervention community (n = 106) with two comparison communities (nA = 56, nB = 49). RESULTS Fifteen months after training, trainees reported significantly higher levels of desired serving behavior than nontrained servers. Though positive effects of server training diminished with time, responsible serving behavior 4 years posttraining remained higher than pretraining levels. The impact appeared greatest for servers with fewer years serving experience, wait-persons, younger servers and servers who worked in establishments without written policies regarding serving practices. CONCLUSIONS The results with regard to modifying server behavior are positive and indicate that server interventions shown to be efficacious should be implemented. Training programs that target specific serving skills in repeat sessions may be most promising for improving server behavior, particularly among both young and new servers working in establishments without written policies regarding serving practices.