Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of hard exudates and red lesions in the macula using a multiscale approach

Diabetic retinopathy (DR) is a complication of diabetes that causes blindness to 1.8 million people in the world. The risk of vision loss from DR increases when pathologies present on the macula. In this paper, we present an automatic system to detect pathologies on the macula such as hard exudates microaneurysms, and hemorrhages. Our approach is a bottom-up implementation, which tries to capture each abnormal structure in the macula in order to detect DR lesions. This technique starts by eliminating the non-uniform illumination thereby enhancing the contrast of red lesions in the images. Possible DR lesion (hard exudates and red lesions) candidates on the macula are extracted by using amplitude-modulation frequency-modulation (AM-FM) features. AM-FM features extract texture information from different frequency scales, providing for an effective method for the detection of hard exudates and red lesions. For each lesion candidate, we also extract shape, color and other texture features that are then combined with AM-FM features. Pathologies in the macula are detected from the candidate lesions using supervised classification with Partial Least Squares.
Campus++: A publish-subscribe architecture for intermittently connected 802.15.4 networks

The aim of this extended abstract is to present Campus++, a location-based publish-subscribe system for intermittently connected delay tolerant networks, exploiting IEEE 802.15. 4 devices, and taking into due account the severe constraints deriving from their physical characteristics. We describe our proposed architectural model and how we implemented our solution in a real test-bed. We point out that our system can be easily adapted to operate in a fully distributed, infrastructure-less way, allowing free communications e.g. in disaster areas or in areas in which ”usual” communications means are either non existent or intentionally made unavailable.
Estimating the Effect of Currency Unions on Trade and Output

Gravity-based cross-sectional evidence indicates that currency unions stimulate trade; cross-sectional evidence indicates that trade stimulates output. This paper estimates the effect that currency union has, via trade, on output per capita. We use economic and geographic data for over 200 countries to quantify the implications of currency unions for trade and output, pursuing a two-state approach. Our estimates at the first stage suggest that belonging to a currency union more than triples trade with the other members of the zone. Moreover, there is no evidence of trade-diversion. Our estimates at the second stage suggest that every one percent increase in trade (relative to GDP) raises income per capita by roughly 1/3 of a percent over twenty years. We combine the two estimates to quantify the effect of currency union on output. Our results support the hypothesis that the beneficial effects of currency unions on economic performance come through the promotion of trade, rather than through a commitment to non-inflationary monetary policy, or other macroeconomic influences.
Charged complexes at the surface of liquid helium

Charged clusters in liquid helium in an external electric field form a two‐dimensional system below the helium surface. This 2D system undergoes a phase transition from a liquid to a Wigner crystal at rather high temperatures. Contrary to the electron Wigner crystal, the Wigner lattice of charged clusters can be detected directly.
Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction

Transaction data is ubiquitous in data mining applications. Examples include market basket data in retail commerce, telephone call records in telecommunications, and Web logs of individual page-requests at Web sites. Profiling consists of using historical transaction data on individuals to construct a model of each individual's behavior. Simple profiling techniques such as histograms do not generalize well from sparse transaction data. In this paper we investigate the application of probabilistic mixture models to automatically generate profiles from large volumes of transaction data. In effect, the mixture model represents each individual's behavior as a linear combination of "basis transactions. " We evaluate several variations of the model on a large retail transaction data set and show that the proposed model provides improved predictive power over simpler histogram-based techniques, as well as being relatively scalable, interpretable, and flexible. In addition we point to applications in outlier detection, customer ranking, interactive visualization, and so forth. The paper concludes by comparing and relating the proposed framework to other transaction-data modeling techniques such as association rules.
Tissue-Specific Strategies of the Very-Long Chain Acyl-CoA Dehydrogenase-Deficient (VLCAD−/−) Mouse to Compensate a Defective Fatty Acid β-Oxidation

Very long-chain acyl-CoA dehydrogenase (VLCAD)-deficiency is the most common long-chain fatty acid oxidation disorder presenting with heterogeneous phenotypes. Similar to many patients with VLCADD, VLCAD-deficient mice (VLCAD−/−) remain asymptomatic over a long period of time. In order to identify the involved compensatory mechanisms, wild-type and VLCAD−/− mice were fed one year either with a normal diet or with a diet in which medium-chain triglycerides (MCT) replaced long-chain triglycerides, as approved intervention in VLCADD. The expression of the mitochondrial long-chain acyl-CoA dehydrogenase (LCAD) and medium-chain acyl-CoA dehydrogenase (MCAD) was quantified at mRNA and protein level in heart, liver and skeletal muscle. The oxidation capacity of the different tissues was measured by LC-MS/MS using acyl-CoA substrates with a chain length of 8 to 20 carbons. Moreover, in white skeletal muscle the role of glycolysis and concomitant muscle fibre adaptation was investigated. In one year old VLCAD−/− mice MCAD and LCAD play an important role in order to compensate deficiency of VLCAD especially in the heart and in the liver. However, the white gastrocnemius muscle develops alternative compensatory mechanism based on a different substrate selection and increased glucose oxidation. Finally, the application of an MCT diet over one year has no effects on LCAD or MCAD expression. MCT results in the VLCAD−/− mice only in a very modest improvement of medium-chain acyl-CoA oxidation capacity restricted to cardiac tissue. In conclusion, VLCAD−/− mice develop tissue-specific strategies to compensate deficiency of VLCAD either by induction of other mitochondrial acyl-CoA dehydrogenases or by enhancement of glucose oxidation. In the muscle, there is evidence of a muscle fibre type adaptation with a predominance of glycolytic muscle fibres. Dietary modification as represented by an MCT-diet does not improve these strategies long-term.
Vision-based teleoperation of unmanned aerial and ground vehicles

We present a novel vision-based teleoperation control framework for a team of an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). Our control law allows a remote human user to teleoperate the team with some useful haptic feedback, while also ensuring the UAV-UGV coordination via the camera installed on the UAV (and seeing the UGV) and the velocity limitation of the UGV. For this, we first elucidate a geometric condition for the UAV and UGV velocities to ensure the UAV-UGV coordination by driving the image feature of the UGV to converge to a desired one on the image surface, while also guaranteeing that the UGV's velocity, which is often much slower than that of the UAV, is under a certain specified bound. The UAV is then tele-controlled to track the teleoperation command as close as possible, yet, only to the extent permissible by this geometric condition. Simulation is performed to illustrate the theory.
An efficient mechanism for mobility support using peer-to-peer overlay networks

Expanding the Internet infrastructure to span mobile devices is a challenging task. The Internet's addressing and routing scheme was originally designed for fixed devices making it inoperable without redirection and tracking functions such as those proposed by mobile IP. However, mobile IP requires prior network setup, and causes a large added overhead. In this paper, we propose a new approach to support mobile IP users using a peer-to-peer overlay network. The proposed overlay exploits locality to efficiently use the network resources and manage location data in a self-organizing and distributed fashion. The simulation results are presented.
Thesaurus entry extraction from an on-line dictionary

The diversity and availability of information sources on the World Wide Web has set the stage for integration and reuse at an unparalleled scale. There remain signi cant hurdles to exploiting the extent of the Web's resources in a consistent, scalable and maintainable fashion. The autonomy and volatility of Web sources complicates maintaining wrappers consistent with the requirements of the data's target application. This paper describes the ArcRank model of relationships between nodes in a directed labeled graph, such as hypertext. The paper presents a ranking algorithm for directed arcs, and the algorithm for extraction of hierarchical relationships between words in a dictionary. Using ArcRank we create a thesaurus style tool to aid in the integration of texts and databases whose content is similar but whose terms are di erent. These algorithms complement handcrafted thesauri, by determining more complete relationships between words, although they are less speci c. Exploiting hierarchies of relationships between words paves the way for broadening and related term queries in web-based repositories.
Multiple View Clustering Using a Weighted Combination of Exemplar-Based Mixture Models

Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead to bad cluster assignments if a view is of poor quality. To deal with this problem, we propose a method that is built upon exemplar-based mixture models, called convex mixture models (CMMs). More specifically, we present a multiview clustering algorithm, based on training a weighted multiview CMM, that associates a weight with each view and learns these weights automatically. Our approach is computationally efficient and easy to implement, involving simple iterative computations. Experiments with several datasets confirm the advantages of assigning weights to the views and the superiority of our framework over single-view and unweighted multiview CMMs, as well as over another multiview algorithm which is based on kernel canonical correlation analysis.
Buprenorphine compared with methadone to treat pregnant women with opioid use disorder: a systematic review and meta‐analysis of safety in the mother, fetus and child

Abstract Aims To assess the safety of buprenorphine compared with methadone to treat pregnant women with opioid use disorder. Methods We searched PubMed, Embase and the Cochrane Library from inception to February 2015 for randomized controlled trials (RCT) and observational cohort studies (OBS) that compared buprenorphine with methadone for treating opioid‐dependent pregnant women. Two reviewers assessed independently the titles and abstracts of all search results and full texts of potentially eligible studies reporting original data for maternal/fetal/infant death, preterm birth, fetal growth outcomes, fetal/congenital anomalies, fetal/child neurodevelopment and/or maternal adverse events. We ascertained each study's risk of bias using validated instruments and assessed the strength of evidence for each outcome using established methods. We computed effect sizes using random‐effects models for each outcome with two or more studies. Results Three RCTs (n = 223) and 15 cohort OBSs (n = 1923) met inclusion criteria. In meta‐analyses using unadjusted data and methadone as comparator, buprenorphine was associated with lower risk of preterm birth [RCT risk ratio (RR) = 0.40, 95% confidence interval (CI) = 0.18, 0.91; OBS RR = 0.67, 95% CI = 0.50, 0.90], greater birth weight [RCT weighted mean difference (WMD) = 277 g, 95% CI = 104, 450; OBS WMD = 265 g, 95% CI = 196, 335] and larger head circumference [RCT WMD = 0.90 cm, 95% CI = 0.14, 1.66; OBS WMD = 0.68 cm, 95% CI = 0.41, 0.94]. No treatment differences were observed for spontaneous fetal death, fetal/congenital anomalies and other fetal growth measures, although the power to detect such differences may be inadequate due to small sample sizes. Conclusions Moderately strong evidence indicates lower risk of preterm birth, greater birth weight and larger head circumference with buprenorphine treatment of maternal opioid use disorder during pregnancy compared with methadone treatment, and no greater harms.
Multiharmonic source-pull/load-pull active setup based on six-port reflectometers: influence of the second harmonic source impedance on RF performances of power transistors

An original measurement system for nonlinear RF power-transistor characterization is presented. This new setup enables the measurement and optimization of output power and/or power-added efficiency (PAE) using active harmonic tuning and six-port reflectometers as vector network analyzers. Two active loops are inserted at both ports of transistors in order to independently control the source and load impedances at the fundamental and at the second harmonic frequency. To the authors' knowledge, this is the only active technique that allows a complete automated multiharmonic load-pull/source-pull measurement system. Experimental results are shown for a commercial GaAs MESFET power transistor at 2 GHz.
Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence — problem structures often present in agent populations — for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.
Toward parallel and distributed learning by meta-learning

Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of very large network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Learning techniques are central to knowledge discovery and the approach proposed in this paper may substantially increase the amount of data a knowledge discovery system can handle effectively. Meta-learning is proposed as a general technique to integrating a number of distinct learning processes. This paper details several meta-learning strategies for integrating independently learned classifiers by the same learner in a parallel and distributed computing environment. Our strategies are particularly suited for massive amounts of data that main-memory-based learning algorithms cannot efficiently handle. The strategies are also independent of the particular learning algorithm used and the underlying parallel and distributed platform. Preliminary experiments using different data sets and algorithms demonstrate encouraging results: parallel learning by meta-learning can achieve comparable prediction accuracy in less space and time than purely serial learning.
Plane Extraction and Map Building Using a Kinect Equipped Mobile Robot

 Abstract — This research work describes a method to create a geometric feature (3D plane) based map using a differential drive mobile robot in an indoor environment. Two algorithms namely Hough Transformation and Random Sample Consensus are used separately to extract multiple 3D planes from the point cloud data and the results are compared. An Octree based data structure is used to create and store the generated planner map of the environment. Furthermore a simple error analysis on the estimated parameters of the plane from both RANSAC and Hough Transformation algorithm in a test environment is presented.
OMP2HMPP: HMPP Source Code Generation from Programs with Pragma Extensions

High-performance computing are based more and more in heterogeneous architectures and GPGPUs have become one of the main integrated blocks in these, as the recently emerged Mali GPU in embedded systems or the NVIDIA GPUs in HPC servers. In both GPGPUs, programming could become a hurdle that can limit their adoption, since the programmer has to learn the hardware capabilities and the language to work with these. We present OMP2HMPP, a tool that, automatically trans-lates a high-level C source code(OpenMP) code into HMPP. The generated version rarely will differs from a hand-coded HMPP version, and will provide an important speedup, near 113%, that could be later improved by hand-coded CUDA. The generated code could be transported either to HPC servers and to embedded GPUs, due to the commonalities between them.
A Single Brief Burst Induces GluR1-dependent Associative Short-term Potentiation: A Potential Mechanism for Short-term Memory

Recent work showed that short-term memory (STM) is selectively reduced in GluR1 knockout mice. This raises the possibility that a form of synaptic modification dependent on GluR1 might underlie STM. Studies of synaptic plasticity have shown that stimuli too weak to induce long-term potentiation induce short-term potentiation (STP), a phenomenon that has received little attention. Here we examined several properties of STP and tested the dependence of STP on GluR1. The minimal requirement for inducing STP was examined using a test pathway and a conditioning pathway. Several closely spaced stimuli in the test pathway, forming a single brief burst, were sufficient to induce STP. Thus, STP is likely to be induced by the similar bursts that occur in vivo. STP induction is associative in nature and dependent on the NMDAR. STP decays with two components, a fast component (1.6 ± 0.26 min) and a slower one (19 ± 6.6 min). To test the role of GluR1 in STP, experiments were conducted on GluR1 knockout mice. We found that STP was greatly reduced. These results, taken together with the behavioral work of D. Sanderson et al. [Sanderson, D. , Good, M. A. , Skelton, K. , Sprengel, R. , Seeburg, P. H. , Nicholas, J., et al. Enhanced long-term and impaired short-term spatial memory in GluA1 AMPA receptor subunit knockout mice: Evidence for a dual-process memory model. Learning and Memory, 2009], provide genetic evidence that STP is a likely mechanism of STM.
Bootstrapping Mobile PINs Using Passwords

We describe a method of deriving PINs from passwords. The method is useful to obtain friction-free user onboarding to mobile platforms. It has significant business benefits to organizations that wish to introduce mobile apps to existing users – but which are reluctant to make the users authenticate with passwords. From the user’s perspective, a PIN is easier to enter than a password, and a derived PIN does not need to be remembered – assuming the user can recall her password. The use of tiered authentication – relying on both PINs and passwords – hardens systems against compromise. This is because transactions relying on PINs can have lower transaction limits and flagging thresholds than transactions authenticated using passwords. Even though our PINs are derived from passwords, they do not contain sufficient information about the passwords to make the passwords easy to infer from compromised PINs. We quantify exactly how much information about the passwords and the derived PINs contain, and how much information is lost – based on reallife password distributions. We also assess the usability of the proposed method using one 25-subject qualitative study and one 100-subject quantitative study. Keywords-bootstrapping, dropbox, entropy, malware, password, PIN.
Identification of linear systems in canonical form through an EM framework

Least-squares estimation has always been the main approach when applying prediction error methods (PEM) in the identification of linear dynamical systems. Regardless of the estimation algorithm, if there are no restrictions on the form of the matrices we want to estimate, the matrices can be determined up to within a linear transformation and thus the result may be different than the true solution and the convergence of iterative algorithms may be affected. In this paper, we apply a new identification procedure based on the Expectation Maximization framework to a family of identifiable state-space models. To our knowledge, this is the first complete solution of Maximum-Likelihood estimation for general linear state-space models.
Nonlinear diffractive optics with curved phases: beam dispersion and transition between light and shadow

We give asymptotic descriptions of smooth oscillating solutions of hyperbolic systems with variable coefficients, in the weakly nonlinear diffractive optics regime. The dependence of the coefficients of the system in the space–time variable (corresponding to propagation in a non-homogeneous medium) implies that the rays are not parallel lines – the same occurs with non-planar initial phases. Approximations are given by WKB asymptotics with 3-scales profiles and curved phases. The fastest scale concerns oscillations, while the slowest one describes the modulation of the envelope, which is along rays for the oscillatory components. We consider two kinds of behaviors at the intermediate scale: ‘weakly decaying’ (Sobolev), giving the transverse evolution of a ‘ray packet’, and ‘shock-type’ profiles describing a region of rapid transition for the amplitude.
A unified formalism for video descriptors

In this paper, we propose a unified formalism for video descriptors. This formalism is based on the descriptors decomposition in three levels: primitive, scattering and projection. With this framework, we are able to rewrite easily all the usual descriptors in the literature such as HOG, HOF, SURF. Then, we propose a new projection method based on approximation with a finite expansion of orthogonal polynomials. Using our framework, we extend all usual descriptors by switching the projection step. The experiments are carried out on the well known KTH dataset and on the more challenging Hollywood 2 action classification dataset and show state of the art results.