Miracle-dz/UniFAR-FTUs huggingface dataset:
Rates of change in tree communities of secondary Neotropical forests following major disturbances

Rates of change in tree communities following major disturbances are determined by a complex set of interactions between local site factors, landscape history and structure, regional species pools and species life histories. Our analysis focuses on vegetation change following abandonment of agricultural fields or pastures, as this is the most extensive form of major disturbance in Neotropical forests. We consider five tree community attributes: stem density, basal area, species density, species richness and species composition. We describe two case studies, in northeastern Costa Rica and Chiapas, Mexico, where both chronosequence and annual tree dynamics studies are being applied. These case studies show that the rates of change in tree communities often deviate from chronosequence trends. With respect to tree species composition, sites of different ages differ more than a single site followed over time through the same age range. Dynamic changes in basal area within stands, on the other hand, generally followed chronosequence trends. Basal area accumulation was more linked with tree growth rates than with net changes in tree density due to recruitment and mortality. Stem turnover rates were poor predictors of species turnover rates, particularly at longer time-intervals. Effects of the surrounding landscape on tree community dynamics within individual plots are poorly understood, but are likely to be important determinants of species accumulation rates and relative abundance patterns.
Timed Extended Invariants for the Passive Testing of Web Services

The service-oriented approach is becoming more and more popular to integrate highly heterogeneous systems. Web services are the natural evolution of conventional middleware technologies to support Web-based and enterprise level integration. Formal testing of such Web-based technology is a key point to guarantee its reliability. In this paper, we choose a non-intrusive approach based on monitoring to propose a conformance passive testing methodology to check that a composed Web service respects its functional requirements. This methodology is based on a set of formal invariants representing properties to be tested including data and time constraints. Passive testing of an industrial system (that uses a composition of Web services) is briefly presented to demonstrate the effectiveness of the proposed approach.
Adaptive Trust Management in MANET

In MANET, trust management is an effective measure of interoperation and collaboration. The authorization decisions to collaborate with a stranger are often tackled by two different trust management systems: a policy-based approach, where authorization decisions are built on logical rules and verifiable properties encoded in signed credentials, and a reputation-based approach, based on collecting, aggregating and disseminating reputation among the principals. However, the overhead caused by proof of compliance on authorization and the un-availability caused by lack of evidence to make decision may negate the strong and objective security advantages of policy-based approach, whilst vagueness, complexity and inaccurate characterization caused by reputation evolution may eliminate the quantitative and flexible advantages of reputation-based approach. Actually an integrated approach would enhance significantly trust management systems. We integrate the notion of policy proof and reputation evolution into trust management such that the decision process is aware of not only the strong and objective security implications, but also the calculability and the availability security implications. Finally, a trust management framework that incorporates trust is presented.
Distributional Neural Networks for Automatic Resolution of Crossword Puzzles

Automatic resolution of Crossword Puzzles (CPs) heavily depends on the quality of the answer candidate lists produced by a retrieval system for each clue of the puzzle grid. Previous work has shown that such lists can be generated using Information Retrieval (IR) search algorithms applied to the databases containing previously solved CPs and reranked with tree kernels (TKs) applied to a syntactic tree representation of the clues. In this paper, we create a labelled dataset of 2 million clues on which we apply an innovative Distributional Neural Network (DNN) for reranking clue pairs. Our DNN is computationally efficient and can thus take advantage of such large datasets showing a large improvement over the TK approach, when the latter uses small training data. In contrast, when data is scarce, TKs outperform DNNs.
On the Dynamics of Analog Min-Sum Iterative Decoders: An Analytical Approach

In this paper, we show an ideal analog min-sum decoder, in the log-likelihood ratio domain, can be considered as a piecewise linear system. Many theoretical aspects of these decoders, thus, can be studied analytically. It is also shown that the dynamic equations can become singular for codes with cycles. When it is non-singular, the corresponding dynamic equations can be solved analytically to derive outputs of the decoder. We study the relationship between singularity and error floor and prove that absorption sets with degree two check nodes are singular graphs and under specific conditions the dynamic equations of an analog min-sum decoder can be reduced to that of an absorption set. The proposed approach paves the way for further analytical analysis on the dynamics of analog min-sum decoders and error floor in low-density parity-check codes.
Tracking Control of a Linear Parabolic PDE with In-domain Point Actuators

This paper addresses the problem of asymptotic tracking control of a linear parabolic partial differential equation with in- domain point actuation. As the considered model is a non-standard partial differential equation, we firstly developed a map that allows transforming this problem into a standard boundary control problem to which existing infinite-dimensional system control methods can be applied. Then, a combination of energy multiplier and differential flatness methods is used to design an asymptotic tracking controller. This control scheme consists of stabilizing state-feedback derived from the energy multiplier method and feed-forward control based on the flatness property of the system. This approach represents a systematic procedure to design tracking control laws for a class of partial differential equations with in-domain point actuation. The applicability and system performance are assessed by simulation studies.
A low complexity impulse radio based UWB transceiver model for automated arrhythmia analysis

In this paper, a 4 GHz clock synchronized transceiver model is proposed for the transmission of biomedical sensor signals like ECG to perform an automated diagnosis of arrhythmia. The system is based on non-coherent architecture which enables the receiver to be extremely simple and largely insensitive to the transmitted pulse shape. By using energy collection approach, a bank of integrators is used to determine the energy of the signal for specific intervals of time, thus making the receiver efficient in a multipath environment. Detection of arrhythmia requires an accurate estimation of the R-R interval of the ECG signal. The proposed model estimates R-R interval and detects different catastrophic arrhythmias.
International Journal of Video& Image Processing and Network Security Adaptive Background Subtraction in Dynamic Environments Using Fuzzy Logic

— Extracting a background from an image is the enabling step for many high-level vision processing tasks, such as object tracking and activity analysis. Although there are a number of object extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background is relatively simple and static. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varying background model. A problem with our system is that by adapting the background model, objects moved are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the object. In this paper, we develop a fuzzy logic inference system to detach the moving object from the background. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust. I. INTRODUCTION Background subtraction is commonly used in the field of video surveillance , optical motion capture , and multimedia application where it needs in the first step to detect the moving objects in the scene. The basic idea is to classified pixel as background or foreground by thresholding the difference between the background image Bt(x, y, t) and the current image It+1(x, y, t). Due of the presence of critical Situations.The detection and segmentation of moving objects in natural video sequences is an important requirement for multimedia indexing and retrieval, but also for adaptation. With the advent of broadly available mobile video players, it has become desirable to adapt video contents to small screens by choosing a suitable compromise between scaling and cropping [1]. This however requires the detection of video-objects that might be of interest. Approaches like Kopf's automatic scaling and cropping rely on pre-trained detectors for fixed kinds of objects like faces or superimposed text [2]. In contrast, background subtraction only relies on the assumption that an objects moves. In our generalized approach, it is only necessary that objects move different from global motion. In surveillance applications, background subtraction has become a standard method for video object segmentation. Even recent publications like [2] still use the scheme that originates from the publication by Stauffer and Grimson [7] who proposed to model the background image pixel-wise by Gaussian Mixture Models (GMM). This exploits the fact that background pixels in subsequent frames of a video should be highly correlated and therefore can be …
Aging Worsens the Effects of Sleep Deprivation on Postural Control

Falls increase with age and cause significant injuries in the elderly. This study aimed to determine whether age modulates the interactions between sleep deprivation and postural control and to evaluate how attention influences these interactions in the elderly. Fifteen young (24±2.7 y.o.) and 15 older adults (64±3.2 y.o.) stood still on a force plate after a night of sleep and after total sleep deprivation. Center of pressure range and velocity were measured with eyes open and with eyes closed while participants performed an interference task, a control task, and no cognitive task. Sleep deprivation increased the antero-posterior range of center of pressure in both age groups and center of pressure speed in older participants only. In elderly participants, the destabilizing effects of sleep deprivation were more pronounced with eyes closed. The interference task did not alter postural control beyond the destabilization induced by sleep loss in older subjects. It was concluded that sleep loss has greater destabilizing effects on postural control in older than in younger participants, and may therefore increase the risk of falls in the elderly.
Modeling, Control Design, and Experimental Validation of an Overactuated Thermal Management System for Engine Dynamometer Applications

Effective engine mapping and calibration are contingent upon tight control of the environment in which the mapping and calibration are performed. Among the most important variables to be controlled are the temperatures of coolant and oil that circulate through the engine block. Because of the large time constants associated with thermodynamic systems, controlling these variables often represents a bottleneck in the engine mapping and calibration processes. In this paper, we examine a particular layout for a thermal management unit, which is currently being used in practice. By developing and analyzing a thermodynamic model of the system, we are able to gain insight into the system dynamics and explore special features to optimize the temperature response. In particular, we will show how the overactuation in the system may be leveraged in the presence of hard saturation constraints and different dynamic actuator authorities. We present design and validation results (both simulation and experimental) for the proposed controller, and compare the performance to the baseline controller in order to quantify improvements.
Heterogeneous Translated Hashing

Multi-modal similarity search has attracted considerable attention to meet the need of information retrieval across different types of media. To enable efficient multi-modal similarity search in large-scale databases recently, researchers start to study multi-modal hashing. Most of the existing methods are applied to search across multi-views among which explicit correspondence is provided. Given a multi-modal similarity search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a Heterogeneous Translated Hashing (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH provides more flexible and discriminative ability by embedding heterogeneous media into different Hamming spaces, compared to almost all existing methods that map heterogeneous data in a common Hamming space. We formulate a joint optimization model to learn hash functions embedding heterogeneous media into different Hamming spaces, and a translator aligning different Hamming spaces. The extensive experiments on two real-world datasets, one publicly available dataset of Flickr, and the other MIRFLICKR-Yahoo Answers dataset, highlight the effectiveness and efficiency of our algorithm.
AWarp: Fast Warping Distance for Sparse Time Series

Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced non-zero observations. Original DTW distance does not take advantage of this sparsity, leading to redundant calculations and a prohibitively large computational cost for long time series. We derive a new time warping similarity measure (AWarp) for sparse time series that works on the run-length encoded representation of sparse time series. The complexity of AWarp is quadratic on the number of observations as opposed to the range of time of the time series. Therefore, AWarp can be several orders of magnitude faster than DTW on sparse time series. AWarp is exact for binary-valued time series and a close approximation of the original DTW distance for any-valued series. We discuss useful variants of AWarp: bounded (both upper and lower), constrained, and multidimensional. We show applications of AWarp to three data mining tasks including clustering, classification, and outlier detection, which are otherwise not feasible using classic DTW, while producing equivalent results. Potential areas of application include bot detection, human activity classification, and unusual review pattern mining.
Overconvergent Witt vectors

Abstract Let A be a finitely generated algebra over a field K of characteristic p > 0. We introduce a subring W†(A) ⊂ W(A), which we call the ring of overconvergent Witt vectors, and prove its basic properties. In a subsequent paper we use the results to define an overconvergent de Rham–Witt complex for smooth varieties over K whose hypercohomology is the rigid cohomology.
Sparse Representations, Compressive Sensing and dictionaries for pattern recognition

In recent years, the theories of Compressive Sensing (CS), Sparse Representation (SR) and Dictionary Learning (DL) have emerged as powerful tools for efficiently processing data in non-traditional ways. An area of promise for these theories is object recognition. In this paper, we review the role of SR, CS and DL for object recognition. Algorithms to perform object recognition using these theories are reviewed. An important aspect in object recognition is feature extraction. Recent works in SR and CS have shown that if sparsity in the recognition problem is properly harnessed then the choice of features is less critical. What becomes critical, however, is the number of features and the sparsity of representation. This issue is discussed in detail.
Assessing the relative reading level of sentence pairs for text simplification

While the automatic analysis of the readability of texts has a long history, the use of readability assessment for text simplification has received only little attention so far. In this paper, we explore readability models for identifying differences in the reading levels of simplified and unsimplified versions of sentences. Our experiments show that a relative ranking is preferable to an absolute binary one and that the accuracy of identifying relative simplification depends on the initial reading level of the unsimplified version. The approach is particularly successful in classifying the relative reading level of harder sentences. In terms of practical relevance, the approach promises to be useful for identifying particularly relevant targets for simplification and to evaluate simplifications given specific readability constraints.
Integrated Connectivity and Coverage Techniques for Wireless Sensor Networks

A wireless sensor network (WSN) consists of a group of energy-constrained sensor nodes with the ability of both sensing and communication, which can be deployed in a field of interesting (FoI) for detecting or monitoring some special events and then forwarding the aggregated data to the designated data center through sink nodes or gateways. In this case, whether the WSN can keep the FoI under strict surveillance and whether the WSN can gather and forward the desired information are two of the most fundamental problem in wireless sensor networks that needs to be solved. Therefore, preserving network connectivity while maximizing coverage by using the limited number of energy constrained nodes is the most critical problem for the deployment of WSNs. In this survey article, we classify and summarize the state-of-the-art algorithms and techniques that addressed for the connectivity-coverage issue.
The Effect of Polyphenols Isolated from Cynanchi wilfordii Radix with Anti-inflammatory, Antioxidant, and Anti-bacterial Activity

Recently, Cynanchi wilfordii Radix has gained wide use in Asian countries as a functional food effective for relieving fatigue, osteoporosis, and constipation, particularly in menopausal disorders. However, its anti-inflammatory and anti-microbial activities have not been explored in detail to date. The anti-inflammatory, antioxidant, and anti-bacterial properties of the Cynanchi wilfordii Radix extracts obtained with water, methanol, ethanol, and acetone were compared. All 4 polyphenol-containing extracts exhibited anti-inflammatory and antioxidant effects. The ethanol extract was found to elicit the most potent reduction of nitric oxide (NO), prostaglandin E2 (PGE2), and cytokine (IL-1β, IL-6, IL-10, and TNF-α) levels, as well as inhibit the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) in a concentration-dependent manner. The evaluation of antioxidant activity also revealed the ethanol extract to have the highest free radical scavenging activity, measured as 85.3±0.4%, which is equivalent to 99.9% of the activity of α -tocopherol. In the assessment of anti-bacterial activity, only ethanol extract was found to inhibit the growth of the Bacillus species Bacillus cereus and Bacillus anthracis. These results show that polyphenols of Cynanchi wilfordii Radix have anti-inflammatory, antioxidant, and anti-bacterial properties that can be exploited and further improved for use as a supplementary functional food, in cosmetics, and for pharmaceutical purposes.
Features of stimulation affecting tonal-speech perception: implications for cochlear prostheses.

Tone languages differ from English in that the pitch pattern of a single-syllable word conveys lexical meaning. In the present study, dependence of tonal-speech perception on features of the stimulation was examined using an acoustic simulation of a CIS-type speech-processing strategy for cochlear prostheses. Contributions of spectral features of the speech signals were assessed by varying the number of filter bands, while contributions of temporal envelope features were assessed by varying the low-pass cutoff frequency used for extracting the amplitude envelopes. Ten normal-hearing native Mandarin Chinese speakers were tested. When the low-pass cutoff frequency was fixed at 512 Hz, consonant, vowel, and sentence recognition improved as a function of the number of channels and reached plateau at 4 to 6 channels. Subjective judgments of sound quality continued to improve as the number of channels increased to 12, the highest number tested. Tone recognition, i.e., recognition of the four Mandarin tone patterns, depended on both the number of channels and the low-pass cutoff frequency. The trade-off between the temporal and spectral cues for tone recognition indicates that temporal cues can compensate for diminished spectral cues for tone recognition and vice versa. An additional tone recognition experiment using syllables of equal duration showed a marked decrease in performance, indicating that duration cues contribute to tone recognition. A third experiment showed that recognition of processed FM patterns that mimic Mandarin tone patterns was poor when temporal envelope and duration cues were removed.
Multiscale Hybrid-Mixed Finite Element Method for Flow Simulation in Fractured Porous Media

The multiscale hybrid-mixed (MHM) method is applied to the numerical approximation of two-dimensional matrix fluid flow in porous media with fractures. The two-dimensional fluid flow in the reservoir and the one-dimensional flow in the discrete fractures are approximated using mixed finite elements. The coupling of the two-dimensional matrix flow with the one-dimensional fracture flow is enforced using the pressure of the one-dimensional flow as a Lagrange multiplier to express the conservation of fluid transfer between the fracture flow and the divergence of the one-dimensional fracture flux. A zero-dimensional pressure (point element) is used to express conservation of mass where fractures intersect. The issuing simulation is then reduced using the MHM method leading to accurate results with a very reduced number of global equations. A general system was developed where fracture geometries and conductivities are specified in an input file and meshes are generated using the public domain mesh generator GMsh. Several test cases illustrate the effectiveness of the proposed approach by comparing the multiscale results with direct simulations.