Miracle-dz/UniFAR-FTUs huggingface dataset:
Copepods come in first: rapid colonization of new temporary ponds

The sequence in which new colonists reach an empty habitat can be crucial for future development and species structure of communities. It is therefore important to assess species composition and abundance in the initial stages of habitat existence. In the present study we focussed on colonization of newly constructed temporary ponds in Donana, Southwest Spain, created by removing 30 or 60 cm of top soil. To confi rm that no egg-bank was present, we conducted hatching experiments using sediment cores from six new ponds and two reference sites. Hatching was not recorded in the sediment of the new ponds with the exception of two rotifer individuals. In contrast, in the reference sites a maximum of 103 individuals per sample hatched, including cyclopoids, cladocer- ans, ostracods and rotifers. In the fi eld, water samples were collected from seven ponds after 19 days of their fi rst hydroperiod. Cyclopoid copepods, mostly Metacyclops minutus, had arrived and dominated all sampled ponds. Other taxa were the monogonont rotifer Brachionus plicatilis in four ponds, and the cladoceran Moina brachiata in one pond. The abundance of zooplankton was negatively correlated with conductivity, suggesting that ponds with higher conductivity undergo delayed colonization. We suggest that fast dispersal and dominance of certain cyclo- poid copepods during early colonization is related to their ability to store sperm and fast individual development. Given that cyclopoid copepods can survive drought periods in the sediment of temporary ponds, fi rst and early colonization by cyclopoids is likely to have a profound effect on the propagule bank and future plankton communi- ties when the ponds refi ll.
On the Computation of Matrix Functions for Square Matrices Having Mixed Eigenvalues Using Numerical Hybrid Method

Abstract: In this paper, a hybrid numerical method for computing matrix functions of square matrices having mixed (distinct real and pure complex) eigenvalues is presented. The suggested technique is first stated and is then proved to be well defined through different stages. The theoretical analysis of the proposal method is then discussed. Some numerical examples are presented to validate the applicability and the accuracy of the obtained analytical results.
Selection and educational attainment: why some children are left behind? Evidence from a middle-income country

ABSTRACT We model schooling as a sequential process and examine why some children are left behind. We focus on the factors that explain selection at early stages of the education system. Our findings for Uruguay suggest that long-term factors, such as parental background or ethnicity matter across all education stages while the effect of short-term factors, such as family income, wear out as individuals progress in the education system, suggesting a severe selection process at early stages.
Estimating Activity at Multiple Scales using Spatial Abstractions

Autonomous robots operating in dynamic environments must maintain beliefs over a hypothesis space that is rich enough to represent the activities of interest at different scales. This is important both in order to accommodate the availability of evidence at varying degrees of coarseness, such as when interpreting and assimilating natural instructions, but also in order to make subsequent reactive planning more efficient. We present an algorithm that combines a topology-based trajectory clustering procedure that generates hierarchically-structured spatial abstractions with a bank of particle filters at each of these abstraction levels so as to produce probability estimates over an agent's navigation activity that is kept consistent across the hierarchy. We study the performance of the proposed method using a synthetic trajectory dataset in 2D, as well as a dataset taken from AIS-based tracking of ships in an extended harbour area. We show that, in comparison to a baseline which is a particle filter that estimates activity without exploiting such structure, our method achieves a better normalised error in predicting the trajectory as well as better time to convergence to a true class when compared against ground truth.
Estimation Stability With Cross-Validation (ESCV)

Cross-validation (CV) is often used to select the regularization parameter in high-dimensional problems. However, when applied to the sparse modeling method Lasso, CV leads to models that are unstable in high-dimensions, and consequently not suited for reliable interpretation. In this article, we propose a model-free criterion ESCV based on a new estimation stability (ES) metric and CV. Our proposed ESCV finds a smaller and locally ES-optimal model smaller than the CV choice so that it fits the data and also enjoys estimation stability property. We demonstrate that ESCV is an effective alternative to CV at a similar easily parallelizable computational cost. In particular, we compare the two approaches with respect to several performance measures when applied to the Lasso on both simulated and real datasets. For dependent predictors common in practice, our main finding is that ESCV cuts down false positive rates often by a large margin, while sacrificing little of true positive rates. ESCV usually outperforms CV in terms of parameter estimation while giving similar performance as CV in terms of prediction. For the two real datasets from neuroscience and cell biology, the models found by ESCV are less than half of the model sizes by CV, but preserves CV's predictive performance and corroborates with subject knowledge and independent work. We also discuss some regularization parameter alignment issues that come up in both approaches. Supplementary materials are available online.
Detection of Region Duplication Forgery in Digital Images Using Wavelets and Log-Polar Mapping

Due to the abundantly available imaging technologies, manipulation of digital images has become a serious problem nowadays, in various fields like medical imaging, digital forensics, journalism, scientific publications, etc. In this paper, we concentrate on detection of a specific category of digital image forgery known as region duplication forgery or copy-move forgery, which is done by copying a block of an image and pasting it on to some other block of the same image. We present a novel approach based on the application of wavelet transform that detects and localizes such forgeries. Our technique works by first applying wavelet transform to the input image to yield a reduced dimension representation. We then perform exhaustive search to identify the similar blocks in the image by mapping them to log-polar coordinates and using phase correlation as the similarity criterion. This is done only once at the lowest resolution of the wavelet transform. Only the matched blocks are carried for comparison to the next level. This drastically reduces the time needed for the detection process. This approach works even if the pasted region has undergone transformations like translation and rotation.
HPL – SSP – 2001 – 4 : Simple table-based modeling of storage devices

Trace driven simulations are too slow for use in solvers. Analytic models require work from a person to understand the array enough to model it. Table based models offer the possibility of automatically measuring the performance of an array for use in a solver. We explain a simplistic way of generating the input points in the table. We then explore three different ways of performing the interpolation of nearby points from the points within the table, and comment on future directions the work could go.
Doubly Selective Channel Estimation Using Exponential Basis Models and Subblock Tracking

Three versions of a novel adaptive channel estimation approach, exploiting the over-sampled complex exponential basis expansion model (CE-BEM), is presented for doubly selective channels, where we track the BEM coefficients rather than the channel tap gains. Since the time-varying nature of the channel is well captured in the CE-BEM by the known exponential basis functions, the time variations of the (unknown) BEM coefficients are likely much slower than those of the channel, and thus more convenient to track. We propose a ¿subblockwise¿ tracking scheme for the BEM coefficients using time-multiplexed (TM) periodically transmitted training symbols. Three adaptive algorithms, including a Kalman filtering scheme based on an assumed autoregressive (AR) model of the BEM coefficients, and two recursive least-squares (RLS) schemes not requiring any model for the BEM coefficients, are investigated for BEM coefficient tracking. Simulation examples illustrate the superior performance of our approach over several existing doubly selective channel estimators.
Gasoline engine valve-train mechanism simulation

The paper describes the engine valve-train mechanism's dynamic analysis method, and introduces how to make dynamic simulation of it, and then makes the conclusions from the simulation results. In the process of simulation, first engine valve-train mechanism is modeled by CATIA software, and then models are imported into ADAMS software, so as to simulate the movement of engine valve-train mechanism. The assumption is made upon the air pressure on the valve, but the assumption is based on theoretical basis, it can meet the requirement of calculation. Finally, the valve-train velocity, acceleration, stress, cam stress and the valve-train stress under different cam speed curves are shown in this paper. Some conclusions including valve-train mechanism's stability, oscillatory and the influence of stress of valves upon different cam speed are given at last.
The Demand for Money in Mexico i

This paper examines the demand for narrow money in Mexico for the period 1980-2005, extending the study by Khamis and Leone (2001). The cointegration analysis suggests the presence of a long term relationship between real money balances, consumption expenditures and the interest rate. The model is estimated in error correction form, indicating the existence of two additional determinants of real money balances in the short run to those found by the referred authors: changes in consumption expenditures and changes in the depreciation rate. The stability tests applied to the estimated model suggest parameter constancy throughout the examined period.
Detection of Verbal Multi-Word Expressions via Conditional Random Fields with Syntactic Dependency Features and Semantic Re-Ranking

A description of a system for identifying Verbal Multi-Word Expressions (VMWEs) in running text is presented. The system mainly exploits universal syntactic dependency features through a Conditional Random Fields (CRF) sequence model. The system competed in the Closed Track at the PARSEME VMWE Shared Task 2017, ranking 2nd place in most languages on full VMWE-based evaluation and 1st in three languages on token-based evaluation. In addition, this paper presents an option to re-rank the 10 best CRF-predicted sequences via semantic vectors, boosting its scores above other systems in the competition. We also show that all systems in the competition would struggle to beat a simple lookup baseline system and argue for a more purpose-specific evaluation scheme.
Proxy signatures for delegating signing operation

In this paper a new type of digital proxy signature is proposed. The proxy signature allows a designated person, called a proxy signer, to sign on behalf of an original signer. Classification of the proxy signatures is shown from the point of view of the degree of delegation, and conditions of a proposed proxy signature for partial delegation are clarified. The proposed proxy signature scheme is based on the discrete logarithm problem. Compared to the consecutive execution of the ordinary digital signature schemes, it has a direct form, and a verifier does not need a public key of a user other than the original signer in the verification stage. Moreover, it requires less amount of computational work than the consecutive execution of the signature schemes. Due to this efficiency together with the delegation property, an organization, e.g. a software company, can very efficiently create many signatures of its own by delegating its signing operations to multiple employees. Another attractive feature of the proposed schemes is their highapplicability to other ordinary signature schemes based on the discrete logarithm problem. For instance, designated confirmer proxy signatures can be constructed. Furthermore, using a proposed on-line proxy updating protocol, the original signer can revoke proxies of dishonest proxy signers.
An influential principal point on camera parameters

This paper proposes a method of camera calibration and the influence of principal point on camera parameters. The camera parameters are represented in terms of a set of intermediate parameters. The intermediate parameters and lens distortion coefficients are iteratively computed. The image coordinates are rectified on each cycle of iteration. Once the iteration is terminated, the initial parameters are computed from the obtained intermediate parameters. Then, the camera parameters and distortion coefficients are iteratively computed from the obtained initial parameters and the image coordinates are rectified too. In the experimental results, the proposed method is tested by change principal point to observe the influential principal point on camera parameters and compared the results with the conventional method.
Using Answer Set Programming and Lambda Calculus to Characterize Natural Language Sentences with Normatives and Exceptions

One way to solve the knowledge acquisition bottleneck is to have ways to translate natural language sentences and discourses to a formal knowledge representation language, especially ones that are appropriate to express domain knowledge in sciences, such as Biology. While there have been several proposals, including by Montague (1970), to give model theoretic semantics for natural language and to translate natural language sentences and discourses to classical logic, none of these approaches use knowledge representation languages that can express domain knowledge involving normative statements and exceptions. In this paper we take a first step to illustrate how one can automatically translate natural language sentences about normative statements and exceptions to representations in the knowledge representation language Answer Set Programming (ASP). To do this, we use λ-calculus representation of words and their composition as dictated by a CCG grammar.
Robust predictive control combined with an adaptive mechanism for constrained uncertain systems subject to disturbances

Abstract: This paper proposes a discrete-time adaptive model predictive control (MPC) algorithm for a class of constrained linear time-invariant systems subject to state-dependent disturbances, which updates the estimate of uncertain system parameters on-line and produces the control input ensuring the constraint fulfillment. This method is based on an adaptive mechanism and a robust MPC algorithm using the comparison model which enables to estimate the future prediction error bound. First, the parameter estimation method for MPC based on the moving horizon estimation is introduced. It allows to predict explicitly the worst-case estimation error bound over the prediction horizon. Second, we propose an adaptive-type MPC strategy developed by combining an on-line parameter estimator with a robust MPC method based on the modified comparison model. The MPC controller designed in this way guarantees constraint fulfillment, closed-loop stability and feasibility in the presence of uncertain system parameters and state-dependent disturbances. A numerical example demonstrates the unique features of the proposed approach.
Tracking Control of Mechanical Systems with Partially Known Friction Model

Two adaptive nonlinear friction compensation schemes are proposed for second-order nonlinear mechanical systems with a partially known nonlinear dynamic friction model to achieve asymptotic position and velocity tracking. The first scheme has auxiliary filtered states so that a simple open-loop observer can be used. The second one has a dual-observer structure to estimate two different nonlinear aspects of the friction state. Conditions for the parameter estimates to converge to the true parameter values are presented. Simulation results are utilized to show control performance and to demonstrate the convergence of the parameter estimates to their true values.
SemTree ontology for enriching Arabic text with lexical semantic annotations

Although the process of semantic annotation of Arabic Web content is essential for the realization of the Semantic Web, the process of semantic annotation cannot be performed without an ontology suitable for the task. In this paper, we describe the design, implementation and evaluation of the SemTree ontology for lexical semantic relations. The ontology was evaluated for usefulness using a prototype system for lexical semantic annotation of Arabic text. Results of the evaluation indicate that the ontology was fit for the purpose of semantic annotations with lexical relations. The evaluation also reveled important recommendations for designing Arabic semantic annotation tools.
Estimation of Physiological Tremor from Accelerometers for Real-Time Applications

Accurate filtering of physiological tremor is extremely important in robotics assisted surgical instruments and procedures. This paper focuses on developing single stage robust algorithms for accurate tremor filtering with accelerometers for real-time applications. Existing methods rely on estimating the tremor under the assumption that it has a single dominant frequency. Our time-frequency analysis on physiological tremor data revealed that tremor contains multiple dominant frequencies over the entire duration rather than a single dominant frequency. In this paper, the existing methods for tremor filtering are reviewed and two improved algorithms are presented. A comparative study is conducted on all the estimation methods with tremor data from microsurgeons and novice subjects under different conditions. Our results showed that the new improved algorithms performed better than the existing algorithms for tremor estimation. A procedure to separate the intended motion/drift from the tremor component is formulated.
Evaluation of Antinociceptive and Anti-Inflammatory Activities of a New Triterpene Saponin from Bauhinia variegata Leaves

A new triterpene saponin, named as 23-hydroxy-3α-[O-α-L-1C4-rhamnopyranosyl-(1´´4´)- O-α-L-4C1-arabinopyranosyl-oxy]olean-12-en-28-oic acid O-α-L-1C4-rhamnopyranosyl- (1´´´´´→4´´´´)-O-β-D-4C1-glucopyranosyl-(1´´´´→6´´´)-O-β-D-4C1-glucopyranosyl ester (9), was isolated from the leaves of Bauhinia variegata Linn. In addition, six flavonoid compounds along with two cinnamic acid derivatives were isolated and identified based on their chromatographic properties, and chemical and spectral data (ESI-high resolution-MSn, 1H NMR, 13C NMR, 1H-1H COSY, HSQC, and HMBC). Compound 9 was found to be nontoxic (LD50) and to have significant anti-inflammatory and antinociceptive activities. It also showed a slight antischistosomal activity.
Noncoding Subgenomic Flavivirus RNA: Multiple Functions in West Nile Virus Pathogenesis and Modulation of Host Responses

Flaviviruses are a large group of positive strand RNA viruses transmitted by arthropods that include many human pathogens such as West Nile virus (WNV), Japanese encephalitis virus (JEV), yellow fever virus, dengue virus, and tick-borne encephalitis virus. All members in this genus tested so far are shown to produce a unique subgenomic flavivirus RNA (sfRNA) derived from the 3' untranslated region (UTR). sfRNA is a product of incomplete degradation of genomic RNA by the cell 5'–3' exoribonuclease XRN1 which stalls at highly ordered secondary RNA structures at the beginning of the 3'UTR. Generation of sfRNA results in inhibition of XRN1 activity leading to an increase in stability of many cellular mRNAs. Mutant WNV deficient in sfRNA generation was highly attenuated displaying a marked decrease in cytopathicity in cells and pathogenicity in mice. sfRNA has also been shown to inhibit the antiviral activity of IFN-α/β by yet unknown mechanism and of the RNAi pathway by likely serving as a decoy substrate for Dicer. Thus, sfRNA is involved in modulating multiple cellular pathways to facilitate viral pathogenicity; however the overlying mechanism linking all these multiple functions of sfRNA remains to be elucidated.
Video Watermarking Techniques for Copyright protection and Content Authentication

The advancement of Internet services and various storage technologies made video piracy as an increasing problem particularly with the proliferation of media sharing through the internet . Thus, research in copyright protection mechanisms and content authentication, where one of which includes digital watermarking has been receiving an increasing interest from scientists especially in designing a seamless algorithm for effective implementation. Basically digital watermarking involves embedding secret symbols known as watermarks within video data which can be used later for copyright detection and authentication verification purposes. This paper presents the state of the art in video watermarking techniques. It provides a critical review on various available techniques. In addition, it addresses the main key performance indicators which include robustness, speed, capacity, fidelity, imperceptibility and computational complexity.
Bayesian Models and Algorithms for Protein Beta-Sheet Prediction

Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein’s structure. We address the problem of β-sheet prediction defined as the prediction of β-strand pairings, interaction types (parallel or anti-parallel), and β-residue interactions (or contact maps). We introduce a Bayesian approach for proteins with six or less β-strands, in which we model the conformational features in a probabilistic framework by combining the amino acid pairing potentials with a priori knowledge of β-strand organizations. To select the optimum β-sheet architecture, we significantly reduce the search space by heuristics that enforce the amino acid pairs with strong interaction potentials. In addition, we find the optimum pairwise alignment between β-strands using dynamic programming, in which we allow any number of gaps in an alignment to model β-bulges more effectively. For proteins with more than six β-strands, we first compute β-strand pairings using the BetaPro method. Then, we compute gapped alignments of the paired β-strands and choose the interaction types and βresidue pairings with maximum alignment scores. We performed a 10-fold cross validation experiment on the BetaSheet916 set and obtained significant improvements in the prediction accuracy.