Miracle-dz/UniFAR-FTUs huggingface dataset:
An alpha-Dominance Expandation Based Algorithm for Many-Objective Optimization

The convergence ability of Pareto-based evolutionary algorithms sharply reduces for many-objective optimization problems because the Pareto dominance is inefficient to rank the solutions and result in too many solutions becoming the non-dominated solutions. To overcome this shortcoming, it is necessary to increase the selection pressure toward the global optimal solutions and well-maintain the diversity of obtained non-dominated solutions. In this paper, an improved α-dominance based on expanding the dominated area of α-dominance is proposed. By redefining each objective function and the optimization problem through non-linear transformations, the dominated area of each solution is further expanded compared to that expanded by α-dominance, which can further enhance the selection pressure. Besides, the new dominance can well maintain the diversity of obtained solutions since the dominated area flexibly changes with different solutions. Moreover, this new dominance can be integrated into any multi-objective evolutionary algorithm to improve the performance of this algorithm. To demonstrate the effectiveness of the new dominance, we conduct the experiments on algorithm NNIA combined by the new dominance (briefly NNIA-NLAD). The experimental results show that the improved α-dominance can help NNIA to find better Pareto Front and maintain the diversity of obtained solutions for many-objective optimization problems.
Semantic Parsing based on Verbal Subcategorization

The aim of this work is to explore new methodologies on Semantic Parsing for unrestricted texts. Our approach follows the current trends in Information Extraction (IE) and is based on the application of a verbal subcategorization lexicon (LEXPIR) by means of complex pattern recognition techniques. LEXPIR is framed on the theoretical model of the verbal subcategorization developed in the Pirapides project.
Consistency Management of Normal Logic Program by Top-down Abductive Proof Procedure

This paper presents a method of computing a revision of a function-free normal logic program. If an added rule is inconsistent with a program, that is, if it leads to a situation such that no stable model exists for a new program, then deletion and addition of rules are performed to avoid inconsistency. We specify a revision by translating a normal logic program into an abductive logic program with abducibles to represent deletion and addition of rules. To compute such deletion and addition, we propose an adaptation of our top-down abductive proof procedure to compute a relevant abducibles to an added rule. We compute a minimally revised program, by choosing a minimal set of abducibles among all the sets of abducibles computed by a top-down proof procedure.
A Functional Antagonistic Relationship between Auxin and Mitochondrial Retrograde Signaling Regulates Alternative Oxidase1a Expression in Arabidopsis1[W][OPEN]

The induction of mitochondrial stress response is antagonized by auxin signaling revealing regulation of mitochondrial respiratory pathway gene expression by auxin. The perception and integration of stress stimuli with that of mitochondrion function are important during periods of perturbed cellular homeostasis. In a continuous effort to delineate these mitochondrial/stress-interacting networks, forward genetic screens using the mitochondrial stress response marker alternative oxidase 1a (AOX1a) provide a useful molecular tool to identify and characterize regulators of mitochondrial stress signaling (referred to as regulators of alternative oxidase 1a [RAOs] components). In this study, we reveal that mutations in genes coding for proteins associated with auxin transport and distribution resulted in a greater induction of AOX1a in terms of magnitude and longevity. Three independent mutants for polarized auxin transport, rao3/big, rao4/pin-formed1, and rao5/multidrug-resistance1/abcb19, as well as the Myb transcription factor rao6/asymmetric leaves1 (that displays altered auxin patterns) were identified and resulted in an acute sensitivity toward mitochondrial dysfunction. Induction of the AOX1a reporter system could be inhibited by the application of auxin analogs or reciprocally potentiated by blocking auxin transport. Promoter activation studies with AOX1a::GUS and DR5::GUS lines further confirmed a clear antagonistic relationship between the spatial distribution of mitochondrial stress and auxin response kinetics, respectively. Genome-wide transcriptome analyses revealed that mitochondrial stress stimuli, such as antimycin A, caused a transient suppression of auxin signaling and conversely, that auxin treatment repressed a part of the response to antimycin A treatment, including AOX1a induction. We conclude that mitochondrial stress signaling and auxin signaling are reciprocally regulated, balancing growth and stress response(s).
The Price of Information in Combinatorial Optimization

Consider a network design application where we wish to lay down a minimum-cost spanning tree in a given graph; however, we only have stochastic information about the edge costs. To learn the precise cost of any edge, we have to conduct a study that incurs a price. Our goal is to find a spanning tree while minimizing the disutility, which is the sum of the tree cost and the total price that we spend on the studies. In a different application, each edge gives a stochastic reward value. Our goal is to find a spanning tree while maximizing the utility, which is the tree reward minus the prices that we pay. Situations such as the above two often arise in practice where we wish to find a good solution to an optimization problem, but we start with only some partial knowledge about the parameters of the problem. The missing information can be found only after paying a probing price, which we call the price of information. What strategy should we adopt to optimize our expected utility/disutility? A classical example of the above setting is Weitzman's "Pandora's box" problem where we are given probability distributions on values of $n$ independent random variables. The goal is to choose a single variable with a large value, but we can find the actual outcomes only after paying a price. Our work is a generalization of this model to other combinatorial optimization problems such as matching, set cover, facility location, and prize-collecting Steiner tree. We give a technique that reduces such problems to their non-price counterparts, and use it to design exact/approximation algorithms to optimize our utility/disutility. Our techniques extend to situations where there are additional constraints on what parameters can be probed or when we can simultaneously probe a subset of the parameters.
Can voice conversion be used to reduce non-native accents?

Voice-conversion (VC) techniques aim to transform utterances from a source speaker to sound as if a target speaker had produced them. For this reason, VC is generally ill-suited for accent-conversion (AC) purposes, where the goal is to capture the regional accent of the source while preserving the voice quality of the target. In this paper, we propose a modification of the conventional training process for VC that allows it to perform as an AC transform. Namely, we pair source and target vectors based not on their ordering within a parallel corpus, as is commonly done in VC, but based on their linguistic similarity. We validate the approach on a corpus containing native-accented and Spanish-accented utterances, and compare it against conventional VC through a series of listening tests. We also analyze whether phonological differences between the two languages (Spanish and American English) help predict the performance of the two methods.
Efficient and Low-Cost 3D Structured Light System Based on a Modified Number-Theoretic Approach

Abstract3D scanning based on structured light (SL) has been proven to be a powerful tool to measure the three-dimensional shape of surfaces, especially in biomechanics. We define a set of conditions that an optimal SL strategy should fulfill in the case of static scenes and then we present an efficient solution based on improving the number-theoretic approach (NTA). The proposal is compared to the well-known Gray code (GC) plus phase shift (PS) technique and the original NTA, all satisfying the same set of conditions but obtaining significant improvements with our implementation. The technique is validated in biomechanical applications such as the scanning of a footprint left on a "foam box" typically made for that purpose, where one of the ultimate goals could be the production of a shoe insole.
An experimental research on how to measure the surface soil moisture on pixial scale

Passive microwave remote sensing provide soil moisture product. The problem of validation of soil moisture products is that the true soil moisture of such a large pixel is difficult to estimate. This paper is a preliminary research on how to measure the true soil moisture of a 30m×30m quadrat. The results suggest that 20 measurement in such a quadrat should gave a reliable estimation of the soil moisture of the quadrat. This could be used as a thumb rule in designing the validation plan of remote sensing soil moisture product. And also could be used to estimate the soil moisture distribution within a space-born microwave radiometer footprint.
EEG-Informed Attended Speaker Extraction From Recorded Speech Mixtures With Application in Neuro-Steered Hearing Prostheses

Objective: We aim to extract and denoise the attended speaker in a noisy two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest. Methods: In this study, we propose a modular processing flow that first extracts the two speech envelopes from the microphone recordings, then selects the attended speech envelope based on the EEG, and finally uses this envelope to inform a multichannel speech separation and denoising algorithm. Results: Strong suppression of interfering (unattended) speech and background noise is achieved, while the attended speech is preserved. Furthermore, EEG-based auditory attention detection (AAD) is shown to be robust to the use of noisy speech signals. Conclusions: Our results show that AAD-based speaker extraction from microphone array recordings is feasible and robust, even in noisy acoustic environments, and without access to the clean speech signals to perform EEG-based AAD. Significance: Current research on AAD always assumes the availability of the clean speech signals, which limits the applicability in real settings. We have extended this research to detect the attended speaker even when only microphone recordings with noisy speech mixtures are available. This is an enabling ingredient for new brain–computer interfaces and effective filtering schemes in neuro-steered hearing prostheses. Here, we provide a first proof of concept for EEG-informed attended speaker extraction and denoising.
Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs

Detecting fake accounts in online social networks (OSNs) protects OSN operators and their users from various malicious activities. Most detection mechanisms attempt to predict and classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attacks in which fake accounts cloak their operation with patterns resembling real user behavior. We herein demonstrate that victims, benign users who control real accounts and have befriended fakes, form a distinct classification category that is useful for designing robust detection mechanisms. First, as attackers have no control over victim accounts and cannot alter their activities, a victim account classifier which relies on user-level activities is relatively harder to circumvent. Second, as fakes are directly connected to victims, a fake account detection mechanism that integrates victim prediction into graphlevel structures is more robust against manipulations of the graph. To validate this new approach, we designed Integro, a scalable defense system that helps OSNs detect fake accounts using a meaningful a user ranking scheme. Integro starts by predicting victim accounts from user-level activities. After that, it integrates these predictions into the graph as weights, so that edges incident to predicted victims have much lower weights than others. Finally, Integro ranks user accounts based on a modified random walk that starts from a known real account. Integro guarantees that most real accounts rank higher than fakes so that OSN operators can take actions against low-ranking fake accounts. We implemented Integro using widely-used, open-source distributed computing platforms in which it scaled nearly linearly. We evaluated Integro against SybilRank, the state-of-the-art in fake account detection, using real-world datasets and a largescale deployment at Tuenti, the largest OSN in Spain. We show that Integro significantly outperforms SybilRank in user ranking quality, where the only requirement is to employ a victim classifier is better than random. Moreover, the deployment of Integro at Tuenti resulted in up to an order of magnitude higher precision in fake accounts detection, as compared to SybilRank.
Tracking of Kuramoto oscillators with input saturation and applications in smart grids

The Kuramoto oscillator has been widely studied because it can model biological, social, chemical, and engineering problems. Conditions for frequency synchronization of a network of Kuramoto oscillators have been well established, but it depends on the connectivity of the network and on the natural frequencies. In this work we propose a consensus-based control strategy that forces the network to follow a virtual agent with constant frequency. Besides, we consider that the control input is constrained and we establish the limits of the set of inputs that assure frequency tracking. The results are extended to the smart grid case, where the nodes of a power network are modeled as Kuramoto oscillators that synchronize to a desired frequency using the injection/absorbsion of power according to a consensus based control. We show that our results are feasible when isolated group of nodes are taken into account, and they can be connected or disconnected to the main grid. Finally, the effects of time-varying sampling are analyzed and sampling period independence is demonstrated.
Analgesic effect of quetiapine in a mouse model of cancer-induced bone pain

Background/Aims Cancer-induced bone pain (CIBP) is one of the most common pains in patients with advanced neoplasms. Because of treatment-associated side effects, more than half of cancer patients are reported to have inadequate and undermanaged pain control. New mechanism-based therapies must be developed to reduce cancer pain. Quetiapine is a commonly used atypical antipsychotic drug. We report a study of the potential analgesic effects of quetiapine in a mouse model of CIBP and examine the mechanism of bone pain by analyzing the expression of various nociceptors. Methods Fifteen male C3H/HeN mice were arbitrarily divided into five groups: control and, CIBP with no treatment, quetiapine treatment, opioid treatment, and melatonin treatment. The mice were tested for mechanical hyperalgesia by determining the nociceptive hind paw withdrawal pressure threshold. Tissues from tibia were removed and subjected to quantitative and qualitative evaluations of transient receptor potential vanilloid 1 (TRPV1), TRPV4, acid-sensing ion channel 1 (ASIC1), ASIC2, and ASIC3 expression. Results Paw withdrawal pressure threshold was improved in the quetiapine treatment group compared with the CIBP group. Expression of TRPV1, TRPV4, ASIC1, ASIC2, and ASIC3 in the CIBP with quetiapine treatment group was significantly lower than that in the CIBP group. Conclusions Our results suggest an analgesic effect of quetiapine in the CIBP animal model and implicate TRPV and ASICs as potential targets for cancer pain management.
Calculations for Tera-Hertz (THz) Radiation Sources

We explore possibilities for THz sources from 0.3 - 30 THz. While still inaccessible, this broad gap is even wider for advanced acceleration schemes extending from X or, at most, W band RF at the low end up to CO2lasers. While physical implementations of these approaches are quite different, both are proving difficult to develop so yet lower frequency, superconducting RF is preferred. Similarly, the validity of modelling techniques varies greatly over this range but generally mandates coupling Maxwell’s equations to the appropriate device transport physics for which there are many options. Here we study radiation from undulatory-shaped transmission lines using finite-difference, time-domain (FDTD) simulations. Also, we present Monte-Carlo techniques for pulse generation. Examples of THz sources demonstrating constructive interference are shown with the goal of optimizing on-chip radiators.
On the scaling characteristics of MMORPG traffic

In this paper a comprehensive scaling analysis of the traffic of the four most popular Massively Multiplayer On-line Role Playing Games (MMORPG) is presented. The examined games are World of Warcraft, Guild Wars, Eve Online and Star Wars Galaxies. Both server and client generated traffic are analyzed in details. Our study reveals the basic statistical properties of the investigated games focusing on the correlation and scaling behavior. Although the examined games are all from the same genre and basic statistics such as the mean packet rate, variation of the packet rate, skewness of the packet rate distributions fall into the same magnitude, the games exhibit diverse traffic characteristics. We have found that in spite of the fact that some similarities can be found among the scaling characteristics of these games they show versatile scaling properties and the games can not be treated with one common model. Reviewed
Ridge-Penalty Regularization for Kernel-CCA

CCA and Kernel-CCA are powerful statistical tools that have been successfully employed for feature extraction. However, when working in high-dimensional signal spaces, care has to be taken to avoid overfitting. This paper discusses the influence of ridge penalty regularization on kernel-CCA by relating it to multivariate linear regression(MLR) and partial least squares(PLS). Experimental results of a pose estimation task will be given.
Exploiting Portuguese Lexical Knowledge Bases for Answering Open Domain Cloze Questions Automatically

We present the task of answering cloze questions automatically and how it can be tackled by exploiting lexical knowledge bases (LKBs). This task was performed in what can be seen as an indirect evaluation of Portuguese LKB. We introduce the LKBs used and the algorithms applied, and then report on the obtained results and draw some conclusions: LKBs are definitely useful resources for this challenging task, and exploiting them, especially with PageRanking-based algorithms, clearly improves the baselines. Moreover, larger LKB, created automatically and not sense-aware led to the best results, as opposed to handcrafted LKB structured on synsets.
Windows-Based Guided Data Capture Software for Mass-Scale Thermophysical and Thermochemical Property Data Collection

Guided data capture software (GDC) is described for mass-scale abstraction from the literature of experimental thermophysical and thermochemical property data for organic chemical systems involving one, two, and three components, chemical reactions, and chemical equilibria. Property values are captured with a strictly hierarchical system based upon rigorous application of the thermodynamic constraints of the Gibbs phase rule with full traceability to source documents. Key features of the program and its adherence to scientific principles are described with particular emphasis on data-quality issues, both in terms of data accuracy and database integrity.
ATP Binding to the KTN/RCK Subunit KtrA from the K+-uptake System KtrAB of Vibrio alginolyticus

Subunit KtrA of the bacterial Na+-dependent K+-translocating KtrAB systems belongs to the KTN/RCK family of regulatory proteins and protein domains. They are located at the cytoplasmic side of the cell membrane. By binding ligands they regulate the activity of a number of K+ transporters and K+ channels. To investigate the function of KtrA from the bacterium Vibrio alginolyticus (VaKtrA), the protein was overproduced in His-tagged form (His10-VaKtrA) and isolated by affinity chromatography. VaKtrA contains a G-rich, ADP-moiety binding β-α-β-fold (“Rossman fold”). Photocross-linking and flow dialysis were used to determine the binding of [32P]ATP and [32P]NAD+ to His10-VaKtrA. Binding of other nucleotides was estimated from the competition by these compounds of the binding of the 32P-labeled nucleotides to the protein. [γ-32P]ATP bound with high affinity to His10-VaKtrA (KD of 9 μm). All other nucleotides tested exhibited KD (Ki) values of 30 μm or higher. Limited proteolysis with trypsin showed that ATP was the only nucleotide that changed the conformation of VaKtrA. ATP specifically promoted complex formation of VaKtrA with the His-tagged form of its K+-translocating partner, VaKtrB-His6, as detected both in an overlay experiment and in an experiment in which VaKtrA was added to VaKtrB-His6 bound to Ni2+-agarose. In intact cells of Escherichia coli both a high of membrane potential and a high cytoplasmic ATP concentration were required for VaKtrAB activity. C-terminal deletions in VaKtrA showed that for in vivo activity at least 169 N-terminal amino acid residues of its total of 220 are required and that its 40 C-terminal residues are dispensable.
Skeleton automata for FPGAs: reconfiguring without reconstructing

While the performance opportunities of field-programmable gate arrays field (FPGAs)field for high-volume query processing are well-known, system makers still have to compromise between desired query expressiveness and high compilation effort. The cost of the latter is the primary limitation in building efficient FPGA/CPU hybrids. In this work we report on an FPGA-based stream processing engine that does not have this limitation. We provide a hardware implementation of XML projection [14] that can be reconfigured in less than a micro-second, yet supports a rich and expressive dialect of XPath. By performing XML projection in the network, we can fully leverage its filtering effect and improve XQuery performance by several factors. These improvements are made possible by a new design approach for FPGA acceleration, called skeleton automata. Skeleton automata separate the structure of finite-state automata from their semantics. Since individual queries only affect the latter, with our approach query workload changes can be accommodated fast and with high expressiveness.
Simple channel coding bounds

New channel coding converse and achievability bounds are derived for a single use of an arbitrary channel. Both bounds are expressed using a quantity called the “smooth 0-divergence”, which is a generalization of Rényi's divergence of order 0. The bounds are also studied in the limit of large block-lengths. In particular, they combine to give a general capacity formula which is equivalent to the one derived by Verdú and Han.
Renormalization group for non-relativistic fermions

A brief introduction is given to the renormalization group for non-relativistic fermions at finite density. It is shown that Landau’s theory of the Fermi liquid arises as a fixed point (with the Landau parameters as marginal couplings) and its instabilities as relevant perturbations. Applications to related areas, nuclear matter, quark matter and quantum dots, are briefly discussed. The focus will be on explaining the main ideas to people in related fields, rather than addressing the experts.