Miracle-dz/UniFAR-FTUs huggingface dataset:
Age and growth of the white croaker Micropogonias furnieri (Perciformes: Sciaenidae) in a coastal area of Southeastern Brazilian Bight

Studies on age and growth of fish populations have direct application in fisheries and contribute for policies of conservation. Our aim was update information on the Micropogonias furnieri growth parameters based on sagittae otoliths annulis readings, and we expected that the studied population from Southeast Brazil can reach more longevity than the reported in the available literature. We examined 286 individuals between August-2010 and July-2011 from Ubatuba Bay, SP. The Total Length ranged 200-480 mm. The determined growth parameters were: Males, L∞ =523.4mm, k = 0.05×year-1, t0 = -8.78 year; Females L∞ = 573.5mm, k = 0.06 × year-1, t0 = -7.49 year. The MI and % of edge type evidenced the formation of one ring per year during autumn/winter, coinciding with low temperature and low growth rate. These results differed from those reported for the Vazzoler’s Population I (summer), and we detected higher longevity (45-48 years) as compared with the recorded in the available information.
Temporary Recovery of the Defect Responsible for Light- and Elevated Temperature-Induced Degradation: Insights Into the Physical Mechanisms Behind LeTID

The effect of light- and elevated temperature-induced degradation (LeTID) can be nonpermanently reversed by charge carrier injection below the degradation temperature (commonly used degradation temperatures are above ∼70 °C). In this study, we show that the rate of temporary recovery depends strongly on the excess carrier density. We observe that the order of the reaction changes from pseudo-zero to first with increasing injection. The rate decreases slightly with increasing temperature. Since the samples can go through multiple degradation/recovery cycles without distinct changes in the degradation kinetics, the experimentally accessible recovered and degraded states are interpreted as manifestations of the equilibrium concentrations of the defect responsible for LeTID at different temperatures. Based on our observations, we argue that the process underlying LeTID degradation is the dissociation of a precursor rather than an association of two or more components. In light of the relation between LeTID susceptibility and bulk hydrogen concentration, we hypothesize that the LeTID precursor dissociates into the LeTID defect and monatomic hydrogen. Numerical simulations of the coupled rate equations including hydrogen interactions well reproduce the experimental observations; according to these results, the presence of a sink for the atomic hydrogen such as dopant atoms is paramount for the LeTID degradation.
Towards a Novel Framework for Automatic Big Data Detection

Big data is a ”relative” concept. It is the combination of <italic>data</italic>, <italic>application</italic>, and <italic>platform</italic> properties. Recently, big data specific technologies have emerged, including software frameworks, databases, hardware accelerators, storage technologies, etc. However, the automatic selection of these solutions for big data computations remains a non-trivial task. Presently, the big data tools are selected by analyzing the problem manually, or by using several performance prediction techniques. The manual identification is based on the data properties only, whereas the performance predictors only estimate basic execution metrics without linking them with big data (3Vs) thresholds. Hence, both ways of identification are mostly incorrect, which can lead to inefficient use of 3Vs optimizations, resulting into global inefficiency, reduced system performance, increasing power consumption, requiring greater effort on the part of the programming team, and misallocation of the hardware resources required for the task. In this regard, <italic>a novel framework</italic> has been proposed for automatic detection of 3Vs (Volume, Velocity, Variety) of big data, using machine learning. The detection is done through <italic>static code features</italic>, <italic>data</italic>, and <italic>platform properties</italic>, leading to relevant tool selection, and code generation, with minimal overheads, lesser programmer interventions, higher usability, and portability. Instead of handling each application with big data specialized solutions, or manually identifying the 3Vs, the framework can automatically detect and link the 3Vs to the relevant optimizations. Several standard applications have been tested using the proposed framework. In the case of volume, the average detection accuracy is up to 97.8% for <italic><bold>seen</bold></italic> and 95.9% for <italic><bold>unseen</bold></italic> applications. In the case of velocity, the average detection accuracy is up to 97.3% for <italic><bold>seen</bold></italic> and 92.6% for <italic><bold>unseen</bold></italic> applications. There is no margin of error in variety detection, as it has straightforward computations without any predictions. Furthermore, an airline recommendation system case study strengthens the effectiveness of the proposed approach.
Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

A high percentage of information that propagates through a social network is sourced from different exogenous sources. E.g., individuals may form their opinions about products based on their own experience or reading a product review, and then share that with their social network. This sharing then diffuses through the network, evolving as a combination of both network and external effects. Besides, different individuals (nodes in a social network) have different degrees of exposition to their external sources, as well. Modeling this influence of external sources is important in order to understand the diffusion process and predict future content sharing patterns. Recognizing this fusion of intrinsic (network) effect and exogenous (external) effect, this paper develops a novel fuzzy relative willingness (FRW) model. Leveraging a fuzzy set approach provides a way to handle the uncertainties arising within the human concept of willingness. We demonstrate that FRW is able to accurately identify both top- $k$ most content producers and diffusion effect based on external influence. We also demonstrate that the fuzzy set theory provides a compelling framework to model uncertainties pertaining to the influence as well as the susceptibility of individuals for both network and exogenous effects.
Inference of Protein-Protein Interaction Networks from Liquid-Chromatography Mass-Spectrometry Data by Approximate Bayesian Computation-Sequential Monte Carlo Sampling

We propose a new algorithm for inference of protein-protein interaction (PPI) networks from noisy time series of Liquid-Chromatography Mass-Spectrometry (LC-MS) proteomic expression data based on Approximate Bayesian Computation - Sequential Monte Carlo sampling (ABC-SMC). The algorithm is an extension of our previous framework PALLAS. The proposed algorithm can be easily modified to handle other complex models of expression data, such as LC-MS data, for which the likelihood function is intractable. Results based on synthetic time series of cytokine LC-MS measurements corresponding to a prototype immunomic network demonstrate that our algorithm is capable of inferring the network topology accurately.
FANet: Features Adaptation Network for 360$^{\circ }$ Omnidirectional Salient Object Detection

Salient object detection (SOD) in 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> omnidirectional images has become an eye-catching problem because of the popularity of affordable 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> cameras. In this paper, we propose a Features Adaptation Network (FANet) to highlight salient objects in 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> omnidirectional images reliably. To utilize the feature extraction capability of convolutional neural networks and capture global object information, we input the equirectangular 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> images and corresponding cube-map 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> images to the feature extraction network (FENet) simultaneously to obtain multi-level equirectangular and cube-map features. Furthermore, we fuse these two kinds of features at each level of the FENet by a projection features adaptation (PFA) module, for selecting these two kinds of features adaptively. Finally, we combine the preliminary adaptation features at different levels by a multi-level features adaptation (MLFA) module, which weights these different-level features adaptively and produces the final saliency maps. Experiments show our FANet outperforms the state-of-the-art methods on the 360<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> omnidirectional SOD datasets.
Trajectory Similarity Assessment On Road Networks Via Embedding Learning

Trajectory similarity assessment is a basic task in trajectory data analysis and application, such as trajectory clustering, route planning and POI recommendation. However, the evaluation of similar trajectories in urban environments often suffer from road network constraints, sampling rate difference and GPS errors, etc. To solve these problems, we propose a trajectory similarity assessment approach based on road network embedding, which can capture both topology and spatiality of road networks for embedding learning. Specifically, it performs random walk to spatial query through depth-first search and introduces distance to optimize the loss function. When mapped to road networks, trajectory embedding can be obtained and the similarity of trajectories can be evaluated. Experiments on real data show that our approach is robust and efficient for trajectory similarity assessment.
An Adjusted Propagation Model for Wireless Sensor Networks in Corn Fields

In this paper, we propose some adjustment parameters for three popular propagation models typically used in vegetated environments. We validate the adjustments proposed with measurements in a small corn field typically found in Colombian farms. The proposed adjustments seem to work properly for wireless sensor networks typically used in the Internet of things for automated assisted farming in small crops.
Asymmetric Intimacy Based Positive Emotions Contagion Peer Public Safety Evacuation Behavior

Behavioral simulation plays an important role in many scenarios, such as emergency evacuation and public safety. The importance of public security in modern life can be seen in the epidemic outbreak in 2020. In particular, the epidemic can affect people’s psychology and emotions and change people’s behavior. Therefore, how to effectively improve people’s positive emotions in a panic has become an urgent problem. In social relations, most behaviors are produced by peer interaction. To simulate the interactions among peers more realistically, this paper studies the diversity and efficiency of evacuation behaviors from the trust of peers, the strength of social relations and empathy. First, a general framework of peer relationships is constructed based on asymmetric intimacy, including the relationship between intimacy relationship modeling. Then, the problem of the dynamic formation of peer groups is studied by employing the calculation method of asymmetric intimacy. The effect of positive emotion transmission mechanism on peer interaction behaviors is explored through asymmetric intimacy. Finally, the companion behaviors of the peers in several scenarios are simulated. The proposed method is compared with different emotion contagion models and the behavioral simulation models. Experimental results show that the positive emotions contagion of the peer behavior simulation method can concrete the macro group behavior and effectively simulate the peer group in an evacuation. The simulation effects are diverse and efficient.
Homéostasis and differentiation of CD8 T lymphocytes in the absence of common gamma chain-dependent cytokines

Cytokines signalling through receptors sharing the γc chain, especially IL-7, are critical for the development and peripheral homeostasis of naive T cells. Furthermore, IL-2, -7, -15 and -21 are pleiotropic factors that can play complimentary or overlapping roles in T cell homeostasis and immune responses to infection. However, identification of their precise function during an anti-viral immune response has been challenging. Indeed, γc deficiency affects not only the survival of naive T cells but also the function of regulatory T cells, limiting the use of γc-deficient mice for assessing T cell immunity. Furthermore, the exact contribution of the γc-dependent cytokines in the differentiation of CD8 T cells remains disputed. Indeed, it is unclear at which step of the differentiation process these cytokines impact and what is their importance on the cellfate decision towards terminal differentiation versus memory generation. In order to assess the impact of γc deficiency on the biology of naive CD8 T cells, we derived P14 TCR transgenic mice on the recombination-activating gene-2 deficient background with or without γc. In this setting, γc -/- naive CD8 T cells fail to accumulate in peripheral lymphoid organs and the few remaining cells are characterized by small size, decreased expression of MHC class I proteins and enhanced apoptosis. By overexpressing human Bcl-2, an anti-apoptotic molecule, the number of peripheral naive CD8 T cells that lack γc could be restored. Nevertheless, as described for naive CD4 T cells, the presence of Bcl-2 could not correct the size and protein synthesis defect of γcdeficient CD8 T cells. We conclude that γc cytokines provide Bcl-2-dependent as well as Bcl-2-independent signals to maintain the phenotype and homeostasis of the peripheral naive CD8 T cell pool. In order to dissect the entire CD8 T cell differentiation program in the absence of γc, we compared the response of CD8 T cells from γc +/+ or γc -/- P14 Bcl-2 mice after challenge with lymphocytic choriomeningitis virus. We demonstrated that although γcdependent signals are dispensable for the initial expansion and the acquisition of cytotoxic functions following antigenic stimulation, they condition the terminal proliferation and differentiation of CD8 effector T cells (i.e.: KLRG1high CD127low shortlived effector T cells) via the transcription factor, T-bet. Moreover, the γc-dependent signals that are critical for memory T cell formation are not rescued by Bcl-2 overexpression. Together, our results define the critical stages for γc cytokines in the programming of terminal effector CD8 T cells and in the Bcl-2-independent survival and homeostatic proliferation of memory CD8 T cells.
Borderline Personality Disorder: A Case for the Right Treatment, at the Right Dose, at the Right Time

Abstract There is now compelling evidence that a range of psychotherapeutic treatments are effective in the treatment of borderline personality disorder (BPD). Such treatments are often lengthy, expensive, subject to high rates of incompletion and are rarely available to people with sub-threshold symptoms. There is broad agreement that some combination of vulnerability, invalidating environment, childhood adversity, disrupted attachment in childhood or trauma play a role in the aetiology of the syndrome of BPD. These factors also contribute to problems with the capacity to mentalise, regulate emotions, tolerate distress and impact on psychosocial development with or without self-damaging and suicidal behaviour. This column takes as a given that people with BPD should receive evidence-based psychological treatments such as dialectical behaviour therapy (DBT), interpersonal therapy and cognitive behavioural therapy in a sufficient dose to be helpful. However, to avert an escalating trajectory which may lead to a diagnosis of BPD the right dose of the right therapy at the right time is necessary. Under-dosing or ineffective psychotherapy can be potentially harmful. This column reviews the evidence, such as it is, for therapeutic approaches which may contribute to more skilful negotiation of life’s difficulties and which may avert deterioration in mental health and quality of life in vulnerable individuals and families.
Lumen & Media Segmentation of IVUS Images via Ellipse Fitting Using a Wavelet-Decomposed Subband CNN

We propose an automatic segmentation method for both lumen and media in IntraVascular UltraSound (IVUS) images using a deep convolutional neural network (CNN). In contrast to previous approaches that broadly fall under the category of labeling each pixel to be either lumen, media or background, we propose to use a structurally regularized CNN via wavelet-based subband decomposition that directly predicts two ellipses that best represent each of lumen and media segments. The proposed architecture significantly reduces computational complexity and offers better performance compared to recent techniques in the literature. We evaluated our network on the publicly available IVUS-Challenge-2011 dataset using two performance metrics, namely Jaccard Measure (JM) and Hausdorff Distance (HD). The evaluation results show that our proposed network outperforms the state-of-the-art lumen and media segmentation methods by a maximum of 8% in JM (Lumen) and nearly 33% in HD (Media).
Joint User Selection and GMD-Based Hybrid Beamforming for Generalized Spatial Modulation Aided Millimeter-wave Massive MIMO Systems

Multiple Input Multiple Output (MIMO) systems with limited Radio Frequency (RF) chains play a pivotal role in the Fifth Generation (5G) of wireless networks. However, the transmitter of Generalized Spatial Modulation MIMO (GSM-MIMO) systems that characterized by a single RF chain and multiple active antennas is capable of reducing both the energy consumption and the transmitter cost. In this paper, combining GSM-MIMO systems with the fully digital Geometric Mean Decomposition (GMD)-based precoding scheme and Analog Beamforming (ABF) into Hybrid Beamforming (HBF) regime is presented for Millimeter-wave (mmWave) massive MIMO systems which is a key enabler for 5G networks. In addition, applying the norm-based user selection algorithm in GSM-MIMO scheme with GMD-based hybrid precoding is proposed, and referred to as Multiuser Steered GSM-MIMO (MUS-GSM-MIMO) scheme. Simulation results demonstrate that the proposed schemes are capable of achieving considerable performance gains over the conventional GSM-MIMO schemes, while avoiding the overwhelming costs of multiple RF chains. Therefore, the proposed schemes are very efficient and highly suitable for large-scale and 5G wireless networks.
Fault Detection Method for Suspension Systems of Maglev Train Based on Optimized Random Matrix Theory

The fault detection of the suspension system in a maglev train is of great importance for its operational safety and reliability. However, in random matrix theory (RMT), the size of the random matrix direct impacts the result of the mean spectral radius (MSR). In this article, a state-of-the-art fault detection method for suspension systems is proposed using optimized RMT. The random matrix with the largest number of eigenvalues is obtained by reshaping the original data, with the help of the auto-correlation length from the correlation analysis. Finally, the optimized MSR is applied to detect the fault. The results of the experiment illustrate that the proposed method is applicable and effective.
Aircraft Turboprop Engine Vibration Monitoring Module

The paper describes electronic vibration monitoring module (EVM) that can be used as standalone diagnostic system or as a part of a complex modular concept of engine electronic control (EEC/FADEC) with aircraft health monitoring system. We present a new generation of EVM for modern small turboprop engines and their vibration diagnostics which has been developed by Unis, a.s. in cooperation with Brno University of Technology and Tomas Bata University in Zlin.
Dungeons & Replicants: Automated Game Balancing via Deep Player Behavior Modeling

Balancing the options available to players in a way that ensures rich variety and viability is a vital factor for the success of any video game, and particularly competitive multiplayer games. Traditionally, this balancing act requires extensive periods of expert analysis, play testing and debates. While automated gameplay is able to predict outcomes of parameter changes, current approaches mainly rely on heuristic or optimal strategies to generate agent behavior. In this paper, we demonstrate the use of deep player behavior models to represent a player population (n = 213) of the massively multiplayer online role-playing game Aion, which are used, in turn, to generate individual agent behaviors. Results demonstrate significant balance differences in opposing enemy encounters and show how these can be regulated. Moreover, the analytic methods proposed are applied to identify the balance relationships between classes when fighting against each other, reflecting the original developers’ design.
A Bayesian Hierarchical Mixture of Gaussian Model for Multi-Speaker DOA Estimation and Separation

In this paper we propose a fully Bayesian hierarchical model for multi-speaker direction of arrival (DoA) estimation and separation in noisy environments, utilizing the W-disjoint orthogonality property of the speech sources. Our probabilistic approach employs a mixture of Gaussians formulation with centroids associated with a grid of candidate speakers' DoAs. The hierarchical Bayesian model is established by attributing priors to the various parameters. We then derive a variational Expectation-Maximization algorithm that estimates the DoAs by selecting the most probable candidates, and separates the speakers using a variant of the multichannel Wiener filter that takes into account the responsibility of each candidate in describing the received data. The proposed algorithm is evaluated using real room impulse responses from a freely-available database, in terms of both DoA estimates accuracy and separation scores. It is shown that the proposed method outperforms competing methods.
Implementation of Network-Coded Cooperation for Energy Efficient Content Distribution in 5G Mobile Small Cells

The continuous increase of mobile data traffic calls for the design of energy-efficient content distribution mechanisms, to be incorporated in the fifth generation of mobile networks, 5G. One of the biggest concerns of the companies and the research community is to reduce the energy consumption in both the user equipments (UEs) and the network equipment. In this article, we present a novel content distribution framework called Network-Coded Cooperative (NCC) Networks, which benefits from the interplay between mobile clouds (MC) and Random Linear Network Coding (RLNC) to reduce the overall energy consumption in the devices that take part in the communication. This novel framework leads to reduced energy consumption by offloading the cellular interface to a link with greater energy efficiency, for instance, WiFi, within the mobile small cell. We evaluate the performance of our framework analytically and in practical implementation (i.e., testbed) in terms of throughput, energy savings, packet decoding ratio, latency, and synchronicity. In comparison to the conventional content distribution system, for the case of four users, the analytical model and the testbed implementation show energy savings of more than 12% and 8%, respectively. Furthermore, network usage is reduced, losses are neutralized, and the content is synchronously distributed to all users.