Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative data detection: bounding performance and complexity reduction

Iterative detection (ID) or turbo decoding is a good approximation to the optimal decoder for a concatenated network of Finite State Machines (FSMs) constructed from constituent codes and ISI channel memory. Iterative detection was used successfully for the first time in the decoding of Parallel Concatenated Convolutional Codes (PCCCs or turbo codes). Applications that have been using iterative detection include Serially Concatenated Convolutional Codes (SCCCs), Trellis Coded Modulation (TCM) over interleaved, frequency-selective fading channels, and Serially Concatenated Continuous Phase Modulation (SCCPM). The Soft Input Soft Output (SISO) module, which is a basic building block in iterative detection, implements an algorithm performing an update of the reliability information of the input and the output symbols of a FSM. For implementation, the complexity should be reduced. Reduced state SISO (RS-SISO) algorithms are used for complexity reduction. When perfect CSI is not available at the receiver, the SISO module should be able to deal with unknown, and possibly time-varying parameters. SISO modules that jointly estimate the parameters and generate soft information will be referred to as Adaptive SISO (A-SISO) modules. In this paper, the RS-SISO algorithm is used for complexity reduction of the A-SISO module, namely RS-A-SISO algorithms. RS-A-SISO modules are applied to SCCPM over frequency-selective fading channels when perfect CSI is not available and trade-offs between performance and complexity are considered. RS-A-SISO algorithms are analyzed using the SNR evolution technique. We present upper bounds for the maximum-likelihood decoding performance of turbo-like codes for specific interleavers. Previous research developed upper bounds for turbo-like codes using the uniformly interleaved assumption, which bounds the performance averaged over all interleavers. These upper bounds for specific interleavers are based on the simple bound and estimated weight distributions including the exact several smallest distance terms because if either estimated weight distributions on their own or the exact several smallest distance terms only are used, an accurate bound can not be obtained. The approximate weight distribution is verified by statistical reliability measure.
Hydrogen Therapy and Its Future Prospects for Ameliorating COVID-19: Clinical Applications, Efficacy, and Modality

Molecular hydrogen is renowned as an odorless and colorless gas. The recommendations developed by China suggest that the inhalation of hydrogen molecules is currently advised in COVID-19 pneumonia treatment. The therapeutic effects of molecular hydrogens have been confirmed after numerous clinical trials and animal-model-based experiments, which have expounded that the low molecular weight of hydrogen enables it to easily diffuse and permeate through the cell membranes to produce a variety of biological impacts. A wide range of both chronic and acute inflammatory diseases, which may include sepsis, pancreatitis, respiratory disorders, autoimmune diseases, ischemia-reperfusion damages, etc. may be treated and prevented by using it. H2 can primarily be inoculated through inhalation, by drinking water (which already contains H2), or by administrating the injection of saline H2 in the body. It may play a pivotal role as an antioxidant, in regulating the immune system, in anti-inflammatory activities (mitochondrial energy metabolism), and cell death (apoptosis, pyroptosis, and autophagy) by reducing the formation of excessive reactive O2 species and modifying the transcription factors in the nuclei of the cells. However, the fundamental process of molecular hydrogen is still not entirely understood. Molecular hydrogen H2 has a promising future in therapeutics based on its safety and possible usefulness. The current review emphasizes the antioxidative, anti-apoptotic, and anti-inflammatory effects of hydrogen molecules along with the underlying principle and fundamental mechanism involved, with a prime focus on the coronavirus disease of 2019 (COVID-19). This review will also provide strategies and recommendations for the therapeutic and medicinal applications of the hydrogen molecule.
Sr ISOTOPES BY LA-MC-ICP-MS PROCEDURES COUPLED WITH THE MACS3 REFERENCE MATERIAL IN A CORAL SAMPLE: A RECORD OF ENVIRONMENTAL CHANGES

The main aim of this work is to demonstrate that the Laser ablation multi-collector inductively coupled plasma mass spectrometry (LA-MC-ICP-MS) is a powerful tool for the analysis of strontium (Sr) isotopes in corals. This work discusses certification strategies for Sr isotopes determination, using reference material (RM) analyses and the results treatment based on detailed data acquired in biological materials, a coral sample. To obtain reliable results, it is essential to properly adjust the mass spectrometer and laser ablation system. Adjusting the equipment to its maximum intensity does not always result in correct 87Sr/86Sr ratios. Therefore, the optimization of the mass spectrometer was performed using the reference material NIST SRM-987 (solution) and adjusting the correct Sr isotope ratio to the reference material (USGS MACS3 and NIST-612, solids) before each analytical session. The protocol applied the solid reference material USGS MACS3 with an isotopic ratio 87Sr/86Sr of 0.72000. The values obtained for this RM varied between 0.7012 and 0.7014, with a correction factor calculated between 0.990 and 0.988. In order to account for potential drifts in the mass spectrometer during an analytical session, the application of bracketing correction and the use of the most convenient reference material are suggested. The analytical uncertainty of Sr data obtained by LA-MC-ICP-MS is comparable to studies carried out on other carbonate materials. The results of ablation techniques are reproducible within the analytical error, which implies that this technique produces robust results when applied to coral carbonates. In addition, several comparative measurements of different reference materials (e.g. USGS MACS3 and NIST 612) and the comparison of the 87Sr/86Sr ratios highlight the robustness of the method. The results along the coral growth axes showed a decrease in the 87Sr/86Sr ratio from the inner to the outer layer of the coral (from 0.70920 to 0.70627), which indicate variations in the availability of particulate matter during the coral growth, probably related to local marine environmental changes.
Regulation of corA, the Magnesium, Nickel, Cobalt Transporter, and Its Role in the Virulence of the Soft Rot Pathogen, Pectobacterium versatile Strain Ecc71

Pectobacterium versatile (formally P. carotovorum) causes disease on diverse plant species by synthesizing and secreting copious amount of plant-cell-wall-degrading exoenzymes including pectate lyases, polygalacturonases, cellulases, and proteases. Exoenzyme production and virulence are controlled by many factors of bacterial, host, and environmental origin. The ion channel forming the magnesium, nickel, and cobalt transporter CorA is required for exoenzyme production and full virulence in strain Ecc71. We investigated CorA’s role as a virulence factor and its expression in P. versatile. Inhibiting the transport function of CorA by growing a CorA+ strain in the presence of specific CorA inhibitor, cobalt (III) hexaammine (Co (III)Hex), has no effect on exoenzyme production. Transcription of pel-1, encoding a pectate lyase isozyme, is decreased in the absence of CorA, suggesting that CorA influences exoenzyme production at the transcriptional level, although apparently not through its transport function. CorA− and CorA+ strains grown in the presence of Co (III)Hex transcriptionally express corA at higher levels than CorA+ strains in the absence of an inhibitor, suggesting the transport role of corA contributes to autorepression. The expression of corA is about four-fold lower in HrpL− strains lacking the hrp-specific extracytoplasmic sigma factor. The corA promoter region contains a sequence with a high similarity to the consensus Hrp box, suggesting that corA is part of Hrp regulon. Our data suggest a complex role, possibly requiring the physical presence of the CorA protein in the virulence of the Pectobacterium versatile strain Ecc71.
Thermotherapy associated with flexibility training does not increase the knee range of movement of healthy adults: systematic review and meta-analysis of randomized clinical trials

The stretching with thermotherapies associated have been related in the literature as a way to increase the range of motion (ROM), but still are not consensus about this efficacy and cost-effectiveness. The muscle stretching is a useful technique in rehabilitation and in physical activities programs, either to gain or maintain the flexibility, as a recovery of musculoskeletal and joint injuries. Objective: To determine the effects of thermotherapies associated to flexibility training in ROM of knee extension in healthy adults. Method: The search strategy was conducted in main databases, as Cochrane Library, LILACS, PEDro, PUBMED/ Medline, Scopus and Web of Science. The searches were done in 2016, and renewed in 2023, in order to achieve new publications along this time. Were selected only randomized clinical trials that have executed a training of hamstrings stretching, associated or not with one or more thermotherapies in healthy young adults, as long as the outcome was knee extension ROM. Only papers in Portuguese or English were assessed. To evaluate risk of bias was used the Cochrane Collaboration’s Risk of Bias Tool, and the methodological quality assessment was rated following the PEDro Scale. Results: Eight articles were included, totalizing 260 participants. The papers showed low methodological quality, and uncertain risk of bias. Only chronic effect of local warming and cryotherapy plus stretching showed a statistically significant difference versus control group. However, the thermotherapy action associated with stretching is still unclear, once the results suggest that even without the thermotherapy there are ROM increases. Conclusion: The stretching is effective in knee extension ROM improvement in healthy adults, with or without thermotherapy. New studies with higher methodological rigor and standardized protocols are needed.
Micrometric displacement sensor based on the strain of a fiber Bragg grating with heterodyne detection of intensity in a Mach-Zehnder interferometer.

This paper describes the implementation and characterization of a displacement sensor according to the strain of a fiber Bragg grating. The system is able to obtain measurements on micrometer scales through heterodyne detection of intensity in a Mach-Zehnder interferometry in fiber optic. For the displacement characterization, the fiber Bragg grating was attached from one end meanwhile at the other end a tension was induced by a high-precision rotation mount. Experimental results are presented for which varying the angular displacement we obtain a linear response in the amplitude of the intensity, determining a sensitivity of 0.77 pW/µm with a displacement size of 48.80 µm in a dynamic range from 0 µm to 488 µm.
An Integer-Only Resource-Minimized RNN on FPGA for Low-Frequency Sensors in Edge-AI

The growth of Artificial Intelligence (AI) and the Internet of Things (IoT) sensors has given rise to a synergistic paradigm known as AIoT, wherein AI functions as the decision-maker and sensors collect information. However, a substantial proportion of AIoT rely on cloud-based AI, which process wirelessly transmitted raw data, increasing power consumption and reducing battery life at sensor nodes. Edge-AI has emerged as a promising alternative, implementing AI directly on sensor nodes, eliminating the need of raw data transmission. Despite its potential, there is a scarcity of hardware architectures optimized for resource-constrained platforms, such as field programmable gate arrays (FPGAs), particularly for low-frequency sensors. This work presents a shared-scale integer-only recurrent neural network (RNN) implemented on a Lattice ICE40UP5K FPGA using a resource-minimized time and layer-multiplexed (TLM) hardware architecture. This architecture adopts real-time processing, setting clock frequency to complete a single RNN timestep preceding the next sensor sample, reducing power consumption significantly. Measurements on this FPGA implementing our proposed architecture applied to a pretrained RNN on cow behavior show a power consumption of $360 \mu \text{W}$ at a clock frequency of 146 kHz and negligible accuracy loss at 8-bit bitwidth. This finding suggests that our methods lead to the most accurate implementation of animal behavior estimation with a power consumption below $500 \mu \text{W}$ on an FPGA. The implementation in Systemverilog and Python code is publicly available, enabling adaptation of the RNN for various tasks involving low-frequency sensors on resource-constrained FPGAs, thereby contributing to the further advancement and democratization of Edge-AI solutions.
On the White's algorithm for fractional programming

In this note we extend for fractional case a method due to White for solving a problem of maximizing over a finite set a function with some special "convexity" properties. Three algorithms applied to a transformation of the initial problem into a maximizing an auxilliary non-fractional function over a bi-product set are given.
BIOLOGIA E MANEJO DE CAPIVARAS (Hydrochoerus hydrochaeris) NO PARQUE ESTADUAL ALBERTO LÖFGREN, SÃO PAULO, BRASIL

Observações comportamentais visando ao manejo de capivaras foram realizadas no Parque Estadual Alberto Löfgren, município de São Paulo, SP, Brasil. Para tanto, se acompanhou um grupo de capivaras de vida livre dessa Unidade de Conservação. Após a contagem do número de indivíduos e da análise das interações intraespecíficas, foi realizado o primeiro manejo destes animais no Parque. O controle dos carrapatos provenientes das capivaras também foi abordado.
ConvMatch: Rethinking Network Design for Two-View Correspondence Learning

Multilayer perceptron (MLP) has become the de facto backbone in two-view correspondence learning, for it can extract effective deep features from unordered correspondences individually. However, the problem of natively lacking context information limits its performance although many context-capturing modules are appended in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage a convolutional neural network (CNN) as the backbone, inherently capable of context aggregation. Specifically, with the observation that sparse motion vectors and a dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating the dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Moreover, we propose global information injection and bilateral convolution, to fit the overall spatial transformation better and accommodate the discontinuities of the motion field in case of large scene disparity. Extensive experiments reveal that ConvMatch consistently outperforms state-of-the-arts for relative pose estimation, homography estimation, and visual localization. Our code is publicly available at https://github.com/SuhZhang/ConvMatch.
Parallel multi-stage features fusion of deep convolutional neural networks for aerial scene classification

ABSTRACT Aerial scene classification is a challenging task in the remote sensing image processing field. Owing to some similar scene, there are only differences in density. To challenge this problem, this paper proposes a novel parallel multi-stage (PMS) architecture formed by a low, middle, and high deep convolutional neural network (DCNN) sub-model. PMS model automatically learns representative and discriminative hierarchical features, which include three 512 dimension vectors, respectively, and the final representative feature created by linear connection. PMS model describes a robust feature of aerial image through three stages feature. Unlike previous methods, we only use transfer learning and deep learning methods to obtain more discriminative features from scene images while improving performance. Experimental results demonstrate that the proposed PMS model has a more superior performance than the state-of-the-art methods, obtaining average classification accuracies of 98.81% and 95.56%, respectively, on UC Merced (UCM) and aerial image dataset (AID) benchmark datasets.
Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition

Existing methods on facial expression recognition (FER) are mainly trained in the setting when multi-class data is available. However, to detect the alien expressions that are absent during training, this type of methods cannot work. To address this problem, we develop a Hierarchical Spatial One Class Facial Expression Recognition Network (HS-OCFER) which can construct the decision boundary of a given expression class (called normal class) by training on only one-class data. Specifically, HS-OCFER consists of three novel components. First, hierarchical bottleneck modules are proposed to enrich the representation power of the model and extract detailed feature hierarchy from different levels. Second, multi-scale spatial regularization with facial geometric information is employed to guide the feature extraction towards emotional facial representations and prevent the model from overfitting extraneous disturbing factors. Third, compact intra-class variation is adopted to separate the normal class from alien classes in the decision space. Extensive evaluations on 4 typical FER datasets from both laboratory and wild scenarios show that our method consistently outperforms state-of-the-art One-Class Classification (OCC) approaches.
The relationship between fine temporal sensitivity and resting beta wave frequencies

THESIS: The Relationship Between Fine Temporal Sensitivity and Resting Beta Wave Frequencies STUDENT: Kristina O. Hernandez COLLEGE: Science and Humanities DATE: July, 2014 PAGES: 56 Various theories and models have been developed to try to explain how people perceive time. Researchers have investigated populations that have temporal deficits, including individuals with Attention Deficit/Hyperactivity Disorder (ADHD), Autism, Schizophrenia, and speech deficits. The current study attempted to find evidence for the Time’s Subjective Expansion (TSE) model by investigating the relationship between resting electroencephalogram (EEG) beta brain wave frequencies and performance on a duration discrimination task in a nonclinical population. There was a positive correlation between resting beta wave frequencies and temporal processing accuracy in the left hemispheric posterior parietal region (P3 and PO7). There was also a positive correlation between resting theta wave frequencies and temporal processing accuracy in the left hemispheric temporal region (T7, TP7). These findings provide support for a left hemispheric advantage for temporal processing.
CSR decoupling within business groups and the risk of perceived greenwashing

Research Summary: Given the growing legitimacy of corporate social responsibility (CSR), many firms engage in symbolic communication to showcase CSR without undertaking commensurate substantive actions. This “ CSR decoupling ” can create a risk of perceived greenwashing, which, in turn, may negatively affect a firm's performance. In this study, we explore an unexamined antecedent of decoupling: interfirm affiliation. Specifically, we use the structure of Business Groups (BGs) to investigate CSR decoupling across rather than within firms. We find that apex firms within a group are more likely to engage in CSR decoupling compared with non-apex firms and, importantly, are partially shielded from greenwashing perceptions by the market. Our research contributes to the literatures on decoupling, perceived greenwashing, and the role of BGs and their CSR practices. Managerial Summary: Companies that engage in symbolic communication about corporate social responsibility (CSR) without substantive actions risk being perceived as “ greenwashers, ” a perception that harms firm performance. Our study demonstrates how, in certain contexts where firms are affiliated with others, this may not occur. For instance, apex firms
Dynamics in Quaternary Ionic Liquids with Non-Flexible Anions: Insights from Mechanical Spectroscopy

The present work investigates how mechanical properties and ion dynamics in ionic liquids (ILs) can be affected by ILs’ design while considering possible relationships between different mechanical and transport properties. Specifically, we study mechanical properties of quaternary ionic liquids with rigid anions by means of Dynamical Mechanical Analysis (DMA). We are able to relate the DMA results to the rheological and transport properties provided by viscosity, conductivity, and diffusion coefficient measurements. A good agreement is found in the temperature dependence of different variables described by the Vogel−Fulcher−Tammann model. In particular, the mechanical spectra of all the measured liquids showed the occurrence of a relaxation, for which the analysis suggested its attribution to a diffusive process, which becomes evident when the ion dynamics are not affected by the fast structural reorganization of flexible anions on a local level.
Interpolative Hyperbolic Realizable Moment Closures for Non-Equilibrium Flows with Heat Transfer

Interpolative Hyperbolic Realizable Moment Closures for Non-Equilibrium Flows with Heat Transfer Boone Rudy Tensuda Master of Applied Science Graduate Department of Aerospace Engineering University of Toronto 2015 The predictive capabilities of a novel, 14-moment, maximum-entropy-based, interpolative closure are explored for multi-dimensional non-equilibrium flows of a monatomic gas with heat transfer. Unlike the maximum-entropy closure on which it is based, the interpolative closure provides closed-form expressions for the closing fluxes while retaining a large region of hyperbolicity. Properties of the moment system are explored via a dispersion analysis and an implicit finite-volume solution procedure is proposed. Multidimensional applications of the closure are then examined for several canonical nonequilibrium flow problems in order to provide an assessment of its capabilities. The predictive capabilities of the closure were found to surpass those of the 10-moment Gaussian closure. It was also found to predict interesting non-equilibrium phenomena, such as counter-gradient heat flux. The proposed implicit solver showed improved computational performance compared to the previously studied semi-implicit technique.