Miracle-dz/UniFAR-FTUs huggingface dataset:
AEROBIC TRAINING PROGRAM ON MUSCULAR STRENGTH AND AEROBIC CAPACITY IN INDIVIDUALS WITH TYPE 2 DIABETES

Taylor, J.D. The impact of a supervised strength and aerobic training program on muscular strength and aerobic capacity in individuals with type 2 diabetes. J. Strength Cond. Res. 21(3):824–830. 2007. —An intervention in the clinical management of individuals with type 2 diabetes is strength and aerobic training. Limited research has been conducted that investigates the effect of a supervised strength and aerobic training program on muscular strength and aerobic capacity in people with type 2 diabetes. The purpose of this 1-group repeated-measures–designed study was to investigate the impact of a supervised strength and aerobic training program on muscular strength and aerobic capacity in subjects with type 2 diabetes. Thirteen subjects with type 2 diabetes completed the training program. Subjects met the American Diabetes Association diagnostic criteria for type 2 diabetes. For each subject, muscular strength (estimated 1 repetition maximum) and aerobic capacity (estimated maximal oxygen uptake) were measured before and after a supervised strength and aerobic training program as well as during a 6-week follow-up. Repeated-measures analysis of variance was used to compare muscular strength and aerobic capacity between pretesting, posttesting, and follow-up testing periods. Significant improvements in muscular strength (p 0.01) and aerobic capacity (p 0.01) were found during posttesting and follow-up testing, as compared to pretesting measures. Yet a significant loss in muscular strength (p 0.01) and no significant change in aerobic capacity (p 0.05) were found during follow-up testing, as compared to posttesting measures. This study indicates that a supervised strength and aerobic training program can significantly improve muscular strength and aerobic capacity in people with type 2 diabetes. Yet improvements in muscular strength due to training will not be maintained if individuals with type 2 diabetes do not adhere to a continuous training program. In addition, aerobic capacity can be improved with training, but aerobic capacity will not continue to improve if people with type 2 diabetes are not compliant with a continuous training program.
Forage plants in Daloa city livestock market: specific diversity, market practices and economic land

— The sale of forage is little known to the majority of people in Côte d'Ivoire. The target of the study is to identify the forage species marketed in the livestock markets of Daloa and to estimate the financial profitability of this activity in the socio-economic life of the actors of the sector. Semi-structured surveys were conducted from September to December 2018 among 45 vendors in the forage marketing chain. Nine forage species divided into five genera and four families were identified. The study showed that these plants come from the non-agglomerated areas of the city, fallows and old plantations. The main species are f forage Moraceae, especially Ficus exasperata, highly sought after by customers. The average selling price of a forage species boot is around 100 to 150 FCFA. The estimated average daily financial income per player is FCFA 750 and varies between 18,750 to FCFA 37,500 per month for a monthly average of FCFA 26,125 and oscillate between 225,000 to FCFA 450,000 for an annual average of 313,500 FCFA. However, although the harvesting and sale of forage trees is a pathway generating substantial income, it is a source of degradation of plant formations already overexploited in Côte d'Ivoire. The study of commercialized forage plants revealed nine (9) species belonging to five (5) genera and four (4) families. Studies have been conducted in some cities in d'Ivoire and other in African countries on the sale of forage plants. In Mali, [8] and in Niger, [9] identified respectively 30 forage species belonging to 7 families in twenty-one markets in twenty-one markets and 34 species (14 families) in twenty-two points of sale in two cities. In Ivory Coast, [10] inventoried 37 forage species in 3 communes of the city of Abidjan. The number of species recorded is smaller and can be explained by of outlets and the cities where the surveys took place. Indeed, our investigations took place in city Daloa alone and only in three markets. study shows that the confirms especially and in But, it differs from those of 93 percent the the men and percent women. The results of the surveys show that the supply of the three livestock markets for forage mainly provided by the periurban of the Vavoua-Daloa and Issia-Daloa axes of the city of Daloa. Surveys indicate that the Gonaté-Daloa axis provides more than 55.89 percent of woody forage from fallows, old plantations and islands of natural formations.
Smooth circle actions on $S^7$ with unbounded periods and non-linearizable multicentres

We will give an example of a smooth free action of $S^1=U(1)$ on $S^7$ whose orbits have unbounded lenghts (equivalently: unbounded periods). As an application of this example we construct a $C^{\infty}$ vector field $X$, defined in a neighbourhood $U$ of $0 \in \mathbb{R}^8$, such that: $U-\{0\}$ is foliated by closed integral curves of $X$, the differential $DX(0)$ at $0$ defines a $1$-parameter group of nondegenerate rotations and $X$ is {\it not} orbitally equivalent to its linearization. This proves in the $C^{\infty}$ category that the classical Poincar\'e Centre Theorem, true for planar nondegenerate centres, is not generalizable to multicentres.
Robust Fault Detection for discrete-time Markovian Jump Systems with mode-dependent time-delays

This paper investigates a fault detection problem for a class of discrete-time Markovian jump systems with norm-bounded uncertainties and mode-dependent time-delays. Attention is focused on constructing the residual generator based on the filter of which its parameters matrices are dependent on the system mode, that is, the fault detection filter is a Markovian jump system as well. The design of fault detection filter is reduced to H-infinity filtering problem by using H-infinity control theory, which can guarantee the difference between the residual and the fault (or, more generally weighted fault) as small as possible in the context of enhancing the robustness of residual to modeling errors, control inputs and unknown inputs. Sufficient condition for the existence of the above filters is established by means of linear matrix inequalities, which can be readily solved by using standard numerical software. A numerical example is given to illustrate the feasibility of the proposed method.
Active Learning for Deterministic Bottom-up Nominal Tree Automata

Nominal set plays a central role in a group-theoretic extension of finite automata to those over an infinite set of data values. Moerman et al. proposed an active learning algorithm for nominal word automata with the equality symmetry. In this paper, we introduce deterministic bottom-up nominal tree automata (DBNTA), which operate on trees whose nodes are labelled with elements of an orbit finite nominal set. We then prove a Myhill-Nerode theorem for the class of languages recognized by DBNTA and propose an active learning algorithm for DBNTA. The algorithm can deal with any data symmetry that admits least support, not restricted to the equality symmetry and/or the total order symmetry. To prove the termination of the algorithm, we define a partial order on nominal sets and show that there is no infinite chain of orbit finite nominal sets with respect to this partial order between any two orbit finite sets.
Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System

Although the smart camera parking system concept has existed for decades, a few approaches have fully addressed the system's scalability and reliability. As the cornerstone of a smart parking system is the ability to detect occupancy, traditional methods use the classification backbone to predict spots from a manual labeled grid. This is time-consuming and loses the system's scalability. Additionally, most of the approaches use deep learning models, making them not error-free and not reliable at scale. Thus, we propose an end-to-end smart camera parking system where we provide an autonomous detecting occupancy by an object detector called OcpDet. Our detector also provides meaningful information from contrastive modules: training and spatial knowledge, which avert false detections during inference. We benchmark OcpDet on the existing PKLot dataset and reach competitive results compared to traditional classification solutions. We also introduce an additional SNU-SPS dataset, in which we estimate the system performance from various views and conduct system evaluation in parking assignment tasks. The result from our dataset shows that our system is promising for real-world applications.
DICE: Data-Efficient Clinical Event Extraction with Generative Models

Event extraction for the clinical domain is an under-explored research area. The lack of training data along with the high volume of domain-specific terminologies with vague entity boundaries makes the task especially challenging. In this paper, we introduce DICE, a robust and data-efficient generative model for clinical event extraction. DICE frames event extraction as a conditional generation problem and introduces a contrastive learning objective to accurately decide the boundaries of biomedical mentions. DICE also trains an auxiliary mention identification task jointly with event extraction tasks to better identify entity mention boundaries, and further introduces special markers to incorporate identified entity mentions as trigger and argument candidates for their respective tasks. To benchmark clinical event extraction, we compose MACCROBAT-EE, the first clinical event extraction dataset with argument annotation, based on an existing clinical information extraction dataset MACCROBAT. Our experiments demonstrate state-of-the-art performances of DICE for clinical and news domain event extraction, especially under low data settings.
Error Parity Fairness: Testing for Group Fairness in Regression Tasks

The applications of Artificial Intelligence (AI) surround decisions on increasingly many aspects of human lives. Society responds by imposing legal and social expectations for the accountability of such automated decision systems (ADSs). Fairness, a fundamental constituent of AI accountability, is concerned with just treatment of individuals and sensitive groups (e.g., based on sex, race). While many studies focus on fair learning and fairness testing for the classification tasks, the literature is rather limited on how to examine fairness in regression tasks. This work presents error parity as a regression fairness notion and introduces a testing methodology to assess group fairness based on a statistical hypothesis testing procedure. The error parity test checks whether prediction errors are distributed similarly across sensitive groups to determine if an ADS is fair. It is followed by a suitable permutation test to compare groups on several statistics to explore disparities and identify impacted groups. The usefulness and applicability of the proposed methodology are demonstrated via a case study on COVID-19 projections in the US at the county level, which revealed race-based differences in forecast errors. Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.
Low-Gain Stabilizers for Linear-Convex Optimal Steady-State Control

We consider the problem of designing a feedback controller which robustly regulates an LTI system to an optimal operating point in the presence of unmeasured disturbances. A general design framework based on so-called optimality models was previously put forward for this class of problems, effectively reducing the problem to that of stabilization of an associated nonlinear plant. This paper presents several simple and fully constructive stabilizer designs to accompany the optimality model designs from [1]. The designs are based on a low-gain integral control approach, which enforces time-scale separation between the exponentially stable plant and the controller. We provide explicit formulas for controllers and gains, along with LMI-based methods for the computation of robust/optimal gains. The results are illustrated via an academic example and an application to power system frequency control.
Minority electron mobilities in GaAs, In0.53Ga0.47As, and GaAS0.50Sb0.50 calculated within an ensemble Monte Carlo model

The minority electron transport is crucial for the performances of heterojunction bipolar transistors (HBTs). Among the III-V semiconductors family, GaAsSb exhibits several advantages making it a serious candidate for the new generation of high speed HBTs. However, this alloy suffers from a lack of experimental studies. Properties such as electronic structure and charge carrier mobilities are of prime importance in theoretical device operation calculations. In this article, we investigate the minority electron mobilities by means of an ensemble Monte Carlo modeling including polar optical phonon–plasmon coupling through a self-consistent dielectric function. Realistic electronic structures are provided by a nonlocal empirical pseudopotential method. It is found that GaAs0.50Sb0. 50 does not show a mobility enhancement for very high acceptor doping levels unlike GaAs and Ga0.47In0. 53As.
Domain Knowledge in A*-Based Causal Discovery

Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. While most previous approaches to causal discovery have implicitly assumed that no expert domain knowledge is available, practitioners can often provide such domain knowledge from prior experience. Recent work has incorporated domain knowledge into constraint-based causal discovery. The majority of such constraint-based methods, however, assume causal faithfulness, which has been shown to be frequently violated in practice. Consequently, there has been renewed attention towards exact-search score-based causal discovery methods, which do not assume causal faithfulness, such as A*-based methods. However, there has been no consideration of these methods in the context of domain knowledge. In this work, we focus on efficiently integrating several types of domain knowledge into A*-based causal discovery. In doing so, we discuss and explain how domain knowledge can reduce the graph search space and then provide an analysis of the potential computational gains. We support these findings with experiments on synthetic and real data, showing that even small amounts of domain knowledge can dramatically speed up A*-based causal discovery and improve its performance and practicality.
Stereo Superpixel Segmentation via Decoupled Dynamic Spatial-Embedding Fusion Network

Stereo superpixel segmentation aims at grouping the discretizing pixels into perceptual regions through left and right views more collaboratively and efficiently. Existing superpixel segmentation algorithms mostly utilize color and spatial features as input, which may impose strong constraints on spatial information while utilizing the disparity information in terms of stereo image pairs. To alleviate this issue, we propose a stereo superpixel segmentation method with a decoupling mechanism of spatial information in this work. To decouple stereo disparity information and spatial information, the spatial information is temporarily removed before fusing the features of stereo image pairs, and a decoupled stereo fusion module (DSFM) is designed to handle the stereo features alignment as well as occlusion problems. Moreover, since the spatial information is vital to superpixel segmentation, we further design a dynamic spatiality embedding module (DSEM) to re-add spatial information, and the weights of spatial information will be adaptively adjusted through the dynamic fusion (DF) mechanism in DSEM for achieving a finer segmentation. Comprehensive experimental results demonstrate that our method can achieve the state-of-the-art performance on the KITTI2015 and Cityscapes datasets, and also verify the efficiency when applied in salient object detection on NJU2K dataset. The source code will be available publicly after paper is accepted.
Domestic sound event detection by shift consistency mean-teacher training and adversarial domain adaptation

Semi-supervised learning and domain adaptation techniques have drawn increasing attention in the field of domestic sound event detection thanks to the availability of large amounts of unlabeled data and the relative ease to generate synthetic strongly-labeled data. In a previous work, several semi-supervised learning strategies were designed to boost the performance of a mean-teacher model. Namely, these strategies include shift consistency training (SCT), interpolation consistency training (ICT), and pseudo-labeling. However, adversarial domain adaptation (ADA) did not seem to improve the event detection accuracy further when we attempt to compensate for the domain gap between synthetic and real data. In this research, we empirically found that ICT tends to pull apart the distributions of synthetic and real data in t-SNE plots. Therefore, ICT is abandoned while SCT, in contrast, is applied to train both the student and the teacher models. With these modifications, the system successfully integrates with an ADA network, and we achieve 47.2% in the F1 score on the DCASE 2020 task 4 dataset, which is 2.1% higher than what was reported in the previous work.
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market

In the modern capital market, the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model — the transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task task using DSE data. Recently, the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper aims to leverage these two effective techniques to discover forecasting ability on the volatile stock market of DSE. We deal with the historical daily and weekly data of eight specific stocks listed in DSE. Our experiments demonstrate promising results and acceptable root-mean-squared error on most of the stocks. We also compare the performance of our model with that of a well-known benchmark stock forecasting model called ARIMA and report satisfactory results.
Extract fundamental frequency based on CNN combined with PYIN

This paper refers to the extraction of multiple fundamental frequencies (multiple F0) based on PYIN, an algorithm for extracting the fundamental frequency (F0) of monophonic music, and a trained convolutional neural networks (CNN) model, where a pitch salience function of the input signal is produced to estimate the multiple F0. The implementation of these two algorithms and their corresponding advantages and disadvantages are discussed in this article. Analysing the different performance of these two methods, PYIN is applied to supplement the F0 extracted from the trained CNN model to combine the advantages of these two algorithms. For evaluation, four pieces played by two violins are used, and the performance of the models are evaluated accoring to the ﬂatness of the F0 curve extracted. The result shows the combined model outperforms the original algorithms when extracting F0 from monophonic music and polyphonic music.
ParaColorizer: Realistic Image Colorization using Parallel Generative Networks

Grayscale image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem makes it even more challenging since the outputs could be multi-modal. The learning-based methods currently in use produce acceptable results for straightforward cases but usually fail to restore the contextual information in the absence of clear figure-ground separation. Also, the images suffer from color bleeding and desaturated backgrounds since a single model trained on full image features is insufficient for learning the diverse data modes. To address these issues, we present a parallel GAN-based colorization framework. In our approach, each separately tailored GAN pipeline colorizes the foreground (using object-level features) or the background (using full-image features). The foreground pipeline employs a Residual-UNet with self-attention as its generator trained using the full-image features and the corresponding object-level features from the COCO dataset. The background pipeline relies on full-image features and additional training examples from the Places dataset. We design a DenseFuse-based fusion network to obtain the final colorized image by feature-based fusion of the parallelly generated outputs. We show the shortcomings of the non-perceptual evaluation metrics commonly used to assess multi-modal problems like image colorization and perform extensive performance evaluation of our framework using multiple perceptual metrics. Our approach outperforms most of the existing learning-based methods and produces results comparable to the state-of-the-art. Further, we performed a runtime analysis and obtained an average inference time of 24ms per image.
Structural and optical properties of micro-diamonds with SiV− color centers

Isolated, micro-metre sized diamonds are grown by micro-wave plasma chemical vapour deposition technique on Si(001) substrates. Each diamond is uniquely identified by markers milled in the Si substrate by Ga+ focused ion beam. The morphology and micro-grain structure analysis, indicates that the diamonds are icosahedral or bi-crystals. Icosahedral diamonds have higher (up to σh = 2.3 GPa), and wider distribution ( Δσh = 4.47 GPa) of hydrostatic stress built up at the micro-crystal grain boundaries, compared to the other crystals. The number and spectral shape of SiV− colour centres incorporated in the micro-diamonds (MDs) is analysed, and estimated by means of temperature dependent photoluminescence measurements, and Monte Carlo simulations. The Monte Carlo simulations indicates that the number of SiV− colour centres is a few thousand per MD.