Miracle-dz/UniFAR-FTUs huggingface dataset:
Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem

This paper addresses a MinMax variant of the Dubins multiple traveling salesman problem (mTSP). This routing problem arises naturally in mission planning applications involving fixed-wing unmanned vehicles and ground robots. We first formulate the routing problem, referred to as the one-in-a-set Dubins mTSP problem (MD-GmTSP), as a mixed-integer linear program (MILP). We then develop heuristic-based search methods for the MD-GmTSP using tour construction algorithms to generate initial feasible solutions relatively fast and then improve on these solutions using variants of the variable neighborhood search (VNS) metaheuristic. Finally, we also explore a graph neural network to implicitly learn policies for the MD-GmTSP using a learning-based approach; specifically, we employ an S-sample batch reinforcement learning method on a shared graph neural network architecture and distributed policy networks to solve the MD-GMTSP. All the proposed algorithms are implemented on modified TSPLIB instances, and the performance of all the proposed algorithms is corroborated. The results show that learning based approaches work well for smaller sized instances, while the VNS based heuristics find the best solutions for larger instances.
Short-Term Traffic Flow Prediction Method with Multiple Factors and Deep Learning

The growing complexity of the transport system has led to a surge in traffic pressure, accidents and a deteriorating traffic environment. Real-time and accurate traffic flow forecasting capabilities are required to achieve timely traffic guidance, improve road safety and travel efficiency, and enhance the traffic environment. One of the reasons for the difficulty in predicting traffic flows is that traffic conditions are highly susceptible to a variety of factors such as weather and traffic accidents, so there is a need for a method of predicting traffic flows that incorporates a variety of influences. This paper proposes that weather-related data, traffic incident effects and time-series data form a multidimensional factor to accurately predict short-time traffic flows. This paper combines the advantages of Long Short-Term Memory Network(LSTM) in multidimensional data processing with the powerful past and future information extraction capability of Bi-directional Gated Recurrent Unit(Bi-GRU), while introducing an attention mechanism to better capture the impact of different moments on the prediction results, and proposes a Multidimensional Factor Fusion Network(MFFN). The model is able to take full account of multiple factor influences and multiplex data to provide highly accurate forecasting results.
On Locating Homoclinic and Heteroclinic Orbits

In earlier papers (see [FD2] and the references there) we have developed a numerical method and derived error estimates for the computation of branches of heteroclinic orbits for a system of autonomous ordinary differential equations in Rn. The idea of the method is to reduce a boundary value problem on the real line to a boundary value problem on a finite interval by using a local (linear or higher order) approximation of the stable and unstable manifolds. A practical limitation for the computation of homoclinic and heteroclinic orbits has been the difficulty in obtaining starting orbits. Typically these were obtained from a closed form solution or via a homotopy from a known solution. Here we consider extensions of our algorithm which allow us to obtain starting orbits in a more systematic way using successive homotopies. As an application, we consider computation of homoclinic orbits in a singular perturbation problem. Keywords: heteroclinic and homoclinic orbits, continuation methods, singular perturbation, numerical analysis
A Divergence Median-based Geometric Detector with A Weighted Averaging Filter

To overcome the performance degradation of the classical fast Fourier transform (FFT)-based constant false alarm rate detector with the limited sample data, a divergence median-based geometric detector on the Riemannian manifold of Heimitian positive definite matrices is proposed in this paper. In particular, an autocorrelation matrix is used to model the correlation of sample data. This method of the modeling can avoid the poor Doppler resolution as well as the energy spread of the Doppler filter banks result from the FFT. Moreover, a weighted averaging filter, conceived from the philosophy of the bilateral filtering in image denoising, is proposed and combined within the geometric detection framework. As the weighted averaging filter acts as the clutter suppression, the performance of the geometric detector is improved. Numerical experiments are given to validate the effectiveness of our proposed method.
Autonomic and vascular function testing in collegiate athletes following SARS-CoV-2 infection: an exploratory study

Introduction: Recent studies suggest that SARS-CoV-2 infection alters autonomic and vascular function in young, otherwise healthy, adults. However, whether these alterations exist in young competitive athletes remains unknown. This study aimed to assess the effects of COVID-19 on cardiac autonomic control and vascular function in collegiate athletes who tested positive for COVID-19, acknowledging the limitations imposed by the early stages of the pandemic. Methods: Sixteen collegiate athletes from various sports underwent a battery of commonly used autonomic and vascular function tests (23 ± 9, range: 12–44 days post-infection). Additionally, data from 26 healthy control participants were included. Results: In response to the Valsalva maneuver, nine athletes had a reduced early phase II blood pressure response and/or reduced Valsalva ratio. A depressed respiratory sinus arrhythmia amplitude was observed in three athletes. Three athletes became presyncopal during standing and did not complete the 10-min orthostatic challenge. Brachial artery flow-mediated dilation, when allometrically scaled to account for differences in baseline diameter, was not different between athletes and controls (10.0% ± 3.5% vs. 7.1% ± 2.4%, p = 0.058). Additionally, no differences were observed between groups when FMD responses were normalized by shear rate (athletes: 0.055% ± 0.026%/s-1, controls: 0.068% ± 0.049%/s-1, p = 0.40). Discussion: Few atypical and borderline responses to autonomic function tests were observed in athletes following an acute SARS-CoV-2 infection. The most meaningful autonomic abnormality being the failure of three athletes to complete a 10-min orthostatic challenge. These findings suggest that some athletes may develop mild alterations in autonomic function in the weeks after developing COVID-19, while vascular function is not significantly impaired.
A Luminance Compensation Method Using Optical Sensors with Optimized Memory Size for High Image Quality AMOLED Displays

This paper proposes a luminance compensation method using optical sensors to achieve high luminance uniformity of active matrix organic light-emitting diode (AMOLED) displays. The proposed method compensates for the non-uniformity of luminance by capturing the luminance of entire pixels and extracting the characteristic parameters. Data modulation using the extracted characteristic parameters is performed to improve luminance uniformity. In addition, memory size is optimized by selecting an optimal bit depth of the extracted characteristic parameters according to the trade-off between the required memory size and luminance uniformity. To verify the proposed compensation method with the optimized memory size, a 40-inch 1920x1080 AMOLED display with a target maximum luminance of 350 cd/m2 is used. The proposed compensation method considering a 4σ range of luminance reduces luminance error from ± 38.64%, ± 36.32%, and ± 43.12% to ± 2.68%, ± 2.64%, and ± 2.76% for red, green, and blue colors, respectively. The optimal bit depth of each characteristic parameter is 6-bit and the total required memory size to achieve high luminance uniformity is 74.6 Mbits.
On the Exploitation of Time-Space Priors for AI-Assisted Biomedical Imaging and Follow-Up

A novel Artificial Intelligence (AI)-assisted inverse scattering (IS) methodology is proposed for the monitoring and follow-up diagnosis of brain strokes. The developed technique leverages the high computational efficiency enabled by the System-by-Design (SbD) paradigm and exploits both a-priori (spatial) information on the imaged patient and acquired (temporal) information retrieved from the reconstructions at a previous instant in order to provide accurate, robust, and reliable images of the pathology.
Experimental Study on Groundwater Dewatering and Recharge of Deep Foundation Pit of a Subway Station in Jinan Water-Rich Area

A large amount of groundwater dewatering is beneficial to the foundation pit construction, but it will cause surface collapse, waste groundwater resources and damage the groundwater environment. In order to maintain the stability of groundwater level and the safety of surrounding buildings in the construction of deep foundation pit, the combination of dewatering and recharge is adopted. Taking the construction of deep foundation pit of a subway station in Jinan water-rich area as an example, the theoretical model of recharge quantity, stratum parameters and groundwater level was obtained through the backward derivation of the precipitation calculation model. 78 dewatering wells and 78 recharge wells were designed, and the on-site dewatering and recharge teat was carried out. The test results show that the water level of a single dewatering well decreases sharply in the initial stage of precipitation, and then the depth drops gradually to a stable level, and finally maintains a fixed value. The water inflow of a single well ranges from 34.54~57.07 m³/d, and the groundwater level can be lowered to 1.53~2.79 m below the foundation pit floor. The recharge volume of a single well in the natural recharge state is 146.88~ 241.44 m3/d, and the higher the recharge pressure, the stronger the recharge capacity of the recharge well. Through field test research, it is found that the recharge capacity of single well of recharge well is much greater than the pumping capacity of single well of dewatering well, and 56 recharge wells can make the recharge rate reach 100%, which optimizes the dewatering and recharge design of deep foundation pit.
Association between Demographic Profile and Mental Health Needs of Children in Statutory Institutions of Care and Protection in Nairobi County, Kenya

Children bring into the justice system their past and current experiences both positive and negative. Despite this, studies show that child justice systems have limited mechanisms for screening and assessment of their psychosocial needs. This lack of internal audit mechanisms by agencies can contribute to worsening of the young person’s mental status. This study screened children in need of care and protection residing in child protection units, remand homes and rescue centres in Nairobi County. Participants constituted 32 female and nine (9) male children recruited from these institutions. The Kiswahili version of strengths and difficulties questionnaire was used to determine their psychosocial needs. A brief questionnaire was developed for use in identifying their social and demographic profiles. The results showed that 64.41 % of respondents had prior contacts with the child protection system. The children had high scores in emotional (M=5.41, SD=1.78) and peer problems (M=3.24, SD=1.97). The study found an association between the nature of the statutory institution the child was residing and hyperactivity problems (p=.0.034), ethnicity and conduct problems (0.009), and between family type and prosocial behaviors (p=0.001). The study findings point to the need for in depth assessments for all children entering statutory institutions and development of evidence-based treatment programs to address their psychosocial wellbeing
A new approach of graded damage modelling

To prevent the problem of spurious localisation in damage mechanics, it is necessary to control the damage gradient amplitude. The body is decomposed in three domains: the undamaged body where ( d = 0 ), the transition zone ( 0 + ≤ d ≤ 1 ) and the totally broken body ( d = 1 ). For the thick level set (TLS) model, damage is a function of the signed distance to the surface d = 0 + . In this article, we propose to control the damage gradient using a convex internal constraint. This point of view produces a new description of graded damage. Analytical solutions on spheres and cylinders under radial loading are given and discussed. For particular internal constraints, the TLS results are recovered.
Comparison of Flat SOM with Spherical SOM. A Case Study

Recently, several spherical self-organizing topographic maps have been introduced in the literature [1-4]. The maps are spherical or toroidal, and thus not bounded, as in the case of e.g. the traditional SOM algorithm and its many versions [5,6], and thus should not suffer from the border effect (border neurons are surrounded by less neurons). The emergence of these spherical maps raises a number of questions. Perhaps one of the most significant one is simply what advantage can one expect to gain from them as opposed to the traditional flat map model? To pursue a satisfactory answer to this fundamental question, the authors aim for a detailed comparison of models through visual and numerical analysis of a case study.
Quasi-Newton methods for Order-value optimization and value-at-risk calculations

The OVO (Order-Value Optimization) problem consists in the minimization of the order-value function Fp(x), defined by Fp(x) = fip(x)(x), where fi1(x)(x) ≤ . ≤ fim(x)(x). The functions f1, . , fm are defined on Ω ⊂ IR and p is an integer between 1 and m. When x is a vector of portfolio positions and fi(x) is the predicted loss under the scenario i, the order-value function is the discrete Valueat-Risk (VaR) function, which is largely used in risk evaluations. The OVO problem is continuous but nonsmooth. A Cauchy-like method with guaranteed convergence to points that satisfy a first order optimality condition was recently introduced by Andreani, Dunder ∗Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970 Campinas SP, Brazil. This author was supported by PRONEXOptimization 76.79. 1008-00, FAPESP (Grant 01-04597-4) and CNPq (Grant 301115/966). e-mail: andreani@ime.unicamp. br †Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970 Campinas SP, This author was supported by PRONEXOptimization 76.79. 1008-00, FAPESP (Grant 01-04597-4) and CNPq. Brazil. e-mail: martinez@ime.unicamp. br ‡Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970 Campinas SP, Brazil. This author was supported by PRONEX-Optimization 76.79. 1008-00 and FAPESP (Grant 02-00171-5). e-mail: otita@ime.unicamp. br §Department of Applied Mathematics, IMECC-UNICAMP, University of Campinas, CP 6065, 13081-970 Campinas SP, Brazil.This author was supported by PRONEX-Optimization 76.79. 1008-00 and FAPESP (Grant 02-14203-6). e-mail: yano@ime.unicamp. br
Dreambooth-based Image Generation Methods for Improving the Performance of CNN

With the continuous progress of digital medicine, how to improve the accuracy of Convolutional Neural Network (CNN) algorithm in the absence of sample size has become a difficult problem. This paper explores how to use the stable diffusion image generation model to generate training samples to improve the accuracy of the CNN image recognition model. In this study, the dreambooth algorithm was used to fine-tune the stable diffusion model and learn the features of brain tumour MRI images, and then the stable diffusion model is used to generate images, and the generated images are used to improve the accuracy of CNN image recognition model. This study first used the dreambooth algorithm to train brain MRI images. After debugging the parameters of the dreambooth algorithm, a relatively good version of the weight was selected. Then this study used the same diffusion model to generate images. After debugging the sampler and adjusting the number of training steps, many attempts were made to get a good sample image, so batch generation is performed. After batch selection of the generated samples, the training set is obtained. The images generated by stable diffusion are then added to the database. The experimental results show that when about 800 images are generated for training, the trained CNN recognition accuracy rate is 60%, which is 3% higher than that of a smaller training set.
Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery

Abstract. Reducing the spectral dimension of hyperspectral data without loss of information is an important process in hyperspectral imagery. By adding the collaborative reconstructive information in linear discriminant analysis (LDA), we propose a discriminant collaborative neighborhood preserving embedding (DCNPE) for feature extraction from hyperspectral images. In the proposed DCNPE, an l2-graph is constructed based on the collaborative representation (CR). The edge weights of graph are calculated by l2-norm minimization using all samples to represent the pointed sample. The proposed DCNPE method aims to find a projection, which can preserve the CR-based reconstruction relationship of data as well as maximize the class discrimination of the data. DCNPE inherits the advantages of both LDA and CR. It not only overcomes the reduced dimension limitation of LDA but also preserves the collaborative neighborhood relations of data through l2-graph. Experimental results on three real HSI datasets certify its effectiveness of dimensionality reduction by showing a better classification performance.
Numerical analysis of confined flow past permeable square cylinder with CE/SE method

The unsteady flow field past a two-dimensional permeable square cylinder was predicted by an updated CE/SE method. The periodic patterns of streamline and vortex shedding were obtained. The evolution of lift and drag coefficients were analysed. Results of 2D computations were compared with the experimental data. It found that the Reynolds number dominated the magnitude and frequency of the vorticity given porosity and Darcy number. It proved that the CE/SE method was an effective tool for predicting viscous flows in porous media.
The Enhancement of CO2 and CH4 Capture on Activated Carbon with Different Degrees of Burn-Off and Surface Chemistry

Activated carbon derived from longan seeds in our laboratory and commercial activated carbon are used to investigate the adsorption of methane (CH4) and carbon dioxide (CO2). The adsorption capacity for activated carbon from longan seeds is greater than commercial activated carbon due to the greater BET area and micropore volume. Increasing the degree of burn-off can enhance the adsorption of CO2 at 273 K from 4 mmol/g to 4.2 and 4.8 mmol/g at 1000 mbar without burn-off, to 19 and 26% with burn-off, respectively. This is because an increase in the degree of burn-off increases the surface chemistry or concentration of functional groups. In the investigation of the effect of the hydroxyl group on the adsorption of CO2 and CH4 at 273 K, it is found that the maximum adsorption capacity of CO2 at 5000 mbar is about 6.4 and 8 mmol/g for cases without and with hydroxyl groups contained on the carbon surfaces. The opposite behavior can be observed in the case of methane, this is due to the stronger electrostatic interaction between the hydroxyl group and carbon dioxide. The simulation results obtained from a Monte Carlo simulation method can be used to support the mechanism in this investigation. Iron oxide is added on carbon surfaces with different concentrations to reveal the effects of ferric compounds on the adsorption of CO2. Iron at a concentration of about 1% on the surface can improve the adsorption capacity. However, excessive amounts of iron led to a limited adsorption capacity. The simulation result shows similar findings to the experimental data. The findings of this study will contribute to the progress of gas separation technologies, paving the way for long-term solutions to climate change and greenhouse gas emissions.
Formulae for Sample Size, Power and Minimum Detectable Relative Risk in Medical Studies

The power calculation approach to the determination of sample size for comparing two independent means and two independent proportions in cross-sectional surveys, cohort studies, case-control studies, clinical trials and community trials is developed. These formulae are inverted to produce expressions for power given sample size and minimum detectable relative risk (or in the case of means, minimum detectable difference) given power and sample size. Although the basic methodology of the approach is well known it is shown that various approximations are necessary in the case of proportions, and the particular approximation used determines the formula obtained. Case-control studies require special formulae of their own. Allowance for unequal sample sizes in the groups to be compared is used throughout. A discussion of the procedures used by the statistical package INSTAT to calculate sample size is included.
Encoding Human Visual Perception Into Deep Hashing for Aerial Image Classification

Accurately calculating the labels of each high-resolution image is an unavoidable technique in remote sensing. In this article, we propose a novel image assortment model that personate each aerial image by optimally encoding a gaze shifting path (GSP). At the same time, wrong semantic model can get absent with it. More specifically, for each aerial image, we reference visually/semantically noticeable representational rogue interiors. To encode their analysis attributes, we mean a small graph comprise of spatially conterminous motivational wall, and extract GSPs on it by active literature algorithm rules. GSP can accurately capture humans perception over many aerial image areas when the notice senses are placed in each image. Subsequently, a double deep learning framework is proposed to intelligently exploit the semantics of these GSPs, with three attributes: label noises reduction, visual manner-unchanging semantics, and adaptive data chart updates are seamlessly integrated. The proposed framework can iteratively solved, with each graphlet re-form into a base. Finally, the GSP-compliant summaries in each aerial have shown the quantized vectors for visual understanding. To qualitatively and quantitatively assess how GSP affects information aerial image classification, we notice that the phantom copy of our progress classification is more accurate than its competitors, and the GSPs propagated by Alzheimer's patients are discriminative from those produced by typical observers, making the classification competitive.