Miracle-dz/UniFAR-FTUs huggingface dataset:
The physics in baseball

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A Deep Learning Approach for Automated Detection and Segmentation of Pressure Ulcers Using Infrared-Based Thermal Imaging

Pressure ulcers are ailments that require constant care and monitoring. They are painful and sometimes life-threatening. Evaluation of pressure ulcers is an important step towards treatment and healing. Presently, evaluation is carried out invasively using methods like PUSH and Bates-Jensen Wound Assessment Tool, that is not comfortable to the patient. Wounds present themselves with altered thermal profile as the activity of degrading or healing cause changes in the temperature of the affected skin/tissues. Infrared thermography is emerging as a viable medical imaging modality. The major upsides of infrared imaging are it being non-radiating and non-contact. Advances in the field of artificial intelligence, and specifically, deep learning, has widened the application domain of thermal imaging in healthcare. In this study, participants (n $=10$ ) with pre-existing pressure ulcers were recruited and imaged using a FLIR IR thermal camera. The acquired images were used to train an object detection model that successfully detected the existing pressure ulcers by drawing a bounding box over them. This allows the region of interest to contain the wound along with surrounding healthy skin for thermal reference. The developed model detected the pressure ulcers with a confidence level of 96-100% in the test images. The present study promises the potential application of IR-based thermal imaging in automated detection and segmentation of pressure ulcers.
Fall Detection System Based on Millimeter Wave Radar and Machine Learning

Nowadays, falling is a growing threat to the elderly. This paper combines millimeter wave radar technology, machine learning algorithm, wireless communication technique and cloud platform to realize a fall detection system. In this project, the millimeter wave radar is used to sample the human posture point cloud data, and we create a data set that consist of point of clouds of two different human poses. Random forest and BP neural network are used to train the fall detection model. The system will send the human posture point data to the trained model and realize the fall detection. Besides, the system will use 4G communication technology to transform the data to the web cloud platform. This web page serves as an warning function, which can report the acceleration, speed and other information. According to our experiments, the millimeter wave radar system that we built in this paper can effectively detect human point cloud, and can send human point cloud data packets to the recognition model to detect human falls. In machine learning part, both Random forest and BP neural network models show very strong robustness after repeated adjusting parameters. Random forest has the advantages of light weight and interpretability, which can reach 93% recognition accuracy. The recognition accuracy of BP neural network is even up to 95%, which is higher than other detection models in previous works. Besides, BP neural network model also has higher recall rate in the categories of human falls, which fully meets the requirements of this project.
Superlinear Convergence of a Minimax Method

To solve a minimax problem Han [1977b] suggested the use of quadratic programs to find search directions. If the matrices in the quadratic programs are positive definite, the method can be shown convergent globally. In this paper we study that for efficiency the matrices should also be good approximations to a certain convex combination of Hessians on some subspace. Therefore, we suggest Powell''s scheme [Powell 1977] for updating these matrices. By doing so, we can avoid computing Hessians. Meanwhile, the matrices maintain positive definiteness and Han''s global convergence theorems can apply. Besides, the convergence of the resulting method is superlinear, indeed.
Capacity of the Trapdoor Channel with Delayed Feedback

We show that the trapdoor channel’s capacity with delayed feedback of two time-instances is given by\begin{equation*}{\text{C}}_2^{{\text{fb}}} = {\log _2}(3/2). \end{equation*}This demonstrates that the feedback capacity degrades sharply even with a single time-instance delay of the channel outputs. The capacity result is established by showing that the delayed feedback capacity can be formulated as a capacity problem with instantaneous feedback and an extended state. Consequently, graph-based methods can be applied to obtain new computable upper and lower bounds on the capacity, which are shown to coincide for the trapdoor channel.
Matroidal Entropy Functions: Constructions, Characterizations and Representations

In this paper, we characterize matroidal entropy functions, i.e., entropy functions in the form h = log v • r, where v ≥ 2 is an integer and r is the rank function of a matroid M. By constructing the variable strength arrays of some matroid operations, we characterized matroidal entropy functions induced by regular matroids and some matroids with the same p-characteristic set as uniform matroid U2,4.
Intrusion Detection Model For Power Information Network Based On Multi-layer Attention Mechanism

As the construction work of energy Internet and new power system continues to advance, the scale of power information network continues to expand, and network attacks against power systems are becoming more and more frequent. Although the existing intrusion detection technologies can defend against some network intrusions and attacks, the detection efficiency and accuracy of related algorithms need to be improved. In this paper, we propose a multi-layer attention mechanism-based intrusion detection model for power information network, which divides feature extraction into two different attention layers at feature level and slice level, and combines recurrent neural networks to fully exploit the value of historical information and current input. Experiments prove that the intrusion detection model proposed in this paper improves the detection accuracy at a lower cost, which in turn improves the security of power information network.
Strong Data Processing Inequalities via Sums of Squares

A hierarchy of semidefinite programming relaxations is described which gives certified upper bounds on the strong data processing (SDPI) constant of a discrete channel. The relaxations rely on a combination of tools from approximation theory and sum-of-squares techniques. By leveraging the properties of rational Padé approximants, we prove that the hierarchy converges to the true SDPI constant. Numerical experiments are performed which verify that these relaxations are very accurate even at low levels of the hierarchy.
MPC Control Strategy for Autonomous Vehicles Driving in Roundabouts

The appearance of autonomous vehicles (AVs) in transportation has increased the attention of the scientific community to develop modern solutions for the control design of AVs in different traffic scenarios. In this paper a control method is proposed for the coordination of autonomous vehicles in roundabout scenarios. For collision avoidance and minimization of traveling time, a Model Predictive Control (MPC) with a centralized controller is introduced to calculate the traveling times of the vehicles. A presented algorithm determines velocity profiles for safety reasons and for the reduction of possible congestion. The operation of the proposed MPC method is tested and demonstrated in CarSim simulation environment.
Automatic Optimization of Volterra Equalizer With Deep Reinforcement Learning for Intensity-Modulated Direct-Detection Optical Communications

Volterra nonlinear equalizer (VNLE) is widely investigated for linear and nonlinear distortions compensation in optical communication systems. Despite the powerful equalization ability, the required high computation complexity limits its real-time implementation on the hardware. To simplify the complexity, the structure of the VNLE needs to be well optimized. Traditionally, manual optimization of the VNLE structure is blind, and traversal search is inefficient and may result in multiple structures. Therefore, the optimal VNLE structure with the best equalization performance and the lowest equalization complexity for the same performance are still unclear. In this paper, a novel search method named AutoVolterra is proposed to efficiently find the optimal structure of VNLE by using deep reinforcement learning to learn the functional relationship between structure parameters and optimization objectives. Experimental demonstration of 50-Gb/s PAM-4 signal transmission in a bandwidth-limited intensity modulation and direct detection system is performed to verify the effectiveness of AutoVolterra. Both AutoVolterra and greedy search are applied to Volterra feedforward equalizer, Volterra decision feedback equalizer and Volterra-Pruning. The bit error ratio and equalizer's complexity results suggest that the structure searched by AutoVolterra can optimize the equalization performance, and also significantly reduce the complexity, and improve the pruning quality of VNLE to assist real-time implementation on the hardware. Besides, AutoVolterra can also provide a reliable equalization performance benchmark and complexity benchmark to help us verify the actual capabilities of other algorithms.
Signaling for MISO Channels Under First- and Second-Moment Constraints

Consider a multiple-input single-output system, where the nonnegative, peak-limited inputs ${X_1}, \ldots ,{X_{{n_{\text{T}}}}} \in [0,\mathcal{A}]$ are subject to first- and second-moment sum-constraints on all antennas. The paper characterizes all probability distributions that can be induced for the "channel image," which is given by the inner product of the input vector with a given channel vector. Key to this result is the description of input vectors that achieve a given deterministic channel image with the smallest energy, where "energy" of an input vector refers to a weighted sum of its one- and two-norms. Minimum-energy input vectors have an interesting structure: depending on the desired channel image, some of the weakest antennas are silenced, and the remaining antennas are chosen according to a shifted and amplitude-constrained beamforming rule.
A Single-Phase Vienna Rectifier With Wide Output Voltage Range

The conventional single-phase Vienna rectifier has been applied in EV chargers with the advantages of a small inductor and high efficiency. However, it has a narrow output voltage regulation range and a large output voltage ripple amplitude. To resolve the above problems, a novel single-phase Vienna rectifier is proposed. The proposed rectifier adds two power switches and two diodes, which makes the output voltage to be controlled more flexible. By the relationship between the input voltage and the output voltage, the proposed rectifier divides three operation modes with the two-modulation strategy. As a result, it has a wider output voltage regulation range and a lower output voltage ripple amplitude. The theories of the topology and the modulation strategy of the proposed rectifier are explained in detail. The mathematical model is deduced and built up. The control strategy is analyzed and provided. The simulations and experiments are carried out to validate the advantages of the proposed rectifier.
ISTA-Net Model-driven Deep Unfolding Network for Hyperspectral and Multispectral Image Fusion

Hyperspectral images (HSI) with high spatial and spectral resolutions have many applications in astronautics, re-mote sensing, and so on. However, it is challenging to obtain HSI with existing imaging techniques due to hardware limitations. In most cases, high-resolution multispectral (HrMS) images or low-resolution hyperspectral (LrHS) images are obtained. Therefore, the fusion of HrMS images and LrHS images for HSI super-resolution has attracted widespread attention. In this paper, we propose a network denoted as a model-based deep unfolding net-work(DuFNet) for hyperspectral image super-resolution (HSSR) task with clear interpretability. Specifically, we integrate the ISTA-Net into a well-established fusion network that is MHF-Net to fully take advantage of the generalization of the ISTA-Net. Experimental results demonstrate that the proposed NAM-DuFNet outperforms existing state-of-the-art methods in terms of subjective and objective results.
Twenty Years of Automated Methods for Mapping Applications on CGRA

Coarse-Grained Reconfigurable Architectures (CGRAs) emerged about 30 years ago. The very first CGRAs were programmed manually. Fortunately, some compilation approaches appeared rapidly to automate the mapping process. Numerous surveys on these architectures exist. Other surveys also gather the tools and methods, but none of them focuses on the mapping process only. This paper focuses solely on automated methods and techniques for mapping applications on CGRA and covers the last two decades of research. This paper aims at providing the terminology, the problem formulation, and a classification of existing methods. The paper ends with research challenges and trends for the future.
Smoothing on Dynamic Concurrency Throttling

Technology scaling has been allowing a growing number of cores in processors to satisfy the increasing demand of new applications, which need to process huge amounts of data in High-Performance Computing (HPC). However, considering that many parallel applications have limited scalability, not always activating the maximum number of available cores to execute an application will provide the best outcome in energy and performance (represented by the Energy-Delay Product, or EDP). Because of that, many works have already proposed different Dynamic Concurrency Throttling (DCT) techniques to adapt thread count as the application executes. However, dynamically tuning (i.e. increasing or decreasing) thread count implies in overheads related to caches warm-up and thread reallocation across the cores. This overhead may become very significant when thread count changes often during execution, which may overcome the benefits brought by DCT. This problem is further aggravated in Non-Uniform Memory Access (NUMA) systems since some cores are more distant, in terms of latency, than others. With that in mind, in this paper, we propose a smoothing-based strategy to minimize the thread count changes and, consequently, mitigate the aforementioned overhead. Our proposal is generic and aims further to improve the optimization results of any DCT technique. As case-study, we performed experiments on two multicore systems with nine well-known benchmarks, showing that our smoothing technique improves EDP results of offline and online state-of-the-art DCT techniques by up to 93% and 89% (both 22% on mean), respectively.
A Review on Therapeutic Effect of Kirtan Kriya Yoga

Kirtan Kriya is a meditation method which was mentioned in the Kundalini yoga . The main focus of paper was on previous research on Kirtan Kriya. The main objective was to identify the therapeutic effects of Kirtan Kriya. Sixteen research articles were found in google scholar. Improving cognition, reducing Alzheimer’s disease, reducing dementia, changing cerebral blood flow, improving sleep, improving memory, reducing stress, lowering anxiety, decreasing depression ,improving psychological well-being, down regulate inflammatory genes, up regulate immune system genes, improving insulin and glucose regulatory genes, increasing telomerase, reducing pain, help in PTSD (Post Traumatic Stress Syndrome) patients, help in Mild Cognitive Impairment patients, improving quality of life and improving concentration were the therapeutic effects which found in this study. Improving cognition is the best therapeutic action of Kirtan Kriya according to the findings.
Fundamental Limits of Multi-Sample Flow Graph Decomposition

The problem of decomposing a graph flow into a small set of paths has a wide range of applications, including transcriptome assembly and routing in data networks. A standard formulation is the sparsest flow decomposition problem, which is known to be NP-hard. In this work, we consider a multi-sample variant of this problem, motivated by the problem of identifying and quantifying proteoforms from mass spectrometry data, where multiple views of the graph can be obtained from multiple biological samples. We derive necessary conditions for the set of samples to unambiguously determine the ground truth set of paths, and we design an algorithm with matching sufficient conditions for a large class of problem instances, making our algorithm information optimal for this class of problem instances. The necessary conditions, combined with a probabilistic model for sample generation, yield a characterization of the number of samples needed for unambiguous recovery of the underlying paths. We analyze the algorithm’s performance on flow data simulated on peptide graphs from real mass spectrometry data.
An Aperiodic Prescribed Performance Control Scheme for Uncertain Nonlinear Systems

This paper proposes a low-complexity feedback control law that is updated aperiodically, in an event-triggered manner, and guarantees prescribed transient and steady state performance for uncertain nonlinear systems affine in the control. By prescribed performance, we mean that the closed-loop error trajectory converges to a predefined arbitrarily small residual set, with convergence rate no less than a certain prespecified value, having maximum overshoot less than a preassigned level. The proposed novel control design is performed in the transformed normalized error, and the triggering mechanism is extracted by guaranteed that these errors always lead to bounded closed loop signals. Moreover, the approach provides a scheme of designing and tuning the control parameters in order to achieve stabilization in a desire state in a pre-defined time T >0. The efficiency of the proposed approach is verified with numerical simulations in MATLAB.
New constructions of binary MDS array codes and locally repairable array codes

In this paper, we firstly present a new construction of binary maximum distance separable (MDS) array codes, from which some types of new MDS array codes of minimum distance 4 with array dimension (p−1)×(ℓ+2) can be deduced. Based on the construction, binary locally repairable array codes (LRACs) of minimum distance 4 are also explored, whose array dimension is (p−1)×2ℓ and column locality is ℓ − 1. Particularly, when 2 is a primitive root module p, a scheduled algorithm for syndrome computation of the LRACs is proposed, which converges to 2 XORs per data bit when ℓ approaches infinity.
Measurement of jet radial proﬁles in Pb–Pb collisions

The jet radial structure and particle transverse momentum ( p T ) composition within jets are presented in centrality-selected Pb–Pb collisions at √ s NN = 2 . 76 TeV. Track-based jets, which are also called charged jets, were reconstructed with a resolution parameter of R = 0 . 3 at midrapidity | η chjet | < 0 . 6 for transverse momenta p T , chjet = 30–120 GeV/ c . Jet–hadron correlations in relative azimuth and pseudorapidity space ( (cid:4) ϕ , (cid:4) η ) are measured to study the distribution of the associated particles around the jet axis for different p T , assoc -ranges between 1 and 20 GeV/ c . The data in Pb–Pb collisions are compared to reference distributions for pp collisions, obtained using embedded PYTHIA simulations. The number of high- p T associate particles (4 < p T , assoc < 20 GeV/ c ) in Pb–Pb collisions is found to be suppressed compared to the reference by 30 to 10%, depending on centrality. The radial particle distribution relative to the jet axis shows a moderate modiﬁcation in Pb–Pb collisions with respect to PYTHIA. High- p T associate particles are slightly more collimated in Pb–Pb collisions compared to the reference, while low- p T associate particles tend to be broadened. The results, which are presented for the ﬁrst time down to p T , chjet = 30 GeV/ c in Pb–Pb collisions, are compatible with both previous jet–hadron-related measurements from the CMS Collaboration and jet shape measurements from the ALICE Collaboration at higher p T , and add further support for
Modified combination of anti-thymocyte globulin (ATG) and post-transplant cyclophosphamide (PTCy) as compared with standard ATG protocol in haploidentical peripheral blood stem cell transplantation for acute leukemia

In haploidentical peripheral blood stem cell transplantation (haplo-PBSCT), the combination of anti-thymocyte globulin and post-transplant cyclophosphamide (ATG/PTCy) has a synergistic impact in preventing graft-versus-host disease (GvHD). However, little is known about the long-term consequences of the new combination approach. Our goal is to evaluate the efficacy of ATG/PTCy versus a standard ATG regimen by focusing at long-term outcomes in a more homogeneous group of patients. We retrospectively included 118 adult patients up to 60 years with acute leukemia who underwent haplo-PBSCT at our single institution, following the same myeloablative conditioning regimen. From 2010 to 2020, 78 patients received a modified combination of ATG (2.5 mg/kg/day, on days −3, −2, and −1) and PTCy (40 mg/kg/day on days +3 and +4) compared to 40 patients who had a standard ATG-based regimen (2.5 mg/kg/day from days −4 to −1) from 2008 to 2015. The median follow-up time for all patients was 5.36 years, respectively. The cumulative incidence (CI) of neutrophil and platelet engraftment, as well as CMV reactivation, did not differ statistically between the two groups. The CI of the acute GvHD of grades II–IV and III–IV and extensive chronic GvHD were considerably lower in the ATG/PTCy (34.6%, 8.97%, and 13.63%) than in the ATG cohort (57.5%, 30%, and 38.23%) as validated by multivariable modeling. Additionally, compared to the ATG arm, the ATG/PTCy was a hazard factor associated with a higher risk of relapse (HR = 2.23, p = 0.039). The probability of 5-year overall survival, disease-free survival, and GvHD-free relapse-free survival in the ATG/PTCy group (53.34%, 49.77%, and 36.04%) was comparable with the ATG group (47.5%, 42.5%, and 22.5%), respectively. Our finding suggested that a modified ATG/PTCy combination resulted in a lower risk of acute and chronic GvHD and a higher risk of relapse than the standard ATG-based protocol but had no effect on long-term outcomes. However, certain adjustments in the immunosuppression protocol are warranted to improve the outcome.