Miracle-dz/UniFAR-FTUs huggingface dataset:
On Nonuniform Convergence Rate Estimates in the Central Limit Theorem

We sharpen the upper bounds for the absolute constant in nonuniform convergence rate estimates in the central limit theorem for sums of independent identically distributed random variables possessing absolute moments of the order $2+\delta$ with some $0<\delta\le1$. In particular, it is demonstrated that under the existence of the third moment this constant does not exceed $18.2$. Also it is shown that the absolute constant in the estimates under consideration can be replaced by a function $C^*(|x|,\delta)$ of the argument $x$ of the difference of the prelimit and limit normal distribution functions for which a positive bounded nonincreasing majorant is found. Moreover, for $\delta=1$ this majorant is asymptotically exact (unimprovable) as $x\to\infty$ and sharpens the estimates due to Nikulin [preprint, arXiv:1004.0552v1 [math.ST], 2010] for all $x$. For the first time a similar result is obtained for the case $\delta\in(0,1)$. As a corollary, we obtain upper estimates for the Kolmogorov functions which .
A Cinnamate 4-HYDROXYLASE1 from Safflower Promotes Flavonoids Accumulation and Stimulates Antioxidant Defense System in Arabidopsis

C4H (cinnamate 4-hydroxylase) is a pivotal gene in the phenylpropanoid pathway, which is involved in the regulation of flavonoids and lignin biosynthesis of plants. However, the molecular mechanism of C4H-induced antioxidant activity in safflower still remains to be elucidated. In this study, a CtC4H1 gene was identified from safflower with combined analysis of transcriptome and functional characterization, regulating flavonoid biosynthesis and antioxidant defense system under drought stress in Arabidopsis. The expression level of CtC4H1 was shown to be differentially regulated in response to abiotic stresses; however, a significant increase was observed under drought exposure. The interaction between CtC4H1 and CtPAL1 was detected using a yeast two-hybrid assay and then verified using a bimolecular fluorescence complementation (BiFC) analysis. Phenotypic and statistical analysis of CtC4H1 overexpressed Arabidopsis demonstrated slightly wider leaves, long and early stem development as well as an increased level of total metabolite and anthocyanin contents. These findings imply that CtC4H1 may regulate plant development and defense systems in transgenic plants via specialized metabolism. Furthermore, transgenic Arabidopsis lines overexpressing CtC4H1 exhibited increased antioxidant activity as confirmed using a visible phenotype and different physiological indicators. In addition, the low accumulation of reactive oxygen species (ROS) in transgenic Arabidopsis exposed to drought conditions has confirmed the reduction of oxidative damage by stimulating the antioxidant defensive system, resulting in osmotic balance. Together, these findings have provided crucial insights into the functional role of CtC4H1 in regulating flavonoid biosynthesis and antioxidant defense system in safflower.
AI Technology for Anti-Aging: an Overview

With the accelerated aging of the global population, the research of anti-aging technology and its application has gradually become one of the hot issues in the biomedical field. In recent years, Artificial Intelligence technologies represented by machine learning, deep learning and cognitive computing, have provided unprecedented methods and tools for biomedical research, and brought breakthroughs to anti-aging, a comprehensive and cutting-edge research topic. This paper first discusses the current problems and challenges that need to be solved in the application of AI technology in anti-aging; then summarizes the current status of AI data research on basic anti-aging applications, analyzes and discusses the research and progress of AI technology in the frontier application areas such as 3D reconstruction of aging structures, aging biomarkers and anti-aging drug development; and finally provides an outlook on the future development trends.
Event-triggered-based fixed-time tracking control with prescribed performance for an underactuated vessel

In this paper, the trajectory tracking control approach for underactuated surface vessels with an off-diagonal inertia matrix is investigated. For the problem with off-diagonal matrix in the model parameters, the coordinate transformation is used to eliminate the challenges of vessel controller design. To make the control system has better tracking performance, a prescribed performance control method is used to design the controller. With the help of an event-triggered mechanism, the communication pressure in the tracking system is relieved. Based on the abovementioned works, the fixed-time stabilisation is considered in the control approach such that the system has fast convergence characteristics. The effectiveness of the proposed control approach is illustrated by implementing some simulations.
EXPONENTIAL STABILITY FOR STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPE WITH IMPULSIVE EFFECTS

This paper is concerned with the exponential stability of stochastic neural networks of neutral type with impulsive effects. By employing the Lyapunov functional and stochastic analysis, a new stability criterion for the stochastic neural network is derived in terms of linear matrix inequality. A numerical example is provided to show the effectiveness and applicability of the obtained result.
Generalized Nash Equilibria of Nonmonetized Noncooperative Games on Lattices

In this paper, we examine noncooperative games with both of the collection of strategies and the ranges of the utilities for the players are lattices, which are said to be nonmonetized. Then we extend the concept of Nash equilibria of noncooperative games to generalized Nash equilibria of nonmonetized noncooperative games. By applying some fixed point theorems in posets and by using the order preserving property of mappings, we prove an existence theorem of generalized Nash equilibria for nonmonetized noncooperative games.
Tunneling, conductance, and wavevector filtering through magnetic barriers in bilayer graphene

M. Ramezani Masir,1 P. Vasilopoulos,2,* and F. M. Peeters1,3,† 1Departement Fysica, Universiteit Antwerpen, Groenenborgerlaan 171, B-2020 Antwerpen, Belgium 2Department of Physics, Concordia University, Montreal, Quebec, Canada H3G 1M8 3Departamento de Física, Universidade Federal do Ceará, Caixa Postal 6030, Campus do Pici, 60455-760 Fortaleza, Ceará, Brazil Received 4 September 2008; published 14 January 2009
On the DAG Decomposition

In this paper, we propose an efficient algorithm to decompose a directed acyclic graph G into a minimized set of node-disjoint chains, which cover all the nodes of G. For any two nodes u and v on a chain, if u is above v then there is a path from u to v in G. The best algorithm for this problem up to now needs O(n) time, where n is the number of the nodes of G. Our algorithm, however, needs only O(n) time, where  is G’s width, defined to be the size of a largest node subset U of G such that for every pair of nodes x, y  U, there does not exist a path from x to y or from y to x. More importantly, by the existing algorithm, O(n) extra space (besides the space for G itself) is required to maintain the transitive closure of G to do the task while ours needs only O(n) extra space.
Compensatory dynamics drive grassland recovery from drought

Grasslands are expected to experience droughts of unprecedented frequency and magnitude in the future. Characterizing grassland responses and recovery from drought is therefore critical to predict the vulnerability of grassland ecosystems to climate change. Most previous studies have focused on ecosystem responses during drought while investigations of post‐drought recovery are rare. Few studies have used functional traits, and in particular bud or clonal traits, to explore the mechanisms underlying grassland responses to and recovery from drought. To address this issue, we experimentally imposed a four‐year drought in a C3‐dominated grassland in northeastern China and monitored recovery for 3 years post‐drought. We investigated the immediate and legacy effects of drought on total above‐ground net primary productivity (ANPP), ANPP of functional groups (rhizomatous grasses, bunch grasses and forbs), and how the legacy effects were driven by plant species diversity, clonal traits and vegetative traits. We found that drought progressively reduced total ANPP over the 4‐year period. The reductions in total ANPP in the first and third drought years were caused by the decrease in ANPP of bunch grasses only, while that of the second year was caused by declines in ANPP of bunch grasses and forbs, and the fourth year decline was linked to all three functional groups. The post‐drought recovery of ANPP, which occurred despite the continued loss of plant species diversity, was mainly driven by rapid recovery of rhizomatous and bunch grasses, which compensated for the slow response by forbs. The rapid post‐drought recovery of these grasses can be attributed to their relatively large, intact bud and shoot densities post‐drought, as well as the recovery of plant height and specific leaf area. The rapid recovery of grasses possibly restricted the growth and distribution of forbs, resulting in reduced forb ANPP and, consequently, lower species diversity during the recovery period. Synthesis. These results highlight the potential for positive legacy effects of drought on ANPP as well as the important and complementary roles of plant reproductive and vegetative traits in mediating ecosystem recovery from drought in a C3‐dominated grassland.
Masking, temporal integration and speech intelligibility in subjects with noise‐induced hearing loss

Three psychoacoustical measures of frequency analysis were obtained from ten normal‐hearing subjects and twelve subjects with noise‐induced hearing loss. First, thresholds of pulsed 500‐ and 4000‐Hz pure tones were measured in the presence of a continuous, 60 dB/Hz spectrum level white‐noise masker. Second, thresholds of pulsed, pure tones were measured in the presence of a continuous, 85‐dB SPL tonal masker of 500 and 4000 Hz. Third, the intensity required to mask pulsed, 500‐ and 4000‐Hz test signals at 10‐dB sensation level was measured with sinusoidal masker of five adjacent frequencies [psychoacoustical tuning curves]. We also measured thresholds for 10‐ and 1000‐ms duration signals of 500 and 4000 Hz as an indication of temporal integration. Two tests of speech intelligibility, one in a speech‐spectrum shaped noise and one in a speech‐babble background, were presented at a fixed intensity. Some hearing‐impaired subjects clearly display increased thresholds in white noise (larger critical ratios), pr.
Evolutionary and Genomic Diversity of True Polyploidy in Tetrapods

Simple Summary Polyploidization, or whole-genome duplication (WGD), represents a dramatic event in evolution. Although its occurrence is much rarer in animals than in plants, distinct WGDs characterize the stem lineages of vertebrates and teleosts. In tetrapods, true polyploids have been described in all major groups and include a wide range of genomic configurations and modes of reproduction. In this work, we provide a comprehensive report on the presence of different types of polyploidy in tetrapods, with a particular focus on its genomic, evolutionary, and ecological diversity. We also describe the main routes of the formation of neopolyploids and discuss the two competing hypotheses that consider polyploidy either as a major force in evolution or, mainly, as an evolutionary dead end. Abstract True polyploid organisms have more than two chromosome sets in their somatic and germline cells. Polyploidy is a major evolutionary force and has played a significant role in the early genomic evolution of plants, different invertebrate taxa, chordates, and teleosts. However, the contribution of polyploidy to the generation of new genomic, ecological, and species diversity in tetrapods has traditionally been underestimated. Indeed, polyploidy represents an important pathway of genomic evolution, occurring in most higher-taxa tetrapods and displaying a variety of different forms, genomic configurations, and biological implications. Herein, we report and discuss the available information on the different origins and evolutionary and ecological significance of true polyploidy in tetrapods. Among the main tetrapod lineages, modern amphibians have an unparalleled diversity of polyploids and, until recently, they were considered to be the only vertebrates with closely related diploid and polyploid bisexual species or populations. In reptiles, polyploidy was thought to be restricted to squamates and associated with parthenogenesis. In birds and mammals, true polyploidy has generally been considered absent (non-tolerated). These views are being changed due to an accumulation of new data, and the impact as well as the different evolutionary and ecological implications of polyploidy in tetrapods, deserve a broader evaluation.
Indoor Wi-Fi Fingerprint Localization Based on SDAE and MLP with Self-attention Mechanism

For received signal strength (RSS) fluctuations, multipath and data redundancy issues based on Wi-Fi fingerprinting, this paper proposes an indoor localization system (SSMLoc), which combines stacked denoising autoencoders (SDAE) and multi-layer perceptron (MLP) with self-attention mechanism (SAM). The system uses SDAE to generate a deep robust representation of RSS data, which is subsequently used to build a localized fingerprint database. A simplified SAM reassigns various weights to the internal properties of deep Wi-Fi fingerprints, allowing the MLP network to better capture the critical features of Wi-Fi fingerprints. The performance of the SSMLoc system using the public dataset UJIIndoorLoc is validated in this paper, and the experimental findings reveal that the system has great robustness and enhances positioning accuracy and stability.
AlGaAs Below Half Bandgap:. the Silicon of Nonlinear Optical Materials

Very few nonlinear optical materials are actually useful for high throughput all-optical devices. However, AlGaAs does satisfy all of the nonlinear optical figures of merit when used with photons of energy less than one half the semiconductor bandgap. Here we review our measurements of the pertinent nonlinear coefficients in waveguides and various device applications to all-optical switching in the communications band around 1550 nm.
Single breath‐hold three‐dimensional whole‐heart T2 mapping with low‐rank plus sparse reconstruction

The purpose of the current study was to develop and evaluate a three‐dimensional single Breath‐hOLd cardiac T2 mapping sequence (3D BOLT) with low‐rank plus sparse (L + S) reconstruction for rapid whole‐heart T2 measurement. 3D BOLT collects three highly accelerated electrocardiogram‐triggered volumes with whole‐heart coverage, all within a single 12‐heartbeat breath‐hold. Saturation pulses are performed every heartbeat to prepare longitudinal magnetization before T2 preparation (T2‐prep) or readout, and the echo time of T2‐prep is varied per volume for variable T2 weighting. Accelerated volumes are reconstructed jointly by an L + S algorithm. 3D BOLT was optimized and validated against gradient spin echo (GraSE) and a previously published approach (three‐dimensional free‐breathing cardiac T2 mapping [3DFBT2]) in both phantoms and human subjects (11 healthy subjects and 10 patients). The repeatability of 3D BOLT was validated on healthy subjects. Retrospective experiments indicated that 3D BOLT with 4.2‐fold acceleration achieved T2 measurements comparable with those obtained with fully sampled data. T2 measured in phantoms using 3D BOLT demonstrated good accuracy and precision compared with the reference (R2 > 0.99). All in vivo imaging was successful and the average left ventricle T2s measured by GraSE, 3DFBT2, and 3D BOLT were comparable and consistent for all healthy subjects (47.0 ± 2.3 vs. 47.7 ± 2.7 vs. 48.4 ± 1.8 ms) and patients (50.8 ± 3.0 vs. 48.6 ± 3.9 vs. 49.1 ± 3.7 ms), respectively. Myocardial T2 measured by 3D BOLT had excellent agreement with 3DFBT2 and there was no significant difference in mean, standard deviation, and coefficient of variation. 3D BOLT showed excellent repeatability (intraclass correlation coefficient: 0.938). The proposed 3D BOLT achieved whole‐heart T2 mapping in a single breath‐hold with good accuracy, precision, and repeatability on T2 measurements.
Does Cognitive Training Reduce Falls across Ten Years?: Data from the ACTIVE Trial

The purpose of this study was to examine the effect of cognitive training on the risk of experiencing a fall across 10 years. The study used data from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) randomized controlled trial. Older adults aged 65–94 were randomly assigned to speed of processing, memory, or reasoning training or to a no-contact control group (n = 2802). The experience of a fall in the prior two months was assessed at baseline and at 1, 2, 3, 5, and 10 years posttest. Cox proportional hazards explored group differences in the total sample, as well as group differences for participants classified as low risk (n = 2360) and high risk (n = 442) for future falls. The data were censored at the first reported fall postbaseline. After baseline, 983 (35.08%) participants across the full sample reported a fall. There were no significant effects of the training in the full sample or in the low-risk sample of participants. However, the participants at greater risk for future falls in the speed of processing training group were 31% less likely (HR = 0.69; 95% CI = 0.48, 0.998, p = 0.049) to experience a subsequent fall across ten years compared to the control group. Reasoning and memory training did not reduce a future fall in the high-risk sample. The speed of processing training reduced the risk of future falls across ten years in the high-risk participants. Future work should examine moderators and mediators of training in at-risk samples.
Changes in Potassium Current and BK Channel Protein Expression in Coronary Artery Smooth Muscle Cells of Spontaneously Hypertensive Rats

The objective of this study was to analyze the changes of potassium current (PC) and large conductance calcium activated potassium channel (BK) protein expression in coronary artery (CA) smooth muscle cells of spontaneously hypertensive rats. Eight healthy male spontaneously hypertensive rats (hypertension group) and 8 normal rats (Wistar group) were selected, and CA smooth muscle cells were isolated from the two groups. The PC of CA smooth muscle cells were recorded by patch clamp whole cell recording technique, and the BK currents and BK tail currents of CA smooth muscle cells were recorded and compared between the two groups. The BK α and BK β proteins expression in the two groups was detected by western blot. The average PC density of CA smooth muscle cells in hypertension group decreased to (68.72±5.14) % after the addition of 100nM IBTX, while that in Wistar group reduced to (33.21±1.76) % after the addition of 100nM IBTX. When the PC of coronary smooth muscle cells of hypertensive rats was + 120mV, + 110mV, + 100mV and + 90mV, the current density after 3mm4-AP inhibition was strikingly higher than that of rats in Wistar group. The tail current of BK channel was recorded by 4-AP inhibition, and the current density of hypertensive rats was dramatically higher than that of rats in Wistar group. There was no significant difference in BK channel BK α protein expression between the two groups. The expression level of BK β protein in hypertension group of rats was markedly higher than that in Wistar group. The BK channel current and protein of CA smooth muscle cells in spontaneously hypertensive rats were significantly higher than those in normal rats, suggesting that the diastolic function of smooth muscle cells was enhanced, which is of great significance to the protection of coronary circulation in hypertension.
Detection method of nickel foam foreign matter based on double-branch lightweight network

Real-time, accurate and rapid detection of defects in nickel foam is a challenging task and plays a crucial role in the quality control of nickel foam production. Currently, research on deep learning-based industrial defect detection usually uses large neural network models to pursue high accuracy, ignoring model complexity and hardware requirements. In order to reduce the application cost, the Detection method of nickel foam foreign matter based on double-branch lightweight network is proposed. First, a lightweight backbone convolutional network is designed to extract features, which reduces computational complexity while effectively extracting features. Then, atrous convolution is used to further optimize the model accuracy, increasing the receptive field of the convolution without losing information, and ensuring the quality of the extracted features. Finally, we use a dual-branch structure for segmentation and classification to dynamically query the detection results of defects. The experimental results on the nickel foam defect dataset show that our model is able to achieve high detection accuracy and single-sheet detection rate, and has strong competitive accuracy and lower detection rate compared with state-of-the-art detectors amount of calculation.
Design and implementation of FPGA-based hardware acceleration system for target detection

Since the application of target detection in convolutional neural networks is becoming more and more widespread, and the computational volume is increasing, the complexity of the network hierarchy and the memory requirements are gradually increasing, so according to the Field Programmable Gate Array (FPGA) parallel computing characteristics, using FPGA to accelerate the convolutional neural network hardware, can design a CPU+FPGA target detection hardware acceleration system. The system adopts the YOLOv2 target detection algorithm and divides the tasks according to the network structure of the algorithm, and at the same time, it quantifies the 32-bit floating-point numbers of the algorithm with 16-bit fixed points, optimizes the convolutional, pooling, and reordering layers of the algorithm, and finally designs the algorithm by using the HLS high-level synthesis. The system uses PYNQ-Z2 platform to implement the convolutional neural network YOLOv2, and the final system performance reaches 27.06GOPs (Giga Operations Per Second, 1 billion operations/s), and the power consumption is only 2.609W, compared to CPU (E5-2620V4), its performance is 6.58 times that of CPU, and the power consumption is only 3% of the CPU, which has certain advantages compared with current target detection systems.