A possible method of using New Hafele-Keating experiment to probe Quantum gravitational perturbations

In case the gravity is quantized, then the differential rotation between the earth's inner core and the mantle should lead to a very weak quantum gravitational perturbation in the directions of east-west. Relative to the kinematic time dilation effect in flat space-time, in curved space-time (gravitational field) around the earth, the kinematic time dilation effect should has a small drift corresponding to the space-time curvature, based on the same reason, in east-west directions the quantum gravitational perturbation will extremely weakly strengthen the earth's gravitational field, and affect the kinematic time dilation effect very slightly. This may be recorded by some ultra-high precision flying clocks in New Hafele-Keating experiment, after a long period, it could be accumulated in to a measurable extra time deviation. In case the extra time deviation successfully extract from actual experimental results, it could be considered as an indirect measurement for quantum gravitational perturbation, the quantum gravitational perturbation may shows that gravity may has a quantized characteristic

Temporal Knowledge Sharing enable Spiking Neural Network Learning from Past and Future

Spiking neural networks have attracted extensive attention from researchers in many fields due to their brain-like information processing mechanism. The proposal of surrogate gradient enables the spiking neural networks to migrate to more complex tasks, and gradually close the gap with the conventional artificial neural networks. Current spiking neural networks utilize the output of all moments to produce the final prediction, which compromises their temporal characteristics and causes a reduction in performance and efficiency. We propose a temporal knowledge sharing approach (TKS) that enables the interaction of information between different moments, by selecting the output of specific moments to compose teacher signals to guide the training of the network along with the real labels. We have validated TKS on both static datasets CIFAR10, CIFAR100, ImageNet-1k and neuromorphic datasets DVS-CIFAR10, NCALTECH101. Our experimental results indicate that we have achieved the current optimal performance in comparison with other algorithms. Experiments on Fine-grained classification datasets further demonstrate our algorithm's superiority with CUB-200-2011, StanfordDogs, and StanfordCars. TKS algorithm helps the model to have stronger temporal generalization capability, allowing the network to guarantee performance with large time steps in the training phase and with small time steps in the testing phase. This greatly facilitates the deployment of SNNs on edge devices

FireFly v2: Advancing Hardware Support for High-Performance Spiking Neural Network with a Spatiotemporal FPGA Accelerator

Spiking Neural Networks (SNNs) are expected to be a promising alternative to Artificial Neural Networks (ANNs) due to their strong biological interpretability and high energy efficiency. Specialized SNN hardware offers clear advantages over general-purpose devices in terms of power and performance. However, there's still room to advance hardware support for state-of-the-art (SOTA) SNN algorithms and improve computation and memory efficiency. As a further step in supporting high-performance SNNs on specialized hardware, we introduce FireFly v2, an FPGA SNN accelerator that can address the issue of non-spike operation in current SOTA SNN algorithms, which presents an obstacle in the end-to-end deployment onto existing SNN hardware. To more effectively align with the SNN characteristics, we design a spatiotemporal dataflow that allows four dimensions of parallelism and eliminates the need for membrane potential storage, enabling on-the-fly spike processing and spike generation. To further improve hardware acceleration performance, we develop a high-performance spike computing engine as a backend based on a systolic array operating at 500-600MHz. To the best of our knowledge, FireFly v2 achieves the highest clock frequency among all FPGA-based implementations. Furthermore, it stands as the first SNN accelerator capable of supporting non-spike operations, which are commonly used in advanced SNN algorithms. FireFly v2 has doubled the throughput and DSP efficiency when compared to our previous version of FireFly and it exhibits 1.33 times the DSP efficiency and 1.42 times the power efficiency compared to the current most advanced FPGA accelerators

FireFly: A High-Throughput and Reconfigurable Hardware Accelerator for Spiking Neural Networks

Spiking neural networks (SNNs) have been widely used due to their strong biological interpretability and high energy efficiency. With the introduction of the backpropagation algorithm and surrogate gradient, the structure of spiking neural networks has become more complex, and the performance gap with artificial neural networks has gradually decreased. However, most SNN hardware implementations for field-programmable gate arrays (FPGAs) cannot meet arithmetic or memory efficiency requirements, which significantly restricts the development of SNNs. They do not delve into the arithmetic operations between the binary spikes and synaptic weights or assume unlimited on-chip RAM resources by using overly expensive devices on small tasks. To improve arithmetic efficiency, we analyze the neural dynamics of spiking neurons, generalize the SNN arithmetic operation to the multiplex-accumulate operation, and propose a high-performance implementation of such operation by utilizing the DSP48E2 hard block in Xilinx Ultrascale FPGAs. To improve memory efficiency, we design a memory system to enable efficient synaptic weights and membrane voltage memory access with reasonable on-chip RAM consumption. Combining the above two improvements, we propose an FPGA accelerator that can process spikes generated by the firing neuron on-the-fly (FireFly). FireFly is implemented on several FPGA edge devices with limited resources but still guarantees a peak performance of 5.53TSOP/s at 300MHz. As a lightweight accelerator, FireFly achieves the highest computational density efficiency compared with existing research using large FPGA devices

Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition

The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread deployment. To address the problem, we leverage Dynamic Vision Sensors (DVS) cameras to detect violent incidents and preserve privacy since it captures pixel brightness variations instead of static imagery. We introduce the Bullying10K dataset, encompassing various actions, complex movements, and occlusions from real-life scenarios. It provides three benchmarks for evaluating different tasks: action recognition, temporal action localization, and pose estimation. With 10,000 event segments, totaling 12 billion events and 255 GB of data, Bullying10K contributes significantly by balancing violence detection and personal privacy persevering. And it also poses a challenge to the neuromorphic dataset. It will serve as a valuable resource for training and developing privacy-protecting video systems. The Bullying10K opens new possibilities for innovative approaches in these domains.Comment: Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark

Risk factors for deep vein thrombosis in patients with pelvic or lower-extremity fractures in the emergency intensive care unit

IntroductionThis study aimed to investigate the incidence of deep vein thrombosis (DVT) in patients with pelvic or lower-extremity fractures in the emergency intensive care unit (EICU), explore the independent risk factors for DVT, and investigate the predictive value of the Autar scale for DVT in these patients.MethodsThe clinical data of patients with single fractures of the pelvis, femur, or tibia in the EICU from August 2016 to August 2019 were retrospectively examined. The incidence of DVT was statistically analyzed. Logistic regression was used to analyze the independent risk factors for DVT in these patients. The receiver-operating characteristic (ROC) curve was used to evaluate the predictive value of the Autar scale for the risk of DVT.ResultsA total of 817 patients were enrolled in this study; of these, 142 (17.38%) had DVT. Significant differences were found in the incidence of DVT among the pelvic fractures, femoral fractures, and tibial fractures (P < 0.001). The multivariate logistic regression analysis showed multiple injuries (OR = 2.210, 95% CI: 1.166–4.187, P = 0.015), fracture site (compared with tibia fracture group, femur fracture group OR = 4.839, 95% CI: 2.688–8.711, P < 0.001; pelvic fracture group OR = 2.210, 95% CI: 1.225–3.988, P = 0.008), and Autar score (OR = 1.198, 95% CI: 1.016–1.353, P = 0.004) were independent risk factors for DVT in patients with pelvic or lower-extremity fractures in the EICU. The area under the ROC curve (AUROC) of the Autar score for predicting DVT was 0.606. When the Autar score was set as the cutoff value of 15.5, the sensitivity and specificity for predicting DVT in patients with pelvic or lower-extremity fractures were 45.1% and 70.7%, respectively.DiscussionFracture is a high-risk factor for DVT. Patients with a femoral fracture or multiple injuries have a higher risk of DVT. In the case of no contraindications, DVT prevention measures should be taken for patients with pelvic or lower-extremity fractures. Autar scale has a certain predictive value for the occurrence of DVT in patients with pelvic or lower-extremity fractures, but it is not ideal

Microarray Analysis of Differentially Expressed Profiles of Circular RNAs in a Mouse Model of Intestinal Ischemia/Reperfusion Injury with and Without Ischemic Postconditioning

Background/Aims: Ischemic postconditioning (iPoC) represents a promising strategy to mitigate ischemia/reperfusion (I/R) injury of the intestine, yet the mechanisms of this treatment remain to be elucidated. Circular RNAs (circRNAs), a novel class of endogenous non-coding RNAs, have recently been recognized as important regulators of gene expression and pathological processes. Here, we aimed to investigate the expression patterns of circRNAs after intestinal I/R with and without iPoC and, furthermore, to explore the potential mechanisms of iPoC in relation to the differentially expressed circRNAs. Methods: The global circRNA and mRNA expression profiles in mouse intestinal mucosa were initially screened by microarray (n = 3 per group) and quantitative real-time PCR was used to validate the expression pattern of circRNAs and mRNAs. Bioinformatics analysis including Gene ontology, KEGG pathway analysis, microRNA binding sites identification and circRNA-miRNA-mRNA network construction were utilized for in-depth mechanism exploration. Results: There were 4 up- and 58 downregulated circRNAs as well as 322 up- and 199 downregulated mRNAs in the intestinal I/R group compared with the sham group, whereas compared with I/R, iPoC treatment significantly upregulated 12 circRNAs and 129 mRNAs and downregulated 21 circRNAs and 174 mRNAs. The expression levels of a randomly selected set of 6 circRNAs and 5 mRNAs were successfully validated by qRT-PCR. Through a systematic comparison of the direction of circRNA expression changes in all groups, we identified two circRNAs, circRNA_012412 and circRNA_016863, that may be closely associated with the protective mechanisms of iPoC. Finally, four possible circRNA_012412/circRNA_016863-miRNA-mRNA pathways were predicted, which may play important roles in endogenous protective signaling in iPoC. Conclusions: This study was the first to comprehensively delineate the expression profiles of circRNAs in a mouse model of intestinal I/R and iPoC and provides novel clues for understanding the mechanisms of iPoC against intestinal I/R injury

Development of a new marker system for identifying the complex members of the low-molecular-weight glutenin subunit gene family in bread wheat (Triticum aestivum L.)

Low-molecular-weight glutenin subunits (LMW-GSs) play an important role in determining the bread-making quality of bread wheat. However, LMW-GSs display high polymorphic protein complexes encoded by multiple genes, and elucidating the complex LMW-GS gene family in bread wheat remains challenging. In the present study, using conventional polymerase chain reaction (PCR) with conserved primers and high-resolution capillary electrophoresis, we developed a new molecular marker system for identifying LMW-GS gene family members. Based on sequence alignment of 13 LMW-GS genes previously identified in the Chinese bread wheat variety Xiaoyan 54 and other genes available in GenBank, PCR primers were developed and assigned to conserved sequences spanning the length polymorphism regions of LMW-GS genes. After PCR amplification, 17 DNA fragments in Xiaoyan 54 were detected using capillary electrophoresis. In total, 13 fragments were identical to previously identified LMW-GS genes, and the other 4 were derived from unique LMW-GS genes by sequencing. This marker system was also used to identify LMW-GS genes in Chinese Spring and its group 1 nulli–tetrasomic lines. Among the 17 detected DNA fragments, 4 were located on chromosome 1A, 5 on 1B, and 8 on 1D. The results suggest that this marker system is useful for large-scale identification of LMW-GS genes in bread wheat varieties, and for the selection of desirable LMW-GS genes to improve the bread-making quality in wheat molecular breeding programmes

New Insights into the Organization, Recombination, Expression and Functional Mechanism of Low Molecular Weight Glutenin Subunit Genes in Bread Wheat

The bread-making quality of wheat is strongly influenced by multiple low molecular weight glutenin subunit (LMW-GS) proteins expressed in the seeds. However, the organization, recombination and expression of LMW-GS genes and their functional mechanism in bread-making are not well understood. Here we report a systematic molecular analysis of LMW-GS genes located at the orthologous Glu-3 loci (Glu-A3, B3 and D3) of bread wheat using complementary approaches (genome wide characterization of gene members, expression profiling, proteomic analysis). Fourteen unique LMW-GS genes were identified for Xiaoyan 54 (with superior bread-making quality). Molecular mapping and recombination analyses revealed that the three Glu-3 loci of Xiaoyan 54 harbored dissimilar numbers of LMW-GS genes and covered different genetic distances. The number of expressed LMW-GS in the seeds was higher in Xiaoyan 54 than in Jing 411 (with relatively poor bread-making quality). This correlated with the finding of higher numbers of active LMW-GS genes at the A3 and D3 loci in Xiaoyan 54. Association analysis using recombinant inbred lines suggested that positive interactions, conferred by genetic combinations of the Glu-3 locus alleles with more numerous active LMW-GS genes, were generally important for the recombinant progenies to attain high Zeleny sedimentation value (ZSV), an important indicator of bread-making quality. A higher number of active LMW-GS genes tended to lead to a more elevated ZSV, although this tendency was influenced by genetic background. This work provides substantial new insights into the genomic organization and expression of LMW-GS genes, and molecular genetic evidence suggesting that these genes contribute quantitatively to bread-making quality in hexaploid wheat. Our analysis also indicates that selection for high numbers of active LMW-GS genes can be used for improvement of bread-making quality in wheat breeding