DVFO: Dynamic Voltage, Frequency Scaling and Workload Offloading for DNN Edge Inference

Due to edge device resource constraints and different characteristics of deep neural network (DNN) models, it is a big challenge to optimize DNN inference performance in terms of energy consumption and inference latency on edge devices. In addition to the dynamic voltage frequency scaling (DVFS) technique, the edge-cloud architecture provides a collaborative approach to efficient DNN inference. However, current edge-cloud collaborative inference methods have not optimized various compute resources on edge devices. Thus, we propose DVFO, a novel DVFS-enabled edge-cloud collaborative inference framework, which jointly optimize DVFS and offloading parameters via deep reinforcement learning (DRL). Specifically, DVFO automatically co-optimizes 1) CPU, GPU and memory frequencies of edge devices, and 2) feature maps to be offloaded to cloud servers. In addition, it leverages a thinking-while-moving concurrent mechanism to accelerate the DRL learning process, and a spatialchannel attention mechanism to extract DNN feature maps of secondary importance for workload offloading. This approach improves energy efficiency and inference latency for different DNN models under various edge-cloud network conditions. Experimental results on different datasets show that DVFO reduces the average energy consumption by 33% compared to state-of-the-art schemes. Moreover, DVFO achieves up to 54% end-to-end inference latency reduction

Numerical Simulations for Large Deformation of Geomaterials Using Molecular Dynamics

From the microperspective, this paper presents a model based on a new type of noncontinuous theoretical mechanical method, molecular dynamics (MD), to simulate the typical soil granular flow. The Hertzian friction formula and viscous damping force are introduced in the MD governing equations to model the granular flow. To show the validity of the proposed approach, a benchmark problem of 2D viscous material flow is simulated. The calculated final flow runout distance of the viscous material agrees well with the result of constrained interpolated profile (CIP) method as reported in the literature. Numerical modeling of the propagation of the collapse of three-dimensional axisymmetric sand columns is performed by the application of MD models. Comparison of the MD computational runout distance and the obtained distance by experiment shows a high degree of similarity. This indicates that the proposed MD model can accurately represent the evolution of the granular flow. The model developed may thus find applications in various problems involving dense granular flow and large deformations, such as landslides and debris flow. It provides a means for predicting fluidization characteristics of soil large deformation flow disasters and for identification and design of appropriate protective measures

Investigation of temperature stress tolerance in Arabidopsis STTM165/166 using electrophysiology and RNA-Seq

Plant electrical signals have been shown to be generated in response to various environmental stresses, but the relationship between these signals and stress tolerance is not well understood. In this study, we used the Arabidopsis STTM165/166 mutant, which exhibits enhanced temperature tolerance, to examine this relationship. Surface recording techniques were utilized to compare the generation ratio and duration characteristics of electrical signals in the STTM165/166 mutant and wild type (WT). Patch-clamp recording was employed to assess ion channel currents, specifically those of calcium ions. The current intensity of the mutant was found to be lower than that of the WT. As calcium ions are involved in the generation of plant electrical signals, we hypothesized that the reduced calcium channel activity in the mutant increased its electrical signal threshold. RNA-Seq analysis revealed differential expression of AHA genes in the STTM165/166 mutant, which may contribute to the prolonged depolarization phenotype. Gene Ontology enrichment of differentially expressed genes (DEGs) identified associations between these DEGs and various stresses, including temperature, salt, and those related to the jasmonic acid and abscisic acid pathways. These findings provide experimental evidence for the use of plant electrical signals in characterizing stress tolerance and explore potential ion mechanisms through patch-clamp recording and DEG Gene Ontology analysis. They also emphasize the need for further research on the relationship between plant electrical signals and stress tolerance.Comment: 20 pages, 5 figure

BCEdge: SLO-Aware DNN Inference Services with Adaptive Batching on Edge Platforms

As deep neural networks (DNNs) are being applied to a wide range of edge intelligent applications, it is critical for edge inference platforms to have both high-throughput and low-latency at the same time. Such edge platforms with multiple DNN models pose new challenges for scheduler designs. First, each request may have different service level objectives (SLOs) to improve quality of service (QoS). Second, the edge platforms should be able to efficiently schedule multiple heterogeneous DNN models so that system utilization can be improved. To meet these two goals, this paper proposes BCEdge, a novel learning-based scheduling framework that takes adaptive batching and concurrent execution of DNN inference services on edge platforms. We define a utility function to evaluate the trade-off between throughput and latency. The scheduler in BCEdge leverages maximum entropy-based deep reinforcement learning (DRL) to maximize utility by 1) co-optimizing batch size and 2) the number of concurrent models automatically. Our prototype implemented on different edge platforms shows that the proposed BCEdge enhances utility by up to 37.6% on average, compared to state-of-the-art solutions, while satisfying SLOs

Temporal Knowledge Question Answering via Abstract Reasoning Induction

In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties. These difficulties often result in the generation of misleading or incorrect information, primarily due to their limited capacity to process evolving factual knowledge and complex temporal logic. In response, we propose a novel, constructivism-based approach that advocates for a paradigm shift in LLM learning towards an active, ongoing process of knowledge synthesis and customization. At the heart of our proposal is the Abstract Reasoning Induction ARI framework, which divides temporal reasoning into two distinct phases: Knowledge-agnostic and Knowledge-based. This division aims to reduce instances of hallucinations and improve LLMs' capacity for integrating abstract methodologies derived from historical data. Our approach achieves remarkable improvements, with relative gains of 29.7\% and 9.27\% on two temporal QA datasets, underscoring its efficacy in advancing temporal reasoning in LLMs. The code will be released at https://github.com/czy1999/ARI.Comment: 17 pages, 10 figure

A New Classification Network for Diagnosing Alzheimer’s Disease in class-imbalance MRI datasets

Automatic identification of Alzheimer’s Disease (AD) through magnetic resonance imaging (MRI) data can eectively assist to doctors diagnose and treat Alzheimer’s. Current methods improve the accuracy of AD recognition, but they are insufficient to address the challenge of small interclass and large intraclass dierences. Some studies attempt to embed patch-level structure in neural networks which enhance pathologic details, but the enormous size and time complexity render these methods unfavorable. Furthermore, several self-attention mechanisms fail to provide contextual information to represent discriminative regions, which limits the performance of these classifiers. In addition, the current loss function is adversely aected by outliers of class imbalance and may fall into local optimal values. Therefore, we propose a 3D Residual RepVGG Attention network (ResRepANet) stacked with several lightweight blocks to identify the MRI of brain disease, which can also trade o accuracy and flexibility. Specifically, we propose a Non-local Context Spatial Attention block (NCSA) and embed it in our proposed ResRepANet, which aggregates global contextual information in spatial features to improve semantic relevance in discriminative regions. In addition, in order to reduce the influence of outliers, we propose a Gradient Density Multiple-weighting Mechanism (GDMM) to automatically adjust the weights of each MRI image via a normalizing gradient norm. Experiments are conducted on datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle Flagship Study of Aging (AIBL). Experiments on both datasets show that the accuracy, sensitivity, specificity, and Area Under the Curve are consistently better than for state-of-the-art methods

Strain improvement of Trichoderma harzianum for enhanced biocontrol capacity: Strategies and prospects

In the control of plant diseases, biocontrol has the advantages of being efficient and safe for human health and the environment. The filamentous fungus Trichoderma harzianum and its closely related species can inhibit the growth of many phytopathogenic fungi, and have been developed as commercial biocontrol agents for decades. In this review, we summarize studies on T. harzianum species complex from the perspective of strain improvement. To elevate the biocontrol ability, the production of extracellular proteins and compounds with antimicrobial or plant immunity-eliciting activities need to be enhanced. In addition, resistance to various environmental stressors should be strengthened. Engineering the gene regulatory system has the potential to modulate a variety of biological processes related to biocontrol. With the rapidly developing technologies for fungal genetic engineering, T. harzianum strains with increased biocontrol activities are expected to be constructed to promote the sustainable development of agriculture

Inherent Redundancy in Spiking Neural Networks

Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SNNs are sparse firing, the analysis and optimization of inherent redundancy in SNNs have been largely overlooked, thus the potential advantages of spike-based neuromorphic computing in accuracy and energy efficiency are interfered. In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs. We argue that the redundancy is induced by the spatio-temporal invariance of SNNs, which enhances the efficiency of parameter utilization but also invites lots of noise spikes. Further, we analyze the effect of spatio-temporal invariance on the spatio-temporal dynamics and spike firing of SNNs. Then, motivated by these analyses, we propose an Advance Spatial Attention (ASA) module to harness SNNs' redundancy, which can adaptively optimize their membrane potential distribution by a pair of individual spatial attention sub-modules. In this way, noise spike features are accurately regulated. Experimental results demonstrate that the proposed method can significantly drop the spike firing with better performance than state-of-the-art SNN baselines. Our code is available in \url{https://github.com/BICLab/ASA-SNN}.Comment: Accepted by ICCV202

Improved Glass Composition Analysis and Identification of Cultural Heritage with Limited Data Using Data Augmentation and CatBoost

Glass artifacts play a significant role in cultural heritage, offering valuable insights into ancient craftsmanship and cultural exchange. However, accurately analyzing and identifying ancient glass objects presents challenges due to limited data. This study aims to enhance the analysis and identification of glass compositions in cultural heritage by employing data augmentation techniques and the CatBoost prediction model. Firstly, data augmentation techniques are applied to expand the limited dataset, increasing sample quantity and diversity to improve the model’s generalization capability. The TOPSIS method is employed to comprehensively evaluate different augmentation factors and select the most suitable ones. Subsequently, the CatBoost prediction model is utilized, and the model parameters are optimized using a random search method to further enhance predictive performance. Experimental research on ancient glass artifacts validates the effectiveness and feasibility of the proposed methods. The final model demonstrates high predictive performance and a good fit on the training set, cross-validation set, and test set. For example, when predicting the sodium oxide content before weathering in glass artifacts, the average R-squared(R2) reaches 0.998, and the Mean Squared Error(MSE) is 0.003. These results signify the accurate prediction of glass artifact compositions and the model’s stable predictive capabilities across different datasets. Utilizing the predicted chemical composition, the identification of glass artifacts achieves a classification accuracy of 100%, indicating the excellence of the model. In conclusion, this study presents an improved approach for analyzing and identifying glass compositions by overcoming the limitations posed by limited data through data augmentation and the CatBoost model. These advancements provide valuable tools and methods for preserving and researching cultural heritage, contributing to the progress of ancient civilization studies and technological development

Nanoscale friction on MoS2/graphene heterostructures

Stacked hetero-structures of two-dimensional materials allow for a design of interactions with corresponding electronic and mechanical properties. We report structure, work function, and frictional properties of 1 to 4 layers of MoS2 grown by chemical vapor deposition on epitaxial graphene on SiC(0001). Experiments were performed by atomic force microscopy in ultra-high vacuum. Friction is dominated by adhesion which is mediated by a deformation of the layers to adapt the shape of the tip apex. Friction decreases with increasing number of MoS2 layers as the bending rigidity leads to less deformation. The dependence of friction on applied load and bias voltage can be attributed to variations in the atomic potential corrugation of the interface, which is enhanced by both load and applied bias. Minimal friction is obtained when work function differences are compensated