Joint Computing Offloading and Resource Allocation for Classification Intelligent Tasks in MEC Systems

Mobile edge computing (MEC) enables low-latency and high-bandwidth applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for intelligent tasks in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum system performance. We focus on classification intelligence tasks and formulate an optimization problem that considers both the accuracy requirements of tasks and the parallel computing capabilities of MEC systems. To solve the optimization problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation, and compression offloading. We use convex optimization and successive convex approximation to derive closed-form expressions for the subcarrier allocation, offloading decisions, computing capacity, and compressed ratio. Based on our solutions, we design an efficient computing offloading and resource allocation algorithm for intelligent tasks in MEC systems. Our simulation results demonstrate that our proposed algorithm significantly improves the performance of intelligent tasks in MEC systems and achieves a flexible trade-off between system revenue and cost considering intelligent tasks compared with the benchmarks.Comment: arXiv admin note: substantial text overlap with arXiv:2307.0274

Phylloxera infestation and the uptake and distribution of 13C and 15N tracers in grape vines

In order to study the reason phylloxera (Daktulosphaira vitifolia Fitch) feeding on roots leads to decreased plant productivity, the uptake and distribution of 13C photosynthates and 15N in the grape vine 'Wuhe 8612' in response to phylloxera infestation were investigated. Phylloxera and grapevines cocultivated in pots were treated with 13CO2 and 15N-urea. The plant weight, nitrogen concentration and accumulation, 15N utilization efficiency, Nitrogen derived from fertilizer (Ndff%), and carbon isotope ratio (δ13C) of different organs were measured. Phylloxera infestation significantly reduced grape weight, shoot length, and N concentration and accumulation in different organs, whereas it increased the ratio between N content of the of roots and above-ground organs. Phylloxera infestation reduced leaf and root nitrogen 15N utilization efficiency, by 24 % and 14 %, respectively compared to controls. Labeled leaves of infested plants took up rather more 13C and 15N and exported a substantial amount of these nutrients to roots. Labeled roots took up rather more 15N and exported a small amount of these nutrients to upper leaves. This study found that phylloxera infestation reduced 13C and 15N uptake in leaves and roots, but increased N and photosynthates, which were mostly distributed to the roots, but also to the upper leaves. These factors together led to weak grape vine growth.

RTN: Reparameterized Ternary Network

To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed-up, memory saving with quantized activation and weights. We first bring up three omitted issues in extremely low-bit networks: the squashing range of quantized values; the gradient vanishing during backpropagation and the unexploited hardware acceleration of ternary networks. By reparameterizing quantized activation and weights vector with full precision scale and offset for fixed ternary vector, we decouple the range and magnitude from the direction to extenuate the three issues. Learnable scale and offset can automatically adjust the range of quantized values and sparsity without gradient vanishing. A novel encoding and computation pat-tern are designed to support efficient computing for our reparameterized ternary network (RTN). Experiments on ResNet-18 for ImageNet demonstrate that the proposed RTN finds a much better efficiency between bitwidth and accuracy, and achieves up to 26.76% relative accuracy improvement compared with state-of-the-art methods. Moreover, we validate the proposed computation pattern on Field Programmable Gate Arrays (FPGA), and it brings 46.46x and 89.17x savings on power and area respectively compared with the full precision convolution.Comment: To appear at AAAI-2

Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes

Robust autonomous driving requires agents to accurately identify unexpected areas in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Previous effort usually resorts to uncertainty estimation and sample synthesis from classification tasks, which ignore the context information and sometimes requires auxiliary datasets with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an energy-guided self-supervised frameworks for anomaly segmentation, which optimizes an anomaly head by maximizing the likelihood of self-generated anomaly pixels. To this end, we design two estimators for anomaly likelihood estimation, one is a simple task-agnostic binary estimator and the other depicts anomaly likelihood as residual of task-oriented energy model. Based on proposed estimators, we further incorporate our framework with likelihood-guided mask refinement process to extract informative anomaly pixels for model training. We conduct extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks, demonstrating that without any auxiliary data or synthetic models, our method can still achieves competitive performance to other SOTA schemes

Quantitative determination and pharmacokinetics of salvianolic acid l, a novel phenolic acid constituent from salvia miltiorrhiza, in rat plasma by high-performance liquid chromatography

A simple, rapid and selective HPLC method was developed for the determination of a novel phenolic acid constituent in rat plasma, salvianolic acid L (SAL), extracted from the dried root of Salvia miltiorrhiza (Danshen). Plasma samples were extracted by ethyl acetate after addition of the internal standard tinidazole. The appropriate separations were achieved using a C18 column with the mobile phase composed of a mixture of acetonitrile/water/formic acid (35:65:0.1, v/v/v) at the flow rate of 0.8 mL/min, and the wavelength of determination by diode-array detector (DAD) detection was 327 nm. Good linearity (r = 0.9996) was obtained within the concentration of 0.05-50 μg/mL. The intra- and inter-day assay precisions ranged from 0.60 to 5.91% and 3.52 to 7.00%, respectively. The accuracy was between 95.8 to 103.8%. In addition, the stability and extraction recovery involved in the method were also validated. This method was successfully applied to investigate the pharmacokinetic study of SAL in rats after a single intravenous administration dose of 2.0, 4.0, and 8.0 mg/kg, respectively.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Learning with Noisy labels via Self-supervised Adversarial Noisy Masking

Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of deep models. The proposed method is simple and flexible, it is tested on both synthetic and real-world noisy datasets, where significant improvements are achieved over previous state-of-the-art methods

Quantitative determination and pharmacokinetics of salvianolic acid l, a novel phenolic acid constituent from salvia miltiorrhiza, in rat plasma by high-performance liquid chromatography

A simple, rapid and selective HPLC method was developed for the determination of a novel phenolic acid constituent in rat plasma, salvianolic acid L (SAL), extracted from the dried root of Salvia miltiorrhiza (Danshen). Plasma samples were extracted by ethyl acetate after addition of the internal standard tinidazole. The appropriate separations were achieved using a C18 column with the mobile phase composed of a mixture of acetonitrile/water/formic acid (35:65:0.1, v/v/v) at the flow rate of 0.8 mL/min, and the wavelength of determination by diode-array detector (DAD) detection was 327 nm. Good linearity (r = 0.9996) was obtained within the concentration of 0.05-50 μg/mL. The intra- and inter-day assay precisions ranged from 0.60 to 5.91% and 3.52 to 7.00%, respectively. The accuracy was between 95.8 to 103.8%. In addition, the stability and extraction recovery involved in the method were also validated. This method was successfully applied to investigate the pharmacokinetic study of SAL in rats after a single intravenous administration dose of 2.0, 4.0, and 8.0 mg/kg, respectively.Colegio de Farmacéuticos de la Provincia de Buenos Aire