Comparisons of GM (1,1), and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan

AbstractThis paper represents the first study to compare seven types of first–order and one–variable grey differential equation model [abbreviated as GM (1, 1)] and back-propagation artificial neural network (BPNN) for predicting hourly particulate matter (PM) including PMio and PM2.5 concentrations in Dali area of Taichung City, Taiwan. Their prediction performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) was 16.76%, 132.95, and 11.53, respectively for PM10 prediction. For PM2.5 prediction, the minimum MAPE, MSE, and RMSE value of 21.64%, 40.41, and 6.36, respectively could be achieved. All statistical values revealed that the predicting performance of GM (1, 1, x(0)), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) could predict the hourly PM variation precisely even comparing with BPNN

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table

Sparse Complementary Pairs with Additional Aperiodic ZCZ Property

This paper presents a novel class of complex-valued sparse complementary pairs (SCPs), each consisting of a number of zero values and with additional zero-correlation zone (ZCZ) property for the aperiodic autocorrelations and crosscorrelations of the two constituent sequences. Direct constructions of SCPs and their mutually-orthogonal mates based on restricted generalized Boolean functions are proposed. It is shown that such SCPs exist with arbitrary lengths and controllable sparsity levels, making them a disruptive sequence candidate for modern low-complexity, low-latency, and low-storage signal processing applications

AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture

Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limits the flexibility of network patterns in learned cell structures. Moreover, due to the topology-agnostic nature of existing works, including both cell-based and node-based approaches, the search process is time consuming and the performance of found architecture may be sub-optimal. To address these problems, we propose AutoShrink, a topology-aware Neural Architecture Search(NAS) for searching efficient building blocks of neural architectures. Our method is node-based and thus can learn flexible network patterns in cell structures within a topological search space. Directed Acyclic Graphs (DAGs) are used to abstract DNN architectures and progressively optimize the cell structure through edge shrinking. As the search space intrinsically reduces as the edges are progressively shrunk, AutoShrink explores more flexible search space with even less search time. We evaluate AutoShrink on image classification and language tasks by crafting ShrinkCNN and ShrinkRNN models. ShrinkCNN is able to achieve up to 48% parameter reduction and save 34% Multiply-Accumulates (MACs) on ImageNet-1K with comparable accuracy of state-of-the-art (SOTA) models. Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1.5 GPU hours, which is 7.2x and 6.7x faster than the crafting time of SOTA CNN and RNN models, respectively

LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning

Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed deep learning systems and aim to achieve differential privacy with good convergence rate and low communication cost. To achieve this goal, we propose a new learning algorithm LEASGD (Leader-Follower Elastic Averaging Stochastic Gradient Descent), which is driven by a novel Leader-Follower topology and a differential privacy model.We provide a theoretical analysis of the convergence rate and the trade-off between the performance and privacy in the private setting.The experimental results show that LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGD by achieving steadily lower loss within the same iterations and by reducing the communication cost by 30%. In addition, LEASGD spends less differential privacy budget and has higher final accuracy result than DPSGD under private setting

_In vivo_ photoacoustic molecular imaging with simultaneous multiple selective targeting using antibody-conjugated gold nanorods

The use of gold nanorods for photoacoustic molecular imaging in vivo with simultaneous multiple selective targeting is reported. The extravasation of multiple molecular probes is demonstrated, and used to probe molecular information of cancer cells. This technique allows molecular profiles representing tumor characteristics to be obtained and a heterogeneous population of cancer cells in a lesion to be determined. The results also show that the image contrast can be enhanced by using a mixture of different molecular probes. In this study, HER2, EGFR, and CXCR4 were chosen as the primary target molecules for examining two types of cancer cells, OECM1 and Cal27. OECM1 cells overexpressed HER2 but exhibited a low expression of EGFR, while Cal27 cells showed the opposite expression profile. Single and double targeting resulted in signal enhancements of up to 3 dB and up to 5 dB, respectively, and hence has potential in improving cancer diagnoses