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This review introduces the potential of 2D electronics for post-Moore era and discusses their current progress in digital circuits, analog circuits, heterogeneous integration, sensing circuits, artificial intelligence chips, and quantum chips in sequence.A comprehensive analysis of the current trends and challenges encountered in the development of 2D materials is summarized.An in-depth roadmap outlining the future development of 2D electronics is presented, and the most accessible and promising avenues for 2D electronics are suggested. Due to the constraints imposed by physical effects and performance degradation, silicon-based chip technology is facing certain limitations in sustaining the advancement of Moore's law. Two-dimensional (2D) materials have emerged as highly promising candidates for the post-Moore era, offering significant potential in domains such as integrated circuits and next-generation computing. Here, in this review, the progress of 2D semiconductors in process engineering and various electronic applications are summarized. A careful introduction of material synthesis, transistor engineering focused on device configuration, dielectric engineering, contact engineering, and material integration are given first. Then 2D transistors for certain electronic applications including digital and analog circuits, heterogeneous integration chips, and sensing circuits are discussed. Moreover, several promising applications (artificial intelligence chips and quantum chips) based on specific mechanism devices are introduced. Finally, the challenges for 2D materials encountered in achieving circuit-level or system-level applications are analyzed, and potential development pathways or roadmaps are further speculated and outlooked.

A growing number of firms are openly disclosing knowledge through academic journals and conferences; however, the impact of this practice on their market value needs further research. From a signalling theory perspective, we investigate the relationship between open knowledge disclosure and firm value and identify potential contingency factors. We propose that open knowledge disclosure conveys a firm’s technical capability and commitment to open science, consequently contributing to its market value. Drawing upon data from listed companies within China’s information and communication technology sector, we confirm that open knowledge disclosure enhances firm value. Furthermore, this enhancement is more pronounced for small firms, young firms, private firms, firms with few patents, firms drafting few technical standards, or firms operating in an immature technology market. Our findings suggest that firms, especially those facing high information asymmetry or lacking alternative signals, can increase their market value by sending positive signals through open knowledge disclosure.

Does the brain track how fast our blood glucose is changing? Knowing such a rate of change would enable the prediction of an upcoming state and a timelier response to this new state. Hypothalamic arousal-orchestrating hypocretin/orexin neurons (HONs) have been proposed to be glucose sensors, yet whether they track glucose concentration (proportional tracking) or rate of change (derivative tracking) is unknown. Using simultaneous recordings of HONs and blood glucose in behaving male mice, we found that maximal HON responses occur in considerable temporal anticipation (minutes) of glucose peaks due to derivative tracking. Analysis of >900 individual HONs revealed glucose tracking in most HONs (98%), with derivative and proportional trackers working in parallel, and many (65%) HONs multiplexed glucose and locomotion information. Finally, we found that HON activity is important for glucose-evoked locomotor suppression. These findings reveal a temporal dimension of brain glucose sensing and link neurobiological and algorithmic views of blood glucose perception in the brain's arousal orchestrators.

This paper presents a novel risk assessment method using Stochastic Colored Petri Nets (SCPN) specifically designed for the loading and unloading process of refined oil. The method incorporates a comprehensive analysis of risk factors by employing event trees and fault trees. Based on the real logistics operation process of an enterprise, four key risk factors and their corresponding evolution processes are identified, including equipment quality, improper operations, wrong instructions, and illegal operations. Subsequently, an SCPN model is constructed to integrate these risk factors and evaluate the system's performance using isomorphic Markov chain analysis. The overall risk assessment of the system is determined based on a risk function, which captures the system's risk level considering the influence of the identified risk factors. The results reveal that personnel engaging in illegal operation behaviors pose a high-risk factor, demanding preventive measures and increased attention. This research provides valuable insights for risk management in the refined oil loading and unloading process, emphasizing the significance of addressing risk factors and enhancing safety measures.

CLIP (Contrastive Language-Image Pre-training) uses contrastive learning from noise image-text pairs to excel at recognizing a wide array of candidates, yet its focus on broad associations hinders the precision in distinguishing subtle differences among fine-grained items. Conversely, Multimodal Large Language Models (MLLMs) excel at classifying fine-grained categories, thanks to their substantial knowledge from pre-training on web-level corpora. However, the performance of MLLMs declines with an increase in category numbers, primarily due to growing complexity and constraints of limited context window size. To synergize the strengths of both approaches and enhance the few-shot/zero-shot recognition abilities for datasets characterized by extensive and fine-grained vocabularies, this paper introduces RAR, a Retrieving And Ranking augmented method for MLLMs. We initially establish a multi-modal retriever based on CLIP to create and store explicit memory for different categories beyond the immediate context window. During inference, RAR retrieves the top-k similar results from the memory and uses MLLMs to rank and make the final predictions. Our proposed approach not only addresses the inherent limitations in fine-grained recognition but also preserves the model's comprehensive knowledge base, significantly boosting accuracy across a range of vision-language recognition tasks. Notably, our approach demonstrates a significant improvement in performance on 5 fine-grained visual recognition benchmarks, 11 few-shot image recognition datasets, and the 2 object detection datasets under the zero-shot recognition setting., Comment: Project: https://github.com/Liuziyu77/RAR

Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series analysis. The self-supervised representation learning can be categorized into two mainstream: contrastive and generative. In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series. We first introduce the basic frameworks for contrastive and generative SSL, respectively, and discuss how to obtain the supervision signal that guides the model optimization. We then implement classical algorithms (SimCLR vs. MAE) for each type and conduct a comparative analysis in fair settings. Our results provide insights into the strengths and weaknesses of each approach and offer practical recommendations for choosing suitable SSL methods. We also discuss the implications of our findings for the broader field of representation learning and propose future research directions. All the code and data are released at \url{https://github.com/DL4mHealth/SSL_Comparison}., Comment: Published at the AI4TS Workshop, IJCAI 2023

The tandem two -cylinder configuration is frequently employed in engineering structures. Its fluid force and vortex shedding are significantly greater than those of widely studied single -cylinder because of the wake -gap interaction. To unveil the underlying mechanism and perform wake control, sensitivity analysis, the adjoint method, and global linear instability were applied to the flow past two tandem cylinders. Utilizing the sensitivity map, the optimal control location was determined. The results indicate that the most sensitive region is located behind the upstream cylinder; therefore, we implemented jet control in that location. The two-dimensional direct numerical simulation was conducted to evaluate the effects of control. The findings indicate that by applying a dimensionless jet flow velocity of 1.0 at the diagonal rear of the upstream cylinder, the fluid force can be effectively reduced with an L/D ratio of 6.0. Additionally, the control effects were evaluated at a range of Reynolds numbers between 75 and 200. Analysis using high -order dynamic mode decomposition (HODMD) reveals that the presence of the jet flow causes a backward shift in the global modes and a substantial reduction in the modal energy. This demonstrates that the jet flow effectively inhibits the occurrence of vortex shedding, resulting in a reduction in both fluid forces and vortex shedding.

We present TinyLLaVA Factory, an open-source modular codebase for small-scale large multimodal models (LMMs) with a focus on simplicity of code implementations, extensibility of new features, and reproducibility of training results. Following the design philosophy of the factory pattern in software engineering, TinyLLaVA Factory modularizes the entire system into interchangeable components, with each component integrating a suite of cutting-edge models and methods, meanwhile leaving room for extensions to more features. In addition to allowing users to customize their own LMMs, TinyLLaVA Factory provides popular training recipes to let users pretrain and finetune their models with less coding effort. Empirical experiments validate the effectiveness of our codebase. The goal of TinyLLaVA Factory is to assist researchers and practitioners in exploring the wide landscape of designing and training small-scale LMMs with affordable computational resources., Comment: Our codebase is made public at https://github.com/TinyLLaVA/TinyLLaVA_Factory with documentation available at https://tinyllava-factory.readthedocs.io/en/latest

Trusted Execution Environments (TEEs) have become a promising solution to secure DNN models on edge devices. However, the existing solutions either provide inadequate protection or introduce large performance overhead. Taking both security and performance into consideration, this paper presents TBNet, a TEE-based defense framework that protects DNN model from a neural architectural perspective. Specifically, TBNet generates a novel Two-Branch substitution model, to respectively exploit (1) the computational resources in the untrusted Rich Execution Environment (REE) for latency reduction and (2) the physically-isolated TEE for model protection. Experimental results on a Raspberry Pi across diverse DNN model architectures and datasets demonstrate that TBNet achieves efficient model protection at a low cost.

Deep neural network (DNN) models have become prevalent in edge devices for real-time inference. However, they are vulnerable to model extraction attacks and require protection. Existing defense approaches either fail to fully safeguard model confidentiality or result in significant latency issues. To overcome these challenges, this paper presents MirrorNet, which leverages Trusted Execution Environment (TEE) to enable secure on-device DNN inference. It generates a TEE-friendly implementation for any given DNN model to protect the model confidentiality, while meeting the stringent computation and storage constraints of TEE. The framework consists of two key components: the backbone model (BackboneNet), which is stored in the normal world but achieves lower inference accuracy, and the Companion Partial Monitor (CPM), a lightweight mirrored branch stored in the secure world, preserving model confidentiality. During inference, the CPM monitors the intermediate results from the BackboneNet and rectifies the classification output to achieve higher accuracy. To enhance flexibility, MirrorNet incorporates two modules: the CPM Strategy Generator, which generates various protection strategies, and the Performance Emulator, which estimates the performance of each strategy and selects the most optimal one. Extensive experiments demonstrate the effectiveness of MirrorNet in providing security guarantees while maintaining low computation latency, making MirrorNet a practical and promising solution for secure on-device DNN inference. For the evaluation, MirrorNet can achieve a 18.6% accuracy gap between authenticated and illegal use, while only introducing 0.99% hardware overhead., Comment: Accepted by ICCAD 2023

Medical image segmentation is critical for diagnosing and treating spinal disorders. However, the presence of high noise, ambiguity, and uncertainty makes this task highly challenging. Factors such as unclear anatomical boundaries, inter-class similarities, and irrational annotations contribute to this challenge. Achieving both accurate and diverse segmentation templates is essential to support radiologists in clinical practice. In recent years, denoising diffusion probabilistic modeling (DDPM) has emerged as a prominent research topic in computer vision. It has demonstrated effectiveness in various vision tasks, including image deblurring, super-resolution, anomaly detection, and even semantic representation generation at the pixel level. Despite the robustness of existing diffusion models in visual generation tasks, they still struggle with discrete masks and their various effects. To address the need for accurate and diverse spine medical image segmentation templates, we propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM). Our approach integrates the diffusion model into a standard U-shaped architecture. At each step, we combine the noise-added image with the labeled mask to guide the diffusion direction accurately towards the target region. Furthermore, to capture specific anatomical a priori information in medical images, we incorporate a shape a priori module. This module efficiently extracts structural semantic information from the input spine images. We evaluate our method on a single dataset of spine images acquired through X-ray imaging. Our results demonstrate that VerseDiff-UNet significantly outperforms other state-of-the-art methods in terms of accuracy while preserving the natural features and variations of anatomy.

Implicit as well as explicit cues are means of communication in driver-pedestrian interaction. With the introduction of automated vehicles (AVs), drivers can engage in non-driving related activities which rise new challenges of communication between AVs and pedestrians. In this context, external human-machine interfaces (eHMIs) are seen as a key contribution in building pedestrians’ trust towards AVs by enabling communication between them. However, a research gap exists regarding the communication of AVs and pedestrian groups. In an intercultural study we investigated the impact of the variables eHMI concept and group size on pedestrians’ street crossing decisions regarding (1) willingness to cross and (2) trust in AVs. Therefore, German (N = 126) and Chinese (N = 79) participants took part in an online-based video study. The results showed that a ”walking person” eHMI had more stable effects with respect to the dependent variables in comparison to a ”smiling face” eHMI in both countries. No main effect of group size on a pedestrian’s willingness to cross or trust in AVs was found. Nevertheless, qualitative data indicated an effect of group size in pedestrian-AV communication processes. Our results therefore contribute to the investigation of communication between AVs and pedestrian groups.

With the development of network and the popularity of e-commerce, the network service industry has shown strong development potential, and many community e-commerce platforms have emerged as the times require. In order to ensure the profit of the supply chain of community e-commerce platform and supervise whether the suppliers of enterprises and grid station service providers try their best to participate in value co-creation, this paper introduces the delay parameter a of community e-commerce platform, constructs a three-party evolutionary game model of "community e-commerce platform-grid station service provider-supplier", simulates the strategies of each agent with matlab, studies the behaviours of community e-commerce platform under different delay parameters, and concludes that the delay parameter a of community e-commerce platform has a great influence on the timing of community e-commerce platform supervision. Finally, three suggestions are put forward for the supervision of the supply chain of community e-commerce platform: (1) encourage consumers to report; (2) formulate the reward and punishment system for the settled enterprises; (3) formulating a reasonable supervision system.

Yan Lin,1,* Xing Gao,1,* Ziyu Liu,1 Zhihui Liu,1 Yongqiang Li,1 Rong Liang,1 Zhiling Liao,2 Jiazhou Ye3 1Department of Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 2Department of Pathology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China; 3Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Rong Liang, Department of Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People’s Republic of China, Tel +86 771 5335155, Fax +86 771 5335155, Email liangrong@gxmu.edu.cn Jiazhou Ye, Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, 530021, People’s Republic of China, Tel +86 771 5331211, Fax +86 771 5331211, Email yejiazhou@gxmu.edu.cnAbstract: Low-grade myofibroblastic sarcoma (LGMS) is a rare, poorly differentiated, malignant tumor. The disease mainly occurs in the head and neck and rarely metastasizes at any age. Currently, there is no consensus regarding the treatment of metastatic LGMS. Here, we report the case of a 45-year-old man who was admitted to our hospital with cough for two weeks and abdominal pain for one week. Preoperative computed tomography revealed a large mass (116× 35 mm) in both the lungs and jejunal mass (maximum diameter, 32 mm). The tumor was excised, and immunohistochemical staining was positive for SMA and CD117 and negative for desmin and CD34, indicating a case of LGMS. The patient was effectively treated with apatinib (250 mg/day) after failure of imatinib, liposomal doxorubicin, and ifosfamide. The progression-free survival time was 8.5 months, and the overall survival time was 17 months after treatment with apatinib. No grade 3 or 4 side effects were observed, except hand-foot syndrome. Wh

Personalized federated learning considers learning models unique to each client in a heterogeneous network. The resulting client-specific models have been purported to improve metrics such as accuracy, fairness, and robustness in federated networks. However, despite a plethora of work in this area, it remains unclear: (1) which personalization techniques are most effective in various settings, and (2) how important personalization truly is for realistic federated applications. To better answer these questions, we propose Motley, a benchmark for personalized federated learning. Motley consists of a suite of cross-device and cross-silo federated datasets from varied problem domains, as well as thorough evaluation metrics for better understanding the possible impacts of personalization. We establish baselines on the benchmark by comparing a number of representative personalized federated learning methods. These initial results highlight strengths and weaknesses of existing approaches, and raise several open questions for the community. Motley aims to provide a reproducible means with which to advance developments in personalized and heterogeneity-aware federated learning, as well as the related areas of transfer learning, meta-learning, and multi-task learning., Comment: 40 pages, 10 figures, 7 tables. EMNIST and Landmarks fine-tuning results are corrected in (and after) v5. Code: https://github.com/google-research/federated/tree/master/personalization_benchmark

While the application of differential privacy (DP) has been well-studied in cross-device federated learning (FL), there is a lack of work considering DP and its implications for cross-silo FL, a setting characterized by a limited number of clients each containing many data subjects. In cross-silo FL, usual notions of client-level DP are less suitable as real-world privacy regulations typically concern the in-silo data subjects rather than the silos themselves. In this work, we instead consider an alternative notion of silo-specific sample-level DP, where silos set their own privacy targets for their local examples. Under this setting, we reconsider the roles of personalization in federated learning. In particular, we show that mean-regularized multi-task learning (MR-MTL), a simple personalization framework, is a strong baseline for cross-silo FL: under stronger privacy requirements, silos are incentivized to federate more with each other to mitigate DP noise, resulting in consistent improvements relative to standard baseline methods. We provide an empirical study of competing methods as well as a theoretical characterization of MR-MTL for mean estimation, highlighting the interplay between privacy and cross-silo data heterogeneity. Our work serves to establish baselines for private cross-silo FL as well as identify key directions of future work in this area., Comment: NeurIPS 2022, 37 pages

We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy mechanism based on the difference of two independent Poisson random variables. To quantify its privacy guarantees, we analyze the privacy loss distribution via a numerical evaluation and provide a sharp bound on the R\'enyi divergence between two shifted Skellam distributions. While useful in both centralized and distributed privacy applications, we investigate how it can be applied in the context of federated learning with secure aggregation under communication constraints. Our theoretical findings and extensive experimental evaluations demonstrate that the Skellam mechanism provides the same privacy-accuracy trade-offs as the continuous Gaussian mechanism, even when the precision is low. More importantly, Skellam is closed under summation and sampling from it only requires sampling from a Poisson distribution -- an efficient routine that ships with all machine learning and data analysis software packages. These features, along with its discrete nature and competitive privacy-accuracy trade-offs, make it an attractive practical alternative to the newly introduced discrete Gaussian mechanism., Comment: Paper published in NeurIPS 2021

Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessing and feature extraction, as well as challenged by poor classification performance. Here, we propose a novel deep learning model, named Attention-Based Convolutional Neural Networks (ABCNN) that taking advantage of CNN and multi-head attention, to directly work on the raw ECG signals and automatically extract the informative dependencies for accurate arrhythmia detection. To evaluate the proposed approach, we conduct extensive experiments over a benchmark ECG dataset. Our main task is to find the arrhythmia from normal heartbeats and, at the meantime, accurately recognize the heart diseases from five arrhythmia types. We also provide convergence analysis of ABCNN and intuitively show the meaningfulness of extracted representation through visualization. The experimental results show that the proposed ABCNN outperforms the widely used baselines, which puts one step closer to intelligent heart disease diagnosis system., Comment: 7 pages, accepted by The 2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS 2021)

Database systems analyze queries to determine upfront which data is needed for answering them and use indexes and other physical design techniques to speed-up access to that data. However, for important classes of queries, e.g., HAVING and top-k queries, it is impossible to determine up-front what data is relevant. To overcome this limitation, we develop provenance-based data skipping (PBDS), a novel approach that generates provenance sketches to concisely encode what data is relevant for a query. Once a provenance sketch has been captured it is used to speed up subsequent queries. PBDS can exploit physical design artifacts such as indexes and zone maps. Our approach significantly improves performance for both disk-based and main-memory database systems., Comment: 20 pages, 14 figures

We consider training models on private data that are distributed across user devices. To ensure privacy, we add on-device noise and use secure aggregation so that only the noisy sum is revealed to the server. We present a comprehensive end-to-end system, which appropriately discretizes the data and adds discrete Gaussian noise before performing secure aggregation. We provide a novel privacy analysis for sums of discrete Gaussians and carefully analyze the effects of data quantization and modular summation arithmetic. Our theoretical guarantees highlight the complex tension between communication, privacy, and accuracy. Our extensive experimental results demonstrate that our solution is essentially able to match the accuracy to central differential privacy with less than 16 bits of precision per value., Comment: International Conference on Machine Learning (ICML), 2021

Ziyu Liu,1,2 Leilei Huang,3,4 Liwei Sun,1 Hui Nie,3,4 Yuqi Liang,1,2 Jinwen Huang,3,4 Fanhong Wu,3,4 Xin Hu1 1The Laboratory of Cancer Biology, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, Jilin, People’s Republic of China; 2Department of Biochemistry and Molecular Biology, School of Life Science, Jilin University, Changchun, Jilin, People’s Republic of China; 3Shanghai Engineering Research Center of Green Fluoropharmaceutical Technology, Shanghai, People’s Republic of China; 4Department of Pharmaceutical Engineering, School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai, People’s Republic of ChinaCorrespondence: Xin Hu; Fanhong Wu Email huxin@jlu.edu.cn; wfh@sit.edu.cnPurpose: Erianin is a small chemical compound extracted from Dendrobium chrysotoxum and has excellent antineoplastic effects against a variety of cancers. Combretastatin A-4 (CA4) is the most effective member of natural phenolic stilbene compounds isolated from the African willow tree Combretum caffrum. Ecust004 (Chemical Formula: C18H21NO7S) is a drug candidate optimized from structure–activity relationship studies of the sulfamate derivatives of Erianin and CA4, which has better bioavailability and pharmacokinetic profiles than Erianin and CA4.Methods: To investigate the antitumor activity of Ecust004 in different types of breast cancer cells, MDA-MB-231 and MCF7 cells were treated with Ecust004. MTT and CCK8 were used to determine the effects of Ecust004 on cell proliferation. Wound-healing and Transwell assays were used to evaluate the migration and invasion level of cells treated with Ecust004. The expression of genes and proteins associated with epithelial–mesenchymal transition was detected by RT-PCR and Western blotting. In vivo studies further clarified the functional effects of Ecust004.Results: Ecust004 treatment decreased the growth and proliferation of MDA-MB-231 and MCF7 cells at a lower dosage than Eriani

We present a multi-agent actor-critic method that aims to implicitly address the credit assignment problem under fully cooperative settings. Our key motivation is that credit assignment among agents may not require an explicit formulation as long as (1) the policy gradients derived from a centralized critic carry sufficient information for the decentralized agents to maximize their joint action value through optimal cooperation and (2) a sustained level of exploration is enforced throughout training. Under the centralized training with decentralized execution (CTDE) paradigm, we achieve the former by formulating the centralized critic as a hypernetwork such that a latent state representation is integrated into the policy gradients through its multiplicative association with the stochastic policies; to achieve the latter, we derive a simple technique called adaptive entropy regularization where magnitudes of the entropy gradients are dynamically rescaled based on the current policy stochasticity to encourage consistent levels of exploration. Our algorithm, referred to as LICA, is evaluated on several benchmarks including the multi-agent particle environments and a set of challenging StarCraft II micromanagement tasks, and we show that LICA significantly outperforms previous methods., Comment: NeurIPS 2020 Camera Ready; first two authors contributed equally

Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extractor. However, existing methods have limitations in achieving (1) unbiased long-range joint relationship modeling under multi-scale operators and (2) unobstructed cross-spacetime information flow for capturing complex spatial-temporal dependencies. In this work, we present (1) a simple method to disentangle multi-scale graph convolutions and (2) a unified spatial-temporal graph convolutional operator named G3D. The proposed multi-scale aggregation scheme disentangles the importance of nodes in different neighborhoods for effective long-range modeling. The proposed G3D module leverages dense cross-spacetime edges as skip connections for direct information propagation across the spatial-temporal graph. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400., Comment: CVPR 2020

Xiaomin She, Yan Lin, Rong Liang, Ziyu Liu, Xing Gao, Jiazhou Ye Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning, People’s Republic of ChinaCorrespondence: Jiazhou Ye Tel +86 7715301253Email nnsz20013@163.comAbstract: RNA-binding proteins (RBPs) act as a key factor in gene regulation by governing RNA metabolism. They contribute to the expression and functions of most RNAs by binding to them and forming complexes. RNA-binding motif protein 38 (RBM38), a member of the RBP family, alters the stability and translation of targeted mRNAs to affect various biological processes, such as cell proliferation, cell cycle arrest, and myogenic differentiation. RBM38 contains a highly conserved RNA recognition motif (RRM) consisting of two subunits, RNP1 and RNP2, which specifically bind to RNAs. Recent studies have revealed that RBM38 regulates the mRNA stability of several tumor-related genes, such as p53, mdm2, p63, p73, p21, and c-Myc, by binding to their 3′ untranslated regions (3′ UTRs); thus, RBM38 modulates targeted gene expression and affects the biological processes of tumors. In addition, abnormal RBM38 expression in some malignant tumors and its correlation with prognosis have been documented in many studies, indicating its value for potential clinical applications. In this review, we present an overview of RBM38, specifically highlighting its relationship with tumor manifestation and development. A brief overview of the potential use of RBM38 in cancer therapy is also included to provide ideas for further research on RBM38.Keywords: RBM38, malignant tumors, p53 family, posttranscriptional regulation

Yan Lin,1 Ziqin He,1 Jiazhou Ye,2 Ziyu Liu,1 Xiaomin She,1 Xing Gao,1 Rong Liang1 1Department of Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, People’s Republic of China; 2Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, People’s Republic of ChinaCorrespondence: Rong LiangDepartment of Medical Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi 530021, People’s Republic of ChinaTel +86 771 5333058Email ronglianggx@126.comAbstract: As a pleiotropic cytokine, interleukin-6 (IL-6) not only regulates the cellular immune response, but it also promotes tumor development by activating multiple carcinogenic pathways. IL-6 expression is significantly elevated in colorectal cancer (CRC) and is closely related to CRC development and patient prognosis. In CRC, IL-6 activates signal transducers and activators of transduction-3 (STAT3) to promote tumor initiation and tumor growth. IL-6/STAT3 signalling has a profound effect on tumor-infiltrating immune cells in the tumor immune microenvironment in CRC. Additionally, IL-6/STAT3 pathway activates downstream target genes to protect tumor cells from apoptosis; drive tumor cell proliferation, cell cycle progression, invasion and metastasis; promote tumor angiogenesis; and stimulate drug resistance. Therefore, a thorough understanding of the many effects of the IL-6/STAT3 pathway in CRC is needed, which the present review examines.Keywords: IL-6/STAT3 pathway, colorectal cancer, tumor development

In-band full-duplex has emerged as an attractive solution for significantly enhancing the throughput of wireless communication systems by doubling the spectrum efficiency compared to conventional half-duplex systems. However, the existence of self-interference is a significant barrier to operate full-duplex systems. In order to cancel the self-interference by analog and digital solutions, time synchronization in digital domain is one of the most important elements in full-duplex systems. The aim of this thesis is to present a time synchronization method for orthogonal frequency division multiplexing (OFDM) full-duplex transceiver based on Long Term Evolution (LTE) and validate its performance by MATLAB simulation. The proposed method acquires time offset information of both desired and self-interference signals in a full-duplex node to enhance the successful synchronization probability. To do this, a Zadoff-Chu sequence with a different root index is used for the primary synchronization signal (PSS) at each node. Further, we discuss about the application of different threshold values for the adaptive cross-correlation peak detection of two kinds of PSSs in a full-duplex node.

A sustainable city relies on renewable energy, which promotes the development of electric vehicles. To support electric vehicles, the concept of charging vehicles while driving has been put forward. Under such circumstances, constructing solar panels on urban roads is an innovative option with great benefits, and the accurate calculation of road photovoltaic power generation is a prerequisite. In this paper, we propose a novel framework for predicting and calculating the solar radiation and electric energy that can be collected from the roads. Google Street View images are collected to measure the sky obstruction of roads which is integrated with the solar radiation model to estimate the irradiation receiving capability. In addition to sky obstruction, we also take the impact of traffic conditions and weather situations into consideration in the calculation. Radiation maps at different times in a year are produced from our work to analyze the roads photovoltaic distribution. In order to test the feasibility of our framework, we take Boston as a case study. Results show that roads in Boston can generate abundant electricity for all future electric vehicles in the city. What's more, main roads through Boston exhibit better power generation potential, and the effect of the traffic condition is limited. Our calculation framework confirms that utilizing solar panels as road surfaces is a great supplement of city power with the unique ability to charge moving cars. © 2019 Elsevier Ltd

Multiclass decomposition splits a multiclass classification problem into a series of independent binary learners and recomposes them by combining their outputs to reconstruct the multiclass classification results. Three widely-used realizations of such decomposition methods are One-Versus-All (OVA), One-Versus-One (OVO), and Error-Correcting-Output-Code (ECOC). While OVA and OVO are quite simple, both of them assume all classes are orthogonal which neglect the latent correlation between classes in real-world. Error-Correcting-Output-Code (ECOC) based decomposition methods, on the other hand, are more preferable due to its integration of the correlation among classes. However, the performance of existing ECOC-based methods highly depends on the design of coding matrix and decoding strategy. Unfortunately, it is quite uncertain and time-consuming to discover an effective coding matrix with appropriate decoding strategy. To address this problem, we propose LightMC, an efficient dynamic multiclass decomposition algorithm. Instead of using fixed coding matrix and decoding strategy, LightMC uses a differentiable decoding strategy, which enables it to dynamically optimize the coding matrix and decoding strategy, toward increasing the overall accuracy of multiclass classification, via back propagation jointly with the training of base learners in an iterative way. Empirical experimental results on several public large-scale multiclass classification datasets have demonstrated the effectiveness of LightMC in terms of both good accuracy and high efficiency., Comment: 10 pages, 2 figures

The game of Chinese Checkers is a challenging traditional board game of perfect information that differs from other traditional games in two main aspects: first, unlike Chess, all checkers remain indefinitely in the game and hence the branching factor of the search tree does not decrease as the game progresses; second, unlike Go, there are also no upper bounds on the depth of the search tree since repetitions and backward movements are allowed. Therefore, even in a restricted game instance, the state-space of the game can still be unbounded, making it challenging for a computer program to excel. In this work, we present an approach that effectively combines the use of heuristics, Monte Carlo tree search, and deep reinforcement learning for building a Chinese Checkers agent without the use of any human game-play data. Experiment results show that our agent is competent under different scenarios and reaches the level of experienced human players.

Rong Liang, Ziyu Liu, Xuemin Piao, Mingtang Zuo, Jinyan Zhang, Zhihui Liu, Yongqiang Li, Yan Lin Department of Medical Oncology, Affiliated Tumor Hospital of Guangxi Medical University, Nanning, Guangxi 530021, People’s Republic of China Abstract: Malignant cancer is one of the most serious diseases that currently endanger human health. As most tumors are diagnosed at an advanced stage, the current treatments show poor therapeutic efficacy, and the patients have poor prognosis. However, a 5-year survival rate higher than 80% could be achieved if tumors are diagnosed at an early stage. Therefore, early diagnosis and treatment play important roles in the prevention and treatment of malignant tumors, and serum tumor markers are important for the early diagnosis of malignant cancers. Recent studies have shown that GP73, a transmembrane protein, has greater diagnostic value in primary liver cancer than in other types of cancers, and research on the regulation of GP73 expression has unveiled broad prospects in anticancer targeted therapy. Thus, GP73, as a new tumor marker, deserves further study. Keywords: GP73, malignant tumors, tumor marker, diagnosis, prognosis

The bump height is one of the key factors which affects the vehicle bump test. If the adopted value of bump height is too small, the test couldn’t get obvious excitation effect for bridge structures. On the other hand, it will cause additional structural damage when the value is rather large. Aiming at this point, the theoretical calculation method to determine limit bump height is proposed in this paper. The vehicle bump test is decomposed into three stages, i.e., the period before rear wheels contacting with bridge, the moment at rear wheels contacting with bridge, the period after rear wheels contacting with bridge. In stage I, the total momentum generated by vehicle rotating around front wheels is calculated. Then, the initial conditions for stage III are derived using the principle of momentum conservation in stage II. Finally, combining with vehicle-bridge coupling equations, the free-decay response of bridge could be calculated to determine the limit bump height in stage III. In this paper, the reliability of the proposed method is validated by finite element method (FEM), and numerical simulations on an actual simply supported hollow slab bridge are used to calculate the dynamic response of bridge considering various transverse positions of vehicle bump and determine the limit bump height.

This paper presents an analytical approach to investigate the free vibration analysis of cracked non-uniform beam carrying spring-mass systems by finite element method and illustrates a valid and reliable damage identification method which using hybrid neural genetic technique. Firstly, based on the finite element method, the dynamic characteristics of non-uniform cracked beam carrying spring-mass systems are obtained. Then, the first five frequencies are used as input parameters by combining genetic algorithm with neural network to identify the damage. Finally, Numerical simulations of direct and inverse problems of non-uniform cracked beams carrying a spring-mass system are carried out.

Based on the D’Alembert principle and the hypothesis of Euler-Bernoulli beam, the vehicle-bridge coupling vibration equations of vehicle bump test were established using mode decomposition method. As bump test applies an impact pulse excitation to bridge structure, the initial conditions to calculate the system vibration equations were derived using momentum conservation law. Then the system vibration equations were solved by using Runge-Kutta method and the dynamic response of bridge was obtained through mode decomposition method. By comparing the maximum bending moment of each section at h height with the design bending moment of bridge, the limit height in bump test was determined. Finally, a simply supported hollow slab bridge was taken as an example, and the dynamic response of this bridge was calculated to determine the limit bump height in dynamic test. The correctness and effectiveness of this method was validated.

The design of key parts of civil engineering structures often undergo several modifications to ensure that the usability of entire structure meets the requirements. Dynamic characteristics are important parameters to evaluate the performance of structure, so it is necessary to calculate dynamic characteristics after each modification. This paper proposes a new method to calculate the dynamic characteristics of the structures rapidly after each modification. According to this method, the structure is decomposed into relatively simple substructures, and the dynamic characteristics of the whole structure can be obtained by the modal synthesis method (MSM). When a substructure has been modified, its dynamic characteristics can be calculated by using the matrix perturbation method (MPM). The proposed method is applied to the numerical calculation of a Y-shape continuous beam bridge, and the results show that the method is correct and efficient.

In many processes it is important to have automatic control in the modern life. For example, PLC systems are using for machine control, water pressure and flow are able to be controlled by DDC program, even a car can be driven by computer. In this article, author will turn our focus on the control system for fermentation tank. The fermentation tank that produces methane is considered as clean and recycle energy source. It is widely used in house, electronic power machine and vehicle around the world. However, its reaction temperature and output concentration control are usually hard to detect without automation system. In this study, the problem is focused to combine fermentation tank and automatic control system in laboratory testing. In this paper, author will be able to use PIC (Peripheral Interface Controller) microcontroller to solve this problem and automatically control the methane tank output methane gas with certain concentration, which could be used as energy source. The temperature and concentration sensors that are chosen as input data of the controller and corresponding algorithm were performed on the PIC. They will be used to realize the composition and thermal state measurement. With those information, the valves of material and water control can be controlled in methane tank. Limited by the experiment equipment and methane reaction tank system, the controller was just tested in the laboratory environment instead of practical application. The test result shows that the controller has its capability to automatically control the stable output of methane gas. In the test, water and material valves are controlled automatically open or close after monitoring temperature and concentration information of the gas in the tank.

Nonlinear magnetization dynamic process in nano-scale magnetic systems is of great scientific interests for its application to magnetic recording technology and spintronic devices. In the dynamic process, thermal fluctuation effects are of critical importance since they are directly related to long term reliability of magnetic devices. Recently, a novel approach to modeling stochastic magnetization dynamics has been proposed[1,2,3]. In this approach, thermal bath effects are accounted for by introducing a jump-noise torque term into the precessional magnetization dynamics equation. In this dissertation, we develop a Monte Carlo type numerical technique for implementation of the approach. There are two central elements of our numerical technique: a ``midpoint'' finite-difference scheme for integration of deterministic precessions and a ``self-scattering'' scheme which results in time-homogenization of a jump-noise process. The numerical technique unconditionally preserves the micromagnetic constraint and appreciably simplifies the random component of Monte Carlo simulations. We perform and illustrate numerous Monte Carlo simulations in the dissertation using numerical examples. The Monte Carlo simulations are ideally suited for implementation on GPUs since they are intrinsically parallelizable in the sense that different realizations of stochastic magnetization dynamics can be computed concurrently. Therefore we develop a parallel algorithm and implement it using an Nvidia GPU card. A speed-up factor of more than 200 is achieved using this GPU implementation in comparison with the tranditional CPU single threaded implementation. Furthermore, we apply the jump-noise process driven magnetization dynamic equation to study random magnetization switching induced by thermal fluctuations. Numerical results demonstrate that the magnetization switching rate has a very different temperature dependence at relatively high and very low temperatures. The high temperature switching

We investigate the decomposition of noncommutative gauge potential $\hat{A_{i}}$, and find it has inner structure, namely, $\hat{A_{i}}$ can be decomposed in two parts $\hat{b_{i}}$ and $\hat{a_{i}}$, here $\hat{b_{i}}$ satisfies gauge transformations while $\hat{a_{i}}$ satisfies adjoint transformations, so dose the Seiberg-Witten mapping of noncommutative U(1) gauge potential. By means of Seiberg-Witten mapping, we construct a mapping of unit vector field between noncommutative space and ordinary space, and find the noncommutative U(1) gauge potential and its gauge field tenser can be expressed in terms of the unit vector field. When the unit vector field has non singularity point, noncommutative gauge potential and gauge field tenser will equal to ordinary gauge potential and gauge field tenser., Comment: 9 pages

By using $\phi$ -mapping method, we discuss the topological structure of the self-duality solution in Jackiw-Pi model in terms of gauge potential decomposition. We set up relationship between Chern-Simons vortices solution and topological number which is determined by Hopf index and and Brouwer degree. We also give the quantization of flux in the case. Then, we study the angular momentum of the vortex, it can be expressed in terms of the flux., Comment: 12 pages, 4 figures,Corrected typos