Abstract:

Abstract:

In the automatic driving systems, logarithm function has been widely used. For example, logarithm function is often used to design loss function in deep learning or convolutional neural network, which serves as the basis for the automatic driving perception system. Therefore, studying the history of invention of logarithm is of great significance to master the concept and application. This paper studies the definition of Napier’s logarithm and his three tables, analyzes two kinds of proof methods of predecessors, and puts forward new proof methods based on the exponential function. Meanwhile, this paper also analyzes Napier’s calculation method. Compared with other alternative methods, the optimization results of Napier’s interval approximation are given. The calculation by MPRF library shows that Napier’s method is more convergent to the true value.

Abstract:

In the field of autonomous driving safety research and application, the limitations of limited testing mileage and exposure to only a single hazardous scenario hinder the improvement of autonomous driving safety performance. To address these issues, testing with adversarial scenarios is considered crucial. However, existing studies utilize generic optimization algorithms as frameworks, resulting in a wastage of computational resources in exploring the parameter space, thereby leading to low efficiency. Moreover, under the constraint of computational cost, these algorithms may not be able to test a sufficient number of diverse failure samples, especially in complex environments. Adversarial scenario testing in complex environments faces three major challenges: information scarcity, sparse distribution of adversarial samples in a vast parameter space, and the difficulty in balancing exploration and exploitation during the search process. To tackle these challenges, this paper proposes an efficient framework for adversarial scenario testing. This framework employs a surrogate model to gather more information about the parameter space, selects small samples to overcome the sparse event constraints in the vast space, and focuses on the unknown regions and adversarial samples for targeted search and update, thereby achieving a balance between exploration and exploitation. Experimental results demonstrate that the proposed method in this paper exhibits a search efficiency four times higher than random sampling and more than double the efficiency compared to general genetic algorithms. Additionally, with a limited number of simulation test runs, it generates a greater number of adversarial test cases that are likely to cause the tested autonomous driving system to fail. Notably, the proposed method can identify many outlier adversarial samples, unveiling failure modes that existing algorithms fail to recognize. Furthermore, the proposed method can swiftly and comprehensively identify the vulnerable scenarios of the tested algorithm, providing support for the testing, validation, and iterative upgrade of autonomous driving algorithms.

Abstract:

Chaos LiDAR has attracted significant attention due to its high resolution, inherent antiinterference capability, and stealth characteristics. However, the performance of chaos LiDAR in longrange target detection and imaging is quite limited by the power of chaotic light sources, sensitivity of linear detectors, and hardware bandwidth. To overcome the bottleneck of chaos LiDAR, this paper proposes the concept of digital chaos LiDAR and conducts theoretical analysis and simulation verification. Through Monte Carlo simulation, this paper studied the detection probability, false probability, and detection range of continuous-wave chaos LiDAR, pulsed chaos LiDAR, and digital chaos LiDAR. The simulation results show that, within the confidence interval where the detection probability is greater than 95% and the false alarm probability is less than 5%, the detection range of digital chaos LiDAR is approximately 35 times and 8 times higher than that of continuous-wave chaos LiDAR and pulsed chaos LiDAR, respectively. With the advantages of ultra-high sensitivity of single-photon detectors and digital output, digital chaos LiDAR is expected to be widely used in the field of long-range target detection and imaging.

Abstract:

To meet the requirements of anti-drone recognition system for drone recognition in the complex background within public places, a target recognition method based on Zebrafish template matching vision recognition and eagle eye visual attention was studied in this paper. By establishing a dataset of drone templates with different postures, combining the eagle eye visual search mechanism with scale invariant feature transformation, the attitude template image is matched with the target to obtain a rough target area. Then calculate the similarity of the Hausdorff distance between the template pose and the target pose to obtain the most similar pose. Experimental results showed that, the anti UAV recognition system can realize the recognition of drones in different complex backgrounds. Compared with the significance target recognition method based on spectral residuals, the average running time is improved by 23.5%. The proposed algorithm has a higher structural similarity index than the differential hash algorithm for finding similar template poses.

Abstract:

To realize efficient path exploration of unmanned platforms in unknown environments, a path planning algorithm based on a hierarchical architecture of “perception planning control” is studied in this work. Real-time construction of two-dimensional grid maps of unknown environments using the Cartographer mapping algorithm at the perception layer. At the planning level, the optimal exploration target point is selected by Canny edge detection, density-based clustering algorithm, and performance function evaluation. Specifically, the concept of continuity of exploration direction is introduced into the efficiency function of planning, overcoming the drawbacks of traditional path planning that repeatedly explores known environments. At the control layer, the shortest path from the current pose to the target point is planned using probability roadmap algorithm, and collision-free tracking of the path is achieved through pure tracking algorithm and vector histogram algorithm. The effectiveness of the algorithm was verified through simulation in three typical environments, and the results showed that the proposed algorithm can achieve higher exploration efficiency and completeness in different environments.

Abstract:

The paper explores the formation control problem of multiple unmanned intelligent vehicles moving in complex environments with the leader-follower method, and designs a formation controller and a formation control scheme by adopting a closed-loop control law, which is advantageous in that it realizes precise control mainly by considering the distance and angle between intelligent vehicles, while referring to the information between the leader and the neighboring followers. Based on the simulated test environment built, the improved control method is tested against the traditional formation method. The experimental results show that the method proposed in this paper has better motion control effects in complex environments.

Abstract:

AlphaFold, which is developed by DeepMind, has made amazing advances in predicting protein structures for life sciences research. Using the vast structural predictions made possible by AlphaFold, a database of over 200 million proteins has been established. Such a database covers the complete proteomes of many organisms. This review outlines the most recent progresses in exploring protein evolution using statistical physical methods based on the AlphaFold database. Traditional protein evolution research often concentrates on the sequences or structures of proteins within the same family, using a narrow microscopic approach. With the new emergence of extensive protein structure predictions by AlphaFold, whereas scientists can expand their horizons to include vast assortments of proteins to make parallels with all proteins in different species and extract statistical trends through macroscopic observation. By comparing the proteins with similar chain lengths in over 40 model organisms, the statistical trends in protein evolution are discovered. For organisms with higher complexity, their constituent proteins present larger radii of gyration, higher flexibility, and higher segregation of hydrophobic and hydrophilic residues in both spatial and sequence. It is also validated by statistical physics analysis that higher organismal complexity correlates with higher functional specialization of constituent proteins. The findings in these studies connect molecular evolution to organism evolution, contributing to the understanding of the origin and evolution of lives.

Abstract:

Photonic nanojet (PNJ) is a high intense, tightly focused light beam on the shadow side surface of lossless dielectric microparticles when the size of the particle is approximately equal to or slightly larger than the wavelength of the incident light. PNJ exhibits exceptional characteristics, including higher intensity than the incident light, the minimum full width at half-maximum less than the diffraction limit of the beamwidth, propagation beyond the evanescent field region and strong backscattering. These properties make PNJ crucial in various applications such as optical signals enhancement, micro-/nano- processing and manufacturing, super-resolution optical imaging, ultra-sensitive trapping and sensing, among others. This review article begins by introducing the origins and discovery of PNJ. Subsequently, it provides an elucidation of the model, theory, morphology features, experimental measurements, and key properties of PNJ. Furthermore, the study investigates and discusses several crucial applications of PNJ. Finally, a comprehensive summary and outlook for PNJ are presented.

Abstract:

Cardiovascular disease is one of leading threats to human life and health. Tissue-engineered vascular scaffolds that can assist the regeneration/repair of disordered vessels have provided promising alternatives for cardiovascular disease treatment. However, existing tissue-engineered vascular scaffolds still confront grand challenges in interfacial adaptations, resulting in high risks of complications upon implantation and unsatisfactory translational application. Recently, tissue-engineered vascular scaffolds capable of programmed deformation have been emerging. Such scaffolds can not only dynamically adapt to three-dimensional vascular shapes with varying diameters but also orderly regulate behaviors and functions of vascular cells, offering new opportunities for addressing the grand challenges of interfacial adaptations. An overview of most-updated advances and perspectives of programmed deformed scaffolds for vascular tissue engineering will provide valuable inspirations to the development and translational applications of new generation of tissue-engineered vascular scaffolds.

Abstract:

Artificial intelligence interpretability refers to the ability of people to understand and interpret the decision-making process of machine learning models. Research in this field aims to improve the transparency of machine learning algorithms, making their decisions more trustworthy and explainable. Interpretability is crucial in artificial intelligence systems, especially in sensitive and critical decision-making domains such as healthcare, finance, and law. By providing interpretability, people can better understand the reasoning behind the model''s decisions, ensuring that they are fair, robust, and ethical. In the continuously evolving field of artificial intelligence, enhancing the interpretability of models is a key step towards achieving trustworthy and sustainable AI. The article outlines the development history of interpretable artificial intelligence and the technical characteristics of various interpretability methods, with a particular focus on interpretability in the medical field. It provides a more in-depth discussion of the limitations of current methods on medical imaging datasets and proposes possible future directions for exploration.

Abstract:

Over the past few decades, the rapid development and widespread adoption of internet technology have propelled humanity into the digital information age, The internet has evolved into a crucial component of human life. With the emergence of the digital lifestyle, individuals are continously generating massive amounts of digital information. Effective and convenient storage of this information is regarded as a significant challenge that needs to be overcome. In this article, we start with introducing the existing storage methods and media, and analyzing the current state of the storage field. Subsequently, we delve into the advantages, core technologies, and the potential applications of DNA as a big data storage medium in the coming days. Furthermore, we propose the future development trends and give insights into DNA-based information storage. We aim to offer new thoughts for the advancement of DNA-based data storage technology.

Abstract:

As food plays an important role in people''s daily lives, a food map showing the geographical distribution of restaurants in a city is of great social value. Social media has covered every aspect of people''s lives; therefore, social media data provides a wealth of data to support automatic cartography. This work proposes an automatic generation method for urban food maps driven by social media data, integrating machine learning and cartographic algorithms to realize the intelligent generation of stylized urban food maps. A visualization system of urban food maps has been developed, which is applied to four cities, Wuhan, Guangzhou, Chongqing, and Chengdu, for case studies. The results show the effectiveness and good visual expressiveness of our method in presenting urban cuisine for cities.

Abstract:

Convolutional Neural Networks (CNNs), as a quintessential representation of deep learning, are the most commonly used neural networks in tasks such as computer vision. However, convolution operations typically account for over 90% of the runtime in CNNs, becoming a bottleneck for performance. Additionally, due to the complexity of current hardware and the diversity of workloads, specific optimizations in previous work often lack performance portability. To address this, we introduce BlazerML, an open-source convolution computation library based on auto-generated code templates from TVM, capable of automatically generating high-performance convolution implementations for any input shape. BlazerML is implemented based on the Winograd algorithm, known for its high performance in fast convolution algorithms. Experimental results demonstrate that BlazerML significantly outperforms current state-of-the-art open-source libraries. On x86 CPUs, running common deep learning network forward inferences, it is faster by 1.18~2.47, 1.18~2.27, and 1.01~1.66 times compared to OnnxRuntime, MNN, and the TVM community version, respectively. On ARM CPUs, for single-layer inference of common deep learning networks, it surpasses ACL and FastConv by 1.26~6.11 and 1.04~4.28 times, respectively.

Abstract:

Recommendation systems can effectively address the problem of information overload, attracting extensive attention from both academia and industry. Collaborative filtering algorithms based on graph neural networks have emerged as a widely adopted technique in recent years. These algorithms can effectively represent user and item features and learn intricate relationships between users and items. Therefore, they have become prevalent in the field of recommendation systems. In this paper, we first categorize the algorithms based on the problems that they aim to solve and then provides a comparison and analysis of representative algorithms within each category. We also summarize commonly used datasets in experiments and briefly introduce the key evaluation metrics. Finally, we discuss the challenges and potential research directions.

Abstract:

Action recognition in the dark is a challenging task in practice because it is difficult to learn robust action representations from low light environments. Furthermore, there is a domain gap between dark scenes and the data used by traditional pretrained models, which results in suboptimal results with the traditional pretrain-finetune approach, and pretraining from scratch is costly. To address this issue, a domain-adaptive pretraining method is proposed to improve action recognition performance in the dark environments. The method integrates an external vision enhancement model for de-darkening to introduce critical knowledge for dark scene processing. It also employs a cross-domain self-distillation framework to reduce the domain gap of visual representations between illuminated and dark scenes. Through extensive experiments in various dark environment action recognition settings, the proposed approach can achieve a Top-1 accuracy of 97.19% on the dark dataset of fully supervised action recognition. In the source-free domain adaptation on the Daily-DA dataset, the accuracy can be improved to 49.11%. In the multi-source domain adaptation scenario on the Daily-DA dataset, the Top-1 accuracy can reach 54.63%.

Abstract:

The growing contradiction between the exponential increase in data amount and the limited storage capacity of existing media is becoming increasingly evident, necessitating the development of new types of media to address this issue. Due to its ultra-high density, low energy consumption and long lifetime for data storage, DNA has attracted significant attention as an emerging storage medium, particularly for massive “cold data”, with the potential to replace current storage methods. In the process of data storage, the effective preservation of DNA plays a crucial role, directly impacting the quality of storage density, stability, storage time, as well as data writing and reading. Due to the limited information available on DNA preservation techniques in the current literature, this paper provides an overview of current research progress and strategies in DNA preservation technology for data storage, discusses the difficulties and challenges faced when applying existing preservation techniques in DNA data storage, and presents prospects for the implementation of DNA data storage.

Abstract:

Effectively transferring knowledge from pre-trained models to downstream video understanding tasks is an important topic in computer vision research. Knowledge transfer becomes more challenging in open world due to poor data conditions. Many recent multimodal pre-training models are inspired by natural language processing and perform transfer learning by designing prompt learning. In this paper, we propose an LLM-powered domain context-assisted open-world action recognition method that leverages the open-world understanding capabilities of large language models. Our approach aligns visual representation with multi-level descriptions of human actions for robust classification, by enriching action labels with contextual knowledge in large language model. In the experiments of open-world action recognition with fully supervised setting, we obtain a Top-1 accuracy of 71.86% on the ARID dataset, and an mAP of 80.93% on the Tiny-VARIT dataset. More important, our method can achieve Top-1 accuracy of 48.63% in source-free video domain adaptation and 54.36% in multi-source video domain adaptation.

Abstract:

The absence of unified standards among smart device brands hinders collaborative management, as it requires dealing with different interfaces and communication protocols of each device, thus complicating the implementation of smart home service management. Moreover, the personalized differences due to lifestyle habits, climate conditions, and other factors also make it difficult for pre-set management rules to meet various requirements. To address these challenges, a spatial-temporal data-driven method for smart home service management is studied in this work. The method involves constructing a temporal knowledge graph for smart homes and utilizing a federated learning-based approach for smart home service management. By recording the state of concept instances in smart home scenarios, the temporal knowledge graph provides temporal data for environmental changes and service statuses. Through federated learning algorithms that incorporate model parameters from different households, personalized model updates and predictions of smart home service statuses are achieved. Experimental results showed that this method can effectively manage smart home devices, accurately meet user demands with satisfied accuracy and rate of convergence.

Abstract:

With the advancement of modern science and technology, the volume of data generated through human production and daily life has surged. Traditional silicon-based storage media such as hard disks and flash memory are gradually becoming inadequate to meet the growing global data storage demands. Consequently, there is a need to explore innovative solutions for data storage. Due to its remarkable advantages, such as incredibly high storage density, extremely long-term storage capabilities, and minimal energy consumption, DNA is regarded as the ideal next-generation storage medium. This review primarily focuses on DNA storage technology. It introduces the fundamental theory and workflow, and subsequently provides an overview of the research status regarding data security in DNA storage technology, within the context of conventional data security. This includes aspects like data encryption, data steganography, data resilience, data erasure, and the detection of bio-cyber attacks. The article also discusses the challenges and emerging trends in data security of DNA storage technology. Through interdisciplinary development, DNA storage technology is expected to resolve the conflict between massive data and limited conventional storage space and can be commercialized in multiple scenarios eventually, all while ensuring broad-spectrum and multi-tier data security.

Abstract:

In the era of data explosion, the global demand for data storage has far exceeded the existing storage capacity. DNA, the natural carrier of genetic information, provides a stable, efficient, and sustainable data storage solution. The process of DNA storage is divided into six main parts: encoding, writing, preservation, access, reading, and decoding; and nanopores are widely used to read information stored in DNA because they are capable of detecting a multitude of signals including DNA sequence and its modifications and structural variants. In this review, we systematically introduce the principle and research history of nanopore-based DNA signal detection, and the applications of nanopore sequencing in DNA storage. In addition, we summarize the applications of machine learning in nanopore detection which presents a new direction for the development of nanopore technology and lays the foundation for building a better DNA storage system.

Abstract:

In this era of massive data, DNA serves as a promising new medium for information storage. Compared to traditional physical storage media, it possesses inherent advantages such as low energy consumption, high storage density, and long storage lifespan. With the rapid development of DNA storage, ensuring information security under new technologies becomes crucial. In this regard, this paper combines research in the fields of encryption and DNA coding, proposing a DNA encryption coding method based on chaotic systems and fountain codes. The encryption principle of chaotic systems is utilized during the DNA fountain code encoding process, preserving the characteristics of DNA fountain codes while ensuring the security of encoded information. This method is applicable to any types of data, achieving high information density and DNA encoding under arbitrary constraints. Furthermore, through simulation experiments, it is demonstrated that this method can effectively resist various cryptographic attacks and possesses error-correction capabilities for data errors generated during the DNA storage process.

Abstract:

At present, traditional storage technologies primarily rely on silicon-based materials as the storage medium. The existing global supply of silicon resources cannot meet the growing demand of data storage. Secondly, with the development of the data era, innovation in storage technology has faced challenges. DNA molecules store rich genetic information, and from the perspective of chemical biology. DNA molecules can be used as a medium for data information storage. This provides a new opportunity for storage technology. Non-natural base nucleotides can expand the genetic alphabet and increase the storage capacity, but there are still many issues to be resolved in their practical applications. This article reviews the progress of DNA storage technology, analysing the current state of DNA storage, unresolved technical challenges, and development prospects. Furthermore, it introduces unnatural base pairs (UBPs) as a new direction in synthetic biology, highlighting their potential advantages and technical challenges in the field of DNA information storage.

Abstract:

Metasurfaces are composed of sub-wavelength scale artificial nanostructures, which enhance the light-matter interaction at resonant wavelengths and improve the signal intensity from biochemical molecules in the near field. The chiral plasmonic metasurfaces can robustly and effectively enhance the chiral signal of chiral molecules and achieve the detection of trace molecules. The biochemical detection technology based on chiral plasmonic metasurfaces is increasingly studied to meet the requirements of high resolution and high sensitivity of detection devices in many fields such as biology, chemistry and environment. This review presented mechanism of biochemical detection of chiral plasmonic metasurfaces and the advances in chiral plasmonic metasurface. In terms of biochemical detection, the recent applications of chiral plasmonic metasurfaces in environmental media sensing, chirality detection, fluorescence detection and surface-enhanced Raman detection were introduced. Finally, the review discussed the application prospect of chiral plasmonic metasurfaces in biochemical detection field.

Abstract:

In this work, microporous membranes were prepared using polyvinylidene fluoride (PVDF) and dimethylacetamide as the solvent via non-solvent induced phase separation technique. The effects of dope compositions (i.e., various additives) and phase change conditions (i.e., evaporation time and relative humidity) on the membrane structure and performance were systematically investigated. It was found that hydrophobic PVDF membranes with large pore size and high permeance could be obtained by adding 16 wt.% isopropanol and 6 wt.% glycerol in the dope solution, followed by evaporation for 4 min at RH 80% before being immersed in water. The membrane possessed a completely open surface and bicontinuous interconnected structure, which contribute to little resistance in the filtration processes. The prepared PVDF membranes showed high pure water flux of 8650.74 LMH after pre-wetting and 200nm-polystyrene-microspheres rejection of more than 99%. In addition, the membrane was hydrophobic with water contact angle of 122°, which make the membrane as a promising candidate used for gas sterilization in the bio-pharmaceutical manufacturing process.

Abstract:

Aiming at the performance problems of photodetectors in optical communication, remote sensing and infrared thermal imaging, the research progress of photodetectors in the near-infrared band at home and abroad was discussed. Compared with traditional compound semiconductor materials, new materials such as silicon-based, graphene, tellurium compounds, transition metal dihalogenated compounds and perovskites have unique structures and properties, and are important materials for the preparation of low-power and high-performance photodetectors. This paper mainly expounds the research progress of silicon-based near-infrared photodetectors based on PN and PiN heterojunction structures, introduces the research progress of near-infrared photodetectors based on two-dimensional materials (graphene, tellurium compounds, transition metal dihalogenated compounds) and perovskite materials, and analyzes and compares the performance parameters of related near-infrared photodetectors, which provides ideas for the subsequent research of high-performance near-infrared photodetectors.

Abstract:

The forged wear-resistant steel balls in production often exhibit poor roundness and burrs, which significantly affect their grinding performance. To solve this problem, an industrial vision inspection method and system is proposed. Roundness of the ball is calculated by the maximum difference between the distances from the ball""s center to its contour. For the default of burr detection, a deep learning detection model is employed. Certain rules to distinguish burrs from the complex textures of the background regions are regulated, which enables the model to be trained effectively. Through analysis of burr features, it is found that burrs often appear as protrusions at the contours and exhibit stripe patterns in terms of brightness and slope. Additionally, capturing images of the high-temperature steel balls using digital filtering imaging effectively removes thermal radiation noise and obtains clear ball images. These images are applied to the YOLOv5 instance segmentation model, resulting in a burr detection rate of 95.3%.

Abstract:

Since the human civilization entered the information age, an exponential growth of digital information globally posed great challenges to data storage. Current data storage devices have many defects, such as limited data density, short lifespan, environment pollution and so on. Deoxyribonucleic acid (DNA), the natural carrier of genetic information, was proposed to be a reasonable alternative due to its high information density, robustness, long half-life and low maintenance cost. Although DNA storage currently faces the challenges of high reading and writhing costs, slow speed and high error rate, it has unique advantages in many fields, such as long-time archival storage, military data encryption and so on. The potential future directions of DNA storage mainly include applications under special scenarios such as space data center and military, encoding-decoding algorithms robust to base errors, in vivo DNA storage, information retrieval without sequencing, and integrated DNA storage system as well as a unified evaluation standard. We hope that in the future, DNA storage can achieve large-scale application, and open a new era of data storage.

Abstract:

DNA molecules exhibit highly promising properties of high storage density and extended lifespan as a medium for next generation digital data storage. Thus, it is expected to act as an alternative to address the global issue of insufficient data storage materials. However, the current advances of DNA-based information storage are mainly focused on "the cold storage of information". Those works have been discussed in this work. However, in this context, It is difficult to realize quick data processing in DNA, such as rewriting, updating, deleting, and erasure. Recently, some studies to develop DNA “hot storage of information” using gene editing technology have been highlighted. In that context, the data processing including destruction, encryption, rewriting, regeneration, decay, recovery, and arithmetic recording can be realized. In this work, the feasibility of using DNA media as an information processing carrier has been comprehensively demonstrated, and corresponding advantages and disadvantages were emphasized. This review aims to highlight the importance of DNA storage technology with the potential low energy consumption, high accuracy, high efficiency, and high security. Additionally, it will show the perspective regarding the integration of DNA characteristics for the next generation of intelligent information storage and processing systems.

Abstract:

With the exponential growth of global data, the current information storage technologies are facing numerous drawbacks such as high maintenance costs and limited storage lifespan, which are gradually becoming more apparent in their inability to meet the increasing demands. Therefore, there is an urgent need to develop new information storage methods to address this issue. DNA, as a natural genetic information carrier, possesses advantages such as high storage density, potential low maintenance costs, and long lifespan, making it a potential new information storage medium. Herein, we aim to provide an overview of the basic principles and processes of DNA data storage technology, along with a review of its historical development. Additionally, we summarize the challenges that the field of DNA-based storage currently faces, such as slow data write and read speeds, as well as the possible solutions to these challenges. Finally, we summarize and highlight the future directions of DNA data storage technology.

Abstract:

The construction of artificial cells with specific cell mimic functions helps to explore the complex biological reaction processes and cellular functions in natural biological cell systems, and provides convenience for the in-depth understanding of the origin of life. Artificial cell construction method, based on the top-down and bottom-up principle, in the past few decades have made great progress and extensive application. Build strategy based on artificial cells, human cells can be divided into "top-down" artificial cells and "bottom-up" cells. Bottom-up complementary branch of synthetic biology is a new, it sought from natural or synthetic ingredients to build artificial cells. One of the goals of bottom-up synthetic biology is to construct or mimic the complex pathways present in the cells of natural organisms. Artificial cells derived from lipids, polymer, lipid/polymer hybrid body, natural cell membrane, metal - organic frameworks and condensed matter and so on. With the real human cells can be various substances in the cell such as proteins, genes, such as mitochondria in the surface or wrapped in internal and are endowed with various functions. Moreover, artificial cells can be used as a drug delivery system and a carrier of information exchange. In addition, artificial cells can also replace the damaged cells to restore the normal operation of the body. Here, we first introduced the method based on bottom-up strategy to build artificial cells and classification; Then the various applications of artificial cells are discussed. Finally, the future development of artificial cells is prospected.

Abstract:

Organic afterglow material is an optical material possessing afterglow properties, and can emit light for an extended period following excitation, thereby increase imaging duration and sensitivity. Organic afterglow materials are commonly utilized in biological analysis due to their flexible design and good biocompatibility. Owing to the light-excitation-free luminescence, the afterglow luminescence can circumvent the interference of tissue auto-fluorescence, and provide higher signal-to-background ratio and sensitivity superior to fluorescence. This review provides an overview of the recent advances in organic afterglow materials, which comprehensively summarizes the reported medical applications of these materials in bioanalysis and afterglow imaging. Finally, the article discusses the prospects and challenges of using organic afterglow materials in molecular construction and clinical imaging.

Abstract:

With the regulations of wearing helmets while driving the electric bicycle, it is urgent to develop a detection algorithm that can accurately detect whether the drivers are wearing helmets. This paper introduces a novel method to detect the helmets based on the YOLO framework. The branch absorption module is proposed to improve the residual backbone network, then the feature fusion is improved through the channel recombination. Finally, the designed structural fusion pruning is applied to further compress the hyper-parameters of the model. The experimental results showed that, the proposed algorithm has higher accuracy and faster speed. Performance of small targets detection also can be improved, with the average accuracy of multiple classification up to 88.8% and detection speed of 29.5fps, which can meet the demand of video surveillance in real applications.

Abstract:

Nowadays, with the rapid development of the Internet, more and more new applications appear gradually, the scale of network expand constantly, and the architecture of network is more and more complicated. As one of the basic technologies for enhancing network controllability, traffic classification can not only provide better QoS for ISPs, but also supervise and manage network effectively, which can ensure the security of the Internet. In this paper we review the research methods and achievements in the field of traffic classification, compare these traditional methods, and point out their advantages and disadvantages. On the other hand, for the real challenges of real-time classification of high-speed network environment, encrypted traffic classification, fine-grained traffic classification, and dynamically changed protocols classification, we describe and analyze the related research progress. Finally, we look ahead the future research direction.

Abstract:

The human gut is densely populated by the gut microbiota. There are accumulating evidences indicating that the gut microbiota plays a significant role in the function of the body, which including the metabolism and energy absorption, the development in the function of gastrointestinal, the modulation of immune system and so on. Many chronic diseases, such as obesity, obesity-associated inflammation, inflammatory bowel disease and depression, are related to gut microbiota dysbiosis. The research of the interaction between intestinal bacteria and human body is instructive to the prevention or treatment of many chronic diseases and maintaining health.

Abstract:

The technology of the robot represents a national high-tech level and the degree of automation. It is helpful to develop the industry of service robots in China if we know the current situation and development trend of service robots research clearly. Recently, robotic cleaners and educational robots have been in great demand. Entertainment robots and surveillance robots are developed rapidly and the market expands quickly. Medical robots begin to enter the modern life and have played an important role in the modern surgery. To satisfy the great market and shorten the distance between China and developed countries, it is necessary to capture the development trend of the technology of service robots. R&D on service robots should focus on the integrated technologies on intelligence, modularization and network.

Abstract:

Gene is the genetic material basis. All life phenomena, like disease and death, are related to Gene. Gene sequencing is a way to read life. With the development of new generation high-throughput sequencing technology, TB or more sequence data will be generated daily. It’s more difficult to interpret these big and complex data than to acquire them. Sequence data interpretation is a critical step in current biological research and has great practical significance. It’s a great challenge for current computer systems and computing models to store, process and analysis massive high throughput sequence data. With survey, especially from BGI (Beijing Genome Institute), the current status, problems and measures taken to process high throughput sequence data will be discussed. However, the challenge is too big to be solved unless more people in different fields work together in depth for a long term.

Abstract:

Repetitive sequences are prevalent in genomes. A large number of experiments have confirmed that they play an important role in biological evolution. At present, the discovery and detection of the repetitive sequences have been becoming a hot topic of genomics. This paper summarizes the research progress in this regard, and briefly analyses the associated tools. Finally, the development of repetitive sequences in future is prospected.

Abstract:

This paper gives a comprehensive introduction to the status of current machine translation research and technology, and analyzes the key problems to be resolved. Finally our idea of the future trends and prospects of machine translation are put forward.

Abstract:

Automatic drive is an important application field of artificial intelligence. In this paper, a novel training strategy for self-driving vehicles was investigated based on the deep reinforcement learning model. The proposed method involves a Q-learning algorithm with filtered experience replay and pre-training with experiences from professional drivers, which accelerates the training process due to reduced exploration spaces. By resampling the input state after clustering, generalization ability of the strategy can be improved due to the individual and independent distribution of the samples. Experimental results show that, in comparison with conventional neural fitted Q-iteration algorithm, the training efficiency and controlling stability can be improved more than 90% and 30% respectively by the proposed approach. Experimental results with more complex testing tracks show that, average travel distance can be improved more than 70% in comparison with the Q-learning algorithm by the proposed method.

Abstract:

Hadoop job schedulers typically use a single resource abstraction and resources are allocated at the level of fixed-size partition of the nodes, called slots. These job schedulers ignore the different demands of jobs and fair allocation of multiple types of resources, leading to poor performance in throughput and average job completion time. This paper studies and implements a Muti-resource Fair Scheduler (MFS) in Hadoop. MFS adopts the idea of Dominant Resource Fairness (DRF). It uses a demand vector to describe demands for resources of a job and allocates resources to the job according to the demand vector. MFS uses resources more efficiently and satisfies multiple jobs with heterogeneous demands for resources. Experiment results show that MFS has higher throughput and shorter average job completion time compared to Hadoop slot-based Fair Scheduler and Capacity Scheduler.

Abstract:

With the increasing concerns of global warming and resource constraints, electric vehicles (EVs) have made great progress during the past decade. The electric driving system of EVs has dinstinct advantages, such as quick response, easy measurement , and precise control of motor torque, available flexible driving architecture, and regenerative braking, etc. Such advantages can be used to improve the performance of vehicle dynamic control. This paper presents the recent research efforts on electric vehicle dynamic control in terms of parameters estimation and dyanmic control scheme and methodology, especially focusing on the tire-road friction estimaion , novel traction control methods. The lateral dynamic control including the electrical differential control, direct yaw moment control, and the integratin chassis cotrol is proposed. Several prospects for vehicle dynamic control are proposed.

Abstract:

The accurate contour delineation of the target and organs at risk (OAR) is essential in treatment planning for image guided radiation therapy. In clinical applications, the contour delineation is often done manually by clinicians, which may be accurate, but time-consuming and tedious for users. Although a lot of automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. In this work, a novel approach of target delineation was proposed. Target delineation of OARs was achieved by using snake model and multiscale curve editing to obtain promising results. It allows users to quickly improve contours by a simple mouse click. Experimental results demonstrate the effectiveness of the proposed method for clinical target delineations.

Abstract:

For the advantages of noninvasive, real-time and quantitative detection, ultrasonic transient elastography has important clinical application value. This work investigates the transient elastography in a few ways and aims to design a transient imaging system. The Displacement tracking algorithm based on correlation techniques and the parabolic interpolation algorithm is proposed to improve the accuracy. A novel match filter is designed to convolute with the estimated displacement in the time direction to boost the SNR of the displacement for a better strain image mapping. The convoluted result shows the match filter can significantly improve the strain image quality and help getting more accurate Youngs modulus estimation. The Time Gain Compensation (TGC) circuit is designed to compensate the attenuated power of the ultrasound signal. And a modified polyacrylamide gel based tissue-mimicking phantom is also developed in this paper, both indentation testing and transient elastography are used to characterize the elastic properties of this phantom. The results are almost consistent with each other.

Abstract:

With the rapid increase in numbers and scales of deep web sites on the Internet, search for data or information from deep web sites by submiting queries to and obtaining results from the backend databases has become a major means in information retrieval from the Web. This area has attracted many researchers to devote their efforts on development of technologies to make better use of information in th deep web. One challenge is searching for and integration of data from various databases in deep web. Since deep web is dominated by text data, research and development of technologies for text information retrieval from deep web have a broad application potential. In this paper, we review the state-of-the-art of deep web research in details and propose some future research directions.

Abstract:

Wireless Capsule Endoscope (WCE) is a very promising tool for the examination of the gastrointestinal (GI) tract. However, there are some problems to be solved for the existed WCE, and one key problem is the accurate localization and tracking of the WCE. Among the possible localization methods, the magnet-based localization technique has its advantages: no need for power, not much space occupation, continuously tracking ability, and no negative effect. In this paper, we present the localization method for the magnet objective inside the WCE based on the magnetic sensor array outside the human body. Through the algorithm and system design we realize real time tracking of 3D position and 2D orientation of the magnet based on the magnetic dipole model. In order to overcome the interference of the human body movement, we propose the multi-magnets’ localization method; also, the 3D positioning and 3D orientation method is proposed, which can be used to make the 3D recovery of the GI tract and the accurate computation of the physiological tissue parameters. The real experiments show that the proposed localization system can run well and obtain the accuracy with 2~3mm for the magnet.

Abstract:

Several of the top ranked supercomputers are based on the hybrid architecture consisting of a large number of CPUs and GPUs. High performance has been obtained for problems with special structures, such as FFT-based imaging processing or N-body based particle calculations. However, for the class of problems described by partial differential equations (PDEs) discretized by finite difference (or other mesh based methods such as finite element) methods, obtaining even reasonably good performance on a CPU/GPU cluster is still a challenge. In this paper, we propose and test an hybrid algorithm which matches the architecture of the cluster. The scalability of the approach is implemented by a domain decomposition method, and the GPU performance is realized by using a smoothed aggregation based algebraic multigrid method. Incomplete factorization, which performs beautifully on CPU but poorly on GPU, is completely avoided in the approach. Numerical experiments are carried out by using up to 32 CPU/GPUs for solving PDE problems discretized by FDM with up to 32 millions unknowns.

Abstract:

Transcranial magnetic stimulation (TMS) is a non-invasive technique that can be used for brain studying and clinical therapy. Firstly, the technology feature and application of the TMS instrument were introduced. Then several TMS coil positioning methods were evaluated and several key problems about TMS coil positioning were discussed. The aim of this study was to propose a new method for TMS coil positioning. The new method combines three aspects of quantitative information including the brain scalp, brain anatomy and brain function and has great advantages and broad application prospects.

Abstract:

Effectively reducing power line interference is always an important issue in electromyography (EMG) signal recordings and analysis. In this study, four commonly used de-noising methods, including digital notch, LMS based adaptive filter, Kalman filter and S transform, which may be suitable for the reduction of power line interference in real-time EMG recordings, were chosen and their performance in reducing the power line interference from EMG signal recordings were quantitatively analyzed and compared. The pilot results of this study showed that Kalman filter presented the best whole performance in attenuating power line interference from EMG signals and S transform de-noising method illustrated the best performance when the power line interference was severe.

Abstract:

Foot drop is the inability to voluntarily dorsiflex the ankle during the swing phase of gait and is usually caused by weakness and damages of the peroneal nerve. The consequences of the foot drop include the decreasing of gait quality, the limiting of mobility, the increasing of falling risk, and great increasing of energy expenditure during walking. Firstly biosignal sensors are used in the drop foot stimulator to detect foot movements. Then the surface drop foot stimulator produces a predefined stimulation profile to the peroneal nerve or tibialis anterial to elicit a dorsiflexion of the foot synchronized with the swing phase of gait to lift the foot. This paper reviewed the fundamentals and current researches of drop foot stimulators. Moreover, the development trends of the closed loop drop foot stimulator were also discussed in the paper.

Abstract:

This paper introduces the scientific computing grid, ScGrid, and it’s middleware SCE. ScGrid is built as one virtual supercomputer, integrating computing resource from more than 30 institutes. It provides unified,?easy to use and reliable scientific computing services. SCE is a lightweight grid middleware, which supports global job scheduling and unified data view. It provides multiple user interfaces including command line, grid portal and APIs. At present, ScGrid has been very successfully used in Chinese Academy of Sciences and widely accepted by more than 200 users.

Abstract:

Human action recognition acts as an important role in human machine interaction. This paper proposes a human body recognition method from depth image based on part size and position features. Random forest classifiers are trained with different parameters. Experimental results demonstrate the feasibility of proposed approach. Recognition accuracy is about 91% and the computation time is about 0.96 us per pixel.

Abstract:

In the past work for long time, since the computation required on object deformation and interaction is intensive, when fluid is interacted with rigid bodies, or especially with animating figure, the demand of real-time simulation and rendering could be hardly achieved. This paper presents a novel approach for generating effects simulated by fluid dynamics and interacted by the figure motions. In order to handle the interaction between fluid effect and deformed figure, firstly, the motion trajectory of character is tracked, and then the fluid dynamics is simulated by the model of Smoothed-Particle Hydrodynamics (SPH). Moreover, during the fluid simulation, an efficient algorithm for particle searching is also proposed, in virtue of parallel processing by GPU. Consequently, the simulation of 3D fluid effects with realistic character interaction can be rendered on a consumer-level PC in real-time.

Abstract:

Random Forests is an important ensemble learning method and it is widely used in data classification and nonparametric regression. In this paper, we review three main theoretical issues of random forests, i.e., the convergence theorem, the generalization error bound and the out-of-bag estimation. In the end, we present an improved Random Forests algorithm, which uses a feature weighting sampling method to sample a subset of features at each node in growing trees. The new method is suitable to solve classification problems of very high dimensional data.

Abstract:

In order to help the hearing loss children, we obtained hearing loss children’s fallible pronunciation texts and the confusing pronunciation text pairs form a good deal of hearing loss children’s audio pronunciation data. We designed a data-driven 3D talking head articulatory animation system, it was driven by the articulatory movements which were collected from a device called Electro-magnetic articulography (EMA) AG500, the system simulated Chinese articulation realistically. In that way, the hearing loss children can observe the speaker’s lips and tongue’s motions during the speaker pronouncing, which could help the hearing loss children train pronunciation and correct the fallible pronunciations. Finally, a perception test was applied to evaluate the system’s performance. The results showed that the 3D talking head system can animate both internal and external articulatory motions effectively. A modified CM model based synthesis method was used to generate the articulatory movements. The root mean square between the real articulatory movements and synthetic articulatory movements was used to measure the synthesis method, and an average value of RMS is 1.25 mm.

Abstract:

Morphological parameter measurement of Pseudosciaena Crocea plays an important role in its genetic selection and quality improvement. In this paper, an automatic detecting system which can measure the Pseudosciaena Crocea morphological parameters such as weight, length and body width was developed based on the machine vision and weighing sensor technology. The system can automatically detect the external morphology parameters by the machine vision, and get weight parameters through the weighing sensor. The mean errors of dimensional measurement and weighting are 0.28% and 0.74% respectively, which shows that the developed system can completely meet the requirements of morphological parameter measurement for Pseudosciaena Crocea. It is a new effective method to the automatic detection of fish morphology parameters.

Abstract:

In this work the thermal behavior of the LiNi1/3Co1/3Mn1/3O2 cathode material for soft packed lithium-ion power batteries during charging and discharging at different C-rate were conducted using the ARC (accelerating rate calorimeter) to provide an adiabatic environment. The overall heat generated by the lithium-ion battery during use, is partly reversible and partly irreversible, due to entropy change and joule heating, respectively. It indicates that the heating generation of lithium-ion cell is decided by the C-rate of charge and discharge. The heat is smaller at low C-rate of charge and discharge. For example, the heating generation of battery increases 7.16℃ at 0.2C-rate and the entropy change heat is clearly embodied. The joule heating is more remarkable than the entropy change during charging and discharging at high C-rate. For instance, the heating generation of cell increased 25.63℃ at 1C-rate. The heat generation of charge is less than discharge at the same C-rate. The DC inter insistence of cell at the SOC (State of Charge) of 0 to 10% increases suddenly, so the heating generation power will reach its maximum in this period during discharge. It is valuable for the design of heat dissipation in lithium-ion battery thermal management.