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Motivated by widespread dominance hierarchy, growth of group sizes, and feedback mechanisms in social species, we are devoted to exploring the scalable second-order consensus of hierarchical groups. More specifically, a hierarchical group consists of a collection of agents with double-integrator dynamics on a directed acyclic graph with additional reverse edges, which characterize feedback mechanisms across hierarchical layers. As the group size grows and the reverse edges appear, we investigate whether the absolute velocity protocol and the relative velocity protocol can preserve the system consensus property without tuning the control gains. It is rigorously proved that the absolute velocity protocol is able to achieve completely scalable second-order consensus but the relative velocity protocol cannot. This result theoretically reveals how the scalable coordination behavior in hierarchical groups is determined by local interaction rules. Moreover, we develop a hierarchical structure in order to achieve scalable second-order consensus for networks of any size and with any number of reverse edges., Comment: 9 pages, 1 figure

Earth-abundant transition metal oxides are promising electrocatalysts for oxidation of biomass alcohols. Here, CoO and Co3O4 are selected as representative cobalt oxide catalysts and grown on carbon fiber paper (CFP) electrodes to reveal the interplay between electronic structure and catalytic activity of catalysts for oxidation of glycerol, diols, and monohydric alcohols. In situ electrochemical tests elucidate that the CoO/CFP electrode has lower interfacial impedance, higher charge transfer, faster oxidation rate, and thereby the higher catalytic activity for alcohol oxidation than the Co3O4/CFP electrode. Especially for glycerol oxidation, the CoO/CFP electrode only requires 1.32 V to reach 10 mA cm−2, the potential is 120 mV lower than that for the Co3O4/CFP electrode. The CoO/CFP electrode can also produce value-added products such as formate, acetate, and glycolate with high selectivity and efficiency at low energy consumptions from oxidation of biomass alcohols. Theoretical calculations further confirm the dominant role of octahedrally coordinated Co-O sites in adsorption, activation, and oxidation of C3-C1 alcohols. This work sheds light on the design of highly efficient transition metal oxide catalysts for oxidation of alcohols by populating octahedral sites in the crystal structure.

Memory corruption attacks (MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and performance. MCAs in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms, we found that MCAs are mainly categorized into two types--return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection (CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CToMP resists the ROP attack, a novel variant of return2libc attacks. Finally, we implement CToMP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CToMP is resilient to various MCAs in unmanned systems with low footprints and system overhead., Comment: This paper has been accepted by SCIENCE CHINA Information Sciences

Human limb motion tracking and recognition plays an important role in medical rehabilitation training, lower limb assistance, prosthetics design for amputees, feedback control for assistive robots, etc. Lightweight wearable sensors, including inertial sensors, surface electromyography sensors, and flexible strain/pressure, are promising to become the next-generation human motion capture devices. Herein, we present a wireless wearable device consisting of a sixteen-channel flexible sponge-based pressure sensor array to recognize various human lower limb motions by detecting contours on the human skin caused by calf gastrocnemius muscle actions. Each sensing element is a round porous structure of thin carbon nanotube/polydimethylsiloxane nanocomposites with a diameter of 4 mm and thickness of about 400 {\mu}m. Ten human subjects were recruited to perform ten different lower limb motions while wearing the developed device. The motion classification result with the support vector machine method shows a macro-recall of about 97.3% for all ten motions tested. This work demonstrates a portable wearable muscle activity detection device with a lower limb motion recognition application, which can be potentially used in assistive robot control, healthcare, sports monitoring, etc., Comment: 17 pages, 6 figures

Mobile and IoT applications have greatly enriched our daily life by providing convenient and intelligent services. However, these smart applications have been a prime target of adversaries for stealing sensitive data. It poses a crucial threat to users' identity security, financial security, or even life security. Research communities and industries have proposed many Information Flow Control (IFC) techniques for data leakage detection and prevention, including secure modeling, type system, static analysis, dynamic analysis, \textit{etc}. According to the application's development life cycle, although most attacks are conducted during the application's execution phase, data leakage vulnerabilities have been introduced since the design phase. With a focus on lifecycle protection, this survey reviews the recent representative works adopted in different phases. We propose an information flow based defensive chain, which provides a new framework to systematically understand various IFC techniques for data leakage detection and prevention in Mobile and IoT applications. In line with the phases of the application life cycle, each reviewed work is comprehensively studied in terms of technique, performance, and limitation. Research challenges and future directions are also pointed out by consideration of the integrity of the defensive chain., Comment: 36 pages, 6 figures, 6 tables

Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading in this work by utilizing variable time condition number estimation and quantum linear regression.The algorithm complexity has been reduced from the classical benchmark O(N^2d) to O(sqrt(d)(kappa)^2(log(1/epsilon))^2 )). It shows quantum advantage, where N is the length of trading data, and d is the number of stocks, kappa is the condition number and epsilon is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.

In social sciences, there is currently rare consensus on the underlying mechanism for cultural evolution, partially due to lack of suitable data. The evolution of first names of newborn babies offers a remarkable example for such researches. In this paper, we employ the historical data on baby names from the United States to investigate the evolutionary process of culture, in particular focusing on how inequality among baby names changes over time. Then we propose a stochastic model where individual choice is determined by both individual preference and social influence, and show that the decrease in the strength of social influence can account for all the observed empirical features. Therefore, we claim that the weakening of social influence drives cultural evolution.

In social sciences, there is currently no consensus on the mechanism for cultural evolution. The evolution of first names of newborn babies offers a remarkable example for the researches in the field. Here we perform statistical analyses on over 100 years of data in the United States. We focus in particular on how the frequency-rank distribution and inequality of baby names change over time. We propose a stochastic model where name choice is determined by personalized preference and social influence. Remarkably, variations on the strength of personalized preference can account satisfactorily for the observed empirical features. Therefore, we claim that personalization drives cultural evolution, at least in the example of baby names.

Conjugated polymers have promising applications as actuators in biomimetic robotics and bio/micromanipulation. For these applications, it is highly desirable to have predictive models available for feasibility study and design optimization. In this paper a geometrically-scalable model is presented for trilayer conjugated polymer actuators based on the diffusive-elastic-metal model. The proposed model characterizes actuation behaviors in terms of intrinsic material parameters and actuator dimensions. Experiments are conducted on polypyrrole actuators of different dimensions to validate the developed scaling laws for the quasi-static force and displacement output, the electrical admittance, and the dynamic displacement response.

Conjugated polymers have promising applications as actuators in biomimetic robotics and bio/micromanipulation. For these applications, it is highly desirable to have predictive models available for feasibility study and design optimization. In this paper a geometrically-scalable model is presented for trilayer conjugated polymer actuators based on the diffusive-elastic-metal model. The proposed model characterizes actuation behaviors in terms of intrinsic material parameters and actuator dimensions. Experiments are conducted on polypyrrole actuators of different dimensions to validate the developed scaling laws for the quasi-static force and displacement output, the electrical admittance, and the dynamic displacement response.

Conjugated polymers have promising applications as actuators in biomimetic robotics and bio/micromanipulation. For these applications, it is highly desirable to have predictive models available for feasibility study and design optimization. In this paper a geometrically-scalable model is presented for trilayer conjugated polymer actuators based on the diffusive-elastic-metal model. The proposed model characterizes actuation behaviors in terms of intrinsic material parameters and actuator dimensions. Experiments are conducted on polypyrrole actuators of different dimensions to validate the developed scaling laws for the quasi-static force and displacement output, the electrical admittance, and the dynamic displacement response.

Conjugated polymers have promising applications as actuators in biomimetic robotics and bio/micromanipulation. For these applications, it is highly desirable to have predictive models available for feasibility study and design optimization. In this paper a geometrically-scalable model is presented for trilayer conjugated polymer actuators based on the diffusive-elastic-metal model. The proposed model characterizes actuation behaviors in terms of intrinsic material parameters and actuator dimensions. Experiments are conducted on polypyrrole actuators of different dimensions to validate the developed scaling laws for the quasi-static force and displacement output, the electrical admittance, and the dynamic displacement response.

Conjugated polymers have promising applications as actuators in biomimetic robotics and bio/micromanipulation.For these applications, it is highly desirable to have predictive models available for feasibility study and designoptimization. In this paper a geometrically-scalable model is presented for trilayer conjugated polymer actuatorsbased on the diffusive-elastic-metal model. The proposed model characterizes actuation behaviors in terms ofintrinsic material parameters and actuator dimensions. Experiments are conducted on polypyrrole actuators ofdifferent dimensions to validate the developed scaling laws for the quasi-static force and displacement output,the electrical admittance, and the dynamic displacement response.

Conjugated polymers have promising applications as actuators in biomimetic robotics and bio/micromanipulation.For these applications, it is highly desirable to have predictive models available for feasibility study and designoptimization. In this paper a geometrically-scalable model is presented for trilayer conjugated polymer actuatorsbased on the diffusive-elastic-metal model. The proposed model characterizes actuation behaviors in terms ofintrinsic material parameters and actuator dimensions. Experiments are conducted on polypyrrole actuators ofdifferent dimensions to validate the developed scaling laws for the quasi-static force and displacement output,the electrical admittance, and the dynamic displacement response.

Conjugated polymers have promising applications as actuators in biomimetic robotics and bio/micromanipulation.For these applications, it is highly desirable to have predictive models available for feasibility study and designoptimization. In this paper a geometrically-scalable model is presented for trilayer conjugated polymer actuatorsbased on the diffusive-elastic-metal model. The proposed model characterizes actuation behaviors in terms ofintrinsic material parameters and actuator dimensions. Experiments are conducted on polypyrrole actuators ofdifferent dimensions to validate the developed scaling laws for the quasi-static force and displacement output,the electrical admittance, and the dynamic displacement response.

The formation control of multiple vehicles using a perceptive frame is studied in this paper. The method is applied to multiple mobile robots. The controller design and lab experiments are shown in the paper. Some useful strategies of coordination are implemented in a perceptive frame by reference projections. The feature of the coordination is illustrated by experiments of three mobile robots.

Recently, supply networks have attracted increasing attention from the scientific community. However, it lacks serious consideration of social preference in Supply Chain Management. In this paper, we develop an evolutionary decision-making model to characterize the effects of suppliers’ altruism in supply networks, and find that the performances of both suppliers and supply chains are improved by introducing the role of altruism. Furthermore, an interesting and reasonable phenomenon is discovered that the suppliers’ and whole network’s profits do not change monotonously with suppliers’ altruistic preference, η, but reach the best at η=0.6 and η=0.4, respectively. This work may shed some light on the in-depth understanding of the effects of altruism for both research and commercial applications.

In social sciences, there is currently rare consensus on the underlying mechanism for cultural evolution, partially due to lack of suitable data. The evolution of first names of newborn babies offers a remarkable example for such researches. In this paper, we employ the historical data on baby names from the United States to investigate the evolutionary process of culture, in particular focusing on how inequality among baby names changes over time. Then we propose a stochastic model where individual choice is determined by both individual preference and social influence, and show that the decrease in the strength of social influence can account for all the observed empirical features. Therefore, we claim that the weakening of social influence drives cultural evolution.