arXiv subjects

Bo Wei

Publications and source records attributed to Bo Wei.

At least 37 records · Page 2Linked to original sources

arXiv2023

Evaluation of ChatGPT-Generated Medical Responses: A Systematic Review and Meta-Analysis

Large language models such as ChatGPT are increasingly explored in medical domains. However, the absence of standard guidelines for performance evaluation has led to methodological inconsistencies. This study aims to summarize the available evidence on evaluating ChatGPT's performance in medicine and provide direction for future research. We searched ten medical literature databases on June 15, 2023, using the keyword "ChatGPT". A total of 3520 articles were identified, of which 60 were reviewed and summarized in this paper and 17 were included in the meta-analysis. The analysis showed that ChatGPT displayed an overall integrated accuracy of 56% (95% CI: 51%-60%, I2 = 87%) in addressing medical queries. However, the studies varied in question resource, question-asking process, and evaluation metrics. Moreover, many studies failed to report methodological details, including the version of ChatGPT and whether each question was used independently or repeatedly. Our findings revealed that although ChatGPT demonstrated considerable potential for application in healthcare, the heterogeneity of the studies and insufficient reporting may affect the reliability of these results. Further well-designed studies with comprehensive and transparent reporting are needed to evaluate ChatGPT's performance in medicine.

stat.ME↗

arXiv2023

Distributed event-triggered aggregative optimization with applications to price-based energy management

This paper studies a distributed continuous-time aggregative optimization problem, which is a fundamental problem in the price-based energy management. The objective of the distributed aggregative optimization is to minimize the sum of local objective functions, which have a specific expression that relies on agents' own decisions and the aggregation of all agents' decisions. To solve the problem, a novel distributed continuous-time algorithm is proposed by combining gradient dynamics with a dynamic average consensus estimator in a two-time scale. The exponential convergence of the proposed algorithm is established under the assumption of a convex global cost function by virtue of the stability theory of singular perturbation systems. Motivated by practical applications, the implementation of the continuous-time algorithm with event-triggered communication is investigated. Simulations on the price-based energy management of distributed energy resources are given to illustrate the proposed method.

math.OC↗

arXiv2023

Overview of Human Activity Recognition Using Sensor Data

Human activity recognition (HAR) is an essential research field that has been used in different applications including home and workplace automation, security and surveillance as well as healthcare. Starting from conventional machine learning methods to the recently developing deep learning techniques and the Internet of things, significant contributions have been shown in the HAR area in the last decade. Even though several review and survey studies have been published, there is a lack of sensor-based HAR overview studies focusing on summarising the usage of wearable sensors and smart home sensors data as well as applications of HAR and deep learning techniques. Hence, we overview sensor-based HAR, discuss several important applications that rely on HAR, and highlight the most common machine learning methods that have been used for HAR. Finally, several challenges of HAR are explored that should be addressed to further improve the robustness of HAR.

eess.SP↗

arXiv2023

General Rational Solutions and Soliton Solutions of the Nonlocal Resonant Nonlinear Schrodinger Equations

General rational solutions for the nonlocal resonant nonlinear Schrodinger equations are derived by using the Hirota bilinear method and the KP hierarchy reduction method. These rational solutions are presented in terms of determinants in which the elements are algebraic expressions. A weaker condition is given for KP reduction in the nonlocal case. The dynamics of first-order solutions are investigated in details. As a special case, we studied rantional solutions of the RNLS equation. Moreover, soliton solutions of the RNLS equation are given by using the Backlund transformation and nonlinear superposition formula.

nlin.SI↗

arXiv2023

Estimation of Complier Expected Shortfall Treatment Effects with a Binary Instrumental Variable

Estimating the causal effect of a treatment or exposure for a subpopulation is of great interest in many biomedical and economical studies. Expected shortfall, also referred to as the super-quantile, is an attractive effect-size measure that can accommodate data heterogeneity and aggregate local information of effect over a certain region of interest of the outcome distribution. In this article, we propose the ComplieR Expected Shortfall Treatment Effect (CRESTE) model under an instrumental variable framework to quantity the CRESTE for a binary endogenous treatment variable. By utilizing the special characteristics of a binary instrumental variable and a specific formulation of Neyman-orthogonalization, we propose a two-step estimation procedure, which can be implemented by simply solving weighted least-squares regression and weighted quantile regression with estimated weights. We develop the asymptotic properties for the proposed estimator and use numerical simulations to confirm its validity and robust finite-sample performance. An illustrative analysis of a National Job Training Partnership Act study is presented to show the practical utility of the proposed method.

stat.ME↗

arXiv2023

Distributed continuous-time strategy-updating rules for noncooperative games with discrete-time communication

In this paper, continuous-time noncooperative games in networks of double-integrator agents are explored. The existing methods require that agents communicate with their neighbors in real time. In this paper, we propose two discrete-time communication schemes based on the designed continuous-time strategy-updating rule for the efficient use of communication resources. First, the property of the designed continuous-time rule is analyzed to ensure that all agents' strategies can converge to the Nash equilibrium. Then, we propose periodic and event-triggered communication schemes for the implementation of the designed rule with discrete-time communication. The rule in the periodic case is implemented synchronously and easily. The rule in the event-triggered case is executed asynchronously without Zeno behaviors. All agents in both cases can asymptotically reach to the Nash equilibrium by interacting with neighbors at discrete times. Simulations are performed in networks of Cournot competition to illustrate the effectiveness of the proposed methods.

math.OC↗

arXiv2022

Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a high-performance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents. Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.

cs.NI↗

arXiv2022

rWiFiSLAM: Effective WiFi Ranging based SLAM System in Ambient Environments

In this paper, we propose rWiFiSLAM, an indoor localisation system based on WiFi ranging measurements. Indoor localisation techniques play an important role in mobile robots when they cannot access good quality GPS signals in indoor environments. Indoor localisation also has many other applications, such as rescue, smart buildings, etc. Inertial Measurement Units (IMU) have been used for Pedestrian Dead Reckoning (PDR) to provide localisation services in the indoor environment as it does not rely on any other signals. Although PDR is a promising technique, it still suffers from unavoidable noise and bias from IMUs in mobile devices. Loop closure is necessary for these scenarios. In this paper, we design an efficient loop closure mechanism based on WiFi ranging measurements along with IMU measurements in a robust pose graph SLAM framework for indoor localisation. One novelty of the proposed method is that we remove the requirement of the full knowledge of the WiFi access point locations, which makes our proposed method feasible for new and/or dynamic environments. We evaluate our designed system in real environments and show the proposed method can achieve sub-meter localisation accuracy and improve the localisation performance by more than 90\% compared with the IMU based PDR.

cs.NI↗

arXiv2022

Dissipative stabilization of linear input delay systems via dynamical state feedback controllers: an optimization based approach

In this note, we present an effective solution to the stabilization of linear input delay systems subject to dissipative constraints while all the effect of input delay is compensated by a controller with novel structure. The method is inspired by the recent development in the mathematical treatment of distributed delays and predictor controllers, which are critical for the derivation of the solution. An important conceptual innovation is the use of a parameterized dynamical state feedback controller (DSFC), where the dimension of the controller equals the dimension of the control input. A sufficient condition for the existence of a dissipative DSFC is obtained via the Krasovskii functional approach, where the condition includes a bilinear matrix inequality (BMI). To solve the BMI, we apply an inner convex approximation algorithm which can be initialized based on an explicit construction of a predictor controller gain. The proposed DSFC can be considered as an extension of the classical predictor controller, thereby capable of compensating all the effects of the pointwise input delay while satisfying dissipative constraints. A numerical example is given to illustrate the effectiveness of our proposed methodology.

math.OC↗

arXiv2022

RSSI-CSI Measurement and Variation Mitigation with Commodity WiFi Device

Owing to the plentiful information released by the commodity devices, WiFi signals have been widely studied for various wireless sensing applications. In many works, both received signal strength indicator (RSSI) and the channel state information (CSI) are utilized as the key factors for precise sensing. However, the calculation and relationship between RSSI and CSI is not explained in detail. Furthermore, there are few works focusing on the measurement variation of the WiFi signal which impacts the sensing results. In this paper, the relationship between RSSI and CSI is studied in detail and the measurement variation of amplitude and phase information is investigated by extensive experiments. In the experiments, transmitter and receiver are directly connected by power divider and RF cables and the signal transmission is quantitatively controlled by RF attenuators. By changing the intensity of attenuation, the measurement of RSSI and CSI is carried out under different conditions. From the results, it is found that in order to get a reliable measurement of the signal amplitude and phase by commodity WiFi, the attenuation of the channels should not exceed 60 dB. Meanwhile, the difference between two channels should be lower than 10 dB. An active control mechanism is suggested to ensure the measurement stability. The findings and criteria of this work is promising to facilitate more precise sensing technologies with WiFi signal.

eess.SP↗

arXiv2022

A Comprehensive Toolbox to Facilitate Quantitative Decision Science in Drug Development: A web-based R shiny application GOahead

Decision-making is critical at each stage of drug development and making informed and transparent Go/No-Go decisions require a sound quantitative decision framework. We designed and implemented GOahead, a comprehensive web-based tool to improve how statisticians and collaborators could prospectively plan and implement the selected Go/No-Go decision approach in real-time. In the paper, we conducted a comprehensive overview of dual-criterion and confidence interval-based approaches to enable quantitative decision-making. illustrative examples are demonstrated for single and two arms designs in both Bayesian and frequentist frameworks, multiple arms design with MCP-MOD is also demonstrated. GOahead can be found on shinyapps server.

stat.AP↗

arXiv2022

A distributed generalized Nash equilibrium seeking algorithm based on extremum seeking control

In this paper, a distributed non-model based seeking algorithm which combines the extremum seeking control (ESC) jointly with learning algorithms is proposed to seek a generalized Nash equilibrium (GNE) for a class of noncooperative games with coupled equality constraint. The strategy of each agent is restricted by both the coupled inter-agent constraint and local inequality constraints. Thanks to the ESC, it is unnecessary to know the specific expressions of agents' cost functions and local constraints and to know the strategies of other agents for the implementation of the proposed GNE seeking algorithm. To deal with the coupled constraints, only the Lagrange multiplier is transmitted among agents with some prior information about the coupled constraints. Moreover, a diminishing dither signal is designed in the seeking algorithm to remove undesirable steady-state oscillations. The non-local convergence of the designed seeking algorithm is analyzed via the singular perturbation theory, averaging analysis and Lyapunov stability theory. Numerical examples are given to verify the effectiveness of our proposed method.

math.OC↗

arXiv2021

Nash Equilibrium Seeking for General Linear Systems with Disturbance Rejection

This paper explores aggregative games in a network of general linear systems subject to external disturbances. To deal with external disturbances, distributed strategy-updating rules based on internal model are proposed for the case with perfect and imperfect information, respectively. Different from existing algorithms based on gradient dynamics, by introducing the integral of gradient of cost functions on the basis of passive theory, the rules are proposed to force the strategies of all players to evolve to Nash equilibrium regardless the effect of disturbances. The convergence of the two strategy-updating rules is analyzed via Lyapunov stability theory, passive theory and singular perturbation theory. Simulations are presented to verify the obtained results.

math.OC↗

arXiv2021

Multi-breather solutions to the Sasa-Satsuma equation

General breather solution to the Sasa-Satsuma (SS) equation is systematically investigated in this paper. We firstly transform the SS equation into a set of three Hirota bilinear equations under proper plane wave background. Starting from a specially arranged tau-function of the Kadomtsev-Petviashvili hierarchy and a set of eleven bilinear equations satisfied, we implement a series steps of reduction procedure, i.e., C-type reduction, dimension reduction and complex conjugate reduction, and reduce these eleven equations to three bilinear equations for the SS equation. Meanwhile, general breather solution to the SS equation is found in determinant of even order. The one- and two-breather solutions are calculated and analyzed in details.

nlin.SI↗

arXiv2021

Adaptive video transmission using QUBO method and Digital Annealer based on Ising machine

With the dramatically increasing video streaming in the total network traffic, it is critical to develop effective algorithms to promote the content delivery service of high quality. Adaptive bitrate (ABR) control is the most essential technique which determines the proper bitrate to be chosen based on network conditions, thus realize high-quality video streaming. In this paper, a novel ABR strategy is proposed based on Ising machine by using the quadratic unconstrained binary optimization (QUBO) method and Digital Annealer (DA) for the first time. The proposed method is evaluated by simulation with the real-world measured throughput, and compared with other state-of-the-art methods. Experiment results show that the proposed QUBO-based method can outperform the existing methods, which demonstrating the superior of the proposed QUBO-based method.

cs.NI↗

arXiv2021

Distributed strategy-updating rules for aggregative games of multi-integrator systems with coupled constraints

In this paper, we explore aggregative games over networks of multi-integrator agents with coupled constraints. To reach the general Nash equilibrium of an aggregative game, a distributed strategy-updating rule is proposed by a combination of the coordination of Lagrange multipliers and the estimation of the aggregator. Each player has only access to partial-decision information and communicates with his neighbors in a weight-balanced digraph which characterizes players' preferences as to the values of information received from neighbors. We first consider networks of double-integrator agents and then focus on multi-integrator agents. The effectiveness of the proposed strategy-updating rules is demonstrated by analyzing the convergence of corresponding dynamical systems via the Lyapunov stability theory, singular perturbation theory and passive theory. Numerical examples are given to illustrate our results.

math.OC↗

arXiv2021

InaudibleKey: Generic Inaudible Acoustic Signal based Key Agreement Protocol for Mobile Devices

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present InaudibleKey, an inaudible acoustic signal-based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey exploits the acoustic channel frequency response of two legitimate devices as a common secret to generating keys. InaudibleKey employs several novel technologies to significantly improve its performance. We conduct extensive experiments to evaluate the proposed system in different real environments. Compared to state-of-the-art works, InaudibleKey improves key generation rate by 3-145 times, extends pairing distance by 3.2-44 times, and reduces information reconciliation counts by 2.5-16 times. Security analysis demonstrates that InaudibleKey is resilient to a number of malicious attacks. We also implement InaudibleKey on modern smartphones and resource-limited IoT devices. Results show that it is energy-efficient and can run on both powerful and resource-limited IoT devices without incurring excessive resource consumption.

cs.NI↗

arXiv2021

Multi-channel Deep Supervision for Crowd Counting

Crowd counting is a task worth exploring in modern society because of its wide applications such as public safety and video monitoring. Many CNN-based approaches have been proposed to improve the accuracy of estimation, but there are some inherent issues affect the performance, such as overfitting and details lost caused by pooling layers. To tackle these problems, in this paper, we propose an effective network called MDSNet, which introduces a novel supervision framework called Multi-channel Deep Supervision (MDS). The MDS conducts channel-wise supervision on the decoder of the estimation model to help generate the density maps. To obtain the accurate supervision information of different channels, the MDSNet employs an auxiliary network called SupervisionNet (SN) to generate abundant supervision maps based on existing groundtruth. Besides the traditional density map supervision, we also use the SN to convert the dot annotations into continuous supervision information and conduct dot supervision in the MDSNet. Extensive experiments on several mainstream benchmarks show that the proposed MDSNet achieves competitive results and the MDS significantly improves the performance without changing the network structure.

cs.CV↗