arXiv subjects

Jian Chen

Publications and source records attributed to Jian Chen.

At least 217 records · Page 12Linked to original sources

arXiv2023

Oxidation kinetics and non-Marcusian charge transfer in dimensionally confined semiconductors

Electrochemical reactions represent essential processes in fundamental chemistry that foster a wide range of applications. Although most electrochemical reactions in bulk substances can be well described by the classical Marcus-Gerischer charge transfer theory, the realistic reaction character and mechanism in dimensionally confined systems remain unknown. Here, we report the multiparametric survey on the kinetics of lateral photooxidation in structurally identical WS2 and MoS2 monolayers, where electrochemical oxidation occurs at the atomically thin monolayer edges. The oxidation rate is correlated quantitatively with various crystallographic and environmental parameters, including the density of reactive sites, humidity, temperature, and illumination fluence. In particular, we observe distinctive reaction barriers of 1.4 and 0.9 eV for the two structurally identical semiconductors and uncover an unusual non-Marcusian charge transfer mechanism in these dimensionally confined monolayers due to the limit in reactant supplies. A scenario of band bending is proposed to explain the discrepancy in reaction barriers. These results add important knowledge into the fundamental electrochemical reaction theory in low-dimensional systems.

cond-mat.mtrl-sci↗

arXiv2023

Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspective

The presence of a large number of bots on social media has adverse effects. The graph neural network (GNN) can effectively leverage the social relationships between users and achieve excellent results in detecting bots. Recently, more and more GNN-based methods have been proposed for bot detection. However, the existing GNN-based bot detection methods only focus on low-frequency information and seldom consider high-frequency information, which limits the representation ability of the model. To address this issue, this paper proposes a Multi-scale with Signed-attention Graph Filter for social bot detection called MSGS. MSGS could effectively utilize both high and low-frequency information in the social graph. Specifically, MSGS utilizes a multi-scale structure to produce representation vectors at different scales. These representations are then combined using a signed-attention mechanism. Finally, multi-scale representations via MLP after polymerization to produce the final result. We analyze the frequency response and demonstrate that MSGS is a more flexible and expressive adaptive graph filter. MSGS can effectively utilize high-frequency information to alleviate the over-smoothing problem of deep GNNs. Experimental results on real-world datasets demonstrate that our method achieves better performance compared with several state-of-the-art social bot detection methods.

cs.CV↗

arXiv2023

Intrinsic Electronic Properties of BN Encapsulated, van der Waals Contacted MoSe$_2$ FETs

Two-dimensional (2D) semiconductors have attracted considerable interest for their unique physical properties. Here, we report the intrinsic cryogenic electronic transport properties in few-layer MoSe$_2$ field-effect transistors (FETs) that are simultaneously van der Waals contacted with gold electrodes and are fully encapsulated in ultraclean hexagonal boron nitride dielectrics. The FETs exhibit electronically favorable channel/dielectric interfaces with low densities of interfacial traps ($10^{10}\,$cm$^{-2}$), which lead to outstanding device characteristics at room temperature, including a near-Boltzmann-limit subthreshold swings ($\lt65\,$mV/dec), a high carrier mobility ($68\,$cm$^{2}\cdot$V$^{-1}\cdot$s$^{-1}$), and a negligible scanning hysteresis ($\lt15\,$mV). The dependence of various contact-related quantities on temperature and carrier density are also systematically characterized to understand the van der Waals contacts between gold and MoSe$_2$. The results provide insightful information on the device physics in van der Waals contacted and encapsulated 2D FETs.

cond-mat.mtrl-sci↗

arXiv2023

Clean BN encapsulated 2D FETs with lithography compatible contacts

Device passivation through ultraclean hexagonal BN encapsulation is proven one of the most effective ways for constructing high-quality devices with atomically thin semiconductors that preserves the ultraclean interface quality and intrinsic charge transport behavior. However, it remains challenging to integrate lithography compatible contact electrodes with flexible distributions and patterns. Here, we report the feasibility in straightforwardly integrating lithography defined contacts into BN encapsulated 2D FETs, giving rise to overall device quality comparable to the state-of-the-art results from the painstaking pure dry transfer processing. Electronic characterization on FETs consisting of WSe$_2$ and MoS$_2$ channels reveals an extremely low scanning hysteresis of ca. 2 mV on average, a low density of interfacial charged impurity of ca. $10^{11}\,$cm$^{-2}$, and generally high charge mobilities over $1000\,$cm$^{2}\cdot$V$^{-1}\cdot$s$^{-1}$ at low temperatures. The overall high device qualities verify the viability in directly integrating lithography defined contacts into BN encapsulated devices to exploit their intrinsic charge transport properties for advanced electronics.

cond-mat.mes-hall↗

arXiv2023

Electrical contact properties between Yb and few-layer WS$_2$

Charge injection mechanism from contact electrodes into two-dimensional (2D) dichalcogenides is an essential topic for exploiting electronics based on 2D channels, but remains not well understood. Here, low-work-function metal ytterbium (Yb) was employed as contacts for tungsten disulfide (WS$_2$) to understand the realistic injection mechanism. The contact properties in WS$_2$ with variable temperature (T) and channel thickness (tch) were synergetically characterized. It is found that the Yb/WS$_2$ interfaces exhibit a strong pinning effect between energy levels and a low contact resistance ($R_\rm{C}$) value down to $5\,kΩ\cdotμ$m. Cryogenic electrical measurements reveal that $R_\rm{C}$ exhibits weakly positive dependence on T till 77 K, as well as a weakly negative correlation with tch. In contrast to the non-negligible $R_\rm{C}$ values extracted, an unexpectedly low effective thermal injection barrier of 36 meV is estimated, indicating the presence of significant tunneling injection in subthreshold regime and the inapplicability of the pure thermionic emission model to estimate the height of injection barrier.

cond-mat.mtrl-sci↗

arXiv2023

Enhancement of Carrier Mobility in Semiconductor Nanostructures by Carrier Distribution Engineering

Two-dimensional (2D) van der Waals semiconductors are appealing for low-power transistors. Here, we show the feasibility in enhancing carrier mobility in 2D semiconductors through engineering the vertical distribution of carriers confined inside the ultrathin channels via symmetrizing gate configuration or increasing channel thickness. Through self-consistently solving the Schrödinger-Poisson equations, the shapes of electron envelope functions are extensively investigated by clarifying their relationship with gate configuration, channel thickness, dielectric permittivity, and electron density. The impacts of electron distribution variation on various carrier scattering matrix elements and overall carrier mobility are insightfully clarified. It is found that the carrier mobility can be generally enhanced in the dual-gated configuration due to the centralization of carrier redistribution in the nanometer-thick semiconductor channels and the rate of increase reaches up to 23% in HfO$_2$ dual-gated 10-layer MoS$_2$ channels. This finding represents a viable strategy for performance optimization in transistors consisting of 2D semiconductors.

cond-mat.mtrl-sci↗

arXiv2023

Wave breaking in the unidirectional non-local wave model

In this paper we study wave breaking in the unidirectional non-local wave model describing the motion of a collision-free plasma in a magnetic field. By analyzing the monotonicity and continuity properties of a system of the Riccati-type differential inequalities involving the extremal slopes of flows, we show a new sufficient condition on the initial data to exhibit wave breaking. Moreover, the estimates of life span and wave breaking rate are derived.

math.AP↗

arXiv2023

Proton Collective Quantum Tunneling Induces Anomalous Thermal Conductivity of Ice under Pressure

Proton tunneling is believed to be non-local in ice but has never been shown experimentally. Here we measured thermal conductivity of ice under pressure up to 50 GPa and found it to increase with pressure until 20 GPa but decrease at higher pressures. We attribute this anomalous drop of thermal conductivity to the collective tunneling of protons at high pressures, supported by large-scale quantum molecular dynamics simulations. The collective tunneling loops span several picoseconds in time and are as large as nanometers in space, which match the phonon periods and wavelengths, leading to strong phonon scattering at high pressures. Our results show direct evidence of collective quantum motion existing in high-pressure ice and provide a new perspective to understanding the coupling between phonon propagation and atomic tunneling.

physics.chem-ph↗

arXiv2023

Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)-Assisted Vehicular Networks

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

cs.DC↗

arXiv2023

Simultaneous Wireless Information and Power Transfer in Near-Field Communications

A near-field simultaneous wireless information and power transfer (SWIPT) network is investigated, where the hybrid beamforming architecture is employed at the base station (BS) for information transmission while charging energy harvesting users. A transmit power minimization problem is formulated by jointly optimizing of the analog beamformer, the baseband digital information/energy beamformers, and the number of dedicated energy beams. To tackle the uncertain number of dedicated energy beams, a semidefinite relaxation based rank-one solution construction method is proposed to obtain the optimal baseband digital beamformers under the fixed analog precoder. Based on the structure of the optimal baseband digital beamformers, it is proved that no dedicated energy beam is required in near-field SWIPT. To further exploit this insight, a penalty-based two-layer (PTL) algorithm is proposed to optimize the analog beamformer and baseband digital information beamformers. By employing the block coordinate descent method, the optimal analog beamformer and baseband digital information beamformers are obtained in the closed-form expressions. Moreover, to reduce the high computational complexity caused by the large number of antennas, a low-complexity two-stage algorithm is proposed. Numerical results illustrate that: 1) the proposed PTL algorithm can achieve the near-optimal performance; and 2) in contract to the far-field SWIPT, single near-field beamformer can focus the energy on multiple locations.

cs.IT↗

arXiv2023

NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings.

cs.AR↗

arXiv2023

RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection

The presence of a large number of bots on social media leads to adverse effects. Although Random forest algorithm is widely used in bot detection and can significantly enhance the performance of weak classifiers, it cannot utilize the interaction between accounts. This paper proposes a Random Forest boosted Graph Neural Network for social bot detection, called RF-GNN, which employs graph neural networks (GNNs) as the base classifiers to construct a random forest, effectively combining the advantages of ensemble learning and GNNs to improve the accuracy and robustness of the model. Specifically, different subgraphs are constructed as different training sets through node sampling, feature selection, and edge dropout. Then, GNN base classifiers are trained using various subgraphs, and the remaining features are used for training Fully Connected Netural Network (FCN). The outputs of GNN and FCN are aligned in each branch. Finally, the outputs of all branches are aggregated to produce the final result. Moreover, RF-GNN is compatible with various widely-used GNNs for node classification. Extensive experimental results demonstrate that the proposed method obtains better performance than other state-of-the-art methods.

cs.LG↗

arXiv2023

Physical Layer Security in Near-Field Communications

A near-field secure transmission framework is proposed. Employing the hybrid beamforming architecture, a multi-antenna base station (BS) transmits confidential information to a multi-antenna legitimate user (U) against a multi-antenna eavesdropper (E) in the near field. A two-stage algorithm is proposed to maximize the near-field secrecy capacity. Based on the fully-digital beamformers obtained in the first stage, the optimal analog beamformers and baseband digital beamformers can be alternatingly derived in the closed-form expressions in the second stage. Numerical results demonstrate that in contrast to the far-field secure communication relying on the angular disparity, the near-field secure communication mainly relies on the distance disparity between U and E.

cs.IT↗

arXiv2023

MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

cs.CV↗

arXiv2023

Energy-Delay Tradeoff in Helper-Assisted NOMA-MEC Systems: A Four-Sided Matching Algorithm

This paper designs a helper-assisted resource allocation strategy in non-orthogonal multiple access (NOMA)-enabled mobile edge computing (MEC) systems, in order to guarantee the quality of service (QoS) of the energy/delay-sensitive user equipments (UEs). To achieve a tradeoff between the energy consumption and the delay, we introduce a novel performance metric, called \emph{energy-delay tradeoff}, which is defined as the weighted sum of energy consumption and delay. The joint optimization of user association, resource block (RB) assignment, power allocation, task assignment, and computation resource allocation is formulated as a mixed-integer nonlinear programming problem with the aim of minimizing the maximal energy-delay tradeoff. Due to the non-convexity of the formulated problem with coupled and 0-1 variables, this problem cannot be directly solved with polynomial complexity. To tackle this challenge, we first decouple the formulated problem into a power allocation, task assignment and computation resource allocation (PATACRA) subproblem. Then, with the solution obtained from the PATACRA subproblem, we equivalently reformulate the original problem as a discrete user association and RB assignment (DUARA) problem. For the PATACRA subproblem, an iterative parametric convex approximation (IPCA) algorithm is proposed. Then, based on the solution obtained from the PATACRA subproblem, we first model the DUARA problem as a four-sided matching problem, and then propose a low-complexity four-sided UE-RB-helper-server matching (FS-URHSM) algorithm. Theoretical analysis demonstrates that the proposed algorithms are guaranteed to converge to stable solutions with polynomial complexity. Finally, simulation results are provided to show the superior performance of our proposed algorithm in terms of the energy consumption and the delay.

cs.IT↗

arXiv2023

STARS-ISAC: How Many Sensors Do We Need?

A simultaneously transmitting and reflecting surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where a novel bi-directional sensing-STARS architecture is devised to facilitate the full-space communication and sensing. Based on the proposed framework, a joint optimization problem is formulated, where the Cramer-Rao bound (CRB) for estimating the 2-dimension direction-of-arrival of the sensing target is minimized. Two cases are considered for sensing performance enhancement. 1) For the two-user case, an alternating optimization algorithm is proposed. In particular, the maximum number of deployable sensors is obtained in the closed-form expressions. 2) For the multi-user case, an extended CRB (ECRB) metric is proposed to characterize the impact of the number of sensors on the sensing performance. Based on the proposed metric, a novel penalty-based double-loop (PDL) algorithm is proposed to solve the ECRB minimization problem. To tackle the coupling of the ECRB, a general decoupling approach is proposed to convert it to a tractable weighted linear summation form. Simulation results reveal that 1) the proposed PDL algorithm can achieve a near-optimal performance with consideration of sensor deployment; 2) without violating the communication under the quality of service requirements, reducing the receive antennas at the BS does not deteriorate the sensing performance; and 3) it is preferable to deploy more passive elements than sensors in terms of achieving optimal sensing performance

cs.IT↗

arXiv2023

Timed Model-Based Mutation Operators for Simulink Models

Model-based mutation analysis is a recent research area, and real-time system testing can benefit from using model mutants. Model-based mutation testing (MBMT) is a particular branch of model-based testing. It generates faulty versions of a model using mutation operators to evaluate and improve test cases. Mutation testing is an effective way to ensure software correctness and has been applied to various application areas. Simulink is a vital modeling language for real-time systems. This paper introduces Simulink model mutation analysis to improve Model-in-the-loop (MIL) testing. We propose a set of Simulink mutation operators based on AUTOSAR, which reflects the temporal correctness when a Simulink model is mapped to Operating System tasks. We implement a mutation framework that generates mutants for implicit clock Simulink models. Finally, we demonstrate how this framework generates mutants to reveal task interference issues in the simulation. Our work integrates the Simulink model with the timed systems to better support mutation testing automation.

cs.SE↗

arXiv2022

Boosting Semi-Supervised Learning with Contrastive Complementary Labeling

Semi-supervised learning (SSL) has achieved great success in leveraging a large amount of unlabeled data to learn a promising classifier. A popular approach is pseudo-labeling that generates pseudo labels only for those unlabeled data with high-confidence predictions. As for the low-confidence ones, existing methods often simply discard them because these unreliable pseudo labels may mislead the model. Nevertheless, we highlight that these data with low-confidence pseudo labels can be still beneficial to the training process. Specifically, although the class with the highest probability in the prediction is unreliable, we can assume that this sample is very unlikely to belong to the classes with the lowest probabilities. In this way, these data can be also very informative if we can effectively exploit these complementary labels, i.e., the classes that a sample does not belong to. Inspired by this, we propose a novel Contrastive Complementary Labeling (CCL) method that constructs a large number of reliable negative pairs based on the complementary labels and adopts contrastive learning to make use of all the unlabeled data. Extensive experiments demonstrate that CCL significantly improves the performance on top of existing methods. More critically, our CCL is particularly effective under the label-scarce settings. For example, we yield an improvement of 2.43% over FixMatch on CIFAR-10 only with 40 labeled data.

cs.CV↗