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Researcher profile

Peng Wu

Peng Wu contributes to research discovery and scholarly infrastructure.

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Computer Visioncond-mat.mes-hallInformation RetrievalArtificial Intelligenceastro-ph.COastro-ph.IMComputation and LanguageDistributed, Parallel, and Cluster ComputingFormal Languages and Automata TheoryGraphicsMachine LearningNetworking and Internet Architecturephysics.acc-phphysics.app-phphysics.flu-dynphysics.ins-detq-fin.STquant-phSoftware Engineering

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Published work

22 published item(s)

preprint2026arXiv

A Forward Simulation-Based Hierarchy of Linearizable Concurrent Objects

In this paper, we systematically investigate the connection between linearizable objects and forward simulation. We prove that the sets of linearizable objects satisfying wait-freedom (resp., lock-freedom or obstruction-freedom) form a bounded join-semilattice under the forward simulation relation, and that the sets of linearizable objects without liveness constraints form a bounded lattice under the same relation. As part of our lattice result, we propose an equivalent characterization of linearizability by reducing checking linearizability w.r.t. sequential specification $Spec$ into checking forward simulation by an object $\mathcal{U}_{Spec}$. To demonstrate the forward simulation relation between linearizable objects, we prove that the objects that are strongly linearizable w.r.t. the same sequential specification and are wait-free (resp., lock-free, obstruction-free) simulate each other, and we prove that the time-stamped queue simulates the Herlihy-Wing queue. We also prove that the Herlihy-Wing queue is simulated by $\mathcal{U}_{Spec}$, and thus, our equivalent characterization of linearizability can be used in the verification of linearizability.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Boosting HDR Image Reconstruction via Semantic Knowledge Transfer

Recovering High Dynamic Range (HDR) images from multiple Standard Dynamic Range (SDR) images become challenging when the SDR images exhibit noticeable degradation and missing content. Leveraging scene-specific semantic priors offers a promising solution for restoring heavily degraded regions. However, these priors are typically extracted from sRGB SDR images, the domain/format gap poses a significant challenge when applying it to HDR imaging. To address this issue, we propose a general framework that transfers semantic knowledge derived from SDR domain via self-distillation to boost existing HDR reconstruction. Specifically, the proposed framework first introduces the Semantic Priors Guided Reconstruction Model (SPGRM), which leverages SDR image semantic knowledge to address ill-posed problems in the initial HDR reconstruction results. Subsequently, we leverage a self-distillation mechanism that constrains the color and content information with semantic knowledge, aligning the external outputs between the baseline and SPGRM. Furthermore, to transfer the semantic knowledge of the internal features, we utilize a Semantic Knowledge Alignment Module (SKAM) to fill the missing semantic contents with the complementary masks. Extensive experiments demonstrate that our framework significantly boosts HDR imaging quality for existing methods without altering the network architecture.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Diagnosing Training Inference Mismatch in LLM Reinforcement Learning

Modern LLM RL systems separate rollout generation from policy optimization. These two stages are expected to produce token probabilities that match exactly. However, implementation differences can make them assign different values to the same sequence under the same model weights, inducing Training-Inference Mismatch (TIM). TIM is difficult to inspect because it is entangled with off-policy drift and common stabilization mechanisms. In this work, we isolate TIM in a zero-mismatch diagnostic setting (VeXact), and show that small token-level numerical disagreements can independently cause training collapse. We further show that TIM changes the effective optimization problem, and identify a set of remedies that could mitigate TIM. Our results suggest that TIM is not benign numerical noise, but a systems-level perturbation that should be treated as a first-order factor in analyzing LLM RL stability.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Guiding Cross-Modal Representations with MLLM Priors via Preference Alignment

Despite Contrastive Language-Image Pretraining (CLIP)'s remarkable capability to retrieve content across modalities, a substantial modality gap persists in its feature space. Intriguingly, we discover that off-the-shelf MLLMs (Multimodal Large Language Models) demonstrate powerful inherent modality alignment properties. While recent MLLM-based retrievers with unified architectures partially mitigate this gap, their reliance on coarse modality alignment mechanisms fundamentally limits their potential. In this work, We introduce MAPLE (Modality-Aligned Preference Learning for Embeddings), a novel framework that leverages the fine grained alignment priors inherent in MLLM to guide cross modal representation learning. MAPLE formulates the learning process as reinforcement learning with two key components: (1) Automatic preference data construction using off-the-shelf MLLM, and (2) a new Relative Preference Alignment (RPA) loss, which adapts Direct Preference Optimization (DPO) to the embedding learning setting. Experimental results show that our preference-guided alignment achieves substantial gains in fine-grained cross-modal retrieval, underscoring its effectiveness in handling nuanced semantic distinctions.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Introduction to the Chinese Space Station Survey Telescope (CSST)

The Chinese Space Station Survey Telescope (CSST) is an upcoming Stage-IV sky survey telescope, distinguished by its large field of view (FoV), high image quality, and multi-band observation capabilities. It can simultaneously conduct precise measurements of the Universe by performing multi-color photometric imaging and slitless spectroscopic surveys. The CSST is equipped with five scientific instruments, i.e. Multi-band Imaging and Slitless Spectroscopy Survey Camera (SC), Multi-Channel Imager (MCI), Integral Field Spectrograph (IFS), Cool Planet Imaging Coronagraph (CPI-C), and THz Spectrometer (TS). Using these instruments, CSST is expected to make significant contributions and discoveries across various astronomical fields, including cosmology, galaxies and active galactic nuclei (AGN), the Milky Way and nearby galaxies, stars, exoplanets, Solar System objects, astrometry, and transients and variable sources. This review aims to provide a comprehensive overview of the CSST instruments, observational capabilities, data products, and scientific potential.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Proactive Recommendation in Social Networks: Steering User Interest with Causal Inference

Recommending items that solely cater to users' historical interests narrows users' horizons. Recent works have considered steering target users beyond their historical interests by directly adjusting items exposed to them. However, the recommended items for direct steering might not align perfectly with the evolution of users' interests, detrimentally affecting the target users' experience. To avoid this issue, we propose a new task named Proactive Recommendation in Social Networks (PRSN) that indirectly steers users' interest by utilizing the influence of social neighbors, i.e., indirect steering by adjusting the exposure of a target item to target users' neighbors. The key to PRSN lies in answering an interventional question: what would a target user' s feedback be on a target item if the item is exposed to the user' s different neighbors? To answer this question, we resort to causal inference and formalize PRSN as: (1) estimating the potential feedback of a user on an item, under the network interference by the item' s exposure to the user' s neighbors; and (2) adjusting the exposure of a target item to target users' neighbors to trade off steering performance and the damage to the neighbors' experience. To this end, we propose a Neighbor Interference Recommendation (NIRec) framework with two modules: (1) an interference representation-based estimation module for modeling potential feedback; (2) a post-learning-based optimization module for adjusting a target item' s exposure to trade off steering performance and the neighbors' experience through greedy search. We conduct extensive semi-simulation experiments on real-world datasets, validating the steering effectiveness of NIRec.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

SliceLens: Fine-Grained and Grounded Error Slice Discovery for Multi-Instance Vision Tasks

Systematic failures of computer vision models on subsets with coherent visual patterns, known as error slices, pose a critical challenge for robust model evaluation. Existing slice discovery methods are primarily developed for image classification, limiting their applicability to multi-instance tasks such as detection, segmentation, and pose estimation. In real-world scenarios, error slices often arise from corner cases involving complex visual relationships, where existing instance-level approaches lacking fine-grained reasoning struggle to yield meaningful insights. Moreover, current benchmarks are typically tailored to specific algorithms or biased toward image classification, with artificial ground truth that fails to reflect real model failures. To address these limitations, we propose SliceLens, a hypothesis-driven framework that leverages LLMs and VLMs to generate and verify diverse failure hypotheses through grounded visual reasoning, enabling reliable identification of fine-grained and interpretable error slices. We further introduce FeSD (Fine-grained Slice Discovery), the first benchmark specifically designed for evaluating fine-grained error slice discovery across instance-level vision tasks, featuring expert-annotated and carefully refined ground-truth slices with precise grounding to local error regions. Extensive experiments on both existing benchmarks and FeSD demonstrate that SliceLens achieves state-of-the-art performance, improving Precision@10 by 0.42 (0.73 vs. 0.31) on FeSD, and identifies interpretable slices that facilitate actionable model improvements, as validated through model repair experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item ratings while omitting the underlying causes behind the ratings. For example, the widely-used Yahoo!R3 dataset contains little information on the causes of the user-movie ratings. A solution could be to conduct surveys and require the users to provide such information. In practice, the user surveys can hardly avoid compliance issues and sparse user responses, which greatly hinders the exploration of causality-based recommendation. To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios. To illustrate the use of our framework, we construct a semi-synthetic dataset with Causal Tags And Ratings (CTAR), based on the movies as well as their descriptive tags and rating information collected from a famous movie rating website. Using the collected data and the causal graph, the user-item-ratings and their corresponding user-item-tags are automatically generated, which provides the reasons (selected tags) why the user rates the items. Descriptive statistics and baseline results regarding the CTAR dataset are also reported. The proposed data generation framework is not limited to recommendation, and the released APIs can be used to generate customized datasets for other research tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting

Recently, many semi-supervised object detection (SSOD) methods adopt teacher-student framework and have achieved state-of-the-art results. However, the teacher network is tightly coupled with the student network since the teacher is an exponential moving average (EMA) of the student, which causes a performance bottleneck. To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2. Based on these networks, a cycle self-training mechanism is built, i.e., S1${\rightarrow}$T1${\rightarrow}$S2${\rightarrow}$T2${\rightarrow}$S1. For S${\rightarrow}$T, we also utilize the EMA weights of the students to update the teachers. For T${\rightarrow}$S, instead of providing supervision for its own student S1(S2) directly, the teacher T1(T2) generates pseudo-labels for the student S2(S1), which looses the coupling effect. Moreover, owing to the property of EMA, the teacher is most likely to accumulate the biases from the student and make the mistakes irreversible. To mitigate the problem, we also propose a distribution consistency reweighting strategy, where pseudo-labels are reweighted based on distribution consistency across the teachers T1 and T2. With the strategy, the two students S2 and S1 can be trained robustly with noisy pseudo labels to avoid confirmation biases. Extensive experiments prove the superiority of CST by consistently improving the AP over the baseline and outperforming state-of-the-art methods by 2.1% absolute AP improvements with scarce labeled data.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Design Considerations for 2D Dirac-Source FETs: Device Parameters, Non-Idealities and Benchmarking

Dirac-source field-effect transistors (DS-FETs) have been proposed as promising candidates for low-power switching devices by leveraging the Dirac cone of graphene as a low-pass energy filter. In particular, using two-dimensional (2D) materials as the channel in a DS-FET is of interest for ultimate scaling purposes. In this paper, we investigate the design considerations for 2D DS-FETs using ballistic simulations based on Landauer formalism. We study the impact of several key device parameters on the device performance, such as graphene doping, Schottky barrier heights, and effective mass of the 2D channel. In addition, we study the impact of non-idealities on the performance of DS-FETs, such as graphene disorder and rethermalization, as well as ways to mitigate them. Finally, we benchmark the performance of DS-FETs for different channel materials, providing a guide for the proper choice of material for 2D DS-FETs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

From Rough to Multifractal volatility: the log S-fBM model

We introduce a family of random measures $M_{H,T} (d t)$, namely log S-fBM, such that, for $H>0$, $M_{H,T}(d t) = e^{ω_{H,T}(t)} d t$ where $ω_{H,T}(t)$ is a Gaussian process that can be considered as a stationary version of an $H$-fractional Brownian motion. Moreover, when $H \to 0$, one has $M_{H,T}(d t) \rightarrow {\widetilde M}_{T}(d t)$ (in the weak sense) where ${\widetilde M}_{T}(d t)$ is the celebrated log-normal multifractal random measure (MRM). Thus, this model allows us to consider, within the same framework, the two popular classes of multifractal ($H = 0$) and rough volatility ($0<H < 1/2$) models. The main properties of the log S-fBM are discussed and their estimation issues are addressed. We notably show that the direct estimation of $H$ from the scaling properties of $\ln(M_{H,T}([t, t+τ]))$, at fixed $τ$, can lead to strongly over-estimating the value of $H$. We propose a better GMM estimation method which is shown to be valid in the high-frequency asymptotic regime. When applied to a large set of empirical volatility data, we observe that stock indices have values around $H=0.1$ while individual stocks are characterized by values of $H$ that can be very close to $0$ and thus well described by a MRM. We also bring evidence that unlike the log-volatility variance $ν^2$ whose estimation appears to be poorly reliable (though used widely in the rough volatility literature), the estimation of the so-called &#34;intermittency coefficient&#34; $λ^2$, which is the product of $ν^2$ and the Hurst exponent $H$, appears to be far more reliable leading to values that seem to be universal for respectively all individual stocks and all stock indices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Mobility overestimation in MoS$_2$ transistors due to invasive voltage probes

Improving carrier mobilities of two-dimensional (2D) semiconductors is highly sought after. Recently, Ng. et al. [1] reported rippled molybdenum disulfide (MoS$_2$) transistors on bulged silicon nitride (SiN$_x$) substrates that exhibit high electron mobilities up to ~900 cm$^2$V$^{-1}$s$^{-1}$. The high mobility values were attributed to the suppression of electron-phonon scattering by the lattice distortion in the rippled MoS$_2$ channel. While the results are compelling, this Matters Arising shows that the mobility values in ref. [1] are likely to be overestimated due to invasive voltage probes in the four-probe measurement setup, which causes a positive threshold voltage shift near the voltage probes and an artificial overestimation of apparent field-effect mobility.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges

Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks. Nevertheless, a unified causal analysis framework has not been established yet. Many causal-based prediction and debiasing studies rarely discuss the causal interpretation of various biases and the rationality of the corresponding causal assumptions. In this paper, we first provide a formal causal analysis framework to survey and unify the existing causal-inspired recommendation methods, which can accommodate different scenarios in RS. Then we propose a new taxonomy and give formal causal definitions of various biases in RS from the perspective of violating the assumptions adopted in causal analysis. Finally, we formalize many debiasing and prediction tasks in RS, and summarize the statistical and machine learning-based causal estimation methods, expecting to provide new research opportunities and perspectives to the causal RS community.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection

Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing on informative areas to produce accurate 3D object detection results. This paper proposes a novel object detection network by semantical point-voxel feature interaction, dubbed PV-RCNN++. Unlike most of existing methods, PV-RCNN++ explores the semantic information to enhance the quality of object detection. First, a semantic segmentation module is proposed to retain more discriminative foreground keypoints. Such a module will guide our PV-RCNN++ to integrate more object-related point-wise and voxel-wise features in the pivotal areas. Then, to make points and voxels interact efficiently, we utilize voxel query based on Manhattan distance to quickly sample voxel-wise features around keypoints. Such the voxel query will reduce the time complexity from O(N) to O(K), compared to the ball query. Further, to avoid being stuck in learning only local features, an attention-based residual PointNet module is designed to expand the receptive field to adaptively aggregate the neighboring voxel-wise features into keypoints. Extensive experiments on the KITTI dataset show that PV-RCNN++ achieves 81.60$\%$, 40.18$\%$, 68.21$\%$ 3D mAP on Car, Pedestrian, and Cyclist, achieving comparable or even better performance to the state-of-the-arts.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RT-WiFi on Software-Defined Radio: Design and Implementation

Applying high-speed real-time wireless technologies in industrial applications has the great potential to reduce the deployment and maintenance costs compared to their wired counterparts. Wireless technologies enhance the mobility and reduce the communication jitter and delay for mobile industrial equipment, such as mobile collaborative robots. Unfortunately, most existing wireless solutions employed in industrial fields either cannot support the desired high-speed communications or cannot guarantee deterministic, real-time performance. A more recent wireless technology, RT-WiFi, achieves a good balance between high-speed data rates and deterministic communication performance. It is however developed on commercial-of-the-shelf (COTS) hardware, and takes considerable effort and hardware expertise to maintain and upgrade. To address these problems, this paper introduces the software-defined radio (SDR)-based RT-WiFi solution which we call SRT-WiFi. SRT-WiFi provides full-stack configurability for high-speed real-time wireless communications. We present the overall system architecture of SRT-WiFi and discuss its key functions which achieve better timing performance and solve the queue management and rate adaptation issues compared to COTS hardware-based RT-WiFi. To achieve effective network management with rate adaptation in multi-cluster SRT-WiFi, a novel scheduling problem is formulated and an effective algorithm is proposed to solve the problem. A multi-cluster SRT-WiFi testbed is developed to validate the design, and extensive experiments are performed to evaluate the performance at both device and system levels.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Preliminary design of DC Magnet Power Supply System for ITER Static Magnetic Field Test facility

ITER (International Thermonuclear Experimental Reactor) static magnetic field (SMF) test facility requires a DC power supply with low voltage, high current, and high stability. Due to the limitation ofswitching loss, there is a contradiction between the output current capability and the output ripple. Large output current usually leads to low switching frequency, and low switching frequency will generate a large number of harmonics. To solve the problems, a topology based on the interleaving parallel buck converter is used and tested in this paper. Moreover, the topology is realized with only a small number of switching metal-oxide-semiconductor field effect transistors (MOSFETs). This article introduces the system design scheme and control method in detail. The analysis of harmonic and simulation are carried out. The validity of proposed scheme and control strategy were confirmed by experiments, the power supply system can supply large current of 15kA and has ability of low ripple.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Unsupervised High-Resolution Portrait Gaze Correction and Animation

This paper proposes a gaze correction and animation method for high-resolution, unconstrained portrait images, which can be trained without the gaze angle and the head pose annotations. Common gaze-correction methods usually require annotating training data with precise gaze, and head pose information. Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels. To address this issue, we first create two new portrait datasets: CelebGaze and high-resolution CelebHQGaze. Second, we formulate the gaze correction task as an image inpainting problem, addressed using a Gaze Correction Module (GCM) and a Gaze Animation Module (GAM). Moreover, we propose an unsupervised training strategy, i.e., Synthesis-As-Training, to learn the correlation between the eye region features and the gaze angle. As a result, we can use the learned latent space for gaze animation with semantic interpolation in this space. Moreover, to alleviate both the memory and the computational costs in the training and the inference stage, we propose a Coarse-to-Fine Module (CFM) integrated with GCM and GAM. Extensive experiments validate the effectiveness of our method for both the gaze correction and the gaze animation tasks in both low and high-resolution face datasets in the wild and demonstrate the superiority of our method with respect to the state of the arts. Code is available at https://github.com/zhangqianhui/GazeAnimationV2

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Effect of dispersion on indistinguishability between single-photon wave-packets

With propagating through a dispersive medium, the temporal-spectral profile of laser pulses should be inevitably modified. Although such dispersion effect has been well studied in classical optics, its effect on a single-photon wave-packet, i.e., the matter wave of a single-photon, has not yet been entirely revealed. In this paper, we investigate the effect of dispersion on indistinguishability of single-photon wave-packets through the Hong-Ou-Mandel (HOM) interference. By dispersively manipulating two indistinguishable single-photon wave-packets before interfering with each other, we observe that the difference of the second-order dispersion between two optical paths of the HOM interferometer can be mapped to the interference curve, indicating that (1) with the same amount of dispersion effect in both paths, the HOM interference curve must be only determined by the intrinsic indistinguishability between the wave-packets, i.e., dispersion cancellation due to the indistinguishability between Feynman paths; (2) unbalanced dispersion effect in two paths cannot be cancelled and will broaden the interference curve thus providing a way to measure the second-order dispersion coefficient. Our results suggest a more comprehensive understanding of the single-photon wave-packet and pave ways to explore further applications of the HOM interference.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision

Violence detection has been studied in computer vision for years. However, previous work are either superficial, e.g., classification of short-clips, and the single scenario, or undersupplied, e.g., the single modality, and hand-crafted features based multimodality. To address this problem, in this work we first release a large-scale and multi-scene dataset named XD-Violence with a total duration of 217 hours, containing 4754 untrimmed videos with audio signals and weak labels. Then we propose a neural network containing three parallel branches to capture different relations among video snippets and integrate features, where holistic branch captures long-range dependencies using similarity prior, localized branch captures local positional relation using proximity prior, and score branch dynamically captures the closeness of predicted score. Besides, our method also includes an approximator to meet the needs of online detection. Our method outperforms other state-of-the-art methods on our released dataset and other existing benchmark. Moreover, extensive experimental results also show the positive effect of multimodal (audio-visual) input and modeling relationships. The code and dataset will be released in https://roc-ng.github.io/XD-Violence/.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs

Test-based automated program repair has been a prolific field of research in software engineering in the last decade. Many approaches have indeed been proposed, which leverage test suites as a weak, but affordable, approximation to program specifications. Although the literature regularly sets new records on the number of benchmark bugs that can be fixed, several studies increasingly raise concerns about the limitations and biases of state-of-the-art approaches. For example, the correctness of generated patches has been questioned in a number of studies, while other researchers pointed out that evaluation schemes may be misleading with respect to the processing of fault localization results. Nevertheless, there is little work addressing the efficiency of patch generation, with regard to the practicality of program repair. In this paper, we fill this gap in the literature, by providing an extensive review on the efficiency of test suite based program repair. Our objective is to assess the number of generated patch candidates, since this information is correlated to (1) the strategy to traverse the search space efficiently in order to select sensical repair attempts, (2) the strategy to minimize the test effort for identifying a plausible patch, (3) as well as the strategy to prioritize the generation of a correct patch. To that end, we perform a large-scale empirical study on the efficiency, in terms of quantity of generated patch candidates of the 16 open-source repair tools for Java programs. The experiments are carefully conducted under the same fault localization configurations to limit biases.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Sub-60 mV/decade switching in a metal-insulator-metal-insulator-semiconductor transistor without ferroelectric component

Negative capacitance field-effect transistors (NC-FETs) have attracted wide interest as promising candidates for steep-slope devices, and sub-60 mV/decade switching has been demonstrated in NC-FETs with various device structures and material systems. However, the detailed mechanisms of the observed steep-slope switching in some of these experiments are under intense debate. Here we show that sub-60 mV/decade switching can be observed in a WS2 transistor with a metal-insulator-metal-insulator-semiconductor (MIMIS) structure - without any ferroelectric component. This structure resembles an NC-FET with internal gate, except that the ferroelectric layer is replaced by a leaky dielectric layer. Through simulations of the charging dynamics during the device characterization using an RC network model, we show that the observed steep-slope switching in our &#34;ferroelectric-free&#34; transistors can be attributed to the internal gate voltage response to the chosen varying gate voltage scan rates. We further show that a constant gate voltage scan rate can also lead to transient sub-60 mV/decade switching in an MIMIS structure with voltage dependent internal gate capacitance. Our results indicate that the observation of sub-60 mV/decade switching alone is not sufficient evidence for the successful demonstration of a true steep-slope switching device and that experimentalists need to critically assess their measurement setups to avoid measurement-related artefacts.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ultra-open High-efficiency Ventilated Metamaterial Absorbers with Customized Broadband Performance

High-efficiency absorption of low-frequency sounds (< 1000 Hz) while maintaining a free flow of fluids remains a significant challenge in acoustical engineering due to the rigid trade-off between absorption and ventilation performances. Although ongoing advances in acoustic metamaterials have unlocked unprecedented possibilities and various metamaterial absorbers have been proposed, most of them only work adequately in the condition of no sound transmissions. Unfortunately, such condition requires a complete block of fluid channels due to longitudinal nature of sounds, which allows them to penetrate any small holes. Otherwise, their absorption performance could be drastically degraded and often cannot exceed 50%. This basic trade-off between absorption and ventilation performances definitely constrains their applications in daily scenarios where free air flows are necessary. Though some ventilated sound barriers with large transmission loss have been demonstrated, they essentially only reflect sounds, which are still there and even may be reflected back. Here, to overcome this general difficulty, we propose and demonstrate an ultra-open ventilated metamaterial absorber. The absorber, aiming at low-frequency sounds, simultaneously ensures high-performance absorption and ventilation, confirmed in experiments. Their mechanism is understood from an effective model of coupled lossy oscillators. Furthermore, the absorbers can be simply stacked to work in a customized broadband, while maintaining a good ventilation. The demonstrated absorber provides a clear scheme for achieving high-performance absorption and ventilation at low frequencies, necessary for applications in environment with free air flows.

0 citations0 reviewsNo code linkedOpen access