arXiv subjects

Zhan Wang

Publications and source records attributed to Zhan Wang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Staying vigilant in the Age of AI: From content generation to content authentication

This paper presents the Yangtze Sea project, an initiative in the battle against Generative AI (GAI)-generated fake con-tent. Addressing a pressing issue in the digital age, we investigate public reactions to AI-created fabrications through a structured experiment on a simulated academic conference platform. Our findings indicate a profound public challenge in discerning such content, highlighted by GAI's capacity for realistic fabrications. To counter this, we introduce an innovative approach employing large language models like ChatGPT for truthfulness assess-ment. We detail a specific workflow for scrutinizing the authenticity of everyday digital content, aimed at boosting public awareness and capability in identifying fake mate-rials. We apply this workflow to an agent bot on Telegram to help users identify the authenticity of text content through conversations. Our project encapsulates a two-pronged strategy: generating fake content to understand its dynamics and developing assessment techniques to mitigate its impact. As part of that effort we propose the creation of speculative fact-checking wearables in the shape of reading glasses and a clip-on. As a computational media art initiative, this project under-scores the delicate interplay between technological progress, ethical consid-erations, and societal consciousness.

cs.CY↗

arXiv2024

FNCC: Fast Notification Congestion Control in Data Center Networks

Congestion control plays a pivotal role in large-scale data centers, facilitating ultra-low latency, high bandwidth, and optimal utilization. Even with the deployment of data center congestion control mechanisms such as DCQCN and HPCC, these algorithms often respond to congestion sluggishly. This sluggishness is primarily due to the slow notification of congestion. It takes almost one round-trip time (RTT) for the congestion information to reach the sender. In this paper, we introduce the Fast Notification Congestion Control (FNCC) mechanism, which achieves sub-RTT notification. FNCC leverages the acknowledgment packet (ACK) from the return path to carry in-network telemetry (INT) information of the request path, offering the sender more timely and accurate INT. To further accelerate the responsiveness of last-hop congestion control, we propose that the receiver notifies the sender of the number of concurrent congested flows, which can be used to adjust the congested flows to a fair rate quickly. Our experimental results demonstrate that FNCC reduces flow completion time by 27.4% and 88.9% compared to HPCC and DCQCN, respectively. Moreover, FNCC triggers minimal pause frames and maintains high utilization even at 400Gbps.

cs.NI↗

arXiv2024

Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality

Viewing omnidirectional images (ODIs) in virtual reality (VR) represents a novel form of media that provides immersive experiences for users to navigate and interact with digital content. Nonetheless, this sense of immersion can be greatly compromised by a blur effect that masks details and hampers the user's ability to engage with objects of interest. In this paper, we present a novel system, called OmniVR, designed to enhance visual clarity during VR navigation. Our system enables users to effortlessly locate and zoom in on the objects of interest in VR. It captures user commands for navigation and zoom, converting these inputs into parameters for the Mobius transformation matrix. Leveraging these parameters, the ODI is refined using a learning-based algorithm. The resultant ODI is presented within the VR media, effectively reducing blur and increasing user engagement. To verify the effectiveness of our system, we first evaluate our algorithm with state-of-the-art methods on public datasets, which achieves the best performance. Furthermore, we undertake a comprehensive user study to evaluate viewer experiences across diverse scenarios and to gather their qualitative feedback from multiple perspectives. The outcomes reveal that our system enhances user engagement by improving the viewers' recognition, reducing discomfort, and improving the overall immersive experience. Our system makes the navigation and zoom more user-friendly.

cs.HC↗

arXiv2024

Generative AI for Visualization: State of the Art and Future Directions

Generative AI (GenAI) has witnessed remarkable progress in recent years and demonstrated impressive performance in various generation tasks in different domains such as computer vision and computational design. Many researchers have attempted to integrate GenAI into visualization framework, leveraging the superior generative capacity for different operations. Concurrently, recent major breakthroughs in GenAI like diffusion model and large language model have also drastically increase the potential of GenAI4VIS. From a technical perspective, this paper looks back on previous visualization studies leveraging GenAI and discusses the challenges and opportunities for future research. Specifically, we cover the applications of different types of GenAI methods including sequence, tabular, spatial and graph generation techniques for different tasks of visualization which we summarize into four major stages: data enhancement, visual mapping generation, stylization and interaction. For each specific visualization sub-task, we illustrate the typical data and concrete GenAI algorithms, aiming to provide in-depth understanding of the state-of-the-art GenAI4VIS techniques and their limitations. Furthermore, based on the survey, we discuss three major aspects of challenges and research opportunities including evaluation, dataset, and the gap between end-to-end GenAI and generative algorithms. By summarizing different generation algorithms, their current applications and limitations, this paper endeavors to provide useful insights for future GenAI4VIS research.

cs.LG↗

arXiv2024

Local Manipulation of Skyrmion Lattice in Fe3GaTe2 at Room Temperature

Motivated by advances in spintronic devices, an extensive exploration is underway to uncover materials that host topologically protected spin textures, exemplified by skyrmions. One critical challenge involved in the potential application of skyrmions in van der Waals (vdW) materials is the attainment and manipulation of skyrmions at room temperature. In this study, we report the creation of intrinsic skyrmion state in van der Waals ferromagnet Fe3GaTe2. By employing variable temperature magnetic force microscopy, the skyrmion lattice can be locally manipulated on Fe3GaTe2 flake. The ordering of skyrmion state is further analyzed. Our result suggest Fe3GaTe2 emerges as a highly promising contender for the realization of skyrmion-based layered spintronic memory devices.

cond-mat.mtrl-sci↗

arXiv2024

VirtuWander: Enhancing Multi-modal Interaction for Virtual Tour Guidance through Large Language Models

Tour guidance in virtual museums encourages multi-modal interactions to boost user experiences, concerning engagement, immersion, and spatial awareness. Nevertheless, achieving the goal is challenging due to the complexity of comprehending diverse user needs and accommodating personalized user preferences. Informed by a formative study that characterizes guidance-seeking contexts, we establish a multi-modal interaction design framework for virtual tour guidance. We then design VirtuWander, a two-stage innovative system using domain-oriented large language models to transform user inquiries into diverse guidance-seeking contexts and facilitate multi-modal interactions. The feasibility and versatility of VirtuWander are demonstrated with virtual guiding examples that encompass various touring scenarios and cater to personalized preferences. We further evaluate VirtuWander through a user study within an immersive simulated museum. The results suggest that our system enhances engaging virtual tour experiences through personalized communication and knowledgeable assistance, indicating its potential for expanding into real-world scenarios.

cs.HC↗

arXiv2023

GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements

In geographic data videos, camera movements are frequently used and combined to present information from multiple perspectives. However, creating and editing camera movements requires significant time and professional skills. This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. First, we analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the design space, we propose a set of adaptive camera shots and further develop an interactive tool called GeoCamera. This interactive tool allows users to flexibly design camera movements for geographic visualizations. We verify the expressiveness of our tool through case studies and evaluate its usability with a user study. The participants find that the tool facilitates the design of camera movements.

cs.HC↗

arXiv2022

The simulation of non-Hermitian disordered system in linear circuits

Non-Hermitian skin effect (NHSE) is a novel phenomenon appearing in non-Hermitian systems. Here, we report the experimental observation of NHSE. Different from the previous non-reciprocal circuit implementation scheme using logic components, we construct our one-dimensional (1D) circuits using linear components only. Besides, we achieve the non-reciprocity by proportionally varying the parameter value of the components. By measuring the voltage response of each site, the information of eigenstates can be mapped out. The results show that the voltage response is always larger on one end of the circuit no matter on which end voltage driving is applied, indicating clearly the presence of the NHSE. Furthermore, we also simulate the interplay of NHSE and Anderson localization (AL) when additional disorder is introduced. Upon increasing the disorder strength, we observe the transformation from the skin effect phase to the localized phase. In the regime of skin effect phase, the eigenstates are all localized at one edge while eigenstates are affected by the voltage supply input in localized phase. Our findings unveil a possible new route for simulation of topological phenomena in non-Hermitian systems.

cond-mat.dis-nn↗

arXiv2022

Probing Operator Spreading via Floquet Engineering in a Superconducting Circuit

Operator spreading, often characterized by out-of-time-order correlators (OTOCs), is one of the central concepts in quantum many-body physics. However, measuring OTOCs is experimentally challenging due to the requirement of reversing the time evolution of systems. Here we apply Floquet engineering to investigate operator spreading in a superconducting 10-qubit chain. Floquet engineering provides an effective way to tune the coupling strength between nearby qubits, which is used to demonstrate quantum walks with tunable couplings, reversed time evolution, and the measurement of OTOCs. A clear light-cone-like operator propagation is observed in the system with multiple excitations, and has a nearly equal velocity as the single-particle quantum walk. For the butterfly operator that is nonlocal (local) under the Jordan-Wigner transformation, the OTOCs show distinct behaviors with (without) a signature of information scrambling in the near integrable system.

quant-ph↗

arXiv2022

Observation of Emergent $\mathbb{Z}_2$ Gauge Invariance in a Superconducting Circuit

Lattice gauge theories (LGTs) are one of the most fundamental subjects in many-body physics, and has recently attracted considerable research interests in quantum simulations. Here we experimentally investigate the emergent $\mathbb{Z}_2$ gauge invariance in a 1D superconducting circuit with 10 transmon qubits. By precisely adjusting staggered longitudinal and transverse fields to each qubit, we construct an effective Hamiltonian containing an LGT and gauge-broken terms. The corresponding matter sector can exhibit a localization, and there also exists a 3-qubit operator, of which the expectation value can retain nonzero for a long time in low-energy regimes. The above localization can be regarded as the confinement of matter fields, and the 3-body operator is the $\mathbb{Z}_2$ gauge generator. These experimental results demonstrate that, despite the absence of gauge structure in the effective Hamiltonian, $\mathbb{Z}_2$ gauge invariance can still emerge in low-energy regimes. Our work provides a method for both theoretically and experimentally studying the rich physics in quantum many-body systems with emergent gauge invariance.

quant-ph↗

arXiv2021

Rapid and Unconditional Parametric Reset Protocol for Tunable Superconducting Qubits

Qubit initialization is a critical task in quantum computation and communication. Extensive efforts have been made to achieve this with high speed, efficiency and scalability. However, previous approaches have either been measurement-based and required fast feedback, suffered from crosstalk or required sophisticated calibration. Here, we report a fast and high-fidelity reset scheme, avoiding the issues above without any additional chip architecture. By modulating the flux through a transmon qubit, we realize a swap between the qubit and its readout resonator that suppresses the excited state population to 0.08% $\pm$ 0.08% within 34 ns (284 ns if photon depletion of the resonator is required). Furthermore, our approach (i) can achieve effective second excited state depletion, (ii) has negligible effects on neighbouring qubits, and (iii) offers a way to entangle the qubit with an itinerant single photon, useful in quantum communication applications.

quant-ph↗

arXiv2021

Hydroelastic lumps in shallow water

Hydroelastic solitary waves propagating on the surface of a three-dimensional ideal fluid through the deformation of an elastic sheet are studied. The problem is investigated based on a Benney-Luke-type equation derived via an explicit non-local formulation of the classic water wave problem. The normal form analysis is carried out for the newly developed equation, which results in the Benney-Roskes-Davey-Stewartson (BRDS) system governing the coupled evolution of the envelope of a carrier wave and the wave-induced mean flow. Numerical results show three types of free solitary waves in the Benney-Luke-type equation: plane solitary wave, lump (i.e., fully localized traveling waves in three dimensions), and transversally periodic solitary wave, and they are counterparts of the BRDS solutions. They are linked together by a dimension-breaking bifurcation where plane solitary waves and lumps can be viewed as two limiting cases, and transversally periodic solitary waves serve as intermediate states. The stability and interaction of solitary waves are investigated via a numerical time integration of the Benney-Luke-type equation. For a localized load moving on the elastic sheet with a constant speed, it is found that there exists a transcritical regime of forcing speed for which there are no steady solutions. Instead, periodic shedding of lumps can be observed if the forcing moves at speed in this range.

physics.flu-dyn↗

arXiv2021

CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

In machine learning applications, the reliability of predictions is significant for assisted decision and risk control. As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN). However, the conventional CPKNN suffers from high variance and bias and long computational time as the feature dimensionality increases. To address these limitations, a new CP framework-conformal prediction with shrunken centroids (CPSC) is proposed. It regularizes the class centroids to attenuate the irrelevant features and shrink the sample space for predictions and reliability quantification. To compare CPKNN and CPSC, we employed them in the classification of 12 categories of alternative herbal medicine with electronic nose as a case and assessed them in two tasks: 1) offline prediction: the training set was fixed and the accuracy on the testing set was evaluated; 2) online prediction with data augmentation: they filtered unlabeled data to augment the training data based on the prediction reliability and the final accuracy of testing set was compared. The result shows that CPSC significantly outperformed CPKNN in both two tasks: 1) CPSC reached a significantly higher accuracy with lower computation cost, and with the same credibility output, CPSC generally achieves a higher accuracy; 2) the data augmentation process with CPSC robustly manifested a statistically significant improvement in prediction accuracy with different reliability thresholds, and the augmented data were more balanced in classes. This novel CPSC provides higher prediction accuracy and better reliability quantification, which can be a reliable assistance in decision support.

q-bio.QM↗

arXiv2021

Classifying herbal medicine origins by temporal and spectral data mining of electronic nose

The origins of herbal medicines are important for their treatment effect, which could be potentially distinguished by electronic nose system. As the odor fingerprint of herbal medicines from different origins can be tiny, the discrimination of origins can be much harder than that of different categories. Better feature extraction methods are significant for this task to be more accurately done, but there lacks systematic studies on different feature extraction methods. In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms. With 50 repetitive experiments with bootstrapping, we compared the effectiveness of the extractions with a two-layer neural network w/o dimensionality reduction methods (principal component analysis, linear discriminant analysis) as the three base classifiers. Compared with the conventional aggregated features, the Fast Fourier Transform method and our novel approach (longitudinal-information-in-a-line) showed an significant accuracy improvement(p < 0.05) on all 3 base classifiers and all three herbal medicine categories. Two of the deep learning algorithm we applied also showed partially significant improvement: one-dimensional convolution neural network(1D-CNN) and a novel graph pooling based framework - multivariate time pooling(MTPool).

q-bio.QM↗

arXiv2021

Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose

Electronic nose has been proven to be effective in alternative herbal medicine classification, but due to the nature of supervised learning, previous research heavily relies on the labelled training data, which are time-costly and labor-intensive to collect. To alleviate the critical dependency on the training data in real-world applications, this study aims to improve classification accuracy via data augmentation strategies. The effectiveness of five data augmentation strategies under different training data inadequacy are investigated in two scenarios: the noise-free scenario where different availabilities of unlabelled data were considered, and the noisy scenario where different levels of Gaussian noises and translational shifts were added to represent sensor drifts. The five augmentation strategies, namely noise-adding data augmentation, semi-supervised learning, classifier-based online learning, Inductive Conformal Prediction (ICP) online learning and our novel ensemble ICP online learning proposed in this study, are experimented and compared against supervised learning baseline, with Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) as the classifiers. Our novel strategy, ensemble ICP online learning, outperforms the others by showing non-decreasing classification accuracy on all tasks and a significant improvement on most simulated tasks (25out of 36 tasks,p<=0.05). Furthermore, this study provides a systematic analysis of different augmentation strategies. It shows at least one strategy significantly improved the classification accuracy with LDA (p<=0.05) and non-decreasing classification accuracy with SVM in each task. In particular, our proposed strategy demonstrated both effectiveness and robustness in boosting the classification model generalizability, which can be employed in other machine learning applications.

cs.LG↗

arXiv2021

Observation of Bloch Oscillations and Wannier-Stark Localization on a Superconducting Processor

The Bloch oscillation (BO) and Wannier-Stark localization (WSL) are fundamental concepts about metal-insulator transitions in condensed matter physics. These phenomena have also been observed in semiconductor superlattices and simulated in platforms such as photonic waveguide arrays and cold atoms. Here, we report experimental investigation of BOs and WSL simulated with a 5-qubit programmable superconducting processor, of which the effective Hamiltonian is an isotropic $XY$ spin chain. When applying a linear potential to the system by properly tuning all individual qubits, we observe that the propagation of a single spin on the chain is suppressed. It tends to oscillate near the neighborhood of their initial positions, which demonstrates the characteristics of BOs and WSL. We verify that the WSL length is inversely correlated to the potential gradient. Benefiting from the precise single-shot simultaneous readout of all qubits in our experiments, we can also investigate the thermal transport, which requires the joint measurement of more than one qubits. The experimental results show that, as an essential characteristic for BOs and WSL, the thermal transport is also blocked under a linear potential. Our experiment would be scalable to more superconducting qubits for simulating various of out-of-equilibrium problems in quantum many-body systems.

quant-ph↗

arXiv2020

Distinct pairing symmetries of superconductivity in infinite-layer nickelates

We report theoretical predictions on the pairing symmetry of the newly discovered superconducting nickelate Nd$_{1-x}$Sr$_{x}$NiO$_{2}$ based on the renormalized mean-field theory for a generalized model Hamiltonian proposed in [Phys. Rev. B \textbf{101}, 020501(R)]. For practical values of the key parameters, we find a transition between a gapped ($d+is$)-wave pairing state in the small doping region to a gapless $d$-wave pairing state in the large doping region, accompanied by an abrupt Fermi surface change at the critical doping. Our overall phase diagram also shows the possibility of a ($d+is$)- to $s$-wave transition if the electron hybridization is relatively small. In either case, the low-doping ($d+is$)-wave state is a gapped superconducting state with broken time-reversal symmetry. Our results are in qualitative agreement with recent experimental observations and predict several key features to be examined in future measurements.

cond-mat.supr-con↗

arXiv2020

t-J model on the effective brick-wall lattice for the recently discovered high-temperature superconductor Ba$_2$CuO$_{3+δ}$

Layered copper oxides have highest superconducting transition temperatures at ambient pressure. Its mechanism remains a grand challenge in condensed matter physics. The essential physics lying in 2-dimensional copper-oxygen layers is well described by a single band Hubbard model or its strong coupling limit t-J model in 2-dimensional square lattice. Recently discovered high temperature superconductor Ba$_2$CuO$_{3+δ}$ with $δ\sim 0.2$ has different crystal structure with large portion of in-plane oxygen vacancies. We observe that an oxygen vacancy breaks the bond of its two neighboring copper atoms, and propose ordered vacancies in Ba$_2$CuO$_{3+δ}$ lead to extended t-J model on an effective brick-wall lattice. For the nearest neighbor hopping, the brick-wall model can be mapped onto t-J model on honeycomb lattice. Our theory explains the superconductivity of Ba$_2$CuO$_{3+δ}$ at high charge carrier density, and predict a time reversal symmetry broken pairing state.

cond-mat.supr-con↗