arXiv subjects

Han Gao

Publications and source records attributed to Han Gao.

At least 73 records · Page 4Linked to original sources

arXiv2024

Microwave-assisted unidirectional superconductivity in Al-InAs nanowire-Al junctions under magnetic fields

Under certain symmetry-breaking conditions, a superconducting system exhibits asymmetric critical currents, dubbed the ``superconducting diode effect". Recently, systems with the ideal superconducting diode efficiency or unidirectional superconductivity have received considerable interest. In this work, we report the study of Al-InAs nanowire-Al Josephson junctions under microwave irradiation and magnetic fields. We observe an enhancement of superconducting diode effect under microwave driving, featured by a horizontal offset of the zero-voltage step in the voltage-current characteristic that increases with microwave power. Devices reach the unidirectional superconductivity regime at sufficiently high driving amplitudes. The offset changes sign with the reversal of the magnetic field direction. Meanwhile, the offset magnitude exhibits a roughly linear response to the microwave power in dBm when both the power and the magnetic field are large. The signatures observed are reminiscent of a recent theoretical proposal using the resistively shunted junction (RSJ) model. However, the experimental results are not fully explained by the RSJ model, indicating a new mechanism for unidirectional superconductivity that is possibly related to non-equilibrium dynamics or dissipation in periodically driven superconducting systems.

cond-mat.mes-hall↗

arXiv2024

Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations

We introduce a generative learning framework to model high-dimensional parametric systems using gradient guidance and virtual observations. We consider systems described by Partial Differential Equations (PDEs) discretized with structured or unstructured grids. The framework integrates multi-level information to generate high fidelity time sequences of the system dynamics. We demonstrate the effectiveness and versatility of our framework with two case studies in incompressible, two dimensional, low Reynolds cylinder flow on an unstructured mesh and incompressible turbulent channel flow on a structured mesh, both parameterized by the Reynolds number. Our results illustrate the framework's robustness and ability to generate accurate flow sequences across various parameter settings, significantly reducing computational costs allowing for efficient forecasting and reconstruction of flow dynamics.

cs.LG↗

arXiv2024

One-dimensional quantum dot array integrated with charge sensors in an InAs nanowire

We report an experimental study of a one-dimensional quintuple-quantum-dot array integrated with two quantum dot charge sensors in an InAs nanowire. The device is studied by measuring double quantum dots formed consecutively in the array and corresponding charge stability diagrams are revealed with both direct current measurements and charge sensor signals. The one-dimensional quintuple-quantum-dot array are then tuned up and its charge configurations are fully mapped out with the two charge sensors. The energy level of each dot in the array can be controlled individually by using a compensated gate architecture (i.e., "virtual gate"). After that, four dots in the array are selected to form two double quantum dots and ultra strong inter-double-dot interaction is obtained. A theoretical simulation based on a 4-dimensional Hamiltonian confirms the strong coupling strength between the two double quantum dots. The highly controllable one-dimensional quantum dot array achieved in this work is expected to be valuable for employing InAs nanowires to construct advanced quantum hardware in the future.

cond-mat.mes-hall↗

arXiv2024

Consistency Regularization for Domain Generalization with Logit Attribution Matching

Domain generalization (DG) is about training models that generalize well under domain shift. Previous research on DG has been conducted mostly in single-source or multi-source settings. In this paper, we consider a third, lesser-known setting where a training domain is endowed with a collection of pairs of examples that share the same semantic information. Such semantic sharing (SS) pairs can be created via data augmentation and then utilized for consistency regularization (CR). We present a theory showing CR is conducive to DG and propose a novel CR method called Logit Attribution Matching (LAM). We conduct experiments on five DG benchmarks and four pretrained models with SS pairs created by both generic and targeted data augmentation methods. LAM outperforms representative single/multi-source DG methods and various CR methods that leverage SS pairs. The code and data of this project are available at https://github.com/Gaohan123/LAM

cs.LG↗

arXiv2024

Partial train and isolate, mitigate backdoor attack

Neural networks are widely known to be vulnerable to backdoor attacks, a method that poisons a portion of the training data to make the target model perform well on normal data sets, while outputting attacker-specified or random categories on the poisoned samples. Backdoor attacks are full of threats. Poisoned samples are becoming more and more similar to corresponding normal samples, and even the human eye cannot easily distinguish them. On the other hand, the accuracy of models carrying backdoors on normal samples is no different from that of clean models.In this article, by observing the characteristics of backdoor attacks, We provide a new model training method (PT) that freezes part of the model to train a model that can isolate suspicious samples. Then, on this basis, a clean model is fine-tuned to resist backdoor attacks.

cs.CV↗

arXiv2024

Video Coding with Cross-Component Sample Offset

Beyond the exploration of traditional spatial, temporal and subjective visual signal redundancy in image and video compression, recent research has focused on leveraging cross-color component redundancy to enhance coding efficiency. Cross-component coding approaches are motivated by the statistical correlations among different color components, such as those in the Y'CbCr color space, where luma (Y) color component typically exhibits finer details than chroma (Cb/Cr) color components. Inspired by previous cross-component coding algorithms, this paper introduces a novel in-loop filtering approach named Cross-Component Sample Offset (CCSO). CCSO utilizes co-located and neighboring luma samples to generate correction signals for both luma and chroma reconstructed samples. It is a multiplication-free, non-linear mapping process implemented using a look-up-table. The input to the mapping is a group of reconstructed luma samples, and the output is an offset value applied on the center luma or co-located chroma sample. Experimental results demonstrate that the proposed CCSO can be applied to both image and video coding, resulting in improved coding efficiency and visual quality. The method has been adopted into an experimental next-generation video codec beyond AV1 developed by the Alliance for Open Media (AOMedia), achieving significant objective coding gains up to 3.5\,\% and 1.8\,\% for PSNR and VMAF quality metrics, respectively, under random access configuration. Additionally, CCSO notably improves the subjective visual quality.

eess.IV↗

arXiv2024

Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the "Edge" of open-set object detection. The suite encompasses two models: Grounding DINO 1.5 Pro, a high-performance model designed for stronger generalization capability across a wide range of scenarios, and Grounding DINO 1.5 Edge, an efficient model optimized for faster speed demanded in many applications requiring edge deployment. The Grounding DINO 1.5 Pro model advances its predecessor by scaling up the model architecture, integrating an enhanced vision backbone, and expanding the training dataset to over 20 million images with grounding annotations, thereby achieving a richer semantic understanding. The Grounding DINO 1.5 Edge model, while designed for efficiency with reduced feature scales, maintains robust detection capabilities by being trained on the same comprehensive dataset. Empirical results demonstrate the effectiveness of Grounding DINO 1.5, with the Grounding DINO 1.5 Pro model attaining a 54.3 AP on the COCO detection benchmark and a 55.7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection. Furthermore, the Grounding DINO 1.5 Edge model, when optimized with TensorRT, achieves a speed of 75.2 FPS while attaining a zero-shot performance of 36.2 AP on the LVIS-minival benchmark, making it more suitable for edge computing scenarios. Model examples and demos with API will be released at https://github.com/IDEA-Research/Grounding-DINO-1.5-API

cs.CV↗

arXiv2024

IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact

Large language models (LLMs) excel in natural language processing but demand intensive computation. To mitigate this, various quantization methods have been explored, yet they compromise LLM performance. This paper unveils a previously overlooked type of outliers in LLMs. Such outliers are found to allocate most of the attention scores on initial tokens of input, termed as pivot tokens, which are crucial to the performance of quantized LLMs. Given that, we propose IntactKV to generate the KV cache of pivot tokens losslessly from the full-precision model. The approach is simple and easy to combine with existing quantization solutions with no extra inference overhead. Besides, IntactKV can be calibrated as additional LLM parameters to boost the quantized LLMs further with minimal training costs. Mathematical analysis also proves that IntactKV effectively reduces the upper bound of quantization error. Empirical results show that IntactKV brings consistent improvement over various quantization methods across different LLMs and downstream tasks, leading to the new state-of-the-art for LLM quantization. The codes are available at https://github.com/ruikangliu/IntactKV.

cs.CL↗

arXiv2024

Tunable moiré bandgap in hBN-aligned bilayer graphene device with in-situ electrostatic gating

Over the years, great efforts have been devoted in introducing a sizable and tunable band gap in graphene for its potential application in next-generation electronic devices. The primary challenge in modulating this gap has been the absence of a direct method for observing changes of the band gap in momentum space. In this study, we employ advanced spatial- and angle-resolved photoemission spectroscopy technique to directly visualize the gap formation in bilayer graphene, modulated by both displacement fields and moiré potentials. The application of displacement field via in-situ electrostatic gating introduces a sizable and tunable electronic bandgap, proportional to the field strength up to 100 meV. Meanwhile, the moiré potential, induced by aligning the underlying hexagonal boron nitride substrate, extends the bandgap by ~ 20 meV. Theoretical calculations, effectively capture the experimental observations. Our investigation provides a quantitative understanding of how these two mechanisms collaboratively modulate the band gap in bilayer graphene, offering valuable guidance for the design of graphene-based electronic devices.

cond-mat.mes-hall↗

arXiv2024

Gate-defined quantum point contacts in a germanium quantum well

We report an experimental study of quantum point contacts defined in a high-quality strained germanium quantum well with layered electric gates. At zero magnetic field, we observe quantized conductance plateaus in units of 2$e^2/h$. Bias-spectroscopy measurements reveal that the energy spacing between successive one-dimensional subbands ranges from 1.5 to 5\,meV as a consequence of the small effective mass of the holes and the narrow gate constrictions. At finite magnetic fields perpendicular to the device plane, the edges of the conductance plateaus get splitted due to the Zeeman effect and Landé $g$ factors are estimated to be $\sim6.6$ for the holes in the germanium quantum well. We demonstrate that all quantum point contacts in the same device have comparable performances, indicating a reliable and reproducible device fabrication process. Thus, our work lays a foundation for investigating multiple forefronts of physics in germanium-based quantum devices that require quantum point contacts as a building block.

cond-mat.mes-hall↗

arXiv2024

Local Intuitionistic Modal Logics and Their Calculi

We investigate intuitionistic modal logics with locally interpreted $\square$ and $\lozenge$. The basic logic LIK is stronger than constructive modal logic WK and incomparable with intuitionistic modal logic IK. We propose an axiomatization of LIK and some of its extensions. We propose bi-nested calculi for LIK and these extensions, thus providing both a decision procedure and a procedure of finite countermodel extraction.

cs.LO↗

arXiv2024

ASPIRe: An Informative Trajectory Planner with Mutual Information Approximation for Target Search and Tracking

This paper proposes an informative trajectory planning approach, namely, \textit{adaptive particle filter tree with sigma point-based mutual information reward approximation} (ASPIRe), for mobile target search and tracking (SAT) in cluttered environments with limited sensing field of view. We develop a novel sigma point-based approximation to accurately estimate mutual information (MI) for general, non-Gaussian distributions utilizing particle representation of the belief state, while simultaneously maintaining high computational efficiency. Building upon the MI approximation, we develop the Adaptive Particle Filter Tree (APFT) approach with MI as the reward, which features belief state tree nodes for informative trajectory planning in continuous state and measurement spaces. An adaptive criterion is proposed in APFT to adjust the planning horizon based on the expected information gain. Simulations and physical experiments demonstrate that ASPIRe achieves real-time computation and outperforms benchmark methods in terms of both search efficiency and estimation accuracy.

cs.RO↗

arXiv2024

Generative Learning for Forecasting the Dynamics of Complex Systems

We introduce generative models for accelerating simulations of complex systems through learning and evolving their effective dynamics. In the proposed Generative Learning of Effective Dynamics (G-LED), instances of high dimensional data are down sampled to a lower dimensional manifold that is evolved through an auto-regressive attention mechanism. In turn, Bayesian diffusion models, that map this low-dimensional manifold onto its corresponding high-dimensional space, capture the statistics of the system dynamics. We demonstrate the capabilities and drawbacks of G-LED in simulations of several benchmark systems, including the Kuramoto-Sivashinsky (KS) equation, two-dimensional high Reynolds number flow over a backward-facing step, and simulations of three-dimensional turbulent channel flow. The results demonstrate that generative learning offers new frontiers for the accurate forecasting of the statistical properties of complex systems at a reduced computational cost.

cs.LG↗

arXiv2024

Rapidity scan with multistage hydrodynamic and statistical thermal models

We calibrate a (3+1)-dimensional multistage hybrid framework using the measured pseudo-rapidity distribution of charged particles and rapidity distribution of net protons for central Au+Au collisions at $\sqrt{s_{\rm NN}}=7.7,\,19.6,\,62.4,\,200$ GeV. We then study the thermodynamic properties of the nuclear matter along the beam direction, and the phase diagram regions probed by the hadronization process near the chemical freeze-out. Using the rapidity-dependent thermal yields of identified particles with full rapidity coverage from the hybrid framework, we apply different scenarios of the statistical thermal model to extract the thermodynamic parameters at the freeze-out, with the known system properties from the hybrid model as a closure test. We find significant theoretical uncertainties in the thermal models when applied to regions away from midrapidity. We also propose a thermal model inspired by the hybrid approach that includes thermal smearing and longitudinal flow for the nuclear matter created at low beam energies.

nucl-th↗

arXiv2023

Thermal dilepton production in heavy-ion collisions at beam-energy-scan (BES) energies

The lepton pair production rate at finite temperature and at next-to-leading-order (NLO) is calculated for quark-gluon plasma at non-zero baryon density. Yields are obtained using a (3+1)D multicomponent simulation capable of reproducing hadronic observables measured in the RHIC Beam Energy Scan. Spectra of intermediate invariant mass dileptons are compared with measurements from the STAR collaboration. The result of a study where the temperature information obtained from the dilepton spectrum from heavy-ion collisions performed at different energies and centralities is presented.

nucl-th↗

arXiv2023

Exploring the freeze-out hypersurface of relativistic nuclear collisions with a rapidity-dependent thermal model

Considering applications to relativistic heavy-ion collisions, we develop a rapidity-dependent thermal model that includes thermal smearing effect and longitudinal boost. We calibrate the model with thermal yields obtained from a multistage hydrodynamic simulation. Through Bayesian analysis, we find that our model extracts freeze-out thermodynamics with better precision than rapidity-independent models. The potential of such a model to constrain longitudinal flow from data is also demonstrated.

nucl-th↗

arXiv2023

Revealing unusual bandgap shifts with temperature and bandgap renormalization effect in phase-stabilized metal halide perovskite thin films

Hybrid organic-inorganic metal halide perovskites are emerging materials in photovoltaics, whose bandgap is one of the most crucial parameters governing their light harvesting performance. Here we present the temperature and photocarrier density dependence of the bandgap in two phase-stabilized perovskite thin films (MA0.3FA0.7PbI3 and MA0.3FA0.7Pb0.5Sn0.5I3) using photoluminescence and absorption spectroscopy. Contrasting bandgap shifts with temperature are observed between the two perovskites. Using X-ray diffraction and in situ high-pressure photoluminescence spectroscopy, we show that thermal expansion plays only a minor role in the large bandgap blueshift, which is attributed to the enhanced structural stability of our samples. Our first-principles calculations further demonstrate the significant impact of thermally induced lattice distortions on the bandgap widening. We propose that the anomalous trends are caused by the competition between static and dynamic distortions. Additionally, both the bandgap renormalization and band filling effects are directly observed for the first time in fluence-dependent photoluminescence measurements and are employed to estimate the exciton effective mass. Our results provide new insights into the basic understanding of thermal and charge-accumulation effects on the band structure of hybrid perovskite thin films.

cond-mat.mtrl-sci↗

arXiv2023

Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation

Turbulent flows have historically presented formidable challenges to predictive computational modeling. Traditional numerical simulations often require vast computational resources, making them infeasible for numerous engineering applications. As an alternative, deep learning-based surrogate models have emerged, offering data-drive solutions. However, these are typically constructed within deterministic settings, leading to shortfall in capturing the innate chaotic and stochastic behaviors of turbulent dynamics. We introduce a novel generative framework grounded in probabilistic diffusion models for versatile generation of spatiotemporal turbulence. Our method unifies both unconditional and conditional sampling strategies within a Bayesian framework, which can accommodate diverse conditioning scenarios, including those with a direct differentiable link between specified conditions and generated unsteady flow outcomes, and scenarios lacking such explicit correlations. A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes. We showcase the versatile turbulence generation capability of our framework through a suite of numerical experiments, including: 1) the synthesis of LES simulated instantaneous flow sequences from URANS inputs; 2) holistic generation of inhomogeneous, anisotropic wall-bounded turbulence, whether from given initial conditions, prescribed turbulence statistics, or entirely from scratch; 3) super-resolved generation of high-speed turbulent boundary layer flows from low-resolution data across a range of input resolutions. Collectively, our numerical experiments highlight the merit and transformative potential of the proposed methods, making a significant advance in the field of turbulence generation.

physics.flu-dyn↗