arXiv subjects

Biao Wu

Publications and source records attributed to Biao Wu.

At least 73 records · Page 4Linked to original sources

arXiv2024

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing a variety of 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 10ms. Out of the 160 participants, 25 teams submitted their code and models. The solutions present novel designs tailored for memory-efficiency and runtime on edge devices. This survey describes the best solutions for real-time SR of compressed high-resolution images.

cs.CV↗

arXiv2024

From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying

Safeguarding the Intellectual Property (IP) of data has become critically important as machine learning applications continue to proliferate, and their success heavily relies on the quality of training data. While various mechanisms exist to secure data during storage, transmission, and consumption, fewer studies have been developed to detect whether they are already leaked for model training without authorization. This issue is particularly challenging due to the absence of information and control over the training process conducted by potential attackers. In this paper, we concentrate on the domain of tabular data and introduce a novel methodology, Local Distribution Shifting Synthesis (\textsc{LDSS}), to detect leaked data that are used to train classification models. The core concept behind \textsc{LDSS} involves injecting a small volume of synthetic data--characterized by local shifts in class distribution--into the owner's dataset. This enables the effective identification of models trained on leaked data through model querying alone, as the synthetic data injection results in a pronounced disparity in the predictions of models trained on leaked and modified datasets. \textsc{LDSS} is \emph{model-oblivious} and hence compatible with a diverse range of classification models. We have conducted extensive experiments on seven types of classification models across five real-world datasets. The comprehensive results affirm the reliability, robustness, fidelity, security, and efficiency of \textsc{LDSS}. Extending \textsc{LDSS} to regression tasks further highlights its versatility and efficacy compared with baseline methods.

cs.LG↗

arXiv2024

IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers

Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting. However, building deep neural networks in real-world scenarios requires not only high accuracy on clean data but also robustness when data distributions shift. While prior methods have proposed that there is a trade-off between accuracy and robustness, we propose IPMix, a simple data augmentation approach to improve robustness without hurting clean accuracy. IPMix integrates three levels of data augmentation (image-level, patch-level, and pixel-level) into a coherent and label-preserving technique to increase the diversity of training data with limited computational overhead. To further improve the robustness, IPMix introduces structural complexity at different levels to generate more diverse images and adopts the random mixing method for multi-scale information fusion. Experiments demonstrate that IPMix outperforms state-of-the-art corruption robustness on CIFAR-C and ImageNet-C. In addition, we show that IPMix also significantly improves the other safety measures, including robustness to adversarial perturbations, calibration, prediction consistency, and anomaly detection, achieving state-of-the-art or comparable results on several benchmarks, including ImageNet-R, ImageNet-A, and ImageNet-O.

cs.CV↗

arXiv2024

SegReg: Segmenting OARs by Registering MR Images and CT Annotations

Organ at risk (OAR) segmentation is a critical process in radiotherapy treatment planning such as head and neck tumors. Nevertheless, in clinical practice, radiation oncologists predominantly perform OAR segmentations manually on CT scans. This manual process is highly time-consuming and expensive, limiting the number of patients who can receive timely radiotherapy. Additionally, CT scans offer lower soft-tissue contrast compared to MRI. Despite MRI providing superior soft-tissue visualization, its time-consuming nature makes it infeasible for real-time treatment planning. To address these challenges, we propose a method called SegReg, which utilizes Elastic Symmetric Normalization for registering MRI to perform OAR segmentation. SegReg outperforms the CT-only baseline by 16.78% in mDSC and 18.77% in mIoU, showing that it effectively combines the geometric accuracy of CT with the superior soft-tissue contrast of MRI, making accurate automated OAR segmentation for clinical practice become possible. See project website https://steve-zeyu-zhang.github.io/SegReg

cs.CV↗

arXiv2024

Quantum Dynamical Tunneling Breaks Classical Conserved Quantities

We discover that quantum dynamical tunneling, occurring between phase space regions in a classically forbidden way, can break conserved quantities in pseudointegrable systems. We rigorously prove that a conserved quantity in a class of typical pseudointegrable systems can be broken quantum mechanically. We then numerically compute the uncertainties of this broken conserved quantity, which remain non-zero for up to $10^5$ eigenstates and exhibit universal distributions similar to energy level statistics. Furthermore, all the eigenstates with large uncertainties show the superpositions of regular orbits with different values of the conserved quantity, showing definitive manifestation of dynamical tunneling. A random matrix model is constructed to successfully reproduce the level statistics in pseudointegrable systems.

nlin.CD↗

arXiv2024

Flexomagnetoelectric effect in Sr2IrO4 thin films

Symmetry engineering is explicitly effective to manipulate and even create phases and orderings in strongly correlated materials. Flexural stress is universally practical to break the space-inversion or time-reversal symmetry. Here, by introducing strain gradient in a centrosymmetric antiferromagnet Sr2IrO4, the space-inversion symmetry is broken accompanying a non-equivalent O p-Ir d orbital hybridization along z axis. Thus, emergent polar phase and out-of-plane magnetic moment have been simultaneously observed in these asymmetric Sr2IrO4 thin films, which both are absent in its ground state. Furthermore, upon the application of magnetic field, such polarization can be controlled by modifying the occupied d orbitals through spin-orbit interaction, giving rise to a flexomagnetoelectric effect. This work provides a general strategy to artificially design multiple symmetries and ferroic orderings in strongly correlated systems.

cond-mat.str-el↗

arXiv2023

Escaping Local Minima with Quantum Coherent Cooling

Quantum cooling has demonstrated its potential in quantum computing, which can reduce the number of control channels needed for external signals. Recent progress also supports the possibility of maintaining quantum coherence in large-scale systems. The limitations of classical algorithms trapped in local minima of cost functions could be overcome using this scheme. According to this, we propose a hybrid quantum-classical algorithm for finding the global minima. Our approach utilizes quantum coherent cooling to facilitate coordinative tunneling through energy barriers if the classical algorithm gets stuck. The encoded Hamiltonian system represents the cost function, and a quantum coherent bath in the ground state serves as a heat sink to absorb energy from the system. Our proposed scheme can be implemented in the circuit quantum electrodynamics (cQED) system using a quantum cavity. The provided numerical evidence demonstrates the quantum advantage in solving spin glass problems.

quant-ph↗

arXiv2023

BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset

Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.

cs.CV↗

arXiv2023

Quantum Dropout: On and Over the Hardness of Quantum Approximate Optimization Algorithm

A combinatorial optimization problem becomes very difficult in situations where the energy landscape is rugged, and the global minimum locates in a narrow region of the configuration space. When using the quantum approximate optimization algorithm (QAOA) to tackle these harder cases, we find that difficulty mainly originates from the QAOA quantum circuit instead of the cost function. To alleviate the issue, we selectively dropout the clauses defining the quantum circuit while keeping the cost function intact. Due to the combinatorial nature of the optimization problems, the dropout of clauses in the circuit does not affect the solution. Our numerical results confirm improvements in QAOA's performance with various types of quantum-dropout implementation.

quant-ph↗

arXiv2023

Mathematics Is Physical

The world of mathematics is often considered abstract, with its symbols, concepts, and topics appearing unrelated to physical objects. However, it is important to recognize that the development of mathematics is fundamentally influenced by a basic fact: mathematicians and computers are physical objects subject to the laws of physics. Through an analysis of the Turing machine, it becomes evident that Turing and his contemporaries overlooked a physical possibility: information carriers can be quantum systems. As a result, computing models like the Turing machine can only process classical information, limiting their computing power. Gödel's incompleteness theorem highlights the basic fact that mathematicians and computers are made up of finite numbers of atoms and molecules. They can only start with a finite number of axioms, use a finite number of symbols and deduction rules, and arrive at theorems with a finite number of steps. While the number of proofs may be infinite after including all future mathematicians and computers, they must still be enumerable. In contrast, the number of mathematical statements is uncountable, meaning that there will always be mathematical statements that cannot be proved true or false. Just as Landauer claimed that information is physical, mathematics is also physical, limited or empowered by the physical entities that carry it out or embody it.

physics.gen-ph↗

arXiv2023

Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing

Pixel-level Scene Understanding is one of the fundamental problems in computer vision, which aims at recognizing object classes, masks and semantics of each pixel in the given image. Since the real-world is actually video-based rather than a static state, learning to perform video semantic segmentation is more reasonable and practical for realistic applications. In this paper, we adopt Mask2Former as architecture and ViT-Adapter as backbone. Then, we propose a recyclable semi-supervised training method based on multi-model ensemble. Our method achieves the mIoU scores of 62.97% and 65.83% on Development test and final test respectively. Finally, we obtain the 2nd place in the Video Scene Parsing in the Wild Challenge at CVPR 2023.

eess.IV↗

arXiv2023

Electrically controllable thermal transport in Josephson junctions based on buckled two-dimensional materials

We investigate the thermal transport properties in superconductor-antiferromagnet-superconductor and superconductor-ferromagnet-superconductor junctions based on buckled two-dimensional materials (BTDMs). Owing to the unique buckled sublattice structures of BTDMs, in both junctions the phase dependence of the thermal conductance can be effectively controlled by perpendicular electric fields. The underlying mechanism for the electrical tunability of thermal conductance is elucidated resorting to the band structures of the magnetic regions. We also reveal the distinct manifestations of antiferromagnetic and ferromagnetic exchange fields in the thermal conductance. These results demonstrate that the perpendicular electric field can serve as a knob to externally manipulate the phase-coherent thermal transport in BTDMs-based Josephson junctions.

cond-mat.supr-con↗

arXiv2022

Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.

eess.IV↗

arXiv2022

Self-Similarity Among Energy Eigenstates

In a quantum system, different energy eigenstates have different properties or features, allowing us define a classifier to divide them into different groups. We find that the ratio of each type of energy eigenstates in an energy shell $[E_{c}-ΔE/2,E_{c}+ΔE/2]$ is invariant with changing width $ΔE$ or Planck constant $\hbar$ as long as the number of eigenstates in the shell is statistically large enough. We give an argument that such self-similarity in energy eigenstates is a general feature for all quantum systems, which is further illustrated numerically with various quantum systems, including circular billiard, double top model, kicked rotor, and Heisenberg XXZ model.

quant-ph↗

arXiv2022

Logarithmic quantum time crystal

We investigate a time-independent many-boson system, whose ground states are quasi-degenerate and become infinitely degenerate in the thermodynamic limit. Out of these quasi-degenerate ground states we construct a quantum state that evolves in time with a period that is logarithmically proportional to the number of particles, that is, T~logN. This boson system in such a state is a quantum time crystal as it approaches the ground state in the thermodynamic limit. The logarithmic dependence of its period on the total particle number N makes it observable experimentally even for systems with very large number of particles. Possible experimental proposals are discussed.

quant-ph↗

arXiv2022

Magnetic molecular orbitals in MnSi

A large body of knowledge about magnetism is attained from models of interacting spins, which usually reside on magnetic ions. Proposals beyond the ionic picture are uncommon and seldom verified by direct observations in conjunction with microscopic theory. Here, using inelastic neutron scattering to study the itinerant near-ferromagnet MnSi, we find that the system's fundamental magnetic units are interconnected, extended molecular orbitals consisting of three Mn atoms each, rather than individual Mn atoms. This result is further corroborated by magnetic Wannier orbitals obtained by ab initio calculations. It contrasts the ionic picture with a concrete example, and presents a novel regime of the spin waves where the wavelength is comparable to the spatial extent of the molecular orbitals. Our discovery brings important insights into not only the magnetism of MnSi, but also a broad range of magnetic quantum materials where structural symmetry, electron itinerancy and correlations act in concert.

cond-mat.str-el↗

arXiv2022

NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://github.com/RenYang-home/NTIRE22_VEnh_SR.

cs.CV↗

arXiv2021

Quantum Computing by Cooling

Interesting problems in quantum computation take the form of finding low-energy states of (pseudo)spin systems with engineered Hamiltonians that encode the problem data. Motivated by the practical possibility of producing very low-temperature spin systems, we propose and exemplify the possibility to compute by coupling the computational spins to a non-Markovian bath of spins that serve as a heat sink. We demonstrate both analytically and numerically that this strategy can achieve quantum advantage in the Grover search problem.

quant-ph↗