Search

We provide a rigorous derivation of Boltzmann's kinetic equation from the hard-sphere system for rarefied gas, that is valid for arbitrarily long time as long as a solution to the Boltzmann equation exists. This is a crucial step in resolving Hilbert's sixth problem, and also extends Lanford's landmark theorem (1975) which justifies such derivation for sufficiently short time. The general strategy follows the paradigm introduced by the first two authors for the long-time derivation of the wave kinetic equation in wave turbulence theory. This is based on proving a long-time cumulant ansatz, which keeps memory of the full collision history of the particle system. At the heart of the matter is a reduction to combinatorial estimates for the Feynman diagrams (referred to as Collision History (CH) molecules), which are then proved by devising an elaborate cutting algorithm, a major novelty of this work., Comment: 152 pages, 21 figures

Silicon photonics faces a persistent challenge in extending photodetection capabilities beyond the 1.6 um wavelength range, primarily due to the lack of appropriate epitaxial materials. Colloidal quantum dots (QDs) present a promising solution here, offering distinct advantages such as infrared wavelength tunability, cost-effectiveness, and facile deposition. Their unique properties position them as a potential candidate for enabling photodetection in silicon photonics beyond the conventional telecom wavelength, thereby expanding the potential applications and capabilities within this domain. In this study, we have successfully integrated lead sulfide (PbS) colloidal quantum dot photodiodes (QDPDs) onto silicon waveguides using standard process techniques. The integrated photodiodes exhibit a remarkable responsivity of 1.3 A/W (with an external quantum efficiency of 74.8%) at a wavelength of 2.1 um, a low dark current of only 106 nA and a bandwidth of 1.1 MHz under a -3 V bias. To demonstrate the scalability of our integration approach, we have developed a compact 8-channel spectrometer incorporating an array of QDPDs. This achievement marks a significant step toward realizing a cost-effective photodetector solution for silicon photonics, particularly tailored for a wide range of sensing applications around the 2 um wavelength range.

Exploration of new dielectrics with large capacitive coupling is an essential topic in modern electronics when conventional dielectrics suffer from the leakage issue near breakdown limit. To address this looming challenge, we demonstrate that rare-earth-metal fluorides with extremely-low ion migration barriers can generally exhibit an excellent capacitive coupling over 20 $\mu$F cm$^{-2}$ (with an equivalent oxide thickness of ~0.15 nm and a large effective dielectric constant near 30) and great compatibility with scalable device manufacturing processes. Such static dielectric capability of superionic fluorides is exemplified by MoS$_2$ transistors exhibiting high on/off current ratios over 10$^8$, ultralow subthreshold swing of 65 mV dec$^{-1}$, and ultralow leakage current density of ~10$^{-6}$ A cm$^{-2}$. Therefore, the fluoride-gated logic inverters can achieve significantly higher static voltage gain values, surpassing ~167, compared to conventional dielectric. Furthermore, the application of fluoride gating enables the demonstration of NAND, NOR, AND, and OR logic circuits with low static energy consumption. Notably, the superconductor-to-insulator transition at the clean-limit Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ can also be realized through fluoride gating. Our findings highlight fluoride dielectrics as a pioneering platform for advanced electronics applications and for tailoring emergent electronic states in condensed matters., Comment: 33 pages, 5 figures

In this paper, we propose a novel learning approach for feed-forward one-shot 4D head avatar synthesis. Different from existing methods that often learn from reconstructing monocular videos guided by 3DMM, we employ pseudo multi-view videos to learn a 4D head synthesizer in a data-driven manner, avoiding reliance on inaccurate 3DMM reconstruction that could be detrimental to the synthesis performance. The key idea is to first learn a 3D head synthesizer using synthetic multi-view images to convert monocular real videos into multi-view ones, and then utilize the pseudo multi-view videos to learn a 4D head synthesizer via cross-view self-reenactment. By leveraging a simple vision transformer backbone with motion-aware cross-attentions, our method exhibits superior performance compared to previous methods in terms of reconstruction fidelity, geometry consistency, and motion control accuracy. We hope our method offers novel insights into integrating 3D priors with 2D supervisions for improved 4D head avatar creation., Comment: ECCV 24 camera ready version. Project page: https://yudeng.github.io/Portrait4D-v2/

Objectives: Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. Methods: Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical / healthcare / clinical", "un-certainty AND medical / healthcare / clinical", and "noise AND medical / healthcare / clinical". Results: A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. Discussion: From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.

Since their discovery, the infinite-layer nickelates have been regarded as an appealing system for gaining deeper insights into high temperature superconductivity (HTSC). However, the synthesis of superconducting samples has been proved to be challenging. Here, we develop an ultrahigh vacuum (UHV) ${\mathrm{\textit{in situ}}}$ reduction method using atomic hydrogen as reducing agent and apply it in lanthanum nickelate system. The reduction parameters, including the reduction temperature (${\mathrm{\textit{T}_{R}}}$) and hydrogen pressure (${\mathrm{\textit{P}_{H}}}$), are systematically explored. We found that the reduction window for achieving superconducting transition is quite wide, reaching nearly 80$^\circ$C in ${\mathrm{\textit{T}_{R}}}$ and 3 orders of magnitude in ${\mathrm{\textit{P}_{H}}}$ when the reduction time is set to 30 mins. And there exists an optimal ${\mathrm{\textit{P}_{H}}}$ for achieving the highest ${\mathrm{\textit{T}_{c}}}$ if both ${\mathrm{\textit{T}_{R}}}$ and reduction time are fixed. More prominently, as confirmed by atomic force microscopy and scanning transmission electron microscopy, the atomically flat surface can be preserved during the ${\mathrm{\textit{in situ}}}$ reduction process, providing advantages over the ${\mathrm{\textit{ex situ}}}$ CaH$_2$ method for surface-sensitive experiments., Comment: 5 pages, 4 figures

Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such data in highly specialized science and engineering domains (e.g., software engineering and security) can be time-consuming and costly, without mentioning the domain gaps between training and inference data if the model needs to be applied to confidential datasets. In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i.e., those without seed entities). To solve this problem, we propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus using the contextualized representations of pre-trained language models. It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types. Extensive experiments on two datasets covering four domains demonstrate the effectiveness of SEType in comparison with various baselines., Comment: 9 pages; Accepted to AAAI 2024 (Code: https://github.com/yuzhimanhua/SEType)

In this article, we consider the singular value asymptotics of compositions of compact linear operators mapping in the real Hilbert space of quadratically integrable functions over the unit interval. Specifically, the composition is given by the compact simple integration operator followed by the non-compact Ces`aro operator possessing a non-closed range. We show that the degree of ill-posedness of that composition is two, which means that the Ces`aro operator increases the degree of illposedness by the amount of one compared to the simple integration operator., Comment: 13 pages, 2 figures, submitted to conference proceedings

Existing one-shot 4D head synthesis methods usually learn from monocular videos with the aid of 3DMM reconstruction, yet the latter is evenly challenging which restricts them from reasonable 4D head synthesis. We present a method to learn one-shot 4D head synthesis via large-scale synthetic data. The key is to first learn a part-wise 4D generative model from monocular images via adversarial learning, to synthesize multi-view images of diverse identities and full motions as training data; then leverage a transformer-based animatable triplane reconstructor to learn 4D head reconstruction using the synthetic data. A novel learning strategy is enforced to enhance the generalizability to real images by disentangling the learning process of 3D reconstruction and reenactment. Experiments demonstrate our superiority over the prior art., Comment: CVPR24 camera ready version. Project page: https://yudeng.github.io/Portrait4D/

In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspectives. Given high-level inputs about the environment and agent profile, our framework harnesses LLMs to produce a series of detailed text descriptions of the avatar agents' facial motions. These descriptions are then processed by our task-agnostic driving engine into motion token sequences, which are subsequently converted into continuous motion embeddings that are further consumed by our standalone neural-based renderer to generate the final photorealistic avatar animations. These streamlined processes allow our framework to adapt to a variety of non-verbal avatar interactions, both monadic and dyadic. Our extensive study, which includes experiments on both newly compiled and existing datasets featuring two types of agents -- one capable of monadic interaction with the environment, and the other designed for dyadic conversation -- validates the effectiveness and versatility of our approach. To our knowledge, we advanced a leap step by combining LLMs and neural rendering for generalized non-verbal prediction and photo-realistic rendering of avatar agents., Comment: Project page: https://dorniwang.github.io/AgentAvatar_project/

In a series of previous works (arXiv:2104.11204, arXiv:2110.04565, arXiv:2301.07063), we gave a rigorous derivation of the homogeneous wave kinetic equation (WKE) up to small multiples of the kinetic timescale, which corresponds to short time solutions to the wave kinetic equation. In this work, we extend this justification to arbitrarily long times that cover the full lifespan of the WKE. This is the first large data, long-time derivation ever obtained in any nonlinear (particle or wave) collisional kinetic limit., Comment: 121 pages, 20 figures. [V2] Added the proof of Proposition 3.2 and some discussions concerning the emergence of time irreversibility

Tracking and modeling unknown rigid objects in the environment play a crucial role in autonomous unmanned systems and virtual-real interactive applications. However, many existing Simultaneous Localization, Mapping and Moving Object Tracking (SLAMMOT) methods focus solely on estimating specific object poses and lack estimation of object scales and are unable to effectively track unknown objects. In this paper, we propose a novel SLAM backend that unifies ego-motion tracking, rigid object motion tracking, and modeling within a joint optimization framework. In the perception part, we designed a pixel-level asynchronous object tracker (AOT) based on the Segment Anything Model (SAM) and DeAOT, enabling the tracker to effectively track target unknown objects guided by various predefined tasks and prompts. In the modeling part, we present a novel object-centric quadric parameterization to unify both static and dynamic object initialization and optimization. Subsequently, in the part of object state estimation, we propose a tightly coupled optimization model for object pose and scale estimation, incorporating hybrids constraints into a novel dual sliding window optimization framework for joint estimation. To our knowledge, we are the first to tightly couple object pose tracking with light-weight modeling of dynamic and static objects using quadric. We conduct qualitative and quantitative experiments on simulation datasets and real-world datasets, demonstrating the state-of-the-art robustness and accuracy in motion estimation and modeling. Our system showcases the potential application of object perception in complex dynamic scenes.

In this note we further discuss the probabilistic scaling introduced by the authors in [21, 22]. In particular we do a case study comparing the stochastic heat equation, the nonlinear wave equation and the nonlinear Schrodinger equation., Comment: Expository paper, 14 pages

Recently, gate-defined Josephson junctions based on magic-angle twisted bilayer graphene (MATBG) have been fabricated. In such a junction, local electrostatic gating can create two superconducting regions connected by an interaction-driven valley-polarized state as the weak link. Due to the spontaneous time-reversal and inversion symmetry breaking of the valley-polarized state, novel phenomena such as the Josephson diode effect have been observed without applying external fields. Importantly, when the so-called nonreciprocity efficiency (which measures the sign and strength of the Josephson effect) changes sign, the energy-phase relation of the junction is approximate $F(\phi) \approx \cos(2\phi)$ where $F$ is the free energy and $\phi$ is the phase difference of the two superconductors. In this work, we show that such a MATBG-based Josephson junction, when shunted by a capacitor, can be used to realize the long-sought-after $0$-$\pi$ qubits which are protected from local perturbation-induced decoherence. Interestingly, by changing the junction parameters, transmon-like qubits with large anharmonicity can also be realized. In short, by utilizing the novel interaction-driven valley-polarized state in MATBG, a single gate-defined Josephson junction can be used to replace complicated superconducting circuits for realizing qubits that are protected from local perturbations.

Freestanding perovskite oxide membranes have drawn great attention recently since they offer exceptional structural tunability and stacking ability, providing new opportunities in fundamental research and potential device applications in silicon-based semiconductor technology. Among different types of sacrificial layers, the $ \mathrm{(Ca, Sr, Ba)}_{3}\mathrm{Al}_{2}\mathrm{O}_{6}$ compounds are most widely used since they can be dissolved in water and prepare high-quality perovskite oxide membranes with clean and sharp surfaces and interfaces. However, the typical transfer process takes a long time (up to hours) in obtaining millimeter-size freestanding membranes, let alone realize wafer-scale samples with high yield. Here, we introduce a new member of the $ \mathrm{SrO-}\mathrm{Al}_{2}\mathrm{O}_{3}$ family,$ \mathrm{Sr}_{4}\mathrm{Al}_{2}\mathrm{O}_{7},$, and demonstrate its high dissolution rate, about 10 times higher than that of $ \mathrm{Sr}_{3}\mathrm{Al}_{2}\mathrm{O}_{6}$. The high-dissolution-rate of $ \mathrm{Sr}_{4}\mathrm{Al}_{2}\mathrm{O}_{7}$ is most likely related to the more discrete Al-O networks and higher concentration of water-soluble Sr-O species in this compound. Our work significantly facilitates the preparation of freestanding membranes and sheds light on the integration of multifunctional perovskite oxides in practical electronic devices., Comment: 11 pages,4 figures

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ~ 600 years using exact methods, whereas our quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier ~ 3.1*10^10 years., Comment: PRL 2023 to appear