arXiv subjects

Jian Liu

Publications and source records attributed to Jian Liu.

At least 307 records · Page 17Linked to original sources

arXiv2023

Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4

Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular benchmarks like LogiQA and ReClor, and newly-released datasets like AR-LSAT. We test the multi-choice reading comprehension and natural language inference tasks with benchmarks requiring logical reasoning. We further construct a logical reasoning out-of-distribution dataset to investigate the robustness of ChatGPT and GPT-4. We also make a performance comparison between ChatGPT and GPT-4. Experiment results show that ChatGPT performs significantly better than the RoBERTa fine-tuning method on most logical reasoning benchmarks. With early access to the GPT-4 API we are able to conduct intense experiments on the GPT-4 model. The results show GPT-4 yields even higher performance on most logical reasoning datasets. Among benchmarks, ChatGPT and GPT-4 do relatively well on well-known datasets like LogiQA and ReClor. However, the performance drops significantly when handling newly released and out-of-distribution datasets. Logical reasoning remains challenging for ChatGPT and GPT-4, especially on out-of-distribution and natural language inference datasets. We release the prompt-style logical reasoning datasets as a benchmark suite and name it LogiEval.

cs.CL↗

arXiv2023

RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense

Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies have shown that private information can still be leaked through shared gradients. To further minimize the risk of privacy leakage, existing defenses usually require clients to locally modify their gradients (e.g., differential privacy) prior to sharing with the server. While these approaches are effective in certain cases, they regard the entire data as a single entity to protect, which usually comes at a large cost in model utility. In this paper, we seek to reconcile utility and privacy in FL by proposing a user-configurable privacy defense, RecUP-FL, that can better focus on the user-specified sensitive attributes while obtaining significant improvements in utility over traditional defenses. Moreover, we observe that existing inference attacks often rely on a machine learning model to extract the private information (e.g., attributes). We thus formulate such a privacy defense as an adversarial learning problem, where RecUP-FL generates slight perturbations that can be added to the gradients before sharing to fool adversary models. To improve the transferability to un-queryable black-box adversary models, inspired by the idea of meta-learning, RecUP-FL forms a model zoo containing a set of substitute models and iteratively alternates between simulations of the white-box and the black-box adversarial attack scenarios to generate perturbations. Extensive experiments on four datasets under various adversarial settings (both attribute inference attack and data reconstruction attack) show that RecUP-FL can meet user-specified privacy constraints over the sensitive attributes while significantly improving the model utility compared with state-of-the-art privacy defenses.

cs.LG↗

arXiv2023

Electro-optically programmable photonic circuits enabled by wafer-scale integration on thin-film lithium niobate

Programmable photonic circuits performing universal linear-optical transformations underpin vital functions in photonic quantum information processing, quantum-enhanced sensor networks, machine learning and many other intriguing applications. Recent advances in photonic integrated circuits facilitate monolithic integration of externally controlled Mach-Zehnder interferometers which can implement arbitrary unitary transformation on a large number of input/output modes. In this work, we demonstrate a 4x4 programmable linear photonic circuit on lithium niobate on insulator platform employing fast, power-efficient and low-loss electro-optical phase shifters, showing enormous advantages in terms of configuration rate and power consumption. Our device is capable of fast switching with 500 ps rise time and 1.7 ns fall time, and possesses a total on-chip power dissipation of only 0.015 mW when operated at 1 MHz modulation, and an insertion loss of 0.15 dB for each modulator and an on-chip extinction ratio of -34 dB for both cross and bar routes.

physics.optics↗

arXiv2023

Persistent hyperdigraph homology and persistent hyperdigraph Laplacians

Hypergraphs are useful mathematical models for describing complex relationships among members of a structured graph, while hyperdigraphs serve as a generalization that can encode asymmetric relationships in the data. However, obtaining topological information directly from hyperdigraphs remains a challenge. To address this issue, we introduce hyperdigraph homology in this work. We also propose topological hyperdigraph Laplacians, which can extract both harmonic spectra and non-harmonic spectra from directed and internally organized data. Moreover, we introduce persistent hyperdigraph homology and persistent hyperdigraph Laplacians through filtration, enabling the capture of topological persistence and homotopic shape evolution of directed and structured data across multiple scales. The proposed methods offer new multiscale algebraic topology tools for topological data analysis.

math.AT↗

arXiv2023

Continuous spin excitations in the three-dimensional frustrated magnet K2Ni2(SO4)3

Continuous spin excitations are widely recognized as one of the hallmarks of novel spin states in quantum magnets, such as quantum spin liquids (QSLs). Here, we report the observation of such kind of excitations in K2Ni2(SO4)3, which consists of two sets of intersected spin-1 Ni2+ trillium lattices. Our inelastic neutron scattering measurement on single crystals clearly shows a dominant excitation continuum, which exhibits a distinct temperature-dependent behavior from that of spin waves, and is rooted in strong quantum spin fluctuations. Further using the self-consistent-gaussian-approximation method, we determined the fourth- and fifth-nearest neighbor exchange interactions are dominant. These two bonds together form a unique three-dimensional network of corner-sharing tetrahedra, which we name as ''hyper-trillium'' lattice. Our results provide direct evidence for the existence of QSL features in K2Ni2(SO4)3 and highlight the potential for the hyper-trillium lattice to host frustrated quantum magnetism.

cond-mat.str-el↗

arXiv2023

Speech Privacy Leakage from Shared Gradients in Distributed Learning

Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis. However, such frameworks are vulnerable to privacy leakage attacks from shared gradients. Despite extensive efforts in the image domain, the exploration of speech privacy leakage from gradients is quite limited. In this paper, we explore methods for recovering private speech/speaker information from the shared gradients in distributed learning settings. We conduct experiments on a keyword spotting model with two different types of speech features to quantify the amount of leaked information by measuring the similarity between the original and recovered speech signals. We further demonstrate the feasibility of inferring various levels of side-channel information, including speech content and speaker identity, under the distributed learning framework without accessing the user's data.

cs.LG↗

arXiv2023

Private Data Valuation and Fair Payment in Data Marketplaces

Data valuation is an essential task in a data marketplace. It aims at fairly compensating data owners for their contribution. There is increasing recognition in the machine learning community that the Shapley value -- a foundational profit-sharing scheme in cooperative game theory -- has major potential to value data, because it uniquely satisfies basic properties for fair credit allocation and has been shown to be able to identify data sources that are useful or harmful to model performance. However, calculating the Shapley value requires accessing original data sources. It still remains an open question how to design a real-world data marketplace that takes advantage of the Shapley value-based data pricing while protecting privacy and allowing fair payments. In this paper, we propose the {\em first} prototype of a data marketplace that values data sources based on the Shapley value in a privacy-preserving manner and at the same time ensures fair payments. Our approach is enabled by a suite of innovations on both algorithm and system design. We firstly propose a Shapley value calculation algorithm that can be efficiently implemented via multiparty computation (MPC) circuits. The key idea is to learn a performance predictor that can directly predict model performance corresponding to an input dataset without performing actual training. We further optimize the MPC circuit design based on the structure of the performance predictor. We further incorporate fair payment into the MPC circuit to guarantee that the data that the buyer pays for is exactly the same as the one that has been valuated. Our experimental results show that the proposed new data valuation algorithm is as effective as the original expensive one. Furthermore, the customized MPC protocol is efficient and scalable.

cs.CR↗

arXiv2023

Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action

Denoising diffusion models have emerged as one of the most powerful generative models in recent years. They have achieved remarkable success in many fields, such as computer vision, natural language processing (NLP), and bioinformatics. Although there are a few excellent reviews on diffusion models and their applications in computer vision and NLP, there is a lack of an overview of their applications in bioinformatics. This review aims to provide a rather thorough overview of the applications of diffusion models in bioinformatics to aid their further development in bioinformatics and computational biology. We start with an introduction of the key concepts and theoretical foundations of three cornerstone diffusion modeling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks, and stochastic differential equations), followed by a comprehensive description of diffusion models employed in the different domains of bioinformatics, including cryo-EM data enhancement, single-cell data analysis, protein design and generation, drug and small molecule design, and protein-ligand interaction. The review is concluded with a summary of the potential new development and applications of diffusion models in bioinformatics.

cs.LG↗

arXiv2023

Artificial Intelligence System for Detection and Screening of Cardiac Abnormalities using Electrocardiogram Images

The artificial intelligence (AI) system has achieved expert-level performance in electrocardiogram (ECG) signal analysis. However, in underdeveloped countries or regions where the healthcare information system is imperfect, only paper ECGs can be provided. Analysis of real-world ECG images (photos or scans of paper ECGs) remains challenging due to complex environments or interference. In this study, we present an AI system developed to detect and screen cardiac abnormalities (CAs) from real-world ECG images. The system was evaluated on a large dataset of 52,357 patients from multiple regions and populations across the world. On the detection task, the AI system obtained area under the receiver operating curve (AUC) of 0.996 (hold-out test), 0.994 (external test 1), 0.984 (external test 2), and 0.979 (external test 3), respectively. Meanwhile, the detection results of AI system showed a strong correlation with the diagnosis of cardiologists (cardiologist 1 (R=0.794, p<1e-3), cardiologist 2 (R=0.812, p<1e-3)). On the screening task, the AI system achieved AUCs of 0.894 (hold-out test) and 0.850 (external test). The screening performance of the AI system was better than that of the cardiologists (AI system (0.846) vs. cardiologist 1 (0.520) vs. cardiologist 2 (0.480)). Our study demonstrates the feasibility of an accurate, objective, easy-to-use, fast, and low-cost AI system for CA detection and screening. The system has the potential to be used by healthcare professionals, caregivers, and general users to assess CAs based on real-world ECG images.

cs.CV↗

arXiv2023

Antibacterial Activity of Zinc Oxide Thin Films by Atomic Layer Deposition for Personal Protective Equipment Applications

The global pandemic has significantly increased the demand for personal protective equipment (PPE). The antimicrobial coating has been broadly applied to PPE to improve its prevention capability, especially after prolonged usage. However, antimicrobial coating by traditional methods, such as chemical vapor deposition, spraying, and slurry coating, suffers from drawbacks such as low efficiency, poor coverage, and loose adhesion to PPE. To overcome these limitations, this work adopted an atomic layer deposition (ALD) technique to deposit a zinc oxide (ZnO) thin film (~ 312.8 nm thick) as an antimicrobial coating and proved its advantage for depositing uniform ZnO coating on PPE with a fabric structure. Analysis by X-ray diffraction, Raman spectroscopy, and X-ray photoelectron spectroscopy confirmed the crystal structure and chemical composition of the ALD-ZnO. Ultraviolet-visible spectra disclosed a high absorption level of about 4.8 from 200 nm to 380 nm wavelength for ALD-ZnO, in contrast to 3.8 for commercial ZnO powders. Moreover, the ALD-ZnO exhibited strong antimicrobial properties when tested against Escherichia coli (E.Coli) in contrast to the control and bare glass samples. The colony-forming unit (CFU/ml) remained zero for all ALD-ZnO samples while varying between 2.30*109 and 4.97*109 with a median of 4.36*109 for the control and between 9.10*108 and 3.27*109 with a median of 2.04x 109 for the bare glass. Statistic analysis using null-hypothesis significance testing revealed that the calculated P value, between bare glass and control, ALD-ZnO and control, and bare glass and control, were all smaller than 0.0001 and significantly smaller than 0.05 alpha value, suggesting a high confidence level of ALD-ZnO as the main factor for preventing E.Coli growth.

physics.med-ph↗

arXiv2022

On-chip single-mode thin film lithium niobate laser based on Sagnac loop reflectors

We demonstrate an on-chip single-mode Er3+-doped thin film lithium niobate (Er: TFLN) laser which consists of a Fabry-Pérot (FP) resonator based on Sagnac loop reflectors (SLRs). The fabricated Er: TFLN laser has a footprint of 6.5 mmx1.5 mm with a loaded quality (Q) factor of 1.6x105 and a free spectral range (FSR) of 63 pm. We generate the single-mode laser around 1550-nm wavelength with a maximum output power of 44.7 μW and a slope efficiency of 0.18 %.

physics.optics↗

arXiv2022

Charge transport in doped conjugated polymers for organic thermoelectrics

Research on conjugated polymers for thermoelectric applications has made tremendous progress in recent years, which is accompanied by surging interest in molecular doping as a means to achieve the high electrical conductivities that are required. A detailed understanding of the complex relationship between the doping process, the structural as well as energetic properties of the polymer films, and the resulting thermoelectric behavior is slowly emerging. This review summarizes recent developments and strategies that permit enhancing the electrical conductivity of p- and n-type conjugated polymers via molecular doping. The impact of the chemical design of both the polymer and the dopant, the processing conditions, and the resulting nanostructure on the doping efficiency and stability of the doped state are discussed. Attention is paid to the interdependence of the electrical and thermal transport characteristics of semiconductor host-dopant systems and the Seebeck coefficient. Strategies that permit to improve the thermoelectric performance, such as an uniaxial alignment of the polymer backbone in both bulk and thin film geometries, manipulation of the dielectric constant of the polymer, and the variation of the dopant size, are explored. A combination of theory and experiment is predicted to yield new chemical design principles and processing schemes that will ultimately give rise to the next generation of organic thermoelectric materials.

physics.chem-ph↗

arXiv2022

Protocol selection for second-order consensus against disturbance

Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error. More specifically, by the orthogonal transformation technique, the analytic expression of the L2 gain of the second-order multi-agent system with absolute velocity protocol is firstly derived, followed by the counterpart with relative velocity protocol. It is shown that both the L2 gains for absolute and relative velocity protocols are determined only by the minimum non-zero eigenvalue of Laplacian matrix and the tunable gains of the state and velocity. Then, we establish the graph conditions to tell which protocol has better anti-disturbance capability. Moreover, we propose a two-step scheme to improve the anti-disturbance capability of second-order multi-agent systems. Finally, simulations are given to illustrate the effectiveness of our findings.

eess.SY↗

arXiv2022

Effects of nucleon-nucleon short-range correlations on inclusive electron scattering

The nucleon-nucleon short-range correlation NN-SRC is one of the key issues of nuclear physics, which typically manifest themselves in high-momentum components of the nuclear momentum distributions. In this letter, the nuclear spectral functions based on the axially deformed relativistic mean-field model are developed to involve the NN-SRC. With the spectral functions, the inclusive electron scattering $ (e,e^{\prime}) $ cross sections are calculated within the PWIA framework, including the quasi-elastic (QE) part and $ Δ$ production part. Especially in the $ Δ$ production region, we reconsider the electromagnetic structures of the nucleon resonance $ Δ$(1232) and the scattering mechanisms, thereby the theoretical calculations are improved effectively and the cross sections are well consistent with the experimental data. The theoretical $ (e,e^{\prime}) $ cross sections are further divided into NN-SRC and mean-field contributions. It is found that, at the kinematics $ 0.5 \,{\rm GeV}^{2}<Q^{2}<1 \,{\rm GeV}^{2} $, the QE peak and $ Δ$ production peak not only reflect the mean-field structure, but also are sensitive to the NN-SRC information. Finally, we provide a new method to extract the strengths of NN-SRC from experimental cross sections for selected nuclei at the suitable kinematics.

nucl-th↗

arXiv2022

Exploring sensitivity of charge-exchange ($p, n$) reactions to the neutron density distribution

$Background:$ The determination of the nuclear neutron properties suffers from uncontrolled uncertainties, which attracted considerable attention recently, such as in the context of the PREX experiment. $Purpose:$ Our aim is to analyze the sensitivity of charge-exchange ($p, n$) reactions to the neutron density distribution $ρ_{n}$ and constrain the neutron characteristics in the nuclear structure models. $Method:$ By combing the folding and the mean-field models, the nucleon-nucleus ($NA$) potential can be obtained from the nuclear density distribution. Further, the ($p, p$) and ($p, n$) cross sections for $^{48}$Ca and $^{208}$Pb are calculated following the distorted-wave Born approximation (DWBA) method. $Results:$ Compared with the ($p, p$) cross section, the effects of $ρ_{n}$ variation on the ($p, n$) cross section are significant, which is due to the impact of isovector properties. Based on the global folding model analyses of data, it is found that $^{48}$Ca and $^{208}$Pb have relatively large neutron skin thickness $ΔR_{n p}$. $Conclusions:$ Results illustrate that the charge-exchange ($p, n$) reaction is a sensitive probe of $ρ_{n}$. The results in this paper can offer useful guides for future experiments of neutron characteristics.

nucl-th↗

arXiv2022

Improved Data Augmentation for Translation Suggestion

Translation suggestion (TS) models are used to automatically provide alternative suggestions for incorrect spans in sentences generated by machine translation. This paper introduces the system used in our submission to the WMT'22 Translation Suggestion shared task. Our system is based on the ensemble of different translation architectures, including Transformer, SA-Transformer, and DynamicConv. We use three strategies to construct synthetic data from parallel corpora to compensate for the lack of supervised data. In addition, we introduce a multi-phase pre-training strategy, adding an additional pre-training phase with in-domain data. We rank second and third on the English-German and English-Chinese bidirectional tasks, respectively.

cs.CL↗

arXiv2022

Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving

Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue. In this paper, we introduce a fully Visual Attention-based Depth (VADepth) network, where spatial attention and channel attention are applied to all stages. By continuously extracting the dependencies of features along the spatial and channel dimensions over a long distance, VADepth network can effectively preserve important details and suppress interfering features to better perceive the scene structure for more accurate depth estimates. In addition, we utilize geometric priors to form scale constraints for scale-aware model training. Specifically, we construct a novel scale-aware loss using the distance between the camera and a plane fitted by the ground points corresponding to the pixels of the rectangular area in the bottom middle of the image. Experimental results on the KITTI dataset show that this architecture achieves the state-of-the-art performance and our method can directly output absolute depth without post-processing. Moreover, our experiments on the SeasonDepth dataset also demonstrate the robustness of our model to multiple unseen environments.

cs.CV↗

arXiv2022

Monolithically integrated active passive waveguide array fabricated on thin film lithium niobate using a single continuous photolithography process

We demonstrate a robust low-loss optical interface by tiling passive (i.e., without doping of active ions) thin film lithium niobate (TFLN) and active (i.e., doped with rare earth ions) TFLN substrates for monolithic integration of passive/active lithium niobate photonics. The tiled substrates composed of both active and passive areas allow to pattern the mask of the integrated active passive photonic device at once using a single continuous photolithography process. The interface loss of tiled substrate is measured as low as 0.26 dB. Thanks to the stability provided by this approach, a four-channel waveguide amplifier is realized in a straightforward manner, which shows a net gain of ~5 dB at 1550-nm wavelength and that of ~8 dB at 1530-nm wavelength for each channel. The robust low-loss optical interface for passive/active photonic integration will facilitate large-scale high performance photonic devices which require on-chip light sources and amplifiers.

physics.optics↗