arXiv subjects

Ying Liu

Publications and source records attributed to Ying Liu.

At least 145 records · Page 8Linked to original sources

arXiv2024

Quantum Anomaly Detection with a Spin Processor in Diamond

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms can not only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, we experimentally demonstrate the anomaly detection of quantum states encoding audio samples with a three-qubit quantum processor consisting of solid-state spins in diamond. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.

quant-ph↗

arXiv2023

YAYI 2: Multilingual Open-Source Large Language Models

As the latest advancements in natural language processing, large language models (LLMs) have achieved human-level language understanding and generation abilities in many real-world tasks, and even have been regarded as a potential path to the artificial general intelligence. To better facilitate research on LLMs, many open-source LLMs, such as Llama 2 and Falcon, have recently been proposed and gained comparable performances to proprietary models. However, these models are primarily designed for English scenarios and exhibit poor performances in Chinese contexts. In this technical report, we propose YAYI 2, including both base and chat models, with 30 billion parameters. YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback. Extensive experiments on multiple benchmarks, such as MMLU and CMMLU, consistently demonstrate that the proposed YAYI 2 outperforms other similar sized open-source models.

cs.CL↗

arXiv2023

Switching dynamics and improved efficiency of free-standing antiferroelectric capacitors

We report the switching dynamics of antiferroelectric Lead Zirconate (PbZrO3) free standing capacitors compared to their epitaxial counterparts. Frequency dependence of hysteresis indicates that freestanding capacitors exhibit a lower dispersion of switching fields, lower residual polarization, and faster switching response as compared to epitaxially-clamped capacitors. As a consequence, freestanding capacitor membranes exhibit better energy storage density and efficiency.

cond-mat.mtrl-sci↗

arXiv2023

Contagion dynamics in time-varying metapopulation networks with node's activity and attractiveness

The metapopulation network model is effectively used to study the spatial spread of epidemics with individuals mobility. Considering the time-varying nature of individual activity and the preferences for attractive destinations in population mobility, this paper develops a time-varying network model in which activity of a population is correlated with its attractiveness. Based on the model, the spreading processes of the SIR disease on different correlated networks are studied, and global migration thresholds are derived. It is observed that increasing the correlation between activity and attractiveness results in a reduced outbreak threshold but suppresses the disease outbreak size and introduces greater heterogeneity in the spatial distribution of infected individuals. We also investigate the impact of non-pharmacological interventions (self-isolation and self-protection) on the spread of epidemics in different correlation networks. The results show that the simultaneous implementation of these measures is more effective in negatively correlated networks than in positively correlated or non-correlated networks, and the prevalence is reduced significantly. In addition, both self-isolation and self-protection strategies increase the migration threshold of the spreading and thus slow the spread of the epidemic. However, the effectiveness of each strategy in reducing the density of infected populations varies depending on different correlated networks. Self-protection is more effective in positively correlated networks, whereas self-isolation is more effective in negatively correlated networks. These findings contribute to a better understanding of epidemic spreading in large-scale time-varying metapopulation networks and provide insights for epidemic prevention and control.

physics.soc-ph↗

arXiv2023

Causal Inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders

Semiparametric efficient estimation of various multi-valued causal effects, including quantile treatment effects, is important in economic, biomedical, and other social sciences. Under the unconfoundedness condition, adjustment for confounders requires estimating the nuisance functions relating outcome or treatment to confounders nonparametrically. This paper considers a generalized optimization framework for efficient estimation of general treatment effects using artificial neural networks (ANNs) to approximate the unknown nuisance function of growing-dimensional confounders. We establish a new approximation error bound for the ANNs to the nuisance function belonging to a mixed smoothness class without a known sparsity structure. We show that the ANNs can alleviate the "curse of dimensionality" under this circumstance. We establish the root-$n$ consistency and asymptotic normality of the proposed general treatment effects estimators, and apply a weighted bootstrap procedure for conducting inference. The proposed methods are illustrated via simulation studies and a real data application.

stat.ME↗

arXiv2023

Scenario Approach for Parametric Markov Models

In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly, without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state of the art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis.

cs.LO↗

arXiv2023

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction. Additionally, ethical considerations and biases in RL-NLP systems are addressed.

cs.CL↗

arXiv2023

Turning Logs into Lumber: Preprocessing Tasks in Process Mining

Event logs are invaluable for conducting process mining projects, offering insights into process improvement and data-driven decision-making. However, data quality issues affect the correctness and trustworthiness of these insights, making preprocessing tasks a necessity. Despite the recognized importance, the execution of preprocessing tasks remains ad-hoc, lacking support. This paper presents a systematic literature review that establishes a comprehensive repository of preprocessing tasks and their usage in case studies. We identify six high-level and 20 low-level preprocessing tasks in case studies. Log filtering, transformation, and abstraction are commonly used, while log enriching, integration, and reduction are less frequent. These results can be considered a first step in contributing to more structured, transparent event log preprocessing, enhancing process mining reliability.

cs.DB↗

arXiv2023

Subset-Reach Estimation in Cross-Media Measurement

We propose two novel approaches to address a critical problem of reach measurement across multiple media -- how to estimate the reach of an unobserved subset of buying groups (BGs) based on the observed reach of other subsets of BGs. Specifically, we propose a model-free approach and a model-based approach. The former provides a coarse estimate for the reach of any subset by leveraging the consistency among the reach of different subsets. Linear programming is used to capture the constraints of the reach consistency. This produces an upper and a lower bound for the reach of any subset. The latter provides a point estimate for the reach of any subset. The key idea behind the latter is to exploit the conditional independence model. In particular, the groups of the model are created by assuming each BG has either high or low reach probability in a group, and the weights of each group are determined through solving a non-negative least squares (NNLS) problem. In addition, we also provide a framework to give both confidence interval and point estimates by integrating these two approaches with training points selection and parameter fine-tuning through cross-validation. Finally, we evaluate the two approaches through experiments on synthetic data.

stat.ME↗

arXiv2023

Giant photon-drag-induced ultrafast photocurrent in diamond for nonlinear photonics

Diamond is emerging as an attractive third-generation wide-bandgap semiconductor for future on-chip nonlinear photonics and quantum optics due to its unique thermal, optical, and mechanical properties. However, the light-driven current under below-bandgap excitation from the second-order nonlinear optical effect in diamond is still challenging. Herein, a giant second-order nonlinear photocurrent is observed in the chemical vapor deposition (CVD) diamond by utilizing terahertz (THz) emission spectroscopy. This ultrafast photocurrent originates from the photon drag effect (PDE), during which the momentum transfer from the incident photons to the charge carriers at the rich grain boundaries of the CVD diamond after the exclusive subgap π-π* transition upon femtosecond laser excitation. Especially, the interplay between circular and linear PDE to the THz generation has been clarified and distinguished under elliptically polarized light excitation. Furthermore, the picosecond ultrafast dynamics of these charge carriers are also verified by the infrared spectroscopy. Owing to the giant photon-drag-induced ultrafast photocurrent, the CVD diamond presents the highest THz emission efficiency compared with the reported carbon allotropes, which expands the new functionality of diamond nonlinear photonics into on-chip THz devices.

physics.optics↗

arXiv2023

The Shackles of Peer Review: Unveiling the Flaws in the Ivory Tower

This essay delves into the ethical dilemmas encountered within the academic peer review process and investigates the prevailing deficiencies in this system. It highlights how established scholars often adhere to mainstream theories not out of genuine belief, but to safeguard their own reputations. This practice perpetuates intellectual conformity, fuels confirmation bias, and stifles dissenting voices. Furthermore, as the number of incorrect papers published by influential scientists increases, it inadvertently encourages more researchers to follow suit, tacitly endorsing incorrect viewpoints. By examining historical instances of suppressed ideas later proven valuable, this essay calls for a reevaluation of academia's commitment to genuine innovation and progress which is usually achieved by applications of fundamental principles in from textbooks.

physics.soc-ph↗

arXiv2023

Ferroelectric Schottky diodes of CuInP$_2$S$_6$ nanosheet

Ferroelectricity in van der Waals (vdW) layered material has attracted a great deal of interest recently. CuInP$_2$S$_6$ (CIPS), the only vdW layered material whose ferroelectricity in the bulk was demonstrated by direct polarization measurements, was shown to remain ferroelectric down to a thickness of a few nanometers. However, its ferroelectric properties have just started to be explored in the context of potential device applications. We report here the preparation and measurements of metal-ferroelectric semiconductor-metal heterostructures using nanosheets of CIPS obtained by mechanical exfoliation. Four bias voltage and polarization dependent resistive states were observed in the current-voltage characteristics, which we attribute to the formation of ferroelectric Schottky diode, along with switching behavior.

cond-mat.mtrl-sci↗

arXiv2023

Observation of odd-parity superconductivity in UTe2

Symmetry properties of the order parameter are among the most fundamental characteristics of a superconductor. The pairing symmetry of recently discovered heavy fermion superconductor UTe2 featuring an exceedingly large upper critical field has attracted a great deal of attention. Even though it is widely believed that UTe2 possesses an odd-parity, spin-triplet pairing symmetry, direct evidence for it is lacking, especially at zero or low magnetic fields. We report here the selection-rule results of Josephson coupling between In, an s-wave superconductor, and UTe2. The orientation dependence of the Josephson coupling suggests very strongly that UTe2 possess an odd-parity pairing state of B_1u in zero magnetic fields. We also report the formation of Andreev surface bound states on the (1-10) surface of UTe2.

cond-mat.supr-con↗

arXiv2023

Pressure Tuned 2D Superconductivity in Black Phosphorus

This paper examines the micro-parameters of superconductors. It studies the modulations from weak van der Waals interaction to strong covalence bonding of superconductors. In particular, we studied layered black phosphorus (BP) as a function of pressure. These results reveal a rich scenario of phase transitions and related quantum phenomena, which show that the phases exhibit superconducting states at a pressure higher than 5.0 GPa. In addition, they indicate an angle-dependent upper critical field that demonstrates the dimensional characteristics of superconductivities. This result suggests that the A17 and cubic phases are three-dimensional (3D). The A7 phase shows a two-dimensional (2D) character. The 2D behavior is related to a weakened, distorted, entangled interlayer coupling.

cond-mat.supr-con↗

arXiv2023

AIC-AB NET: A Neural Network for Image Captioning with Spatial Attention and Text Attributes

Image captioning is a significant field across computer vision and natural language processing. We propose and present AIC-AB NET, a novel Attribute-Information-Combined Attention-Based Network that combines spatial attention architecture and text attributes in an encoder-decoder. For caption generation, adaptive spatial attention determines which image region best represents the image and whether to attend to the visual features or the visual sentinel. Text attribute information is synchronously fed into the decoder to help image recognition and reduce uncertainty. We have tested and evaluated our AICAB NET on the MS COCO dataset and a new proposed Fashion dataset. The Fashion dataset is employed as a benchmark of single-object images. The results show the superior performance of the proposed model compared to the state-of-the-art baseline and ablated models on both the images from MSCOCO and our single-object images. Our AIC-AB NET outperforms the baseline adaptive attention network by 0.017 (CIDEr score) on the MS COCO dataset and 0.095 (CIDEr score) on the Fashion dataset.

cs.CV↗

arXiv2023

Observation of microscopic confinement dynamics by a tunable topological $θ$-angle

The topological $θ$-angle is central to the understanding of a plethora of phenomena in condensed matter and high-energy physics such as the strong CP problem, dynamical quantum topological phase transitions, and the confinement--deconfinement transition. Difficulties arise when probing the effects of the topological $θ$-angle using classical methods, in particular through the appearance of a sign problem in numerical simulations. Quantum simulators offer a powerful alternate venue for realizing the $θ$-angle, which has hitherto remained an outstanding challenge due to the difficulty of introducing a dynamical electric field in the experiment. Here, we report on the experimental realization of a tunable topological $θ$-angle in a Bose--Hubbard gauge-theory quantum simulator, implemented through a tilted superlattice potential that induces an effective background electric field. We demonstrate the rich physics due to this angle by the direct observation of the confinement--deconfinement transition of $(1+1)$-dimensional quantum electrodynamics. Using an atomic-precision quantum gas microscope, we distinguish between the confined and deconfined phases by monitoring the real-time evolution of particle--antiparticle pairs, which exhibit constrained (ballistic) propagation for a finite (vanishing) deviation of the $θ$-angle from $π$. Our work provides a major step forward in the realization of topological terms on modern quantum simulators, and the exploration of rich physics they have been theorized to entail.

cond-mat.quant-gas↗

arXiv2023

A Novel Isomer of Volleyballene Sc$_{20}$C$_{60}$

The Stone-Wales defect is a well-known and significant defective structure in carbon materials, impacting their mechanical, chemical, and electronic properties. Recently, a novel metal-carbon nanomaterial named Volleyballene has been discovered, characterized by a C-C bond bridging two carbon pentagons. Using first-principles calculations, a stable Stone-Wales-defective counterpart of Volleyballene, exhibiting $T_h$ symmetry, has been proposed by rotating the C-C bond by 90$°$. Although its binding energy per atom is slightly higher than Volleyballene ($ΔE_b$ = 0.009 eV/atom), implying marginally lower structural stability, it can maintain its bond structure until the effective temperature reaches about 1538.56 K, indicating greater thermodynamic stability. Additionally, its highest vibration frequency is 1346.2 cm$^{-1}$, indicating strong chemical bond strength. A theoretical analysis of the Sc$_{20}$C$_{60}$+Sc$_{20}$C$_{60}$ binary systems highlights that the stable building block may be applied in potential nano-assembly.

cond-mat.mtrl-sci↗

arXiv2023

Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation

Word sense disambiguation (WSD), which aims to determine an appropriate sense for a target word given its context, is crucial for natural language understanding. Existing supervised methods treat WSD as a classification task and have achieved remarkable performance. However, they ignore uncertainty estimation (UE) in the real-world setting, where the data is always noisy and out of distribution. This paper extensively studies UE on the benchmark designed for WSD. Specifically, we first compare four uncertainty scores for a state-of-the-art WSD model and verify that the conventional predictive probabilities obtained at the end of the model are inadequate to quantify uncertainty. Then, we examine the capability of capturing data and model uncertainties by the model with the selected UE score on well-designed test scenarios and discover that the model reflects data uncertainty satisfactorily but underestimates model uncertainty. Furthermore, we explore numerous lexical properties that intrinsically affect data uncertainty and provide a detailed analysis of four critical aspects: the syntactic category, morphology, sense granularity, and semantic relations. The code is available at https://github.com/RyanLiut/WSD-UE.

cs.CL↗