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Jin Wang

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preprint2026arXiv

Brightest GRB flare observed in GRB 221009A: bridge the last gap between flare and prompt emission in GRB

Flares are usually observed during the afterglow phase of Gamma-Ray Bursts (GRBs) in soft X-ray, optical and radio bands, but rarely in gamma-ray band. Despite the extraordinary brightness, GECAM-C has accurately measured both the bright prompt emission and flare emission of GRB 221009A without instrumental effects, offering a good opportunity to study the relation between them. In this work, we present a comprehensive analysis of flare emission of GRB 221009A, which is composed of a series of flares. Among them, we identify an exceptionally bright flare with a record-breaking isotropic energy $E_{\rm iso} = 1.82 \times 10^{53}$ erg of GRB flares. It exhibits the highest peak energy ever detected in GRB flares, $E_{\rm peak} \sim 300$ keV, making it a genuine gamma-ray flare. It also shows rapid rise and decay timescales, significantly shorter than those of typical X-ray flares observed in soft X-ray or optical band, but comparable to those observed in prompt emissions. Despite these exceptional properties, the flare shares several common properties with typical GRB flares. We note that this is the first observation of a GRB flare in the keV-MeV band with sufficiently high temporal resolution and high statistics, which bridges the last gap between prompt emission and flare.

preprint2026arXiv

ChemBART: A Pre-trained BART Model Assisting Organic Chemistry Analysis

Recent advances in large language models (LLMs) have demonstrated transformative potential across diverse fields. While LLMs have been applied to molecular simplified molecular input line entry system (SMILES) in computer-aided synthesis planning (CASP), existing methodologies typically address single tasks, such as precursor prediction. We introduce ChemBART, a SMILES-based LLM pre-trained on chemical reactions, which enables a unified model for multiple downstream chemical tasks--achieving the paradigm of "one model, one pre-training, multiple tasks." By leveraging outputs from a mask-filling pre-training task on reaction expressions, ChemBART effectively solves a variety of chemical problems, including precursor/reagent generation, temperature-yield regression, molecular property classification, and optimizing the policy and value functions within a reinforcement learning framework, integrated with Monte Carlo tree search for multi-step synthesis route design. Unlike single-molecule pre-trained LLMs constrained to specific applications, ChemBART addresses broader chemical challenges and integrates them for comprehensive synthesis planning. Crucially, ChemBART-designed multi-step synthesis routes and reaction conditions directly inspired wet-lab validation, which confirmed shorter pathways with ~30% yield improvement over literature benchmarks. Our work validates the power of reaction-focused pre-training and showcases the broad utility of ChemBART in advancing the complete synthesis planning cycle.

preprint2026arXiv

Evaluating transfer learning strategies for improving dairy cattle body weight prediction in small farms using depth-image and point-cloud data

Computer vision provides automated, non-invasive, and scalable tools for monitoring dairy cattle, thereby supporting management, health assessment, and phenotypic data collection. Although transfer learning is commonly used for predicting body weight from images, its effectiveness and optimal fine-tuning strategies remain poorly understood in livestock applications, particularly beyond the use of pretrained ImageNet or COCO weights. In addition, while both depth images and three-dimensional point-cloud data have been explored for body weight prediction, direct comparisons of these two modalities in dairy cattle are limited. Therefore, the objectives of this study were to 1) evaluate whether transfer learning from a large farm enhances body weight prediction on a small farm with limited data, and 2) compare the predictive performance of depth-image- and point-cloud-based approaches under three experimental designs. Top-view depth images and point-cloud data were collected from 1,201, 215, and 58 cows at large, medium, and small dairy farms, respectively. Four deep learning models were evaluated: ConvNeXt and MobileViT for depth images, and PointNet and DGCNN for point clouds. Transfer learning markedly improved body weight prediction on the small farm across all four models, outperforming single-source learning and achieving gains comparable to or greater than joint learning. These results indicate that pretrained representations generalize well across farms with differing imaging conditions and dairy cattle populations. No consistent performance difference was observed between depth-image- and point-cloud-based models. Overall, these findings suggest that transfer learning is well suited for small farm prediction scenarios where cross-farm data sharing is limited by privacy, logistical, or policy constraints, as it requires access only to pretrained model weights rather than raw data.

preprint2026arXiv

HearSay Benchmark: Do Audio LLMs Leak What They Hear?

While Audio Large Language Models (ALLMs) have achieved remarkable progress in understanding and generation, their potential privacy implications remain largely unexplored. This paper takes the first step to investigate whether ALLMs inadvertently leak user privacy solely through acoustic voiceprints and introduces $\textit{HearSay}$, a comprehensive benchmark constructed from over 22,000 real-world audio clips. To ensure data quality, the benchmark is meticulously curated through a rigorous pipeline involving automated profiling and human verification, guaranteeing that all privacy labels are grounded in factual records. Extensive experiments on $\textit{HearSay}$ yield three critical findings: $\textbf{Significant Privacy Leakage}$: ALLMs inherently extract private attributes from voiceprints, reaching 92.89% accuracy on gender and effectively profiling social attributes. $\textbf{Insufficient Safety Mechanisms}$: Alarmingly, existing safeguards are severely inadequate; most models fail to refuse privacy-intruding requests, exhibiting near-zero refusal rates for physiological traits. $\textbf{Reasoning Amplifies Risk}$: Chain-of-Thought (CoT) reasoning exacerbates privacy risks in capable models by uncovering deeper acoustic correlations. These findings expose critical vulnerabilities in ALLMs, underscoring the urgent need for targeted privacy alignment. The codes and dataset are available at https://github.com/JinWang79/HearSay_Benchmark

preprint2026arXiv

IPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts

Current image quality assessment methods are heavily biased towards global distortions (e.g., noise, blur), neglecting local perceptual artifacts such as ghosting, lens flare, and moire effects. Although significant progress has been made in artifact removal, the fundamental problem of automatic artifact detection remains largely unexplored. In this paper, we formalize the Image Perceptual Artifact Detection (IPAD) task to address this gap. We contribute a benchmark dataset comprising 3,520 artifact images, including 520 real-captured and 3,000 synthetic samples, each paired with pixel-level masks across three representative artifact categories. The core challenge of IPAD lies in the localized, subtle, and semantically weak nature of these artifacts, which makes them prone to missed detection. To overcome this, we introduce IPAD-CLIP, a novel framework built upon CLIP that enhances artifact discrimination in both textual and visual spaces while preserving generalization capabilities. Our key insight is that local artifacts often exhibit strong correlations with specific semantic contexts. Accordingly, we learn artifact-aware text embeddings to explicitly model the object-artifact relationships, resulting in enhanced representations that clear differentiate between clean and artifact prompts. These text embeddings are then used as anchors to shift the visual encoder's attention from high-level semantics to subtle, low-level artifacts. Extensive experiments demonstrate that IPAD-CLIP offers a resource-efficient adaptation of CLIP for detection, significantly outperforming advanced image anomaly detection and manipulation detection methods on our benchmark. To the best of our knowledge, this is the first study addressing multi-class local perceptual artifact detection in terms of both dataset and model.

preprint2026arXiv

LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models

Aligning Large Language Models (LLMs) with human preferences is critical, yet traditional fine-tuning methods are computationally expensive and inflexible. While test-time alignment offers a promising alternative, existing approaches often rely on distorted trajectory-level signals or inefficient sampling, fundamentally capping performance and failing to preserve the generative diversity of the base model. This paper introduces LLMdoctor, a novel framework for efficient test-time alignment that operates via a patient-doctor paradigm. It integrates token-level reward acquisition with token-level flow-guided preference optimization (TFPO) to steer a large, frozen patient LLM with a smaller, specialized doctor model. Unlike conventional methods that rely on trajectory-level rewards, LLMdoctor first extracts fine-grained, token-level preference signals from the patient model's behavioral variations. These signals then guide the training of the doctor model via TFPO, which establishes flow consistency across all subtrajectories, enabling precise token-by-token alignment while inherently preserving generation diversity. Extensive experiments demonstrate that LLMdoctor significantly outperforms existing test-time alignment methods and even surpasses the performance of full fine-tuning approaches like DPO.

preprint2026arXiv

Multi parameter discrimination using multiple spectral troughs in a cascaded fiber sensor

Accurate monitoring of temperature, axial strain, and refractive index is critical for structural health monitoring, industrial process control, and environmental sensing. However, conventional optical fiber sensors are often limited by strong parameter cross sensitivity, poor discrimination capability, and increased system complexity when multiple sensing units are required. In this work, a compact multi-parameter optical fiber sensing platform is proposed based on a cascaded single-mode fiber, multimode fiber, and long-period fiber grating structure, combined with a wavelength-based spectral demodulation strategy. Within the cascaded configuration, multiple characteristic spectral troughs arising from distinct physical mechanisms coexist in a single transmission spectrum. Interference-induced troughs are generated by the multimode fiber section, while a resonance-induced trough is introduced by the long-period fiber grating. Although none of these troughs responds exclusively to a single parameter, each exhibits simultaneous and linearly independent responses to temperature, axial strain, and refractive index with distinct sensitivity magnitudes and trends. Consequently, each trough can be described by a unique sensitivity vector, enabling robust multi-parameter discrimination through multi-wavelength spectral demodulation.

preprint2026arXiv

Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation

Generating high-quality 3D characters from single images remains a significant challenge in digital content creation, particularly due to complex body poses and self-occlusion. In this paper, we present RCM (Rotate your Character Model), an advanced image-to-video diffusion framework tailored for high-quality novel view synthesis (NVS) and 3D character generation. Compared to existing diffusion-based approaches, RCM offers several key advantages: (1) transferring characters with any complex poses into a canonical pose, enabling consistent novel view synthesis across the entire viewing orbit, (2) high-resolution orbital video generation at 1024x1024 resolution, (3) controllable observation positions given different initial camera poses, and (4) multi-view conditioning supporting up to 4 input images, accommodating diverse user scenarios. Extensive experiments demonstrate that RCM outperforms state-of-the-art methods in both novel view synthesis and 3D generation quality.

preprint2026arXiv

Slip viscosity and strain-rate viscosity in Taylor-Couette laminar flows: Experimental falsification and end-wall effects

The viscous force should be shear force, the difference between the strain-rate viscosity and the slip viscosity is that the former has conjugate shear force, while the latter does not. The study in this paper verifies the physical authenticity of two viscosity models through Taylor Couette laminar flow experiments with inner and outer cylinders rotating at the same angular velocity, and numerically investigate the influence of relative cylinder spacing and rotational speed on the circumferential velocity under the slip model. The experimental results of LDV measurement with a relative cylinder spacing of 0.3 indicate that the maximum deviation from rigid-body rotation is about 0.86%, which is consistent with the theoretical prediction of slip viscosity model. The numerical simulations show that the end-walls have no effect under the strain-rate viscosity model; but when the slip viscosity model is introduced, the end-walls inevitably bring about the circumferential velocity profile changing along the axial direction, and result in a three-dimensional (3D) spiral streamline pattern influenced by the relative cylinder spacing and angular speed of cylinders.

preprint2026arXiv

TAGRPO: Boosting GRPO on Image-to-Video Generation with Direct Trajectory Alignment

Recent studies have demonstrated the efficacy of integrating Group Relative Policy Optimization (GRPO) into flow matching models, particularly for text-to-image and text-to-video generation. However, we find that directly applying these techniques to image-to-video (I2V) models often fails to yield consistent reward improvements. To address this limitation, we present TAGRPO, a robust post-training framework for I2V models inspired by contrastive learning. Our approach is grounded in the observation that rollout videos generated from identical initial noise provide superior guidance for optimization. Leveraging this insight, we propose a novel GRPO loss applied to intermediate latents, encouraging direct alignment with high-reward trajectories while maximizing distance from low-reward counterparts. Furthermore, we introduce a memory bank for rollout videos to enhance diversity and reduce computational overhead. Despite its simplicity, TAGRPO achieves significant improvements over DanceGRPO in I2V generation.

preprint2024arXiv

Information retrieval from Hawking radiation in the non-isometric model of black hole interior: theory and quantum simulations

The non-isometric holographic model of the black hole interior stands out as a potential resolution of the long-standing black hole information puzzle since it remedies the friction between the effective calculation and the microscopic description. In this study, combining the final-state projection model, the non-isometric model of black hole interior and Hayden-Preskill thought experiment, we investigate the information recovery from decoding Hawking radiation and demonstrate the emergence of the Page time in this setup. We incorporate the effective modes into the scrambling inside the horizon, which are usually disregarded in Hayden-Preskill protocols, and show that the Page time can be identified as the transition of information transmission channels from the EPR projection to the local projections. This offers a new perspective on the Page time. We compute the decoupling condition under which retrieving information is feasible and show that this model computes the black hole entropy consistent with the quantum extremal surface calculation. Assuming the full knowledge of the dynamics of the black hole interior, we show how Yoshida-Kitaev decoding strategy can be employed in the modified Hayden-Preskill protocol. Furthermore, we perform experimental tests of both probabilistic and Grover's search decoding strategies on the 7-qubit IBM quantum processors to validate our analytical findings and confirm the feasibility of retrieving information in the non-isometric model. This study would stimulate more interests to explore black hole information problem on the quantum processors.

preprint2023arXiv

Entanglement and work statistics in the driven open system

We study the entanglement and work statistics in a driven two-qubit system. The regulation of periodic driving has much more versatility and universality in contrast to reservoir engineering in static systems. We found the quasi-steady state entanglement can be amplified effectively by the external drive in certain parameter regimes. The drive extends the range of temperatures or temperature differences at which entanglement can emerge. From the view of the effective Hamiltonian, the addition of the driving alters the inter-qubit coupling and system-bath coupling, which are crucial in determining the quasi-steady state. The work statistics are also investigated. The driven system, as a continuous quantum thermal machine, output work continuously and steadily at the quasi-steady state. There is a distinct operation of modes and corresponding performance by changing driving. It can also be understood that the drive changes the effective Hamiltonian, and further the modes of energy exchanges between the system and the baths as well as the work reservoir.

preprint2023arXiv

High precision atom interferometer-based dynamic gravimeter measurement by eliminating the cross-coupling effect

A dynamic gravimeter with an atomic interferometer (AI) can perform absolute gravity measurements with high precision. AI-based dynamic gravity measurement is a type of joint measurement that uses AI sensors and a classical accelerometer. The coupling of the two sensors may degrade the measurement precision. In this study, we analyzed the cross-coupling effect and introduced a recovery vector to suppress this effect. We improved the phase noise of the interference fringe by a factor of 1.9 by performing marine gravity measurements using an AI-based gravimeter and optimizing the recovery vector. Marine gravity measurements were performed, and high gravity measurement precision was achieved. The external and inner coincidence accuracies of the gravity measurement are 0.42 mGal and 0.46 mGal, which were improved by factors of 4.18 and 4.21 by optimizing the cross-coupling effect.

preprint2023arXiv

Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach

Multi-access Edge Computing (MEC) is an emerging computing paradigm that extends cloud computing to the network edge to support resource-intensive applications on mobile devices. As a crucial problem in MEC, service migration needs to decide how to migrate user services for maintaining the Quality-of-Service when users roam between MEC servers with limited coverage and capacity. However, finding an optimal migration policy is intractable due to the dynamic MEC environment and user mobility. Many existing studies make centralized migration decisions based on complete system-level information, which is time-consuming and also lacks desirable scalability. To address these challenges, we propose a novel learning-driven method, which is user-centric and can make effective online migration decisions by utilizing incomplete system-level information. Specifically, the service migration problem is modeled as a Partially Observable Markov Decision Process (POMDP). To solve the POMDP, we design a new encoder network that combines a Long Short-Term Memory (LSTM) and an embedding matrix for effective extraction of hidden information, and further propose a tailored off-policy actor-critic algorithm for efficient training. The extensive experimental results based on real-world mobility traces demonstrate that this new method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms and can achieve near-optimal results on various MEC scenarios.

preprint2023arXiv

Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning

Dataset discovery from data lakes is essential in many real application scenarios. In this paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes (with table union search as the main use case). Our proposed framework features a contrastive learning method to train column encoders from pre-trained language models in a fully unsupervised manner. The column encoder of Starmie captures the rich contextual semantic information within tables by leveraging a contrastive multi-column pre-training strategy. We utilize the cosine similarity between column embedding vectors as the column unionability score and propose a filter-and-verification framework that allows exploring a variety of design choices to compute the unionability score between two tables accordingly. Empirical evaluation results on real table benchmark datasets show that Starmie outperforms the best-known solutions in the effectiveness of table union search by 6.8 in MAP and recall. Moreover, Starmie is the first to employ the HNSW (Hierarchical Navigable Small World) index for accelerate query processing of table union search which provides a 3,000X performance gain over the linear scan baseline and a 400X performance gain over an LSH index (the state-of-the-art solution for data lake indexing).

preprint2023arXiv

Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision

Computer vision (CV), a non-intrusive and cost-effective technology, has furthered the development of precision livestock farming by enabling optimized decision-making through timely and individualized animal care. The availability of affordable two- and three-dimensional camera sensors, combined with various machine learning and deep learning algorithms, has provided a valuable opportunity to improve livestock production systems. However, despite the availability of various CV tools in the public domain, applying these tools to animal data can be challenging, often requiring users to have programming and data analysis skills, as well as access to computing resources. Moreover, the rapid expansion of precision livestock farming is creating a growing need to educate and train animal science students in CV. This presents educators with the challenge of efficiently demonstrating the complex algorithms involved in CV. Thus, the objective of this study was to develop ShinyAnimalCV, an open-source cloud-based web application. This application provides a user-friendly interface for performing CV tasks, including object segmentation, detection, three-dimensional surface visualization, and extraction of two- and three-dimensional morphological features. Nine pre-trained CV models using top-view animal data are included in the application. ShinyAnimalCV has been deployed online using cloud computing platforms. The source code of ShinyAnimalCV is available on GitHub, along with detailed documentation on training CV models using custom data and deploying ShinyAnimalCV locally to allow users to fully leverage the capabilities of the application. ShinyAnimalCV can contribute to CV research and teaching in the animal science community.

preprint2022arXiv

Conditional entropy production and quantum fluctuation theorem of dissipative information: Theory and experiments

We study quantum conditional entropy production, which quantifies the irreversibility of system-environment evolution from the perspective of a third system, called the reference. The reference is initially correlated with the system. We show that the quantum unconditional entropy production with respect to the system is less than the conditional entropy production with respect to the reference, where the latter includes a reference-induced dissipative information. The dissipative information pinpoints the distributive correlation established between the environment and the reference, even though they do not interact directly. When reaching the thermal equilibrium, the system-environment evolution has a zero unconditional entropy production. However, one can still have a nonzero conditional entropy production with respect to the reference, which characterizes the informational nonequilibrium of the system-environment evolution in the view point of the reference. The additional contribution to the conditional entropy production, the dissipative information, characterizes a minimal thermodynamic cost that the system pays for maintaining the correlation with the reference. Positive dissipative information also characterizes potential work waste. We prove that both types of entropy production and the dissipative information follow quantum fluctuation theorems when a two-point measurement is applied. We verify the quantum fluctuation theorem for the dissipative information experimentally on IBM quantum computers. We also present examples based on the qubit collisional model and demonstrate universal nonzero dissipative information in the qubit Maxwell's demon protocol.

preprint2022arXiv

Development of a compact high-resolution absolute gravity gradiometer based on atom interferometers

We present a compact high-resolution gravity gradiometer based on dual Rb-85 atom interferometers using stimulated Raman transitions. A baseline L=44.5 cm and an interrogation time T=130 ms are realized in a sensor head with volume of less than 100 liters. Experimental parameters are optimized to improve the short-term sensitivity while a rejection algorithm relying on inversion of the Raman wave vector is implemented to improve the long-term stability. After an averaging time of 17000 s, a phase resolution of 104 μrad is achieved, which corresponds to a gravity gradient resolution of 0.86 E. As far as we know, this is the sub-E atom gravity gradiometer with the highest level of compactness to date. After the evaluation and correction of system errors induced by light shift, residual Zeeman shift, Coriolis effect and self-attraction effect, the instrument serves as an absolute gravity gradiometer and with it the local gravity gradient is measured to be 3114 (53) E.

preprint2022arXiv

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing methods rely on dense video frame annotations, which require a tremendous amount of human effort. To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner. One major challenge of end-to-end one-shot video grounding is the existence of videos frames that are either irrelevant to the language query or the labeled frames. Another challenge relates to the limited supervision, which might result in ineffective representation learning. To address these challenges, we designed an end-to-end model via Information Tree for One-Shot video grounding (IT-OS). Its key module, the information tree, can eliminate the interference of irrelevant frames based on branch search and branch cropping techniques. In addition, several self-supervised tasks are proposed based on the information tree to improve the representation learning under insufficient labeling. Experiments on the benchmark dataset demonstrate the effectiveness of our model.

preprint2022arXiv

Explaining Deepfake Detection by Analysing Image Matching

This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels. To this end, three hypotheses from the perspective of image matching are proposed as follows. 1. Deepfake detection models indicate real/fake images based on visual concepts that are neither source-relevant nor target-relevant, that is, considering such visual concepts as artifact-relevant. 2. Besides the supervision of binary labels, deepfake detection models implicitly learn artifact-relevant visual concepts through the FST-Matching (i.e. the matching fake, source, target images) in the training set. 3. Implicitly learned artifact visual concepts through the FST-Matching in the raw training set are vulnerable to video compression. In experiments, the above hypotheses are verified among various DNNs. Furthermore, based on this understanding, we propose the FST-Matching Deepfake Detection Model to boost the performance of forgery detection on compressed videos. Experiment results show that our method achieves great performance, especially on highly-compressed (e.g. c40) videos.

preprint2022arXiv

Full quantum theory of nonequilibrium phonon condensation and phase transition

Frölich condensation is a room-temperature nonequilibrium phenomenon which is expected to occur in many physical and biological systems. Though predicted theoretically a half century ago, the nature of such condensation remains elusive. In this Letter, we derive a full quantum theory of Fröhlich condensation from the Wu-Austin Hamiltonian and present for the first time an analytical proof that a second-order phase transition induced by nonequilibrium and nonlinearity emerges in the large-$D$ limit with and without decorrelation approximation. This critical behavior cannot be witnessed if external sources are treated classically. We show that the phase transition is accompanied by large fluctuations in the statistical distribution of condensate phonons and that the Mandel-Q factor which characterizes fluctuations becomes negative in the limit of excessive external energy input. In contrast with the cold atom equilibrium BEC, the Fröhlich condensate is a result of the nonequilibrium driving where the pump plays a role of setting the number of particles, and the medium plays a role of setting the temperature. Hence, BEC can either arise by reducing the medium temperature at fixed pump (equilibrium case), or by increasing the pump at fixed medium temperature (nonequilibrium case).

preprint2022arXiv

Incrementally Stochastic and Accelerated Gradient Information mixed Optimization for Manipulator Motion Planning

This paper introduces a novel motion planner, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO informed by the mixed momenta for an efficient constrained optimization based on the penalty method. In the stochastic part, we generate the adaptive stochastic momenta via the random selection of sub-functionals based on the adaptive momentum (Adam) method to solve the body-obstacle stuck case. Due to the slow convergence of the stochastic part, we integrate the accelerated gradient descent (AGD) to improve the planning efficiency. Moreover, we adopt the Bayesian tree inference (BTI) to transform the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO), which improves the computation efficiency and success rate further. Finally, we tune the key parameters and benchmark iSAGO against the other 5 planners on LBR-iiwa in a bookshelf and AUBO-i5 on a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.

preprint2022arXiv

Investigating the environmental dependence of ultralight scalar dark matter with atom interferometers

We study the environmental dependence of ultralight scalar dark matter (DM) with linear interactions to the standard model particles. The solution to the DM field turns out to be a sum of the cosmic harmonic oscillation term and the local exponential fluctuation term. The amplitude of the first term depends on the local DM density and the mass of the DM field. The second term is induced by the local distribution of matter, such as the Earth. And it depends not only on the mass of the Earth, but also the density of the Earth. Then, we compute the phase shift induced by the DM field in atom interferometers (AIs), through solving the trajectories of atoms. Especially, the AI signal for the violation of weak equivalence principle (WEP) caused by the DM field is calculated. Depending on the values of the DM coupling parameters, contributions to the WEP violation from the first and second terms of the DM field can be either comparable or one larger than the other. Finally, we give some constraints to DM coupling parameters using results from the terrestrial atomic WEP tests.

preprint2022arXiv

Kinetics of Hawking-Page phase transition with the non-Markovian effects

Based on the free energy landscape description of Hawking-Page phase transition, the transition process from the Schwarzschild-anti-de Sitter black hole to the thermal anti-de Sitter space are considered to be stochastic under the thermal fluctuations. If the correlation time of the effective thermal bath is comparable or even longer than the oscillating time of the spacetime state in the potential well on the free energy landscape, the non-Markovian model of the black hole phase transition is required to study the kinetics of the transition processes. The non-Markovian or memory effect is represented by the time dependent friction kernel and the kinetics is then governed by the generalized Langevin equation complemented by the free energy potential. As the concrete examples, we study the effects of the exponentially decay friction kernel and the oscillatory friction kernel on the kinetics of Hawking-Page phase transition. For the exponentially decayed friction, the non-Markovian effects promote the transition process, and for the oscillatory friction, increasing the oscillating frequency also speeds up the transition process.

preprint2022arXiv

Locally Aggregated Feature Attribution on Natural Language Model Understanding

With the growing popularity of deep-learning models, model understanding becomes more important. Much effort has been devoted to demystify deep neural networks for better interpretability. Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result. However, direct application of these gradient-based methods to NLP tasks is not trivial due to the fact that the input consists of discrete tokens and the "reference" tokens are not explicitly defined. In this work, we propose Locally Aggregated Feature Attribution (LAFA), a novel gradient-based feature attribution method for NLP models. Instead of relying on obscure reference tokens, it smooths gradients by aggregating similar reference texts derived from language model embeddings. For evaluation purpose, we also design experiments on different NLP tasks including Entity Recognition and Sentiment Analysis on public datasets as well as key feature detection on a constructed Amazon catalogue dataset. The superior performance of the proposed method is demonstrated through experiments.

preprint2022arXiv

Machop: an End-to-End Generalized Entity Matching Framework

Real-world applications frequently seek to solve a general form of the Entity Matching (EM) problem to find associated entities. Such scenarios include matching jobs to candidates in job targeting, matching students with courses in online education, matching products with user reviews on e-commercial websites, and beyond. These tasks impose new requirements such as matching data entries with diverse formats or having a flexible and semantics-rich matching definition, which are beyond the current EM task formulation or approaches. In this paper, we introduce the problem of Generalized Entity Matching (GEM) that satisfies these practical requirements and presents an end-to-end pipeline Machop as the solution. Machop allows end-users to define new matching tasks from scratch and apply them to new domains in a step-by-step manner. Machop casts the GEM problem as sequence pair classification so as to utilize the language understanding capability of Transformers-based language models (LMs) such as BERT. Moreover, it features a novel external knowledge injection approach with structure-aware pooling methods that allow domain experts to guide the LM to focus on the key matching information thus further contributing to the overall performance. Our experiments and case studies on real-world datasets from a popular recruiting platform show a significant 17.1% gain in F1 score against state-of-the-art methods along with meaningful matching results that are human-understandable.

preprint2022arXiv

Metastable dynamics of neural circuits and networks

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.

preprint2022arXiv

Path integral and instantons for the process and phase transition rate of the RNAdS black hole

We propose a new approach to study the dynamical phase transition of RNAdS black holes on the underlying free energy landscape. By formulating a path integral framework, we can quantify the kinetic paths representing the history from the initial state to the end state, which provides us a visualized yet quantified picture about how the phase transition proceeds. Based on these paths, we derive the analytical formulas for the time evolution of the transition probability and provide a physical interpretation of the contribution to the probability from one "pseudomolecule" ("anti-pseudomolecule"), which is actually the phase transition rate from the small(large) to the large(small) black hole state. These numerical results show a good consistency with the underlying free energy landscape topography.

preprint2022arXiv

Quantum cosmology of the flat universe via closed real-time path integral

Quantum cosmology is crucial to understand the evolution of the early universe. Despite significant progress, challenges still remain. For example, the role of time in quantum cosmology is unclear. Furthermore, the influence of the environment on the evolution of the quantum universe is challenging. In this work, we studied the evolution of the quantum universe non-perturbatively using the closed real-time path integral. The environments coupled to the quantum universe being considered are the radiation, the non-relativistic matter, or the dark matter. We evaluated the influence functional of the massless scalar field coupled with the flat FRW universe. We studied the evolution of the quantum universe by setting the initial state of spacetime as a Gaussian wave packet. In different scenarios, we show that the classical trajectory of the universe is consistent with the quantum evolution of the wave packet. The coherence, the absolute quantum fluctuation and the Gibbs entropy all monotonically increase with time, yet the relative quantum fluctuation decreases with time. We show that for a given size of the radiation dominated universe, the lower temperature corresponds to a more quantum universe. We find that the minimal coupling of the free massless scalar field with the flat FRW spacetime generally gives rise to the memory characterized via non-Markovian correlations. Finally, we show that under higher radiation temperatures, a small universe has a higher chance of a transition to a bigger universe.

preprint2022arXiv

Self-Alignment of a Large-Area Dual-Atom-Interferometer Gyroscope Using Parameter Decoupled Phase Seeking Calibrations

We realize a Mach-Zehnder-type dual-atom-interferometer gyroscope with an interrogation arm of 40 cm length and the interference area up to 1.2 cm$^2$. The precise angular alignment of the large-scale separated Raman lasers is demonstrated by seeking the phase intersection of Ramsey-Bord$\acute{e}$ interferometers after the gravity effect is compensated and by decoupling the velocity dependent crosstalk phase shifts, and applied to build the Mach-Zehnder atom interferometer. Then a compact inertial rotation sensor is realized based on dual large-area Mach-Zehnder atom interferometers by precisely aligning the large-scale separated Raman lasers, in which the coherence is well preserved and the common noise is differentially suppressed. The sensor presents a sensitivity of $1.5\times10^{-7}$ rad/s/Hz$^{1/2}$, and a stability of $9.5\times10^{-10}$ rad/s at 23000 s. The absolute rotation measurement is carried out by adjusting the atomic velocity which corresponds to modulating the scale factor.

preprint2022arXiv

Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration

A great surge in the development of global navigation satellite systems (GNSS) excavates the potential for prosperity in many state-of-the-art technologies, e.g., autonomous ground vehicle navigation. Nevertheless, the GNSS is vulnerable to various ground interferences, which significantly break down the continuity of the navigation system. Meanwhile, the GNSS-based next-generation navigation devices are being developed to be smaller, more low-cost, and lightweight, as the commercial market forecasts. This work aims to answer whether the smartphone inertial measurement unit (IMU) is sufficient to support the GNSS baseband. Thus, a cascaded ultra-tightly coupled GNSS/inertial navigation system (INS) technique, where consumer-level smartphone sensors are used, is applied to improve the baseband of GNSS software-defined radios (SDRs). A Doppler value is predicted based on an integrated extended Kalman filter (EKF) navigator where the pseudorange-state-based measurements of GNSS and INS are fused. It is used to assist numerically controlled oscillators (NCOs) in the GNSS baseband. Then, an ultra-tight integration platform is built with the upgraded GNSS SDR, of which baseband processing is integrated with INS mechanization. Finally, tracking and carrier-based positioning performances are assessed in the proposed platform for the smartphone-IMU-aided GNSS baseband via kinematic field tests. The experimental results prove that extra hardware with only a few dollars instead of more expensive ones can improve the GNSS baseband efficiently.

preprint2022arXiv

Ultralight scalar dark matter detection with ZAIGA

ZAIGA is a proposed underground long-baseline atom interferometer (AI) facility, aiming for experimental research on gravitation and related problems. In this paper, we study the possibility of detecting the ultralight scalar dark matter (DM) with ZAIGA. According to a popular scalar DM model, the DM field contains a background oscillation term and a local exponential fluctuation term. In order to calculate the proposed constraints on DM coupling parameters, we need to first compute the DM signals in ZAIGA. For the case of two AIs vertically separated by 300 meters, the DM-induced differential phase consists of three contributions, coming from the DM-induced changes in atomic internal energy levels, atomic masses and the gravitational acceleration. For the case of two AIs horizontally separated by several kilometers, the signal comes from the DM-induced changes in atomic internal energy levels. With the current and future technical parameters of ZAIGA, we then obtain the proposed constraints on five DM coupling parameters. It turns out that our proposed constraints could be several orders of magnitude better than the ones set by the MICROSCOPE space mission.

preprint2022arXiv

Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019

This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a "meta-learner", "data ingestor", "model selector", "model/learner", and "evaluator". This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free "AutoDL self-service".

preprint2021arXiv

Absolute quantification of real-time PCR data with stage signal difference analysis

Real-time PCR, or Real-time Quantitative PCR (qPCR) is an effective approach to quantify nucleic acid samples. Given the complicated reaction system along with thermal cycles, there has been long-term confusion on accurately calculating the initial nucleic acid amounts from the fluorescence signals. Although many improved algorithms had been proposed, the classical threshold method is still the primary choice in the routine application. In this study, we will first illustrate the origin of the linear relationship between the threshold value and logarithm of the initial nucleic acid amount by reconstructing the PCR reaction process with stochastic simulations. We then develop a new method for the absolute quantification of nucleic acid samples with qPCR. By monitoring the fluorescence signal changes in every stage of the thermal cycle, we are able to calculate a representation of the step-wise efficiency change. This is the first work calculated PCR efficiency change directly from the fluorescence signal, without fitting or sophisticated analysis. Our results revealed that the efficiency change during the PCR process is complicated and can not be modeled simply by monotone function model. Based on the calculated efficiency, we illustrate a new absolute qPCR analysis method for accurately determining nucleic acid amount. The efficiency problem is completely avoided in this new method.

preprint2021arXiv

Defect-free arbitrary-geometry assembly of mixed-species atom arrays

Optically trapped mixed-species single atom arrays with arbitrary geometries are an attractive and promising platform for various applications, because tunable quantum systems with multiple components provide extra degrees of freedom for experimental control. Here, we report the first demonstration of two-dimensional $6\times4$ dual-species atom assembly with a filling fraction of 0.88 (0.89) for $^{85}$Rb ($^{87}$Rb) atoms. This mixed-species atomic synthetic is achieved via rearranging initially randomly distributed atoms using a sorting algorithm (heuristic heteronuclear algorithm) which is proposed for bottom-up atom assembly with both user-defined geometries and two-species atom number ratios. Our fully tunable hybrid-atom system of scalable advantages is a good starting point for high-fidelity quantum logic, many-body quantum simulation and forming defect-free single molecule arrays.

preprint2021arXiv

Equilibrium and nonequilibrium quantum correlations between two detectors in curved space time

We investigate the equilibrium and nonequilibrium quantum information correlations encoded in two-qubit system (near the horizon of a Kerr black hole). We study the impact of mass and the angular momentum, and further the local curvature or accelerations on the behaviors of the quantum correlations between two qubits. We show the quantum information of two qubits is encoded in the space time structure. In nonequilibrium case, the nonequilibrium can also contribute to the correlations.

preprint2021arXiv

Excitation Energy Transfer under Strong Laser Drive

Strong molecule-light interaction enables the control of molecular structures and dynamical processes. A model with strong laser drive is proposed to greatly enhance the intermolecular distance of resonant energy transfer, where the molecules are strongly driven by an optical cavity. The optimal Rabi frequency and quantum yield of energy transfer are observed, resulting from the trade off between dipole-dipole interaction and molecule-cavity coupling. When the strong drive at certain Rabi frequency is applied, a larger spatial range of effective energy transfer and a slower decay rate with the distance compared to the Förster mechanism of resonant energy transfer are observed in our model. Our work sheds light on spectroscopic study of the cooperative energy transfer in molecular polaritons.

preprint2021arXiv

Hayden-Preskill protocol and decoding Hawking radiation at finite temperature

We study the Hayden-Preskill thought experiment at finite temperature and obtain the decoupling condition that the information thrown into an old black hole can be extracted by decoding the Hawking radiation. We then consider the decoding Hayden-Preskill protocol at finite temperature assuming the observer outside the black hole who has the access to the full radiation and the unitary dynamics of the black hole. We also consider the cases when the Hawking radiation has noise and decoherence in the storage. The decoding probabilities and the corresponding fidelities are calculated. It is shown that for all the three cases we have considered, the decoding fidelities are less than unity in general. This result indicates that at finite temperature, the decoding strategy and the recovery algorithm is harder to realize than that at infinite temperature.

preprint2021arXiv

High fidelity entanglement of neutral atoms via a Rydberg-mediated single-modulated-pulse controlled-PHASE gate

Neutral atom platform has become an attractive choice to study the science of quantum information and quantum simulation, where intense efforts have been devoted to the entangling processes between individual atoms. For the development of this area, two-qubit controlled-PHASE gate via Rydberg blockade is one of the most essential elements. Recent theoretical studies have suggested the advantages of introducing non-trivial waveform modulation into the gate protocol, which is anticipated to improve its performance towards the next stage. We report our recent experimental results in realizing a two-qubit controlled-PHASE($C_Z$) gate via off-resonant modulated driving(ORMD) embedded in two-photon transition for Rb atoms. It relies upon a single modulated driving pulse with a carefully calculated smooth waveform to gain the appropriate phase accumulations required by the two-qubit gate. Combining this $C_Z$ gate with global microwave pulses, two-atom entanglement is generated with the raw fidelity of 0.945(6). Accounting for state preparation and measurement (SPAM) errors, we extract the entanglement operation fidelity to be 0.980(7). Our work features completing the $C_Z$ gate operation within a single pulse to avoid shelved Rydberg population, thus demonstrate another promising route for realizing high-fidelity two-qubit gate for neutral atom platform.

preprint2021arXiv

Parallel implementations of random time algorithm for chemical network stochastic simulations

In this study, we have developed a parallel version of the random time simulation algorithm. Firstly, we gave a rigorous basis of the random time description of the stochastic process of chemical reaction network time evolution. And then we reviewed the random time simulation algorithm and gave the implementations for the parallel version of next reaction random time algorithm. The discussion of computational complexity suggested a factor of $M$ (which is the connection number of the network) folds time consuming reduction for random time simulation algorithm as compared to other exact stochastic simulation algorithms, such as the Gillespie algorithm. For large-scale system, such like the protein-protein interaction network, $M$ is on order of $10^8$. We further demonstrate the power of random time simulation with a GPGPU parallel implementation which achieved roughly 100 folds acceleration as compared with CPU implementations. Therefore the stochastic simulation method we developed here can be of great application value for simulating time evolution process of large-scale network.

preprint2021arXiv

Some nonlinear inverse relations of Bell polynomials via the Lagrange inversion formula

In this paper, by means of the classical Lagrange inversion formula, we establish a general nonlinear inverse relations which is a partial solution to the problem proposed in the paper [J. Wang, Nonlinear inverse relations for the Bell polynomials via the Lagrange inversion formula, J. Integer Seq., Vol. 22 (2019), Article 19.3.8. (https://cs.uwaterloo.ca/journals/JIS/VOL22/Wang/wang53.pdf). As applications of this inverse relation, we not only find a short proof of another nonlinear inverse relation due to Birmajer et al., but also set up a few convolution identities concerning the Mina polynomials.

preprint2020arXiv

Explaining Away Attacks Against Neural Networks

We investigate the problem of identifying adversarial attacks on image-based neural networks. We present intriguing experimental results showing significant discrepancies between the explanations generated for the predictions of a model on clean and adversarial data. Utilizing this intuition, we propose a framework which can identify whether a given input is adversarial based on the explanations given by the model. Code for our experiments can be found here: https://github.com/seansaito/Explaining-Away-Attacks-Against-Neural-Networks.

preprint2020arXiv

Field-induced oscillation of magnetization blocking in holmium metallacrown magnet

Single-molecule magnets (SMMs) are promising elements for quantum informatics. In the presence of strong magnetic anisotropy, they exhibit magnetization blocking - a magnetic memory effect at the level of a single molecule. Recent studies have shown that the SMM performance scales with the height of magnetization blocking barrier. By employing molecular engineering this can be significantly modified, remaining independent from other external factors such as magnetic field. Taking advantage of hyperfine coupling of electronic and nuclear spins further enhances their functionality, however, a poor understanding of relaxation mechanisms in such SMMs limits the exploitation of nuclear-spin molecular qubits. Here we report the opening discovery of field-dependent oscillation of the magnetization blocking barrier in a new holmium metallacrown magnet driven by the switch of relaxation mechanisms involving hyperfine interaction. Single-crystal magnetic hysteresis measurements combined with first-principles calculations reveal an activated temperature dependence of magnetic relaxation dominated either by incoherent quantum tunneling of magnetization at anti-crossing points of exchange-hyperfine states or by Orbach-like processes at crossing points. We demonstrate that these relaxation mechanisms can be consecutively switched on and off by increasing the external field, which paves a way for manipulating the magnetization dynamics of SMMs using hyperfine interaction.

preprint2020arXiv

Further study on elliptic interpolation formulas for the elliptic Askey-Wilson polynomials and allied identities

In this paper, we introduce the so-called elliptic Askey-Wilson polynomials which are homogeneous polynomials in two special theta functions. With regard to the significance of polynomials of such kind, we establish some general elliptic interpolation formulas by the methods of matrix inversions and of polynomial representations. Furthermore, we find that the basis of elliptic interpolation space due to Schlosser can be uniquely characterized via the elliptic Askey-Wilson polynomials. As applications of these elliptic interpolation formulas, we establish some new elliptic function identities, including an extension of Weierstrass' theta identity, a generalized elliptic Karlsson-Minton type identity, and an elliptic analogue of Gasper's summation formula for very-well-poised ${}_{6+2m}ϕ_{5+2m}$ series.

preprint2020arXiv

GJRA: A Global Joint Resource Allocation Scheme for UAV service of PEC in IIoTs

The Industrial Internet of Things (IIoT) is an emerging paradigm to make industrial operations more efficient and intelligent by deploying a massive number of wireless devices to industry scenes. However, due to the limited computing capability and batteries, the Industrial Internet of Things Devices (IIoTDs) can't perform the computation-intensive or delay-sensitive tasks well and provide long-term services in practical. To tackle these challenges, we present an effective global joint resource allocation scheme for Unmanned Aerial Vehicle (UAV) service of Pervasive Edge Computing (PEC) in Industrial Internet of Things (IIoTs) and studied a collaborative UAV server-IIoTDs scheme in this paper. In our proposed scheme, the IIoTDs can keep high-efficiency performance even their battery ran out, by deploying the UAV as a PEC server and a mobile power source to provide task offloading and energy harvesting opportunities for IIoTDs. In consideration of the offloading position selection, devices resource allocation and system performance, we aim to minimize the overall service latency of all IIoTDs consisting of task computation latency and offloading latency, by joint optimizing the task offloading decisions, charging resources allocation, connection management, and UAV computation resources allocation. However, the formulated optimization problem is a mixed-integer nonlinear programming (MINLP) problem which is challenging to solve in general. In order to address the problem, we decompose it into multiple convex sub-problems based on block-coordinate descent (BCD) method to obtain the optimal solution. Performance evaluation demonstrates that our scheme outperforms the existing schemes in terms of the overall service latency of IIoTDs.

preprint2020arXiv

Influence of equilibrium and nonequilibrium environments on macroscopic realism through the Leggett-Garg inequalities

We study the macroscopic realism (macrorealism) through the two- and three-time Leggett-Garg inequalities (LGIs) in a two interacting qubits system. The two qubits are coupled either with two bosonic (thermal or photonic) baths or fermionic (electronic) baths. We study both how the equilibrium and nonequilibrium environments influence the LGIs. One way to characterize the nonequilibrium condition is by the temperature difference (for the bosonic bath) or the chemical potential difference (for the fermionic bath). We also study the heat or particle current and the entropy production rate generated by the nonequilibrium environments. Analytical forms of LGIs and the maximal value of LGIs based on the quantum master equation beyond the secular approximation are derived. The LGI functions and the corresponding maximal value have separated contributions, the part describing the coherent evolution and the part describing the coupling between the system and environments. The environment-coupling part can be from the equilibrium environment or the nonequilibrium environment. The nonequilibrium dynamics is quantified by the Bloch-Redfield equation which is beyond the Lindblad form. We found that the nonequilibriumness quantified by the temperature difference or the chemical potential difference can lead to the LGIs violations or the increase of the maximal value of LGIs, restoring the quantum nature from certain equilibrium cases where LGIs are preserved. The corresponding nonequilibrium thermodynamic cost is quantified by the nonzero entropy production rate. Our finding of the nonequilibrium promoted LGIs violations suggests a new strategy for the design of quantum information processing and quantum computational devices to maintain the quantum nature and quantum correlations for long.

preprint2020arXiv

New Fluctuation Theorem on Maxwell's Demon

With the increasing interest for the control of the system at the nano and mesoscopic scales, studies have been focused on the limit of the energy dissipation in an open system by refining the concept of the Maxwell's demon. The well-known Sagawa-Ueda fluctuation theorem provides an explanation of the demon: the absence of a part of demon's information leads to an improper entropy production which violates the thermodynamic 2nd law. Realizing that the demon contributes not only to the system but also to the environments, we introduce the dissipative information to quantify the total contribution of the demon, rather than using an improper entropy production. We prove a set of new fluctuation theorems based on this, which can be used to uncover the truth behind the demon: The controlled system does not violate the 2nd law at any coarse-grained level for the demon's control. However, there exists an inevitable demon-induced dissipative information which always increases the entropy production. A consequence of these theorems is that, less work and more heat can be extracted and generated respectively by a demon than the limits predicted by the Ueda-Sagawa theorem. We also suggest a possible realization of the experimental estimation of these work and heat bounds, which can be measured and tested.

preprint2020arXiv

Proof of the $(α,β)$--inversion formula conjectured by Hsu and Ma

In light of the well-known fact that the $n$th divided difference of any polynomial of degree $m$ must be zero while $m<n$,the present paper proves the $(α,β)$-inversion formula conjectured by Hsu and Ma [J. Math. Res. $\&$ Exposition 25(4) (2005) 624]. As applications of $(α,β)$-inversion, we not only recover some known matrix inversions due to Gasper, Schlosser, and Warnaar, but also fin three new matrix inversions related to elliptic divisibility sequence and theta functions.

preprint2020arXiv

Suppression of Coriolis error in weak equivalence principle test using ^[85]Rb-^[87]Rb dual-species atom interferometer

Coriolis effect is an important error source in the weak equivalence principle (WEP) test using atom interferometer. In this paper, the problem of Coriolis error in WEP test is studied theoretically and experimentally. In theoretical simulation, Coriolis effect is analyzed by establishing an error model. The measurement errors of Eotvos coefficient (eta) in WEP test related to experimental parameters, such as horizontal-velocity difference and horizontal-position difference of atomic clouds, horizontal-position difference of detectors and rotation compensation of Raman laser's mirror are calculated. In experimental investigation, the position difference between Rb-85 and Rb-87 atomic clouds is reduced to 0.1 mm by optimizing the experimental parameters, an alternating detection method is used to suppress the error caused by detection position difference, thus the Coriolis error related to atomic clouds and detectors is eliminated to 1.1E-9. This Coriolis error is further corrected by compensating the rotation of Raman laser's mirror, and the total uncertainty of eta measurement related to Coriolis effect is reduced as 4.4E-11.

preprint2020arXiv

The nonequilibrium back-reaction of Hawking radiation to a Schwarzschild black hole

We investigate the nonequilibrium back reaction on the Schwarzschild black hole from the radiation field. The back reactions are characterized by the membrane close to the black hole. When the membrane is thin, we found that larger temperature difference can lead to more significant negative surface tension, larger thermodynamic dissipation cost and back reaction in energy and entropy as well as larger black hole area. This may be relevant to the primordial black holes in early universe. Moreover, our nonequilibrium model can resolve the inconsistency issue of the black hole back reaction under zero mass limit in the equilibrium case. In the thick membrane case, the nonequilibrium back reaction is found to be more significant than that in the thin membrane case. The nonequilibrium temperature difference can increase the energy and entropy loss as well as the thermodynamic dissipation of the black hole and the membrane back reactions. The nonequilibrium dissipation cost characterized by the entropy production rate appears to be significant compared to the entropy rate radiated by the black hole under finite temperature difference. This may shed light on the black hole information paradox due to the information loss from the entropy production rate in the nonequilibirum cases. The nonequilibrium thermodynamic fluctuations can also reflect the effects of the back-reactions of the Hawking radiation on the evolution of a black hole.

preprint2020arXiv

Two new transformation formulas for ${}_{8}ψ_{8}$ and ${}_{8}W_{7}$ series associated with Weierstrass' theta identity

In this paper, we establish two new transformation formulas for ${}_{8}ψ_{8}$ and ${}_8ϕ_7$ series by means of Slater's general transformation for bilateral series. As applications, some specific transformation formulas are presented among which include a general form of Weierstrass' theta identity and new proofs of Bailey's VWP ${}_6ψ_6$ and Jackson's ${}_8ϕ_7$ summation formula.

preprint2020arXiv

YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our approach achieved good results for both the SI and TC subtasks. The macro-F1-score for the SI subtask is 0.406, and the micro-F1-score for the TC subtask is 0.505. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set. This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques. The code of this paper is availabled at: https://github.com/daojiaxu/semeval_11.

preprint2020arXiv

YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis

In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest. However, the classification and recognition of memes is much more complicated than that of social text since it involves visual cues and language understanding. To address this issue, this paper proposed a parallel-channel model to process the textual and visual information in memes and then analyze the sentiment polarity of memes. In the shared task of identifying and categorizing memes, we preprocess the dataset according to the language behaviors on social media. Then, we adapt and fine-tune the Bidirectional Encoder Representations from Transformers (BERT), and two types of convolutional neural network models (CNNs) were used to extract the features from the pictures. We applied an ensemble model that combined the BiLSTM, BIGRU, and Attention models to perform cross domain suggestion mining. The officially released results show that our system performs better than the baseline algorithm. Our team won nineteenth place in subtask A (Sentiment Classification). The code of this paper is availabled at : https://github.com/YuanLi95/Semveal2020-Task8-emotion-analysis.

preprint2019arXiv

Balanced Coherence Times of Mixed-Species Atomic Qubits in a Dual $3\times3$ Magic-Intensity Optical Dipole Trap Array

In this work, we construct a polarization-mediated magic-intensity (MI) optical dipole trap (ODT) array, in which the detrimental effects of light shifts on the mixed-species qubits are efficiently mitigated so that the coherence times of the mixed-species qubits are both substantially enhanced and balanced for the first time. This mixed-species magic trapping technique relies on the tunability of the coefficient of the third-order cross term and ground state hyperpolarizability, which are inherently dependent on the degree of circular polarization of the trap laser. Experimentally, polarization of the ODT array for $^{85}$Rb qubits is finely adjusted to a definite value so that its working magnetic field required for magic trapping amounts to the one required for magically trapping $^{87}$Rb qubits in another ODT array with fully circular polarization. Ultimately, in such a polarization-mediated MI-ODT array, the coherence times of $^{87}$Rb and $^{85}$Rb qubits are respectively enhanced up to 891$\pm$47 ms and 943$\pm$35 ms. Furthermore, a new source of dephasing effect is revealed, which arises from the noise of the elliptic polarization, and the reduction in corresponding dephasing effect on the $^{85}$Rb qubits is attainable by use of shallow magic intensity. It is anticipated that the novel mixed-species MI-ODT array is a versatile platform for building scalable quantum computers with neutral atoms.

preprint2019arXiv

DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks

Accurate real-time forecasting of key performance indicators (KPIs) is an essential requirement for various LTE/5G radio access network (RAN) automation. However, an accurate prediction can be very challenging in large-scale cellular environments due to complex spatio-temporal dynamics, network configuration changes and unavailability of real-time network data. In this work, we introduce a reusable analytics framework that enables real-time KPI prediction using a hierarchical deep learning architecture. Our prediction approach, namely DeepAuto, stacks multiple long short-term memory (LSTM) networks horizontally to capture instantaneous, periodic and seasonal patterns in KPI time-series. It further merge with feed-forward networks to learn the impact of network configurations and other external factors. We validate the approach by predicting two important KPIs, including cell load and radio channel quality, using large-scale real network streaming measurement data from the operator. For cell load prediction, DeepAuto model showed up to 15% improvement in Root Mean Square Error (RMSE) compared to naive method of using recent measurements for short-term horizon and up to 32% improvement for longer-term prediction.

preprint2019arXiv

Investigations of the Underlying Mechanisms of HIF-1α and CITED2 Binding to TAZ1

The TAZ1 domain of CREB binding protein is crucial for transcriptional regulation and recognizes multiple targets. The interactions between TAZ1 and its specific targets are related to the cellular hypoxic negative feedback regulation. Previous experiments reported that one of the TAZ1 targets CITED2 is an efficient competitor of another target HIF-1α. Here by developing the structure-based models of TAZ1 complexes we have uncovered the underlying mechanisms of the competitions between HIF-1α and CITED2 binding to TAZ1. Our results are consistent with the experimental hypothesis on the competition mechanisms and the apparent affinity. In addition, the simulations prove the dominant position of forming TAZ1-CITED2 complex in both thermodynamics and kinetics. For thermodynamics, TAZ1-CITED2 is the lowest basin located on the free energy surface of binding in the ternary system. For kinetics, the results suggest that CITED2 binds to TAZ1 faster than HIF-1α. Besides, the analysis of contact map and f values in this study will be helpful for further experiments on TAZ1 systems.

preprint2019arXiv

Orbit Design for Space Atom-Interferometer AIGSO

Atom Interferometric Gravitational-wave (GW) Space Observatory (AIGSO) is a mission concept mainly aimed at the middle-frequency (0.1 Hz - 10 Hz) GW detection. AIGSO proposes to have three spacecraft in linear formation with extension of 10 km. The three spacecraft need to maintain 5 km + 5 km constant arm-length formation. In this study, we address the issue of orbit design and thruster requirement. The acceleration to maintain the formation can be designed to be less than 30 pm/s$^2$ and the thruster requirement is in the 30 nN range. Application to other arm-length-maintaining missions is also discussed.

preprint2019arXiv

Preparation of a Heteronuclear Two-atom System in the 3D Motional Ground State in an Optical Tweezer

We report the realization of a heteronuclear two-atom of $^{87}$Rb-$^{85}$Rb in the ground state of an optical tweezer (OT). Starting by trapping two different isotopic single atoms, a $^{87}$Rb and a $^{85}$Rb in two strongly focused and linearly polarized OT with 4 $μ$m apart, we perform simultaneously three dimensional Raman sideband cooling for both atoms and the obtained 3D ground state probabilities of $^{87}$Rb and $^{85}$Rb are 0.91(5) and 0.91(10) respectively. There is no obvious crosstalk observed during the cooling process. We then merge them into one tweezer via a species-dependent transport, where the species-dependent potentials are made by changing the polarization of the OTs for each species from linear polarization to the desired circular polarization. The measurable increment of vibrational quantum due to merging is $0.013(1)$ for the axial dimension. This two-atom system can be used to investigate cold collisional physics, to form quantum logic gates, and to build a single heteronuclear molecule. It can also be scaled up to few-atom regime and extended to other atomic species and molecules, and thus to ultracold chemistry.

preprint2019arXiv

The dynamical and thermodynamical origin of dissipative chaos

Chaos is usually referred to the sensitivity to initial conditions in which the nonlinearity plays a crucial role. Beyond such a mathematical description, the understanding of the underlying physical origin of the chaos is still not very clear. Here we study the dissipative chaos from the perspective of the nonequilibrium dynamics. This was not fully investigated in the traditional chaos theory, despite of the Lorenz's original discovery of chaos from the nonequilibrium atmosphere. We found that the nonequilibriumness as the degree of detailed balance breaking can be quantified by the appearance of the steady state probability flux in the state space. We uncovered that the dynamical origin of the onset and offset of the dissipative chaos such as Lorentz attractor is from the sudden appearance and disappearance of such nonequilibrium flux. We also uncovered that the dissipation associated with the flux quantified by the entropy production rate gives the thermodynamical origin of dissipative chaos. The sharp changes in the degree of nonequilibriumness by the flux and the entropy production rate also provide alternative quantitative indicators for the onset and offset of the dissipative chaos.

preprint2019arXiv

ZAIGA: Zhaoshan Long-baseline Atom Interferometer Gravitation Antenna

The Zhaoshan long-baseline Atom Interferometer Gravitation Antenna (ZAIGA) is a new type of underground laser-linked interferometer facility, and is currently under construction. It is in the 200-meter-on-average underground of a mountain named Zhaoshan which is about 80 km southeast to Wuhan. ZAIGA will be equipped with long-baseline atom interferometers, high-precision atom clocks, and large-scale gyros. ZAIGA facility will take an equilateral triangle configuration with two 1-km-apart atom interferometers in each arm, a 300-meter vertical tunnel with atom fountain and atom clocks mounted, and a tracking-and-ranging 1-km-arm-length prototype with lattice optical clocks linked by locked lasers. The ZAIGA facility will be used for experimental research on gravitation and related problems including gravitational wave detection, high-precision test of the equivalence principle of micro-particles, clock based gravitational red-shift measurement, rotation measurement and gravito-magnetic effect.

preprint2018arXiv

K-nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.

preprint2016arXiv

A framework towards understanding mesoscopic phenomena: Emergent unpredictability, symmetry breaking and dynamics across scales

By integrating 4 lines of thoughts: symmetry breaking originally advanced by Anderson, bifurcation from nonlinear dynamics, Landau's theory of phase transition, and the mechanism of emergent rare events studied by Kramers, we introduce a possible framework for understanding mesoscopic dynamics that links (i) fast lower level microscopic motions, (ii) movements within each basin at the mid-level, and (iii) higher-level rare transitions between neighboring basins, which have rates that decrease exponentially with the size of the system. In this mesoscopic framework, multiple attractors arise as emergent properties of the nonlinear systems. The interplay between the stochasticity and nonlinearity leads to successive jump-like transitions among different basins. We argue each transition is a dynamic symmetry breaking, with the potential of exhibiting Thom-Zeeman catastrophe as well as phase transition with the breakdown of ergodicity (e.g., cell differentiation). The slow-time dynamics of the nonlinear mesoscopic system is not deterministic, rather it is a discrete stochastic jump process. The existence of these discrete states and the Markov transitions among them are both emergent phenomena. This emergent stochastic jump dynamics then serves as the stochastic element for the nonlinear dynamics of a higher level aggregates on an even larger spatial and slower time scales (e.g., evolution). This description captures the hierarchical structure outlined by Anderson and illustrates two distinct types of limit of a mesoscopic dynamics: A long-time ensemble thermodynamics in terms of time $t$ tending infinity followed by the size of the system $N$ tending infinity, and a short-time trajectory steady state with $N$ tending infinity followed by $t$ tending infinity. With these limits, symmetry breaking and cusp catastrophe are two perspectives of the same mesoscopic system on different time scales.

preprint2016arXiv

A multiscale nonequilibrium model for melting of metal powder bed subjected to constant heat flux

A multiscale numerical model based on nonequilibrium thermal effect for melting of metal powder bed subjected to constant heat flux is developed. The volume shrinkage due to density change is taken into account. The nonequilibrium model is discretized by an implicit finite difference method and solved numerically using an iterative tri-diagonal matrix algorithm. The evolutions of powder bed surface temperature and various interfacial locations as well as the melting temperature range during the melting process are investigated. The results show that liquid region, upper and lower parts of mushy zone are formed on the top of unsintered zone as the melting progresses. The duration of the preheating stage shortens and the melting rate accelerates as the initial porosity or initial temperature increases while particle size has much less effect on the melting process. The parametric study shows the melting temperature range of the powder bed widens with increasing initial porosity, decreasing initial temperature or increasing particle size.

preprint2016arXiv

A Variational Algorithm for Bayesian Variable Selection

There has been an intense development on the estimation of a sparse regression coefficient vector in statistics, machine learning and related fields. In this paper, we focus on the Bayesian approach to this problem, where sparsity is incorporated by the so-called spike-and-slab prior on the coefficients. Instead of replying on MCMC for posterior inference, we propose a fast and scalable algorithm based on variational approximation to the posterior distribution. The updating scheme employed by our algorithm is different from the one proposed by Carbonetto and Stephens (2012). Those changes seem crucial for us to show that our algorithm can achieve asymptotic consistency even when the feature dimension diverges exponentially fast with the sample size. Empirical results have demonstrated the effectiveness and efficiency of the proposed algorithm.

preprint2016arXiv

An Ensemble EM Algorithm for Bayesian Variable Selection

We study the Bayesian approach to variable selection in the context of linear regression. Motivated by a recent work by Rockova and George (2014), we propose an EM algorithm that returns the MAP estimate of the set of relevant variables. Due to its particular updating scheme, our algorithm can be implemented efficiently without inverting a large matrix in each iteration and therefore can scale up with big data. We also show that the MAP estimate returned by our EM algorithm achieves variable selection consistency even when $p$ diverges with $n$. In practice, our algorithm could get stuck with local modes, a common problem with EM algorithms. To address this issue, we propose an ensemble EM algorithm, in which we repeatedly apply the EM algorithm on a subset of the samples with a subset of the covariates, and then aggregate the variable selection results across those bootstrap replicates. Empirical studies have demonstrated the superior performance of the ensemble EM algorithm.

preprint2016arXiv

Coherence preservation of a single neutral atom qubit transferred between magic-intensity optical traps

We demonstrate that the coherence of a single mobile atomic qubit can be well preserved during a transfer process among different optical dipole traps (ODTs). This is a prerequisite step in realizing a large-scale neutral atom quantum information processing platform. A qubit encoded in the hyperfine manifold of $^{87}$Rb atom is dynamically extracted from the static quantum register by an auxiliary moving ODT and reinserted into the static ODT. Previous experiments were limited by decoherences induced by the differential light shifts of qubit states. Here we apply a magic-intensity trapping technique which mitigates the detrimental effects of light shifts and substantially enhances the coherence time to $225 \pm 21\,\mathrm{ms}$. The experimentally demonstrated magic trapping technique relies on the previously neglected hyperpolarizability contribution to the light shifts, which makes the light shift dependence on the trapping laser intensity to be parabolic. Because of the parabolic dependence, at a certain "magic" intensity, the first order sensitivity to trapping light intensity variations over ODT volume is eliminated. We experimentally demonstrate the utility of this approach and measure hyperpolarizability for the first time. Our results pave the way for constructing a scalable quantum-computing architectures with single atoms trapped in an array of magic ODTs.

preprint2016arXiv

Extracting the differential phase in dual atom interferometers by modulating magnetic fields

We present a new scheme for measuring the differential phase in dual atom interferometers. The magnetic field is modulated in one interferometer, and the differential phase can be extracted without measuring the amplitude of the magnetic field by combining the ellipse and linear fitting methods. The gravity gradient measurements are discussed based on dual atom interferometers. Numerical simulation shows that the systematic error of the differential phase measurement is largely decreased when the duration of the magnetic field is symmetrically modulated. This combined fitting scheme has a high accuracy for measuring an arbitrary differential phase in dual atom interferometers.

preprint2016arXiv

Oscillatory Double-Diffusive Convection in a Horizontal Cavity with Soret and Dufour Effects

Oscillatory double-diffusive convection in horizontal cavity with Soret and Dufour effects is investigated numerically based on SIMPLE algorithm with QUICK scheme in non-uniform staggered grid system. The results show that double-diffusive convection develops from steady-state convection-dominated, periodic oscillatory, quasi-periodic oscillatory to chaotic flow, and finally return to periodic oscillation as buoyancy ratio increases. Moreover, fundamental frequency and fluctuation amplitude increase with buoyancy ratio. As Rayleigh number increases, transition trendy of oscillatory convection is similar to that of buoyancy ratio. But the return of periodic oscillation from chaos is not obtained as Rayleigh number increases. As aspect ratio decreases, the oscillatory convection evolves from periodic into steady-state. In addition, fundamental frequency increases at first and then decreases while fluctuation amplitude decreases with aspect ratio.

preprint2015arXiv

Curl flux, coherence, and population landscape of molecular systems: Nonequilibrium quantum steady state, energy (charge) transport, and thermodynamics

We established a theoretical framework in terms of the curl flux, population landscape, and coherence for non-equilibrium quantum systems at steady state, through exploring the energy and charge transport in molecular processes. The curl quantum flux plays the key role in determining transport properties and the system reaches equilibrium when flux vanishes. The novel curl quantum flux reflects the degree of non-equilibriumness and the time-irreversibility. We found an analytical expression for the quantum flux and its relationship to the environmental pumping (non-equilibriumness quantified by the voltage away from the equilibrium) and the quantum tunneling. Furthermore, we investigated another quantum signature, the coherence, quantitatively measured by the non-zero off diagonal element of the density matrix. Besides the environment-assistance which can give dramatic enhancement of coherence and quantum flux with high voltage at a fixed tunneling strength, the quantum flux is promoted by the coherence in the regime of small tunneling while reduced by the coherence in the regime of large tunneling, due to the non-monotonic relationship between the coherence and tunneling. In view of the system as a quantum heat engine, we studied the non-equilibrium thermodynamics and established the analytical connections of curl quantum flux to the transport quantities such as energy (charge) transfer efficiency, chemical reaction efficiency, energy dissipation, heat and electric currents observed in the experiments. We observed a perfect transfer efficiency in chemical reactions at high voltage (chemical potential difference). Our theoretical predicted behavior of the electric current with respect to the voltage is in good agreements with the recent experiments on electron transfer in single molecules.

preprint2015arXiv

Formation of Non-reciprocal Bands in Magnetized Diatomic Plasmonic Chains

We show that non-reciprocal bands can be formed in a magnetized periodic chain of spherical plasmonic particles with two particles per unit cell. Simplified form of symmetry operators in dipole approximations are used to demonstrate explicitly the relation between spectral non-reciprocity and broken spatial-temporal symmetries. Due to hybridization among plasmon modes and free photon modes, strong spectral non-reciprocity appears in region slightly below the lightline, where highly directed guiding of energy can be supported. The results may provide a clear guidance on the design of one-way waveguides.

preprint2015arXiv

Influence of separating distance between atomic sensors for gravitational wave detection

We consider a recent scheme of gravitational wave detection using atomic interferometers as inertial sensors, and reinvestigate its configuration using the concept of sensitivity functions. We show that such configuration can suppress noise without influencing the gravitational wave signal. But the suppression is insufficient for the direct observation of gravitational wave signals, so we analyse the behaviour of the different noises influencing the detection scheme. As a novel method, we study the relations between the measurement sensitivity and the distance between two interferometers, and find that the results derived from vibration noise and laser frequency noise are in stark contrast to that derived from the shot noise, which is significant for the configuration design of gravitational wave detectors using atomic interferometers.

preprint2015arXiv

Interaction induced decay of a heteronuclear two-atom system

Two-atom systems in small traps are of fundamental interest, first of all for understanding the role of interactions in degenerate cold gases and for the creation of quantum gates in quantum information processing with single-atom traps. One of the key quantities is the inelastic relaxation (decay) time when one of the atoms or both are in a higher hyperfine state. Here we measure this quantity in a heteronuclear system of $^{87}$Rb and $^{85}$Rb in a micro optical trap and demonstrate experimentally and theoretically the presence of both fast and slow relaxation processes, depending on the choice of the initial hyperfine states. The developed experimental method allows us to single out a particular relaxation process and, in this sense, our experiment is a "superclean platform" for collisional physics studies. Our results have also implications for engineering of quantum states via controlled collisions and creation of two-qubit quantum gates.

preprint2015arXiv

Non-reciprocal mu-near-zero mode in PT-symmetric magnetic domains

We find that a new type of non-reciprocal modes exist at an interface between two \emph{parity-time} ($\mathcal{PT}$) symmetric magnetic domains (MDs) near the frequency of zero effective permeability. The new mode is non-propagating and purely magnetic when the two MDs are semi-infinite while it becomes propagating in the finite case. In particular, two pronounced nonreciprocal responses could be observed via the excitation of this mode: one-way optical tunneling for oblique incidence and unidirectional beam shift at normal incidence. When the two MDs system becomes finite in size, it is found that perfect-transmission mode could be achieved if $\mathcal{PT}$-symmetry is maintained. The unique properties of such an unusual mode are investigated by analytical modal calculation as well as numerical simulations. The results suggest a new approach to the design of compact optical isolator.

preprint2015arXiv

On the Optimal Provider Selection for Repair in Distributed Storage System with Network Coding

In large-scale distributed storage systems (DSS), reliability is provided by redundancy spread over storage servers across the Internet. Network coding (NC) has been widely studied in DSS because it can improve the reliability with low repair time. To maintain reliability, an unavailable storage server should be firstly replaced by a new server, named new comer. Then, multiple storage servers, called providers, should be selected from surviving servers and send their coded data through the Internet to the new comer for regenerating the lost data. Therefore, in a large-scale DSS, provider selection and data routing during the regeneration phase have great impact on the performance of regeneration time. In this paper, we investigate a problem of optimal provider selection and data routing for minimizing the regeneration time in the DSS with NC. Specifically, we first define the problem in the DSS with NC. For the case that the providers are given, we model the problem as a mathematical programming. Based on the mathematical programming, we then formulate the optimal provider selection and data routing problem as an integer linear programming problem and develop an efficient near-optimal algorithm based on linear programming relaxation (BLP). Finally, extensive simulation experiments have been conducted, and the results show the effectiveness of the proposed algorithm.

preprint2015arXiv

Shape, orientation and magnitude of the curl quantum flux, the coherence and the statistical correlations in energy transport at nonequilibrium steady state

We provide a quantitative description of the nonequilibriumness based on the model of coupled oscillators interacting with multiple energy sources. This can be applied to the study of vibrational energy transport in molecules. The curl quantum flux quantifying the nonequilibriumness and time-irreversibility is quantified in the coherent representation and we find the geometric description of the shape and polarization of the flux which provides the connection between the microscopic description of quantum nonequilibriumness and the macroscopic observables, i.e., correlation function. We use the Wilson loop integral to quantify the magnitude of curl flux, which is shown to be correlated to the correlation function as well. Coherence contribution is explicitly demonstrated to be non-trivial and to considerably promote the heat transport quantified by heat current and efficiency. This comes from the fact that coherence effect is microscopically reflected by the geometric description of the flux. To uncover the effect of vibron-phonon coupling between the vibrational modes of molecular stretching (vibron) and the molecular chain (phonon), we further explore the influences of localization, which leads to the coherent and incoherent regimes that are characterised by the current-current correlations.

preprint2015arXiv

Test of Equivalence Principle at $10^{-8}$ Level by a Dual-species Double-diffraction Raman Atom Interferometer

We report an improved test of the weak equivalence principle by using a simultaneous $^{85}$Rb-$^{87}$Rb dual-species atom interferometer. We propose and implement a four-wave double-diffraction Raman transition scheme for the interferometer, and demonstrate its ability in suppressing common-mode phase noise of Raman lasers after their frequencies and intensity ratios are optimized. The statistical uncertainty of the experimental data for Eötvös parameter $η$ is $0.8\times10^{-8}$ at 3200 s. With various systematic errors corrected the final value is $η=(2.8\pm3.0)\times10^{-8}$. The major uncertainty is attributed to the Coriolis effect.

preprint2015arXiv

The Assistance of Molecular Vibrations on Coherent Energy Transfer in Photosynthesis from the View of Quantum Heat Engine

Recently the quantum nature in the energy transport in solar cell and light-harvesting complexes have attracted much attention, as being triggered by the experimental observations. We model the light-harvesting complex (i.e., PEB50 dimer) as a quantum heat engine (QHE) and study the effect of the undamped intra-molecule vibrational modes on the coherent energy transfer process and quantum transport. We find that the exciton-vibration interaction has non-trivial contribution to the promotion of quantum yield as well as transport properties of the quantum heat engine at steady state, by enhancing the quantum coherence quantified by entanglement entropy. The perfect quantum yield over 90% has been obtained, with theexciton-vibration coupling. We attribute these improvements to the renormalization of the electronic couplings effectively induced by exciton-vibration interaction and the subsequent delocalization of excitons. Finally we demonstrate that the thermal relaxation and dephasing can help the excitation energy transfer in PEB50 dimer.

preprint2015arXiv

Topology-Aware Node Selection for Data Regeneration in Heterogeneous Distributed Storage Systems

Distributed storage systems introduce redundancy to protect data from node failures. After a storage node fails, the lost data should be regenerated at a replacement storage node as soon as possible to maintain the same level of redundancy. Minimizing such a regeneration time is critical to the reliability of distributed storage systems. Existing work commits to reduce the regeneration time by either minimizing the regenerating traffic, or adjusting the regenerating traffic patterns, whereas nodes participating data regeneration are generally assumed to be given beforehand. However, such regeneration time also depends heavily on the selection of the participating nodes. Selecting different participating nodes actually involve different data links between the nodes. Real-world distributed storage systems usually exhibit heterogeneous link capacities. It is possible to further reduce the regeneration time via exploiting such link capacity differences and avoiding the link bottlenecks. In this paper, we consider the minimization of the regeneration time by selecting the participating nodes in heterogeneous networks. We analyze the regeneration time and propose node selection algorithms for overlay networks and real-world topologies. Considering that the flexible amount of data blocks from each provider may deeply influence the regeneration time, several techniques are designed to enhance our schemes in overlay networks. Experimental results show that our node selection schemes can significantly reduce the regeneration time for each topology, especially in practical networks with heterogeneous link capacities.

preprint2015arXiv

Vibrational and coherence dynamics of molecules

We {\it analytically} investigate the population and coherence dynamics and relaxations in the vibrational energy transport in molecules. The corresponding two time scales $t_1$ and $t_2$ are explored. Coherence-population entanglement is found to considerably promote the time scale $t_2$ for dephasing and the amplitude of coherence. This is attributed to the suppression of the environment-induced drift force by coherence. Moreover the population imbalance (magnetization) is shown to be significantly amplified with the coherence-population entanglement. Contrary to the previous studies, we exactly elucidate a coherent process by showing $t_1<t_2$. We predict the relaxation of vibrational and orientational dynamics of OH-stretching modes in agreement with the recent experiments, when applied to the water molecules dissolved in D$_2$O. Finally we explore the coherence effect on the heat current at the macroscopic level.

preprint2013arXiv

Configurations of a new atomic interferometer for gravitational wave detection

Recently, the configuration using atomic interferometers (AIs) had been suggested for the detection of gravitational waves. A new AI with some additional laser pulses for implementing large momentum transfer was also put forward, in order to improve the influence of shot noise and laser frequency noise. In the paper, we use the sensitivity function to analyze all possible configurations of the new AI and to distinguish how many momenta are transferred in a specific configuration. With the analysis for the new configuration, we explore the detection scheme of gravitational wave further, in particular, for the amelioration of the laser frequency noise. We find that the amelioration is definite in such scheme, but novelly, in some cases the frequency noise can be canceled completely by using a proper data processing method.

preprint2013arXiv

Super-resolution image transfer by a vortex-like metamaterial

We propose a vortex-like metamaterial device that is capable of transferring image along a spiral route without losing subwavelength information of the image. The super-resolution image can be guided and magnified at the same time with one single design. Our design may provide insights in manipulating super-resolution image in a more flexible manner. Examples are given and illustrated with numerical simulations.

preprint2013arXiv

Suppressing phase decoherence of a single atom qubit with CPMG sequence

We experimentally demonstrate the strong suppression of dephasing of a qubit stored in a single \textsuperscript{87}Rb atom in an optical dipole trap by using Carr-Purcell-Meiboom-Gill(CPMG) pulse sequences. Regarded as a repetition of spin echo, CPMG sequence is an optimal choice for suppressing both inhomogeneous and homogeneous phase decoherence mechanisms. In the trap with 830 nm wavelength and 0.7 mK potential depth, the spin relaxation time of single atoms is showed to be 830.8 ms. We obtain the reversible inhomogeneous dephasing time of $T_{2}^{\ast}$=1.4 ms. The homogeneous dephasing time is $T_{2}^{\prime}$ =102.7 ms in the spin echo process, by employing CPMG sequence with pulse number n = 6 the homogeneous dephasing is further suppressed by a factor of 3, and its corresponding coherence time is extended to $T_{2}^{\prime}$=304.5 ms.

preprint2012arXiv

A new mechanism of development and differentiation through slow binding/unbinding of regulatory proteins to the genes

Understanding the differentiation, a biological process from a multipotent stem or progenitor state to a mature cell is critically important. We develop a theoretical framework to quantify the underlying potential landscape and biological paths for cell development and differentiation. We propose a new mechanism of differentiation and development through binding/unbinding of regulatory proteins to the gene promoters. We found indeed the differentiated states can emerge from the slow promoter binding/unbinding processes. Furthermore, under slow promoter binding/unbinding, we found multiple meta-stable differentiated states. This can explain the origin of multiple states observed in the recent experiments. In addition, the kinetic time quantified by mean first passage transition time for the differentiation and reprogramming strongly depends on the time scale of the promoter binding/unbinding processes. We discovered an optimal speed for differentiation for certain binding/unbinding rates of regulatory proteins to promoters. More experiments in the future might be able to tell if cells differentiate at at that optimal speed. In addition, we quantify kinetic pathways for the differentiation and reprogramming. We found that they are irreversible. This captures the non-equilibrium dynamics in multipotent stem or progenitor cells. Such inherent time-asymmetry as a result of irreversibility of state transition pathways as shown may provide the origin of time arrow for cell development.

preprint2012arXiv

Bag-of-Words Representation for Biomedical Time Series Classification

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.

preprint2012arXiv

Combining red- and blue-detuned optical potentials to form a Lamb-Dicke trap for a single neutral atom

We propose and demonstrate a scheme for strongly radially confining a single neutral atom in a bichromatic far-off resonance optical dipole trap(BFORT) . BFORT is composed of a blue-detuned Laguerre-Gaussian $LG^1_ 0$ beam and a red-detuned Gaussian beam. The trapping radial dimension of a single atom trapped in the Gaussian FORT can be greatly compressed by imposing a blue-detuned Laguerre-Gaussian $LG^1_ 0$ beam with moderate potential depth. By modulating the potential depth of the Gaussian FORT we observed that the resonant and parametric excitation of the oscillatory motion of a single atom in this BFORT and obtained the oscillation frequency that well fits prediction from the theoretical model. The frequency measurement shows that effective trapping dimension can be greatly sharper than that diffraction limited of microscopic objective we used. Then we show that the excess scattering rate due to imposing blue detuned light can be eliminated when single atoms is close to ground-state theoretically. So BFORT suits the purpose of acting as a Lamb-Dicke trap for further cooling a single neutral atom to motion ground-state and finding application in quantum information progressing.

preprint2012arXiv

One-way Tamm plasmon-polaritons on the interface of magnetophotonic crystals and conducting metal oxides

We demonstrate theoretically the existence of one-way Tamm plasmon-polaritons on the interface between magnetophotonic crystals and conducting metal oxides. In contrast to conventional surface plasmon-polaritons (SPPs), Tamm plasmon-polariton (TPPs) occur at frequencies above the bulk plasma frequency of the conducting materials, provided that the dispersion curves of such surface modes lie outside the light cone for the conducting oxides and simultaneously fall into the photonic band gap of the magnetophotonic crystal. The nonreciprocal properties of TPPs are caused by violation of the periodicity and time reversal symmetry in the structure. Calculations on the field distribution and transmission spectra through the structure are employed to confirm the theoretical results, which could potentially impact on a broad range of SPP-related phenomena in applications.

preprint2011arXiv

A Secured Health Care Application Architecture for Cyber-Physical Systems

Cyber-physical systems (CPS) can be viewed as a new generation of systems with integrated control, communication and computational capabilities. Like the internet transformed how humans interact with one another, cyber-physical systems will transform how people interact with the physical world. Currently, the study of CPS is still in its infancy and there exist many research issues and challenges ranging from electricity power, health care, transportation and smart building etc. In this paper, an introduction of CPeSC3 (cyber physical enhanced secured wireless sensor networks (WSNs) integrated cloud computing for u-life care) architecture and its application to the health care monitoring and decision support systems is given. The proposed CPeSC3 architecture is composed of three main components, namely 1) communication core, 2) computation core, and 3) resource scheduling and management core. Detailed analysis and explanation are given for relevant models such as cloud computing, real time scheduling and security models. Finally, a medical health care application scenario is presented based on our practical test-bed which has been built for 3 years.

preprint2011arXiv

Potential and Flux Decomposition for Dynamical Systems and Non-Equilibrium Thermodynamics: Curvature, Gauge Field and Generalized Fluctuation-Dissipation Theorem

The driving force of the dynamical system can be decomposed into the gradient of a potential landscape and curl flux (current). The fluctuation-dissipation theorem (FDT) is often applied to near equilibrium systems with detailed balance. The response due to a small perturbation can be expressed by a spontaneous fluctuation. For non-equilibrium systems, we derived a generalized FDT that the response function is composed of two parts: (1) a spontaneous correlation representing the relaxation which is present in the near equilibrium systems with detailed balance; (2) a correlation related to the persistence of the curl flux in steady state, which is also in part linked to a internal curvature of a gauge field. The generalized FDT is also related to the fluctuation theorem. In the equal time limit, the generalized FDT naturally leads to non-equilibrium thermodynamics where the entropy production rate can be decomposed into spontaneous relaxation driven by gradient force and house keeping contribution driven by the non-zero flux that sustains the non-equilibrium environment and breaks the detailed balance.

preprint2011arXiv

Zeeman Splitting of Photonic Angular Momentum States in Gyromagnetic Cylinder

We show that under the presence of a static magnetic field the photon eigen-frequencies of a circular gyromagnetic cylinder experience a splitting that is proportional to the angular momentum density of light at the cylinder surface. Such a splitting of the photonic states is similar to the Zeeman splitting of electronic states in atoms. This leads to some unusual decoupling properties of these non-degenerate photonic angular momentum states, which are demonstrated through numerical simulations.

preprint2010arXiv

A qualitative test for intrinsic size effect on ferroelectric phase transitions

The size effect in ferroelectrics is treated as a competition between the geometrical symmetry of the ferroelectric sample and its crystalline symmetry. The manifestation of this competition is shown to be polarization rotation, which is driven by temperature and/or size variations, thus providing a qualitative indication of intrinsic finite size effect on ferroelectrics. The concept is demonstrated in a simple case of PbTiO3 nanowires having their axis parallel to [111]C direction, where the size effect is shown to include polarization rotation and an additional phase transition.

Jin Wang

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90 published item(s)

Brightest GRB flare observed in GRB 221009A: bridge the last gap between flare and prompt emission in GRB

ChemBART: A Pre-trained BART Model Assisting Organic Chemistry Analysis

Evaluating transfer learning strategies for improving dairy cattle body weight prediction in small farms using depth-image and point-cloud data

HearSay Benchmark: Do Audio LLMs Leak What They Hear?

IPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts

LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models

Multi parameter discrimination using multiple spectral troughs in a cascaded fiber sensor

Rotate Your Character: Revisiting Video Diffusion Models for High-Quality 3D Character Generation

Slip viscosity and strain-rate viscosity in Taylor-Couette laminar flows: Experimental falsification and end-wall effects

TAGRPO: Boosting GRPO on Image-to-Video Generation with Direct Trajectory Alignment

Information retrieval from Hawking radiation in the non-isometric model of black hole interior: theory and quantum simulations

Entanglement and work statistics in the driven open system

High precision atom interferometer-based dynamic gravimeter measurement by eliminating the cross-coupling effect

Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach

Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning

Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision

Conditional entropy production and quantum fluctuation theorem of dissipative information: Theory and experiments

Development of a compact high-resolution absolute gravity gradiometer based on atom interferometers

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

Explaining Deepfake Detection by Analysing Image Matching

Full quantum theory of nonequilibrium phonon condensation and phase transition

Incrementally Stochastic and Accelerated Gradient Information mixed Optimization for Manipulator Motion Planning

Investigating the environmental dependence of ultralight scalar dark matter with atom interferometers

Kinetics of Hawking-Page phase transition with the non-Markovian effects

Locally Aggregated Feature Attribution on Natural Language Model Understanding

Machop: an End-to-End Generalized Entity Matching Framework

Metastable dynamics of neural circuits and networks

Path integral and instantons for the process and phase transition rate of the RNAdS black hole

Quantum cosmology of the flat universe via closed real-time path integral

Self-Alignment of a Large-Area Dual-Atom-Interferometer Gyroscope Using Parameter Decoupled Phase Seeking Calibrations

Supporting GNSS Baseband Using Smartphone IMU and Ultra-Tight Integration

Ultralight scalar dark matter detection with ZAIGA

Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019

Absolute quantification of real-time PCR data with stage signal difference analysis

Defect-free arbitrary-geometry assembly of mixed-species atom arrays

Equilibrium and nonequilibrium quantum correlations between two detectors in curved space time

Excitation Energy Transfer under Strong Laser Drive

Hayden-Preskill protocol and decoding Hawking radiation at finite temperature

High fidelity entanglement of neutral atoms via a Rydberg-mediated single-modulated-pulse controlled-PHASE gate

Parallel implementations of random time algorithm for chemical network stochastic simulations

Some nonlinear inverse relations of Bell polynomials via the Lagrange inversion formula

Explaining Away Attacks Against Neural Networks

Field-induced oscillation of magnetization blocking in holmium metallacrown magnet

Further study on elliptic interpolation formulas for the elliptic Askey-Wilson polynomials and allied identities

GJRA: A Global Joint Resource Allocation Scheme for UAV service of PEC in IIoTs

Influence of equilibrium and nonequilibrium environments on macroscopic realism through the Leggett-Garg inequalities

New Fluctuation Theorem on Maxwell's Demon

Proof of the $(α,β)$--inversion formula conjectured by Hsu and Ma

Suppression of Coriolis error in weak equivalence principle test using ^[85]Rb-^[87]Rb dual-species atom interferometer

The nonequilibrium back-reaction of Hawking radiation to a Schwarzschild black hole

Two new transformation formulas for ${}_{8}ψ_{8}$ and ${}_{8}W_{7}$ series associated with Weierstrass' theta identity

YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis

Balanced Coherence Times of Mixed-Species Atomic Qubits in a Dual $3\times3$ Magic-Intensity Optical Dipole Trap Array

DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks

Investigations of the Underlying Mechanisms of HIF-1α and CITED2 Binding to TAZ1

Orbit Design for Space Atom-Interferometer AIGSO

Preparation of a Heteronuclear Two-atom System in the 3D Motional Ground State in an Optical Tweezer

The dynamical and thermodynamical origin of dissipative chaos

ZAIGA: Zhaoshan Long-baseline Atom Interferometer Gravitation Antenna

K-nearest Neighbor Search by Random Projection Forests

A framework towards understanding mesoscopic phenomena: Emergent unpredictability, symmetry breaking and dynamics across scales

A multiscale nonequilibrium model for melting of metal powder bed subjected to constant heat flux

A Variational Algorithm for Bayesian Variable Selection

An Ensemble EM Algorithm for Bayesian Variable Selection

Coherence preservation of a single neutral atom qubit transferred between magic-intensity optical traps

Extracting the differential phase in dual atom interferometers by modulating magnetic fields

Oscillatory Double-Diffusive Convection in a Horizontal Cavity with Soret and Dufour Effects

Curl flux, coherence, and population landscape of molecular systems: Nonequilibrium quantum steady state, energy (charge) transport, and thermodynamics

Formation of Non-reciprocal Bands in Magnetized Diatomic Plasmonic Chains

Influence of separating distance between atomic sensors for gravitational wave detection

Interaction induced decay of a heteronuclear two-atom system

Non-reciprocal mu-near-zero mode in PT-symmetric magnetic domains

On the Optimal Provider Selection for Repair in Distributed Storage System with Network Coding