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Jia Wang

Jia Wang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.quant-gasquant-phphysics.atom-phMachine LearningComputer Visiongr-qcArtificial Intelligencehep-thphysics.acc-phphysics.chem-phphysics.plasm-phcond-mat.mes-hallcond-mat.supr-conq-fin.CPq-fin.STq-fin.TRDatabasesInformation Theorymath.ITnucl-thphysics.atm-clusComputation and Languagecond-mat.dis-nncond-mat.mtrl-scicond-mat.stat-mechCryptography and Securityeess.IVeess.SYInformation Retrievalmath.OAmath.OCmath.PRmath.STNetworking and Internet ArchitectureNeural and Evolutionary Computingnucl-exphysics.opticsphysics.soc-phSocial and Information NetworksStatistics TheorySystems and Control

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Published work

63 published item(s)

preprint2026arXiv

Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation

Large Multimodal Models (LMMs) have recently shown remarkable promise in low-level visual perception tasks, particularly in Image Quality Assessment (IQA), demonstrating strong zero-shot capability. However, achieving state-of-the-art performance often requires computationally expensive fine-tuning methods, which aim to align the distribution of quality-related token in output with image quality levels. Inspired by recent training-free works for LMM, we introduce IQARAG, a novel, training-free framework that enhances LMMs' IQA ability. IQARAG leverages Retrieval-Augmented Generation (RAG) to retrieve some semantically similar but quality-variant reference images with corresponding Mean Opinion Scores (MOSs) for input image. These retrieved images and input image are integrated into a specific prompt. Retrieved images provide the LMM with a visual perception anchor for IQA task. IQARAG contains three key phases: Retrieval Feature Extraction, Image Retrieval, and Integration & Quality Score Generation. Extensive experiments across multiple diverse IQA datasets, including KADID, KonIQ, LIVE Challenge, and SPAQ, demonstrate that the proposed IQARAG effectively boosts the IQA performance of LMMs, offering a resource-efficient alternative to fine-tuning for quality assessment.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Forge-and-Quench: Enhancing Image Generation for Higher Fidelity in Unified Multimodal Models

Integrating image generation and understanding into a single framework has become a pivotal goal in the multimodal domain. However, how understanding can effectively assist generation has not been fully explored. Unlike previous works that focus on leveraging reasoning abilities and world knowledge from understanding models, this paper introduces a novel perspective: leveraging understanding to enhance the fidelity and detail richness of generated images. To this end, we propose Forge-and-Quench, a new unified framework that puts this principle into practice. In the generation process of our framework, an MLLM first reasons over the entire conversational context, including text instructions, to produce an enhanced text instruction. This refined instruction is then mapped to a virtual visual representation, termed the Bridge Feature, via a novel Bridge Adapter. This feature acts as a crucial link, forging insights from the understanding model to quench and refine the generation process. It is subsequently injected into the T2I backbone as a visual guidance signal, alongside the enhanced text instruction that replaces the original input. To validate this paradigm, we conduct comprehensive studies on the design of the Bridge Feature and Bridge Adapter. Our framework demonstrates exceptional extensibility and flexibility, enabling efficient migration across different MLLM and T2I models with significant savings in training overhead, all without compromising the MLLM's inherent multimodal understanding capabilities. Experiments show that Forge-and-Quench significantly improves image fidelity and detail across multiple models, while also maintaining instruction-following accuracy and enhancing world knowledge application. Models and codes are available at https://github.com/YanbingZeng/Forge-and-Quench.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Generalized Poincaré inequality for quantum Markov semigroups

We prove a noncommutative $(p,p)$-Poincaré inequality for trace-symmetric quantum Markov semigroups on tracial von Neumann algebras, assuming only the existence of a spectral gap. Extending semi-commutative results of Huang and Tropp, our argument uses Markov dilations to obtain chain-rule estimates for Dirichlet forms and employs amalgamated free products to define an appropriate noncommutative derivation. We further generalize the argument to non-tracial $σ$-finite von Neumann algebras under the weaker assumption of GNS-detailed balance, using Haagerup's reduction and Kosaki's interpolation theorem. As applications, we recover noncommutative Khintchine and sub-exponential concentration inequalities.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RTPrune: Reading-Twice Inspired Token Pruning for Efficient DeepSeek-OCR Inference

DeepSeek-OCR leverages visual-text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods for conventional vision-language models (VLMs) fail to preserve textual fidelity due to improper compression mechanisms. By analyzing the decoding process of DeepSeek-OCR, we find that a distinct two-stage reading trajectory: the model initially prioritizes the majority of high-norm tokens, then subsequently redistributes its attention to the remaining ones. Motivated by this insight, we propose RTPrune, a two-stage token pruning method tailored for DeepSeek-OCR. In the first stage, we prioritize high-norm visual tokens that capture salient textual and structural information. In the second stage, the remaining tokens are paired and merged based on optimal transport theory to achieve efficient feature aggregation. We further introduce a dynamic pruning ratio that adapts to token similarity and textual density for OCR tasks, enabling a better efficiency-accuracy trade-off. Extensive experiments demonstrate state-of-the-art performance, as evidenced by 99.47% accuracy and 1.23$\times$ faster prefill on OmniDocBench, achieved with 84.25% token retention when applied to DeepSeek-OCR-Large.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

STEP3-VL-10B Technical Report

We present STEP3-VL-10B, a lightweight open-source foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence. STEP3-VL-10B is realized through two strategic shifts: first, a unified, fully unfrozen pre-training strategy on 1.2T multimodal tokens that integrates a language-aligned Perception Encoder with a Qwen3-8B decoder to establish intrinsic vision-language synergy; and second, a scaled post-training pipeline featuring over 1k iterations of reinforcement learning. Crucially, we implement Parallel Coordinated Reasoning (PaCoRe) to scale test-time compute, allocating resources to scalable perceptual reasoning that explores and synthesizes diverse visual hypotheses. Consequently, despite its compact 10B footprint, STEP3-VL-10B rivals or surpasses models 10$\times$-20$\times$ larger (e.g., GLM-4.6V-106B, Qwen3-VL-235B) and top-tier proprietary flagships like Gemini 2.5 Pro and Seed-1.5-VL. Delivering best-in-class performance, it records 92.2% on MMBench and 80.11% on MMMU, while excelling in complex reasoning with 94.43% on AIME2025 and 75.95% on MathVision. We release the full model suite to provide the community with a powerful, efficient, and reproducible baseline.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Achieving High Efficiency And Enhanced Beam Quality In Laser Wakefield Acceleration

Laser wakefield acceleration, characterized by the extremely high electric field gradient exceeding 100GV/m, is regarded as a compact and cost affordable technology for the next generation of particle colliders and light sources. However, it has always been a major challenge to effectively increase the energy transfer efficiency from the laser to the accelerated beam, while ensuring the beam quality remains suitable for practical applications. This study demonstrates that the laser with shorter pulse duration allows for a two-step dechirping process of the accelerated electron beam with charge of nanocoulomb level. The electron beams with an energy spread of 1% can be generated with the energy transfer efficiency of 10% to 30% in a large parameter space. For example, one electron beam with the energy of 420MeV, the charge of 5.5nC and the RMS energy spread of 2% can be produced using an 8.3J laser pulse with 7.2fs duration.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

AttentionLut: Attention Fusion-based Canonical Polyadic LUT for Real-time Image Enhancement

Recently, many algorithms have employed image-adaptive lookup tables (LUTs) to achieve real-time image enhancement. Nonetheless, a prevailing trend among existing methods has been the employment of linear combinations of basic LUTs to formulate image-adaptive LUTs, which limits the generalization ability of these methods. To address this limitation, we propose a novel framework named AttentionLut for real-time image enhancement, which utilizes the attention mechanism to generate image-adaptive LUTs. Our proposed framework consists of three lightweight modules. We begin by employing the global image context feature module to extract image-adaptive features. Subsequently, the attention fusion module integrates the image feature with the priori attention feature obtained during training to generate image-adaptive canonical polyadic tensors. Finally, the canonical polyadic reconstruction module is deployed to reconstruct image-adaptive residual 3DLUT, which is subsequently utilized for enhancing input images. Experiments on the benchmark MIT-Adobe FiveK dataset demonstrate that the proposed method achieves better enhancement performance quantitatively and qualitatively than the state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Knowledge-Based Decision Support System for In Vitro Fertilization Treatment

In Vitro Fertilization (IVF) is the most widely used Assisted Reproductive Technology (ART). IVF usually involves controlled ovarian stimulation, oocyte retrieval, fertilization in the laboratory with subsequent embryo transfer. The first two steps correspond with follicular phase of females and ovulation in their menstrual cycle. Therefore, we refer to it as the treatment cycle in our paper. The treatment cycle is crucial because the stimulation medications in IVF treatment are applied directly on patients. In order to optimize the stimulation effects and lower the side effects of the stimulation medications, prompt treatment adjustments are in need. In addition, the quality and quantity of the retrieved oocytes have a significant effect on the outcome of the following procedures. To improve the IVF success rate, we propose a knowledge-based decision support system that can provide medical advice on the treatment protocol and medication adjustment for each patient visit during IVF treatment cycle. Our system is efficient in data processing and light-weighted which can be easily embedded into electronic medical record systems. Moreover, an oocyte retrieval oriented evaluation demonstrates that our system performs well in terms of accuracy of advice for the protocols and medications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An Accelerated Proximal Gradient-based Model Predictive Control Algorithm

In this letter, an accelerated quadratic programming (QP) algorithm is proposed based on the proximal gradient method. The algorithm can achieve convergence rate $O(1/p^α)$, where $p$ is the iteration number and $α$ is the given positive integer. The proposed algorithm improves the convergence rate of existing algorithms that achieve $O(1/p^{2})$. The key idea is that iterative parameters are selected from a group of specific high order polynomial equations. The performance of the proposed algorithm is assessed on the randomly generated model predictive control (MPC) optimization problems. The experimental results show that our algorithm can outperform the state-of-the-art optimization software MOSEK and ECOS for the small size MPC problems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Crossover polarons in a strongly interacting Fermi superfluid

We investigate the zero-temperature quasiparticle properties of a mobile impurity immersed in a strongly interacting Fermi superfluid at the crossover from a Bose-Einstein condensate (BEC) to a Bardeen--Cooper--Schrieffer (BCS) superfluid, by using a many-body $T$-matrix approach that excludes Efimov trimer bound states. Termed BEC-BCS crossover polaron, or crossover polaron in short, this quasiparticle couples to elementary excitations of a many-body background and therefore could provide a useful probe of the underlying strongly interacting Fermi superfluid. Due to the existence of a significant pairing gap $Δ$, we find that the repulsive polaron branch becomes less well-defined. In contrast, the attractive polaron branch is protected by the pairing gap and becomes more robust at finite momentum. It remains as a delta-function peak in the impurity spectral function below a threshold $2Δ$. Above the threshold, the attractive polaron enters the particle-hole continuum and starts to get damped. We predict the polaron energy, residue and effective mass for realistic Bose-Fermi mixtures, where the minority bosonic atoms play the role of impurity. These results are practically useful for future cold-atom experiments on crossover polarons.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements

It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. We evaluate the performance of our approach with backtesting on historical trading data of SPDR SP 500 Trust ETF over eight years. The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Entanglement dynamics of an open moving-biparticle system driven by classical-field

In this work, the entanglement dynamics of a moving-biparticle system driven by an external classical field are investigated, where the moving-biparticle system is coupled with a zero temperature common environment. The analytical expressions of the density operator and the entanglement can be obtained by using the dressed-state basis when the total excitation number is one. We also discuss in detail the effects of different parameters on the entanglement dynamics. The results show that the classical driving can not only protect the entanglement, but also effectively eliminate the influence of the qubit velocity and the detuning on the quantum entanglement.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exact quasiparticle properties of a heavy polaron in BCS Fermi superfluids

We present the Ramsey response and radio-frequency spectroscopy of a heavy impurity immersed in an interacting Fermi superfluid, using exact functional determinant approach. We describe the Fermi superfluid through the conventional Bardeen-Cooper-Schrieffer theory and investigate the role of the pairing gap on quasiparticle properties revealed by the two spectroscopies. The energy cost for pair breaking prevents Anderson\textquoteright s orthogonality catastrophe that occurs in a non-interacting Fermi gas and allows the existence of polaron quasiparticles in the exactly solvable heavy impurity limit. Hence, we rigorously confirm the remarkable features such as dark continuum, molecule-hole continuum and repulsive polaron. For a magnetic impurity scattering at finite temperature, we predict additional resonances related to the sub-gap Yu-Shiba-Rusinov bound state, whose positions can be used to measure the superfluid pairing gap. For a non-magnetic scattering at zero temperature, we surprisingly find undamped repulsive polarons. These exact results might be readily observed in quantum gas experiments with Bose-Fermi mixtures that have a large-mass ratio.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach

One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Several model compression methods, such as gradient quantization and sparsification, have been proposed to improve the communication efficiency of model aggregation. However, the information-theoretic minimum communication cost for a given distortion of gradient estimators is still unknown. In this paper, we study the fundamental limit of communication cost of model aggregation in distributed learning from a rate-distortion perspective. By formulating the model aggregation as a vector Gaussian CEO problem, we derive the rate region bound and sum-rate-distortion function for the model aggregation problem, which reveals the minimum communication rate at a particular gradient distortion upper bound. We also analyze the communication cost at each iteration and total communication cost based on the sum-rate-distortion function with the gradient statistics of real-world datasets. It is found that the communication gain by exploiting the correlation between worker nodes is significant for SignSGD, and a high distortion of gradient estimator can achieve low total communication cost in gradient compression.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations

Recently, graph neural networks (GNNs) have been widely used to develop successful recommender systems. Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user. Indeed, explaining GNN-based recommendations is unique, and existing GNN explanation methods are inappropriate for two reasons. First, traditional GNN explanation methods are designed for node, edge, or graph classification tasks rather than ranking, as in recommender systems. Second, standard machine learning explanations are usually intended to support skilled decision-makers. Instead, recommendations are designed for any end-user, and thus their explanations should be provided in user-understandable ways. In this work, we propose GREASE, a novel method for explaining the suggestions provided by any black-box GNN-based recommender system. Specifically, GREASE first trains a surrogate model on a target user-item pair and its $l$-hop neighborhood. Then, it generates both factual and counterfactual explanations by finding optimal adjacency matrix perturbations to capture the sufficient and necessary conditions for an item to be recommended, respectively. Experimental results conducted on real-world datasets demonstrate that GREASE can generate concise and effective explanations for popular GNN-based recommender models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Heavy polarons in ultracold atomic Fermi superfluids at the BEC-BCS crossover: formalism and applications

We investigate the system of a heavy impurity embedded in a paired two-component Fermi gas at the crossover from a Bose-Einstein condensate (BEC) to a Bardeen--Cooper--Schrieffer (BCS) superfluid via an extension of the functional determinant approach (FDA). FDA is an exact numerical approach applied to study manifestations of Anderson\textquoteright s orthogonality catastrophe (OC) in the system of a static impurity immersed in an ideal Fermi gas. Here, we extend the FDA to a strongly correlated superfluid background described by a BCS mean-field wavefunction. In contrast to the ideal Fermi gas case, the pairing gap in the BCS superfluid prevents the OC and leads to genuine polaron signals in the spectrum. Thus, our exactly solvable model can provide a deeper understanding of polaron physics. In addition, we find that the polaron spectrum can be used to measure the superfluid pairing gap, and in the case of a magnetic impurity, the energy of the sub-gap Yu-Shiba-Rusinov (YSR) bound state. Our theoretical predictions can be examined with state-of-art cold-atom experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Injection induced by coaxial laser interference in laser wakefield accelerators

A new injection scheme using the interference of two coaxial laser pulses is proposed for generating high quality beams in laser wakefield accelerators. In this scheme, a relatively loosely focused laser pulse drives the plasma wakefield, and a tightly focused laser pulse with similar intensity triggers interference ring pattern which creates onion-like multi sheaths in the plasma wakefield. Due to the wavefront curvature change after the focal position of the tightly focused laser, the innermost sheath of the wakefield expands, which slows down the effective phase velocity of the wakefield and triggers injection of plasma electrons. Particle-in-cell simulations show that high quality electron beams with low energy spread (a few per mill), high charge (hundred picocoulomb) and small emittance (sub millimeter milliradian) at the same time can be generated using moderate laser parameters for properly chosen phase differences between the two lasers.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multidimensional Spectroscopy of Time-Dependent Impurities in Ultracold Fermions

We investigate the system of a heavy impurity immersed in a degenerated Fermi gas, where the impurity's internal degree of freedom (pseudospin) is manipulated by a series of radiofrequency (RF) pulses at several different times. Applying the functional determinant approach, we carry out an essentially exact calculation of the Ramsey-interference-type responses to the RF pulses. These responses are universal functions of the multiple time intervals between the pulses for all time and can be regarded as multidimensional (MD) spectroscopy of the system in the time domain. A Fourier transformation of the time intervals gives the MD spectroscopy in the frequency domain, providing insightful information on the many-body correlation and relaxation via the cross-peaks, e.g., the off-diagonal peaks in a two-dimensional spectrum. These features are inaccessible for the conventional, one-dimensional absorption spectrum. Our scheme provides a new method to investigate many-body nonequilibrium physics beyond the linear response regime with the accessible tools in cold atoms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On the Rate-Distortion-Perception Function

Rate-distortion-perception theory generalizes Shannon's rate-distortion theory by introducing a constraint on the perceptual quality of the output. The perception constraint complements the conventional distortion constraint and aims to enforce distribution-level consistencies. In this new theory, the information-theoretic limit is characterized by the rate-distortion-perception function. Although a coding theorem for the rate-distortion-perception function has recently been established, the fundamental nature of the optimal coding schemes remains unclear, especially regarding the role of randomness in encoding and decoding. It is shown in the present work that except for certain extreme cases, the rate-distortion-perception function is achievable by deterministic codes. This paper also clarifies the subtle differences between two notions of perfect perceptual quality and explores some alternative formulations of the perception constraint.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Photo-excitation measurement of Tan's contact for a strongly interacting Fermi gas

We derive theoretically an exact relation between Tan's universal contact and the photo-excitation rate of a strongly interacting Fermi gas, in the case of optically transferring fermionic pairs to a more tightly bound molecular state. Our deviation generalizes the relation between Tan's contact and the closed-channel molecular fraction found earlier by Werner, Tarruell and Castin in Eur. Phys. J. B \textbf{68}, 401 (2009). We use the relation to understand the recent low-temperature photo-excitation measurement in a strongly interacting $^{6}$Li Fermi gas {[}Liu \textit{et al.}, arXiv:1903.12321{]} and show that there is a reasonable agreement between theory and experiment close to the unitary limit. We propose that our relation can be applied to accurately measure Tan's contact coefficient at finite temperature in future experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pseudopotentials for Two-dimentional Ultracold Scattering in the Presence of Synthetic Spin-orbit-coupling

We derive a pseudopotential in two dimensions (2D) with the presence of a 2D Rashba spin-orbit-coupling (SOC), following the same spirit of frame transformation in {[}Phys. Rev. A 95, 020702(R) (2017){]}. The frame transformation correctly describes the non-trivial phase accumulation and partial wave couplings due to the presence of SOC and gives rise to a different pseudopotential than the free-space one, even when the length scale of SOC is significantly larger than the two-body potential range. As an application, we apply our pseudopotential with the Lippmann-Schwinger equation to obtain an analytical scattering matrix. To demonstrate the validity, we compare our results with a numerical scattering calculation of finite-range potential and show perfect agreement over a wide range of scattering energy and SOC strength. Our results also indicate that the differences between our pseudopotential and the original free-space pseudopotential are essential to reproduce scattering observables correctly.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

With the development of online travel services, it has great application prospects to timely mine users' evaluation emotions for travel services and use them as indicators to guide the improvement of online travel service quality. In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon. SCCL model aims at the lack of consideration of local features and emotional semantic features of the text in the language model that can efficiently extract text context features like BERT and GRU. Then make the following improvements to their shortcomings. On the one hand, based on BERT-BiGRU, the capsule network is introduced to extract local features while retaining good context features. On the other hand, the sentiment lexicon is introduced to extract the emotional sequence of the text to provide richer emotional semantic features for the model. To enhance the universality of the sentiment lexicon, the improved SO-PMI algorithm based on TF-IDF is used to expand the lexicon, so that the lexicon can also perform well in the field of online travel reviews.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Optimal Beam-loading in Two-bunch Nonlinear Plasma Wakefield Accelerators

Due to the highly nonlinear nature of the beam-loading, it is at present not possible to analytically determine the beam parameters needed in a two-bunch plasma wakefield accelerator for maintaining a low energy spread. Therefore in this paper, by using the Broyden-Fletcher-Goldfarb-Shanno algorithm for the parameter scanning with the code QuickPIC and the polynomial regression together with k-fold cross-validation method, we obtain two fitting formulas for calculating the parameters of tri-Gaussian electron beams when minimizing the energy spread based on the beam-loading effect in a nonlinear plasma wakefield accelerator. One formula allows the optimization of the normalized charge per unit length of a trailing beam to achieve the minimal energy spread, i.e. the optimal beam-loading. The other one directly gives the transformer ratio when the trailing beam achieves the optimal beam-loading. A simple scaling law for charges of drive beams and trailing beams is obtained from the fitting formula, which indicates that the optimal beam-loading is always achieved for a given charge ratio of the two beams when the length and separation of two beams and the plasma density are fixed. The formulas can also help obtain the optimal plasma densities for the maximum accelerated charge and the maximum acceleration efficiency under the optimal beam-loading respectively. These two fitting formulas will significantly enhance the efficiency for designing and optimizing a two-bunch plasma wakefield acceleration stage.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two-dimensional spectroscopic diagnosis of quantum coherence in Fermi polarons

We present a full microscopic many-body calculation of a recently-proposed nonlinear two-dimensional spectroscopy for Fermi polarons, and show that the quantum coherence between the attractive and repulsive polarons, which has never been experimentally examined, can be unambiguously revealed via quantum beats at the two off-diagonal crosspeaks in the two-dimensional spectrum. We predict that particle-hole excitations make the two crosspeaks asymmetric and lead to an additional side peak near the diagonal repulsive polaron peak. Our simulated spectra can be readily examined in future cold-atom experiments, where the two-dimensional spectroscopy is to be implemented by using a Ramsey interference sequence of rf pulses in the time domain. Our results also provide a first-principle understanding of the recent two-dimensional coherent spectroscopy of interacting excitons and trions in doped monolayer transition metal dichalcogenides.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Anderson localization transition in a robust $\mathcal{PT}$-symmetric phase of a generalized Aubry-Andre model

We study a generalized Aubry-Andre model that obeys $\mathcal{PT}$-symmetry. We observe a robust $\mathcal{PT}$-symmetric phase with respect to system size and disorder strength, where all eigenvalues are real despite the Hamiltonian being non-hermitian. This robust $\mathcal{PT}$-symmetric phase can support an Anderson localization transition, giving a rich phase diagram as a result of the interplay between disorder and $\mathcal{PT}$-symmetry. Our model provides a perfect platform to study disorder-driven localization phenomena in a $\mathcal{PT}$-symmetric system.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets

Financial markets are a complex dynamical system. The complexity comes from the interaction between a market and its participants, in other words, the integrated outcome of activities of the entire participants determines the markets trend, while the markets trend affects activities of participants. These interwoven interactions make financial markets keep evolving. Inspired by stochastic recurrent models that successfully capture variability observed in natural sequential data such as speech and video, we propose CLVSA, a hybrid model that consists of stochastic recurrent networks, the sequence-to-sequence architecture, the self- and inter-attention mechanism, and convolutional LSTM units to capture variationally underlying features in raw financial trading data. Our model outperforms basic models, such as convolutional neural network, vanilla LSTM network, and sequence-to-sequence model with attention, based on backtesting results of six futures from January 2010 to December 2017. Our experimental results show that, by introducing an approximate posterior, CLVSA takes advantage of an extra regularizer based on the Kullback-Leibler divergence to prevent itself from overfitting traps.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Financial Markets Prediction with Deep Learning

Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

First-order Bose-Einstein condensation with three-body interacting bosons

Bose-Einstein condensation, observed in either strongly interacting liquid helium or weakly interacting atomic Bose gases, is widely known to be a second-order phase transition. Here, we predict a first-order Bose-Einstein condensation in a cloud of harmonically trapped bosons interacting with both attractive two-body interaction and repulsive three-body interaction, characterized respectively by an $s$-wave scattering length $a<0$ and a three-body scattering hypervolume $D>0$. It happens when the harmonic trapping potential is weak, so with increasing temperature the system changes from a low-temperature liquid-like quantum droplet to a normal gas, and therefore experiences a first-order liquid-to-gas transition. At large trapping potential, however, the quantum droplet can first turn into a superfluid gas, rendering the condensation transition occurred later from a superfluid gas to a normal gas smooth. We determine a rich phase diagram and show the existence of a tri-critical point, where the three phases - quantum droplet, superfluid gas and normal gas - meet together. We argue that an ensemble of spin-polarized tritium atoms could be a promising candidate to observe the predicted first-order Bose-Einstein condensation, across which the condensate fraction or central condensate density jumps to zero and the surface-mode frequencies diverge.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Scissor-cross ionization injection in laser wakefield accelerators

We propose to use a frequency doubled pulse colliding with the driving pulse at an acute angle to trigger ionization injection in a laser wakefield accelerator. This scheme effectively reduces the duration that injection occurs, thus high injection quality is obtained. Three-dimensional particle-in-cell simulations show that electron beams with energy of ~500 MeV, charge of ~40 pC, energy spread of ~1% and normalized emittance of a few millimeter milliradian can be produced by ~100 TW laser pulses. By adjusting the angle between the two pulses, the intensity of the trigger pulse and the gas dope ratio, the charge and energy spread of the electron beam can be controlled.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Alleviation of Gradient Exploding in GANs: Fake Can Be Real

In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples are considered as real ones during the training process. This strategy can reduce the gradient value that generator receives in the region where gradient exploding happens. We show the process of an unbalanced generation and a vicious circle issue resulted from gradient exploding in practical training, which explains the instability of GANs. We also theoretically prove that gradient exploding can be alleviated by penalizing the difference between discriminator outputs and fake-as-real consideration for very close real and fake samples. Accordingly, Fake-As-Real GAN (FARGAN) is proposed with a more stable training process and a more faithful generated distribution. Experiments on different datasets verify our theoretical analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Spatial separation of 2-propanol monomer and its ionization-fragmentation pathways

The spatial separation of 2-propanol monomer from its clusters in a molecular beam by an electrostatic deflector was demonstrated. Samples of 2-propanol monomer with a purity of 90 % and a beam density of $7\times10^6~\text{cm}^{-3}$ were obtained. These samples were utilized to study the femtosecond-laser-induced strong-field multi-photon ionization and fragmentation of 2-propanol using non-resonant 800 nm light with peak intensities of $3-7\times10^{13}~W/cm^{2}$.

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preprint2020arXiv

Spatial Separation of the Conformers of Methyl Vinyl Ketone

Methyl vinyl ketone (C$_4$H$_6$O) is a volatile, labile organic compound of importance in atmospheric chemistry. We prepared a molecular beam of methyl vinyl ketone with a rotational temperature of 1.2(2)~K and demonstrated the spatial separation of the \emph{s-cis} and \emph{s-trans} conformers of methyl vinyl ketone using the electrostatic deflector. The resulting sample density was $1.5(2)\times10^{8}~\text{cm}^{-3}$ for the direct beam in the laser ionization region. These conformer-selected methyl vinyl ketone samples are well suited for conformer-specific chemical reactivity studies such as in Diels-Alder cycloaddition reactions.

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preprint2020arXiv

Ultradilute self-bound quantum droplets in Bose-Bose mixtures at finite temperature

We theoretically investigate the finite-temperature structure and collective excitations of a self-bound ultradilute Bose droplet in a flat space realized in a binary Bose mixture with attractive inter-species interactions on the verge of mean-field collapse. As the droplet formation relies critically on the repulsive force provided by Lee-Huang-Yang quantum fluctuations, which can be easily compensated by thermal fluctuations, we find a significant temperature effect in the density distribution and collective excitation spectrum of the Bose droplet. A finite-temperature phase diagram as a function of the number of particles is determined. We show that the critical number of particles at the droplet-to-gas transition increases dramatically with increasing temperature. Towards the bulk threshold temperature for thermally destabilizing an infinitely large droplet, we find that the excitation-forbidden, self-evaporation region in the excitation spectrum, predicted earlier by Petrov using a zero-temperature theory, shrinks and eventually disappears. All the collective excitations, including both surface modes and compressional bulk modes, become softened at the droplet-to-gas transition. The predicted temperature effects of a self-bound Bose droplet in this work could be difficult to measure experimentally due to the lack of efficient thermometry at low temperatures. However, these effects may already present in the current cold-atom experiments.

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preprint2020arXiv

Uniform Interpolation Constrained Geodesic Learning on Data Manifold

In this paper, we propose a method to learn a minimizing geodesic within a data manifold. Along the learned geodesic, our method can generate high-quality interpolations between two given data samples. Specifically, we use an autoencoder network to map data samples into latent space and perform interpolation via an interpolation network. We add prior geometric information to regularize our autoencoder for the convexity of representations so that for any given interpolation approach, the generated interpolations remain within the distribution of the data manifold. Before the learning of a geodesic, a proper Riemannianmetric should be defined. Therefore, we induce a Riemannian metric by the canonical metric in the Euclidean space which the data manifold is isometrically immersed in. Based on this defined Riemannian metric, we introduce a constant speed loss and a minimizing geodesic loss to regularize the interpolation network to generate uniform interpolation along the learned geodesic on the manifold. We provide a theoretical analysis of our model and use image translation as an example to demonstrate the effectiveness of our method.

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preprint2019arXiv

Efficient training and design of photonic neural network through neuroevolution

Recently, optical neural networks (ONNs) integrated in photonic chips has received extensive attention because they are expected to implement the same pattern recognition tasks in the electronic platforms with high efficiency and low power consumption. However, the current lack of various learning algorithms to train the ONNs obstructs their further development. In this article, we propose a novel learning strategy based on neuroevolution to design and train the ONNs. Two typical neuroevolution algorithms are used to determine the hyper-parameters of the ONNs and to optimize the weights (phase shifters) in the connections. In order to demonstrate the effectiveness of the training algorithms, the trained ONNs are applied in the classification tasks for iris plants dataset, wine recognition dataset and modulation formats recognition. The calculated results exhibit that the training algorithms based on neuroevolution are competitive with other traditional learning algorithms on both accuracy and stability. Compared with previous works, we introduce an efficient training method for the ONNs and demonstrate their broad application prospects in pattern recognition, reinforcement learning and so on.

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preprint2016arXiv

Many-body localization in Ising models with random long-range interactions

We theoretically investigate the many-body localization phase transition in a one-dimensional Ising spin chain with random long-range spin-spin interactions, $V_{ij}\propto\left|i-j\right|^{-α}$, where the exponent of the interaction range $α$ can be tuned from zero to infinitely large. By using exact diagonalization, we calculate the half-chain entanglement entropy and the energy spectral statistics and use them to characterize the phase transition towards the many-body localization phase at infinite temperature and at sufficiently large disorder strength. We perform finite-size scaling to extract the critical disorder strength and the critical exponent of the divergent localization length. With increasing $α$, the critical exponent experiences a sharp increase at about $α=1$ and then gradually decreases to a value found earlier in a disordered short-ranged interacting spin chain. For $α<1$, we find that the system is mostly localized and the increase in the disorder strength may drive a transition between two many-body localized phases. In contrast, for $α>1$, the transition is from a thermalized phase to the many-body localization phase. Our predictions could be experimentally tested with ion-trap quantum emulator with programmable random long-range interactions, or with randomly distributed Rydberg atoms or polar molecules in lattices.

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preprint2016arXiv

Strongly correlated Fermi superfluid near an orbital Feshbach resonance: Stability, equation of state and Leggett mode

We theoretically study the superfluid phase of a strongly correlated $^{173}$Yb Fermi gas near its orbital Feshbach resonance, by developing a quantitative pair-fluctuation theory within a two-band model. We examine the density excitation spectrum of the system and determine a stability phase diagram. We find that the $^{173}$Yb Fermi gas is intrinsically metastable and has a peculiar equation of state, due to the small but positive singlet scattering length. The massive Leggett mode, arising from the fluctuation of the relative phase of two order parameters, is severely damped. We discuss the parameter space where an undamped Leggett mode may exist.

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preprint2015arXiv

Chosen-plaintext attack of an image encryption scheme based on modified permutation-diffusion structure

Since the first appearance in Fridrich's design, the usage of permutation-diffusion structure for designing digital image cryptosystem has been receiving increasing research attention in the field of chaos-based cryptography. Recently, a novel chaotic Image Cipher using one round Modified Permutation-Diffusion pattern (ICMPD) was proposed. Unlike traditional permutation-diffusion structure, the permutation is operated on bit level instead of pixel level and the diffusion is operated on masked pixels, which are obtained by carrying out the classical affine cipher, instead of plain pixels in ICMPD. Following a \textit{divide-and-conquer strategy}, this paper reports that ICMPD can be compromised by a chosen-plaintext attack efficiently and the involved data complexity is linear to the size of the plain-image. Moreover, the relationship between the cryptographic kernel at the diffusion stage of ICMPD and modulo addition then XORing is explored thoroughly.

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preprint2015arXiv

Entropy relations and the application of black holes with cosmological constant and Gauss-Bonnet term

Based on the entropy relations, we derive thermodynamic bound for entropy and area of horizons of Schwarzschild-dS black hole, including the event horizon, Cauchy horizon and negative horizon (i.e. the horizon with negative value), which are all geometrical bound and made up of the cosmological radius. Consider the first derivative of entropy relations together, we get the first law of thermodynamics for all horizons. We also obtain the Smarr relation of horizons by using the scaling discussion. For thermodynamics of all horizons, the cosmological constant is treated as a thermodynamical variable. Especially for thermodynamics of negative horizon, it is defined well in the $r<0$ side of spacetime. The validity of this formula seems to work well for three-horizons black holes. We also generalize the discussion to thermodynamics for event horizon and Cauchy horizon of Gauss-Bonnet charged flat black holes, as the Gauss-Bonnet coupling constant is also considered as thermodynamical variable. These give further clue on the crucial role that the entropy relations of multi-horizons play in black hole thermodynamics and understanding the entropy at the microscopic level.

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preprint2015arXiv

Estimation of the time of change in panel data

We consider the problem of estimating the common time of a change in the mean parameters of panel data when dependence is allowed between the panels in the form of a common factor. A CUSUM type estimator is proposed, and we establish first and second order asymptotics that can be used to derive consistent confidence intervals for the time of change. Our results improve upon existing theory in two primary directions. Firstly, the conditions we impose on the model errors only pertain to the order of their long run moments, and hence our results hold for nearly all stationary time series models of interest, including nonlinear time series like the ARCH and GARCH processes. Secondly, we study how the asymptotic distribution and norming sequences of the estimator depend on the magnitude of the changes in each panel and the common factor loadings. The performance of our results in finite samples is demonstrated with a Monte Carlo simulation study, and we consider applications to two real data sets: the exchange rates of 23 currencies with respect to the US dollar, and the GDP per capita in 113 countries.

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preprint2015arXiv

Network Cartography: Seeing the Forest and the Trees

Real-world networks are often complex and large with millions of nodes, posing a great challenge for analysts to quickly see the big picture for more productive subsequent analysis. We aim at facilitating exploration of node-attributed networks by creating representations with conciseness, expressiveness, interpretability, and multi-resolution views. We develop such a representation as a {\it map} --- among the first to explore principled network cartography for general networks. In parallel with common maps, ours has landmarks, which aggregate nodes homogeneous in their traits and interactions with nodes elsewhere, and roads, which represent the interactions between the landmarks. We capture such homogeneity by the similar roles the nodes played. Next, to concretely model the landmarks, we propose a probabilistic generative model of networks with roles as latent factors. Furthermore, to enable interactive zooming, we formulate novel model-based constrained optimization. Then, we design efficient linear-time algorithms for the optimizations. Experiments using real-world and synthetic networks show that our method produces more expressive maps than existing methods, with up to 10 times improvement in network reconstruction quality. We also show that our method extracts landmarks with more homogeneous nodes, with up to 90\% improvement in the average attribute/link entropy among the nodes over each landmark. Sense-making of a real-world network using a map computed by our method qualitatively verify the effectiveness of our method.

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preprint2015arXiv

Revealing the origin of super-Efimov states in the hyperspherical formalism

Super-Efimov states are a new kind of universal three-body bound states predicted for three identical fermions with $p$-wave resonant interactions in two dimensions by a recent field-theoretic calculation [Phys.~Rev.~Lett.~\textbf{110}, 235301 (2013)]. The binding energies of these states obey a dramatic double exponential scaling $E_n=E_*\exp(-2 e^{πn/s_0+θ})$ with universal scaling $s_0=4/3$ and three-body parameters $E_*$ and $θ$. We use the hyperspherical formalism and show that the super-Efimov states originate from an emergent effective potential $-1/4ρ^2-(s_0^2+1/4)/ρ^2\ln^2\left(ρ\right)$ at large hyperradius $ρ$. Moreover, for pairwise interparticle potentials with van der Waals tails, our numerical calculation indicates that the three-body parameters $E_*$ and $θ$ are also universal and the ground super-Efimov state shall cross the threshold when the $2$D $p$-wave scattering area is about $-42.0\, l_\text{vdW}^2$ with $l_\text{vdW}$ the van der Waals length.

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preprint2015arXiv

Thermodynamic relations for entropy and temperature of multi-horizons black holes

We present some entropy and temperature relations of multi-horizons, even including the "virtual" horizon. These relations are related to product, division and sum of entropy and temperature of multi-horizons. We obtain the additional thermodynamic relations of both static and rotating black holes in three and four dimensional (A)dS spacetime. Especially, a new dimensionless, charges-independence and $T_+S_+=T_-S_-$ like relation is presented. This relation does not depend on the mass, electric charge, angular momentum and cosmological constant, as it is always a constant. These relations lead us to get some interesting thermodynamic bound of entropy and temperature, including the Penrose inequality which is the first geometrical inequality of black holes. Besides, based on these new relations, one can obtain the first law of thermodynamics and Smarr relation for all horizons of black hole.

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preprint2014arXiv

Controlling ultracold chemical reactions via Rydberg-dressed interactions

We show that ultracold chemical reactions can be manipulated and controlled by using Rydberg-dressed interactions. Scattering in the ultracold regime is sensitive to long-range interactions, especially when weakly bound (or quasi-bound) states exist near the collision threshold. We investigate how, by Rydberg-dressing a reactant, one enhances its polarizability and modifies the long-range van der Waals collision complex, which can alter chemical reaction rates by shifting the position of near threshold bound states. We carry out a full quantum mechanical scattering calculation for the benchmark system H$_2$+D, and show that resonances can be moved substantially and that rate coefficients at cold and ultracold temperatures can be increased by several orders of magnitude.

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preprint2014arXiv

Effect of initial fluctuations on the collective flow in intermediate-energy heavy ion collisions

A systemical analysis of the initial fluctuation effect on the collective flows for Au+Au at 1$A$ GeV has been presented in the framework of Isospin-dependent Quantum Molecular Dynamics model (IQMD), and a special focus on the initial fluctuation effect on the squeeze-out is emphasized. The flows calculated by the participant plane reconstructedby the initial geometry in coordinate space are compared with those calculated by both the ideal reaction plane and event plane methods. It is found that initial fluctuation weakens squeeze-out effect, and somediscrepancies between the flows extracted by the above different plane methods appearwhich indicate that the flows are affected by the evolution of dynamics. In addition, we found that the squeeze-out flow is also proportional to initial eccentricity. Our calculations also qualitatively give the similar trend for the excitation function of the elliptic flow of the FOPI experimental data. Finally we address the nucleon number scaling of the flows for light particles. Even though initial fluctuation decreases the ratio of $v_4/v_2^2$ as well as $v_3/(v_1v_2$) a lot, all fragments to mass number 4 keep the same curve and shows independent of transverse momentum.

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preprint2014arXiv

Rydberg Electrons in a Bose-Einstein Condensate

We investigate a hybrid system composed of ultracold Rydberg atoms immersed in an atomic Bose-Einstein condensate (BEC). The coupling between the Rydberg electrons and BEC atoms leads to the excitation of phonons, the exchange of which induces Yukawa interaction between Rydberg atoms. Due to the small electron mass, the effective charge associated with this quasi-particle-mediated interaction can be large, while its range is equal to the healing length of the BEC, which can be tuned by adjusting the scattering length of the BEC atoms. We find that for small healing lengths, the distortion of the BEC can "image" the wave function density of the Rydberg electron, while large healing lengths induce an attractive Yukawa potential between the two Rydberg atoms that can form a new type of ultra-long-range molecule. We discuss both cases for a realistic system.

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preprint2014arXiv

The "universal property" of Horizon Entropy Sum of Black Holes in Four Dimensional Asymptotical (anti-)de-Sitter Spacetime Background

We present a new ``universal property'' of entropy, that is the ``entropy sum'' relation of black holes in four dimensional (anti-)de-Sitter asymptotical background. They depend only on the cosmological constant with the necessary effect of the un-physical ``virtual'' horizon included in the spacetime where only the cosmological constant, mass of black hole, rotation parameter and Maxwell field exist. When there is more extra matter field in the spacetime, one will find the ``entropy sum'' is also dependent of the strength of these extra matter field. For both cases, we conclude that the ``entropy sum'' does not depend on the conserved charges $M$, $Q$ and $J$, while it does depend on the property of background spacetime. We will mainly test the ``entropy sum'' relation in static, stationary black hole and some black hole with extra matter source (scalar hair and higher curvature) in the asymptotical (anti-)de-sitter spacetime background. Besides, we point out a newly found counter example of the mass independence of the ''entropy product'' relation in the spacetime with extra scalar hair case, while the ``entropy sum'' relation still holds. These result are indeed suggestive to some underlying microscopic mechanism. Moreover, the cosmological constant and extra matter field dependence of the ``entropy sum'' of all horizon seems to reveal that ``entropy sum'' is more general as it is only related to the background field. For the case of asymptotical flat spacetime without any matter source, we give a note for the Kerr black hole case in appendix. One will find only mass dependence of ``entropy sum'' appears. It makes us believe that, considering the dependence of ``entropy sum'', the mass background field may be regarded as the next order of cosmological constant background field and extra matter field.

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preprint2014arXiv

The Entropy Relations of Black Holes with Multihorizons in Higher Dimensions

We study the entropy relations of multi-horizons black holes in higher dimensional (A)dS spacetime with maximal symmetries, including Einstein-Maxwell gravity and $f(R)$(-Maxwell) gravity. These additional equalities in thermodynamics are expected to be useful to understanding the origin of black hole entropy at the microscopic level. Revisiting the entropy product introduced by Cvetic etc, in our case, it has an unexpected behavior. It is shown that this electric charge $Q$ plays an important role in this entropy product. The entropy product of charged black holes only depends on the electric charge $Q$ and is mass independence. When $Q$ vanishes in the solution, it turns to mass dependence, even when including the effect of the un-physical ``virtual'' horizons. In this sense, the ``universal relation'' of this entropy product is destroyed. Then we introduce another kind of ``universal'' entropy relation, which only depends on the cosmological constant $Λ$ and the background topology $k$, and which does not depend on the conserved charges $Q$, nor even the mass $M$.

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preprint2014arXiv

The Entropy Sum of (A)dS Black Holes in Four and Higher Dimensions

We present the "entropy sum" relation of (A)dS charged black holes in higher dimensional Einstein-Maxwell gravity, $f(R)$ gravity, Gauss-Bonnet gravity and gauged supergravity. For their "entropy sum" with the necessary effect of the un-physical "virtual" horizon included, we conclude the general results that the cosmological constant dependence and Gauss-Bonnet coupling constant dependence do hold in both the four and six dimensions, while the "entropy sum" is always vanishing in odd dimensions. Furthermore, the "entropy sum" of all horizons is related to the geometry of the horizons in four and six dimensions. In these explicitly four cases, one also finds that the conserved charges $M$ (the mass), $Q$ (the charge from Maxwell field or supergravity) and the parameter $a$ (the angular momentum) play no role in the "entropy sum" relations.

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preprint2013arXiv

Comparison of classical and quantal calculations of helium three-body recombination

A general method to study classical scattering in $n$-dimension is developed. Through classical trajectory calculations, the three-body recombination is computed as a function of the collision energy for helium atoms, as an example. Quantum calculations are also performed for the $J^Π$ = $0^{+}$ symmetry of the three-body recombination rate in order to compare with the classical results, yielding good agreement for $E\gtrsim$ 1 K. The classical threshold law is derived and numerically confirmed for the Newtonian three-body recombination rate. Finally, a relationship is found between the quantum and classical three-body hard hypersphere elastic cross sections which is analogous to the well-known shadow scattering in two-body collisions.

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preprint2012arXiv

Electron Density Dependence of in-plane Spin Relaxation Anisotropy in GaAs/AlGaAs Two-Dimensional Electron Gas

We investigated the spin dynamics of two-dimensional electrons in (001) GaAs/AlGaAs heterostructure using the time resolved Kerr rotation technique under a transverse magnetic field. The in-plane spin lifetime is found to be anisotropic below 150k due to the interference of Rashba and Dresselhaus spin-orbit coupling and D'yakonov-Perel' spin relaxation. The ratio of in-plane spin lifetimes is measured directly as a function of temperature and pump power, showing that the electron density in 2DEG channel strongly affects the Rashba spin-orbit coupling.

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preprint2012arXiv

Nonequilibrium electronic transport through a polymer chain: Role of solitons

Nonequilibrium electronic transport through a polymer chain is investigated by the scattering state operator method. The polymer chain is described by an electron-lattice coupling model and its two ends are connected with metal electrodes of different chemical potentials. The scattering states are shown to be a set of complete eigenstates of electrons in the system at nonequilibrium steady state. With the method, we show that the nonequilibrium Peierls transition (NEPT) does not survive the lattice relaxation and the soliton-antisoliton pair excitations. Furthermore the electronic transport though the chain is shown to be accomplished through the soliton-lattice energy band.

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preprint2012arXiv

Origin of the Three-body Parameter Universality in Efimov Physics

In recent years extensive theoretical and experimental studies of universal few-body physics have led to advances in our understanding of universal Efimov physics [1]. The Efimov effect, once considered a mysterious and esoteric effect, is today a reality that many experiments in ultracold quantum gases have successfully observed and continued to explore [2-14]. Whereas theory was the driving force behind our understanding of Efimov physics for decades, recent experiments have contributed an unexpected discovery. Specifically, measurements have found that the so-called three-body parameter determining several properties of the system is universal, even though fundamental assumptions in the theory of the Efimov effect suggest that it should be a variable property that depends on the precise details of the short-range two- and three-body interactions. The present Letter resolves this apparent contradiction by elucidating unanticipated implications of the two-body interactions. Our study shows that the three-body parameter universality emerges because a universal effective barrier in the three-body potentials prevents the three particles from simultaneously getting close to each other. Our results also show limitations on this universality, as it is more likely to occur for neutral atoms and less likely to extend to light nuclei.

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preprint2012arXiv

Rydberg states of triatomic hydrogen and deuterium

The triatomic hydrogen ion (H$_3^+$) has spurred tremendous interest in astrophysics in recent decades, and Rydberg states of H$_3$ have also maintained an important role for understanding H$_3^+$ experiments. In a previous study [J. Chem. Phys. \textbf{133}, 234302 (2010)], radiative transitions between neutral H$_3$ Rydberg states were calculated at wavelengths near 7 microns, and could be compared with mid-infrared laser lines observed in hydrogen/rare gas discharges. The present study extends the investigation to wavelengths near 10 -- 13 microns. Rydberg states of D$_3$ are also treated.

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preprint2012arXiv

Small Count Privacy and Large Count Utility in Data Publishing

While the introduction of differential privacy has been a major breakthrough in the study of privacy preserving data publication, some recent work has pointed out a number of cases where it is not possible to limit inference about individuals. The dilemma that is intrinsic in the problem is the simultaneous requirement of data utility in the published data. Differential privacy does not aim to protect information about an individual that can be uncovered even without the participation of the individual. However, this lack of coverage may violate the principle of individual privacy. Here we propose a solution by providing protection to sensitive information, by which we refer to the answers for aggregate queries with small counts. Previous works based on $\ell$-diversity can be seen as providing a special form of this kind of protection. Our method is developed with another goal which is to provide differential privacy guarantee, and for that we introduce a more refined form of differential privacy to deal with certain practical issues. Our empirical studies show that our method can preserve better utilities than a number of state-of-the-art methods although these methods do not provide the protections that we provide.

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preprint2012arXiv

Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data

Operational network data, management data such as customer care call logs and equipment system logs, is a very important source of information for network operators to detect problems in their networks. Unfortunately, there is lack of efficient tools to automatically track and detect anomalous events on operational data, causing ISP operators to rely on manual inspection of this data. While anomaly detection has been widely studied in the context of network data, operational data presents several new challenges, including the volatility and sparseness of data, and the need to perform fast detection (complicating application of schemes that require offline processing or large/stable data sets to converge). To address these challenges, we propose Tiresias, an automated approach to locating anomalous events on hierarchical operational data. Tiresias leverages the hierarchical structure of operational data to identify high-impact aggregates (e.g., locations in the network, failure modes) likely to be associated with anomalous events. To accommodate different kinds of operational network data, Tiresias consists of an online detection algorithm with low time and space complexity, while preserving high detection accuracy. We present results from two case studies using operational data collected at a large commercial IP network operated by a Tier-1 ISP: customer care call logs and set-top box crash logs. By comparing with a reference set verified by the ISP's operational group, we validate that Tiresias can achieve >94% accuracy in locating anomalies. Tiresias also discovered several previously unknown anomalies in the ISP's customer care cases, demonstrating its effectiveness.

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preprint2012arXiv

Truss Decomposition in Massive Networks

The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also efficient to compute, k-truss represents the "core" of a k-core that keeps the key information of, while filtering out less important information from, the k-core. However, existing algorithms for computing k-truss are inefficient for handling today's massive networks. We first improve the existing in-memory algorithm for computing k-truss in networks of moderate size. Then, we propose two I/O-efficient algorithms to handle massive networks that cannot fit in main memory. Our experiments on real datasets verify the efficiency of our algorithms and the value of k-truss.

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preprint2012arXiv

Universal three-body parameter in heteronuclear atomic systems

In Efimov physics, a three-body parameter (3BP), previously regarded as nonuniversal, uniquely defines bound and scattering properties of three particles. A universal 3BP, however, have been recently shown in experiments and theory in ultracold homonuclear gases. Our present study further predicts a universal 3BP for heteronuclear atomic systems near broad Feshbach resonances, and provides physical interpretations for its origin. We show that for a system composed of two light and one heavy atoms, the physical origin of the universal 3BP is similar to the homonuclear case while for systems composed by one light and two heavy atoms the universality of the 3BP is now mostly controlled by the heavy-heavy interatomic properties. This new universality is explained by the universal properties of the van der Waals interactions in a simple Born-Oppenheimer (BO) picture. Finally, we show the numerically determined 3BPs for some combinations of alkali atoms used in ultracold experiments.

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preprint2012arXiv

Universal three-body recombination via resonant d-wave interactions

For a system of three identical bosons interacting via short-range forces, when two of the atoms are about to form a two-body s-wave dimer, there exists an infinite number of three-body bound states. This effect is the well-known Efimov effect. These three-body states (Efimov states) are found to be universal for ultracold atomic gases and the lowest Efimov state crosses the three-body break-up threshold when the s-wave two-body scattering length is $a \approx -9.73 r_{\rm vdW}$, $r_{\rm vdW}$ being the van der Waals length. This article focuses on a generalized version of this Efimov scenario, where two of the atoms are about to form a two-body d-wave dimer, which leads to strong d-wave interactions. In a recent paper [B. Gao, Phys. Rev. A. {\bf 62}, 050702(R) (2000)], Bo Gao has predicted that for broad resonances the d-wave dimer is always formed near $a \approx 0.956 r_{\rm vdW}$. Here we find that a single universal three-body state associated with the d-wave dimer is also formed near the three-body break-up threshold at $a \approx 1.09 r_{\rm vdW}$ and its signature can be found through enhancement of the three-body recombination. The three-body effective potential curves that are crucial for understanding the recombination dynamics are also calculated and analyzed. An improved method to calculate the couplings, effective potential curves, and recombination rate coefficients is presented.

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preprint2011arXiv

Laser-induced fluorescence studies of HfF+ produced by autoionization

Autoionization of Rydberg states of HfF, prepared using the optical-optical double resonance (OODR) technique, holds promise to create HfF+ in a particular Zeeman level of a rovibronic state for an electron electric dipole moment (eEDM) search. We characterize a vibronic band of Rydberg HfF at 54 cm-1 above the lowest ionization threshold and directly probe the state of the ions formed from this vibronic band by performing laser-induced fluorescence (LIF) on the ions. The Rydberg HfF molecules show a propensity to decay into only a few ion rotational states of a given parity and are found to preserve their orientation qualitatively upon autoionization. We show empirically that we can create 30% of the total ion yield in a particular |J+,M+> state and present a simplified model describing autoionization from a given Rydberg state that assumes no angular dynamics.

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preprint2011arXiv

Numerical study of three-body recombination for systems with many bound states

Three-body recombination processes are treated numerically for a system of three identical bosons. The two-body model potentials utilized support many bound states, a major leap in complexity that produces an intricate structure of sharp nonadiabatic avoided crossings in the three-body hyperradial adiabatic potentials at short distances. This model thus displays the usual difficulties of more realistic systems. To overcome the numerical challenges associated with sharp avoided crossings, the slow variable discretization (SVD) approach is adopted in the region of small hyperradii. At larger hyperradii, where the adiabatic potentials and couplings are smooth, the traditional adiabatic method suffices. Despite the high degree of complexity, recombination into deeply bound states behaves regularly due to the dominance of one decay pathway, resulting from the strong coupling between different recombination channels. Moreover, the usual Wigner threshold law must be modified for excited incident recombination channels.

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preprint2010arXiv

Quantum-defect analysis of $np$ and $nd$ $\rm{H_3}$ Rydberg energy levels

In this work, Rydberg energy levels of the triatomic hydrogen molecule ($\rm{H_3}$) are studied with multichannel quantum-defect theory. We extract the body-frame p-wave quantum defects from highly accurate \emph{ab initio} electronic potential surfaces and calculate the quantum defects of higher momentum states by using the long-range multipole potential model. To obtain laboratory-frame quantum defects, we perform a rovibrational-frame transformation with accurate rovibrational states of $\rm{H_3^+}$. Finally, we use the laboratory-frame quantum defects to calculate Rydberg energy levels for the fundamental neutral triatomic molecule $\rm{H_3}$.

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