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Yun Li

Yun Li 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-gascond-mat.mtrl-sciquant-phComputer Visioncond-mat.mes-hallMachine Learningmath.PRApplicationsComputation and LanguageMethodologyArtificial Intelligencecond-mat.othereess.SYmath.STphysics.soc-phStatistics TheorySystems and Controlastro-ph.COastro-ph.EPastro-ph.SRBiomoleculesDiscrete Mathematicseess.IVeess.SPhep-exhep-phInformation RetrievalInformation Theorymath-phmath.ITmath.MPmath.OCNetworking and Internet Architecturephysics.ao-phPopulations and Evolution

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Published work

49 published item(s)

preprint2026arXiv

Edge-Cloud Collaborative Reconstruction via Structure-Aware Latent Diffusion for Downstream Remote Sensing Perception

The exponential surge in high-resolution remote sensing data faces a severe bottleneck in satellite-to-ground transmission. Limited downlink bandwidth forces the use of extreme high-ratio compression, which irreversibly destroys high-frequency structural details essential for downstream machine perception tasks like object detection. While current super-resolution techniques attempt to recover these details, regression-based methods often yield over-smoothed textures, and generative diffusion models frequently introduce structural hallucinations that mislead detection systems. To address this trade-off, we propose the Structure-Aware Latent Diffusion (SALD) framework, an asymmetric edge-cloud collaborative SR system. At the resource-constrained edge, the system decouples imagery into a highly compressed low-frequency payload and a lightweight soft structural prior. Transmitting this decoupled representation minimizes bandwidth consumption. On the powerful cloud side, we introduce a Structure-Gated Large Kernel (SGLK) module and a Semantic-Guidance Engine (SGE) within the diffusion backbone. These modules leverage the transmitted structural priors to gate large-kernel convolutions, effectively capturing long-range dependencies inherent in aerial scenes while actively suppressing generative hallucinations. Extensive experiments on both the MSCM and UCMerced datasets demonstrate that, even under extreme bandwidth constraints, SALD achieves superior perceptual quality (LPIPS) and significantly enhances downstream performance in both scene classification and small-target detection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Chinese Idiom Paraphrasing

Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters. Due to the properties of non-compositionality and metaphorical meaning, Chinese Idioms are hard to be understood by children and non-native speakers. This study proposes a novel task, denoted as Chinese Idiom Paraphrasing (CIP). CIP aims to rephrase idioms-included sentences to non-idiomatic ones under the premise of preserving the original sentence's meaning. Since the sentences without idioms are easier handled by Chinese NLP systems, CIP can be used to pre-process Chinese datasets, thereby facilitating and improving the performance of Chinese NLP tasks, e.g., machine translation system, Chinese idiom cloze, and Chinese idiom embeddings. In this study, CIP task is treated as a special paraphrase generation task. To circumvent difficulties in acquiring annotations, we first establish a large-scale CIP dataset based on human and machine collaboration, which consists of 115,530 sentence pairs. We further deploy three baselines and two novel CIP approaches to deal with CIP problems. The results show that the proposed methods have better performances than the baselines based on the established CIP dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Learning-based Predictive Control of Battery Management for Frequency Regulation

This paper proposes a deep learning-based optimal battery management scheme for frequency regulation (FR) by integrating model predictive control (MPC), supervised learning (SL), reinforcement learning (RL), and high-fidelity battery models. By taking advantage of deep neural networks (DNNs), the derived DNN-approximated policy is computationally efficient in online implementation. The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation. The SL process speeds up the subsequent RL process by providing a good initialization. By utilizing RL algorithms, one prominent property of the proposed scheme is that it can learn from the data generated by simulating the FR policy on the high-fidelity battery simulator to adjust the DNN-approximated policy, which is originally based on low-fidelity battery model. A case study using real-world data of FR signals and prices is performed. Simulation results show that, compared to conventional MPC schemes, the proposed deep learning-based scheme can effectively achieve higher economic benefits of FR participation while maintaining lower online computational cost.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Effects from Hadronic Structure of Photon on $B\toϕγ$ and $B_s\to(ρ^0,ω)γ$ Decays

Using the perturbative QCD approach, we studied the effects from hadronic structure of photon on the pure annihilation rediative decays $B\toϕγ$ and $B_s\to(ρ^0,ω)γ$. These decays have small branching fractions due to the power suppression by the $Λ/m_B$, which make them very sensitive to the next-leading power corrections. The quark components and the related two-particle distribution amplitudes of a final state photon are introduced. The branching fractions can be enhanced remarkably by the factorizable and nonfactorizable emission diagrams. The branching fraction of $B\to ϕγ$ even increases by about 40 times, and those of $B_s \to ρ^0γ$ and $B_s \to ωγ$ are at the order of ${\cal O}(10^{-10})$. We also note that the ratio of branching fractions of $B_s \to ρ^0γ$ and $B_s \to ωγ$ is very sensitive to the effects from hadronic structure of photon. All above results could be tested in future.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Functional law of large numbers and central limit theorem for slow-fast McKean-Vlasov equations

In this paper, we study the asymptotic behavior of a fully-coupled slow-fast McKean-Vlasov stochastic system. Using the non-linear Poisson equation on Wasserstein space, we first establish the strong convergence in the averaging principle of the functional law of large numbers type. In particular, the diffusion coefficient of the slow process can depend on the distribution of the fast motion. Then we consider the stochastic fluctuations of the original system around its average, and prove that the normalized difference will converge weakly to a linear McKean-Vlasov Ornstein-Uhlenbeck type process, which can be viewed as a functional central limit theorem. Extra drift and diffusion coefficients involving the expectation are characterized explicitly. Furthermore, the optimal rates of the convergence are also obtained.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks

Emerging six generation (6G) is the integration of heterogeneous wireless networks, which can seamlessly support anywhere and anytime networking. But high Quality-of-Trust should be offered by 6G to meet mobile user expectations. Artificial intelligence (AI) is considered as one of the most important components in 6G. Then AI-based trust management is a promising paradigm to provide trusted and reliable services. In this article, a generative adversarial learning-enabled trust management method is presented for 6G wireless networks. Some typical AI-based trust management schemes are first reviewed, and then a potential heterogeneous and intelligent 6G architecture is introduced. Next, the integration of AI and trust management is developed to optimize the intelligence and security. Finally, the presented AI-based trust management method is applied to secure clustering to achieve reliable and real-time communications. Simulation results have demonstrated its excellent performance in guaranteeing network security and service quality.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Magnetotransport due to conductivity fluctuations in non-magnetic ZrTe2 nanoplates

Transition metal dichalcogenides with nontrivial band structures exhibit various fascinating physical properties and have sparked intensively research interest. Here, we performed systematic magnetotransport measurements on mechanical exfoliation prepared ZrTe2 nanoplates. We revealed that the negative longitudinal magnetoresistivity observed at high field region in the presence of parallel electric and magnetic fields could stem from the conductivity fluctuations due to the excess Zr in the nanoplates. In addition, the parametric plot, the planar Hall resistivity as function of the in-plane anisotropic magnetoresistivity, has an ellipse-shaped pattern with shifted orbital center, which further strengthen the evidence for the conductivity fluctuations. Our work provides some useful insights into transport phenomena in topological materials.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Near-real-time estimates of daily CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions. Carbon Monitor Cities provides daily, city-level estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP) were performed, and we estimate the overall uncertainty to be 21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries. A more complete description of this dataset is published in Scientific Data (https://doi.org/10.1038/s41597-022-01657-z).

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Poisson equation on Wasserstein space and diffusion approximations for McKean-Vlasov equation

We consider the fully-coupled McKean-Vlasov equation with multi-time-scale potentials, and all the coefficients depend on the distributions of both the slow component and the fast motion. By studying the smoothness of the solution of the non-linear Poisson equation on Wasserstein space, we derive the asymptotic limit as well as the quantitative error estimate of the convergence for the slow process. Extra homogenized drift term containing derivative in the measure argument of the solution of the Poisson equation appears in the limit, which seems to be new and is unique for systems involving the fast distribution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Prompt-Learning for Short Text Classification

In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks. Recently, as an effective method for tuning Pre-trained Language Models for specific downstream tasks, prompt-learning has attracted a vast amount of attention and research. The main intuition behind the prompt-learning is to insert the template into the input and convert the text classification tasks into equivalent cloze-style tasks. However, most prompt-learning methods expand label words manually or only consider the class name for knowledge incorporating in cloze-style prediction, which will inevitably incur omissions and bias in short text classification tasks. In this paper, we propose a simple short text classification approach that makes use of prompt-learning based on knowledgeable expansion. Taking the special characteristics of short text into consideration, the method can consider both the short text itself and class name during expanding label words space. Specifically, the top $N$ concepts related to the entity in the short text are retrieved from the open Knowledge Graph like Probase, and we further refine the expanded label words by the distance calculation between selected concepts and class labels. Experimental results show that our approach obtains obvious improvement compared with other fine-tuning, prompt-learning, and knowledgeable prompt-tuning methods, outperforming the state-of-the-art by up to 6 Accuracy points on three well-known datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific. Each dataset may be limited to a specific range of symptoms, overall disease severity, and demographic attributes; further, dataset formats may differ, impacting model performance. This paper proposes a side-aware meta-learning for cross-dataset tinnitus diagnosis, which can effectively classify tinnitus in subjects of divergent ages and genders from different data collection processes. Owing to the superiority of meta-learning, our method does not rely on large-scale datasets like conventional deep learning models. Moreover, we design a subject-specific training process to assist the model in fitting the data pattern of different patients or healthy people. Our method achieves a high accuracy of 73.8\% in the cross-dataset classification. We conduct an extensive analysis to show the effectiveness of side information of ears in enhancing model performance and side-aware meta-learning in improving the quality of the learned features.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Weight-dependent Gates for Network Pruning

In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. This paper argues that the pruning decision should depend on the convolutional weights, and thus proposes novel weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary gates to prune or keep the filters automatically. To prune the network under efficiency constraints, a switchable Efficiency Module is constructed to predict the hardware latency or FLOPs of candidate pruned networks. Combined with the proposed Efficiency Module, W-Gates can perform filter pruning in an efficiency-aware manner and achieve a compact network with a better accuracy-efficiency trade-off. We have demonstrated the effectiveness of the proposed method on ResNet34, ResNet50, and MobileNet V2, respectively achieving up to 1.33/1.28/1.1 higher Top-1 accuracy with lower hardware latency on ImageNet. Compared with state-of-the-art methods, W-Gates also achieves superior performance.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Generative Adversarial U-Net for Domain-free Medical Image Augmentation

The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. Without a sufficient number of training samples, deep learning based models are very likely to suffer from over-fitting problem. The common solution is image manipulation such as image rotation, cropping, or resizing. Those methods can help relieve the over-fitting problem as more training samples are introduced. However, they do not really introduce new images with additional information and may lead to data leakage as the test set may contain similar samples which appear in the training set. To address this challenge, we propose to generate diverse images with generative adversarial network. In this paper, we develop a novel generative method named generative adversarial U-Net , which utilizes both generative adversarial network and U-Net. Different from existing approaches, our newly designed model is domain-free and generalizable to various medical images. Extensive experiments are conducted over eight diverse datasets including computed tomography (CT) scan, pathology, X-ray, etc. The visualization and quantitative results demonstrate the efficacy and good generalization of the proposed method on generating a wide array of high-quality medical images.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Task Aligned Generative Meta-learning for Zero-shot Learning

Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer the correlation between visual features and attributes to train the classifier for unseen classes. However, such models may have a strong bias towards seen classes during training. Meta-learning has been introduced to mitigate the basis, but meta-ZSL methods are inapplicable when tasks used for training are sampled from diverse distributions. In this regard, we propose a novel Task-aligned Generative Meta-learning model for Zero-shot learning (TGMZ). TGMZ mitigates the potentially biased training and enables meta-ZSL to accommodate real-world datasets containing diverse distributions. TGMZ incorporates an attribute-conditioned task-wise distribution alignment network that projects tasks into a unified distribution to deliver an unbiased model. Our comparisons with state-of-the-art algorithms show the improvements of 2.1%, 3.0%, 2.5%, and 7.6% achieved by TGMZ on AWA1, AWA2, CUB, and aPY datasets, respectively. TGMZ also outperforms competitors by 3.6% in generalized zero-shot learning (GZSL) setting and 7.9% in our proposed fusion-ZSL setting.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Agglomerative Neural Networks for Multi-view Clustering

Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, the pairwise comparison cannot portray the inter-view relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present Agglomerative Neural Network (ANN) based on Constrained Laplacian Rank to cluster multi-view data directly while avoiding a dedicated postprocessing step (e.g., using K-means). We further extend ANN with learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multi-view clustering approaches on four popular datasets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures and extensibility in our case study and explain its robustness and effectiveness of data-driven modifications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Conductive Domain Walls in Non-Oxide Ferroelectrics Sn2P2S6

The conductive domain wall (CDW) is extensively investigated in ferroelectrics, which can be considered as a quasi-two-dimensional reconfigurable conducting channel embedded into an insulating material. Therefore, it is highly important for the application of ferroelectric nanoelectronics. Hitherto, most CDW investigations are restricted in oxides, and limited work has been reported in non-oxides to the contrary. Here, by successfully synthesizing the non-oxide ferroelectric Sn2P2S6 single crystal, we observed and confirmed the domain wall conductivity by using different scanning probe techniques which origins from the nature of inclined domain walls. Moreover, the domains separated by CDW also exhibit distinguishable electrical conductivity due to the interfacial polarization charge with opposite signs. The result provides a novel platform for understanding electrical conductivity behavior of the domains and domain walls in non-oxide ferroelectrics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LSBert: A Simple Framework for Lexical Simplification

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. In this paper, we propose a lexical simplification framework LSBert based on pretrained representation model Bert, that is capable of (1) making use of the wider context when both detecting the words in need of simplification and generating substitue candidates, and (2) taking five high-quality features into account for ranking candidates, including Bert prediction order, Bert-based language model, and the paraphrase database PPDB, in addition to the word frequency and word similarity commonly used in other LS methods. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and obtains obvious improvement compared with these baselines, outperforming the state-of-the-art by 29.8 Accuracy points on three well-known benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Matching methods for obtaining survival functions to estimate the effect of a time-dependent treatment

In observational studies of survival time featuring a binary time-dependent treatment, the hazard ratio (an instantaneous measure) is often used to represent the treatment effect. However, investigators are often more interested in the difference in survival functions. We propose semiparametric methods to estimate the causal effect of treatment among the treated with respect to survival probability. The objective is to compare post-treatment survival with the survival function that would have been observed in the absence of treatment. For each patient, we compute a prognostic score (based on the pre-treatment death hazard) and a propensity score (based on the treatment hazard). Each treated patient is then matched with an alive, uncensored and not-yet-treated patient with similar prognostic and/or propensity scores. The experience of each treated and matched patient is weighted using a variant of Inverse Probability of Censoring Weighting to account for the impact of censoring. We propose estimators of the treatment-specific survival functions (and their difference), computed through weighted Nelson-Aalen estimators. Closed-form variance estimators are proposed which take into consideration the potential replication of subjects across matched sets. The proposed methods are evaluated through simulation, then applied to estimate the effect of kidney transplantation on survival among end-stage renal disease patients using data from a national organ failure registry.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Operator level hard-to-soft transition for $β$-ensembles

The soft and hard edge scaling limits of $β$-ensembles can be characterized as the spectra of certain random Sturm-Liouville operators. It has been shown that by tuning the parameter of the hard edge process one can obtain the soft edge process as a scaling limit. We prove that this limit can be realized on the level of the corresponding random operators. More precisely, the random operators can be coupled in a way so that the scaled versions of the hard edge operators converge to the soft edge operator a.s. in the norm resolvent sense.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Taking the pulse of COVID-19: A spatiotemporal perspective

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Impact of Unmeasured Within- and Between-Cluster Confounding on the Bias of Effect Estimators from Fixed Effect, Mixed effect and Instrumental Variable Models

Instrumental variable methods are popular choices in combating unmeasured confounding to obtain less biased effect estimates. However, we demonstrate that alternative methods may give less biased estimates depending on the nature of unmeasured confounding. Treatment preferences of clusters (e.g., physician practices) are the most f6requently used instruments in instrumental variable analyses (IVA). These preference-based IVAs are usually conducted on data clustered by region, hospital/facility, or physician, where unmeasured confounding often occurs within or between clusters. We aim to quantify the impact of unmeasured confounding on the bias of effect estimators in IVA, as well as alternative methods including ordinary least squares regression, linear mixed models (LMM) and fixed effect models (FE) to study the effect of a continuous exposure (e.g., treatment dose). We derive bias formulae of estimators from these four methods in the presence of unmeasured within- and/or between-cluster confounders. We show that IVAs can provide consistent estimates when unmeasured within-cluster confounding exists, but not when between-cluster confounding exists. On the other hand, FEs and LMMs can provide consistent estimates when unmeasured between-cluster confounding exits, but not for within-cluster confounding. Whether IVAs are advantageous in reducing bias over FEs and LMMs depends on the extent of unmeasured within-cluster confounding relative to between-cluster confounding. Furthermore, the impact of unmeasured between-cluster confounding on IVA estimates is larger than the impact of unmeasured within-cluster confounding on FE and LMM estimates. We illustrate these methods through data applications. Our findings provide guidance for choosing appropriate methods to combat the dominant types of unmeasured confounders and help interpret statistical results in the context of unmeasured confounding

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preprint2020arXiv

Using Multiple Imputation to Classify Potential Outcomes Subgroups

With medical tests becoming increasingly available, concerns about over-testing and over-treatment dramatically increase. Hence, it is important to understand the influence of testing on treatment selection in general practice. Most statistical methods focus on average effects of testing on treatment decisions. However, this may be ill-advised, particularly for patient subgroups that tend not to benefit from such tests. Furthermore, missing data are common, representing large and often unaddressed threats to the validity of statistical methods. Finally, it is desirable to conduct analyses that can be interpreted causally. We propose to classify patients into four potential outcomes subgroups, defined by whether or not a patient's treatment selection is changed by the test result and by the direction of how the test result changes treatment selection. This subgroup classification naturally captures the differential influence of medical testing on treatment selections for different patients, which can suggest targets to improve the utilization of medical tests. We can then examine patient characteristics associated with patient potential outcomes subgroup memberships. We used multiple imputation methods to simultaneously impute the missing potential outcomes as well as regular missing values. This approach can also provide estimates of many traditional causal quantities. We find that explicitly incorporating causal inference assumptions into the multiple imputation process can improve the precision for some causal estimates of interest. We also find that bias can occur when the potential outcomes conditional independence assumption is violated; sensitivity analyses are proposed to assess the impact of this violation. We applied the proposed methods to examine the influence of 21-gene assay, the most commonly used genomic test, on chemotherapy selection among breast cancer patients.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Bound state and non-Markovian dynamics of a quantum emitter around a surface plasmonic nanostructure

A bound state between a quantum emitter (QE) and surface plasmon polaritons (SPPs) can be formed, where the QE is partially stabilized in its excited state. We put forward a general approach for calculating the energy level shift at a negative frequency $ω$, which is just the negative of the nonresonant part for the energy level shift at positive frequency $-ω$. We also propose an efficient formalism for obtaining the long-time value of the excited-state population without calculating the eigenfrequency of the bound state or performing a time evolution of the system, in which the probability amplitude for the excited state in the steady limit is equal to one minus the integral of the evolution spectrum over the positive frequency range. With the above two quantities obtained, we show that the non-Markovian decay dynamics in the presence of a bound state can be obtained by the method based on the Green's function expression for the evolution operator. A general criterion for identifying the existence of a bound state is presented. These are numerically demonstrated for a QE located around a nanosphere and in a gap plasmonic nanocavity. These findings are instructive in the fields of coherent light-matter interactions.

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preprint2016arXiv

Combinatorial Constructions of Optimal $(m, n,4,2)$ Optical Orthogonal Signature Pattern Codes

Optical orthogonal signature pattern codes (OOSPCs) play an important role in a novel type of optical code-division multiple-access (CDMA) network for 2-dimensional image transmission. There is a one-to-one correspondence between an $(m, n, w, λ)$-OOSPC and a $(λ+1)$-$(mn,w,1)$ packing design admitting an automorphism group isomorphic to $\mathbb{Z}_m\times \mathbb{Z}_n$. In 2010, Sawa gave the first infinite class of $(m, n, 4, 2)$-OOSPCs by using $S$-cyclic Steiner quadruple systems. In this paper, we use various combinatorial designs such as strictly $\mathbb{Z}_m\times \mathbb{Z}_n$-invariant $s$-fan designs, strictly $\mathbb{Z}_m\times \mathbb{Z}_n$-invariant $G$-designs and rotational Steiner quadruple systems to present some constructions for $(m, n, 4, 2)$-OOSPCs. As a consequence, our new constructions yield more infinite families of optimal $(m, n, 4, 2)$-OOSPCs. Especially, we shall see that in some cases an optimal $(m, n, 4, 2)$-OOSPC can not achieve the Johnson bound.

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preprint2016arXiv

POLARBEAR Constraints on Cosmic Birefringence and Primordial Magnetic Fields

We constrain anisotropic cosmic birefringence using four-point correlations of even-parity $E$-mode and odd-parity $B$-mode polarization in the cosmic microwave background measurements made by the POLARization of the Background Radiation (POLARBEAR) experiment in its first season of observations. We find that the anisotropic cosmic birefringence signal from any parity-violating processes is consistent with zero. The Faraday rotation from anisotropic cosmic birefringence can be compared with the equivalent quantity generated by primordial magnetic fields if they existed. The POLARBEAR nondetection translates into a 95% confidence level (C.L.) upper limit of 93 nanogauss (nG) on the amplitude of an equivalent primordial magnetic field inclusive of systematic uncertainties. This four-point correlation constraint on Faraday rotation is about 15 times tighter than the upper limit of 1380 nG inferred from constraining the contribution of Faraday rotation to two-point correlations of $B$-modes measured by Planck in 2015. Metric perturbations sourced by primordial magnetic fields would also contribute to the $B$-mode power spectrum. Using the POLARBEAR measurements of the $B$-mode power spectrum (two-point correlation), we set a 95% C.L. upper limit of 3.9 nG on primordial magnetic fields assuming a flat prior on the field amplitude. This limit is comparable to what was found in the Planck 2015 two-point correlation analysis with both temperature and polarization. We perform a set of systematic error tests and find no evidence for contamination. This work marks the first time that anisotropic cosmic birefringence or primordial magnetic fields have been constrained from the ground at subdegree scales.

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preprint2016arXiv

Probing Carrier Transport and Structure-property Relationship of Highly Ordered Organic Semiconductors at Two-dimensional Limit

One of the basic assumptions in organic field-effect transistors, the most fundamental device unit in organic electronics, is that charge transport occurs two-dimensionally in the first few molecular layers near the dielectric interface. Although the mobility of bulk organic semiconductors has increased dramatically, direct probing of intrinsic charge transport in the two-dimensional limit has not been possible due to excessive disorders and traps in ultrathin organic thin films. Here, highly ordered mono- to tetra-layer pentacene crystals are realized by van der Waals (vdW) epitaxy on hexagonal BN. We find that the charge transport is dominated by hopping in the first conductive layer, but transforms to band-like in subsequent layers. Such abrupt phase transition is attributed to strong modulation of the molecular packing by interfacial vdW interactions, as corroborated by quantitative structural characterization and density functional theory calculations. The structural modulation becomes negligible beyond the second conductive layer, leading to a mobility saturation thickness of only ~3nm. Highly ordered organic ultrathin films provide a platform for new physics and device structures (such as heterostructures and quantum wells) that are not possible in conventional bulk crystals.

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preprint2015arXiv

A van der Waals pn heterojunction with organic/inorganic semiconductors

van der Waals (vdW) heterojunctions formed by two-dimensional (2D) materials have attracted tremendous attention due to their excellent electrical/optical properties and device applications. However, current 2D heterojunctions are largely limited to atomic crystals, and hybrid organic/inorganic structures are rarely explored. Here, we fabricate hybrid 2D heterostructures with p-type dioctylbenzothienobenzothiophene (C8-BTBT) and n-type MoS2. We find that few-layer C8-BTBT molecular crystals can be grown on monolayer MoS2 by vdW epitaxy, with pristine interface and controllable thickness down to monolayer. The operation of the C8-BTBT/MoS2 vertical heterojunction devices is highly tunable by bias and gate voltages between three different regimes: interfacial recombination, tunneling and blocking. The pn junction shows diode-like behavior with rectifying ratio up to 105 at the room temperature. Our devices also exhibit photovoltaic responses with power conversion efficiency of 0.31% and photoresponsivity of 22mA/W. With wide material combinations, such hybrid 2D structures will offer possibilities for opto-electronic devices that are not possible from individual constituents.

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preprint2015arXiv

Berry Curvature of interacting bosons in a honeycomb lattice

We consider soft-core bosons with onsite interaction loaded in the honeycomb lattice with different site energies for the two sublattices. Using both a mean-field approach and quantum Monte-Carlo simulations, we show that the topology of the honeycomb lattice results in a non-vanishing Berry curvature for the band structure of the single-particle excitations of the system. This Berry curvature induces an anomalous Hall effect. It is seen by studying the time evolution of a wavepacket, namely a superfluid ground state in a harmonic trap, subjected either to a constant force (Bloch oscillations) or to a sudden shift of the trap center.

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preprint2015arXiv

Bose-Einstein condensation with spin-orbit coupling

The recent realization of synthetic spin-orbit coupling represents an outstanding achievement in the physics of ultracold quantum gases. In this review we explore the properties of a spin-orbit-coupled Bose-Einstein condensate with equal Rashba and Dresselhaus strengths. This system presents a rich phase diagram, which exhibits a tricritical point separating a zero-momentum phase, a spin-polarized plane-wave phase, and a stripe phase. In the stripe phase translational invariance is spontaneously broken, in analogy with supersolids. Spin-orbit coupling also strongly affects the dynamics of the system. In particular, the excitation spectrum exhibits intriguing features, including the suppression of the sound velocity, the emergence of a roton minimum in the plane-wave phase, and the appearance of a double gapless band structure in the stripe phase. Finally, we discuss a combined procedure to make the stripes visible and stable, thus allowing for a direct experimental detection.

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preprint2015arXiv

Stable dilute supersolid of two-dimensional dipolar bosons

We consider two-dimensional bosonic dipoles oriented perpendicularly to the plane. On top of the usual two-body contact and long-range dipolar interactions we add a contact three-body repulsion as expected, in particular, for dipoles in the bilayer geometry with tunneling. The three-body repulsion is crucial for stabilizing the system, and we show that our model allows for stable continuous space supersolid states in the dilute regime and calculate the zero-temperature phase diagram.

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preprint2015arXiv

The dynamical evolution of low-mass hydrogen-burning stars, brown dwarfs and planetary-mass objects formed through disc fragmentation

Theory and simulations suggest that it is possible to form low-mass hydrogen-burning stars, brown dwarfs and planetary-mass objects via disc fragmentation. As disc fragmentation results in the formation of several bodies at comparable distances to the host star, their orbits are generally unstable. Here, we study the dynamical evolution of these objects. We set up the initial conditions based on the outcomes of the SPH simulations of Stamatellos & Whitworth, and for comparison we also study the evolution of systems resulting from lower-mass fragmenting discs. We refer to these two sets of simulations as set 1 and set 2. At 10 Myr, approximately half of the host stars have one companion left, and approximately 22% (set 1) to 9.8% (set 2) of the host stars are single. Systems with multiple secondaries in relatively stable configurations are common (about 30% and 44%, respectively). The majority of the companions are ejected within 1 Myr with velocities mostly below 5 km/s, with some runaway escapers with velocities over 30 km/s. About 6% (set 1) and 2% (set 2) of the companions pair up into very low-mass binary systems. The majority of these pairs escape as very low-mass binaries, while others remain bound to the host star in hierarchical configurations (often with retrograde inner orbits). Physical collisions with the host star (0.43 and 0.18 events per host star for set 1 and set 2) and between companions (0.08 and 0.04 events per host star for set 1 and set 2) are relatively common and their frequency increases with increasing disc mass. Our study predicts observable properties of very low-mass binaries, low-mass hierarchical systems, the brown dwarf desert, and free-floating brown dwarfs and planetary-mass objects in and near young stellar groupings, which can be used to distinguish between different formation scenarios of very low-mass stars, brown dwarfs and planetary-mass objects.

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preprint2015arXiv

Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models

Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of coded projections using a spatial light modulator (e.g., a digital micro-mirror device) and a few optical sensors. This approach finds use in imaging applications where full-frame sensors are either too expensive (e.g., for short-wave infrared wavelengths) or unavailable. Existing SMC systems reconstruct static scenes using techniques from compressive sensing (CS). For videos, however, existing acquisition and recovery methods deliver poor quality. In this paper, we propose the CS multi-scale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs. Our framework features novel sensing matrices that enable the efficient computation of a low-resolution video preview, while enabling high-resolution video recovery using convex optimization. To further improve the quality of the reconstructed videos, we extract optical-flow estimates from the low-resolution previews and impose them as constraints in the recovery procedure. We demonstrate the efficacy of our CS-MUVI framework for a host of synthetic and real measured SMC video data, and we show that high-quality videos can be recovered at roughly $60\times$ compression.

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preprint2014arXiv

Approach for making visible and stable stripes in a spin-orbit-coupled Bose-Einstein superfluid

The striped phase exhibited by a spin-$1/2$ Bose-Einstein condensate with spin-orbit coupling is characterized by the spontaneous breaking of two continuous symmetries: gauge and translational symmetry. This is a peculiar feature of supersolids and is the consequence of interaction effects. We propose an approach to produce striped configurations with high-contrast fringes, making their experimental detection in atomic gases a realistic perspective. Our approach, whose efficiency is directly confirmed by three-dimensional Gross-Pitaevskii simulations, is based on the space separation of the two spin components into a two-dimensional bilayer configuration, causing the reduction of the effective interspecies interaction and the increase of the stability of the striped phase. We also explore the effect of a $π/2$ Bragg pulse, causing the increase of the fringe wavelength, and of a $π/2$ rf pulse, revealing the coherent nature of the order parameter in the spin channel.

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preprint2014arXiv

Arginine-Phosphate Salt Bridges Between Histones and DNA: Intermolecular Actuators that Control Nucleosome Architecture

Structural bioinformatics and van der Waals density functional theory are combined to investigate the mechanochemical impact of a major class of histone-DNA interactions, namely the formation of salt bridges between arginine residues in histones and phosphate groups on the DNA backbone. Principal component analysis reveals that the configurational fluctuations of the sugar-phosphate backbone display sequence-specific variability, and clustering of nucleosomal crystal structures identifies two major salt bridge configurations: a monodentate form in which the arginine end-group guanidinium only forms one hydrogen bond with the phosphate, and a bidentate form in which it forms two. Density functional theory calculations highlight that the combination of sequence, denticity and salt bridge positioning enable the histones to tunably activate specific backbone deformations via mechanochemical stress. The results suggest that selection for specific placements of van der Waals contacts, with high-precision control of the spatial distribution of intermolecular forces, may serve as an underlying evolutionary design principle for the structure and function of nucleosomes, a conjecture that is corroborated by previous experimental studies.

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preprint2014arXiv

Two-dimensional Quasi-Freestanding Molecular Crystals for High-Performance Organic Field-Effect Transistors

Two-dimensional atomic crystals are extensively studied in recent years due to their exciting physics and device applications. However, a molecular counterpart, with scalable processability and competitive device performance, is still challenging. Here, we demonstrate that high-quality few-layer dioctylbenzothienobenzothiophene molecular crystals can be grown on graphene or boron nitride substrate via van der Waals epitaxy, with precisely controlled thickness down to monolayer, large-area single crystal, low process temperature and patterning capability. The crystalline layers are atomically smooth and effectively decoupled from the substrate due to weak van der Waals interactions, affording a pristine interface for high-performance organic transistors. As a result, monolayer dioctylbenzothienobenzothiophene molecular crystal field-effect transistors on boron nitride show record-high carrier mobility up to 10cm2V-1s-1 and aggressively scaled saturation voltage around 1V. Our work unveils an exciting new class of two-dimensional molecular materials for electronic and optoelectronic applications.

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preprint2014arXiv

Universal Outlier Hypothesis Testing

Outlier hypothesis testing is studied in a universal setting. Multiple sequences of observations are collected, a small subset of which are outliers. A sequence is considered an outlier if the observations in that sequence are distributed according to an ``outlier'' distribution, distinct from the ``typical'' distribution governing the observations in all the other sequences. Nothing is known about the outlier and typical distributions except that they are distinct and have full supports. The goal is to design a universal test to best discern the outlier sequence(s). It is shown that the generalized likelihood test is universally exponentially consistent under various settings. The achievable error exponent is also characterized. In the other settings, it is also shown that there cannot exist any universally exponentially consistent test.

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preprint2014arXiv

Universal Sequential Outlier Hypothesis Testing

Universal outlier hypothesis testing is studied in a sequential setting. Multiple observation sequences are collected, a small subset of which are outliers. A sequence is considered an outlier if the observations in that sequence are generated by an "outlier" distribution, distinct from a common "typical" distribution governing the majority of the sequences. Apart from being distinct, the outlier and typical distributions can be arbitrarily close. The goal is to design a universal test to best discern all the outlier sequences. A universal test with the flavor of the repeated significance test is proposed and its asymptotic performance is characterized under various universal settings. The proposed test is shown to be universally consistent. For the model with identical outliers, the test is shown to be asymptotically optimal universally when the number of outliers is the largest possible and with the typical distribution being known, and its asymptotic performance otherwise is also characterized. An extension of the findings to the model with multiple distinct outliers is also discussed. In all cases, it is shown that the asymptotic performance guarantees for the proposed test when neither the outlier nor typical distribution is known converge to those when the typical distribution is known.

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preprint2013arXiv

Modulation of electron carrier density at the n-type LaAlO3/SrTiO3 interface by water adsorption

We investigate energetic stability and dissociation dynamics of water adsorption at the LaAlO3 surface of the n-type LaAlO3/SrTiO3 (LAO/STO) interface and its effect on electronic properties of the interface by carrying out first-principles electronic structure calculations. In an ambient atmosphere at room temperature the configuration of 1 monolayer (ML) of water molecules including 3/4 ML of dissociated water molecules adsorbed at the surface is found to be most stable, the configuration of 1 ML of dissociated water molecules is metastable. Water molecule dissociation induces a shift-up of the valence band maximum (VBM) of the LAO surface, reducing the gap between the VBM of the LAO surface and the conduction band minimum of the STO. For the LAO/STO interface with three LAO unit-cell layers, once the coverage of dissociated water molecules reaches 1/2 ML the gap is closed, the interface becomes metallic and the carrier density at the LAO/STO interface increases with increasing the coverage of dissociated water molecules. Our findings suggest two ways to control the conductivity at the LAO/STO interface: (I) insulator-metal transition by adsorbing a mount of water at the bare surface; (II) carrier density change by the transition between the most stable and the metastable adsorption configurations for 1 ML coverage in an ambient atmosphere at room temperature.

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preprint2013arXiv

Superstripes and the excitation spectrum of a spin-orbit-coupled Bose-Einstein condensate

Using Bogoliubov theory we calculate the excitation spectrum of a spinor Bose-Einstein condensed gas with equal Rashba and Dresselhaus spin-orbit coupling in the stripe phase. The emergence of a double gapless band structure is pointed out as a key signature of Bose-Einstein condensation and of the spontaneous breaking of translational invariance symmetry. In the long wavelength limit the lower and upper branches exhibit, respectively, a clear spin and density nature. For wave vectors close to the first Brillouin zone, the lower branch acquires an important density character responsible for the divergent behavior of the structure factor and of the static response function, reflecting the occurrence of crystalline order. The sound velocities are calculated as functions of the Raman coupling for excitations propagating orthogonal and parallel to the stripes. Our predictions provide new perspectives for the identification of supersolid phenomena in ultracold atomic gases.

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preprint2012arXiv

Anisotropic dynamics of a spin-orbit coupled Bose-Einstein condensate

By calculating the density response function we identify the excitation spectrum of a Bose-Einstein condensate with equal Rashba and Dresselhaus spin-orbit coupling. We find that the velocity of sound along the direction of spin-orbit coupling is deeply quenched and vanishes when one approaches the second-order phase transition between the plane wave and the zero momentum quantum phases. We also point out the emergence of a roton minimum in the excitation spectrum for small values of the Raman coupling, providing the onset of the transition to the stripe phase. Our findings point out the occurrence of a strong anisotropy in the dynamic behavior of the gas. A hydrodynamic description accounting for the collective oscillations in both uniform and harmonically trapped gases is also derived.

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preprint2012arXiv

Quantum tri-criticality and phase transitions in spin-orbit coupled Bose-Einstein condensates

We consider a spin-orbit coupled configuration of spin-1/2 interacting bosons with equal Rashba and Dresselhaus couplings. The phase diagram of the system is discussed with special emphasis to the role of the interaction treated in the mean-field approximation. For a critical value of the density and of the Raman coupling we predict the occurrence of a characteristic tri-critical point separating the spin mixed, the phase separated and the single minimum states of the Bose gas. The corresponding quantum phases are investigated analyzing the momentum distribution, the longitudinal and transverse spin-polarization and the emergence of density fringes. The effect of harmonic trapping as well as the role of the breaking of spin symmetry in the interaction Hamiltonian are also discussed.

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preprint2012arXiv

Sum rules, dipole oscillation and spin polarizability of a spin-orbit coupled quantum gas

Using a sum rule approach we investigate the dipole oscillation of a spin-orbit coupled Bose-Einstein condensate confined in a harmonic trap. The crucial role played by the spin polarizability of the gas is pointed out. We show that the lowest dipole frequency exhibits a characteristic jump at the transition between the stripe and spin-polarized phase. Near the second order transition between the spin-polarized and the single minimum phase the lowest frequency is vanishingly small for large condensates, reflecting the divergent behavior of the spin polarizability. We compare our results with recent experimental measurements as well as with the predictions of effective mass approximation.

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preprint2011arXiv

Limit of Spin Squeezing in Finite Temperature Bose-Einstein Condensates

We show that, at finite temperature, the maximum spin squeezing achievable using interactions in Bose-Einstein condensates has a finite limit when the atom number $N\to \infty$ at fixed density and interaction strength. We calculate the limit of the squeezing parameter for a spatially homogeneous system and show that it is bounded from above by the initial non-condensed fraction.

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preprint2011arXiv

Universal contact and collective excitations of a strongly interacting Fermi gas

We study the relationship between Tan's contact parameter and the macroscopic dynamic properties of an ultracold trapped gas, such as the frequencies of the collective oscillations and the propagation of sound in one-dimensional (1D) configurations. We find that the value of the contact, extracted from the most recent low-temperature measurements of the equation of state near unitarity, reproduces with accuracy the experimental values of the collective frequencies of the radial breathing mode at the lowest temperatures. The available experiment results for the 1D sound velocities near unitarity are also investigated.

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preprint2010arXiv

Atom chip based generation of entanglement for quantum metrology

Atom chips provide a versatile `quantum laboratory on a microchip' for experiments with ultracold atomic gases. They have been used in experiments on diverse topics such as low-dimensional quantum gases, cavity quantum electrodynamics, atom-surface interactions, and chip-based atomic clocks and interferometers. A severe limitation of atom chips, however, is that techniques to control atomic interactions and to generate entanglement have not been experimentally available so far. Such techniques enable chip-based studies of entangled many-body systems and are a key prerequisite for atom chip applications in quantum simulations, quantum information processing, and quantum metrology. Here we report experiments where we generate multi-particle entanglement on an atom chip by controlling elastic collisional interactions with a state-dependent potential. We employ this technique to generate spin-squeezed states of a two-component Bose-Einstein condensate and show that they are useful for quantum metrology. The observed 3.7 dB reduction in spin noise combined with the spin coherence imply four-partite entanglement between the condensate atoms and could be used to improve an interferometric measurement by 2.5 dB over the standard quantum limit. Our data show good agreement with a dynamical multi-mode simulation and allow us to reconstruct the Wigner function of the spin-squeezed condensate. The techniques demonstrated here could be directly applied in chip-based atomic clocks which are currently being set up.

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preprint2010arXiv

Enhanced and reduced atom number fluctuations in a BEC splitter

We measure atom number statistics after splitting a gas of ultracold 87Rb atoms in a purely magnetic double-well potential created on an atom chip. Well below the critical temperature for Bose-Einstein condensation T_c, we observe reduced fluctuations down to -4.9dB below the atom shot noise level. Fluctuations rise to more than +3.8dB close to T_c, before reaching the shot noise level for higher temperatures. We use two-mode and classical field simulations to model these results. This allows us to confirm that the super-shot noise fluctuations directly originate from quantum statistics.

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preprint2010arXiv

Particle number fluctuations in a cloven trapped Bose gas at finite temperature

We study fluctuations in the atom number difference between two halves of a harmonically trapped Bose gas in three dimensions. We solve the problem analytically for non interacting atoms. In the interacting case we find an analytical solution in the Thomas-Fermi and high temperature limit in good agreement with classical field simulations. In the large system size limit, fluctuations in the number difference are maximal for a temperature $T\simeq 0.7 T_c$ where $T_c$ is the critical temperature, independently of the trap anisotropy. The occurrence of this maximum is due to an interference effect between the condensate and the non-condensed field.

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preprint2009arXiv

Polarization screening and induced carrier density at the interface of LaAlO$_3$ overlayer on SrTiO$_3$ (001)

We investigate the role of lattice polarization in determination of induced carrier density at the $n$-type interface of LaAlO$_3$ overlayer on SrTiO$_3$ (001) by carrying out density-functional-theory calculations. When no oxygen vacancy or defect is present, the magnitude of polarization screening in the LaAlO$_3$ layers is found to be correlated with the carrier charge induced at the interface. For the interfaces with a few LaAlO$_3$ layers, the induced charge carrier is compensated by the electrostatic screening and consequently its density remains far less than 0.5 electrons per unit cell.

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