BZPEERPapers, people, evidence and research paths
Menu

Discover

SearchFind papers, people, topics, institutions and opportunities from one place.GraphSee how papers, authors, topics and evidence connect.

Research tools

HomeContinue from the papers, people and topics that matter now.CollectionsTurn saved papers into reading lists, evidence maps and shareable context.ResearchReconstruct the path that led you from search to paper to question.CollaborationMove from a paper, topic or expert request into a focused thread.PublishCreate full-text research articles with metadata, formulas and versions.

Network

ExpertsFind specialists whose work and reviews explain why they can help.CommunitiesJoin research groups organized around papers, topics and shared evidence.

Opportunities

ListingsBrowse roles, calls and collaborations connected to real research context.

Account

Log inReturn to your saved papers, graph views and active threads.Create accountStart saving papers, building a research trail and joining teams.
Log inCreate account

Source author record

Nan Zhang

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

ResearcherUnclaimed source record
Databasesphysics.opticshep-thMethodologygr-qcphysics.soc-phSocial and Information Networksastro-ph.COComputationDistributed, Parallel, and Cluster Computingeess.SPhep-phMachine Learningastro-ph.EPastro-ph.GAcond-mat.mes-hallcond-mat.mtrl-scicond-mat.str-elData Structures and Algorithmseess.ASNetworking and Internet Architecturephysics.app-phq-fin.CPSound

Catalog footprint

What is connected

37works
24topics
4close collaborators

Actions

Connect this record

Log in to claim
Open graphBrowse works

Research graph

See the researcher in context

Open full explorer

Inspect adjacent papers, topics, institutions and collaborators without losing the researcher page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

37 published item(s)

preprint2026arXiv

A census of star-formation and gas mass tracers in two lensed $z \sim 4$ dusty star-forming galaxies

We present new and archival Atacama Large Millimeter/submillimeter Array (ALMA) observations of two strongly lensed dusty star-forming galaxies (DSFGs) selected from the South Pole Telescope survey, SPT0418-47 $(z = 4.225)$ and SPT2147-50 $(z = 3.760)$. We study the [C II], CO(7-6), [C I](2-1), and, in SPT0418-47, $p$-H$_2$O emission, which along with the underlying continuum (rest-frame 160 $μ$m and 380 $μ$m) are routinely used as tracers of gas mass and/or star-formation rate (SFR). We perform a pixel-by-pixel analysis of both sources in the image plane to study the resolved Kennicutt-Schmidt relation, finding generally good agreement between the slopes of the SFR versus gas mass surface density using the different tracers. Using lens modeling methods, we find that the dust emission is more compact than the line emission in both sources, with CO(7-6) and [C I](2-1) similar in extent and [C II] the most extended, reminiscent of recent findings of extended [C II] spatial distributions in galaxies at similar cosmic epochs. We develop the [C I](2-1) / CO(7-6) flux density ratio as an observable proxy for gas depletion timescale ($τ_{\rm dep}$), which can be applied to large samples of DSFGs, in lieu of more detailed inferences of this timescale which require analysis of observations at multiple wavelengths. Furthermore, the extended [C II] emission in both sources, compared to the total continuum and line emission, suggests that [C II], used in recent years as a molecular gas mass and SFR tracer in high-$z$ galaxies, may not always be a suitable tracer of these physical quantities.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Why Do Reasoning Models Lose Coverage? The Role of Data and Forks in the Road

Recent progress in large language models has led to the emergence of reasoning models, which have shown strong performance on complex tasks through specialized fine-tuning procedures. While these methods reliably improve pass@1 accuracy, prior works have observed that they show a coverage shrinkage behavior, where pass@k degrades relative to the base model. In this paper, we investigate the reasoning shrinkage arise under SFT-based post-training. We hypothesize that this behavior is driven by properties of the fine-tuning data, specifically related to decision points or "forks in the road" scenarios where model faces indecipherable patterns with multiple valid reasoning paths. To test this hypothesis, we design controlled case studies that simulate such decision-point settings, spanning indecipherable nodes in graph branching, and reasoning modes. By tracking post-training dynamics in these settings, we find that the shrinkage phenomenon is tightly correlated with the prevalence of decision-point scenarios in the training data. We also demonstrate that this shrinkage behavior can be partially mitigated through targeted data synthesis design of decision-points, and a more systematic diversity-encouraging decoding mechanism. Our findings identify data-centric factors as a key driver of shrinkage in reasoning models and highlight diversity-aware designs as an effective lever for controlling it.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Dynamic Service Scheduling and Resource Management in Energy-Harvesting Multi-access Edge Computing

Multi-access Edge Computing (MEC) delivers low-latency services by hosting applications near end-users. To promote sustainability, these systems are increasingly integrated with renewable Energy Harvesting (EH) technologies, enabling operation where grid electricity is unavailable. However, balancing the intermittent nature of harvested energy with dynamic user demand presents a significant resource allocation challenge. This work proposes an online strategy for an MEC system powered exclusively by EH to address this trade-off. Our strategy dynamically schedules computational tasks with dependencies and governs energy consumption through real-time decisions on server frequency scaling and service module migration. Experiments using real-world datasets demonstrate our algorithm's effectiveness in efficiently utilizing harvested energy while maintaining low service latency.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

For Intelligent and Higher Spectrum Efficiency: A Variable Packing Ratio Transmission System Based on Faster-than-Nyquist and Deep Learning

With the rapid development of various services in wireless communications, spectrum resource has become increasingly valuable. Faster than Nyquist (FTN) signaling, proposed in the 1970s, is a promising paradigm for improving spectrum utilization. This paper proposes intelligent variable-packing-ratio (VPR)-based transmissions for high spectrum efficiency (SE) and security, respectively. Aided by deep learning (DL)-based estimation, the proposed scheme for high SE can achieve a higher capacity with negligible modification to existing communication paradigms (e.g., spectrum allocation or frame structure). Also, for VPR-based secure transmission, a dynamic generation scheme is proposed to produce randomly distributed positions to switch the packing ratio, which can effectively avoid detections and attacks. In addition, we propose a simplified DL-based packing ratio estimation for both of these two scenarios so that the receiver can estimate the packing ratio without any in-band or out-band control messages. Simulation results show that the proposed simplified estimation achieves nearly the same accuracy and convergence speed as the original multi-branch fully-connected structure with a complexity reduction of 20 folds. Finally, we derive the closed-form SE of the proposed VPR transmission under different channels. The numerical results validate the correctness of the derivation and demonstrate the SE gains of the VPR scheme beyond conventional Nyquist transmission.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Joint Estimation Approach to Sparse Additive Ordinary Differential Equations

Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. In this article, we propose a novel joint estimation approach for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by considering the likelihood, ODE fidelity and sparse regularization simultaneously. We design a block coordinate descent algorithm for optimizing the non-convex and non-differentiable objective function. The global convergence of the algorithm is established. The simulation study and two applications demonstrate the superior performance of the proposed method in estimation and improved performance of identifying the sparse structure.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A spherical harmonics method for processing anisotropic X-ray atomic pair distribution functions

A general spherical harmonics method is described for extracting anisotropic pair distribution functions (PDF) in this work. In the structural study of functional crystallized materials, the investigation of the local structures under the application of external stimuli, such as electric field and stress, is in urgent need. A well-established technique for local structure studies is PDF analysis, but the extraction of the X-ray PDF data is usually based on angular integrations of isotropic X-ray structure functions, which is no longer valid for the anisotropic responses of the materials under orientation-dependent stimuli. Therefore, we have developed an advanced spherical harmonics method to transform two-dimensional X-ray diffraction patterns into anisotropic PDF data, based on three-dimensional diffraction geometry and Fourier transform. The electrical-field-induced local structural change in the PbZr0.54Ti0.46O3 ceramics is then presented to demonstrate the method's effectiveness.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Density Regression with Conditional Support Points

Density regression characterizes the conditional density of the response variable given the covariates, and provides much more information than the commonly used conditional mean or quantile regression. However, it is often computationally prohibitive in applications with massive data sets, especially when there are multiple covariates. In this paper, we develop a new data reduction approach for the density regression problem using conditional support points. After obtaining the representative data, we exploit the penalized likelihood method as the downstream estimation strategy. Based on the connections among the continuous ranked probability score, the energy distance, the $L_2$ discrepancy and the symmetrized Kullback-Leibler distance, we investigate the distributional convergence of the representative points and establish the rate of convergence of the density regression estimator. The usefulness of the methodology is illustrated by modeling the conditional distribution of power output given multivariate environmental factors using a large scale wind turbine data set. Supplementary materials for this article are available online.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DT-SV: A Transformer-based Time-domain Approach for Speaker Verification

Speaker verification (SV) aims to determine whether the speaker's identity of a test utterance is the same as the reference speech. In the past few years, extracting speaker embeddings using deep neural networks for SV systems has gone mainstream. Recently, different attention mechanisms and Transformer networks have been explored widely in SV fields. However, utilizing the original Transformer in SV directly may have frame-level information waste on output features, which could lead to restrictions on capacity and discrimination of speaker embeddings. Therefore, we propose an approach to derive utterance-level speaker embeddings via a Transformer architecture that uses a novel loss function named diffluence loss to integrate the feature information of different Transformer layers. Therein, the diffluence loss aims to aggregate frame-level features into an utterance-level representation, and it could be integrated into the Transformer expediently. Besides, we also introduce a learnable mel-fbank energy feature extractor named time-domain feature extractor that computes the mel-fbank features more precisely and efficiently than the standard mel-fbank extractor. Combining Diffluence loss and Time-domain feature extractor, we propose a novel Transformer-based time-domain SV model (DT-SV) with faster training speed and higher accuracy. Experiments indicate that our proposed model can achieve better performance in comparison with other models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamical Modeling for non-Gaussian Data with High-dimensional Sparse Ordinary Differential Equations

Ordinary differential equations (ODE) have been widely used for modeling dynamical complex systems. For high-dimensional ODE models where the number of differential equations is large, it remains challenging to estimate the ODE parameters and to identify the sparse structure of the ODE models. Most existing methods exploit the least-square based approach and are only applicable to Gaussian observations. However, as discrete data are ubiquitous in applications, it is of practical importance to develop dynamic modeling for non-Gaussian observations. New methods and algorithms are developed for both parameter estimation and sparse structure identification in high-dimensional linear ODE systems. First, the high-dimensional generalized profiling method is proposed as a likelihood-based approach with ODE fidelity and sparsity-inducing regularization, along with efficient computation based on parameter cascading. Second, two versions of the two-step collocation methods are extended to the non-Gaussian set-up by incorporating the iteratively reweighted least squares technique. Simulations show that the profiling procedure has excellent performance in latent process and derivative fitting and ODE parameter estimation, while the two-step collocation approach excels in identifying the sparse structure of the ODE system. The usefulness of the proposed methods is also demonstrated by analyzing three real datasets from Google trends, stock market sectors, and yeast cell cycle studies.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Generating a dynamical M2 brane from super-gravitons in a pp-wave background

We present a detail study of dynamically generating a M2 brane from super-gravitons (or D0 branes) in a pp-wave background possessing maximal spacetime SUSY. We have three kinds of dynamical solutions depending on the excess energy which appears as an order parameter signalling a critical phenomenon about the solutions. As the excess energy is below a critical value, we have two branches of the solution, one can have its size zero while the other cannot for each given excess energy. However there can be an instanton tunnelling between the two. Once the excess energy is above the critical value, we have a single solution whose dynamical behavior is basically independent of the background chosen and whose size can be zero at some instant. A by product of this study is that the size of particles or extended objects can grow once there is a non-zero excess energy even without the presence of a background flux, therefore lending support to the spacetime uncertainty principle.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hall anomaly, Quantum Oscillations and Possible Lifshitz Transitions in Kondo Insulator YbB$_{12}$: Evidence for Unconventional Charge Transport

In correlated electronic systems, strong interactions and the interplay between different degrees of freedom may give rise to anomalous charge transport properties, which can be tuned by external parameters like temperature and magnetic field. Recently, magnetic quantum oscillations and metallic low-temperature thermal conductivity have been observed in the Kondo insulator YbB$_{12}$, whose resistivity is a few orders of magnitude higher than those of conventional metals. As yet, these unusual observations are not fully understood. Here we present a detailed investigation of the behavior of YbB$_{12}$ under intense magnetic fields using both transport and torque magnetometry measurements. A low-field Hall anomaly, reminiscent of the Hall response associated with "strange-metal" physics, develops at $T < 1.5$ K. At two characteristic magnetic fields ($μ_0H_1= 19.6$ T and $μ_0H_2 \sim 31$ T), signatures appear in the Hall coefficient, magnetic torque, and magnetoresistance. We suggest that they are likely to be field-induced Lifshitz transitions. Moreover, above 35 T, the background resistivity displays an unusual, nonmetallic $T^α$-behavior, with $α$ being field-dependent and varying between -1.5 and -2. By normalizing the Shubnikov-de Haas oscillation amplitude to this $T^α$-dependence, the calculated cyclotron mass becomes more consistent with that deduced from de Haas-van Alphen oscillations. Our results support a novel two-fluid scenario in YbB$_{12}$: a Fermi-liquid-like fluid of charge-neutral quasiparticles coexists with charge carriers that remain in a nonmetallic state. The former experience successive Lifshitz transitions and develop Landau quantization in applied magnetic fields, whilst scattering between both fluids allows the Shubnikov-de Haas effect to be observed in the electrical transport.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Optimal Subsampling for Large Sample Ridge Regression

Subsampling is a popular approach to alleviating the computational burden for analyzing massive datasets. Recent efforts have been devoted to various statistical models without explicit regularization. In this paper, we develop an efficient subsampling procedure for the large sample linear ridge regression. In contrast to the ordinary least square estimator, the introduction of the ridge penalty leads to a subtle trade-off between bias and variance. We first investigate the asymptotic properties of the subsampling estimator and then propose to minimize the asymptotic-mean-squared-error criterion for optimality. The resulting subsampling probability involves both ridge leverage score and L2 norm of the predictor. To further reduce the computational cost for calculating the ridge leverage scores, we propose the algorithm with efficient approximation. We show by synthetic and real datasets that the algorithm is both statistically accurate and computationally efficient compared with existing subsampling based methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Prospects for detecting axion-like particles via the decay $Z\rightarrow af\bar{f}$ at future $Z$ factories

We investigate the prospects for detecting axion-like particles (ALPs, dubbed as "a") via the decay $Z\rightarrow a f\bar{f}$ at future $Z$ factories. Considering the decay channels $a\rightarrow μ^+ μ^-$ and $a\rightarrow b \bar{b}$ , four types of signals $μ^+ μ^- /E$, $b b /E$, $e^+ e^- μ^+ μ^-$ and $e^+ e^- b b$ are explored. We demonstrate that these channels are promising for detecting ALPs at $Z$ factories and obtain the sensitivity bounds on the couplings $g_{aZZ}$ and $g_{aγZ}$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A 6G White Paper on Connectivity for Remote Areas

In many places all over the world rural and remote areas lack proper connectivity that has led to increasing digital divide. These areas might have low population density, low incomes, etc., making them less attractive places to invest and operate connectivity networks. 6G could be the first mobile radio generation truly aiming to close the digital divide. However, in order to do so, special requirements and challenges have to be considered since the beginning of the design process. The aim of this white paper is to discuss requirements and challenges and point out related, identified research topics that have to be solved in 6G. This white paper first provides a generic discussion, shows some facts and discusses targets set in international bodies related to rural and remote connectivity and digital divide. Then the paper digs into technical details, i.e., into a solutions space. Each technical section ends with a discussion and then highlights identified 6G challenges and research ideas as a list.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Complementary lateral-spin-orbit building blocks for programmable logic and in-memory computing

Current-driven switching of nonvolatile spintronic materials and devices based on spin-orbit torques offer fast data processing speed, low power consumption, and unlimited endurance for future information processing applications. Analogous to conventional CMOS technology, it is important to develop a pair of complementary spin-orbit devices with differentiated magnetization switching senses as elementary building blocks for realizing sophisticated logic functionalities. Various attempts using external magnetic field or complicated stack/circuit designs have been proposed, however, plainer and more feasible approaches are still strongly desired. Here we show that a pair of two locally laser annealed perpendicular Pt/Co/Pt devices with opposite laser track configurations and thereby inverse field-free lateral spin-orbit torques (LSOTs) induced switching senses can be adopted as such complementary spin-orbit building blocks. By electrically programming the initial magnetization states (spin down/up) of each sample, four Boolean logic gates of AND, OR, NAND and NOR, as well as a spin-orbit half adder containing an XOR gate, were obtained. Moreover, various initialization-free, working current intensity-programmable stateful logic operations, including the material implication (IMP) gate, were also demonstrated by regarding the magnetization state as a logic input. Our complementary LSOT building blocks provide an applicable way towards future efficient spin logics and in-memory computing architectures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Constraints on the generalized natural inflation after Planck 2018

Based on the dynamics of single scalar field slow-roll inflation and the theory of reheating, we investigate the generalized natural inflationary (GNI) model. Concretely, we give constraints on the scalar spectral index $n_{s}$ and tensor-to scalar ratio $r$ for $Λ$CDM $+r$ model according to the latest data from Plack 2018 TT,TE,EE+lowE+lensing (P18) and BICEP2/Keck 2015 season (BK15), i.e., $n_{s}=0.9659\pm0.0044$ at $68\%$ confidence level (CL) and $r<0.0623$ at $95\%$CL. We find that the GNI model is favored by P18 plus BK15 in the ranges of $\log_{10}(f/M_{p})=0.62^{+0.17}_{-0.18}$ and $m=0.35^{+0.13}_{-0.23}$ at $68\%$CL. In addition, the corresponding predictions of the general and two-phase reheating are respectively discussed. It follows that the parameter $m$ has the significant effect on the model behaviors.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Graded nanocomposite metamaterials for a double-sided radiative cooling architecture with a record breaking cooling power density

As an emerging electricity-free cooling technology, radiative cooling employs outer space as the heat sink. With this, a sky-facing thermal emitter is usually required. Due to the black-body radiation limit at ambient temperature, the maximum cooling power density for a single-faced radiative cooling device is ~156.9 W/m2. Here we report a double-sided radiative cooling architecture using graded nanocomposite metamaterials (GNM) designed for a vertically aligned thermal emitter. This GNM structure possesses an optical absorption of over 90% throughout the solar spectrum, and exceeds 90% reflection in the mid-infrared spectral region. With this configuration, both sides of a planar thermal emitter can be used to perform radiative cooling and a record cooling power density beyond 280 W/m2 was realized in a single thin-film thermal emitter. Under the standard pressure, we realized a temperature reduction of 14 degree Celsius below the ambient temperature in the laboratory environment, and over 12 degree Celsius in the outdoor test.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Divergence of First Order Resonance Widths at Low Eccentricities

Orbital resonances play an important role in the dynamics of planetary systems. Classical theoretical analyses found in textbooks report that libration widths of first order mean motion resonances diverge for nearly circular orbits. Here we examine the nature of this divergence with a non-perturbative analysis of a few first order resonances interior to a Jupiter-mass planet. We show that a first order resonance has two branches, the pericentric and the apocentric resonance zone. As the eccentricity approaches zero, the centers of these zones diverge away from the nominal resonance location but their widths shrink. We also report a novel finding of "bridges" between adjacent first order resonances: at low eccentricities, the apocentric libration zone of a first order resonance smoothly connects with the pericentric libration zone of the neighboring first order resonance. These bridges may facilitate resonant migration across large radial distances in planetary systems, entirely in the low eccentricity regime.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Primordial Gravitational Waves Spectrum in the Coupled-Scalar-Tachyon Bounce Universe

We derive in detail the equations of motion for the tensorial modes of primordial metric perturbations in the Coupled-Scalar-Tachyon Bounce Universe. We solve for the gravitational wave equations in the pre-bounce contraction and the post-bounce expansion epochs. To match the solutions of the tensor perturbations, we idealise the bounce process yet retaining the essential physical properties of the bounce universe. We put forward two matching conditions: one ensures the continuity of the gravitational wave functions and the other respects the symmetric nature of the bounce dynamics. The matching conditions connect the two independent modes of gravitational waves solutions before and after the bounce. We further analyse the scale dependence and time dependence of the gravitational waves spectra in the bounce universe and compare them with the primordial spectrum in the single field inflation scenario. We discuss the implications to early universe physics and present model independent observational signatures extracted from the bounce universe.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Discover Aggregates Exceptions over Hidden Web Databases

Nowadays, many web databases "hidden" behind their restrictive search interfaces (e.g., Amazon, eBay) contain rich and valuable information that is of significant interests to various third parties. Recent studies have demonstrated the possibility of estimating/tracking certain aggregate queries over dynamic hidden web databases. Nonetheless, tracking all possible aggregate query answers to report interesting findings (i.e., exceptions), while still adhering to the stringent query-count limitations enforced by many hidden web databases providers, is very challenging. In this paper, we develop a novel technique for tracking and discovering exceptions (in terms of sudden changes of aggregates) over dynamic hidden web databases. Extensive real-world experiments demonstrate the superiority of our proposed algorithms over baseline solutions.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

HDBSCAN: Density based Clustering over Location Based Services

Location Based Services (LBS) have become extremely popular and used by millions of users. Popular LBS run the entire gamut from mapping services (such as Google Maps) to restaurants (such as Yelp) and real-estate (such as Redfin). The public query interfaces of LBS can be abstractly modeled as a kNN interface over a database of two dimensional points: given an arbitrary query point, the system returns the k points in the database that are nearest to the query point. Often, k is set to a small value such as 20 or 50. In this paper, we consider the novel problem of enabling density based clustering over an LBS with only a limited, kNN query interface. Due to the query rate limits imposed by LBS, even retrieving every tuple once is infeasible. Hence, we seek to construct a cluster assignment function f(.) by issuing a small number of kNN queries, such that for any given tuple t in the database which may or may not have been accessed, f(.) outputs the cluster assignment of t with high accuracy. We conduct a comprehensive set of experiments over benchmark datasets and popular real-world LBS such as Yahoo! Flickr, Zillow, Redfin and Google Maps.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Query Reranking As A Service

The ranked retrieval model has rapidly become the de facto way for search query processing in client-server databases, especially those on the web. Despite of the extensive efforts in the database community on designing better ranking functions/mechanisms, many such databases in practice still fail to address the diverse and sometimes contradicting preferences of users on tuple ranking, perhaps (at least partially) due to the lack of expertise and/or motivation for the database owner to design truly effective ranking functions. This paper takes a different route on addressing the issue by defining a novel {\em query reranking problem}, i.e., we aim to design a third-party service that uses nothing but the public search interface of a client-server database to enable the on-the-fly processing of queries with any user-specified ranking functions (with or without selection conditions), no matter if the ranking function is supported by the database or not. We analyze the worst-case complexity of the problem and introduce a number of ideas, e.g., on-the-fly indexing, domination detection and virtual tuple pruning, to reduce the average-case cost of the query reranking algorithm. We also present extensive experimental results on real-world datasets, in both offline and live online systems, that demonstrate the effectiveness of our proposed techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Aggregate Estimations over Location Based Services

Location based services (LBS) have become very popular in recent years. They range from map services (e.g., Google Maps) that store geographic locations of points of interests, to online social networks (e.g., WeChat, Sina Weibo, FourSquare) that leverage user geographic locations to enable various recommendation functions. The public query interfaces of these services may be abstractly modeled as a kNN interface over a database of two dimensional points on a plane: given an arbitrary query point, the system returns the k points in the database that are nearest to the query point. In this paper we consider the problem of obtaining approximate estimates of SUM and COUNT aggregates by only querying such databases via their restrictive public interfaces. We distinguish between interfaces that return location information of the returned tuples (e.g., Google Maps), and interfaces that do not return location information (e.g., Sina Weibo). For both types of interfaces, we develop aggregate estimation algorithms that are based on novel techniques for precisely computing or approximately estimating the Voronoi cell of tuples. We discuss a comprehensive set of real-world experiments for testing our algorithms, including experiments on Google Maps, WeChat, and Sina Weibo.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

End-fire injection of guided light into optical microcavity

Coupling light into microdisk plays a key role in a number of applications such as resonant filters and optical sensors. While several approaches have successfully coupled light into microdisk efficiently, most of them suffer from the ultrahigh sensitivity to the environmental vibration. Here we demonstrate a robust mechanism, which is termed as end-fire injection. By connecting an input waveguide to a circular microdisk directly, the mechanism shows that light can be efficiently coupled into optical microcavity. The coupling efficiency can be as high as 0.75 when the input signals are on resonances. Our numerical results reveal that the high coupling efficiency is attributed to the constructive interference between the whispering gallery modes and the input signals. We have also shown that the end-fire injection can be further extended to the long-lived resonances with low refractive index such as n = 1.45. We believe our results will shed light on the applications of optical microcavities.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Experimental demonstration of PT-symmetric stripe lasers

Recently, the coexistence of parity-time (PT) symmetric laser and absorber has gained tremendous research attention. While the PT symmetric absorber has been observed in microwave metamaterials, the experimental demonstration of PT symmetric laser is still absent. Here we experimentally study PT-symmetric laser absorber in stripe waveguide. Using the concept of PT symmetry to exploit the light amplification and absorption, PT-symmetric laser absorbers have been successfully obtained. Different from the single-mode PT symmetric lasers, the PT-symmetric stripe lasers have been experimentally confirmed by comparing the relative wavelength positions and mode spacing under different pumping conditions. When the waveguide is half pumped, the mode spacing is doubled and the lasing wavelengths shift to the center of every two initial lasing modes. All these observations are consistent with the theoretical predictions and confirm the PT-symmetry breaking well.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Inversed Vernier effect based single-mode laser emission in coupled microdisks

Recently, on-chip single-mode laser emission has attracted considerable research attention due to its wide applications. While most of single-mode lasers in coupled microdisks or microrings have been qualitatively explained by either Vernier effect or inversed Vernier effect, none of them have been experimentally confirmed. Here, we studied the mechanism for single-mode operation in coupled microdisks. We found that the mode numbers had been significantly reduced to nearly single-mode within a large pumping power range from threshold to gain saturation. The detail laser spectra showed that the largest gain and the first lasing peak were mainly generated by one disk and the laser intensity was proportional to the frequency detuning. The corresponding theoretical analysis showed that the experimental observations were dominated by internal coupling within one cavity, which was similar to the recently explored inversed Vernier effect in two coupled microrings. We believe our finding will be important for understanding the previous experimental findings and the development of on-chip single-mode laser.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Leveraging History for Faster Sampling of Online Social Networks

How to enable efficient analytics over such data has been an increasingly important research problem. Given the sheer size of such social networks, many existing studies resort to sampling techniques that draw random nodes from an online social network through its restrictive web/API interface. Almost all of them use the exact same underlying technique of random walk - a Markov Chain Monte Carlo based method which iteratively transits from one node to its random neighbor. Random walk fits naturally with this problem because, for most online social networks, the only query we can issue through the interface is to retrieve the neighbors of a given node (i.e., no access to the full graph topology). A problem with random walks, however, is the "burn-in" period which requires a large number of transitions/queries before the sampling distribution converges to a stationary value that enables the drawing of samples in a statistically valid manner. In this paper, we consider a novel problem of speeding up the fundamental design of random walks (i.e., reducing the number of queries it requires) without changing the stationary distribution it achieves - thereby enabling a more efficient "drop-in" replacement for existing sampling-based analytics techniques over online social networks. Our main idea is to leverage the history of random walks to construct a higher-ordered Markov chain. We develop two algorithms, Circulated Neighbors and Groupby Neighbors Random Walk (CNRW and GNRW) and prove that, no matter what the social network topology is, CNRW and GNRW offer better efficiency than baseline random walks while achieving the same stationary distribution. We demonstrate through extensive experiments on real-world social networks and synthetic graphs the superiority of our techniques over the existing ones.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Rank-Based Inference over Web Databases

In recent years, there has been much research in Ranked Retrieval model in structured databases, especially those in web databases. With this model, a search query returns top-k tuples according to not just exact matches of selection conditions, but a suitable ranking function. This paper studies a novel problem on the privacy implications of database ranking. The motivation is a novel yet serious privacy leakage we found on real-world web databases which is caused by the ranking function design. Many such databases feature private attributes - e.g., a social network allows users to specify certain attributes as only visible to him/herself, but not to others. While these websites generally respect the privacy settings by not directly displaying private attribute values in search query answers, many of them nevertheless take into account such private attributes in the ranking function design. The conventional belief might be that tuple ranks alone are not enough to reveal the private attribute values. Our investigation, however, shows that this is not the case in reality. To address the problem, we introduce a taxonomy of the problem space with two dimensions, (1) the type of query interface and (2) the capability of adversaries. For each subspace, we develop a novel technique which either guarantees the successful inference of private attributes, or does so for a significant portion of real-world tuples. We demonstrate the effectiveness and efficiency of our techniques through theoretical analysis, extensive experiments over real-world datasets, as well as successful online attacks over websites with tens to hundreds of millions of users - e.g., Amazon Goodreads and Renren.com.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

The combination of directional outputs and single-mode operation in circular microdisk with broken PT symmetry

Monochromaticity and directionality are two key characteristics of lasers. However, the combination of directional emission and single-mode operation is quite challenging, especially for the on-chip devices. Here we propose a microdisk laser with single-mode operation and directional emissions by exploiting the recent developments associated with parity-time (PT) symmetry. This is accomplished by introducing one-dimensional periodic gain and loss into a circular microdisk, which induces a coupling between whispering gallery modes with different radial numbers. The lowest threshold mode is selected at the positions with least initial wavelength difference. And the directional emissions are formed by the introduction of additional grating vectors by the periodic distribution of gain and loss regions. We believe this research will impact the practical applications of on-chip microdisk lasers.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Aggregate Estimation Over Dynamic Hidden Web Databases

Many databases on the web are "hidden" behind (i.e., accessible only through) their restrictive, form-like, search interfaces. Recent studies have shown that it is possible to estimate aggregate query answers over such hidden web databases by issuing a small number of carefully designed search queries through the restrictive web interface. A problem with these existing work, however, is that they all assume the underlying database to be static, while most real-world web databases (e.g., Amazon, eBay) are frequently updated. In this paper, we study the novel problem of estimating/tracking aggregates over dynamic hidden web databases while adhering to the stringent query-cost limitation they enforce (e.g., at most 1,000 search queries per day). Theoretical analysis and extensive real-world experiments demonstrate the effectiveness of our proposed algorithms and their superiority over baseline solutions (e.g., the repeated execution of algorithms designed for static web databases).

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Lifshitz effects on holographic $p$-wave superfluid

In the probe limit, we numerically build a holographic $p$-wave superfluid model in the four-dimensional Lifshitz black hole coupled to a Maxwell-complex vector field. We observe the rich phase structure and find that the Lifshitz dynamical exponent $z$ contributes evidently to the effective mass of the matter field and dimension of the gravitational background. Concretely, we obtain the Cave of Winds appeared only in the five-dimensional anti-de Sitter~(AdS) spacetime, and the increasing $z$ hinders not only the condensate but also the appearance of the first-order phase transition. Furthermore, our results agree with the Ginzburg-Landau results near the critical temperature. In addition, the previous AdS superfluid model is generalized to the Lifshitz spacetime.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Lifshitz Scaling Effects on Holographic Superconductors

Via numerical and analytical methods, the effects of the Lifshitz dynamical exponent $z$ on holographic superconductors are studied in some detail, including $s$ wave and $p$ wave models. Working in the probe limit, we find that the behaviors of holographic models indeed depend on concrete value of $z$. We obtain the condensation and conductivity in both Lifshitz black hole and soliton backgrounds with general $z$. For both $s$ wave and $p$ wave models in the black hole backgrounds, as $z$ increases, the phase transition becomes more difficult and the growth of conductivity is suppressed. For the Lifshitz soliton backgrounds, when $z$ increases ($z=1,~2,~3$), the critical chemical potential decreases in the $s$ wave cases but increases in the $p$ wave cases. For $p$ wave models in both Lifshitz black hole and soliton backgrounds, the anisotropy between the AC conductivity in different spatial directions is suppressed when $z$ increases. The analytical results uphold the numerical results.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Walk, Not Wait: Faster Sampling Over Online Social Networks

In this paper, we introduce a novel, general purpose, technique for faster sampling of nodes over an online social network. Specifically, unlike traditional random walk which wait for the convergence of sampling distribution to a predetermined target distribution - a waiting process that incurs a high query cost - we develop WALK-ESTIMATE, which starts with a much shorter random walk, and then proactively estimate the sampling probability for the node taken before using acceptance-rejection sampling to adjust the sampling probability to the predetermined target distribution. We present a novel backward random walk technique which provides provably unbiased estimations for the sampling probability, and demonstrate the superiority of WALK-ESTIMATE over traditional random walks through theoretical analysis and extensive experiments over real world online social networks.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Digging Deeper into Deep Web Databases by Breaking Through the Top-k Barrier

A large number of web databases are only accessible through proprietary form-like interfaces which require users to query the system by entering desired values for a few attributes. A key restriction enforced by such an interface is the top-k output constraint - i.e., when there are a large number of matching tuples, only a few (top-k) of them are preferentially selected and returned by the website, often according to a proprietary ranking function. Since most web database owners set k to be a small value, the top-k output constraint prevents many interesting third-party (e.g., mashup) services from being developed over real-world web databases. In this paper we consider the novel problem of "digging deeper" into such web databases. Our main contribution is the meta-algorithm GetNext that can retrieve the next ranked tuple from the hidden web database using only the restrictive interface of a web database without any prior knowledge of its ranking function. This algorithm can then be called iteratively to retrieve as many top ranked tuples as necessary. We develop principled and efficient algorithms that are based on generating and executing multiple reformulated queries and inferring the next ranked tuple from their returned results. We provide theoretical analysis of our algorithms, as well as extensive experimental results over synthetic and real-world databases that illustrate the effectiveness of our techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Faster Random Walks By Rewiring Online Social Networks On-The-Fly

Many online social networks feature restrictive web interfaces which only allow the query of a user's local neighborhood through the interface. To enable analytics over such an online social network through its restrictive web interface, many recent efforts reuse the existing Markov Chain Monte Carlo methods such as random walks to sample the social network and support analytics based on the samples. The problem with such an approach, however, is the large amount of queries often required (i.e., a long "mixing time") for a random walk to reach a desired (stationary) sampling distribution. In this paper, we consider a novel problem of enabling a faster random walk over online social networks by "rewiring" the social network on-the-fly. Specifically, we develop Modified TOpology (MTO)-Sampler which, by using only information exposed by the restrictive web interface, constructs a "virtual" overlay topology of the social network while performing a random walk, and ensures that the random walk follows the modified overlay topology rather than the original one. We show that MTO-Sampler not only provably enhances the efficiency of sampling, but also achieves significant savings on query cost over real-world online social networks such as Google Plus, Epinion etc.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Optimal Algorithms for Crawling a Hidden Database in the Web

A hidden database refers to a dataset that an organization makes accessible on the web by allowing users to issue queries through a search interface. In other words, data acquisition from such a source is not by following static hyper-links. Instead, data are obtained by querying the interface, and reading the result page dynamically generated. This, with other facts such as the interface may answer a query only partially, has prevented hidden databases from being crawled effectively by existing search engines. This paper remedies the problem by giving algorithms to extract all the tuples from a hidden database. Our algorithms are provably efficient, namely, they accomplish the task by performing only a small number of queries, even in the worst case. We also establish theoretical results indicating that these algorithms are asymptotically optimal -- i.e., it is impossible to improve their efficiency by more than a constant factor. The derivation of our upper and lower bound results reveals significant insight into the characteristics of the underlying problem. Extensive experiments confirm the proposed techniques work very well on all the real datasets examined.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Parallel Binomial American Option Pricing with (and without) Transaction Costs

We present a parallel algorithm that computes the ask and bid prices of an American option when proportional transaction costs apply to the trading of the underlying asset. The algorithm computes the prices on recombining binomial trees, and is designed for modern multi-core processors. Although parallel option pricing has been well studied, none of the existing approaches takes transaction costs into consideration. The algorithm that we propose partitions a binomial tree into blocks. In any round of computation a block is further partitioned into regions which are assigned to distinct processors. To minimise load imbalance the assignment of nodes to processors is dynamically adjusted before each new round starts. Synchronisation is required both within a round and between two successive rounds. The parallel speedup of the algorithm is proportional to the number of processors used. The parallel algorithm was implemented in C/C++ via POSIX Threads, and was tested on a machine with 8 processors. In the pricing of an American put option, the parallel speedup against an efficient sequential implementation was 5.26 using 8 processors and 1500 time steps, achieving a parallel efficiency of 65.75%.

0 citations0 reviewsNo code linkedOpen access