arXiv subjects

Jing Yang

Publications and source records attributed to Jing Yang.

At least 325 records · Page 18Linked to original sources

arXiv2021

Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization

Despite the significant interests and many progresses in decentralized multi-player multi-armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized lower bound in the heterogeneous MP-MAB setting remains open. In this paper, we propose BEACON -- Batched Exploration with Adaptive COmmunicatioN -- that closes this gap. BEACON accomplishes this goal with novel contributions in implicit communication and efficient exploration. For the former, we propose a novel adaptive differential communication (ADC) design that significantly improves the implicit communication efficiency. For the latter, a carefully crafted batched exploration scheme is developed to enable incorporation of the combinatorial upper confidence bound (CUCB) principle. We then generalize the existing linear-reward MP-MAB problems, where the system reward is always the sum of individually collected rewards, to a new MP-MAB problem where the system reward is a general (nonlinear) function of individual rewards. We extend BEACON to solve this problem and prove a logarithmic regret. BEACON bridges the algorithm design and regret analysis of combinatorial MAB (CMAB) and MP-MAB, two largely disjointed areas in MAB, and the results in this paper suggest that this previously ignored connection is worth further investigation.

stat.ML↗

arXiv2021

A fusion learning method to subgroup analysis of Alzheimer's disease

Uncovering the heterogeneity in the disease progression of Alzheimer's is a key factor to disease understanding and treatment development, so that interventions can be tailored to target the subgroups that will benefit most from the treatment, which is an important goal of precision medicine. However, in practice, one top methodological challenge hindering the heterogeneity investigation is that the true subgroup membership of each individual is often unknown. In this article, we aim to identify latent subgroups of individuals who share a common disorder progress over time, to predict latent subgroup memberships, and to estimate and infer the heterogeneous trajectories among the subgroups. To achieve these goals, we apply a concave fusion learning method proposed in Ma and Huang (2017) and Ma et al. (2019) to conduct subgroup analysis for longitudinal trajectories of the Alzheimer's disease data. The heterogeneous trajectories are represented by subject-specific unknown functions which are approximated by B-splines. The concave fusion method can simultaneously estimate the spline coefficients and merge them together for the subjects belonging to the same subgroup to automatically identify subgroups and recover the heterogeneous trajectories. The resulting estimator of the disease trajectory of each subgroup is supported by an asymptotic distribution. It provides a sound theoretical basis for further conducting statistical inference in subgroup analysis..

stat.ME↗

arXiv2021

Federated Linear Contextual Bandits

This paper presents a novel federated linear contextual bandits model, where individual clients face different $K$-armed stochastic bandits coupled through common global parameters. By leveraging the geometric structure of the linear rewards, a collaborative algorithm called Fed-PE is proposed to cope with the heterogeneity across clients without exchanging local feature vectors or raw data. Fed-PE relies on a novel multi-client G-optimal design, and achieves near-optimal regrets for both disjoint and shared parameter cases with logarithmic communication costs. In addition, a new concept called collinearly-dependent policies is introduced, based on which a tight minimax regret lower bound for the disjoint parameter case is derived. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.

stat.ML↗

arXiv2021

Explainable Fact-checking through Question Answering

Misleading or false information has been creating chaos in some places around the world. To mitigate this issue, many researchers have proposed automated fact-checking methods to fight the spread of fake news. However, most methods cannot explain the reasoning behind their decisions, failing to build trust between machines and humans using such technology. Trust is essential for fact-checking to be applied in the real world. Here, we address fact-checking explainability through question answering. In particular, we propose generating questions and answers from claims and answering the same questions from evidence. We also propose an answer comparison model with an attention mechanism attached to each question. Leveraging question answering as a proxy, we break down automated fact-checking into several steps -- this separation aids models' explainability as it allows for more detailed analysis of their decision-making processes. Experimental results show that the proposed model can achieve state-of-the-art performance while providing reasonable explainable capabilities.

cs.CL↗

arXiv2021

Scalable Fact-checking with Human-in-the-Loop

Researchers have been investigating automated solutions for fact-checking in a variety of fronts. However, current approaches often overlook the fact that the amount of information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages.

cs.CL↗

arXiv2021

Timely Status Updating Over Erasure Channels Using an Energy Harvesting Sensor: Single and Multiple Sources

A status updating system is considered in which data from multiple sources are sampled by an energy harvesting sensor and transmitted to a remote destination through an erasure channel. The goal is to deliver status updates of all sources in a timely manner, such that the cumulative long-term average age-of-information (AoI) is minimized. The AoI for each source is defined as the time elapsed since the generation time of the latest successful status update received at the destination from that source. Transmissions are subject to energy availability, which arrives in units according to a Poisson process, with each energy unit capable of carrying out one transmission from only one source. The sensor is equipped with a unit-sized battery to save the incoming energy. A scheduling policy is designed in order to determine which source is sampled using the available energy. The problem is studied in two main settings: no erasure status feedback, and perfect instantaneous feedback.

cs.IT↗

arXiv2021

Ultra-high pressure disordered eight-coordinated phase of Mg$_2$GeO$_4$: Analogue for super-Earth mantles

Mg2GeO4 is an analogue for the ultra-high pressure behavior of Mg2SiO4, so we have investigated magnesium germanate to 275 GPa and over 2000 K using a laser-heated diamond anvil cell combined with in situ synchrotron X-ray diffraction and density functional theory (DFT) computations. The experimental results are consistent with a novel phase with disordered Mg and Ge, in which germanium adopts eight-fold coordination with oxygen: the cubic Th3P4- type structure. Simulations using the special quasirandom structure (SQS) method suggest partial order in the tetragonal I-42d structure, indistinguishable from I-43d Th3P4 in our experiments. These structures have not been reported before in any oxide. If applicable to silicates, the formation of this highly coordinated and intrinsically disordered phase would have important implications for the interior mineralogy of large, rocky extrasolar planets.

cond-mat.mtrl-sci↗

arXiv2021

In-memory computing based on all-optically controlled memristor

Artificial intelligence is widely used in everyday life. However, an insufficient computing efficiency due to the so-called von Neumann bottleneck cannot satisfy the demand for real-time processing of rapidly growing data. Memristive in-memory computing is a promising candidate for highly efficient data processing. However, performance of memristors varies significantly because of microstructure change induced by electric-driven matter migration. Here, we propose an all-optically controlled (AOC) memristor with a simple Au/ZnO/Pt sandwich structure based on a purely electronic tuning mechanism of memconductance. The memconductance can be reversibly tuned only by light irradiation with different wavelengths. The device can be used to perform in-memory computation such as nonvolatile neuromorphic computing and Boolean logic functions. Moreover, no microstructure change is involved during the operation of our AOC memristor which demonstrates superior operation stability. Based on this and its structural simplicity, the device has attractive application prospects for the next generation of computing systems.

physics.app-ph↗

arXiv2021

Long-wavelength fluctuations and dimensionality crossover in confined liquids

The phase behavior of liquids confined in a slit geometry does not reveal a crossover from a three to a two-dimensional behavior as the gap size decreases. Indeed, the prototypical two-dimensional hexatic phase only occurs in liquids confined to a monolayer. Here, we demonstrate that the dimensionality crossover is apparent in the lateral size dependence of the relaxation dynamics of confined liquids, developing a Debye model for the density of vibrational states of confined systems and performing extensive numerical simulations. In confined systems, Mermin-Wagner fluctuations enhance the amplitude of vibrational motion or Debye-Waller factor by a quantity scaling as the inverse gap width and proportional to the logarithm of the aspect ratio, as a clear signature of a two-dimensional behaviour. As the temperature or lateral system size increases, the crossover to a size-independent relaxation dynamics occurs when structural relaxation takes place before the vibrational modes with the longest wavelength develop.

cond-mat.soft↗

arXiv2021

Core shell NaErF4 at NaYF4 upconversion nanoparticles qualify a NIR speckle wavemeter by a visible CCD

Speckle patterns have been widely confirmed that can be utilized to reconstruct the wavelength information. In order to achieve higher resolution, a varies of optical diffusing waveguides have been investigated with a focus on their wavelength sensitivity. However, it has been a challenge to reach the balance among cost, volumes, resolution, and stability. In this work, we designed a compact cylindrical random scattering waveguide (CRSW) as the light diffuser only by mixing TiO2 particles and ultra-violate adhesive. The speckle patterns are generated by the light multiple scattering in the CRSW. Importantly, the thin layer of upconversion nanoparticles (UCNPs) were sprayed on the end face of the CRSW. This allows the near infrared (NIR) light to be converted to the visible light, breaking the imaging limitation of visible cameras in the NIR range. We further designed a convolution neural network (CNN) to recognize the wavelength of the speckle patterns with good robustness and excellent ability of transfer learning, resulting in the achievement of a high resolution of 20 kHz ( 0.16 fm) at around 1550 nm with temperature resistance of 2 celsius. Our results provide a low-cost, compact, and simple NIR wavemeter in particular with the ultra high resolution and good temperature stability.

physics.optics↗

arXiv2021

Deep-learning-based Hyperspectral imaging through a RGB camera

Hyperspectral image (HSI) contains both spatial pattern and spectral information which has been widely used in food safety, remote sensing, and medical detection. However, the acquisition of hyperspectral images is usually costly due to the complicated apparatus for the acquisition of optical spectrum. Recently, it has been reported that HSI can be reconstructed from single RGB image using convolution neural network (CNN) algorithms. Compared with the traditional hyperspectral cameras, the method based on CNN algorithms is simple, portable and low cost. In this study, we focused on the influence of the RGB camera spectral sensitivity (CSS) on the HSI. A Xenon lamp incorporated with a monochromator were used as the standard light source to calibrate the CSS. And the experimental results show that the CSS plays a significant role in the reconstruction accuracy of an HSI. In addition, we proposed a new HSI reconstruction network where the dimensional structure of the original hyperspectral datacube was modified by 3D matrix transpose to improve the reconstruction accuracy.

eess.IV↗

arXiv2021

The D-plus Discriminant and Complexity of Root Clustering

Let $p(x)$ be an integer polynomial with $m\ge 2$ distinct roots $ρ_1,\ldots,ρ_m$ whose multiplicities are $\boldsymbolμ=(μ_1,\ldots,μ_m)$. We define the D-plus discriminant of $p(x)$ to be $D^+(p):= \prod_{1\le i<j\le m}(ρ_i-ρ_j)^{μ_i+μ_j}$. We first prove a conjecture that $D^+(p)$ is a $\boldsymbolμ$-symmetric function of its roots $ρ_1,\ldots,ρ_m$. Our main result gives an explicit formula for $D^+(p)$, as a rational function of its coefficients. Our proof is ideal-theoretic, based on re-casting the classic Poisson resultant as the "symbolic Poisson formula". The D-plus discriminant first arose in the complexity analysis of a root clustering algorithm from Becker et al. (ISSAC 2016). The bit-complexity of this algorithm is proportional to a quantity $\log(|D^+(p)|^{-1})$. As an application of our main result, we give an explicit upper bound on this quantity in terms of the degree of $p$ and its leading coefficient.

cs.SC↗

arXiv2021

Multi-Spectrally Constrained Transceiver Design against Signal-Dependent Interference

This paper focuses on the joint synthesis of constant envelope transmit signal and receive filter aimed at optimizing radar performance in signal-dependent interference and spectrally contested-congested environments. To ensure the desired Quality of Service (QoS) at each communication system, a precise control of the interference energy injected by the radar in each licensed/shared bandwidth is imposed. Besides, along with an upper bound to the maximum transmitted energy, constant envelope (with either arbitrary or discrete phases) and similarity constraints are forced to ensure compatibility with amplifiers operating in saturation regime and bestow relevant waveform features, respectively. To handle the resulting NP-hard design problems, new iterative procedures (with ensured convergence properties) are devised to account for continuous and discrete phase constraints, capitalizing on the Coordinate Descent (CD) framework. Two heuristic procedures are also proposed to perform valuable initializations. Numerical results are provided to assess the effectiveness of the conceived algorithms in comparison with the existing methods.

eess.SP↗

arXiv2021

Concentrated solutions to fractional Schrödinger equations with prescribed $L^2$-norm

We investigate the existence and local uniqueness of normalized $k$-peak solutions for the fractional Schrödinger equations with attractive interactions with a class of degenerated trapping potential with non-isolated critical points. Precisely, applying the finite dimensional reduction method, we first obtain the existence of $k$-peak concentrated solutions and especially describe the relationship between the chemical potential $μ$ and the attractive interaction $a$. Second, after precise analysis of the concentrated points and the Lagrange multiplier, we prove the local uniqueness of the $k$-peak solutions with prescribed $L^2$-norm, by use of the local Pohozaev identities, the blow-up analysis and the maximum principle associated to the nonlocal operator $(-Δ)^s$. To our best knowledge, there is few results on the excited normalized solutions of the fractional Schrödinger equations before this present work. The main difficulty lies in the non-local property of the operator $(-Δ)^s$. First, it makes the standard comparison argument in the ODE theory invalid to use in our analysis. Second, because of the algebraic decay involving the approximate solutions, the estimates, on the Lagrange multiplier for example, would become more subtle. Moreover,when studying the corresponding harmonic extension problem, several local Pohozaev identities are constructed and we have to estimate several kinds of integrals that never appear in the classic local Schrödinger problems. In addition, throughout our discussion, we need to distinct the different cases of $p$, which are called respectively that the mass-subcritical, the mass-critical, and the mass-supercritical case, due to the mass-constraint condition. Another difficulty comes from the influence of the different degenerate rates along different directions at the critical points of the potential.

math.AP↗

arXiv2021

On the vertex-degree based invariants of digraphs

Let $D=(V,A)$ be a digraphs without isolated vertices. A vertex-degree based invariant $I(D)$ related to a real function $φ$ of $D$ is defined as a summation over all arcs, $I(D) = \frac{1}{2}\sum_{uv\in A}{φ(d_u^+,d_v^-)}$, where $d_u^+$ (resp. $d_u^-$) denotes the out-degree (resp. in-degree) of a vertex $u$. In this paper, we give the extremal values and extremal digraphs of $I(D)$ over all digraphs with $n$ non-isolated vertices. Applying these results, we obtain the extremal values of some vertex-degree based topological indices of digraphs, such as the Randić index, the Zagreb index, the sum-connectivity index, the $GA$ index, the $ABC$ index and the harmonic index, and the corresponding extremal digraphs.

math.CO↗

arXiv2021

Seasonal association between viral causes of hospitalised acute lower respiratory infections and meteorological factors in China: a retrospective study

Acute lower respiratory infections caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses. Our aim was to identify the dominant meteorological factors and to model their effects on common respiratory viruses in different regions of China. We analysed monthly virus data on patients from 81 sentinel hospitals in 22 provinces in mainland China from 2009 to 2013. The geographical detector method was used to quantify the explanatory power of each meteorological factor, individually and interacting in pairs. 28369 hospitalised patients with ALRI were tested, 10387 were positive for at least one virus, including RSV, influenza virus, PIV, ADV, hBoV, hCoV and hMPV. RSV and influenza virus had annual peaks in the north and biannual peaks in the south. PIV and hBoV had higher positive rates in the spring summer months. hMPV had an annual peak in winter spring, especially in the north. ADV and hCoV exhibited no clear annual seasonality. Temperature, atmospheric pressure, vapour pressure, and rainfall had most explanatory power on most respiratory viruses in each region. Relative humidity was only dominant in the north, but had no significant explanatory power for most viruses in the south. Hours of sunlight had significant explanatory power for RSV and influenza virus in the north, and for most viruses in the south. Wind speed was the only factor with significant explanatory power for human coronavirus in the south. For all viruses, interactions between any two of the paired factors resulted in enhanced explanatory power, either bivariately or non-linearly.

stat.AP↗

arXiv2021

Document Layout Analysis via Dynamic Residual Feature Fusion

The document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region, which has wide application such as optical character recognition (OCR) systems and document retrieval. However, it is a challenge to build a DLA system because the training data is very limited and lacks an efficient model. In this paper, we propose an end-to-end united network named Dynamic Residual Fusion Network (DRFN) for the DLA task. Specifically, we design a dynamic residual feature fusion module which can fully utilize low-dimensional information and maintain high-dimensional category information. Besides, to deal with the model overfitting problem that is caused by lacking enough data, we propose the dynamic select mechanism for efficient fine-tuning in limited train data. We experiment with two challenging datasets and demonstrate the effectiveness of the proposed module.

cs.CV↗

arXiv2021

Confocal super-resolution microscopy based on a spatial mode sorter

Spatial resolution is one of the most important specifications of an imaging system. Recent results in quantum parameter estimation theory reveal that an arbitrarily small distance between two incoherent point sources can always be efficiently determined through the use of a spatial mode sorter. However, extending this procedure to a general object consisting of many incoherent point sources remains challenging, due to the intrinsic complexity of multi-parameter estimation problems. Here, we generalize the Richardson-Lucy (RL) deconvolution algorithm to address this challenge. We simulate its application to an incoherent confocal microscope, with a Zernike spatial mode sorter replacing the pinhole used in a conventional confocal microscope. We test different spatially incoherent objects of arbitrary geometry, and we find that the resolution enhancement of sorter-based microscopy is on average over 30% higher than that of a conventional confocal microscope using the standard RL deconvolution algorithm. Our method could potentially be used in diverse applications such as fluorescence microscopy and astronomical imaging.

physics.optics↗