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Source author record

Fang Wang

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

ResearcherUnclaimed source record
Computer VisionComputation and Languageastro-ph.GAmath.APArtificial Intelligenceastro-ph.HEcond-mat.mtrl-sciMachine Learningmath.DGmath.NAastro-ph.COCryptography and Securityeess.SYFormal Languages and Automata Theorygr-qcHardware Architecturehep-phInformation Theorymath.ITMethodologyNeural and Evolutionary Computingnucl-exnucl-thNumerical Analysisphysics.acc-phphysics.opticsphysics.soc-phq-fin.GNquant-phSystems and Control

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Published work

35 published item(s)

preprint2026arXiv

Convergence Analysis of Weighted Median Opinion Dynamics with Higher-Order Effects

The weighted median mechanism provides a robust alternative to weighted averaging in opinion dynamics. Existing models, however, are predominantly formulated on pairwise interaction graphs, which limits their ability to represent higher-order environmental effects. In this work, a generalized weighted median opinion dynamics model is proposed by incorporating high-order interactions through a simplicial complex representation. The resulting dynamics are formulated as a nonlinear discrete-time system with synchronous opinion updates, in which intrinsic agent interactions and external environmental influences are jointly modeled. Sufficient conditions for asymptotic consensus are established for heterogeneous systems composed of opinionated and unopinionated agents. For homogeneous opinionated systems, convergence and convergence rates are rigorously analyzed using the Banach fixed-point theorem. Theoretical results demonstrate the stability of the proposed dynamics under mild conditions, and numerical simulations are provided to corroborate the analysis. This work extends median-based opinion dynamics to high-order interaction settings and provides a system-level framework for stability and consensus analysis.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

EndPrompt: Efficient Long-Context Extension via Terminal Anchoring

Extending the context window of large language models typically requires training on sequences at the target length, incurring quadratic memory and computational costs that make long-context adaptation expensive and difficult to reproduce. We propose EndPrompt, a method that achieves effective context extension using only short training sequences. The core insight is that exposing a model to long-range relative positional distances does not require constructing full-length inputs: we preserve the original short context as an intact first segment and append a brief terminal prompt as a second segment, assigning it positional indices near the target context length. This two-segment construction introduces both local and long-range relative distances within a short physical sequence while maintaining the semantic continuity of the training text--a property absent in chunk-based simulation approaches that split contiguous context. We provide a theoretical analysis grounded in Rotary Position Embedding and the Bernstein inequality, showing that position interpolation induces a rigorous smoothness constraint over the attention function, with shared Transformer parameters further suppressing unstable extrapolation to unobserved intermediate distances. Applied to LLaMA-family models extending the context window from 8K to 64K, EndPrompt achieves an average RULER score of 76.03 and the highest average on LongBench, surpassing LCEG (72.24), LongLoRA (72.95), and full-length fine-tuning (69.23) while requiring substantially less computation. These results demonstrate that long-context generalization can be induced from sparse positional supervision, challenging the prevailing assumption that dense long-sequence training is necessary for reliable context-window extension. The code is available at https://github.com/clx1415926/EndPrompt.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Logic-Guided Multistage Inference for Explainable Multidefendant Judgment Prediction

Crime disrupts societal stability, making law essential for balance. In multidefendant cases, assigning responsibility is complex and challenges fairness, requiring precise role differentiation. However, judicial phrasing often obscures the roles of the defendants, hindering effective AI-driven analyses. To address this issue, we incorporate sentencing logic into a pretrained Transformer encoder framework to enhance the intelligent assistance in multidefendant cases while ensuring legal interpretability. Within this framework an oriented masking mechanism clarifies roles and a comparative data construction strategy improves the model's sensitivity to culpability distinctions between principals and accomplices. Predicted guilt labels are further incorporated into a regression model through broadcasting, consolidating crime descriptions and court views. Our proposed masked multistage inference (MMSI) framework, evaluated on the custom IMLJP dataset for intentional injury cases, achieves significant accuracy improvements, outperforming baselines in role-based culpability differentiation. This work offers a robust solution for enhancing intelligent judicial systems, with publicly code available.

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preprint2026arXiv

Measuring Maximum Activations in Open Large Language Models

The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characterized outlier features and massive activations on pre-2024 LLaMA-style models, and the downstream activation-quantization stack inherits that picture without revisiting it for the post-LLaMA open-model boom. We ask the deployment-oriented question: how large can activations get in modern open LLMs, and how does this magnitude vary across families, generations, and training stages? Under a unified pipeline (5,000-sample multi-domain corpus, family-specific tokenization, identical hooks across embeddings, hidden states, attention, MLP/MoE, SwiGLU gates, and final norm), we measure global and layerwise maxima on 27 checkpoints from 8 open families spanning dense, MoE, vision-language, intermediate-training, and instruction-tuned variants. We find that (i) global maxima span over nearly four orders of magnitude at comparable parameter counts, with Qwen3.5 and MoE checkpoints in the 10^2 to 10^3 range and Gemma3-27B-it reaching ~7 x 10^5; (ii) cross-family and cross-generation comparisons break simple monotonic scaling; and (iii) MoE checkpoints exhibit 14.0-23.4x lower peaks than matched-scale dense counterparts, while the residual stream carries the global maximum in 22/24 checkpoints. A lightweight INT-8 sanity check shows that measured maxima co-vary with low-bit reconstruction error via activation-scale selection. We conclude that maximum activation magnitude is a model property tied to family, architecture, and training stage - not a simple byproduct of size - and should be measured and reported alongside any open-weight release before low-bit deployment. The code is publicly available at https://github.com/clx1415926/Max_act_llm.

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preprint2024arXiv

GPBiLQ and GPQMR: Two iterative methods for unsymmetric partitioned linear systems

We introduce two iterative methods, GPBiLQ and GPQMR, for solving unsymmetric partitioned linear systems. The basic mechanism underlying GPBiLQ and GPQMR is a novel simultaneous tridiagonalization via biorthogonality that allows for short-recurrence iterative schemes. Similar to the biconjugate gradient method, it is possible to develop another method, GPBiCG, whose iterate (if it exists) can be obtained inexpensively from the GPBiLQ iterate. Whereas the iterate of GPBiCG may not exist, the iterates of GPBiLQ and GPQMR are always well defined as long as the biorthogonal tridiagonal reduction process does not break down. We discuss connections between the proposed methods and some existing methods, and give numerical experiments to illustrate the performance of the proposed methods.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Graph Neural Prompting with Large Language Models

Large language models (LLMs) have shown remarkable generalization capability with exceptional performance in various language modeling tasks. However, they still exhibit inherent limitations in precisely capturing and returning grounded knowledge. While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost. Therefore, how to enhance pre-trained LLMs using grounded knowledge, e.g., retrieval-augmented generation, remains an open question. In this work, we propose Graph Neural Prompting (GNP), a novel plug-and-play method to assist pre-trained LLMs in learning beneficial knowledge from KGs. GNP encompasses various designs, including a standard graph neural network encoder, a cross-modality pooling module, a domain projector, and a self-supervised link prediction objective. Extensive experiments on multiple datasets demonstrate the superiority of GNP on both commonsense and biomedical reasoning tasks across different LLM sizes and settings. Code is available at https://github.com/meettyj/GNP.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Investigation of Bare-bones Algorithms from Quantum Perspective: A Quantum Dynamical Global Optimizer

Recent decades, the emergence of numerous novel algorithms makes it a gimmick to propose an intelligent optimization system based on metaphor, and hinders researchers from exploring the essence of search behavior in algorithms. However, it is difficult to directly discuss the search behavior of an intelligent optimization algorithm, since there are so many kinds of intelligent schemes. To address this problem, an intelligent optimization system is regarded as a simulated physical optimization system in this paper. The dynamic search behavior of such a simplified physical optimization system are investigated with quantum theory. To achieve this goal, the Schroedinger equation is employed as the dynamics equation of the optimization algorithm, which is used to describe dynamic search behaviours in the evolution process with quantum theory. Moreover, to explore the basic behaviour of the optimization system, the optimization problem is assumed to be decomposed and approximated. Correspondingly, the basic search behaviour is derived, which constitutes the basic iterative process of a simple optimization system. The basic iterative process is compared with some classical bare-bones schemes to verify the similarity of search behavior under different metaphors. The search strategies of these bare bones algorithms are analyzed through experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Training Entire-Space Models for Target-oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types of instance. In the first type, aspect terms are associated with at least one opinion word, while in the second type, aspect terms do not have corresponding opinion words. However, previous researches trained and evaluated their models with only the first type of instance, resulting in a sample selection bias problem. Specifically, TOWE models were trained with only the first type of instance, while these models would be utilized to make inference on the entire space with both the first type of instance and the second type of instance. Thus, the generalization performance will be hurt. Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space. To validate the sample selection bias problem, four popular TOWE datasets containing only aspect terms associated with at least one opinion word are extended and additionally include aspect terms without corresponding opinion words. Experimental results on these datasets show that training TOWE models on entire space will significantly improve model performance and evaluating TOWE models only on the first type of instance will overestimate model performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end \emph{i}MeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooth's region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, \emph{i}MeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at \textcolor[rgb]{0,0,0}{$0.964\pm0.054$}, significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of $0.597\pm0.761 \, mm$ in distances between the prediction and ground truth for $66$ landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.

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preprint2021arXiv

Estimation of transmitted wavefronts at defocused positions in a broad bandwidth range

Wavefront aberrations can reflect the imaging quality of high-performance optical systems better than geometric aberrations. Although laser interferometers have emerged as the main tool for measurement of transmitted wavefronts, their application is greatly limited, as they are typically designed for operation at specific wavelengths. In a previous study, we proposed a method for determining the wavefront transmitted by an optical system at any wavelength in a certain band. Although this method works well for most monochromatic systems, where the image plane is at the focal point for the transmission wavelength, for general multi-color systems, it is more practical to measure the wavefront at the defocused image plane. Hence, in this paper, we have developed a complete method for determining transmitted wavefronts in a broad bandwidth at any defocused position, enabling wavefront measurements for multi-color systems. Here, we assume that in small ranges, the Zernike coefficients have a linear relationship with position, such that Zernike coefficients at defocused positions can be derived from measurements performed at the focal point. We conducted experiments to verify these assumptions, validating the new method. The experimental setup has been improved so that it can handle multi-color systems, and a detailed experimental process is summarized. With this technique, application of broadband transmission wavefront measurement can be extended to most general optical systems, which is of great significance for characterization of achromatic and apochromatic optical lenses.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

IRO: Integrity and Reliability Enhanced Ring ORAM

Memory security and reliability are two of the major design concerns in cloud computing systems. State-of-the-art memory security-reliability co-designs (e.g. Synergy) have achieved a good balance on performance, confidentiality, integrity, and reliability. However, these works merely rely on encryption to ensure data confidentiality, which has been proven unable to prevent information leakage from memory access patterns. Ring ORAM is an attractive confidential protection protocol to hide memory access patterns to the untrusted storage system. Unfortunately, it does not compatible with the security-reliability co-designs. A forced combination would result in more severe performance loss. In this paper, we propose IRO, an Integrity and Reliability enhanced Ring ORAM design. To reduce the overhead of integrity verification, we propose a low overhead integrity tree RIT and use a Minimum Update Subtree Tree (MUST) to reduce metadata update overhead. To improve memory reliability, we present Secure Replication to provide channel-level error resilience for the ORAM tree and use the mirrored channel technique to guarantee the reliability of the MUST. Last, we use the error correction pointer (ECP) to repair permanent memory cell fault to further improve device reliability and lifetime. A compact metadata design is used to reduce the storage and consulting overhead of the ECP. IRO provides strong security and reliability guarantees, while the resulting storage and performance overhead is very small. Our evaluation shows that IRO only increases 7.54% execution time on average over the Baseline under two channels four AES-GCM units setting. With enough AES-GCM units to perform concurrent MAC computing, IRO can reduce 2.14% execution time of the Baseline.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Large Time Behavior and Diffusion Limit for a System of Balance Laws From Chemotaxis in Multi-dimensions

We consider the Cauchy problem for a system of balance laws derived from a chemotaxis model with singular sensitivity in multiple space dimensions. Utilizing energy methods, we first prove the global well-posedness of classical solutions to the Cauchy problem when only the energy of the first order spatial derivatives of the initial data is sufficiently small, and the solutions are shown to converge to the prescribed constant equilibrium states as time goes to infinity. Then we prove that the solutions of the fully dissipative model converge to those of the corresponding partially dissipative model when the chemical diffusion coefficient tends to zero.

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preprint2016arXiv

A Symbolic Decision Procedure for Symbolic Alternating Finite Automata

We introduce Symbolic Alternating Finite Automata (s-AFA) as an expressive, succinct, and decidable model for describing sets of finite sequences over arbitrary alphabets. Boolean operations over s-AFAs have linear complexity, which is in sharp contrast with the quadratic cost of intersection and union for non-alternating symbolic automata. Due to this succinctness, emptiness and equivalence checking are PSpace-hard. We introduce an algorithm for checking the equivalence of two s-AFAs based on bisimulation up to congruence. This algorithm allows us to exploit the power of SAT and SMT solvers to efficiently search the state space of the s-AFAs. We evaluate our decision procedure on two verification and security applications: 1) checking satisfiability of linear temporal logic formulas over finite traces, and 2) checking equivalence of Boolean combinations of regular expressions. Our experiments show that our technique often outperforms existing techniques and it can be beneficial in both such applications.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Improving Bandwidth Efficiency of FBMC-OQAM Through Virtual Symbols

Filter bank multicarrier (FBMC) systems that are based on offset quadrature amplitude modulation (OQAM), namely, FBMC-OQAM, have been criticized for their inefficiency in the use of spectral resources, because of the long ramp-up and ramp-down tails at the beginning and the end of each data packet, respectively. We propose a novel method for shortening these tails. By appending a set of virtual (i.e., none data carrying) symbols to the beginning and the end of each packet, and clever selection of these symbols, we show that the ramp-up and rampdown tails in FMBC-OQAM can be suppressed to an extent that they deem as negligible and thus may be ignored. This shortens the length of signal burst in each FBMC-OQAM packet, hence, improves on its bandwidth efficiency, viz., the same data is transmitted over a shorter period of time. We develop an optimization method that allows computation of virtual symbols, for each data packet. Simulation results show that, compared to existing methods, the proposed tail-shortening approach leads to a superior out-of-band (OOB) emission performance and a much lower error vector magnitude (EVM) for the demodulated symbols.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On the positivity of scattering operators for Poincaré-Einstein manifolds

In this paper, we mainly study the scattering operators for the Poincaré-Einstein manifolds. Those operators give the fractional GJMS operators $P_{2γ}$ for the conformal infinity. If a Poincaré-Einstein manifolds $(X^{n+1}, g_+)$ is locally conformally flat and there exists an representative $g$ for the conformal infinity $(M, [g])$ such that the scalar curvature $R$ is a positive constant and $Q_4>0$, then we prove that $P_{2γ}$ is positive for $γ\in (1,2)$ and thus the first real scattering pole is less than $\frac{n}{2}-2$.

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preprint2016arXiv

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. VI. Velocity-resolved Reverberation Mapping of H$β$ Line

In the sixth of the series of papers reporting on a large reverberation mapping (RM) campaign of active galactic nuclei (AGNs) with high accretion rates, we present velocity-resolved time lags of H$β$ emission lines for nine objects observed in the campaign during 2012$-$2013. In order to correct the line-broadening caused by seeing and instruments before the analysis of velocity-resolved RM, we adopt Richardson-Lucy deconvolution to reconstruct their H$β$ profiles. The validity and effectiveness of the deconvolution are checked out by Monte Carlo simulation. Five among the nine objects show clear dependence of time delay on velocity. Mrk 335 and Mrk 486 show signatures of gas inflow whereas the clouds in the broad-line regions (BLRs) of Mrk 142 and MCG +06-26-012 tend to be radial outflowing. Mrk 1044 is consistent with the case of virialized motions. The lags of the rest four are not velocity-resolvable. The velocity-resolved RM of super-Eddington accreting massive black holes (SEAMBHs) shows that they have diversity of the kinematics in their BLRs. Comparing with the AGNs with sub-Eddington accretion rates, we do not find significant differences in the BLR kinematics of SEAMBHs.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Compositional Memory for Visual Question Answering

Visual Question Answering (VQA) emerges as one of the most fascinating topics in computer vision recently. Many state of the art methods naively use holistic visual features with language features into a Long Short-Term Memory (LSTM) module, neglecting the sophisticated interaction between them. This coarse modeling also blocks the possibilities of exploring finer-grained local features that contribute to the question answering dynamically over time. This paper addresses this fundamental problem by directly modeling the temporal dynamics between language and all possible local image patches. When traversing the question words sequentially, our end-to-end approach explicitly fuses the features associated to the words and the ones available at multiple local patches in an attention mechanism, and further combines the fused information to generate dynamic messages, which we call episode. We then feed the episodes to a standard question answering module together with the contextual visual information and linguistic information. Motivated by recent practices in deep learning, we use auxiliary loss functions during training to improve the performance. Our experiments on two latest public datasets suggest that our method has a superior performance. Notably, on the DARQUAR dataset we advanced the state of the art by 6$\%$, and we also evaluated our approach on the most recent MSCOCO-VQA dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Multi-color optical monitoring of Mrk 501 from 2010 to 2015

We have monitored the BL Lac object Mrk 501 in optical $V$, $R$ and $I$ bands from 2010 to 2015. For Mrk 501, the presence of strong host galaxy component can affect the results of photometry. After subtracting the host galaxy contributions, the source shows intraday and long-term variabilities for optical flux and color indices. The average variability amplitudes of $V$, $R$ and $I$ bands are $22.05\%, 22.25\%, 23.82\%$ respectively, and the value of duty cycle 14.87 per cent. A minimal variability timescale of 106 minutes is detected. No significant time lag between $V$ and $I$ bands is found on one night. The bluer-when-brighter (BWB) trend is dominant for Mrk 501 on intermediate, short and intraday timescales which supports the shock-in-jet model. For the long timescale, Mrk 501 in different state can have different BWB trend. The corresponding results of non-correcting host galaxy contributions are also presented.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Sketch-based 3D Shape Retrieval using Convolutional Neural Networks

Retrieving 3D models from 2D human sketches has received considerable attention in the areas of graphics, image retrieval, and computer vision. Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features. We argue that this two stage approach (view selection -- matching) is pragmatic but also problematic because the "best views" are subjective and ambiguous, which makes the matching inputs obscure. This imprecise nature of matching further makes it challenging to choose features manually. Instead of relying on the elusive concept of "best views" and the hand-crafted features, we propose to define our views using a minimalism approach and learn features for both sketches and views. Specifically, we drastically reduce the number of views to only two predefined directions for the whole dataset. Then, we learn two Siamese Convolutional Neural Networks (CNNs), one for the views and one for the sketches. The loss function is defined on the within-domain as well as the cross-domain similarities. Our experiments on three benchmark datasets demonstrate that our method is significantly better than state of the art approaches, and outperforms them in all conventional metrics.

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preprint2015arXiv

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. III. Detection of Fe II Reverberation in Nine Narrow-Line Seyfert 1 Galaxies

This is the third in a series of papers reporting on a large reverberation-mapping campaign aimed to study the properties of active galactic nuclei (AGNs) with high accretion rates. We present new results on the variability of the optical Fe II emission lines in 10 AGNs observed by the Yunnan Observatory 2.4m telescope during 2012--2013. We detect statistically significant time lags, relative to the AGN continuum, in nine of the sources. This accurate measurement is achieved by using a sophisticated spectral fitting scheme that allows for apparent flux variations of the host galaxy, and several narrow lines, due to the changing observing conditions. Six of the newly detected lags are indistinguishable from the Hbeta lags measured in the same sources. Two are significantly longer and one is slightly shorter. Combining with Fe II lags reported in previous studies, we find a Fe II radius--luminosity relationship similar to the one for Hbeta, although our sample by itself shows no clear correlation. The results support the idea that Fe II emission lines originate in photoionized gas which, for the majority of the newly reported objects, is indistinguishable from the Hbeta-emitting gas. We also present a tentative correlation between the lag and intensity of Fe II and Hbeta and comment on its possible origin.

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preprint2014arXiv

Comment on "Fault-Tolerate Quantum Private Comparison Based on GHZ States and ECC"

A two-party quantum private comparison scheme using GHZ states and error-correcting code (ECC) was introduced in Li et al.'s paper [Int. J. Theor. Phys. 52: 2818-2815, 2013], which holds the capability of fault-tolerate and could be performed in a none-ideal scenario. However, this study points out there exists a fatal loophole under a special attack, namely the twice-Hadamard-CNOT attack. A malicious party may intercept the other's particles, firstly executes the Hadamard operations on these intercepted particles and his (her) own ones respectively, and then sequentially performs twice CNOT operations on them and the auxiliary particles prepared in advance. As a result, the secret input will be revealed without being detected through measuring the auxiliary particles. For resisting this special attack, an improvement is proposed by applying a permutation operator before TP sends the particle sequences to all the participants.

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preprint2014arXiv

Radiation field for Einstein vacuum equations with spacial dimension $n\geq 4$

In this paper, the radiation field is defined for solutions to Einstein vacuum equations which are close to Minkowski space-time with spacial dimension $n\geq 4$. The regularity properties and asymptotic behavior of those Einstein vacuum solutions are established at the same time. In particular, the map from Cauchy intial data to the radiation field is proved to be an isomorphism when restricting to a small neighborhood of Minkowski data in suitable weighted b-Sobolev spaces.

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preprint2014arXiv

Radiation fields on Schwarzschild spacetime

In this paper we define the radiation field for the wave equation on the Schwarzschild black hole spacetime. In this context it has two components: the rescaled restriction of the time derivative of a solution to null infinity and to the event horizon. In the process, we establish some regularity properties of solutions of the wave equation on the spacetime. In particular, we prove that the regularity of the solution across the event horizon and across null infinity is determined by the regularity and decay rate of the initial data at the event horizon and at infinity. We also show that the radiation field is unitary with respect to the conserved energy and prove support theorems for each piece of the radiation field.

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preprint2014arXiv

SDSS J013127.34$-$032100.1: A newly discovered radio-loud quasar at $z=5.18$ with extremely high luminosity

Only very few z>5 quasars discovered to date are radio-loud, with a radio-to-optical flux ratio (radio-loudness parameter) higher than 10. Here we report the discovery of an optically luminous radio-loud quasar, SDSS J013127.34-032100.1 (J0131-0321 in short), at z=5.18+-0.01 using the Lijiang 2.4m and Magellan telescopes. J0131-0321 has a spectral energy distribution consistent with that of radio-loud quasars. With an i-band magnitude of 18.47 and radio flux density of 33 mJy, its radio-loudness parameter is ~100. The optical and near-infrared spectra taken by Magellan enable us to estimate its bolometric luminosity to be L_bol ~ 1.1E48 erg/s, approximately 4.5 times greater than that of the most distant quasar known to date. The black hole mass of J0131-0321 is estimated to be 2.7E9 solar masses, with an uncertainty up to 0.4 dex. Detailed physical properties of this high-redshift, radio-loud, potentially super-Eddington quasar can be probed in the future with more dedicated and intensive follow-up observations using multi-wavelength facilities.

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preprint2014arXiv

Supermassive black holes with high accretion rates in active galactic nuclei: II. the most luminous standard candles in the Universe

This is the second in a series of papers reporting on a large reverberation mapping (RM) campaign to measure black hole (BH) mass in high accretion rate active galactic nuclei (AGNs). The goal is to identify super-Eddington accreting massive black holes (SEAMBHs) and to use their unique properties to construct a new method for measuring cosmological distances. Based on theoretical models, the saturated bolometric luminosity of such sources is proportional to the BH mass which can be used to obtain their distance. Here we report on five new RM measurements and show that in four of the cases we can measure the BH mass and three of these sources are SEAMBHs. Together with the three sources from our earlier work, we now have six new sources of this type. We use a novel method based on a minimal radiation efficiency to identify nine additional SEAMBHs from earlier RM-based mass measurements. We use a Bayesian analysis to determine the parameters of the new distance expression, and the method uncertainties, from the observed properties of the objects in the sample. The ratio of the newly measured distances to the standard cosmological ones has a mean scatter of 0.14 dex, indicating that SEAMBHs can be use as cosmological distance probes. With their high luminosity, long period of activity and large numbers at high redshifts, SEAMBHs have a potential to extend the cosmic distance ladder beyond the range now explored by type Ia supernovae.

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preprint2014arXiv

Test for a Mean Vector with Fixed or Divergent Dimension

It has been a long history in testing whether a mean vector with a fixed dimension has a specified value. Some well-known tests include the Hotelling $T^2$-test and the empirical likelihood ratio test proposed by Owen [Biometrika 75 (1988) 237-249; Ann. Statist. 18 (1990) 90-120]. Recently, Hotelling $T^2$-test has been modified to work for a high-dimensional mean, and the empirical likelihood method for a mean has been shown to be valid when the dimension of the mean vector goes to infinity. However, the asymptotic distributions of these tests depend on whether the dimension of the mean vector is fixed or goes to infinity. In this paper, we propose to split the sample into two parts and then to apply the empirical likelihood method to two equations instead of d equations, where d is the dimension of the underlying random vector. The asymptotic distribution of the new test is independent of the dimension of the mean vector. A simulation study shows that the new test has a very stable size with respect to the dimension of the mean vector, and is much more powerful than the modified Hotelling $T^2$-test.

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preprint2014arXiv

The low level radio frequency control system for DC-SRF photo-injector at Peking University

A low level radio frequency (LLRF) control system is designed and constructed at Peking University, which is for the DC-SRF photo injector operating at 2K. Besides with continuous wave (CW), the system is also reliable with pulsed RF and pulsed beam, the stability of amplitude and phase can achieve 0.13% and 0.1°respectively. It is worth noting that the system works perfectly when the cavity is driven at both generator driven resonator (GDR) and self-excited loop (SEL), the latter is useful in measuring the performance of the cavity.

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preprint2014arXiv

Weak Galerkin method for the coupled Darcy-Stokes flow

A family of weak Galerkin finite element discretization is developed for solving the coupled Darcy-Stokes equation. The equation in consideration admits the Beaver-Joseph-Saffman condition on the interface. By using the weak Galerkin approach, in the discrete space we are able to impose the normal continuity of velocity explicitly. Or in other words, strong coupling is achieved in the discrete space. Different choices of weak Galerkin finite element spaces are discussed, and error estimates are given.

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preprint2014arXiv

Which factor dominates the industry evolution? A synergy analysis based on China's ICT industry

Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on industry convergence and technology progress. Firstly, we use self-organization method and Haken Model to establish synergetic evolution equations, select technology progress and industry convergence as the key variables of industry evolution system; then use patent licensing data of china's listed ICT companies to measure industry convergence rate and apply DEA Malmquist index method to calculate technology progress level; furthermore apply simultaneous equation estimation method to investigate the synergetic industry evolution process. From 2002 to 2012, China's ICT industry develops rapidly; it has the most obvious convergence and powerful technology progress compared with other industries. We choose china's listed ICT industry to make empirical analysis. Our main findings are: a) technology progress is the order parameter which dominates industry system evolution. Moreover, industry convergence is the control parameter which is influenced by technology progress; b) Development of technology progress is the core factor for causing evolution of industry system, and industry convergence is the outcome of technology progress; c) Especially, it is important that the dominated role of technology progress will be sustained, even though in the environment of convergence, companies also need focus on self-innovation, rather than only adapt to the new industry evolution trend.

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preprint2013arXiv

Beyond Physical Connections: Tree Models in Human Pose Estimation

Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to address three questions: 1) are simple tree models sufficient? more specifically, 2) how to use tree models effectively in human pose estimation? and 3) how shall we use combined parts together with single parts efficiently? Assuming we have a set of single parts and combined parts, and the goal is to estimate a joint distribution of their locations. We surprisingly find that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure. This suggests one can straightforwardly use a mixed representation of single and combined parts to approximate their joint distribution in a simple tree model. As such, one only needs to build Visual Categories of the combined parts, and then perform inference on the learned latent tree. Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset. Experiments on animal images from the VOC challenge further support our findings.

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preprint2013arXiv

Learning Visual Symbols for Parsing Human Poses in Images

Parsing human poses in images is fundamental in extracting critical visual information for artificial intelligent agents. Our goal is to learn self-contained body part representations from images, which we call visual symbols, and their symbol-wise geometric contexts in this parsing process. Each symbol is individually learned by categorizing visual features leveraged by geometric information. In the categorization, we use Latent Support Vector Machine followed by an efficient cross validation procedure to learn visual symbols. Then, these symbols naturally define geometric contexts of body parts in a fine granularity. When the structure of the compositional parts is a tree, we derive an efficient approach to estimating human poses in images. Experiments on two large datasets suggest our approach outperforms state of the art methods.

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preprint2013arXiv

Realizing Chemical Codoping in Oxide Semiconductors

We demonstrate experimentally a chemical codoping approach that would simultaneously narrow the band gap and control the band edge positions of oxide semiconductors. Using TiO2 as an example, we show that a sequential doping scheme with nitrogen (N) leading the way, followed by phosphorous (P), is crucial for the incorporation of both N and P into the anion sites. Various characterization techniques confirm the formation of the N-P bonds, and as a consequence of the chemical codoping, the band gap of the TiO2 is reduced from 3.0 eV to 1.8 eV. The realization of chemical codoping could be an important step forward in improving the general performance of electronic and optoelectronic materials and devices.

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preprint2013arXiv

Study on the Fermi level of microstructured Silicon with impurities introduced by chalcogenides and their affect on solar cell efficiency

Microstructured Silicon, which is obtained by irradiating the surface of a Silicon wafer with femtosecond laser pulses under certain circumstances, has unusual optical properties such as the strong absorption of light with wavelength from 0.25μm to 17μm. So it holds great promise in the intermediate band solar cell (IBSC). Some articles have discussed the electronic structure associating with simple substitutional impurities in Silicon introduced by chalcogenides. And on this basis, after high temperature annealing treatment, we establish the mode of impurity levels of microstructured Silicon introduced by sulfur and oxygen. Using generalized statistics of multi-level,we analyze the probability of electronic in all local energy levels and the relationship among Fermi level, temperature and the density of impurities. Then the theoretical conversion efficiency of the corresponding IBSC is discussed with the Detailed Balance Theory. And the issue of making high efficiency solar cells based on femtosecond laser microstructured Silicon is discussed in detail.

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preprint2011arXiv

Isobaric yield ratios in heavy-ion reactions, and symmetry energy of neutron-rich nuclei at intermediate energies

The isobaric yield ratios of the fragments produced in the neutron-rich 48Ca and 64Ni projectile fragmentation are analyzed in the framework of a modified Fisher model. The correlations between the isobaric yield ratios (R) and the energy coefficients in the Weiszäcker-Beth semiclassical mass formula (the symmetry-energy term asym, the Coulomb-energy term ac, and the pairing-energy term ap) and the difference between the chemical potentials of the neutron and proton (μn-μp) are investigated. Simple correlations between (μn-μp)/T, ac/T, asym/T, and ap/T (where T is the temperature), and lnR are obtained. It is suggested that (μn-μp)/T, ac/T, asym/T, and ap/T of neutron-rich nuclei can be extracted using isobaric yield ratios for heavy-ion collisions at intermediate energies.

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