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Xu Wang

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

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Machine Learningmath.CVphysics.atom-phmath.DGArtificial Intelligencemath.NAComputer Visionnucl-thmath.AGphysics.ins-detSoftware Engineeringmath.APNumerical AnalysisApplicationsastro-ph.HEComputation and LanguageCryptography and Securityhep-exLogic in Computer Sciencemath.SGSystems and Controlastro-ph.EPastro-ph.IMComputer Science and Game Theorycond-mat.dis-nncond-mat.mtrl-scicond-mat.softcond-mat.str-elcond-mat.supr-concs.CYeess.ASeess.IVeess.SPeess.SYGraphicsmath.DSmath.PRmath.STMultiagent SystemsMultimediaNetworking and Internet Architecturephysics.app-phphysics.chem-phq-fin.GNQuantitative MethodsSoundStatistics Theory

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Published work

70 published item(s)

preprint2026arXiv

Response-G1: Explicit Scene Graph Modeling for Proactive Streaming Video Understanding

Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall short due to their implicit, query-agnostic modeling of visual evidence. We introduce Response-G1, a novel framework that establishes explicit, structured alignment between the accumulated video evidence and the query's expected response conditions via scene graphs. The framework operates in three fine-tuning-free stages: (1) online query-guided scene graph generation from streaming clips; (2) memory-based retrieval of the most semantically relevant historical scene graphs; and (3) retrieval-augmented trigger prompting for per-frame "silence/response" decisions. By grounding both evidence and conditions in a shared graph representation, Response-G1 achieves more interpretable and accurate response timing decisions. Experimental results on established benchmarks demonstrate the superiority of our method in both proactive and reactive tasks, validating the advantage of explicit scene graph modeling and retrieval in streaming video understanding.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

MiMo-Audio: Audio Language Models are Few-Shot Learners

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

TBDD: A New Trust-based, DRL-driven Framework for Blockchain Sharding in IoT

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it's vulnerable to the $1\%$ attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (\textsc{TbDd}) framework, crafted to counter shard collusion risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, \textsc{TbDd} discerns node types and performs targeted resharding against potential threats. The model maximizes tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Rigorous evaluations prove \textsc{TbDd}'s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

A Survey on Evaluation of Large Language Models

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner. However, the inevitable network asynchrony, the over-dependence on a central coordinator, and the lack of an open and fair incentive mechanism collectively hinder its further development. We propose \textsc{IronForge}, a new generation of FL framework, that features a Directed Acyclic Graph (DAG)-based data structure and eliminates the need for central coordinators to achieve fully decentralized operations. \textsc{IronForge} runs in a public and open network, and launches a fair incentive mechanism by enabling state consistency in the DAG, so that the system fits in networks where training resources are unevenly distributed. In addition, dedicated defense strategies against prevalent FL attacks on incentive fairness and data privacy are presented to ensure the security of \textsc{IronForge}. Experimental results based on a newly developed testbed FLSim highlight the superiority of \textsc{IronForge} to the existing prevalent FL frameworks under various specifications in performance, fairness, and security. To the best of our knowledge, \textsc{IronForge} is the first secure and fully decentralized FL framework that can be applied in open networks with realistic network and training settings.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation

Recently, there have been increasing calls for computer science curricula to complement existing technical training with topics related to Fairness, Accountability, Transparency, and Ethics. In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation. This paper presents an early use of our approach in a college-level computer science course. Through an in-class activity, we report empirical data for the initial effectiveness of our approach. Our results suggest that the use of the Value Cards toolkit can improve students' understanding of both the technical definitions and trade-offs of performance metrics and apply them in real-world contexts, help them recognize the significance of considering diverse social values in the development of deployment of algorithmic systems, and enable them to communicate, negotiate and synthesize the perspectives of diverse stakeholders. Our study also demonstrates a number of caveats we need to consider when using the different variants of the Value Cards toolkit. Finally, we discuss the challenges as well as future applications of our approach.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

$λ$-domain VVC Rate Control Based on Game Theory

Versatile Video Coding (VVC) has set a new milestone in high-efficiency video coding. In the standard encoder, the $λ$-domain rate control is incorporated for its high accuracy and good Rate-Distortion (RD) performance. In this paper, we formulate this task as a Nash equilibrium problem that effectively bargains between multiple agents, {\it i.e.}, Coding Tree Units (CTUs) in the frame. After that, we calculate the optimal $λ$ value with a two-step strategy: a Newton method to iteratively obtain an intermediate variable, and a solution of Nash equilibrium to obtain the optimal $λ$. Finally, we propose an effective CTU-level rate allocation with the optimal $λ$ value. To the best of our knowledge, we are the first to combine game theory with $λ$-domain rate control. Experimental results with Common Test Conditions (CTC) demonstrate the efficiency of the proposed method, which outperforms the state-of-the-art CTU-level rate allocation algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Semantic Web Technology Index

Semantic Web (SW) technology has been widely applied to many domains such as medicine, health care, finance, geology. At present, researchers mainly rely on their experience and preferences to develop and evaluate the work of SW technology. Although the general architecture (e.g., Tim Berners-Lee's Semantic Web Layer Cake) of SW technology was proposed many years ago and has been well-known, it still lacks a concrete guideline for standardizing the development of SW technology. In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology. This index consists of 10 criteria that quantify the quality as a score of 0 ~ 10. We address each criterion in detail for a clear explanation from three aspects: 1) what is the criterion? 2) why do we consider this criterion and 3) how do the current studies meet this criterion? Finally, we present the validation of this index by providing some examples of how to apply the index to the validation cases. We conclude that the index is a useful standard to guide and evaluate the work in SW technology.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Curvature of the base manifold of a Monge-Ampère fibration and its existence

In this paper, we consider a special relative Kähler fibration that satisfies a homogenous Monge-Ampère equation, which is called a Monge-Ampère fibration. There exist two canonical types of generalized Weil-Petersson metrics on the base complex manifold of the fibration. For the second generalized Weil-Petersson metric, we obtain an explicit curvature formula and prove that the holomorphic bisectional curvature is non-positive, the holomorphic sectional curvature, the Ricci curvature, and the scalar curvature are all bounded from above by a negative constant. For a holomorphic vector bundle over a compact Kähler manifold, we prove that it admits a projectively flat Hermitian structure if and only if the associated projective bundle fibration is a Monge-Ampère fibration. In general, we can prove that a relative Kähler fibration is Monge-Ampère if and only if an associated infinite rank Higgs bundle is Higgs-flat. We also discuss some typical examples of Monge-Ampère fibrations.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Emergence of superconducting dome in insulating ZrNx films via nitrogen manipulation

Reproducing the electronic phase diagram of strongly correlated high-transition-temperature (high-Tc) superconductors in materials other than Cu-, Fe-, and Ni-based compounds has been a challenging task. Only very recently, a few material systems have partially achieved this goal by band engineering. In this work, we combine film growth, charge transport, magnetometry, Terahertz Spectroscopy, Raman scattering, and Scanning Transmission Electron Microscopy to investigate superconductivity and the normal state of ZrNx, which reveals a phase diagram that bears extraordinary similarities to those of high-Tc superconductors. Remarkably, even though superconductivity of ZrNx can be characterized within the Bardeen-Cooper-Schrieffer paradigm and its normal state can be understood within the Fermi liquid framework, by tunning the N chemical concentration, we observe the evolution of a superconducting dome in the close vicinity of a strongly insulating state and a normal state resistivity mimics its counterpart of the high-Tc superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Gaze Estimation Approach Using Deep Differential Residual Network

Gaze estimation, which is a method to determine where a person is looking at given the person's full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with $angular-error$ of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Graph Neural Networks for Double-Strand DNA Breaks Prediction

Double-strand DNA breaks (DSBs) are a form of DNA damage that can cause abnormal chromosomal rearrangements. Recent technologies based on high-throughput experiments have obvious high costs and technical challenges.Therefore, we design a graph neural network based method to predict DSBs (GraphDSB), using DNA sequence features and chromosome structure information. In order to improve the expression ability of the model, we introduce Jumping Knowledge architecture and several effective structural encoding methods. The contribution of structural information to the prediction of DSBs is verified by the experiments on datasets from normal human epidermal keratinocytes (NHEK) and chronic myeloid leukemia cell line (K562), and the ablation studies further demonstrate the effectiveness of the designed components in the proposed GraphDSB framework. Finally, we use GNNExplainer to analyze the contribution of node features and topology to DSBs prediction, and proved the high contribution of 5-mer DNA sequence features and two chromatin interaction modes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search

Aiming at two molecular graph datasets and one protein association subgraph dataset in OGB graph classification task, we design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search). At the same time, we improve it based on the GNN topology design method F2GNN to further design the feature selection and fusion strategies, so as to further improve the performance of the model in the graph property prediction task while overcoming the over smoothing problem of deep GNN training. Finally, a performance breakthrough is achieved on these three datasets, which is significantly better than other methods with fixed aggregate function. It is proved that the NAS method has high generalization ability for multiple tasks and the advantage of our method in processing graph property prediction tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nuclear excitation cross section of $^{229}$Th via inelastic electron scattering

Nuclear excitation cross section of $^{229}$Th from the ground state to the low-lying isomeric state via inelastic electron scattering is calculated, on the level of Dirac distorted wave Born approximation. With electron energies below 100 eV, inelastic scattering is very efficient in the isomeric excitation, yielding excitation cross sections on the order of 10$^{-27}$ to 10$^{-26}$ cm$^2$. Systematic analyses are presented on elements affecting the excitation cross section, including the ion-core potential, the relativistic effect, the knowledge of the reduced nuclear transition probabilities, etc.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Periodic repeating fast radio bursts: interaction between a magnetized neutron star and its planet in an eccentric orbit

Fast radio bursts (FRBs) are mysterious transient phenomena. The study of repeating FRBs may provide useful information about their nature due to their redetectability. The two most famous repeating sources are FRBs 121102 and 180916, with a period of 157 days and 16.35 days, respectively. Previous studies suggest that the periodicity of FRBs is likely associated with neutron star (NS) binary systems. Here we introduce a new model which proposes that periodic repeating FRBs are due to the interaction of a NS with its planet in a highly elliptical orbit. The periastron of the planet is very close to the NS so that it would be partially disrupted by tidal force every time it passes through the periastron. Fragments generated in the process could interact with the compact star through the Alfvén wing mechanism and produce FRBs. The model can naturally explain the repeatability of FRBs with a period ranging from a few days to several hundred days, but it generally requires that the eccentricity of the planet's orbit should be large enough. Taking FRBs 121102 and 180916 as examples, it is shown that the main features of the observed repeating behaviors can be satisfactorily accounted for.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Real-space Observation of Incommensurate Spin Density Wave and Coexisting Charge Density Wave on Cr(001) surface

In itinerant magnetic systems, a spin density wave (SDW) state can be induced by Fermi surface nesting and electron-electron interaction. It may intertwine with other orders such as charge density wave (CDW), while their relation is still yet to be understood. Here via spin-polarized scanning tunneling microscopy, we directly observed long-range spin modulation on Cr(001) surface, which corresponds to the well-known incommensurate SDW of bulk Cr. It displays 6.0 nm in-plane period and anti-phase behavior between adjacent (001) planes. Meanwhile, we simultaneously observed the coexisting CDW with half the period of SDW. Such SDW/CDW have highly correlated domain structures and are in-phase. Surprisingly, the CDW displays a contrast inversion around a density-of-states dip at -22 meV, indicating an anomalous CDW gap opened below EF. These observations support that the CDW is a secondary order driven by SDW. Our work is not only a real-space characterization of incommensurate SDW, but also provides insights on how SDW and CDW coexist.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Recollision induced nuclear excitation of $^{229}$Th

Previously we proposed a new approach of exciting the $^{229}$Th nucleus using laser-driven electron recollision [W. Wang et al., Phys. Rev. Lett. 127, 052501 (2021)]. The current article is aimed to elaborate the method by explaining further theoretical details and presenting extended new results. The method has also been improved by adopting the electronic excitation cross sections calculated recently by Tkalya [E. V. Tkalya, Phys. Rev. Lett. 124, 242501 (2020)]. The new cross sections are obtained from Dirac distorted-wave calculations instead of from Dirac plane-wave calculations as we used previously. The distorted-wave cross sections are shown to be 5 to 6 orders of magnitude higher than the plane-wave results. With the excitation cross sections updated, the probability of isomeric excitation of $^{229}$Th from electron recollision is calculated to be on the order of $10^{-12}$ per nucleus per (femtosecond) laser pulse. Dependency of the excitation probability on various laser parameters is calculated and discussed, including the laser intensity, the laser wavelength, and the laser pulse duration.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Strong convergence rate of the Euler scheme for SDEs driven by additive rough fractional noises

The strong convergence rate of the Euler scheme for SDEs driven by additive fractional Brownian motions is studied, where the fractional Brownian motion has Hurst parameter $H\in(\frac13,\frac12)$ and the drift coefficient is not required to be bounded. The Malliavin calculus, the rough path theory and the $2$D Young integral are utilized to overcome the difficulties caused by the low regularity of the fractional Brownian motion and the unboundedness of the drift coefficient. The Euler scheme is proved to have strong order $2H$ for the case that the drift coefficient has bounded derivatives up to order three and have strong order $H+\frac12$ for linear cases. Numerical simulations are presented to support the theoretical results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Tidal Deformability of Strange Quark Planets and Strange Dwarfs

Strange quark matter, which is composed of u, d, and s quarks, could be the true ground of matter. According to this hypothesis, compact stars may actually be strange quark stars, and there may even be stable strange quark dwarfs and strange quark planets. The detection of the binary neutron star merger event GW170817 provides us new clues on the equation of state of compact stars. In this study, the tidal deformability of strange quark planets and strange quark dwarfs are calculated. It is found that the tidal deformability of strange quark objects is smaller than that of normal matter counterparts. For a typical 0.6 M$_\odot$ compact star, the tidal deformability of a strange dwarf is about 1.4 times less than that of a normal white dwarf. The difference is even more significant between strange quark planets and normal matter planets. Additionally, if the strange quark planet is a bare one (i.e., not covered by a normal matter curst), the tidal deformability will be extremely small, which means bare strange quark planets will hardly be distorted by tidal forces. Our study clearly proves the effectiveness of identifying strange quark objects via searching for strange quark planets through gravitational-wave observations.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

An inverse source problem for the stochastic wave equation

This paper is concerned with an inverse source problem for the stochastic wave equation driven by a fractional Brownian motion. Given the random source, the direct problem is to study the solution of the stochastic wave equation. The inverse problem is to determine the statistical properties of the source from the expectation and covariance of the final-time data. For the direct problem, it is shown to be well-posed with a unique mild solution. For the inverse problem, the uniqueness is proved for a certain class of functions and the instability is characterized. Numerical experiments are presented to illustrate the reconstructions by using a truncation-based regularization method.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Attention Models for Point Clouds in Deep Learning: A Survey

Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks. However, creating feature representation of robust, discriminative from unordered and irregular point clouds is challenging. In this paper, our ultimate goal is to provide a comprehensive overview of the point clouds feature representation which uses attention models. More than 75+ key contributions in the recent three years are summarized in this survey, including the 3D objective detection, 3D semantic segmentation, 3D pose estimation, point clouds completion etc. We provide a detailed characterization (1) the role of attention mechanisms, (2) the usability of attention models into different tasks, (3) the development trend of key technology.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Design and testing of an sTGC ASIC interface board for the ATLAS New Small Wheel upgrade

The ATLAS experiment will replace the present Small Wheel (SW) detector with a New Small Wheel detector (NSW) aiming to improve the performance of muon triggering and precision tracking in the endcap region at the High-Luminosity LHC. Small-strip Thin Gap Chamber (sTGC) is one of the two new detector technologies used in this upgrade. A few custom-designed ASICs are needed for the sTGC detector. We designed an sTGC ASIC interface board to test ASIC-to-ASIC communication and validate the functionality of the entire system. A test platform with the final readout system is set up and the whole sTGC readout chain is demonstrated for the first time. Key parameters in the readout chain are discussed and the results are shown.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Inverse random potential scattering for elastic waves

This paper is concerned with the inverse elastic scattering problem for a random potential in three dimensions. Interpreted as a distribution, the potential is assumed to be a microlocally isotropic Gaussian random field whose covariance operator is a classical pseudo-differential operator. Given the potential, the direct scattering problem is shown to be well-posed in the distribution sense by studying the equivalent Lippmann--Schwinger integral equation. For the inverse scattering problem, we demonstrate that the microlocal strength of the random potential can be uniquely determined with probability one by a single realization of the high frequency limit of the averaged compressional or shear backscattered far-field pattern of the scattered wave. The analysis employs the integral operator theory, the Born approximation in the high frequency regime, the microlocal analysis for the Fourier integral operators, and the ergodicity of the wave field.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Prioritize Crowdsourced Test Reports via Deep Screenshot Understanding

Crowdsourced testing is increasingly dominant in mobile application (app) testing, but it is a great burden for app developers to inspect the incredible number of test reports. Many researches have been proposed to deal with test reports based only on texts or additionally simple image features. However, in mobile app testing, texts contained in test reports are condensed and the information is inadequate. Many screenshots are included as complements that contain much richer information beyond texts. This trend motivates us to prioritize crowdsourced test reports based on a deep screenshot understanding. In this paper, we present a novel crowdsourced test report prioritization approach, namely DeepPrior. We first represent the crowdsourced test reports with a novelly introduced feature, namely DeepFeature, that includes all the widgets along with their texts, coordinates, types, and even intents based on the deep analysis of the app screenshots, and the textual descriptions in the crowdsourced test reports. DeepFeature includes the Bug Feature, which directly describes the bugs, and the Context Feature, which depicts the thorough context of the bug. The similarity of the DeepFeature is used to represent the test reports' similarity and prioritize the crowdsourced test reports. We formally define the similarity as DeepSimilarity. We also conduct an empirical experiment to evaluate the effectiveness of the proposed technique with a large dataset group. The results show that DeepPrior is promising, and it outperforms the state-of-the-art approach with less than half the overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Active Gate Drive with Gate-Drain Discharge Compensation for Voltage Balancing in Series-Connected SiC MOSFETs

Imbalanced voltage sharing during the turn-off transient is a challenge for series-connected silicon carbide (SiC) MOSFET application. This article first discusses the influence of the gate-drain discharge deviation on the voltage imbalance ratio, and its primary causes are also presented and verified by LTspice simulation. Accordingly, a novel active gate drive, which aims to compensate the discharge difference between devices connected in series, is proposed and analyzed. By only using the original output of the driving IC, the proposed gate drive is realized by implementing an auxiliary circuit on the existing commercial gate drive. Therefore, unlike other active gate drives for balancing control, no extra isolations for power/signal are needed, and the number of the devices in series is unlimited. The auxiliary circuit includes three sub-circuits as a high-bandwidth current sink for regulating switching performance, a relative low-frequency but reliable sampling and control circuit for closed-loop control, and a trigger combining the former and the latter. The operational principle and the design guideline for each part are presented in detail. Experimental results validate the performance of the proposed gate drive and its voltage balancing control algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An inverse random source problem for Maxwell's equations

This paper is concerned with an inverse random source problem for the three-dimensional time-harmonic Maxwell equations. The source is assumed to be a centered complex-valued Gaussian vector field with correlated components, and its covariance operator is a pseudo-differential operator. The well-posedness of the direct source scattering problem is established and the regularity of the electromagnetic field is given. For the inverse source scattering problem, the micro-correlation strength matrix of the covariance operator is shown to be uniquely determined by the high frequency limit of the expectation of the electric field measured in an open bounded domain disjoint with the support of the source. In particular, we show that the diagonal entries of the strength matrix can be uniquely determined by only using the amplitude of the electric field. Moreover, this result is extended to the almost surely sense by deducing an ergodic relation for the electric field over the frequencies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Automate Obstructive Sleep Apnea Diagnosis Using Convolutional Neural Networks

Identifying sleep problem severity from overnight polysomnography (PSG) recordings plays an important role in diagnosing and treating sleep disorders such as the Obstructive Sleep Apnea (OSA). This analysis traditionally is done by specialists manually through visual inspections, which can be tedious, time-consuming, and is prone to subjective errors. One of the solutions is to use Convolutional Neural Networks (CNN) where the convolutional and pooling layers behave as feature extractors and some fully-connected (FCN) layers are used for making final predictions for the OSA severity. In this paper, a CNN architecture with 1D convolutional and FCN layers for classification is presented. The PSG data for this project are from the Cleveland Children's Sleep and Health Study database and classification results confirm the effectiveness of the proposed CNN method. The proposed 1D CNN model achieves excellent classification results without manually preprocesssing PSG signals such as feature extraction and feature reduction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Cohomologies of complex manifolds with symplectic $(1,1)$-forms

Let $(X, J)$ be a complex manifold with a non-degenerated smooth $d$-closed $(1,1)$-form $ω$. Then we have a natural double complex $\overline{\partial}+\overline{\partial}^Λ$, where $\overline{\partial}^Λ$ denotes the symplectic adjoint of the $\overline{\partial}$-operator. We study the Hard Lefschetz Condition on the Dolbeault cohomology groups of $X$ with respect to the symplectic form $ω$. In \cite{TW}, we proved that such a condition is equivalent to a certain symplectic analogous of the $\partial\overline{\partial}$-Lemma, namely the $\overline{\partial}\, \overline{\partial}^Λ$-Lemma, which can be characterized in terms of Bott--Chern and Aeppli cohomologies associated to the above double complex. We obtain Nomizu type theorems for the Bott--Chern and Aeppli cohomologies and we show that the $\overline{\partial}\, \overline{\partial}^Λ$-Lemma is stable under small deformations of $ω$, but not stable under small deformations of the complex structure. However, if we further assume that $X$ satisfies the $\partial\overline{\partial}$-Lemma then the $\overline{\partial}\, \overline{\partial}^Λ$-Lemma is stable.

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preprint2020arXiv

Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes

Background and Objective: Biometric measurements of fetal head are important indicators for maternal and fetal health monitoring during pregnancy. 3D ultrasound (US) has unique advantages over 2D scan in covering the whole fetal head and may promote the diagnoses. However, automatically segmenting the whole fetal head in US volumes still pends as an emerging and unsolved problem. The challenges that automated solutions need to tackle include the poor image quality, boundary ambiguity, long-span occlusion, and the appearance variability across different fetal poses and gestational ages. In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes. Methods: The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features in a composite and hierarchical way. With little computation overhead, HAS proves to be effective in addressing boundary ambiguity and deficiency. To enhance the spatial consistency in segmentation, we further organize multiple segmentors in a cascaded fashion to refine the results by revisiting context in the prediction of predecessors. Results: Validated on a large dataset collected from 100 healthy volunteers, our method presents superior segmentation performance (DSC (Dice Similarity Coefficient), 96.05%), remarkable agreements with experts. With another 156 volumes collected from 52 volunteers, we ahieve high reproducibilities (mean standard deviation 11.524 mL) against scan variations. Conclusion: This is the first investigation about whole fetal head segmentation in 3D US. Our method is promising to be a feasible solution in assisting the volumetric US-based prenatal studies.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Nuclear fission in intense laser fields

Rapid-advancing intense laser technologies enable the possibility of a direct laser-nucleus coupling. In this paper the effect of intense laser fields on a series of nuclear fission processes, including proton decay, alpha decay, and cluster decay, is theoretically studied with the help of nuclear double folding potentials. The results show that the half-lives of these decay processes can be modified by non-negligible amounts, for example on the order of 0.01 or 0.1 percents in intense laser fields available in the forthcoming years. In addition to numerical results, an approximate analytical formula is derived to connect the laser-induced modification to the decay half-life and the decay energy.

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preprint2020arXiv

On the mechanical beta relaxation in glass and its relation to the double-peak phenomenon in impulse excited vibration at high temperatures

A viscoelastic model is established to reveal the relation between alpha-beta relaxation of glass and the double-peak phenomenon in the experiments of impulse excited vibration. In the modelling, the normal mode analysis (NMA) of potential energy landscape (PEL) picture is employed to describe mechanical alpha and beta relaxations in a glassy material. The model indicates that a small beta relaxation can lead to an apparent double-peak phenomenon resulted from the free vibration of a glass beam when the frequency of beta relaxation peak is close to the natural frequency of specimen. The theoretical prediction is validated by the acoustic spectrum of a fluorosilicate glass beam excited by a mid-span impulse. Furthermore, the experimental results indicate a negative temperature-dependence of the frequency of beta relaxation in the fluorosilicate glass S-FSL5 which can be explained based on the physical picture of fragmented oxide-network patches in liquid-like regions.

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preprint2020arXiv

Price Competition with LTE-U and WiFi

LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of LTE-U and WiFi in unlicensed spectrum In this paper, we instead investigate the impact of such a technology from an economic perspective. We consider a model in which an incumbent service provider (SP) deploys a duty cycle-based technology like LTE-U in an unlicensed band along with operating in a licensed band and competes with one or more entrants that only operate in the unlicensed band using a different technology like WiFi. We characterize the impact of a technology like LTE-U on the market outcome and show that the welfare impacts of this technology are subtle, depending in part on the amount of unlicensed spectrum and number of entrants. The difference in spectral efficiency between LTE and WiFi also plays a role in the competition among SPs. Finally, we investigate the impact of the duty cycle and the portion of unlicensed spectrum used by the technology.

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preprint2020arXiv

Statistical Inference for Networks of High-Dimensional Point Processes

Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, this paper develops a new statistical inference procedure for high-dimensional Hawkes processes. The key ingredient for this inference procedure is a new concentration inequality on the first- and second-order statistics for integrated stochastic processes, which summarize the entire history of the process. Combining recent results on martingale central limit theory with the new concentration inequality, we then characterize the convergence rate of the test statistics. We illustrate finite sample validity of our inferential tools via extensive simulations and demonstrate their utility by applying them to a neuron spike train data set.

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preprint2020arXiv

Substantially enhanced deuteron-triton fusion probabilities in intense low-frequency laser fields

Deuteron-triton (DT) fusion is the primary fusion reaction used in controlled fusion research, mainly for its relatively high reaction cross sections compared to other fusion options. Even so, to attain appreciable reaction probabilities very high temperatures (on the order of 10-100 million kelvins) are required, which are extremely challenging to achieve and maintain. We show that intense low-frequency laser fields, such as those in the near-infrared regime for the majority of intense laser facilities around the world, are highly effective in transferring energy to the DT system and enhancing the DT fusion probabilities. The fusion probabilities are shown to be enhanced by at least an order of magnitude in 800-nm laser fields with intensities on the order of 10$^{21}$ W/cm$^2$. The demanding temperature requirement of controlled nuclear fusion may be relaxed if intense low-frequency lasers are exploited.

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preprint2020arXiv

Timing Performance of a Micro-Channel-Plate Photomultiplier Tube

The spatial dependence of the timing performance of the R3809U-50 Micro-Channel-Plate PMT (MCP-PMT) by Hamamatsu was studied in high energy muon beams. Particle position information is provided by a GEM tracker telescope, while timing is measured relative to a second MCP-PMT, identical in construction. In the inner part of the circular active area (radius r$<$5.5\,mm) the time resolution of the two MCP-PMTs combined is better than 10~ps. The signal amplitude decreases in the outer region due to less light reaching the photocathode, resulting in a worse time resolution. The observed radial dependence is in quantitative agreement with a dedicated simulation. With this characterization, the suitability of MCP-PMTs as $\text{t}_\text{0}$ reference detectors has been validated.

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preprint2020arXiv

Ultra-broadband acoustic ventilation barriers via hybrid-functional metasurfaces

Ventilation barriers allowing simultaneous sound blocking and free airflow passage are of great challenge but necessary for particular scenarios calling for sound-proofing ventilation. Previous works based on local resonance or Fano-like interference serve a narrow working range around the resonant or destructive-interference frequency. Efforts made on broadband designs show a limited bandwidth typically smaller than half an octave. Here, we theoretically design an ultra-broadband ventilation barrier via hybridizing dissipation and interference. Confirmed by experiments, the synergistic effect from our hybrid-functional metasurface significantly expand the scope of its working frequencies, leading to an effective blocking of more than 90% of incident energy in the range of 650-2000 Hz, while its structural thickness is only 53 mm $(\sim λ/ 10)$. Our design shows great flexibility in customizing the broadband and is capable of handling sound coming from various directions, which has potential in air-permeable yet sound-proofing applications.

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preprint2019arXiv

Algebraic fiber spaces and curvature of higher direct images

In this article we are interested in the differential geometric properties of certain higher direct images of exterior powers of the sheaf of relative differentials twisted with a line bundle. We obtain explicit curvature formulas, especially in case where the said line bundle satisfies a natural curvature assumption. Several applications are obtained, including a proof of a result by Viehweg-Zuo in the context of a canonically polarized family of maximal variation.

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preprint2019arXiv

An inverse random source problem for the time fractional diffusion equation driven by a fractional Brownian motion

This paper is concerned with the mathematical analysis of the inverse random source problem for the time fractional diffusion equation, where the source is assumed to be driven by a fractional Brownian motion. Given the random source, the direct problem is to study the stochastic time fractional diffusion equation. The inverse problem is to determine the statistical properties of the source from the expectation and variance of the final time data. For the direct problem, we show that it is well-posed and has a unique mild solution under a certain condition. For the inverse problem, the uniqueness is proved and the instability is characterized. The major ingredients of the analysis are based on the properties of the Mittag--Leffler function and the stochastic integrals associated with the fractional Brownian motion.

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preprint2019arXiv

Optimal Strong Convergence Rate of a Backward Euler Type Scheme for the Cox--Ingersoll--Ross Model Driven by Fractional Brownian Motion

In this paper, we investigate the optimal strong convergence rate of numerical approximations for the Cox--Ingersoll--Ross model driven by fractional Brownian motion with Hurst parameter $H\in(1/2,1)$. To deal with the difficulties caused by the unbounded diffusion coefficient, we study an auxiliary equation based on Lamperti transformation. By means of Malliavin calculus, we prove that the backward Euler scheme applied to this auxiliary equation ensures the positivity of the numerical solution, and is of strong order one. Furthermore, a numerical approximation for the original model is obtained and converges with the same order.

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preprint2016arXiv

A flat Higgs bundle structure on the complexified Kähler cone

We shall construct a natural Higgs bundle structure on the complexified Kähler cone of a compact Kähler manifold, which can be seen as an analogy of the classical Higgs bundle structure associated to a variation of Hodge structure. In the proof of the flat-ness of our Higgs bundle, we find a commutator identity that can be used to decode the variational properties of the polarized Hodge-Lefschetz module structure on the fibres of our Higgs bundle. Thus we can use a generalized version of Lu's Hodge metric to study the curvature property of the complexified Kähler cone. In particular, it implies that the above Hodge metric defines a Kähler metric on the complexified Kähler cone with negative holomorphic sectional curvature, which can be seen as a new result on Wilson's conjecture.

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preprint2016arXiv

A Multifaceted Panel Data Gravity Model Analysis of Peru's Foreign Trade

Peru's abundant natural resources and friendly trade policies has made the country a major economic player in both South America and the global community. Consequently, exports are playing an increasingly important role in Peru's national economy. Indeed, growing from 13.1% as of 1994, exports now contribute approximately 21% of the GDP of Peru as of 2015. Given Peru's growing global influence, the time is ripe for a thorough analysis of the most important factors governing its export performance. Thus, within the framework of the augmented gravity model of trade, this paper examines Peru's export performance and attempts to identify the dominant economic factors that should be further developed to increase the value of exports. The analysis was conducted from three different aspects: (1) general economic parameters' effect on Peru's export value, (2) more specific analysis into a major specific trade good, copper, and (3) the impact that regional trade agreements have had on Peru's export performance. Our panel data analysis results for each dataset revealed interesting economic trends and were consistent with the theoretical expectations of the gravity model: namely positive coefficients for economic size and negative coefficients for distance. This report's results can be a reference for the proper direction of Peruvian economic policy so as to enhance economic growth in a sustainable direction.

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preprint2016arXiv

Approximation of Invariant Measure for Damped Stochastic Nonlinear Schrödinger Equation via an Ergodic Numerical Scheme

In order to inherit numerically the ergodicity of the damped stochastic nonlinear Schrödinger equation with additive noise, we propose a fully discrete scheme, whose spatial direction is based on spectral Galerkin method and temporal direction is based on a modification of the implicit Euler scheme. We not only prove the unique ergodicity of the numerical solutions of both spatial semi-discretization and full discretization, but also present error estimations on invariant measures, which gives order $2$ in spatial direction and order ${\frac12}$ in temporal direction.

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preprint2016arXiv

Convergence in probability of an ergodic and conformal multi-symplectic numerical scheme for a damped stochastic NLS equation

In this paper, we investigate the convergence order in probability of a novel ergodic numerical scheme for damped stochastic nonlinear Schrödinger equation with an additive noise. Theoretical analysis shows that our scheme is of order one in probability under appropriate assumptions for the initial value and noise. Meanwhile, we show that our scheme possesses the unique ergodicity and preserves the discrete conformal multi-symplectic conservation law. Numerical experiments are given to show the longtime behavior of the discrete charge and the time average of the numerical solution, and to test the convergence order, which verify our theoretical results.

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preprint2016arXiv

Curvature of higher direct image sheaves and its application on negative-curvature criterion for the Weil-Petersson metric

We shall show that $q$-semipositivity of the vector bundle $E$ over a Kähler total space $\mathcal X$ implies the Griffiths-semipositivity of the $q$-th direct image of $\mathcal O(K_{\mathcal X/B}\otimes E)$. As an application, we shall give a negative-curvature criterion for the generalized Weil-Petersson metric on the base manifold.

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preprint2016arXiv

Electronic Single Molecule Identification of Carbohydrate Isomers by Recognition Tunneling

Glycans play a central role as mediators in most biological processes, but their structures are complicated by isomerism. Epimers and anomers, regioisomers, and branched sequences contribute to a structural variability that dwarfs those of nucleic acids and proteins, challenging even the most sophisticated analytical tools, such as NMR and mass spectrometry. Here, we introduce an electron tunneling technique that is label-free and can identify carbohydrates at the single-molecule level, offering significant benefits over existing technology. It is capable of analyzing sub-picomole quantities of sample, counting the number of individual molecules in each subset in a population of coexisting isomers, and is quantitative over more than four orders of magnitude of concentration. It resolves epimers not well separated by ion-mobility and can be implemented on a silicon chip. It also provides a readout mechanism for direct single-molecule sequencing of linear oligosaccharides.

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preprint2016arXiv

Entropy of a semigroup of maps from a set-valued view

In this paper, we introduce a new entropy-like invariant, named Hausdorff metric entropy, for finitely generated semigroups acting on compact metric spaces from a set-valued view and study its properties. We establish the relation between Hausdorff metric entropy and topological entropy of a semigroup defined by Biś. Some examples with positive or zero Hausdorff metric entropy are given. Moreover, some notions of chaos are also well generalized for finitely generated semigroups from a set-valued view.

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preprint2016arXiv

Extension of ATLAST/LUVOIR's Capabilities to 5 Microns, or Beyond

ATLAST is a particular realization of LUVOIR, the Large Ultraviolet Optical Infrared telescope, a ~10 m diameter space telescope being defined for consideration in the 2020 Decadal Review of astronomy and astrophysics. ATLAST/LUVOIR is generally thought of as an ambient temperature (~300 K) system, and little consideration has been given to using it at infrared wavelengths longward of ~2 μm. We assess the scientific and technical benefits of operating such a telescope further into the infrared, with particular emphasis on the study of exoplanets, which is a major science theme for ATLAST/LUVOIR. For the study of exoplanet atmospheres, the capability to work at least out to 5.0 μm is highly desirable. Such an extension of the long wavelength limit of ATLAST would greatly increase its capabilities for studies of exoplanet atmospheres and provide powerful capabilities for the study of a wide range of astrophysical questions. We present a concept for a fiber-fed grating spectrometer which would enable R = 200 spectroscopy on ATLAST with minimal impact on the other focal planet instruments. We conclude that it is technically feasible and highly desirable scientifically to extend the wavelength range of ATLAST to at least 5 μm.

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preprint2016arXiv

Numerical Analysis on Ergodic Limit of Approximations for Stochastic NLS Equation via Multi-symplectic Scheme

We consider a finite dimensional approximation of the stochastic nonlinear Schrödinger equation driven by multiplicative noise, which is derived by applying a symplectic method to the original equation in spatial direction. Both the unique ergodicity and the charge conservation law for this finite dimensional approximation are obtained on the unit sphere. To simulate the ergodic limit over long time for the finite dimensional approximation, we discretize it further in temporal direction to obtain a fully discrete scheme, which inherits not only the stochastic multi-symplecticity and charge conservation law of the original equation but also the unique ergodicity of the finite dimensional approximation. The temporal average of the fully discrete numerical solution is proved to converge to the ergodic limit with order one with respect to the time step for a fixed spatial step. Numerical experiments verify our theoretical results on charge conservation, ergodicity and weak convergence.

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preprint2016arXiv

Setup of a photomultiplier tube test bench for use at LHAASO-KM2A

To fulfill the requirements for testing the photomultiplier tubes (PMTs) of the electromagnetic detec- tor at the Large High Altitude Air Shower Observatory (LHAASO), a multi-functional PMT test bench with a two dimensional scanning system has been developed. With this 2D scanning system, 16 PMTs can be scanned simultaneously for characteristics tests, including uniformity, cathode transit time difference, single photo-electron spectrum, gain vs. high voltage, linear behavior and dark noise. The programmable hardware and intelligent software of the test bench make it convenient to use and provide reliable results. The test methods are described in detail and primary results are presented.

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preprint2015arXiv

Fast Landmark Subspace Clustering

Kernel methods obtain superb performance in terms of accuracy for various machine learning tasks since they can effectively extract nonlinear relations. However, their time complexity can be rather large especially for clustering tasks. In this paper we define a general class of kernels that can be easily approximated by randomization. These kernels appear in various applications, in particular, traditional spectral clustering, landmark-based spectral clustering and landmark-based subspace clustering. We show that for $n$ data points from $K$ clusters with $D$ landmarks, the randomization procedure results in an algorithm of complexity $O(KnD)$. Furthermore, we bound the error between the original clustering scheme and its randomization. To illustrate the power of this framework, we propose a new fast landmark subspace (FLS) clustering algorithm. Experiments over synthetic and real datasets demonstrate the superior performance of FLS in accelerating subspace clustering with marginal sacrifice of accuracy.

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preprint2015arXiv

Nonparametric Bayesian Regression on Manifolds via Brownian Motion

This paper proposes a novel framework for manifold-valued regression and establishes its consistency as well as its contraction rate. It assumes a predictor with values in the interval $[0,1]$ and response with values in a compact Riemannian manifold $M$. This setting is useful for applications such as modeling dynamic scenes or shape deformations, where the visual scene or the deformed objects can be modeled by a manifold. The proposed framework is nonparametric and uses the heat kernel (and its associated Brownian motion) on manifolds as an averaging procedure. It directly generalizes the use of the Gaussian kernel (as a natural model of additive noise) in vector-valued regression problems. In order to avoid explicit dependence on estimates of the heat kernel, we follow a Bayesian setting, where Brownian motion on $M$ induces a prior distribution on the space of continuous functions $C([0,1], M)$. For the case of discretized Brownian motion, we establish the consistency of the posterior distribution in terms of the $L_{q}$ distances for any $1 \leq q < \infty$. Most importantly, we establish contraction rate of order $O(n^{-1/4+ε})$ for any fixed $ε>0$, where $n$ is the number of observations. For the continuous Brownian motion we establish weak consistency.

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preprint2015arXiv

Spectral Convergence Rate of Graph Laplacian

Laplacian Eigenvectors of the graph constructed from a data set are used in many spectral manifold learning algorithms such as diffusion maps and spectral clustering. Given a graph constructed from a random sample of a $d$-dimensional compact submanifold $M$ in $\mathbb{R}^D$, we establish the spectral convergence rate of the graph Laplacian. It implies the consistency of the spectral clustering algorithm via a standard perturbation argument. A simple numerical study indicates the necessity of a denoising step before applying spectral algorithms.

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preprint2014arXiv

Holomorphic maps with large images

We show that each pseudoconvex domain $Ω\subset {\mathbb C}^n$ admits a holomorphic map $F$ to ${\mathbb C}^m$ with $|F|\le C_1 e^{C_2 \hatδ^{-6}}$, where $\hatδ$ is the minimum of the boundary distance and $(1+|z|^2)^{-1/2}$, such that every boundary point is a Casorati-Weierstrass point of $F$. Based on this fact, we introduce a new anti-hyperbolic concept --- universal dominability. We also show that for each $α>6$ and each pseudoconvex domain $Ω\subset {\mathbb C}^n$, there is a holomorphic function $f$ on $Ω$ with $|f|\le C_αe^{C_α' \hatδ^{-α}}$, such that every boundary point is a Picard point of $F$. Applications to the construction of holomorphic maps of a given domain onto some ${\mathbb C}^m$ are given.

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preprint2014arXiv

Impact of Wavelength and Modulation Conversion on Transluscent Elastic Optical Networks Using MILP

Compared to legacy wavelength division multiplexing networks, elastic optical networks (EON) have added flexibility to network deployment and management. EONs can include previously available technology, such as signal regeneration and wavelength conversion, as well as new features such as finer-granularity spectrum assignment and modulation conversion. Yet each added feature adds to the cost of the network. In order to quantify the potential benefit of each technology, we present a link-based mixed-integer linear programming (MILP) formulation to solve the optimal resource allocation problem. We then propose a recursive model in order to either augment existing network deployments or speed up the resource allocation computation time for larger networks with higher traffic demand requirements than can be solved using an MILP. We show through simulation that systems equipped with signal regenerators or wavelength converters require a notably smaller total bandwidth, depending on the topology of the network. We also show that the suboptimal recursive solution speeds up the calculation and makes the running-time more predictable, compared to the optimal MILP.

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preprint2014arXiv

Riemannian Multi-Manifold Modeling

This paper advocates a novel framework for segmenting a dataset in a Riemannian manifold $M$ into clusters lying around low-dimensional submanifolds of $M$. Important examples of $M$, for which the proposed clustering algorithm is computationally efficient, are the sphere, the set of positive definite matrices, and the Grassmannian. The clustering problem with these examples of $M$ is already useful for numerous application domains such as action identification in video sequences, dynamic texture clustering, brain fiber segmentation in medical imaging, and clustering of deformed images. The proposed clustering algorithm constructs a data-affinity matrix by thoroughly exploiting the intrinsic geometry and then applies spectral clustering. The intrinsic local geometry is encoded by local sparse coding and more importantly by directional information of local tangent spaces and geodesics. Theoretical guarantees are established for a simplified variant of the algorithm even when the clusters intersect. To avoid complication, these guarantees assume that the underlying submanifolds are geodesic. Extensive validation on synthetic and real data demonstrates the resiliency of the proposed method against deviations from the theoretical model as well as its superior performance over state-of-the-art techniques.

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preprint2013arXiv

Big Deformation in 17C

Reaction- and interaction cross sections of 17C on a carbon target have been reanalyzed by use of the modified Glauber model. The analysis with a deformed Woods-Saxon density/potential suggests a big deformation structure for 17C. The existence of a tail in the density distribution supports its possibility of being a one-neutron halo structure. Under a deformed core plus single-particle assumption, analysis shows a dominant d-wave of the valence neutron in 17C. Key words Cross section, Glauber model, Density distribution, Halo, Deformation.

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preprint2013arXiv

Revisiting Timed Specification Theory II : Realisability

In this paper we present an assume-guarantee specification theory (aka interface theory from [14]) for modular synthesis and verification of real-time systems with critical timing constraints. It is a further step of our earlier work [10] which achieved an elegant algebraic specification theory for real-time systems endowed with the capability to freeze time. In this paper we relinquish such (unrealisable) capability and target more realistic systems without the ability to stop time. Our theory, in a combined process-algebraic and reactive-synthesis style, provides the operations of parallel composition for system integration, logical conjunction/disjunction for viewpoint fusion and independent development, and quotient for incremental synthesis. We show that a substitutive refinement preorder, which is a coarsening of the pre-congruence in [10], constitutes the weakest pre-congruence preserving freedom of incompatibility errors. The coarsening requires a shift in the focus of our theory to a more game-theoretical treatment, where the coarsening constitutes a reactive synthesis game named normalisation and is efficiently implementable by a novel local bot-backpropagation algorithm. Previously, timed concurrent games have been studied in [1,14,13], where one of the key concern is the removal of time-blocking strategies by applying blame assignment [13]. Our timed games also have the issue of time-blocking strategies, which may arise through the composition of specifications. However, due to our distinctively different formulation of timed games, we have discovered another elegant solution to the problem without blame assignment. Our solution utilises a second reactive synthesis game called realisation, which is dual to normalisation and implementable by the dual local top-backpropagation algorithm.

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preprint2013arXiv

Variation of the Bergman kernels under deformations of complex structures

Inspired by Berndtsson's work on the subharmonicity property of the Bergman kernel, we give a local variation formula of the full Bergman kernels associated to deformations of complex manifolds. In compact case, it follows from the reproducing property of the Bergman kernel and the curvature formula of the 0-th direct image sheaf. In general, following Schumacher's idea, we use the Lie derivative to compute the variation. An equivalent criterion for the triviality of holomorphic motions of planar domains in terms of the Bergman kernel is given as an application.

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preprint2012arXiv

Classical Theory of High Field Atomic Ionization Using Elliptical Polarization

Important information about strong-field atomic or molecular ionization can be missed when using linearly polarized laser fields. The field strength at which an electron was ionized, or the time during a pulse of the ionization event are examples of such missing information. In treating single, double, and triple ionization events we show that information of this kind is made readily available by use of elliptical polarization.

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preprint2012arXiv

Revisiting Timed Specification Theories: A Linear-Time Perspective

We consider the setting of component-based design for real-time systems with critical timing constraints. Based on our earlier work, we propose a compositional specification theory for timed automata with I/O distinction, which supports substitutive refinement. Our theory provides the operations of parallel composition for composing components at run-time, logical conjunction/disjunction for independent development, and quotient for incremental synthesis. The key novelty of our timed theory lies in a weakest congruence preserving safety as well as bounded liveness properties. We show that the congruence can be characterised by two linear-time semantics, timed-traces and timed-strategies, the latter of which is derived from a game-based interpretation of timed interaction.

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preprint2010arXiv

Elliptical Polarization and Probability of Double Ionization

The degree of elliptical polarization of intense short laser pulses is shown to be related to the timing of strong-field non-sequential double ionization. Higher ellipticity is predicted to force the initiation of double ionization into a narrower time window, and this "pins" the ionizing field strength in an unexpected way, leading to the first experimentally testable formula for double ionization probability as a function of ellipticity.

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preprint2009arXiv

Effects of Elliptical Polarization on Strong-Field Short-Pulse Double Ionization

We predict new end-of-pulse behavior in high-field atomic double ionization. Calculations of atomic electron trajectories in short intense laser pulses confirm our analysis of elliptical polarization. We exhibit a four-band structure in ion momentum distributions under various ellipticities, and predict that sequential and non-sequential double ionization can be cleanly distinguished under elliptical polarization.

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