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Xin Li

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Computer Vision gr-qc math.OA Machine Learning astro-ph.CO hep-ph eess.IV Computational Complexity cond-mat.mtrl-sci Artificial Intelligence cond-mat.soft hep-ex math.KT Computation and Language cond-mat.mes-hall Cryptography and Security Information Theory math.DS math.IT astro-ph.HE physics.chem-ph hep-lat quant-ph astro-ph.GA Biological Physics cond-mat.supr-con hep-th Multimedia physics.app-ph q-fin.MF cond-mat.stat-mech cond-mat.str-el Data Structures and Algorithms math-ph math.CO math.MP math.NA math.NT Networking and Internet Architecture cond-mat.quant-gas cs.CY Digital Libraries eess.SP Information Retrieval math.FA physics.comp-ph physics.gen-ph Robotics Social and Information Networks Applications astro-ph.EP astro-ph.SR Cell Behavior cond-mat.dis-nn Genomics Graphics Human-Computer Interaction math.GR math.OC Molecular Networks Numerical Analysis physics.class-ph physics.data-an physics.flu-dyn physics.optics q-fin.TR Tissues and Organs astro-ph.IM Computational Engineering, Finance, and Science cond-mat.other Discrete Mathematics eess.SY Hardware Architecture math.AG math.AP math.CA math.GN math.GT math.MG math.RA math.RT math.ST nlin.CD nucl-th Other Computer Science physics.ins-det physics.plasm-ph physics.soc-ph Populations and Evolution Programming Languages q-fin.ST Quantitative Methods Statistics Theory Subcellular Processes Systems and Control

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preprint2026arXiv

Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding

This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object segmentation. By introducing previously unreleased challenging data and analyzing the cutting-edge, multimodal solutions submitted by participants, this report highlights the community's latest technical advancements and charts promising future directions for robust video scene comprehension.

preprint2025arXiv

Observation of the $γ$-ray Emission from W43 with LHAASO

In this paper, we report the detection of the very-high-energy (VHE, $ 100{\rm\ GeV} < E < 100{\rm\ TeV} $) and ultra-high-energy (UHE, $E > 100\rm\ TeV$) $γ$-ray emissions from the direction of the young star-forming region W43, observed by the Large High Altitude Air Shower Observation (LHAASO). The extended $γ$-ray source was detected with a significance of ${\sim}16\,σ$ by KM2A and ${\sim}17\,σ$ by WCDA, respectively. The angular extension of this $γ$-ray source is about 0.5 degrees, corresponding to a physical size of about 50 pc. We discuss the origin of the $γ$-ray emission and possible cosmic ray acceleration in the W43 region using multi-wavelength data. Our findings suggest that W43 is likely another young star cluster capable of accelerating cosmic rays (CRs) to at least several hundred TeV.

preprint2025arXiv

Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

We report a dedicated study of the newly discovered extended UHE $γ$-ray source 1LHAASO J0056+6346u. Analyzing 979 days of LHAASO-WCDA data and 1389 days of LHAASO-KM2A data, we observed a significant excess of $γ$-ray events with both WCDA and KM2A. Assuming a point power-law source with a fixed spectral index, the significance maps reveal excesses of ${\sim}12.65\,σ$, ${\sim}22.18\,σ$, and ${\sim}10.24\,σ$ in the energy ranges of 1--25 TeV, 25--100 TeV, and $> 100$ TeV, respectively. We use a 3D likelihood algorithm to derive the morphological and spectral parameters, and the source is detected with significances of $12.65\,σ$ by WCDA and $25.27\,σ$ by KM2A. The best-fit positions derived from WCDA and KM2A data are (R.A. = $13.96^\circ\pm0.09^\circ$, Decl. = $63.92^\circ\pm0.05^\circ$) and (R.A. = $14.00^\circ\pm0.05^\circ$, Decl. = $63.79^\circ\pm0.02^\circ$), respectively. The angular size ($r_{39}$) of 1LHAASO J0056+6346u is $0.34^\circ\pm0.04^\circ$ at 1--25 TeV and $0.24^\circ\pm0.02^\circ$ at $> 25$ TeV. The differential flux of this UHE $γ$-ray source can be described by an exponential cutoff power-law function: $(2.67\pm0.25) \times 10^{-15} (E/20\,\text{TeV})^{-1.97\pm0.10} e^{-E/(55.1\pm7.2)\,\text{TeV}} \,\text{TeV}^{-1}\,\text{cm}^{-2}\,\text{s}^{-1}$. To explore potential sources of $γ$-ray emission, we investigated the gas distribution around 1LHAASO J0056+6346u. 1LHAASO J0056+6346u is likely to be a TeV PWN powered by an unknown pulsar, which would naturally explain both its spatial and spectral properties. Another explanation is that this UHE $γ$-ray source might be associated with gas content illuminated by a nearby CR accelerator, possibly the SNR candidate G124.0+1.4.

preprint2025arXiv

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Black holes (BH), one of the most intriguing objects in the universe, can manifest themselves through electromagnetic radiation initiated by the accretion flow. Some stellar-mass BHs drive relativistic jets when accreting matter from their companion stars, forming microquasars. Non-thermal emission from the radio to tera-electronvolt (TeV) gamma-ray band has been observed from microquasars, indicating the acceleration of relativistic particles. Here we report detection of four microquasars (SS 433, V4641 Sgr, GRS 1915+105, MAXI J1820+070) of spectrum extending to the ultrahigh-energy (UHE; photon energy $E>100$ TeV) band and one microquasar (Cygnus X-1) of spectrum approaching 100 TeV, using the Large High Altitude Air Shower Observatory (LHAASO). Notably, the total emission associated with SS 433 cannot be interpreted with a single leptonic component. In the UHE band, its emission is in spatial coincidence with a giant atomic cloud, which is consistent with a hadronic origin. An elongated source is discovered from V4641 Sgr with the spectrum continuing up to 800 TeV. The detection of UHE gamma rays demonstrates that accreting BHs and their environments can operate as extremely efficient accelerators of particles out of 1 peta-electronvolt (PeV), suggesting microquasars to be important contributors to Galactic cosmic rays especially around the `knee' region.

preprint2024arXiv

Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information

Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI. Blind beamforming only requires measuring the received signal power at the user terminal for a sequence of randomly generated phase shifts across all IRSs. The main idea is to extract the key statistical quantity for beamforming by exploring only a small portion of the whole solution space of phase shifts. We show that blind beamforming guarantees a signal-to-noise ratio (SNR) boost of Theta(N^{2L}) under certain conditions, where L is the number of IRSs and N is the number of reflecting elements per IRS. The proposed conditions for achieving the optimal SNR boost of Theta(N^{4}) in a double-IRS system are much easier to satisfy than the existing ones in the literature. Most importantly, the proposed conditions can be extended to a fully general L-IRS system. The above result significantly improves upon the state of the art in the area of multi-IRS-assisted communication. Moreover, blind beamforming is justified via field tests and simulations. In particular, as shown in our field tests at 2.6 GHz, our method yields up to 17 dB SNR boost; to the best of our knowledge, this is the first time that the use of multiple IRSs gets verified in the real world.

preprint2023arXiv

Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment

In this paper, formation tracking for a multi-AUV system (MAS) using an improved adaptive sliding mode control method is studied in the Three Dimensional (3-D) underwater environment. Firstly, the kinematics model and the dynamic model of the AUVs are given as the Six Dimensions of Freedom (6-DOF) considered. Then, control law based on the mathematical model of the AUVs is proposed based on the improved sliding mode method. A second order sliding mode control method is adopted to eliminate the chatting phenomenon of the controller. Thirdly, considering the water flow in the underwater working environment of the AUVs, an adaptive module is added to the controller. With the adaptive approach, the finite disturbances caused by water flow could be handled with the controller. The proposed method achieves stability by substituting an adaptive continuous term for the switching term in the controller. At last, a robust sliding mode controller with continuous model predictive control strategy for the multi-AUV system is developed to achieve leader-follower formation tracking under the presence of bounded flow disturbances, and simulations are implemented to confirm the effectiveness of the proposed method.

preprint2023arXiv

Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security

The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation. In this paper, we propose sophisticated beamforming designs for multi-user DFRC systems by additionally taking the physical layer security (PLS) into account. We show that appropriately designed radar waveforms can also act as the traditional artificial noise conceived for drowning out the eavesdropping channel and for attaining increased design degrees of freedom (DoF). The joint beamforming design is formulated as a non-convex optimization problem for striking a compelling trade-off amongst the conflicting design objectives of radar transmit beampattern, communication quality of service (QoS), and the PLS level. Then, we propose a semidefinite relaxation (SDR)-based algorithm and a reduced-complexity version to tackle the non-convexity, where the globally optimal solutions are found. Moreover, a robust beamforming method is also developed for considering realistic imperfect channel state information (CSI) knowledge. Finally, simulation results are provided for corroborating our theoretical results and show the proposed methods' superiority.

preprint2022arXiv

A discrete-module-finite-element hydroelasticity method in analyzing dynamic response of floating flexible structures

A discrete-module-finite element (DMFE) based hydroelasticity method has been proposed and well developed. Firstly, a freely floating flexible structure is discretized into several macro-submodules in two horizontal directions to perform a multi-rigid-body hydrodynamic analysis. Each macro-submodule is then abstracted to a lumped mass at the center of gravity that bears the external forces including inertia force, hydrodynamic force and hydrostatic force. Apart from external forces, all lumped masses are also subjected to structural forces that reflect the structural deformation features of the original flexible structure. The key to calculating the structural forces is derivation of the equivalent overall structural stiffness matrix with respect to the displacements of all lumped masses, which is tackled following the finite element procedure. More specifically, each macro-submodule is discretized into a number of microelements to derive the corresponding structural stiffness matrix, which is manipulated to a new one including only the nodes at the position of the lumped masses and surrounding boundaries by using the substructure approach, and subsequently the target overall stiffness matrix is obtained by combining together all macro-submodules. Finally, based on equivalence between external and structural forces, the DMFE method establishes the hydroelastic equation on all lumped masses with their displacements as unknown variables. Solving the equation gives the displacement response of all lumped masses. Displacement and structural force responses are first calculated on the interfaces of every two adjacent macro-submodules, after which at any given position of the flexible structure, the recovery of displacement is based on the structural stiffness matrix of the corresponding macro-submodule and the recovery of structural force uses the spline interpolation scheme.

preprint2022arXiv

A Logarithm Depth Quantum Converter: From One-hot Encoding to Binary Encoding

Within the quantum computing, there are two ways to encode a normalized vector $\{ α_i \}$. They are one-hot encoding and binary coding. The one-hot encoding state is denoted as $\left | ψ_O^{(N)} \right \rangle=\sum_{i=0}^{N-1} α_i \left |0 \right \rangle^{\otimes N-i-1} \left |1 \right \rangle \left |0 \right \rangle ^{\otimes i}$ and the binary encoding state is denoted as $\left | ψ_B^{(N)} \right \rangle=\sum_{i=0}^{N-1} α_i \left |b_i \right \rangle$, where $b_i$ is interpreted in binary of $i$ as the tensor product sequence of qubit states. In this paper, we present a method converting between the one-hot encoding state and the binary encoding state by taking the Edick state as the transition state, where the Edick state is defined as $\left | ψ_E^{(N)} \right \rangle=\sum_{i=0}^{N-1} α_i \left |0 \right \rangle^{\otimes N-i-1} \left |1 \right \rangle ^{\otimes i}$. Compared with the early work, our circuit achieves the exponential speedup with $O(\log^2 N)$ depth and $O(N)$ size.

preprint2022arXiv

A multi-domain virtual network embedding algorithm with delay prediction

Virtual network embedding (VNE) is an crucial part of network virtualization (NV), which aims to map the virtual networks (VNs) to a shared substrate network (SN). With the emergence of various delay-sensitive applications, how to improve the delay performance of the system has become a hot topic in academic circles. Based on extensive research, we proposed a multi-domain virtual network embedding algorithm based on delay prediction (DP-VNE). Firstly, the candidate physical nodes are selected by estimating the delay of virtual requests, then particle swarm optimization (PSO) algorithm is used to optimize the mapping process, so as to reduce the delay of the system. The simulation results show that compared with the other three advanced algorithms, the proposed algorithm can significantly reduce the system delay while keeping other indicators unaffected.

preprint2022arXiv

A Survey on Applications of Digital Human Avatars toward Virtual Co-presence

This paper investigates different approaches to build and use digital human avatars toward interactive Virtual Co-presence (VCP) environments. We evaluate the evolution of technologies for creating VCP environments and how the advancement in Artificial Intelligence (AI) and Computer Graphics affect the quality of VCP environments. We categorize different methods in the literature based on their applications and methodology and compare various groups and strategies based on their applications, contributions, and limitations. We also have a brief discussion about the approaches that other forms of human representation, rather than digital human avatars, have been utilized in VCP environments. Our goal is to fill the gap in the research domain where there is a lack of literature review investigating different approaches for creating avatar-based VCP environments. We hope this study will be useful for future research involving human representation in VCP or Virtual Reality (VR) environments. To the best of our knowledge, it is the first survey research that investigates avatar-based VCP environments. Specifically, the categorization methodology suggested in this paper for avatar-based methods is new.

preprint2022arXiv

Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs

Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae. However, the neighbor landmarks often disturb each other for the homogeneous appearance of vertebrae, which makes vertebral landmark localization extremely difficult. In this paper, we propose multi-stage cascaded convolutional neural networks (CNNs) to split the single task into two sequential steps, i.e., center point localization to roughly locate 17 center points of vertebrae, and corner point localization to find 4 corner points for each vertebra without distracted by others. Landmarks in each step are located gradually from a set of initialized points by regressing offsets via cascaded CNNs. Principal Component Analysis (PCA) is employed to preserve a shape constraint in offset regression to resist the mutual attraction of vertebrae. We evaluate our method on the AASCE dataset that consists of 609 tight spinal anterior-posterior X-ray images and each image contains 17 vertebrae composed of the thoracic and lumbar spine for spinal shape characterization. Experimental results demonstrate our superior performance of vertebral landmark localization over other state-of-the-arts with the relative error decreasing from 3.2e-3 to 7.2e-4.

preprint2022arXiv

Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study

Purpose: We model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named EXP-SEA (Experimental Platform for Search Engine Advertising) supporting experimental studies of collective behaviors in the context of search engine advertising. Design: We implement the EXP-SEA to validate the proposed simulation framework, also conduct three experimental studies on the aggregate impact of electronic word-of-mouth, the competition level, and strategic bidding behaviors. EXP-SEA supports heterogeneous participants, various auction mechanisms, and also ranking and pricing algorithms. Findings: Findings from our three experiments show that (a) both the market profit and advertising indexes such as number of impressions and number of clicks are larger when the eWOM effect presents, meaning social media certainly has some effect on search engine advertising outcomes, (b) the competition level has a monotonic increasing effect on the market performance, thus search engines have an incentive to encourage both the eWOM among search users and competition among advertisers, and (c) given the market-level effect of the percentage of advertisers employing a dynamic greedy bidding strategy, there is a cut-off point for strategic bidding behaviors. Originality: This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.

preprint2022arXiv

AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results

This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compressed image, and Track~2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 videos) and 30 additional videos. In this challenge, there are 12 teams and 2 teams that submitted the final results to Track 1 and Track 2, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution on compressed image and video. The proposed LDV 3.0 dataset is available at https://github.com/RenYang-home/LDV_dataset. The homepage of this challenge is at https://github.com/RenYang-home/AIM22_CompressSR.

preprint2022arXiv

An Adaptive Rank Continuation Algorithm for General Weighted Low-rank Recovery

This paper is devoted to proposing a general weighted low-rank recovery model and designing a fast SVD-free computational scheme to solve it. First, our generic weighted low-rank recovery model unifies several existing approaches in the literature.~Moreover, our model readily extends to the non-convex setting. Algorithm-wise, most first-order proximal algorithms in the literature for low-rank recoveries require computing singular value decomposition (SVD). As SVD does not scale appropriately with the dimension of the matrices, these algorithms become slower when the problem size becomes larger. By incorporating the variational formulation of the nuclear norm into the sub-problem of proximal gradient descent, we avoid computing SVD, which results in significant speed-up. Moreover, our algorithm preserves the rank identification property of nuclear norm [33] which further allows us to design a rank continuation scheme that asymptotically achieves the minimal iteration complexity. Numerical experiments on both toy examples and real-world problems, including structure from motion (SfM) and photometric stereo, background estimation, and matrix completion, demonstrate the superiority of our proposed algorithm.

preprint2022arXiv

Asymmetry in the number of L4 and L5 Jupiter Trojans driven by jumping Jupiter

Context. More than 10000 Jupiter Trojans have been detected so far. They are moving around the L4 and L5 triangular Lagrangian points of the Sun-Jupiter system and their distributions can provide important clues to the early evolution of the Solar System. Aims. The number asymmetry of the L4 and L5 Jupiter Trojans is a longstanding problem. We aim to test a new mechanism in order to explain this anomalous feature by invoking the jumping-Jupiter scenario. Methods. First, we introduce the orbital evolution of Jupiter caused by the giant planet instability in the early Solar System. In this scenario, Jupiter could undergo an outward migration at a very high speed. We then investigate how such a jump changes the numbers of the L4 (N4) and L5 (N5) Trojans. Results. The outward migration of Jupiter can distort the co-orbital orbits near the Lagrangian points, resulting in L4 Trojans being more stable than the L5 ones. We find that, this mechanism could potentially explain the unbiased number asymmetry of N4/N5~1.6 for the known Jupiter Trojans. The uncertainties of the system parameters, e.g. Jupiter's eccentricity and inclination, the inclination distribution of Jupiter Trojans, are also taken into account and our results about the L4/L5 asymmetry have been further validated. However, the resonant amplitudes of the simulated Trojans are excited to higher values compared to the current population. A possible solution is that collisions among the Trojans may reduce their resonant amplitudes.

preprint2022arXiv

C-envelopes for operator algebras with a coaction and co-universal C-algebras for product systems

A cosystem consists of a possibly nonselfadoint operator algebra equipped with a coaction by a discrete group. We introduce the concept of C*-envelope for a cosystem; roughly speaking, this is the smallest C*-algebraic cosystem that contains an equivariant completely isometric copy of the original one. We show that the C*-envelope for a cosystem always exists and we explain how it relates to the usual C*-envelope. We then show that for compactly aligned product systems over group-embeddable right LCM semigroups, the C*-envelope is co-universal, in the sense of Carlsen, Larsen, Sims and Vittadello, for the Fock tensor algebra equipped with its natural coaction. This yields the existence of a co-universal C*-algebra, generalizing previous results of Carlsen, Larsen, Sims and Vittadello, and of Dor-On and Katsoulis. We also realize the C*-envelope of the tensor algebra as the reduced cross sectional algebra of a Fell bundle introduced by Sehnem, which, under a mild assumption of normality, we then identify to the quotient of the Fock algebra by the image of Sehnem's strong covariance ideal. In another application, we obtain a reduced Hao-Ng isomorphism theorem for the co-universal algebras.

preprint2022arXiv

Context-aware Visual Tracking with Joint Meta-updating

Visual object tracking acts as a pivotal component in various emerging video applications. Despite the numerous developments in visual tracking, existing deep trackers are still likely to fail when tracking against objects with dramatic variation. These deep trackers usually do not perform online update or update single sub-branch of the tracking model, for which they cannot adapt to the appearance variation of objects. Efficient updating methods are therefore crucial for tracking while previous meta-updater optimizes trackers directly over parameter space, which is prone to over-fit even collapse on longer sequences. To address these issues, we propose a context-aware tracking model to optimize the tracker over the representation space, which jointly meta-update both branches by exploiting information along the whole sequence, such that it can avoid the over-fitting problem. First, we note that the embedded features of the localization branch and the box-estimation branch, focusing on the local and global information of the target, are effective complements to each other. Based on this insight, we devise a context-aggregation module to fuse information in historical frames, followed by a context-aware module to learn affinity vectors for both branches of the tracker. Besides, we develop a dedicated meta-learning scheme, on account of fast and stable updating with limited training samples. The proposed tracking method achieves an EAO score of 0.514 on VOT2018 with the speed of 40FPS, demonstrating its capability of improving the accuracy and robustness of the underlying tracker with little speed drop.

preprint2022arXiv

DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition

Human action recognition has recently become one of the popular research topics in the computer vision community. Various 3D-CNN based methods have been presented to tackle both the spatial and temporal dimensions in the task of video action recognition with competitive results. However, these methods have suffered some fundamental limitations such as lack of robustness and generalization, e.g., how does the temporal ordering of video frames affect the recognition results? This work presents a novel end-to-end Transformer-based Directed Attention (DirecFormer) framework for robust action recognition. The method takes a simple but novel perspective of Transformer-based approach to understand the right order of sequence actions. Therefore, the contributions of this work are three-fold. Firstly, we introduce the problem of ordered temporal learning issues to the action recognition problem. Secondly, a new Directed Attention mechanism is introduced to understand and provide attentions to human actions in the right order. Thirdly, we introduce the conditional dependency in action sequence modeling that includes orders and classes. The proposed approach consistently achieves the state-of-the-art (SOTA) results compared with the recent action recognition methods, on three standard large-scale benchmarks, i.e. Jester, Kinetics-400 and Something-Something-V2.

preprint2022arXiv

Domain-knowledge-aided machine learning method for properties prediction of soft magnetic metallic glasses

A machine learning (ML) method aided by domain knowledge was proposed to predict saturated magnetization (Bs) and critical diameter (Dmax) of soft magnetic metallic glass (MGs). Two datasets were established based on published experimental works about soft magnetic MGs. A general feature space was proposed and proved to be adaptive for ML model training for different prediction tasks. It was found that the predictive performance of ML models was better than traditional physical knowledge-based estimation methods. In addition, domain knowledge aided feature selection can greatly reduce the number of features without significantly reducing the prediction accuracy. Finally, binary classification of the critical size of soft magnetic metallic glass was studied.

preprint2022arXiv

DR-GAN: Distribution Regularization for Text-to-Image Generation

This paper presents a new Text-to-Image generation model, named Distribution Regularization Generative Adversarial Network (DR-GAN), to generate images from text descriptions from improved distribution learning. In DR-GAN, we introduce two novel modules: a Semantic Disentangling Module (SDM) and a Distribution Normalization Module (DNM). SDM combines the spatial self-attention mechanism and a new Semantic Disentangling Loss (SDL) to help the generator distill key semantic information for the image generation. DNM uses a Variational Auto-Encoder (VAE) to normalize and denoise the image latent distribution, which can help the discriminator better distinguish synthesized images from real images. DNM also adopts a Distribution Adversarial Loss (DAL) to guide the generator to align with normalized real image distributions in the latent space. Extensive experiments on two public datasets demonstrated that our DR-GAN achieved a competitive performance in the Text-to-Image task.

preprint2022arXiv

Efficient Linear and Affine Codes for Correcting Insertions/Deletions

This paper studies \emph{linear} and \emph{affine} error-correcting codes for correcting synchronization errors such as insertions and deletions. We call such codes linear/affine insdel codes. Linear codes that can correct even a single deletion are limited to have information rate at most $1/2$ (achieved by the trivial 2-fold repetition code). Previously, it was (erroneously) reported that more generally no non-trivial linear codes correcting $k$ deletions exist, i.e., that the $(k+1)$-fold repetition codes and its rate of $1/(k+1)$ are basically optimal for any $k$. We disprove this and show the existence of binary linear codes of length $n$ and rate just below $1/2$ capable of correcting $Ω(n)$ insertions and deletions. This identifies rate $1/2$ as a sharp threshold for recovery from deletions for linear codes, and reopens the quest for a better understanding of the capabilities of linear codes for correcting insertions/deletions. We prove novel outer bounds and existential inner bounds for the rate vs. (edit) distance trade-off of linear insdel codes. We complement our existential results with an efficient synchronization-string-based transformation that converts any asymptotically-good linear code for Hamming errors into an asymptotically-good linear code for insdel errors. Lastly, we show that the $\frac{1}{2}$-rate limitation does not hold for affine codes by giving an explicit affine code of rate $1-ε$ which can efficiently correct a constant fraction of insdel errors.

preprint2022arXiv

Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach

Predicting gene functions is a challenge for biologists in the post genomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.

preprint2022arXiv

Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions

Gene/protein interactions provide critical information for a thorough understanding of cellular processes. Recently, considerable interest and effort has been focused on the construction and analysis of genome-wide gene networks. The large body of biomedical literature is an important source of gene/protein interaction information. Recent advances in text mining tools have made it possible to automatically extract such documented interactions from free-text literature. In this paper, we propose a comprehensive framework for constructing and analyzing large-scale gene functional networks based on the gene/protein interactions extracted from biomedical literature repositories using text mining tools. Our proposed framework consists of analyses of the network topology, network topology-gene function relationship, and temporal network evolution to distill valuable information embedded in the gene functional interactions in literature. We demonstrate the application of the proposed framework using a testbed of P53-related PubMed abstracts, which shows that literature-based P53 networks exhibit small-world and scale-free properties. We also found that high degree genes in the literature-based networks have a high probability of appearing in the manually curated database and genes in the same pathway tend to form local clusters in our literature-based networks. Temporal analysis showed that genes interacting with many other genes tend to be involved in a large number of newly discovered interactions.

preprint2022arXiv

Graph-based Facial Affect Analysis: A Review

As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems. Early methods focus on extracting appearance and geometry features associated with human affects while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. This paper provides a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their non-deep or deep learning methods, emphasizing the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.

preprint2022arXiv

Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation

The rapid development of intelligent tasks, e.g., segmentation, detection, classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot be optimized in an end-to-end manner. To solve this problem, some pioneering works have applied reinforcement learning to implement image-wise semantic compression. Nevertheless, video semantic compression has not been explored since its complex reference architectures and compression modes. In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC. Specifically, to simplify the complex mode decision of video semantic coding, we divided the action space into frame-level and CTU-level spaces in a hierarchical manner, and then explore the best mode selection for them progressively with the cooperation of frame-level and CTU-level agents. Moreover, since the modes of video semantic coding will exponentially increase with the number of frames in a Group of Pictures (GOP), we carefully investigate the effects of different mode selections for video semantic coding and design a simple but effective mode simplification strategy for it. We have validated our HRLVSC on the video segmentation task with HEVC reference software HM16.19. Extensive experimental results demonstrated that our HRLVSC can achieve over 39% BD-rate saving for video semantic coding under the Low Delay P configuration.

preprint2022arXiv

Improved Decoding of Expander Codes

We study the classical expander codes, introduced by Sipser and Spielman \cite{SS96}. Given any constants $0< α, \varepsilon < 1/2$, and an arbitrary bipartite graph with $N$ vertices on the left, $M < N$ vertices on the right, and left degree $D$ such that any left subset $S$ of size at most $αN$ has at least $(1-\varepsilon)|S|D$ neighbors, we show that the corresponding linear code given by parity checks on the right has distance at least roughly $\frac{αN}{2 \varepsilon }$. This is strictly better than the best known previous result of $2(1-\varepsilon ) αN$ \cite{Sudan2000note, Viderman13b} whenever $\varepsilon < 1/2$, and improves the previous result significantly when $\varepsilon $ is small. Furthermore, we show that this distance is tight in general, thus providing a complete characterization of the distance of general expander codes. Next, we provide several efficient decoding algorithms, which vastly improve previous results in terms of the fraction of errors corrected, whenever $\varepsilon < \frac{1}{4}$. Finally, we also give a bound on the list-decoding radius of general expander codes, which beats the classical Johnson bound in certain situations (e.g., when the graph is almost regular and the code has a high rate). Our techniques exploit novel combinatorial properties of bipartite expander graphs. In particular, we establish a new size-expansion tradeoff, which may be of independent interests.

preprint2022arXiv

Intrinsic new properties of a quantum spin liquid

Quantum fluctuations are expected to lead to highly entangled spin-liquid states in certain two-dimensional spin-1/2 compounds. We have synthesized and measured thermodynamic properties and muon spin relaxation rates in the copper-based two-dimensional triangular-lattice spin liquids Lu$_3$Cu$_2$Sb$_3$O$_{14}$ and Lu$_3$CuZnSb$_3$O$_{14}$. The former is the least disordered of this kind discovered to date. Magnetic entropy generation at high temperatures has been ruled out after carefully correcting for the lattice specific heat. Surprisingly, roughly half of the magnetic entropy is missing down to temperatures of O(10$^{-3}$) the exchange energy, independent of magnetic field up to $gμ_B H \gtrsim k_BΘ_W$, where $Θ_W$ is the Weiss temperature. The magnetic specific heat divided by temperature $C_M(T)/T$ and muon spin relaxation rate $λ(T)$ are both temperature-independent at low temperatures, followed by logarithmic decreases with increasing temperature. This behavior can be simply characterized by scale-invariant time-dependent fluctuations with a single parameter. Since no cooperative effects due to impurities are observed, the measured properties are intrinsic. They are evidence that in Lu$_3$Cu$_2$Sb$_3$O$_{14}$ massive quantum fluctuations lead to either a gigantic specific heat peak from singlet excitations at very low temperatures or, perhaps less likely, an extensively degenerate possibly topological singlet ground state.

preprint2022arXiv

Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression

As a commonly-used image compression format, JPEG has been broadly applied in the transmission and storage of images. To further reduce the compression cost while maintaining the quality of JPEG images, lossless transcoding technology has been proposed to recompress the compressed JPEG image in the DCT domain. Previous works, on the other hand, typically reduce the redundancy of DCT coefficients and optimize the probability prediction of entropy coding in a hand-crafted manner that lacks generalization ability and flexibility. To tackle the above challenge, we propose the learned lossless JPEG transcoding framework via Joint Lossy and Residual Compression. Instead of directly optimizing the entropy estimation, we focus on the redundancy that exists in the DCT coefficients. To the best of our knowledge, we are the first to utilize the learned end-to-end lossy transform coding to reduce the redundancy of DCT coefficients in a compact representational domain. We also introduce residual compression for lossless transcoding, which adaptively learns the distribution of residual DCT coefficients before compressing them using context-based entropy coding. Our proposed transcoding architecture shows significant superiority in the compression of JPEG images thanks to the collaboration of learned lossy transform coding and residual entropy coding. Extensive experiments on multiple datasets have demonstrated that our proposed framework can achieve about 21.49% bits saving in average based on JPEG compression, which outperforms the typical lossless transcoding framework JPEG-XL by 3.51%.

preprint2022arXiv

Learning Optical Flow with Adaptive Graph Reasoning

Estimating per-pixel motion between video frames, known as optical flow, is a long-standing problem in video understanding and analysis. Most contemporary optical flow techniques largely focus on addressing the cross-image matching with feature similarity, with few methods considering how to explicitly reason over the given scene for achieving a holistic motion understanding. In this work, taking a fresh perspective, we introduce a novel graph-based approach, called adaptive graph reasoning for optical flow (AGFlow), to emphasize the value of scene/context information in optical flow. Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph. The proposed AGFlow can effectively exploit the context information and incorporate it within the matching procedure, producing more robust and accurate results. On both Sintel clean and final passes, our AGFlow achieves the best accuracy with EPE of 1.43 and 2.47 pixels, outperforming state-of-the-art approaches by 11.2% and 13.6%, respectively.

preprint2022arXiv

Left regular representations of Garside categories I. C*-algebras and groupoids

We initiate the study of C*-algebras and groupoids arising from left regular representations of Garside categories, a notion which originated from the study of Braid groups. Every higher rank graph is a Garside category in a natural way. We develop a general classification result for closed invariant subspaces of our groupoids as well as criteria for topological freeness and local contractiveness, properties which are relevant for the structure of the corresponding C*-algebras. Our results provide a conceptual explanation for previous results on gauge-invariant ideals of higher rank graph C*-algebras. As another application, we give a complete analysis of the ideal structures of C*-algebras generated by left regular representations of Artin-Tits monoids.

preprint2022arXiv

Low-Degree Polynomials Extract from Local Sources

We continue a line of work on extracting random bits from weak sources that are generated by simple processes. We focus on the model of locally samplable sources, where each bit in the source depends on a small number of (hidden) uniformly random input bits. Also known as local sources, this model was introduced by De and Watson (TOCT 2012) and Viola (SICOMP 2014), and is closely related to sources generated by $\mathsf{AC}^0$ circuits and bounded-width branching programs. In particular, extractors for local sources also work for sources generated by these classical computational models. Despite being introduced a decade ago, little progress has been made on improving the entropy requirement for extracting from local sources. The current best explicit extractors require entropy $n^{1/2}$, and follow via a reduction to affine extractors. To start, we prove a barrier showing that one cannot hope to improve this entropy requirement via a black-box reduction of this form. In particular, new techniques are needed. In our main result, we seek to answer whether low-degree polynomials (over $\mathbb{F}_2$) hold potential for breaking this barrier. We answer this question in the positive, and fully characterize the power of low-degree polynomials as extractors for local sources. More precisely, we show that a random degree $r$ polynomial is a low-error extractor for $n$-bit local sources with min-entropy $Ω(r(n\log n)^{1/r})$, and we show that this is tight. Our result leverages several new ingredients, which may be of independent interest. Our existential result relies on a new reduction from local sources to a more structured family, known as local non-oblivious bit-fixing sources. To show its tightness, we prove a "local version" of a structural result by Cohen and Tal (RANDOM 2015), which relies on a new "low-weight" Chevalley-Warning theorem.

preprint2022arXiv

Low-Rank Phase Retrieval with Structured Tensor Models

We study the low-rank phase retrieval problem, where the objective is to recover a sequence of signals (typically images) given the magnitude of linear measurements of those signals. Existing solutions involve recovering a matrix constructed by vectorizing and stacking each image. These algorithms model this matrix to be low-rank and leverage the low-rank property to decrease the sample complexity required for accurate recovery. However, when the number of available measurements is more limited, these low-rank matrix models can often fail. We propose an algorithm called Tucker-Structured Phase Retrieval (TSPR) that models the sequence of images as a tensor rather than a matrix that we factorize using the Tucker decomposition. This factorization reduces the number of parameters that need to be estimated, allowing for a more accurate reconstruction in the under-sampled regime. Interestingly, we observe that this structure also has improved performance in the over-determined setting when the Tucker ranks are chosen appropriately. We demonstrate the effectiveness of our approach on real video datasets under several different measurement models.

preprint2022arXiv

Machine learning prediction for mean motion resonance behaviour -- The planar case

Most recently, machine learning has been used to study the dynamics of integrable Hamiltonian systems and the chaotic 3-body problem. In this work, we consider an intermediate case of regular motion in a non-integrable system: the behaviour of objects in the 2:3 mean motion resonance with Neptune. We show that, given initial data from a short 6250 yr numerical integration, the best-trained artificial neural network (ANN) can predict the trajectories of the 2:3 resonators over the subsequent 18750 yr evolution, covering a full libration cycle over the combined time period. By comparing our ANN's prediction of the resonant angle to the outcome of numerical integrations, the former can predict the resonant angle with an accuracy as small as of a few degrees only, while it has the advantage of considerably saving computational time. More specifically, the trained ANN can effectively measure the resonant amplitudes of the 2:3 resonators, and thus provides a fast approach that can identify the resonant candidates. This may be helpful in classifying a huge population of KBOs to be discovered in future surveys.

preprint2022arXiv

Mechanical feedback controls the emergence of dynamical memory in growing tissue monolayers

The growth of a tissue, which depends on cell-cell interactions and biologically relevant process such as cell division and apoptosis, is regulated by a mechanical feedback mechanism. We account for these effects in a minimal two-dimensional model in order to investigate the consequences of mechanical feedback, which is controlled by a critical pressure, $p_c$. A cell can only grow and divide if the pressure it experiences, due to interaction with its neighbors, is less than $p_c$. Because temperature is an irrelevant variable in the model, the cell dynamics is driven by self-generated active forces (SGAFs) that are created by cell division. It is shown that even in the absence of intercellular interactions, cells undergo diffusive behavior. The SGAF driven diffusion is indistinguishable from the well-known dynamics of a free Brownian particle at a fixed finite temperature. When intercellular interactions are taken into account, we find persistent temporal correlations in the force-force autocorrelation function ($FAF$) that extends over timescale of several cell division times. The time-dependence of the $FAF$ reveals memory effects, which increases as pc increases. The observed non-Markovian effects emerge due to the interplay of cell division and mechanical feedback, and is inherently a non-equilibrium phenomenon.

preprint2022arXiv

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

Data augmentation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as NER, data augmentation methods often suffer from token-label misalignment, which leads to unsatsifactory performance. In this work, we propose Masked Entity Language Modeling (MELM) as a novel data augmentation framework for low-resource NER. To alleviate the token-label misalignment issue, we explicitly inject NER labels into sentence context, and thus the fine-tuned MELM is able to predict masked entity tokens by explicitly conditioning on their labels. Thereby, MELM generates high-quality augmented data with novel entities, which provides rich entity regularity knowledge and boosts NER performance. When training data from multiple languages are available, we also integrate MELM with code-mixing for further improvement. We demonstrate the effectiveness of MELM on monolingual, cross-lingual and multilingual NER across various low-resource levels. Experimental results show that our MELM presents substantial improvement over the baseline methods.

preprint2022arXiv

MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification

This paper presents a novel person re-identification model, named Multi-Head Self-Attention Network (MHSA-Net), to prune unimportant information and capture key local information from person images. MHSA-Net contains two main novel components: Multi-Head Self-Attention Branch (MHSAB) and Attention Competition Mechanism (ACM). The MHSAB adaptively captures key local person information, and then produces effective diversity embeddings of an image for the person matching. The ACM further helps filter out attention noise and non-key information. Through extensive ablation studies, we verified that the Multi-Head Self-Attention Branch (MHSAB) and Attention Competition Mechanism (ACM) both contribute to the performance improvement of the MHSA-Net. Our MHSA-Net achieves competitive performance in the standard and occluded person Re-ID tasks.

preprint2022arXiv

Model Attribution of Face-swap Deepfake Videos

AI-created face-swap videos, commonly known as Deepfakes, have attracted wide attention as powerful impersonation attacks. Existing research on Deepfakes mostly focuses on binary detection to distinguish between real and fake videos. However, it is also important to determine the specific generation model for a fake video, which can help attribute it to the source for forensic investigation. In this paper, we fill this gap by studying the model attribution problem of Deepfake videos. We first introduce a new dataset with DeepFakes from Different Models (DFDM) based on several Autoencoder models. Specifically, five generation models with variations in encoder, decoder, intermediate layer, input resolution, and compression ratio have been used to generate a total of 6,450 Deepfake videos based on the same input. Then we take Deepfakes model attribution as a multiclass classification task and propose a spatial and temporal attention based method to explore the differences among Deepfakes in the new dataset. Experimental evaluation shows that most existing Deepfakes detection methods failed in Deepfakes model attribution, while the proposed method achieved over 70% accuracy on the high-quality DFDM dataset.

preprint2022arXiv

Multitude of exceptional points in van der Waals magnets

Several works have recently addressed the emergence of exceptional points (EPs), i.e., spectral singularities of non-Hermitian Hamiltonians, in the long-wavelength dynamics of coupled magnetic systems. Here, by focusing on the driven magnetization dynamics of a van der Waals ferromagnetic bilayer, we show that exceptional points can appear over extended portions of the first Brillouin zone as well. Furthermore, we demonstrate that the effective non-Hermitian magnon Hamiltonian, whose eigenvalues are purely real or come in complex-conjugate pairs, respects an unusual wavevector-dependent pseudo-Hermiticity. Finally, for both armchair and zigzag nanoribbon geometries, we discuss both the complex and purely real spectra of the topological edge states and their experimental implications.

preprint2022arXiv

Neural Collaborative Graph Machines for Table Structure Recognition

Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply combine visual cues with other modalities via early fusion to reason their graph relationships. However, neither early fusion nor individually reasoning in terms of multiple modalities can be appropriate for all varieties of table structures with great diversity. Instead, different modalities are expected to collaborate with each other in different patterns for different table cases. In the community, the importance of intra-inter modality interactions for table structure reasoning is still unexplored. In this paper, we define it as heterogeneous table structure recognition (Hetero-TSR) problem. With the aim of filling this gap, we present a novel Neural Collaborative Graph Machines (NCGM) equipped with stacked collaborative blocks, which alternatively extracts intra-modality context and models inter-modality interactions in a hierarchical way. It can represent the intra-inter modality relationships of tabular elements more robustly, which significantly improves the recognition performance. We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intra-modality cues, which is vital for diversified table cases. Experimental results on benchmarks demonstrate our proposed NCGM achieves state-of-the-art performance and beats other contemporary methods by a large margin especially under challenging scenarios.

preprint2022arXiv

NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition

Recently, Vision Transformers (ViT), with the self-attention (SA) as the de facto ingredients, have demonstrated great potential in the computer vision community. For the sake of trade-off between efficiency and performance, a group of works merely perform SA operation within local patches, whereas the global contextual information is abandoned, which would be indispensable for visual recognition tasks. To solve the issue, the subsequent global-local ViTs take a stab at marrying local SA with global one in parallel or alternative way in the model. Nevertheless, the exhaustively combined local and global context may exist redundancy for various visual data, and the receptive field within each layer is fixed. Alternatively, a more graceful way is that global and local context can adaptively contribute per se to accommodate different visual data. To achieve this goal, we in this paper propose a novel ViT architecture, termed NomMer, which can dynamically Nominate the synergistic global-local context in vision transforMer. By investigating the working pattern of our proposed NomMer, we further explore what context information is focused. Beneficial from this "dynamic nomination" mechanism, without bells and whistles, the NomMer can not only achieve 84.5% Top-1 classification accuracy on ImageNet with only 73M parameters, but also show promising performance on dense prediction tasks, i.e., object detection and semantic segmentation. The code and models will be made publicly available at https://github.com/TencentYoutuResearch/VisualRecognition-NomMer

preprint2022arXiv

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://github.com/RenYang-home/NTIRE21_VEnh

preprint2022arXiv

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

preprint2022arXiv

Observations of pores and surrounding regions with CO 4.66 μm lines by BBSO/CYRA

Solar observations of carbon monoxide (CO) indicate the existence of lower-temperature gas in the lower solar chromosphere. We present an observation of pores, and quiet-Sun, and network magnetic field regions with CO 4.66 μm lines by the Cryogenic Infrared Spectrograph (CYRA) at Big Bear Solar Observatory. We used the strong CO lines at around 4.66 μm to understand the properties of the thermal structures of lower solar atmosphere in different solar features with various magnetic field strengths. AIA 1700 Å images, HMI continuum images and magnetograms are also included in the observation. The data from 3D radiation magnetohydrodynamic (MHD) simulation with the Bifrost code are also employed for the first time to be compared with the observation. We used the RH code to synthesize the CO line profiles in the network regions. The CO 3-2 R14 line center intensity changes to be either enhanced or diminished with increasing magnetic field strength, which should be caused by different heating effects in magnetic flux tubes with different sizes. We find several "cold bubbles" in the CO 3-2 R14 line center intensity images, which can be classified into two types. One type is located in the quiet-Sun regions without magnetic fields. The other type, which has rarely been reported in the past, is near or surrounded by magnetic fields. Notably, some are located at the edge of the magnetic network. The two kinds of cold bubbles and the relationship between cold bubble intensities and network magnetic field strength are both reproduced by the 3D MHD simulation with the Bifrost and RH codes. The simulation also shows that there is a cold plasma blob near the network magnetic fields, causing the observed cold bubbles seen in the CO 3-2 R14 line center image. Our observation and simulation illustrate that the magnetic field plays a vital role in the generation of some CO cold bubbles.

preprint2022arXiv

On Coupled Dirac Systems under Boundary Condition

In this article we study the existence of solutions for the Dirac systems \begin{equation}\label{e:0.1} \left\{ \begin{array}{c} Pu=\frac{\partial H}{\partial v}(x,u,v) \quad\hbox{on} \ M, Pv=\frac{\partial H}{\partial u}(x,u,v) \quad\hbox{on} \ M, B_{\text{CHI}}u= B_{\text{CHI}}v=0\quad\hbox{on} \ \partial M \end{array} \right. \end{equation} where $M$ is an $m$-dimensional compact oriented Riemannian spin manifold with smooth boundary $\partial M$, $P$ is the Dirac operator under the boundary condition $B_{\text{CHI}}u= B_{\text{CHI}}v=0$ on $\partial M$, $ u,v\in C^{\infty}(M,ΣM)$ are spinors. Using an analytic framework of proper products of fractional Sobolev spaces, the solutions existence results of the coupled Dirac systems are obtained for nonlinearity with superquadratic growth rates.

preprint2022arXiv

Relational Representation Learning in Visually-Rich Documents

Relational understanding is critical for a number of visually-rich documents (VRDs) understanding tasks. Through multi-modal pre-training, recent studies provide comprehensive contextual representations and exploit them as prior knowledge for downstream tasks. In spite of their impressive results, we observe that the widespread relational hints (e.g., relation of key/value fields on receipts) built upon contextual knowledge are not excavated yet. To mitigate this gap, we propose DocReL, a Document Relational Representation Learning framework. The major challenge of DocReL roots in the variety of relations. From the simplest pairwise relation to the complex global structure, it is infeasible to conduct supervised training due to the definition of relation varies and even conflicts in different tasks. To deal with the unpredictable definition of relations, we propose a novel contrastive learning task named Relational Consistency Modeling (RCM), which harnesses the fact that existing relations should be consistent in differently augmented positive views. RCM provides relational representations which are more compatible to the urgent need of downstream tasks, even without any knowledge about the exact definition of relation. DocReL achieves better performance on a wide variety of VRD relational understanding tasks, including table structure recognition, key information extraction and reading order detection.

preprint2022arXiv

RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment

Coronary CT Angiography (CCTA) is susceptible to various distortions (e.g., artifacts and noise), which severely compromise the exact diagnosis of cardiovascular diseases. The appropriate CCTA Vessel-level Image Quality Assessment (CCTA VIQA) algorithm can be used to reduce the risk of error diagnosis. The primary challenges of CCTA VIQA are that the local part of coronary that determines final quality is hard to locate. To tackle the challenge, we formulate CCTA VIQA as a multiple-instance learning (MIL) problem, and exploit Transformer-based MIL backbone (termed as T-MIL) to aggregate the multiple instances along the coronary centerline into the final quality. However, not all instances are informative for final quality. There are some quality-irrelevant/negative instances intervening the exact quality assessment(e.g., instances covering only background or the coronary in instances is not identifiable). Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA. Based on the above two modules, we propose a Reinforced Transformer Network (RTN) for automatic CCTA VIQA based on end-to-end optimization. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the real-world CCTA dataset, exceeding previous MIL methods by a large margin.

preprint2022arXiv

Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System

This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based diagnosis system that appears to be accurate but fails when tested in new hospitals/datasets. Recent studies indicate the system might learn shortcut and non-relevant features instead of generalizable features, so-called good features. We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets. With that, we formulate a novel model training scheme for the deep neural network to learn good features for classification and/or detection tasks ensuring a consistent generalization performance on OOD test sets. The experimental results qualitatively and quantitatively demonstrate the superior performance of our method using the benchmark CXR image data sets on classification tasks.

preprint2022arXiv

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge. Parti treats text-to-image generation as a sequence-to-sequence modeling problem, akin to machine translation, with sequences of image tokens as the target outputs rather than text tokens in another language. This strategy can naturally tap into the rich body of prior work on large language models, which have seen continued advances in capabilities and performance through scaling data and model sizes. Our approach is simple: First, Parti uses a Transformer-based image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens. Second, we achieve consistent quality improvements by scaling the encoder-decoder Transformer model up to 20B parameters, with a new state-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22 on MS-COCO. Our detailed analysis on Localized Narratives as well as PartiPrompts (P2), a new holistic benchmark of over 1600 English prompts, demonstrate the effectiveness of Parti across a wide variety of categories and difficulty aspects. We also explore and highlight limitations of our models in order to define and exemplify key areas of focus for further improvements. See https://parti.research.google/ for high-resolution images.

preprint2022arXiv

Search for the correlations between host properties and ${\rm DM_{host}}$ of fast radio bursts: constraints on the baryon mass fraction in IGM

The application of fast radio bursts (FRBs) as probes to investigate astrophysics and cosmology requires the proper modelling of the dispersion measures of Milky Way (${\rm DM_{MW}}$) and host galaxy (${\rm DM_{host}}$). ${\rm DM_{MW}}$ can be estimated using the Milky Way electron models, such as NE2001 model and YMW16 model. However, ${\rm DM_{host}}$ is hard to model due to limited information on the local environment of FRBs. In this paper, using 17 well-localized FRBs, we search for the possible correlations between ${\rm DM_{host}}$ and the properties of host galaxies, such as the redshift, the stellar mass, the star-formation rate, the age of galaxy, the offset of FRB site from galactic center, and the half-light radius. We find no strong correlation between ${\rm DM_{host}}$ and any of the host property. Assuming that ${\rm DM_{host}}$ is a constant for all host galaxies, we constrain the fraction of baryon mass in the intergalactic medium today to be $f_{\rm IGM,0}=0.78_{-0.19}^{+0.15}$. If we model ${\rm DM_{host}}$ as a log-normal distribution, however, we obtain a larger value, $f_{\rm IGM,0}=0.83_{-0.17}^{+0.12}$. Based on the limited number of FRBs, no strong evidence for the redshift evolution of $f_{\rm IGM}$ is found.

preprint2022arXiv

Self-Supervised Tracking via Target-Aware Data Synthesis

While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensive and exhaustive annotation, we study self-supervised learning for visual tracking. In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data by simulating various appearance variations during tracking, including appearance variations of objects and background interference. Since the target state is known in all synthesized data, existing deep trackers can be trained in routine ways using the synthesized data without human annotation. The proposed target-aware data-synthesis method adapts existing tracking approaches within a self-supervised learning framework without algorithmic changes. Thus, the proposed self-supervised learning mechanism can be seamlessly integrated into existing tracking frameworks to perform training. Extensive experiments show that our method 1) achieves favorable performance against supervised learning schemes under the cases with limited annotations; 2) helps deal with various tracking challenges such as object deformation, occlusion, or background clutter due to its manipulability; 3) performs favorably against state-of-the-art unsupervised tracking methods; 4) boosts the performance of various state-of-the-art supervised learning frameworks, including SiamRPN++, DiMP, and TransT.

preprint2022arXiv

SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning

This work explores how to learn robust and generalizable state representation from image-based observations with deep reinforcement learning methods. Addressing the computational complexity, stringent assumptions and representation collapse challenges in existing work of bisimulation metric, we devise Simple State Representation (SimSR) operator. SimSR enables us to design a stochastic approximation method that can practically learn the mapping functions (encoders) from observations to latent representation space. In addition to the theoretical analysis and comparison with the existing work, we experimented and compared our work with recent state-of-the-art solutions in visual MuJoCo tasks. The results shows that our model generally achieves better performance and has better robustness and good generalization.

preprint2022arXiv

Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment

Existing learning-based methods for blind image quality assessment (BIQA) are heavily dependent on large amounts of annotated training data, and usually suffer from a severe performance degradation when encountering the domain/distribution shift problem. Thanks to the development of unsupervised domain adaptation (UDA), some works attempt to transfer the knowledge from a label-sufficient source domain to a label-free target domain under domain shift with UDA. However, it requires the coexistence of source and target data, which might be impractical for source data due to the privacy or storage issues. In this paper, we take the first step towards the source-free unsupervised domain adaptation (SFUDA) in a simple yet efficient manner for BIQA to tackle the domain shift without access to the source data. Specifically, we cast the quality assessment task as a rating distribution prediction problem. Based on the intrinsic properties of BIQA, we present a group of well-designed self-supervised objectives to guide the adaptation of the BN affine parameters towards the target domain. Among them, minimizing the prediction entropy and maximizing the batch prediction diversity aim to encourage more confident results while avoiding the trivial solution. Besides, based on the observation that the IQA rating distribution of single image follows the Gaussian distribution, we apply Gaussian regularization to the predicted rating distribution to make it more consistent with the nature of human scoring. Extensive experimental results under cross-domain scenarios demonstrated the effectiveness of our proposed method to mitigate the domain shift.

preprint2022arXiv

Spin interactions and topological magnonics in chromium trihalide CrClBrI

The discovery of spontaneous magnetism in van der Waal (vdW) magnetic monolayers has opened up an unprecedented platform for investigating magnetism in purely two-dimensional systems. Recently, it has been shown that the magnetic properties of vdW magnets can be easily tuned by adjusting the relative composition of halides. Motivated by these experimental advances, here we derive a model for a trihalide CrClBrI monolayer from symmetry principles and we find that, in contrast to its single-halide counterparts, it can display highly anisotropic nearest- and next-to-nearest neighbor Dzyaloshinskii-Moriya and Heisenberg interactions. Depending on the parameters, the DM interactions are responsible for the formation of exotic chiral spin states, such as skyrmions and spin cycloids, as shown by our Monte Carlo simulations. Focusing on a ground state with a two-sublattice unit cell, we find spin-wave bands with nonvanishing Chern numbers. The resulting magnon edge states yield a magnon thermal Hall conductivity that changes sign as function of temperature and magnetic field, suggesting chromium trihalides as a candidate for testing topological magnon transport in two-dimensional noncollinear spin systems.

preprint2022arXiv

Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition

Chinese characters carry a wealth of morphological and semantic information; therefore, the semantic enhancement of the morphology of Chinese characters has drawn significant attention. The previous methods were intended to directly extract information from a whole Chinese character image, which usually cannot capture both global and local information simultaneously. In this paper, we develop a stroke-based autoencoder(SAE), to model the sophisticated morphology of Chinese characters with the self-supervised method. Following its canonical writing order, we first represent a Chinese character as a series of stroke images with a fixed writing order, and then our SAE model is trained to reconstruct this stroke image sequence. This pre-trained SAE model can predict the stroke image series for unseen characters, as long as their strokes or radicals appeared in the training set. We have designed two contrasting SAE architectures on different forms of stroke images. One is fine-tuned on existing stroke-based method for zero-shot recognition of handwritten Chinese characters, and the other is applied to enrich the Chinese word embeddings from their morphological features. The experimental results validate that after pre-training, our SAE architecture outperforms other existing methods in zero-shot recognition and enhances the representation of Chinese characters with their abundant morphological and semantic information.

preprint2022arXiv

StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment

Deep neural networks (DNNs) have shown great potential in non-reference image quality assessment (NR-IQA). However, the annotation of NR-IQA is labor-intensive and time-consuming, which severely limits their application especially for authentic images. To relieve the dependence on quality annotation, some works have applied unsupervised domain adaptation (UDA) to NR-IQA. However, the above methods ignore that the alignment space used in classification is sub-optimal, since the space is not elaborately designed for perception. To solve this challenge, we propose an effective perception-oriented unsupervised domain adaptation method StyleAM for NR-IQA, which transfers sufficient knowledge from label-rich source domain data to label-free target domain images via Style Alignment and Mixup. Specifically, we find a more compact and reliable space i.e., feature style space for perception-oriented UDA based on an interesting/amazing observation, that the feature style (i.e., the mean and variance) of the deep layer in DNNs is exactly associated with the quality score in NR-IQA. Therefore, we propose to align the source and target domains in a more perceptual-oriented space i.e., the feature style space, to reduce the intervention from other quality-irrelevant feature factors. Furthermore, to increase the consistency between quality score and its feature style, we also propose a novel feature augmentation strategy Style Mixup, which mixes the feature styles (i.e., the mean and variance) before the last layer of DNNs together with mixing their labels. Extensive experimental results on two typical cross-domain settings (i.e., synthetic to authentic, and multiple distortions to one distortion) have demonstrated the effectiveness of our proposed StyleAM on NR-IQA.

preprint2022arXiv

SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed with diverse distortion types and degrees. To warp the compressed images into the shared representation space while maintaining the complex distortion information, we extract the hierarchical feature representations from each stage of the Swin Transformer. Besides, we utilize cross attention operation to map the extracted feature representations into a learned Swin distance space. Experimental results show that the proposed metric achieves higher consistency with human's perceptual judgment compared with both traditional methods and learning-based methods on CLIC datasets.

preprint2022arXiv

The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training

The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing interest in the multimedia community, owing to the excellent ability of learning visual representation from unlabeled data. Aiming at learning representations with high semantics abstracted, a group of works attempts to reconstruct non-semantic pixels with large-ratio masking strategy, which may suffer from "over-smoothing" problem, while others directly infuse semantics into targets in off-line way requiring extra data. Different from them, we shift the perspective to the Fourier domain which naturally has global perspective and present a new Masked Image Modeling (MIM), termed Geminated Gestalt Autoencoder (Ge$^2$-AE) for visual pre-training. Specifically, we equip our model with geminated decoders in charge of reconstructing image contents from both pixel and frequency space, where each other serves as not only the complementation but also the reciprocal constraints. Through this way, more robust representations can be learned in the pre-trained encoders, of which the effectiveness is confirmed by the juxtaposing experimental results on downstream recognition tasks. We also conduct several quantitative and qualitative experiments to investigate the learning behavior of our method. To our best knowledge, this is the first MIM work to solve the visual pre-training through the lens of frequency domain.

preprint2022arXiv

The eigenvectors-eigenvalues identity and Sun's conjectures on determinants and permanents

In this paper, we prove a conjecture raised by Zhi-Wei Sun in 2018 by the eigenvectors-eigenvalues identity found by Denton, Parke, Tao and X. Zhang in 2019.

preprint2022arXiv

The HI Gas Disk Thickness of the Ultra-diffuse Galaxy AGC 242019

Ultra-diffuse galaxies (UDGs) are as faint as dwarf galaxies but whose sizes are similar to those of spiral galaxies. A variety of formation mechanisms have been proposed, some of which could result in different disk thicknesses. In this study, we measure the radial profile of the HI scale height (h_g) and flaring angle (h_g/R) of AGC 242019 through the joint Poisson-Boltzmann equation based on its well spatially-resolved HI gas maps. The mean HI scale height of AGC 242019 is <h_g> \approx 537.15 \pm 89.4 pc, and the mean flaring angle is <h_g/R> \approx 0.19 \pm 0.03. As a comparison, we also derive the disk thickness for a sample of 14 dwarf irregulars. It is found that the HI disk of AGC 242019 has comparable thickness to dwarfs. This suggests that AGC 242019 is unlikely to experience much stronger stellar feedback than dwarf galaxies, which otherwise leads to a thicker disk for this galaxy.

preprint2022arXiv

Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence

Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and downscaling degradation kernel. To improve real world applicability of such models, there are growing interests to develop models optimized for arbitrary upscaling factors. Our proposed method is the first to treat arbitrary rescaling, both upscaling and downscaling, as one unified process. Using joint optimization of both directions, the proposed model is able to learn upscaling and downscaling simultaneously and achieve bidirectional arbitrary image rescaling. It improves the performance of current arbitrary upscaling models by a large margin while at the same time learns to maintain visual perception quality in downscaled images. The proposed model is further shown to be robust in cycle idempotence test, free of severe degradations in reconstruction accuracy when the downscaling-to-upscaling cycle is applied repetitively. This robustness is beneficial for image rescaling in the wild when this cycle could be applied to one image for multiple times. It also performs well on tests with arbitrary large scales and asymmetric scales, even when the model is not trained with such tasks. Extensive experiments are conducted to demonstrate the superior performance of our model.

preprint2022arXiv

Unsupervised Learning of Accurate Siamese Tracking

Unsupervised learning has been popular in various computer vision tasks, including visual object tracking. However, prior unsupervised tracking approaches rely heavily on spatial supervision from template-search pairs and are still unable to track objects with strong variation over a long time span. As unlimited self-supervision signals can be obtained by tracking a video along a cycle in time, we investigate evolving a Siamese tracker by tracking videos forward-backward. We present a novel unsupervised tracking framework, in which we can learn temporal correspondence both on the classification branch and regression branch. Specifically, to propagate reliable template feature in the forward propagation process so that the tracker can be trained in the cycle, we first propose a consistency propagation transformation. We then identify an ill-posed penalty problem in conventional cycle training in backward propagation process. Thus, a differentiable region mask is proposed to select features as well as to implicitly penalize tracking errors on intermediate frames. Moreover, since noisy labels may degrade training, we propose a mask-guided loss reweighting strategy to assign dynamic weights based on the quality of pseudo labels. In extensive experiments, our tracker outperforms preceding unsupervised methods by a substantial margin, performing on par with supervised methods on large-scale datasets such as TrackingNet and LaSOT. Code is available at https://github.com/FlorinShum/ULAST.

preprint2022arXiv

Vector-quantized Image Modeling with Improved VQGAN

Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised representation learning. When trained on ImageNet at $256\times256$ resolution, we achieve Inception Score (IS) of 175.1 and Fr'echet Inception Distance (FID) of 4.17, a dramatic improvement over the vanilla VQGAN, which obtains 70.6 and 17.04 for IS and FID, respectively. Based on ViT-VQGAN and unsupervised pretraining, we further evaluate the pretrained Transformer by averaging intermediate features, similar to Image GPT (iGPT). This ImageNet-pretrained VIM-L significantly beats iGPT-L on linear-probe accuracy from 60.3% to 73.2% for a similar model size. VIM-L also outperforms iGPT-XL which is trained with extra web image data and larger model size.

preprint2022arXiv

WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning

Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving highly precise alignment is still challenging, especially when nodes with long-range connectivity to the labeled anchors are encountered. To alleviate this limitation, we purposefully designed WL-Align which adopts a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of "across-network Weisfeiler-Lehman relabeling" and "proximity-preserving representation learning". The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors' connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the "exact matching" scenario. Data and code of WL-Align are available at https://github.com/ChenPengGang/WLAlignCode.

preprint2021arXiv

Analysis-suitable unstructured T-splines: Multiple extraordinary points per face

Analysis-suitable T-splines (AST-splines) are a promising candidate to achieve a seamless integration between the design and the analysis of thin-walled structures in industrial settings. In this work, we generalize AST-splines to allow multiple extraordinary points within the same face. This generalization drastically increases the flexibility to build geometries using AST-splines; e.g., much coarser meshes can be generated to represent a certain geometry. The AST-spline spaces detailed in this work have $C^1$ inter-element continuity near extraordinary points and $C^2$ inter-element continuity elsewhere. We mathematically show that AST-splines with multiple extraordinary points per face are linearly independent and their polynomial basis functions form a non-negative partition of unity. We numerically show that AST-splines with multiple extraordinary points per face lead to optimal convergence rates for second- and fourth-order linear elliptic problems. To illustrate a possible isogeometric framework that is already available, we design the B-pillar and the side outer panel of a car using T-splines with the commercial software Autodesk Fusion360, import the control nets into our in-house code to build AST-splines, and import the Bézier extraction information into the commercial software LS-DYNA to solve eigenvalue problems. The results are compared with conventional finite elements. Good agreement is found, but conventional finite elements require significantly more degrees of freedom to reach a converged solution than AST-splines.

preprint2021arXiv

Boundary quotient C*-algebras of semigroups

We study two classes of operator algebras associated with a unital subsemigroup $P$ of a discrete group $G$: one related to universal structures, and one related to co-universal structures. First we provide connections between universal C*-algebras that arise variously from isometric representations of $P$ that reflect the space $\mathcal{J}$ of constructible right ideals, from associated Fell bundles, and from induced partial actions. This includes connections of appropriate quotients with the strong covariance relations in the sense of Sehnem. We then pass to the reduced representation $\mathrm{C}^*_λ(P)$ and we consider the boundary quotient $\partial \mathrm{C}^*_λ(P)$ related to the minimal boundary space. We show that $\partial \mathrm{C}^*_λ(P)$ is co-universal in two different classes: (a) with respect to the equivariant constructible isometric representations of $P$; and (b) with respect to the equivariant C*-covers of the reduced nonselfadjoint semigroup algebra $\mathcal{A}(P)$. If $P$ is an Ore semigroup, or if $G$ acts topologically freely on the minimal boundary space, then $\partial \mathrm{C}^*_λ(P)$ coincides with the usual C*-envelope $\mathrm{C}^*_{\text{env}}(\mathcal{A}(P))$ in the sense of Arveson. This covers total orders, finite type and right-angled Artin monoids, the Thompson monoid, multiplicative semigroups of nonzero algebraic integers, and the $ax+b$-semigroups over integral domains that are not a field. In particular, we show that $P$ is an Ore semigroup if and only if there exists a canonical $*$-isomorphism from $\partial \mathrm{C}^*_λ(P)$, or from $\mathrm{C}^*_{\text{env}}(\mathcal{A}(P))$, onto $\mathrm{C}^*_λ(G)$. If any of the above holds, then $\mathcal{A}(P)$ is shown to be hyperrigid.

preprint2021arXiv

Configuring Intelligent Reflecting Surface with Performance Guarantees: Optimal Beamforming

This work proposes linear time strategies to optimally configure the phase shifts for the reflective elements of an intelligent reflecting surface (IRS). Specifically, we show that the binary phase beamforming can be optimally solved in linear time to maximize the received signal-to-noise ratio (SNR). For the general K-ary phase beamforming, we develop a linear time approximation algorithm that guarantees performance within a constant fraction (1+\cos(π/K))/2 of the global optimum, e.g., it can attain over 85% of the optimal performance for the quadrature beamforming with K=4. According to the numerical results, the proposed approximation algorithm for discrete IRS beamforming outperforms the existing algorithms significantly in boosting the received SNR.

preprint2021arXiv

DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning

Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of the deep neural network's gradients, or as a result of an explicit sparsification process. Existing communication primitives are agnostic to the peculiarities of deep learning; consequently, they impose unnecessary communication overhead. This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored for distributed deep learning. DeepReduce decomposes sparse tensors in two sets, values and indices, and allows both independent and combined compression of these sets. We support a variety of common compressors, such as Deflate for values, or run-length encoding for indices. We also propose two novel compression schemes that achieve superior results: curve fitting-based for values and bloom filter-based for indices. DeepReduce is orthogonal to existing gradient sparsifiers and can be applied in conjunction with them, transparently to the end-user, to significantly lower the communication overhead. As proof of concept, we implement our approach on Tensorflow and PyTorch. Our experiments with large real models demonstrate that DeepReduce transmits fewer data and imposes lower computational overhead than existing methods, without affecting the training accuracy.

preprint2021arXiv

Extreme Suppression of Antiferromagnetic Order and Critical Scaling in a Two-Dimensional Random Quantum Magnet

Sr$_2$CuTeO$_6$ is a square-lattice Néel antiferromagnet with superexchange between first-neighbor $S=1/2$ Cu spins mediated by plaquette centered Te ions. Substituting Te by W, the affected impurity plaquettes have predominantly second-neighbor interactions, thus causing local magnetic frustration. Here we report a study of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$ using neutron diffraction and $μ$SR techniques, showing that the Néel order vanishes already at $x = 0.025 \pm 0.005$. We explain this extreme order suppression using a two-dimensional Heisenberg spin model, demonstrating that a W-type impurity induces a deformation of the order parameter that decays with distance as $1/r^2$ at temperature $T=0$. The associated logarithmic singularity leads to loss of order for any $x>0$. Order for small $x>0$ and $T>0$ is induced by weak interplane couplings. In the nonmagnetic phase of Sr$_2$CuTe$_{1-x}$W$_x$O$_6$, the $μ$SR relaxation rate exhibits quantum critical scaling with a large dynamic exponent, $z \approx 3$, consistent with a random-singlet state.

preprint2021arXiv

Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution

Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations. The key to solving this more challenging real image super-resolution (RealSR) problem lies in learning feature representations that are both informative and content-aware. In this paper, we propose an Omni-frequency Region-adaptive Network (ORNet) to address both challenges, here we call features of all low, middle and high frequencies omni-frequency features. Specifically, we start from the frequency perspective and design a Frequency Decomposition (FD) module to separate different frequency components to comprehensively compensate the information lost for real LR image. Then, considering the different regions of real LR image have different frequency information lost, we further design a Region-adaptive Frequency Aggregation (RFA) module by leveraging dynamic convolution and spatial attention to adaptively restore frequency components for different regions. The extensive experiments endorse the effective, and scenario-agnostic nature of our OR-Net for RealSR.

preprint2021arXiv

Leveraging Planar Regularities for Point Line Visual-Inertial Odometry

With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. Because pure sparse 3D points provide a structureless representation of the environment, generating 3D mesh from sparse points can further model the environment topology and produce dense mapping. To improve the accuracy of 3D mesh generation and localization, we propose a tightly-coupled monocular VIO system, PLP-VIO, which exploits point features and line features as well as plane regularities. The co-planarity constraints are used to leverage additional structure information for the more accurate estimation of 3D points and spatial lines in state estimator. To detect plane and 3D mesh robustly, we combine both the line features with point features in the detection method. The effectiveness of the proposed method is verified on both synthetic data and public datasets and is compared with other state-of-the-art algorithms.

preprint2021arXiv

Machine learning prediction of magnetic properties of Fe-based metallic glasses considering local structures

Magnetism prediction is of great significance for Fe-based metallic glasses (FeMGs), which have shown great commercial value. Theories or models established based on condensed matter physics exhibit several exceptions and limited accuracy. In this work, machine learning (ML) models learned from a large amount of experimental data were trained based on eXtreme gradient boosting (XGBoost), artificial neural networks (ANN), and random forest to predict the magnetic properties of FeMGs. The XGBoost and ANN models exhibited comparably excellent predictive performance, with R^2 >= 0.903, mean absolute percentage error (MAPE) <= 6.17, and root mean squared error (RMSE) <= 0.098. The trained ML models aggregate the influence of 13 factors, which is difficult to achieve in traditional physical models. The influence of local structure, which was represented by the experimental parameter of the supercooled liquid region, presented a significant impact on the predictive performance of ML models. The developed ML-based method here can predict the magnetic properties of FeMGs by considering multiple factors simultaneously, including complex local structures.

preprint2021arXiv

Saxl Conjecture for triple hooks

We make some progresses on Saxl conjecture. Firstly, we show that the probability that a partition is comparable in dominance order to the staircase partition tends to zero as the staircase partition grows. Secondly, for partitions whose Durfee size is $k$ where $k\geq3$, by semigroup property, we show that there exists a number $n_k$ such that if the tensor squares of the first $n_k$ staircase partitions contain all irreducible representations corresponding to partitions with Durfee size $k$, then all tensor squares contain partitions with Durfee size $k$. Specially, we show that $n_3=14$ and $n_4=28$. Furthermore, with the help of computer we show that the Saxl conjecture is true for all triple-hooks (i.e. partitions with Durfee size 3). Similar results for chopped square and caret shapes are also discussed.

preprint2020arXiv

5G Technologies Based Remote E-Health: Architecture, Applications, and Solutions

Currently, many countries are facing the problems of aging population, serious imbalance of medical resources supply and demand, as well as uneven geographical distribution, resulting in a huge demand for remote e-health. Particularly, with invasions of COVID-19, the health of people and even social stability have been challenged unprecedentedly. To contribute to these urgent problems, this article proposes a general architecture of the remote e-health, where the city hospital provides the technical supports and services for remote hospitals. Meanwhile, 5G technologies supported telemedicine is introduced to satisfy the high-speed transmission of massive multimedia medical data, and further realize the sharing of medical resources. Moreover, to turn passivity into initiative to prevent COVID-19, a broad area epidemic prevention and control scheme is also investigated, especially for the remote areas. We discuss their principles and key features, and foresee the challenges, opportunities, and future research trends. Finally, a node value and content popularity based caching strategy is introduced to provide a preliminary solution of the massive data storage and low-latency transmission.

preprint2020arXiv

A Chinese Corpus for Fine-grained Entity Typing

Fine-grained entity typing is a challenging task with wide applications. However, most existing datasets for this task are in English. In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4,800 mentions manually labeled through crowdsourcing. Each mention is annotated with free-form entity types. To make our dataset useful in more possible scenarios, we also categorize all the fine-grained types into 10 general types. Finally, we conduct experiments with some neural models whose structures are typical in fine-grained entity typing and show how well they perform on our dataset. We also show the possibility of improving Chinese fine-grained entity typing through cross-lingual transfer learning.

preprint2020arXiv

A highly sensitive piezoresistive sensor based on MXene and polyvinyl butyral with a wide detection limit and low power consumption

As a new class of two-dimensional transition-metal carbide and carbonitride, MXene have been widely used in the energy storage, sensor, catalysis, electromagnetic interference shielding and other field. It is a challenge to simultaneously realize a sensor of extremely high sensitivity, wide detection limits, low power consumption and good mechanical stability. In this work, taking advantage of high conductivity of MXene and porous structure of polyvinyl butyral, a highly sensitive piezoresistive sensor was fabricated. The fabricated MXene/PVB-based sensor exhibits highly sensitive reliably with a factor of ~11.9 kPa^-1, ~1.15 kPa^-1 and ~0.20 kPa^-1 in the ranges of 31.2 Pa-312 Pa, 312 Pa- 62.4 kPa and 62.4 kPa-1248.4 kPa, respectively. The sensor has a wide detection range (~31.2 Pa to ~2.205 MPa), low detection limit (6.8 Pa), low detection voltage (0.1 mV), low power consumption (~3.6 * 10^-10 W), fast response time ( ~110 ms), as well as good mechanical stability (over 10,000 maximum-pressure cycles). Moreover, it is demonstrated that the sensor can detect subtle bending and release activities of human, including arterial pulses and voice signal, which is potentially suitable as a wide detection range, highly sensitive and low power consumption piezoresistive sensor. This work provides a new avenue to expand the application of MXene-based flexible pressure sensor in the field of wide sensing range and ultra-low power consumption.

preprint2020arXiv

A new method to test the cosmic distance duality relation using the strongly lensed gravitational waves

We propose a new method to test the cosmic distance duality relation using the strongly lensed gravitational waves. The simultaneous observations of image positions, the relative time delay between different images, the redshift measurements of lens and source, together with the mass modelling of the lens galaxy, provide the angular diameter distance to the gravitational wave source. On the other hand, from the observation of gravitational wave signals the luminosity distance to the source can be obtained. This is to our knowledge the first method to simultaneously measure both the angular diameter distance and luminosity distance from the same source. Thus, the strongly lensed gravitational waves provide a unique way to test the cosmic distance duality relation. With the construction of the third generation gravitational detectors such as the Einstein Telescope in the future, it is possible to test the cosmic distance duality relation at an accuracy of several percent.

preprint2020arXiv

A picture of pseudogap phase related to charge fluxes

Recently, charge density fluctuations or charge fluxes attract strong interests in understanding the unconventional superconductivity. In this paper, a new emergent configuration in cuprates is identified by density functional theory simulations, called the charge pseudoplane, which exhibits the property of confining the dynamic charge fluxes for higher superconducting transition temperatures. It further redefines the fundamental collective excitation in cuprates as pQon with the momentum-dependent and ultrafast localization-delocalization duality. It is shown that both pseudogap and superconducting phases can be born from and intertwined through the charge flux confinement property of the charge pseudoplane region. Our experimental simulations based on the new picture provide good agreements with previous angle resolved photoemission spectroscopy and scanning tunneling microscopy results. Our work thus opens a new perspective on the origin of pseudogap phase and other related phases in cuprates, and further provides a critical descriptor to search and design higher temperature superconductors.

preprint2020arXiv

A Real-Time Deep Network for Crowd Counting

Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situation, we hope the model run as fast as possible while keeping accuracy. In this paper, we propose a compact convolutional neural network for crowd counting which learns a more efficient model with a small number of parameters. With three parallel filters executing the convolutional operation on the input image simultaneously at the front of the network, our model could achieve nearly real-time speed and save more computing resources. Experiments on two benchmarks show that our proposed method not only takes a balance between performance and efficiency which is more suitable for actual scenes but also is superior to existing light-weight models in speed.

preprint2020arXiv

AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results

This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the low-resolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. Due to this massive information loss, it is hard to accurately restore the missing information. Track 1 is set up to gauge the state-of-the-art for such a demanding task, where fidelity to the ground truth is measured by PSNR and SSIM. Perceptually higher quality can be achieved in trade-off for fidelity by generating plausible high-frequency content. Track 2 therefore aims at generating visually pleasing results, which are ranked according to human perception, evaluated by a user study. In contrast to single image super-resolution (SISR), VSR can benefit from additional information in the temporal domain. However, this also imposes an additional requirement, as the generated frames need to be consistent along time.

preprint2020arXiv

Automatic Lumbar Spinal CT Image Segmentation with a Dual Densely Connected U-Net

The clinical treatment of degenerative and developmental lumbar spinal stenosis (LSS) is different. Computed tomography (CT) is helpful in distinguishing degenerative and developmental LSS due to its advantage in imaging of osseous and calcified tissues. However, boundaries of the vertebral body, spinal canal and dural sac have low contrast and hard to identify in a CT image, so the diagnosis depends heavily on the knowledge of expert surgeons and radiologists. In this paper, we develop an automatic lumbar spinal CT image segmentation method to assist LSS diagnosis. The main contributions of this paper are the following: 1) a new lumbar spinal CT image dataset is constructed that contains 2393 axial CT images collected from 279 patients, with the ground truth of pixel-level segmentation labels; 2) a dual densely connected U-shaped neural network (DDU-Net) is used to segment the spinal canal, dural sac and vertebral body in an end-to-end manner; 3) DDU-Net is capable of segmenting tissues with large scale-variant, inconspicuous edges (e.g., spinal canal) and extremely small size (e.g., dural sac); and 4) DDU-Net is practical, requiring no image preprocessing such as contrast enhancement, registration and denoising, and the running time reaches 12 FPS. In the experiment, we achieve state-of-the-art performance on the lumbar spinal image segmentation task. We expect that the technique will increase both radiology workflow efficiency and the perceived value of radiology reports for referring clinicians and patients.

preprint2020arXiv

Bi2Te3/Si thermophotovoltaic cells converting low temperature radiation into electricity

The thermophotovoltaic cells which convert the low temperature radiation into electricity are of significance due to their potential applications in many fields. In this work, Bi2Te3/Si thermophotovoltaic cells which work under the radiation from the blackbody with the temperature of 300 K-480 K are presented. The experimental results show that the cells can output electricity even under the radiation temperature of 300 K. The band structure of Bi2Te3/Si heterojunctions and the defects in Bi2Te3 thin films lower the conversion efficiency of the cells. It is also demonstrated that the resistivity of Si and the thickness of Bi2Te3 thin films have important effects on Bi2Te3/Si thermophotovoltaic cells. Although the cells' output power is small, this work provides a possible way to utilize the low temperature radiation.

preprint2020arXiv

Building a PubMed knowledge graph

PubMed is an essential resource for the medical domain, but useful concepts are either difficult to extract or are ambiguated, which has significantly hindered knowledge discovery. To address this issue, we constructed a PubMed knowledge graph (PKG) by extracting bio-entities from 29 million PubMed abstracts, disambiguating author names, integrating funding data through the National Institutes of Health (NIH) ExPORTER, collecting affiliation history and educational background of authors from ORCID, and identifying fine-grained affiliation data from MapAffil. Through the integration of the credible multi-source data, we could create connections among the bio-entities, authors, articles, affiliations, and funding. Data validation revealed that the BioBERT deep learning method of bio-entity extraction significantly outperformed the state-of-the-art models based on the F1 score (by 0.51%), with the author name disambiguation (AND) achieving a F1 score of 98.09%. PKG can trigger broader innovations, not only enabling us to measure scholarly impact, knowledge usage, and knowledge transfer, but also assisting us in profiling authors and organizations based on their connections with bio-entities. The PKG is freely available on Figshare (https://figshare.com/s/6327a55355fc2c99f3a2, simplified version that exclude PubMed raw data) and TACC website (http://er.tacc.utexas.edu/datasets/ped, full version).

preprint2020arXiv

C*-algebras of right LCM one-relator monoids and Artin-Tits monoids of finite type

We study C*-algebras generated by left regular representations of right LCM one-relator monoids and Artin-Tits monoids of finite type. We obtain structural results concerning nuclearity, ideal structure and pure infiniteness. Moreover, we compute K-theory. Based on our K-theory results, we develop a new way of computing K-theory for certain group C*-algebras and crossed products.

preprint2020arXiv

Cartan subalgebras and the UCT problem, II

We show that outer approximately represenbtable actions of a finite cyclic group on UCT Kirchberg algebras satisfy a certain quasi-freeness type property if the corresponding crossed products satisfy the UCT and absorb a suitable UHF algebra tensorially. More concretely, we prove that for such an action there exists an inverse semigroup of homogeneous partial isometries that generates the ambient C*-algebra and whose idempotent semilattice generates a Cartan subalgebra. We prove a similar result for actions of finite cyclic groups with the Rokhlin property on UCT Kirchberg algebras absorbing a suitable UHF algebra. These results rely on a new construction of Cartan subalgebras in certain inductive limits of Cartan pairs. We also provide a characterisation of the UCT problem in terms of finite order automorphisms, Cartan subalgebras and inverse semigroups of partial isometries of the Cuntz algebra $\mathcal{O}_2$. This generalizes earlier work of the authors.

preprint2020arXiv

Cascade Graph Neural Networks for RGB-D Salient Object Detection

In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient object detection fromRGB-D images is how to fully leverage the two complementary data sources. Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors.In this work, we introduceCascade Graph Neural Networks(Cas-Gnn),a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection. Cas-Gnn processes the two data sources individually and employs a novelCascade Graph Reasoning(CGR) module to learn powerful dense feature embeddings, from which the saliency map can be easily inferred. Contrast to the previous approaches, the explicitly modeling and reasoning of high-level relations between complementary data sources allows us to better overcome challenges such as occlusions and ambiguities. Extensive experiments demonstrate that Cas-Gnn achieves significantly better performance than all existing RGB-DSOD approaches on several widely-used benchmarks.

preprint2020arXiv

Constructing Menger manifold C-diagonals in classifiable C-algebras

We construct C*-diagonals with connected spectra in all classifiable stably finite C*-algebras which are unital or stably projectionless with continuous scale. For classifiable stably finite C*-algebras with torsion-free $K_0$ and trivial $K_1$, we further determine the spectra of the C*-diagonals up to homeomorphism. In the unital case, the underlying space turns out to be the Menger curve. In the stably projectionless case, the space is obtained by removing a non-locally-separating copy of the Cantor space from the Menger curve. We show that each of our classifiable C*-algebras has continuum many pairwise non-conjugate such Menger manifold C*-diagonals. Along the way, we also obtain a complete classification of C*-diagonals in all one-dimensional non-commutative CW complexes.

preprint2020arXiv

Context-aware Helpfulness Prediction for Online Product Reviews

Modeling and prediction of review helpfulness has become more predominant due to proliferation of e-commerce websites and online shops. Since the functionality of a product cannot be tested before buying, people often rely on different kinds of user reviews to decide whether or not to buy a product. However, quality reviews might be buried deep in the heap of a large amount of reviews. Therefore, recommending reviews to customers based on the review quality is of the essence. Since there is no direct indication of review quality, most reviews use the information that ''X out of Y'' users found the review helpful for obtaining the review quality. However, this approach undermines helpfulness prediction because not all reviews have statistically abundant votes. In this paper, we propose a neural deep learning model that predicts the helpfulness score of a review. This model is based on convolutional neural network (CNN) and a context-aware encoding mechanism which can directly capture relationships between words irrespective of their distance in a long sequence. We validated our model on human annotated dataset and the result shows that our model significantly outperforms existing models for helpfulness prediction.

preprint2020arXiv

Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation

Online recommendation services recommend multiple commodities to users. Nowadays, a considerable proportion of users visit e-commerce platforms by mobile devices. Due to the limited screen size of mobile devices, positions of items have a significant influence on clicks: 1) Higher positions lead to more clicks for one commodity. 2) The 'pseudo-exposure' issue: Only a few recommended items are shown at first glance and users need to slide the screen to browse other items. Therefore, some recommended items ranked behind are not viewed by users and it is not proper to treat this kind of items as negative samples. While many works model the online recommendation as contextual bandit problems, they rarely take the influence of positions into consideration and thus the estimation of the reward function may be biased. In this paper, we aim at addressing these two issues to improve the performance of online mobile recommendation. Our contributions are four-fold. First, since we concern the reward of a set of recommended items, we model the online recommendation as a contextual combinatorial bandit problem and define the reward of a recommended set. Second, we propose a novel contextual combinatorial bandit method called UBM-LinUCB to address two issues related to positions by adopting the User Browsing Model (UBM), a click model for web search. Third, we provide a formal regret analysis and prove that our algorithm achieves sublinear regret independent of the number of items. Finally, we evaluate our algorithm on two real-world datasets by a novel unbiased estimator. An online experiment is also implemented in Taobao, one of the most popular e-commerce platforms in the world. Results on two CTR metrics show that our algorithm outperforms the other contextual bandit algorithms.

preprint2020arXiv

Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests

A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users. Two models with disjoint and hybrid payoffs are considered to characterize the phenomenon that users' preferences towards different items vary differently over time. In the disjoint payoff model, the reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous and distinct changes across different arms. An efficient learning algorithm that is adaptive to abrupt reward changes is proposed and theoretical regret analysis is provided to show that a sublinear scaling of regret in the time length $T$ is achieved. The algorithm is further extended to a more general setting with hybrid payoffs where the reward of playing an arm is determined by both an arm-specific preference vector and a joint coefficient vector shared by all arms. Empirical experiments are conducted on real-world datasets to verify the advantages of the proposed learning algorithms against baseline ones in both settings.

preprint2020arXiv

Cooperation among tumor cell subpopulations leads to intratumor heterogeneity

Heterogeneity is a hallmark of all cancers. Tumor heterogeneity is found at different levels -- interpatient, intrapatient, and intratumor heterogeneity. All of them pose challenges for clinical treatments. The latter two scenarios can also increase the risk of developing drug resistance. Although the existence of tumor heterogeneity has been known for two centuries, a clear understanding of its origin is still elusive, especially at the level of intratumor heterogeneity (ITH). The coexistence of different subpopulations within a single tumor has been shown to play crucial roles during all stages of carcinogenesis. Here, using concepts from evolutionary game theory and public goods game, often invoked in the context of the tragedy of commons, we explore how the interactions among subclone populations influence the establishment of ITH. By using an evolutionary model, which unifies several experimental results in distinct cancer types, we develop quantitative theoretical models for explaining data from {\it in vitro} experiments involving pancreatic cancer as well as {\it vivo} data in glioblastoma multiforme. Such physical and mathematical models complement experimental studies, and could optimistically also provide new ideas for the design of efficacious therapies for cancer patients.

preprint2020arXiv

Correlating surface energy with adsorption energy by means of intrinsic characteristics of substrates

Surface energy is fundamental in controlling surface properties and surface-driven processes like heterogeneous catalysis, as adsorption energy is. It is thus crucial to establish an effective scheme to determine surface energy and its relation with adsorption energy. Herein, we propose a model to quantify the effects of the intrinsic characteristics of materials on the material-dependent property and anisotropy of surface energy, based on the period number and group number of bulk atoms, and the valence-electron number, electronegativity and coordination of surface atoms. Our scheme holds for elemental crystals in both solid and liquid phases, body-centered-tetragonal intermetallics, fluorite-structure intermetallics, face-centered-cubic intermetallics, Mg-based surface alloys and semiconductor compounds, which further identifies a quantitative relation between surface energy and adsorption energy and rationalizes the material-dependent error of first-principle methods in calculating the two quantities. This model is predictive with easily accessible parameters and thus allows the rapid screening of materials for targeted properties.

preprint2020arXiv

COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays

During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and clinical workflow. In view of this need, we present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases with a small amount of COVID-19 cases, and a trained lightweight medical student (MS) network to perform on-device COVID-19 patient triage and follow-up. To tackle the challenge of vastly similar and dominant fore- and background in medical images, we employ novel loss functions and training schemes for the MS network to learn the robust features. We demonstrate the significant potential of COVID-MobileXpert for rapid deployment via extensive experiments with diverse MS architecture and tuning parameter settings. The source codes for cloud and mobile based models are available from the following url: https://github.com/xinli0928/COVID-Xray.

preprint2020arXiv

Defending against adversarial attacks on medical imaging AI system, classification or detection?

Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems. Although an array of adversarial training and/or loss function based defense techniques have been developed and proved to be effective in computer vision, defending against adversarial attacks on medical images remains largely an uncharted territory due to the following unique challenges: 1) label scarcity in medical images significantly limits adversarial generalizability of the AI system; 2) vastly similar and dominant fore- and background in medical images make it hard samples for learning the discriminating features between different disease classes; and 3) crafted adversarial noises added to the entire medical image as opposed to the focused organ target can make clean and adversarial examples more discriminate than that between different disease classes. In this paper, we propose a novel robust medical imaging AI framework based on Semi-Supervised Adversarial Training (SSAT) and Unsupervised Adversarial Detection (UAD), followed by designing a new measure for assessing systems adversarial risk. We systematically demonstrate the advantages of our robust medical imaging AI system over the existing adversarial defense techniques under diverse real-world settings of adversarial attacks using a benchmark OCT imaging data set.

preprint2020arXiv

Designable ductility of a nano-network from few-layered graphene bonded with benzene: A molecular dynamics study

In nanoscale, motion operation of a nano-objective is usually realized by displacement load, which put forwards high requirement for ductility of material. Since pristine graphene has low ductility, once the stretching strain exceeds its critical value, it breaks in brittle style and loses ability to bear the external load quickly. Herein, to improve the ductility, a corrugated sandwich carbon nano-network model based on few-layered graphene is proposed, in which the two surface layers are bonded with several corrugated core layers via benzene molecules. Effects of factors such as the geometry of the carbon network, temperature, and strain rate, on the ductility are evaluated by molecular dynamics simulations. Conclusions are drawn for potential application of the new two-dimensional material with designable ductility.

preprint2020arXiv

Detection of Genuine and Posed Facial Expressions of Emotion: A Review

Facial expressions of emotion play an important role in human social interactions. However, posed acting is not always the same as genuine feeling. Therefore, the credibility assessment of facial expressions, namely, the discrimination of genuine (spontaneous) expressions from posed(deliberate/volitional/deceptive) ones, is a crucial yet challenging task in facial expression understanding. Rapid progress has been made in recent years for automatic detection of genuine and posed facial expressions. This paper presents a general review of the relevant research, including several spontaneous vs. posed (SVP) facial expression databases and various computer vision based detection methods. In addition, a variety of factors that will influence the performance of SVP detection methods are discussed along with open issues and technical challenges.

preprint2020arXiv

Deterministic scheme for two-dimensional type-II Dirac points and experimental realization in acoustics

Low-energy electrons near Dirac/Weyl nodal points mimic massless relativistic fermions. However, as they are not constrained by Lorentz invariance, they can exhibit tipped-over type-II Dirac/Weyl cones which provide highly anisotropic physical properties and responses, creating unique possibilities. Recently, they have been observed in several quantum and classical systems. Yet, there is still no simple and deterministic strategy to realize them since their nodal points are accidental degeneracies, unlike symmetry-guaranteed type-I counterparts. Here, we propose a band-folding scheme for constructing type-II Dirac points, and we use a tight-binding analysis to unveil its generality and deterministic nature. Through realizations in acoustics, type-II Dirac points are experimentally visualized and investigated using near-field mappings. As a direct effect of tipped-over Dirac cones, strongly tilted kink states originating from their valley-Hall properties are also observed. This deterministic scheme could serve as platform for further investigations of intriguing physics associated with various strongly Lorentz-violating nodal points.

preprint2020arXiv

Dual Role of Cell-Cell Adhesion In Tumor Suppression and Proliferation Due to Collective Mechanosensing

It is known that mechanical interactions couple a cell to its neighbors, enabling a feedback loop to regulate tissue growth. However, the interplay between cell-cell adhesion strength, local cell density and force fluctuations in regulating cell proliferation is poorly understood. Here, we show that spatial variations in the tumor growth rates, which depend on the location of cells within tissue spheroids, are strongly influenced by cell-cell adhesion. As the strength of the cell-cell adhesion increases, intercellular pressure initially decreases, enabling dormant cells to more readily enter into a proliferative state. We identify an optimal cell-cell adhesion regime where pressure on a cell is a minimum, allowing for maximum proliferation. We use a theoretical model to validate this novel collective feedback mechanism coupling adhesion strength, local stress fluctuations and proliferation.Our results, predicting the existence of a non-monotonic proliferation behavior as a function of adhesion strength, are consistent with experimental results. Several experimental implications of the proposed role of cell-cell adhesion in proliferation are quantified, making our model predictions amenable to further experimental scrutiny. We show that the mechanism of contact inhibition of proliferation, based on a pressure-adhesion feedback loop, serves as a unifying mechanism to understand the role of cell-cell adhesion in proliferation.

preprint2020arXiv

Efficiency in Real-time Webcam Gaze Tracking

Efficiency and ease of use are essential for practical applications of camera based eye/gaze-tracking. Gaze tracking involves estimating where a person is looking on a screen based on face images from a computer-facing camera. In this paper we investigate two complementary forms of efficiency in gaze tracking: 1. The computational efficiency of the system which is dominated by the inference speed of a CNN predicting gaze-vectors; 2. The usability efficiency which is determined by the tediousness of the mandatory calibration of the gaze-vector to a computer screen. To do so, we evaluate the computational speed/accuracy trade-off for the CNN and the calibration effort/accuracy trade-off for screen calibration. For the CNN, we evaluate the full face, two-eyes, and single eye input. For screen calibration, we measure the number of calibration points needed and evaluate three types of calibration: 1. pure geometry, 2. pure machine learning, and 3. hybrid geometric regression. Results suggest that a single eye input and geometric regression calibration achieve the best trade-off.

preprint2020arXiv

Evolution of superconductivity and antiferromagnetic order in Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$

The vanadium doping effects on superconductivity and magnetism of iron pnictides are investigated in Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ by transport, susceptibility and neutron scattering measurements. The doping of magnetic impurity V causes a fast suppression of superconductivity with T$_c$ reduced at a rate of 7.4~K/1\%V. On the other hand, the long-range commensurate $C$-type antiferromagnetic order is recovered upon the V doping. The value of ordered magnetic moments of Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ follows a dome-like evolution versus doping concentration x. A possible Griffiths-type antiferromagnetic region of multiple coexisting phases in the phase diagram of Ba(Fe$_{0.92-x}$Co$_{0.08}$V$_x$)$_2$As$_2$ is identified, in accordance with previous theoretical predictions based on a cooperative behavior of the magnetic impurities and the conduction electrons mediating the Ruderman-Kittel-Kasuya-Yosida interactions between them.

preprint2020arXiv

Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability

Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate the explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.

preprint2020arXiv

GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving

Despite the advancement in the technology of autonomous driving cars, the safety of a self-driving car is still a challenging problem that has not been well studied. Motion prediction is one of the core functions of an autonomous driving car. Previously, we propose a novel scheme called GRIP which is designed to predict trajectories for traffic agents around an autonomous car efficiently. GRIP uses a graph to represent the interactions of close objects, applies several graph convolutional blocks to extract features, and subsequently uses an encoder-decoder long short-term memory (LSTM) model to make predictions. Even though our experimental results show that GRIP improves the prediction accuracy of the state-of-the-art solution by 30%, GRIP still has some limitations. GRIP uses a fixed graph to describe the relationships between different traffic agents and hence may suffer some performance degradations when it is being used in urban traffic scenarios. Hence, in this paper, we describe an improved scheme called GRIP++ where we use both fixed and dynamic graphs for trajectory predictions of different types of traffic agents. Such an improvement can help autonomous driving cars avoid many traffic accidents. Our evaluations using a recently released urban traffic dataset, namely ApolloScape showed that GRIP++ achieves better prediction accuracy than state-of-the-art schemes. GRIP++ ranked #1 on the leaderboard of the ApolloScape trajectory competition in October 2019. In addition, GRIP++ runs 21.7 times faster than a state-of-the-art scheme, CS-LSTM.

preprint2020arXiv

Holographic boiling and generalized thermodynamic description beyond local equilibrium

Tuning a very simple two-component holographic superfluid model, we can have a first order phase transition between two superfluid phases in the probe limit. Inspired by the potential landscape discussion, an intuitive physical picture for systems with first order phase transitions is provided. We stress that holography perfectly offers a generalized thermodynamic description of certain strongly coupled systems even out of local equilibrium, which enables us to carefully study domain wall structures of the system under first order phase transitions, either static or in real time dynamics. We numerically construct the 1D domain wall configuration and compute the surface tension of the domain wall from its generalized grand potential. We also numerically simulate the real time dynamics of a 2D bubble nucleation process (holographic boiling). The surface tension of the 1D domain wall nicely matches the final state of the 2D bubble nucleation process when the bubble radius is large enough.

preprint2020arXiv

How to Investigate the Historical Roots and Evolution of Research Fields in China? A Case Study on iMetrics Using RootCite

This paper aimed to provide an approach to investigate the historical roots and evolution of research fields in China by extending the reference publication year spectroscopy (RPYS). RootCite, an open source software accepts raw data from both the Web of Science and the China Social Science Citation Index (CSSCI), was developed using python. We took iMetrics in China as the research case. 5,141 Chinese iMetrics related publications with 73,376 non-distinct cited references (CR) collected from the CSSCI were analyzed using RootCite. The results showed that the first CR in the field can be dated back to 1882 and written in English; but the majority (64.2%) of the CR in the field were Chinese publications. 17 peaks referring to 18 seminal works (13 in English and 5 in Chinese) were located during the period from 1900 to 2017. The field shared the same roots with that in the English world but has its own characteristics, and it was then shaped by contributions from both the English world and China. The five Chinese works have played irreplaceable and positive roles in the historical evolutionary path of the field, which should not be ignored, especially for the evolution of the field. This research demonstrated how RootCite aided the task of identifying the origin and evolution of research fields in China, which could be valuable for extending RPYS for countries with other languages.

preprint2020arXiv

Hybrid Graph Neural Networks for Crowd Counting

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges:(i) multi-scale relations for capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can distill rich relations between the nodes to obtain more powerful representations, leading to robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art approaches by a large margin.

preprint2020arXiv

Improve SGD Training via Aligning Mini-batches

Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of a feature extractor (i.e. last hidden layer) and a linear classifier (i.e. output layer) that is trained jointly with stochastic gradient descent (SGD). In each iteration of SGD, a mini-batch from the training data is sampled and the true gradient of the loss function is estimated as the noisy gradient calculated on this mini-batch. From the feature learning perspective, the feature extractor should be updated to learn meaningful features with respect to the entire data, and reduce the accommodation to noise in the mini-batch. With this motivation, we propose In-Training Distribution Matching (ITDM) to improve DNN training and reduce overfitting. Specifically, along with the loss function, ITDM regularizes the feature extractor by matching the moments of distributions of different mini-batches in each iteration of SGD, which is fulfilled by minimizing the maximum mean discrepancy. As such, ITDM does not assume any explicit parametric form of data distribution in the latent feature space. Extensive experiments are conducted to demonstrate the effectiveness of our proposed strategy.

preprint2020arXiv

Infrared diagnostics of the solar magnetic field with Mg I 12 $μ$m lines: forward-model results

The Mg I 12.32 and 12.22 $μ$m lines are a pair of emission lines that present a great advantage for accurate solar magnetic field measurement. They potentially contribute to the diagnosis of solar atmospheric parameters through their high magnetic sensitivity. The goal of this study is to understand the radiation transfer process of these lines in detail and explore the ability of magnetic field diagnosis in the infrared. We calculated the Stokes profiles and response functions of the two Mg I 12 $μ$m lines based on one-dimensional solar atmospheric models using the Rybicki-Hummer (RH) radiative transfer code. The integration of these profiles with respect to the wavelength was used to generate calibration curves related to the longitudinal and transverse fields. The traditional single-wavelength calibration curve based on the weak-field approximation was also tested to determine if it is suitable for the infrared. The 12.32 $μ$m line is more suitable for a magnetic field diagnosis because its relative emission intensity and polarization signal are stronger than that of the 12.22 $μ$m line. The result from the response functions illustrates that the derived magnetic field and velocity with 12.32 $μ$m line mainly originate from the height of 450 km, while that for the temperature is about 490 km. The calibration curves obtained by the wavelength-integrated method show a nonlinear distribution. For the Mg I 12.32 $μ$m line, the longitudinal (transverse) field can be effectively inferred from Stokes V/I (Q/I and U/I) in the linear range below $\sim 600$ G ($\sim 3000$ G) in quiet regions and below $\sim 400$ G ($\sim 1200$ G) in penumbrae. Within the given linear range, the method is a supplement to the magnetic field calibration when the Zeeman components are incompletely split.

preprint2020arXiv

Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization

Image demosaicing and super-resolution are two important tasks in color imaging pipeline. So far they have been mostly independently studied in the open literature of deep learning; little is known about the potential benefit of formulating a joint demosaicing and super-resolution (JDSR) problem. In this paper, we propose an end-to-end optimization solution to the JDSR problem and demonstrate its practical significance in computational imaging. Our technical contributions are mainly two-fold. On network design, we have developed a Residual-Dense Squeeze-and-Excitation Networks (RDSEN) supported by a pre-demosaicing network (PDNet) as the pre-processing step. We address the issue of spatio-spectral attention for color-filter-array (CFA) data and discuss how to achieve better information flow by concatenating Residue-Dense Squeeze-and-Excitation Blocks (RDSEBs) for JDSR. Experimental results have shown that significant PSNR/SSIM gain can be achieved by RDSEN over previous network architectures including state-of-the-art RCAN. On perceptual optimization, we propose to leverage the latest ideas including relativistic discriminator and pre-excitation perceptual loss function to further improve the visual quality of textured regions in reconstructed images. Our extensive experiment results have shown that Texture-enhanced Relativistic average Generative Adversarial Network (TRaGAN) can produce both subjectively more pleasant images and objectively lower perceptual distortion scores than standard GAN for JDSR. Finally, we have verified the benefit of JDSR to high-quality image reconstruction from real-world Bayer pattern data collected by NASA Mars Curiosity.

preprint2020arXiv

K-theory for generalized Lamplighter groups

We compute K-theory for the reduced group C*-algebras of generalized Lamplighter groups.

preprint2020arXiv

Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration

Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inherent interference among hybrid distortions which compromises the restoration performance. To decompose such interference, we introduce the concept of Disentangled Feature Learning to achieve the feature-level divide-and-conquer of hybrid distortions. Specifically, we propose the feature disentanglement module (FDM) to distribute feature representations of different distortions into different channels by revising gain-control-based normalization. We also propose a feature aggregation module (FAM) with channel-wise attention to adaptively filter out the distortion representations and aggregate useful content information from different channels for the construction of raw image. The effectiveness of the proposed scheme is verified by visualizing the correlation matrix of features and channel responses of different distortions. Extensive experimental results also prove superior performance of our approach compared with the latest HD-IR schemes.

preprint2020arXiv

Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication

With the rise of online e-commerce platforms, more and more customers prefer to shop online. To sell more products, online platforms introduce various modules to recommend items with different properties such as huge discounts. A web page often consists of different independent modules. The ranking policies of these modules are decided by different teams and optimized individually without cooperation, which might result in competition between modules. Thus, the global policy of the whole page could be sub-optimal. In this paper, we propose a novel multi-agent cooperative reinforcement learning approach with the restriction that different modules cannot communicate. Our contributions are three-fold. Firstly, inspired by a solution concept in game theory named correlated equilibrium, we design a signal network to promote cooperation of all modules by generating signals (vectors) for different modules. Secondly, an entropy-regularized version of the signal network is proposed to coordinate agents' exploration of the optimal global policy. Furthermore, experiments based on real-world e-commerce data demonstrate that our algorithm obtains superior performance over baselines.

preprint2020arXiv

LIRA: Lifelong Image Restoration from Unknown Blended Distortions

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.

preprint2020arXiv

LO-Net: Deep Real-time Lidar Odometry

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through individually designed feature selection, feature matching, and pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a new mask-weighted geometric constraint loss, LO-Net can effectively learn feature representation for LO estimation, and can implicitly exploit the sequential dependencies and dynamics in the data. We also design a scan-to-map module, which uses the geometric and semantic information learned in LO-Net, to improve the estimation accuracy. Experiments on benchmark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM.

preprint2020arXiv

Locally Decodable Codes with Randomized Encoding

We initiate a study of locally decodable codes with randomized encoding. Standard locally decodable codes are error correcting codes with a deterministic encoding function and a randomized decoding function, such that any desired message bit can be recovered with good probability by querying only a small number of positions in the corrupted codeword. This allows one to recover any message bit very efficiently in sub-linear or even logarithmic time. Besides this straightforward application, locally decodable codes have also found many other applications such as private information retrieval, secure multiparty computation, and average-case complexity. However, despite extensive research, the tradeoff between the rate of the code and the number of queries is somewhat disappointing. For example, the best known constructions still need super-polynomially long codeword length even with a logarithmic number of queries, and need a polynomial number of queries to achieve a constant rate. In this paper, we show that by using a randomized encoding, in several models we can achieve significantly better rate-query tradeoff. In addition, our codes work for both the standard Hamming errors, and the more general and harder edit errors.

preprint2020arXiv

LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 730K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. To evaluate a tracker on different attributes, we define 4 scenario attributes and 12 challenge attributes in the evaluation dataset. By releasing LSOTB-TIR, we encourage the community to develop deep learning based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance. Furthermore, we re-train several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.

preprint2020arXiv

Multi-node Bert-pretraining: Cost-efficient Approach

Recently, large scale Transformer-based language models such as BERT, GPT-2, and XLNet have brought about exciting leaps in state-of-the-art results for many Natural Language Processing (NLP) tasks. One of the common trends in these recent models is a significant increase in model complexity, which introduces both more weights and computation. Moreover, with the advent of large-scale unsupervised datasets, training time is further extended due to the increased amount of data samples within a single training epoch. As a result, to train these models within a reasonable time, machine learning (ML) programmers often require advanced hardware setups such as the premium GPU-enabled NVIDIA DGX workstations or specialized accelerators such as Google's TPU Pods. Our work addresses this limitation and demonstrates that the BERT pre-trained model can be trained within 2 weeks on an academic-size cluster of widely available GPUs through careful algorithmic and software optimizations. In this paper, we present these optimizations on how to improve single device training throughput, distribute the training workload over multiple nodes and GPUs, and overcome the communication bottleneck introduced by the large data exchanges over the network. We show that we are able to perform pre-training on BERT within a reasonable time budget (12 days) in an academic setting, but with a much less expensive and less aggressive hardware resource requirement than in previously demonstrated industrial settings based on NVIDIA DGX machines or Google's TPU Pods.

preprint2020arXiv

Multi-scale Grouped Dense Network for VVC Intra Coding

Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams IMC at the bit-rate of 0.15 in Lowrate track. Moreover, our MSGDN-GAN could achieve the better subjective performance.

preprint2020arXiv

On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax losses are often used to boost performance of the unweighted version. In this paper, motivated to explain the reweighting mechanism, we explicate the learning property of those two loss functions by analyzing the necessary condition (e.g., gradient equals to zero) after training CNNs to converge to a local minimum. The analysis immediately provides us explanations for understanding (1) quantitative effects of the class-wise reweighting mechanism: deterministic effectiveness for binary classification using logistic loss yet indeterministic for multi-class classification using softmax loss; (2) disadvantage of logistic loss for single-label multi-class classification via one-vs.-all approach, which is due to the averaging effect on predicted probabilities for the negative class (e.g., non-target classes) in the learning process. With the disadvantage and advantage of logistic loss disentangled, we thereafter propose a novel reweighted logistic loss for multi-class classification. Our simple yet effective formulation improves ordinary logistic loss by focusing on learning hard non-target classes (target vs. non-target class in one-vs.-all) and turned out to be competitive with softmax loss. We evaluate our method on several benchmark datasets to demonstrate its effectiveness.

preprint2020arXiv

Operator algebras of higher rank numerical semigroups

A higher rank numerical semigroup is a positive cone whose seminormalization is isomorphic to the free abelian semigroup. The corresponding nonselfadjoint semigroup algebras are known to provide examples that answer Arveson's Dilation Problem to the negative. Here we show that these algebras share the polydisc as the character space in a canonical way. We subsequently use this feature in order to identify higher rank numerical semigroups from the corresponding nonselfadjoint algebras.

preprint2020arXiv

Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data

Dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D Fast Fourier Transforms (FFT), correlation and pair distribution function are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics on the particle-by-particle level. Beyond the macroscopic descriptors, we utilize the knowledge of local particle geometries and configurations to explore the evolution of local geometries and reconstruct the interaction potential between the particles. Finally, we use the machine learning-based feature extraction to define particle neighborhood free of physics constraints. This approach allowed separating the possible classes of particle behavior, identify the associated transition probabilities, and further extend this analysis to identify slow modes and associated configurations, allowing for systematic exploration and predictive modeling of the time dynamics of the system. Overall, this work establishes the DL based workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

preprint2020arXiv

Reply to "Comment on 'Apical charge flux-modulated in-plane transport properties of cuprate superconductors'"

This Reply to preceding Comment of arXiv:1909.09867 shows why the statements in the Comment are misleading. We point out that our physical picture and theirs are fundamentally different, therefore the claim of using their correlation to include ours shows a very limited physical relevance, which instead may impede the precise understanding of either of the two pictures.

preprint2020arXiv

Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene

Trajectory forecasting, or trajectory prediction, of multiple interacting agents in dynamic scenes, is an important problem for many applications, such as robotic systems and autonomous driving. The problem is a great challenge because of the complex interactions among the agents and their interactions with the surrounding scenes. In this paper, we present a novel method for the robust trajectory forecasting of multiple intelligent agents in dynamic scenes. The proposed method consists of three major interrelated components: an interaction net for global spatiotemporal interactive feature extraction, an environment net for decoding dynamic scenes (i.e., the surrounding road topology of an agent), and a prediction net that combines the spatiotemporal feature, the scene feature, the past trajectories of agents and some random noise for the robust trajectory prediction of agents. Experiments on pedestrian-walking and vehicle-pedestrian heterogeneous datasets demonstrate that the proposed method outperforms the state-of-the-art prediction methods in terms of prediction accuracy.

preprint2020arXiv

Space Efficient Deterministic Approximation of String Measures

We study approximation algorithms for the following three string measures that are widely used in practice: edit distance (ED), longest common subsequence (LCS), and longest increasing sequence (LIS). All three problems can be solved exactly by standard algorithms that run in polynomial time with roughly $Θ(n)$ space, where $n$ is the input length, and our goal is to design deterministic approximation algorithms that run in polynomial time with significantly smaller space. Towards this, we design several algorithms that achieve $1+ε$ or $1-ε$ approximation for all three problems, where $ε>0$ can be any constant and even slightly sub constant. Our algorithms are flexible and can be adjusted to achieve the following two regimes of parameters: 1) space $n^δ$ for any constant $δ>0$ with running time essentially the same as or slightly more than the standard algorithms; and 2) space $\mathsf{polylog}(n)$ with (a larger) polynomial running time, which puts the approximation versions of the three problems in Steve's class (\textbf{SC}). Our algorithms significantly improve previous results in terms of space complexity, where all known results need to use space at least $Ω(\sqrt{n})$. Some of our algorithms can also be adapted to work in the asymmetric streaming model [SS13], and output the corresponding sequence. Furthermore, our results can be used to improve a recent result by Farhadi et. al. [FHRS20] about approximating ED in the asymmetric streaming model, reducing the running time from being exponential in [FHRS20] to a polynomial. Our algorithms are based on the idea of using recursion as in Savitch's theorem [Sav70], and a careful adaption of previous techniques to make the recursion work. Along the way we also give a new logspace reduction from longest common subsequence to longest increasing sequence, which may be of independent interest.

preprint2020arXiv

Spatially Heterogeneous Dynamics of Cells in a Growing Tumor Spheroid: Comparison Between Theory and Experiments

Collective cell movement, characterized by multiple cells that are in contact for substantial periods of time and undergo correlated motion, plays a central role in cancer and embryogenesis. Recent imaging experiments have provided time-dependent traces of individual cells, thus providing an unprecedented picture of tumor spheroid growth. By using simulations of a minimal cell model, we analyze the experimental data that map the movement of cells in fibrosarcoma tumor spheroid embedded in a collagen matrix. Both simulations and experiments show that cells in the core of the spheroid exhibit subdiffusive glassy dynamics (mean square displacement, $Δ(t) \approx t^α$ with $α< 1$), whereas cells in the periphery exhibit superdiffusive motion, $Δ(t) \approx t^α$ with $α>1)$. The motion of most of the cells near the periphery undergo highly persistent and correlated directional motion, thus explaining the observed superdiffusive behavior. The $α$ values for cells in the core and periphery, extracted from simulations and experiments are in near quantitative agreement with each other, which is surprising given that no parameter in the model was used to fit the measurements. The qualitatively different dynamics of cells in the core and periphery is captured by the fourth order susceptibility, introduced to characterize metastable states in glass forming systems. Analyses of the velocity autocorrelation of individual cells show remarkable spatial heterogeneity with no two cells exhibiting similar behavior. The prediction that $α$ should depend on the location of the cells in the tumor is amenable to experimental test. The highly heterogeneous dynamics of cells in the tumor spheroid provides a plausible mechanism for the origin of intratumor heterogeneity.

preprint2020arXiv

Statistical Issues and Recommendations for Clinical Trials Conducted During the COVID-19 Pandemic

The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials. Its effects on trial data create multiple potential statistical issues. The scale of impact is unprecedented, but when viewed individually, many of the issues are well defined and feasible to address. A number of strategies and recommendations are put forward to assess and address issues related to estimands, missing data, validity and modifications of statistical analysis methods, need for additional analyses, ability to meet objectives and overall trial interpretability.

preprint2020arXiv

Strongly lensed gravitational waves as the probes to test the cosmic distance duality relation

The cosmic distance relation (DDR) associates the angular diameters distance ($D_A$) and luminosity distance ($D_L$) by a simple formula, i.e., $D_L=(1+z)^2D_A$. The strongly lensed gravitational waves (GWs) provide a unique way to measure $D_A$ and $D_L$ simultaneously to the GW source, hence can be used as probes to test DDR. In this paper, we prospect the use of strongly lensed GW events from the future Einstein Telescope to test DDR. We write the possible deviation of DDR as $(1+z)^2D_A/D_L=η(z)$, and consider two different parametrizations of $η(z)$, namely, $η_1(z)=1+η_0 z$ and $η_2(z)=1+η_0 z/(1+z)$. Numerical simulations show that, with about 100 strongly lensed GW events observed by ET, the parameter $η_0$ can be constrained at $1.3\%$ and $3\%$ levels for the first and second parametrizations, respectively.

preprint2020arXiv

The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error

Innovations in artificial intelligence (AI) are occurring at speeds faster than ever witnessed before. However, few studies have managed to measure or depict this increasing velocity of innovations in the field of AI. In this paper, we combine data on AI from arXiv and Semantic Scholar to explore the pace of AI innovations from three perspectives: AI publications, AI players, and AI updates (trial and error). A research framework and three novel indicators, Average Time Interval (ATI), Innovation Speed (IS) and Update Speed (US), are proposed to measure the pace of innovations in the field of AI. The results show that: (1) in 2019, more than 3 AI preprints were submitted to arXiv per hour, over 148 times faster than in 1994. Furthermore, there was one deep learning-related preprint submitted to arXiv every 0.87 hours in 2019, over 1,064 times faster than in 1994. (2) For AI players, 5.26 new researchers entered into the field of AI each hour in 2019, more than 175 times faster than in the 1990s. (3) As for AI updates (trial and error), one updated AI preprint was submitted to arXiv every 41 days, with around 33% of AI preprints having been updated at least twice in 2019. In addition, as reported in 2019, it took, on average, only around 0.2 year for AI preprints to receive their first citations, which is 5 times faster than 2000-2007. This swift pace in AI illustrates the increase in popularity of AI innovation. The systematic and fine-grained analysis of the AI field enabled to portrait the pace of AI innovation and demonstrated that the proposed approach can be adopted to understand other fast-growing fields such as cancer research and nano science.

preprint2020arXiv

Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach

Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (MAN) approach for more powerful ABSA without the need for aspect tags using two new tag-free data sets crawled directly from TripAdvisor ({https://www.tripadvisor.com}). With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and it can also detect the vital aspect(s) leading to the overall sentiment polarity among different aspects via a new aspect ranking scheme. We carry out extensive experiments to demonstrate the strong performance of MAN compared to other state-of-the-art ABSA approaches and the explainability of our approach by visualizing and interpreting attention weights in case studies.

preprint2020arXiv

Towards Evaluating the Robustness of Chinese BERT Classifiers

Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks. Meanwhile, character-level Chinese NLP models, including BERT for Chinese, have also demonstrated that they can outperform the existing models. In this paper, we show that, however, such BERT-based models are vulnerable under character-level adversarial attacks. We propose a novel Chinese char-level attack method against BERT-based classifiers. Essentially, we generate "small" perturbation on the character level in the embedding space and guide the character substitution procedure. Extensive experiments show that the classification accuracy on a Chinese news dataset drops from 91.8% to 0% by manipulating less than 2 characters on average based on the proposed attack. Human evaluations also confirm that our generated Chinese adversarial examples barely affect human performance on these NLP tasks.

preprint2020arXiv

Tuned Hybrid Non-Uniform Subdivision Surfaces with Optimal Convergence Rates

This paper presents an enhanced version of our previous work, hybrid non-uniform subdivision surfaces [19], to achieve optimal convergence rates in isogeometric analysis. We introduce a parameter $λ$ ($\frac{1}{4}<λ<1$) to control the rate of shrinkage of irregular regions, so the method is called tuned hybrid non-uniform subdivision (tHNUS). Our previous work corresponds to the case when $λ=\frac{1}{2}$. While introducing $λ$ in hybrid subdivision significantly complicates the theoretical proof of $G^1$ continuity around extraordinary vertices, reducing $λ$ can recover the optimal convergence rates when tuned hybrid subdivision functions are used as a basis in isogeometric analysis. From the geometric point of view, the tHNUS retains comparable shape quality as [19] under non-uniform parameterization. Its basis functions are refinable and the geometric mapping stays invariant during refinement. Moreover, we prove that a tuned hybrid subdivision surface is globally $G^1$-continuous. From the analysis point of view, tHNUS basis functions form a non-negative partition of unity, are globally linearly independent, and their spline spaces are nested. We numerically demonstrate that tHNUS basis functions can achieve optimal convergence rates for the Poisson's problem with non-uniform parameterization around extraordinary vertices.

preprint2020arXiv

Ultra-open High-efficiency Ventilated Metamaterial Absorbers with Customized Broadband Performance

High-efficiency absorption of low-frequency sounds (< 1000 Hz) while maintaining a free flow of fluids remains a significant challenge in acoustical engineering due to the rigid trade-off between absorption and ventilation performances. Although ongoing advances in acoustic metamaterials have unlocked unprecedented possibilities and various metamaterial absorbers have been proposed, most of them only work adequately in the condition of no sound transmissions. Unfortunately, such condition requires a complete block of fluid channels due to longitudinal nature of sounds, which allows them to penetrate any small holes. Otherwise, their absorption performance could be drastically degraded and often cannot exceed 50%. This basic trade-off between absorption and ventilation performances definitely constrains their applications in daily scenarios where free air flows are necessary. Though some ventilated sound barriers with large transmission loss have been demonstrated, they essentially only reflect sounds, which are still there and even may be reflected back. Here, to overcome this general difficulty, we propose and demonstrate an ultra-open ventilated metamaterial absorber. The absorber, aiming at low-frequency sounds, simultaneously ensures high-performance absorption and ventilation, confirmed in experiments. Their mechanism is understood from an effective model of coupled lossy oscillators. Furthermore, the absorbers can be simply stacked to work in a customized broadband, while maintaining a good ventilation. The demonstrated absorber provides a clear scheme for achieving high-performance absorption and ventilation at low frequencies, necessary for applications in environment with free air flows.

preprint2020arXiv

Vispi: Automatic Visual Perception and Interpretation of Chest X-rays

Medical imaging contains the essential information for rendering diagnostic and treatment decisions. Inspecting (visual perception) and interpreting image to generate a report are tedious clinical routines for a radiologist where automation is expected to greatly reduce the workload. Despite rapid development of natural image captioning, computer-aided medical image visual perception and interpretation remain a challenging task, largely due to the lack of high-quality annotated image-report pairs and tailor-made generative models for sufficient extraction and exploitation of localized semantic features, particularly those associated with abnormalities. To tackle these challenges, we present Vispi, an automatic medical image interpretation system, which first annotates an image via classifying and localizing common thoracic diseases with visual support and then followed by report generation from an attentive LSTM model. Analyzing an open IU X-ray dataset, we demonstrate a superior performance of Vispi in disease classification, localization and report generation using automatic performance evaluation metrics ROUGE and CIDEr.

preprint2019arXiv

Anion charge-lattice volume dependent Li ion migration in compounds with the face-centered cubic anion frameworks

The proper design principles are essential for the efficient development of superionic conductors. However, the existing design principles are mainly proposed from the perspective of crystal structures. In this work, the face-centered cubic (fcc) anion frameworks were creatively constructed to study the effects of anion charge and lattice volume on the stability of lithium ion occupation and lithium ion migration. Both the large negative anion charges and large lattice volumes would increase the relative stabilities of lithium-anion tetrahedron, and make Li ions prefer to occupy the tetrahedral sites. For a tetrahedral Li ion migration to its adjacent tetrahedral site through an octahedral transition state, the smaller the negative anion charge is, the lower the lithium ion migration barrier will be. While for an octahedral Li ion migration to its adjacent octahedral site through a tetrahedral transition state, the larger negative anion charge is, the lower the lithium ion migration barrier will be. New design principles for developing superionic conductors with the fcc anion framework were proposed. Low Li ion migration barriers would be achieved by adjusting the non-lithium elements within the same crystal structure framework to obtain the desired electronegativity difference between the anion element and non-lithium cation element.

preprint2019arXiv

Bi2Te3/Sb2Te3 Heterojunction and Thermophotovoltaic Cells Absorbing the Radiation from Room-temperature Surroundings

The thermophotovoltaic cells which can convert the infrared radiation from room-temperature surroundings into electricity are of significance due to their potential applications in many fields. In this work, narrow bandgap Bi2Te3/Sb2Te3 thin film thermophotovoltaic cells were fabricated, and the formation mechanism of Bi2Te3/Sb2Te3 p-n heterojunctions was investigated. During the formation of the heterojunctions at room temperature, both electrons and holes diffuse in the same direction from n-type Bi2Te3 thin films to p-type Sb2Te3 thin films rather than conventional bi-directional diffusion. Because the strong intrinsic excitation generates a large number of intrinsic carriers which weaken the built-in electric field of the heterojunctions, their I-V curves become similar to straight lines. It is also demonstrated that Bi2Te3/Sb2Te3 thermophotovoltaic cells can output electrical power under the infrared radiation from a room-temperature heat source. This work proves that it is possible to convert the infrared radiation from dark and room-temperature surroundings into electricity through narrow bandgap thermophotovoltaic cells.

preprint2019arXiv

Combined Mean Field Limit and Non-relativistic Limit of Vlasov-Maxwell Particle System to Vlasov-Poisson System

In this paper we consider the mean field limit and non-relativistic limit of relativistic Vlasov-Maxwell particle system to Vlasov-Poisson equation. With the relativistic Vlasov-Maxwell particle system being a starting point, we carry out the estimates (with respect to $N$ and $c$) between the characteristic equation of both Vlasov-Maxwell particle model and Vlasov-Poisson equation, where the probabilistic method is exploited. In the last step, we take both large $N$ limit and non-relativistic limit (meaning $c$ tending to infinity) to close the argument.

preprint2019arXiv

Superconducting Praseodymium Superhydrides

Superhydrides have complex hydrogenic sublattices and are important prototypes for studying metallic hydrogen and high-temperature superconductors. Encouraged by the results on LaH10, in consideration of the differences between La and Pr, Pr-H system is especially worth studying because of the magnetism and valence-band f-electrons in element Pr. Here we successfully synthesized praseodymium superhydrides (PrH9) in laser-heated diamond anvil cells. Synchrotron X-ray diffraction (XRD) analysis demonstrated the presence of previously predicted F43m-PrH9 and unexpected P63/mmc-PrH9 phases. Moreover, Fm3m-PrH3, P4/nmm-PrH(3-δ) and Fm3m-PrH(1+x) were found below 52 GPa. F43m-PrH9 and P63/mmc-PrH9 were stable above 100 GPa in experiment. Experimental studies of electrical resistance in the PrH9 sample showed the emergence of superconducting transition (Tc) below 9K and a dependent Tc on applied magnetic field. Theoretical calculations indicate that magnetic order and electron-phonon interaction coexist in a very close range of pressures in the PrH9 sample which may contribute to its low superconducting temperature Tc. Our results highlight the intimate connections among hydrogenic sublattices, density of states, magnetism and superconductivity in Pr-based superhydrides.

preprint2018arXiv

Cross-scale predictive dictionaries

Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations. However, solving inverse problems with sparsifying dictionaries can be computationally expensive, especially when the dictionary under consideration has a large number of atoms. In this paper, we incorporate additional structure on to dictionary-based sparse representations for visual signals to enable speedups when solving sparse approximation problems. The specific structure that we endow onto sparse models is that of a multi-scale modeling where the sparse representation at each scale is constrained by the sparse representation at coarser scales. We show that this cross-scale predictive model delivers significant speedups, often in the range of 10-60$\times$, with little loss in accuracy for linear inverse problems associated with images, videos, and light fields.

preprint2016arXiv

Analysing health professionals' learning interactions in online social networks: A social network analysis approach

Online Social Networking may be a way to support health professionals' need for continuous learning through interaction with peers and experts. Understanding and evaluating such learning is important but difficult, and Social Network Analysis (SNA) offers a solution. This paper demonstrates how SNA can be used to study levels of participation as well as the patterns of interactions that take place among health professionals in a large online professional learning network. Our analysis has shown that their learning network is highly centralised and loosely connected. The level of participation is low in general, and most interactions are structured around a small set of users consisting of moderators and core members. The structural patterns of interaction indicates there is a chance of small group learning occurring and requires further investigation to identify those potential learning groups. This first stage of analysis, to be followed by longitudinal study of the dynamics of interaction and complemented by content analysis of their discussion, may contribute to greater sophistication in the analysis and utilisation of new environments for health professional learning.

preprint2016arXiv

Computing approximate PSD factorizations

We give an algorithm for computing approximate PSD factorizations of nonnegative matrices. The running time of the algorithm is polynomial in the dimensions of the input matrix, but exponential in the PSD rank and the approximation error. The main ingredient is an exact factorization algorithm when the rows and columns of the factors are constrained to lie in a general polyhedron. This strictly generalizes nonnegative matrix factorizations which can be captured by letting this polyhedron to be the nonnegative orthant.

preprint2016arXiv

Direct Meissner Effect Observation of Superconductivity in Compressed H2S

Recently, an extremely high superconducting temperature (Tc) of ~200 K has been reported in the sulfur hydride system above 100 GPa. This result is supported by theoretical predictions and verified experimentally. The crystal structure of the superconducting phase was also identified experimentally, confirming the theoretically predicted structure as well as a decomposition mechanism from H2S to H3S+S. Even though nuclear resonant scattering has been successfully used to provide magnetic evidence for a superconducting state, a direct measurement of the important Meissner effect is still lacking. Here we report in situ alternating-current magnetic susceptibility measurements on compressed H2S under high pressures. It is shown that superconductivity suddenly appears at 117 GPa and that Tc reaches 183 K at 149 GPa before decreasing monotonically with a further increase in pressure. This evolution agrees with both theoretical calculations and earlier experimental measurements. The idea of conventional high temperature superconductivity in hydrogen-dominant compounds has thus been realized in the sulfur hydride system under hydrostatic pressure, opening further exciting perspectives for possibly realizing room temperature superconductivity in hydrogen-based compounds.

preprint2016arXiv

Effect of GRB spectra on the empirical luminosity correlations and the GRB Hubble diagram

The spectra of gamma-ray bursts (GRBs) in a wide energy range can usually be well described by the Band function, which is a two smoothly jointed power laws cutting at a breaking energy. Below the breaking energy, the Band function reduces to a cut-off power law, while above the breaking energy it is a simple power law. However, for some detectors (such as the Swift-BAT) whose working energy is well below or just near the breaking energy, the observed spectra can be fitted to cut-off power law with enough precision. Besides, since the energy band of Swift-BAT is very narrow, the spectra of most GRBs can be fitted well even using a simple power law. In this paper, with the most up-to-date sample of Swift-BAT GRBs, we study the effect of different spectral models on the empirical luminosity correlations, and further investigate the effect on the reconstruction of GRB Hubble diagram. We mainly focus on two luminosity correlations, i.e., the Amati relation and Yonetoku relation. We calculate these two luminosity correlations on both the case that the GRB spectra are modeled by Band function and cut-off power law. It is found that both luminosity correlations only moderately depend on the choice of GRB spectra. Monte Carlo simulations show that Amati relation is insensitive to the high-energy power-law index of the Band function. As a result, the GRB Hubble diagram calibrated using luminosity correlations is almost independent on the GRB spectra.

preprint2016arXiv

Explicit Non-Malleable Extractors, Multi-Source Extractors and Almost Optimal Privacy Amplification Protocols

We make progress in the following three problems: 1. Constructing optimal seeded non-malleable extractors; 2. Constructing optimal privacy amplification protocols with an active adversary, for any security parameter; 3. Constructing extractors for independent weak random sources, when the min-entropy is extremely small (i.e., near logarithmic). For the first two problems, the best known non-malleable extractors by Chattopadhyay, Goyal and Li [CGL16], and by Cohen [Coh16a,Coh16b] all require seed length and min-entropy at least $\log^2 (1/ε)$, where $ε$ is the error of the extractor. As a result, the best known explicit privacy amplification protocols with an active adversary, which achieve 2 rounds of communication and optimal entropy loss in [Li15c,CGL16], can only handle security parameter up to $s=Ω(\sqrt{k})$, where $k$ is the min-entropy of the shared secret weak random source. For larger $s$ the best known protocol with optimal entropy loss in [Li15c] requires $O(s/\sqrt{k})$ rounds of communication. In this paper we give an explicit non-malleable extractor that only requires seed length and min-entropy $\log^{1+o(1)} (n/ε)$, which also yields a 2-round privacy amplification protocol with optimal entropy loss for security parameter up to $s=k^{1-α}$ for any constant $α>0$. For the third problem, previously the best known extractor which supports the smallest min-entropy due to Li [Li13a], requires min-entropy $\log^{2+δ} n$ and uses $O(1/δ)$ sources, for any constant $δ>0$. A very recent result by Cohen and Schulman [CS16] improves this, and constructed explicit extractors that use $O(1/δ)$ sources for min-entropy $\log^{1+δ} n$, any constant $δ>0$. In this paper we further improve their result, and give an explicit extractor that uses $O(1)$ (an absolute constant) sources for min-entropy $\log^{1+o(1)} n$.

preprint2016arXiv

Gamma-ray burst polarization reduction induced by the Lorentz invariance violation

It has been observed that photons in the prompt emission of some gamma-ray bursts (GRBs) are highly polarized. The high polarization is used by some authors to give a strict constraint on the Lorentz invariance violation (LIV). If the Lorentz invariance is broken, the polarization vector of a photon may rotate during its propagation. The rotation angle of polarization vector depends on both the photon energy and the distance of source. It is believed that if high polarization is observed, then the relative rotation angle (denoted by $α$) of polarization vector of the highest energy photon with respect to that of the lowest energy photon should be no more than $π/2$. Otherwise, the net polarization will be severely suppressed, thus couldn't be as high as what was actually observed. In this paper, we will give a detailed calculation on the evolution of GRB polarization arising from LIV effect duration the propagation. It is shown that the polarization degree rapidly decrease as $α$ increases, and reaches a local minimum at $α\approx π$, then increases until $α\approx 3π/2$, after that decreases again until $α\approx 2π$, etc. The polarization degree as a function of $α$ oscillates with a quasi-period $T\approx π$, while the oscillating amplitude gradually decreases to zero. Moreover, we find that a considerable amount (more than $60\%$ of the initial polarization) of polarization degree can be conserved when $α\approx π/2$. The polarization observation in a higher and wider energy band, a softer photon spectrum, and a higher redshift GRB is favorable in order to tightly constrain LIV effect.

preprint2016arXiv

Improved Non-Malleable Extractors, Non-Malleable Codes and Independent Source Extractors

In this paper we give improved constructions of several central objects in the literature of randomness extraction and tamper-resilient cryptography. Our main results are: (1) An explicit seeded non-malleable extractor with error $ε$ and seed length $d=O(\log n)+O(\log(1/ε)\log \log (1/ε))$, that supports min-entropy $k=Ω(d)$ and outputs $Ω(k)$ bits. Combined with the protocol in \cite{DW09}, this gives a two round privacy amplification protocol with optimal entropy loss in the presence of an active adversary, for all security parameters up to $Ω(k/\log k)$. (2) An explicit non-malleable two-source extractor for min-entropy $k \geq (1-γ)n$, some constant $γ>0$, that outputs $Ω(k)$ bits with error $2^{-Ω(n/\log n)}$. Combined with the connection in \cite{CG14b} this gives a non-malleable code in the two-split-state model with relative rate $Ω(1/\log n)$. This exponentially improves previous constructions, all of which only achieve rate $n^{-Ω(1)}$.\footnote{The work of Aggarwal et. al \cite{ADKO15} had a construction which "achieves" constant rate, but recently the author found an error in their proof.} (3)A two-source extractor for min-entropy $O(\log n \log \log n)$, which also implies a $K$-Ramsey graph on $N$ vertices with $K=(\log N)^{O(\log \log \log N)}$. We also obtain a seeded non-malleable $9$-source extractor with optimal seed length, which in turn gives a $10$-source extractor for min-entropy $O(\log n)$. Previously the best known extractor for such min-entropy requires $O(\log \log n)$ sources \cite{CohL16}. Independent of our work, Cohen \cite{Cohen16} obtained similar results to (1) and the two-source extractor, except the dependence on $ε$ is $\log(1/ε)(\log \log (1/ε))^{O(1)}$ and the two-source extractor requires min-entropy $\log n (\log \log n)^{O(1)}$.

preprint2016arXiv

Partial transformation groupoids attached to graphs and semigroups

We introduce the notion of continuous orbit equivalence for partial dynamical systems, and give an equivalent characterization in terms of Cartan-isomorphisms for partial C*-crossed products. Both graph C*-algebras and semigroup C*-algebras can be described as C*-algebras attached to partial dynamical systems. As applications, for graphs, we generalize and explain a result of Matsumoto and Matui relating orbit equivalence and Cartan-isomorphism, and for semigroups, we strengthen several structural results for semigroup C*-algebras concerning amenability, nuclearity as well as simplicity of boundary quotients. We also discuss pure infiniteness for partial transformation groupoids arising from graphs and semigroups.

preprint2016arXiv

Review of the three candidate hohlraums in ICF

In this paper, we give a review of three hohlraum geometries, including cylindrical, octahedral and six-cylinder-port hohlraums, in inertial confinement fusion (ICF) mainly from theoretical side. Every hohlraum has its own strengths and weaknesses. Although there is a problem of drive asymmetry in the cylindrical hohlraums due to some non-ideal factors, the success of ignition is still possible if more laser energy is available beyond the US National Ignition Facility (NIF) in the future. Octahedral hohlraums can provide the high symmetry flux on capsule. However, octahedral hohlraums suffer from several problems due to the complicated three-dimensional plasma conditions inside. And up to now, there is no one target design with the octahedral hohlraums in which each problem can be solved at the same time. Six-cylinder-port hohlraums combine the merits in theory of both cylindrical and octahedral hohlraums to a certain extent. We introduce a target design with good performance by using the six-cylinder-port hohlraums, in which the key issues of concern, such as laser energy, drive symmetry, and laser plasma interaction (LPI), etc, are all balanced.

preprint2016arXiv

Segmenting a Surface Mesh into Pants Using Morse Theory

A pair of pants is a genus zero orientable surface with three boundary components. A pants decomposition of a surface is a finite collection of unordered pairwise disjoint simple closed curves embedded in the surface that decompose the surface into pants. In this paper we present two Morse theory based algorithms for pants decomposition of a surface mesh. Both algorithms operates on a choice of an appropriate Morse function on the surface. The first algorithm uses this Morse function to identify handles that are glued systematically to obtain a pant decomposition. The second algorithm uses the Reeb graph of the Morse function to obtain a pant decomposition. Both algorithms work for surfaces with or without boundaries. Our preliminary implementation of the two algorithms shows that both algorithms run in much less time than an existing state-of-the-art method, and the Reeb graph based algorithm achieves the best time efficiency. Finally, we demonstrate the robustness of our algorithms against noise.

preprint2016arXiv

SICS: Secure In-Cloud Service Function Chaining

There is an increasing trend that enterprises outsource their network functions to the cloud for lower cost and ease of management. However, network function outsourcing brings threats to the privacy of enterprises since the cloud is able to access the traffic and rules of in-cloud network functions. Current tools for secure network function outsourcing either incur large performance overhead or do not support real-time updates. In this paper, we present SICS, a secure service function chain outsourcing framework. SICS encrypts each packet header and use a label for in-cloud rule matching, which enables the cloud to perform its functionalities correctly with minimum header information leakage. Evaluation results show that SICS achieves higher throughput, faster construction and update speed, and lower resource overhead at both enterprise and cloud sides, compared to existing solutions.

preprint2016arXiv

Speculative Futures Trading under Mean Reversion

This paper studies the problem of trading futures with transaction costs when the underlying spot price is mean-reverting. Specifically, we model the spot dynamics by the Ornstein-Uhlenbeck (OU), Cox-Ingersoll-Ross (CIR), or exponential Ornstein-Uhlenbeck (XOU) model. The futures term structure is derived and its connection to futures price dynamics is examined. For each futures contract, we describe the evolution of the roll yield, and compute explicitly the expected roll yield. For the futures trading problem, we incorporate the investor's timing option to enter or exit the market, as well as a chooser option to long or short a futures upon entry. This leads us to formulate and solve the corresponding optimal double stopping problems to determine the optimal trading strategies. Numerical results are presented to illustrate the optimal entry and exit boundaries under different models. We find that the option to choose between a long or short position induces the investor to delay market entry, as compared to the case where the investor pre-commits to go either long or short.

preprint2016arXiv

The significance of anisotropic signals hiding in the type Ia supernovae

We use two different methods, i.e., dipole-fitting (DF) and hemisphere comparison (HC), to search for the anisotropic signals hiding in the Union2.1 data set. We find that the directions of maximum matter density derived using these two methods are about $114^{\circ}$ away from each other. We construct four Union2.1-like mock samples to test the statistical significance of these two methods. It is shown that DF method is statistically significant, while HC method is strongly biased by the distribution of data points in the sky. Then we assume that the anisotropy of distance modulus is mainly induced by the anisotropy of matter density, which is modeled to be the dipole form $Ω_M=Ω_{M0}(1-\cosθ)$. We fit our model to Union2.1, and find that the direction of maximum matter density is well consistent with the direction derived using DF method, but it is very different from the direction previously claimed. Monte Carlo simulations show that our method is statistically more significant than HC method, although it is not as significant as DF method. The statistical significance can be further improved if the data points are homogeneously distributed in the sky. Due to the low quality of present supernovae data, however, it is still premature to claim that the Universe has any preferred direction.

preprint2016arXiv

Video Scene Parsing with Predictive Feature Learning

In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we contribute two novel methods that constitute a unified parsing framework. (1) \textbf{Predictive feature learning}} from nearly unlimited unlabeled video data. Different from existing methods learning features from single frame parsing, we learn spatiotemporal discriminative features by enforcing a parsing network to predict future frames and their parsing maps (if available) given only historical frames. In this way, the network can effectively learn to capture video dynamics and temporal context, which are critical clues for video scene parsing, without requiring extra manual annotations. (2) \textbf{Prediction steering parsing}} architecture that effectively adapts the learned spatiotemporal features to scene parsing tasks and provides strong guidance for any off-the-shelf parsing model to achieve better video scene parsing performance. Extensive experiments over two challenging datasets, Cityscapes and Camvid, have demonstrated the effectiveness of our methods by showing significant improvement over well-established baselines.

preprint2015arXiv

$X(3915)$ as a $D_s \bar D_s$ bound state

We suggest that the observed properties of the charmoniumlike resonance $X(3915)$ can possibly be explained if it is an $S-$wave molecular bound state of $D_s \bar D_s$ meson pair with binding energy about 18\,MeV. In particular, the decays of $X(3915)$ to pairs of non-strange $D$ mesons are suppressed and proceed at a rate comparable to that of the decay $X(3915) \to ωJ/ψ$, whose branching fraction can be as large as about 0.3. Other major types of decay of $X(3915)$ with a comparable (or slightly smaller) rate are the transition $X(3915) \to ηη_c$ and the decays into light hadrons due to the annihilation of the $c \bar c$ quark pair. The existence of a bound state should lead to an enhancement in the spectrum of the invariant mass for the $D_s \bar D_s$ near threshold in $B$ decays, e.g. in $B \to K D_s \bar D_s$ which enhancement can be tested experimentally.

preprint2015arXiv

A unified description for dipoles of the fine-structure constant and SnIa Hubble diagram in Finslerian universe

We propose a Finsler spacetime scenario of the anisotropic universe. The Finslerian universe requires both the fine-structure constant and accelerating cosmic expansion have dipole structure, and the directions of these two dipoles are the same. Our numerical results show that the dipole direction of SnIa Hubble diagram locates at $(l,b)=(314.6^\circ\pm20.3^\circ,-11.5^\circ\pm12.1^\circ)$ with magnitude $B=(-3.60\pm1.66)\times10^{-2}$. And the dipole direction of the fine-structure constant locates at $(l,b)=(333.2^\circ\pm8.8^\circ,-12.7^\circ\pm6.3^\circ)$ with magnitude $B=(0.97\pm0.21)\times10^{-5}$. The angular separation between the two dipole directions is about $18.2^\circ$.

preprint2015arXiv

An asymmetric ARCH model and the non-stationarity of Clustering and Leverage effects

We propose a new volatility model based on two stylized facts of the volatility in the stock market: clustering and leverage effect. We calibrate our model parameters, in the leading order, with 77 years Dow Jones Industrial Average data. We find in the short time scale (10 to 50 days) the future volatility is sensitive to the sign of past returns, i.e. asymmetric feedback or leverage effect. However, in the long time scale (300 to 1000 days) clustering becomes the main factor. We study non-stationary features by using moving windows and find that clustering and leverage effects display time evolutions that are rather nontrivial. The structure of our model allows us to shed light on a few surprising facts recently found by Chicheportiche and Bouchaud.

preprint2015arXiv

Anisotropic inflation in Finsler spacetime

We suggest the universe is Finslerian in the stage of inflation. The Finslerian background spacetime breaks rotational symmetry and induces parity violation. The primordial power spectrum is given for quantum fluctuation of the inflation field. It depends not only on the magnitude of wavenumber but also on the preferred direction. We derive the gravitational field equations in the perturbed Finslerian background spacetime, and obtain a conserved quantity outside the Hubble horizon. The angular correlation coefficients are presented in our anisotropic inflation model. The parity violation feature of Finslerian background spacetime requires that the anisotropic effect only appears in angular correlation coefficients if $l'=l+1$. The numerical results of the angular correlation coefficients are given to describe the anisotropic effect.

preprint2015arXiv

Are long gamma-ray bursts standard candles?

Gamma-ray bursts (GRBs) are widely proposed as an effective probe to trace the Hubble diagram of the Universe in high redshift range. However, the calibration of GRBs is not as easy as that of type-Ia supernovae (SNe Ia). Most calibrating methods at present make use one or some of the empirical luminosity correlations, e.g., Amati relation. One of the underlying assumptions of these calibrating methods is that the empirical correlation is universal over all redshifts. In this paper, we check to what extent this assumption holds. Assuming that SNe Ia exactly trace the Hubble diagram of the Universe, we re-investigate the Amati relation for low redshift ($z<1.4$) and high redshift ($z>1.4$) GRBs, respectively. It is found that the Amati relation of low-$z$ GRBs differs from that of high-$z$ GRBs at more than $3σ$ confidence level. This result is insensitive to cosmological models. We should be cautious when using Amati relation to reconstruct the Hubble diagram of the Universe.

preprint2015arXiv

Constraining the Lorentz invariance violation from the continuous spectra of short gamma-ray bursts

In quantum gravity, a foamy structure of space-time leads to Lorentz invariance violation (LIV). As the most energetic astrophysical processes in the Universe, gamma-ray bursts (GRBs) provide an effective way to probe quantum gravity effects. We use continuous spectra of 20 short GRBs detected by the Swift satellite to give a conservative lower limit of quantum gravity energy scale $M_\textrm{QG} $. Due to the LIV effect, photons with different energy have different velocities. This will lead to the delayed arrival of high energy photons relative to the low energy ones. Based on the fact that the LIV-induced time delay can't be longer than the duration of a GRB, we present the most conservative estimation of the quantum gravity energy scales from 20 short GRBs. The most strict constraint, $M_\textrm{QG}>5.05\times10^{14}$ GeV, is from GRB 140622A.

preprint2015arXiv

Continuous orbit equivalence rigidity

We take the first steps towards a better understanding of continuous orbit equivalence, i.e., topological orbit equivalence with continuous cocycles. First, we characterise continuous orbit equivalence in terms of isomorphisms of C*-crossed products preserving Cartan subalgebras. This is the topological analogue of the classical result by Singer and Feldman-Moore in the measurable setting. Secondly, we turn to continuous orbit equivalence rigidity, i.e., the question whether for certain classes of topological dynamical systems, continuous orbit equivalence implies conjugacy. We show that this is not always the case by constructing topological dynamical systems (actions of free abelian groups, and also non-abelian free groups) which are continuously orbit equivalent but not conjugate. Furthermore, we prove positive rigidity results. For instance, it turns out that general topological Bernoulli actions are rigid when compared with actions of nilpotent groups, and that topological Bernoulli actions of duality groups are rigid when compared with actions of solvable groups. The same is true for certain subshifts of full shifts over finite alphabets.

preprint2015arXiv

Dimensions of Biquadratic and Bicubic Spline Spaces over Hierarchical T-meshes

This paper discusses the dimensions of biquadratic C1 spline spaces and bicubic C2 spline spaces over hierarchical T-meshes using the smoothing cofactor-conformality method. We obtain the dimension formula of biquadratic C1 spline spaces over hierarchical T-meshes in a concise way. In addition, we provide a dimension formula for bicubic C2 spline spaces over hierarchical T-mesh with fewer restrictions than that in the previous literature. A dimension formula for bicubic C2 spline spaces over a new type hierarchical T-mesh is also provided.

preprint2015arXiv

Gibbs-Markov-Young structures with (stretched) exponential tail for partially hyperbolic attractors

We study partially hyperbolic sets $K$ on a Riemannian manifold $M$ whose tangent space splits as $T_K M=E^{cu}\oplus E^{s}$, for which the center-unstable direction $E^{cu}$ is non-uniformly expanding on some local unstable disk. We prove that the (stretched) exponential decay of recurrence times for an induced scheme can be deduced under the assumption of (stretched) exponential decay of the time that typical points need to achieve some uniform expanding in the center-unstable direction. This extends a result by Alves and Pinheiro to the (stretched) exponential case. As an application of our main result we obtain (stretched) exponential decay of correlations and exponentially large deviations for a class of partially hyperbolic diffeomorphisms introduced by Alves, Bonatti and Viana.

preprint2015arXiv

Improved Constructions of Two-Source Extractors

In a recent breakthrough \cite{CZ15}, Chattopadhyay and Zuckerman gave an explicit two-source extractor for min-entropy $k \geq \log^C n$ for some large enough constant $C$. However, their extractor only outputs one bit. In this paper, we improve the output of the two-source extractor to $k^{Ω(1)}$, while the error remains $n^{-Ω(1)}$. Our improvement is obtained by giving a better extractor for $(q, t, γ)$ non-oblivious bit-fixing sources, which can output $t^{Ω(1)}$ bits instead of one bit as in \cite{CZ15}.

preprint2015arXiv

Model-independent distance calibration of high-redshift gamma-ray bursts and constrain on the $Λ$CDM model

Gamma-ray bursts (GRBs) are luminous enough to be detectable up to redshift $z\sim 10$. They are often proposed as complementary tools to type-Ia supernovae (SNe Ia) in tracing the Hubble diagram of the Universe. The distance calibrations of GRBs usually make use one or some of the empirical luminosity correlations, such as $τ_{\rm lag}-L$, $V-L$, $E_p-L$, $E_p-E_γ$, $τ_{\rm RT}-L$ and $E_p-E_{\rm iso}$ relations. These calibrating methods are based on the underling assumption that the empirical luminosity correlations are universal over all redshift range. In this paper, we test the possible redshift dependence of six luminosity correlations by dividing GRBs into low-$z$ and high-$z$ classes according to their redshift smaller or larger than 1.4. It is shown that the $E_p-E_γ$ relation for low-$z$ GRBs is consistent with that for high-$z$ GRBs within $1σ$ uncertainty. The intrinsic scatter of $V-L$ relation is too larger to make a convincing conclusion. For the rest four correlations, however, low-$z$ GRBs differ from high-$z$ GRBs at more than $3σ$ confidence level. As such, we calibrate GRBs using the $E_p-E_γ$ relation in a model-independent way. The constraint of high-$z$ GRBs on the $Λ$CDM model gives $Ω_M=0.302\pm 0.142(1σ)$, well consistent with the Planck 2015 results.

preprint2015arXiv

Non-Malleable Extractors and Codes, with their Many Tampered Extensions

Randomness extractors and error correcting codes are fundamental objects in computer science. Recently, there have been several natural generalizations of these objects, in the context and study of tamper resilient cryptography. These are seeded non-malleable extractors, introduced in [DW09]; seedless non-malleable extractors, introduced in [CG14b]; and non-malleable codes, introduced in [DPW10]. However, explicit constructions of non-malleable extractors appear to be hard, and the known constructions are far behind their non-tampered counterparts. In this paper we make progress towards solving the above problems. Our contributions are as follows. (1) We construct an explicit seeded non-malleable extractor for min-entropy $k \geq \log^2 n$. This dramatically improves all previous results and gives a simpler 2-round privacy amplification protocol with optimal entropy loss, matching the best known result in [Li15b]. (2) We construct the first explicit non-malleable two-source extractor for min-entropy $k \geq n-n^{Ω(1)}$, with output size $n^{Ω(1)}$ and error $2^{-n^{Ω(1)}}$. (3) We initiate the study of two natural generalizations of seedless non-malleable extractors and non-malleable codes, where the sources or the codeword may be tampered many times. We construct the first explicit non-malleable two-source extractor with tampering degree $t$ up to $n^{Ω(1)}$, which works for min-entropy $k \geq n-n^{Ω(1)}$, with output size $n^{Ω(1)}$ and error $2^{-n^{Ω(1)}}$. We show that we can efficiently sample uniformly from any pre-image. By the connection in [CG14b], we also obtain the first explicit non-malleable codes with tampering degree $t$ up to $n^{Ω(1)}$, relative rate $n^{Ω(1)}/n$, and error $2^{-n^{Ω(1)}}$.

preprint2015arXiv

On K-theoretic invariants of semigroup C*-algebras attached to number fields, Part II

This paper continues the study of K-theoretic invariants for semigroup C*-algebras attached to ax+b-semigroups over rings of algebraic integers in number fields. We show that from the semigroup C*-algebra together with its canonical commutative subalgebra, it is possible to reconstruct the zeta function of the underlying number field as well as its ideal class group (as a group). In addition, we give an alternative interpretation of this result in terms of dynamical systems.

preprint2015arXiv

Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit

Motivated by the industry practice of pairs trading, we study the optimal timing strategies for trading a mean-reverting price spread. An optimal double stopping problem is formulated to analyze the timing to start and subsequently liquidate the position subject to transaction costs. Modeling the price spread by an Ornstein-Uhlenbeck process, we apply a probabilistic methodology and rigorously derive the optimal price intervals for market entry and exit. As an extension, we incorporate a stop-loss constraint to limit the maximum loss. We show that the entry region is characterized by a bounded price interval that lies strictly above the stop-loss level. As for the exit timing, a higher stop-loss level always implies a lower optimal take-profit level. Both analytical and numerical results are provided to illustrate the dependence of timing strategies on model parameters such as transaction cost and stop-loss level.

preprint2015arXiv

Optimal Multiple Trading Times Under the Exponential OU Model with Transaction Costs

This paper studies the timing of trades under mean-reverting price dynamics subject to fixed transaction costs. We solve an optimal double stopping problem to determine the optimal times to enter and subsequently exit the market, when prices are driven by an exponential Ornstein-Uhlenbeck process. In addition, we analyze a related optimal switching problem that involves an infinite sequence of trades, and identify the conditions under which the double stopping and switching problems admit the same optimal entry and/or exit timing strategies. Among our results, we find that the investor generally enters when the price is low, but may find it optimal to wait if the current price is sufficiently close to zero. In other words, the continuation (waiting) region for entry is disconnected. Numerical results are provided to illustrate the dependence of timing strategies on model parameters and transaction costs.

preprint2015arXiv

Primordial power spectrum of tensor perturbations in Finsler spacetime

We first investigate the gravitational wave in the flat Finsler spacetime. In the Finslerian universe, we derive the perturbed gravitational field equation with tensor perturbations. The Finslerian background spacetime breaks rotational symmetry and induces parity violation. Then we obtain the modified primordial power spectrum of tensor perturbations. The parity violation feature requires that the anisotropic effect contributes to $TT,TE,EE,BB$ angular correlation coefficients with $l'=l+1$ and $TB,EB$ with $l'=l$. The numerical results show that the anisotropic contributions to angular correlation coefficients depend on $m$, and $TE$ and $ET$ angular correlation coefficients are different.

preprint2015arXiv

Signal photon flux generated by high-frequency relic gravitational waves

The power spectrum of primordial tensor perturbations $\mathcal{P}_t$ increases rapidly in high frequency region if the spectral index $n_t>0$. It is shown that the amplitude of relic gravitational wave $h_t$($5\times10^9$Hz) varies from $10^{-36}$ to $10^{-25}$ while $n_t$ varies from $-6.25\times 10^{-3}$ to $0.87$. High frequency gravitational waves detector that is proposed by F.-Y. Li detects gravitational waves through observing the perturbed photon flux that is generated by interaction between the relic gravitational waves and electromagnetic system. It is shown that the perturbative photon flux $N_x^1$($5\times10^9$Hz) varies from $1.40\times10^{-4}\rm s^{-1}$ to $2.85\times10^{7}\rm s^{-1}$ while $n_t$ varies from $-6.25\times 10^{-3}$ to $0.87$. Correspondingly, the ratio of the transverse perturbative photon flux $N_x^1$ to the background photon flux varies from $10^{-28}$ to $10^{-16}$.

preprint2015arXiv

Tailor the functionalities of metasurfaces: From perfect absorption to phase modulation

Metasurfaces in metal/insulator/metal configuration have recently been widely used in photonics research, with applications ranging from perfect absorption to phase modulation, but why and when such structures can realize what kind of functionalities are not yet fully understood. Here, based on a coupled-mode theory analysis, we establish a complete phase diagram in which the optical properties of such systems are fully controlled by two simple parameters (i.e., the intrinsic and radiation losses), which are in turn dictated by the geometrical/material parameters of the underlying structures. Such a phase diagram can greatly facilitate the design of appropriate metasurfaces with tailored functionalities (e.g., perfect absorption, phase modulator, electric/magnetic reflector, etc.), demonstrated by our experiments and simulations in the Terahertz regime. In particular, our experiments show that, through appropriate structural/material tuning, the device can be switched across the functionality phase boundaries yielding dramatic changes in optical responses. Our discoveries lay a solid basis for realizing functional and tunable photonic devices with such structures.

preprint2015arXiv

Testing the isotropy of the Universe by using the JLA compilation of type-Ia supernovae

We probe the possible anisotropy of the Universe by using the JLA compilation of type-Ia supernovae. We apply the Markov Chain Monte Carlo (MCMC) method to constrain the amplitude and direction of anisotropy in three cosmological models. For the dipole-modulated $Λ$CDM model, the anisotropic amplitude is consistent with zero at $68\%$ C.L., and has an upper bound $A_D<1.98\times10^{-3}$ at $95\%$ C.L. Regardless of much larger uncertainty, we find the dipole direction of JLA is amazingly opposite to that of Union2. Similar results are found for the dipole-modulated $w$CDM and CPL models. Thus, the Universe is still well consistent with the isotropy according to the JLA compilation.

preprint2015arXiv

Three-Source Extractors for Polylogarithmic Min-Entropy

We continue the study of constructing explicit extractors for independent general weak random sources. The ultimate goal is to give a construction that matches what is given by the probabilistic method --- an extractor for two independent $n$-bit weak random sources with min-entropy as small as $\log n+O(1)$. Previously, the best known result in the two-source case is an extractor by Bourgain \cite{Bourgain05}, which works for min-entropy $0.49n$; and the best known result in the general case is an earlier work of the author \cite{Li13b}, which gives an extractor for a constant number of independent sources with min-entropy $\mathsf{polylog(n)}$. However, the constant in the construction of \cite{Li13b} depends on the hidden constant in the best known seeded extractor, and can be large; moreover the error in that construction is only $1/\mathsf{poly(n)}$. In this paper, we make two important improvements over the result in \cite{Li13b}. First, we construct an explicit extractor for \emph{three} independent sources on $n$ bits with min-entropy $k \geq \mathsf{polylog(n)}$. In fact, our extractor works for one independent source with poly-logarithmic min-entropy and another independent block source with two blocks each having poly-logarithmic min-entropy. Thus, our result is nearly optimal, and the next step would be to break the $0.49n$ barrier in two-source extractors. Second, we improve the error of the extractor from $1/\mathsf{poly(n)}$ to $2^{-k^{Ω(1)}}$, which is almost optimal and crucial for cryptographic applications. Some of the techniques developed here may be of independent interests.

preprint2014arXiv

$χ_{c0} \, ω$ production in $e^+e^-$ annihilation through $ψ(4160)$

We argue that the recent BESIII data on the cross section for the process $e^+e^- \to χ_{c0} \, ω$ in the center of mass energy range 4.21 - 4.42 GeV can be described by the contribution of the known charmonium-like resonance $ψ(4160)$ with the mass of about 4190\,MeV. The value of the coupling in the transition $ψ(4160) \to χ_{c0} \, ω$ needed for this mechanism is comparable to that in another known similar transition $χ_{c0}(2P) \to J/ψ\, ω$. The suggested mechanism also naturally explains the reported relative small value of the cross section for the final states $χ_{c1} \, ω$ and $χ_{c2} \, ω$ above their respective thresholds.

preprint2014arXiv

A new approach to recent constructions of C*-algebras from modular index theory

We present a new approach to C*-algebras recently constructed in the context of modular index theory by Carey, Phillips, Putnam and Rennie. It turns out that their constructions can be identified with full corners of ideals in semigroup C*-algebras. This new point of view leads to a systematic analysis of these algebras and their canonical maximal abelian subalgebras.

preprint2014arXiv

Almost sure mixing rates for non-uniformly expanding maps

We consider random perturbations of non-uniformly expanding maps, possibly having a non-degenerate critical set. We prove that, if the Lebesgue measure of the set of points failing the non-uniform expansion or the slow recurrence to the critical set at a certain time, for almost all random orbits, decays in a (stretched) exponential fashion, then the decay of correlations along random orbits is stretched exponential, up to some waiting time. As applications, we obtain almost sure stretched exponential decay of random correlations for Viana maps, as for a class of non-uniformly expanding local diffeomorphisms and a quadratic family of interval maps.

preprint2014arXiv

Constraining anisotropy of the universe from different groups of type-Ia supernovae

Recent released Planck data and other astronomical observations show that the universe may be anisotropic on large scales. Inspired by this, anisotropic cosmological models have been proposed. We note that the Finsler-Randers spacetime provides an appropriate framework for the anisotropic cosmology. By adding an arbitrary 1-form to the Friedmann-Robertson-Walker (FRW) line element, a privileged axis in the universe is picked out. The distance-redshift relation is modified to be direction-dependent. We wish that the anisotropic cosmological model may be tested crossly by independent observations. Type-Ia supernovae (SNe Ia) calibrated from four different light curve fitters are used to constrain possible anisotropy of the universe. The magnitudes of anisotropy are all between 2% --- 5%, but the systematic uncertainty cannot be excluded. The directions of privileged axis seem to differ from each other. The statistical significance is not high enough to make a convincing conclusion. Nevertheless, the $1σ$ contours in the $(l,b)$ plane obtained from four groups of SNe Ia have an overlap, centering at $(l,b)\approx (170^{\circ},0^{\circ})$. Monte Carlo simulation shows that the anisotropy is unlikely to be caused by selection effect.

preprint2014arXiv

Constraining the Anisotropy of the Universe from Supernovae and Gamma-ray Bursts

Recently, an anisotropic cosmological model was proposed. An arbitrary 1-form, which picks out a privileged axis in the universe, was added to the Friedmann-Robertson-Walker line element. The distance-redshift relation was modified such that it is direction dependent. In this paper, we use the Union2 dataset and 59 high-redshift gamma-ray bursts to give constraints on the anisotropy of the universe. The results show that the magnitude of anisotropy is about $D=-0.044\pm0.018$, and the privileged axis points towards the direction $(l_0,b_0)=(306.1^{\circ}\pm 18.7^{\circ},-18.2^{\circ}\pm 11.2^{\circ})$ in the galactic coordinate system. The anisotropy is small and the isotropic cosmological model is an excellent approximation.

preprint2014arXiv

Contribution of $Z_b$ resonances to $Υ(5S) \to πππχ_b$

We discuss the recently presented Belle results on the decays $Υ(5S) \to πππχ_{bJ}(1P)$. The data indicate that in addition to the $ω$ emission, $Υ(5S) \to ωχ_{bJ}$, there is a significant non resonant background in the three pion spectrum. We suggest that a sizable fraction of this background may be associated with the cascade process $Υ(5S) \to πZ_b \to πρχ_b$ involving the $Z_b(10610)$ and $Z_b(10650)$ resonances. If confirmed by the data, this would be the first observation of transition from the $Z_b$ resonances to lower bottomonium with emission of a light meson state different from a single pion, which may provide a new input in understanding of the internal dynamics of these resonances.

preprint2014arXiv

Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths.We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.

preprint2014arXiv

Distributed Collaborative Monitoring in Software Defined Networks

We propose a Distributed and Collaborative Monitoring system, DCM, with the following properties. First, DCM allow switches to collaboratively achieve flow monitoring tasks and balance measurement load. Second, DCM is able to perform per-flow monitoring, by which different groups of flows are monitored using different actions. Third, DCM is a memory-efficient solution for switch data plane and guarantees system scalability. DCM uses a novel two-stage Bloom filters to represent monitoring rules using small memory space. It utilizes the centralized SDN control to install, update, and reconstruct the two-stage Bloom filters in the switch data plane. We study how DCM performs two representative monitoring tasks, namely flow size counting and packet sampling, and evaluate its performance. Experiments using real data center and ISP traffic data on real network topologies show that DCM achieves highest measurement accuracy among existing solutions given the same memory budget of switches.

preprint2014arXiv

Dynamical Crossover in Soft Colloids below the Overlap Concentration

The dynamics of soft colloids in solutions is characterized by internal collective motion as well as center-of-mass diffusion. Using neutron scattering we demonstrate that the competition between the relaxation processes associated with these two degrees of freedom results in strong dependence of dynamics and structure on colloid concentration, c, even well below the overlap concentration c*. We show that concurrent with increasing inter-particle collisions, substantial structural dehydration and slowing-down of internal dynamics occur before geometrically defined colloidal overlap develops. While previous experiments have shown that the average size of soft colloids changes very little below c*, we find a marked change in both the internal structure and internal dynamics with concentration. The competition between these two relaxation processes gives rise to a new dynamically-defined dilute threshold concentration well below c*.

preprint2014arXiv

Full-range Gate-controlled Terahertz Phase Modulations with Graphene Metasurfaces

Local phase control of electromagnetic wave, the basis of a diverse set of applications such as hologram imaging, polarization and wave-front manipulation, is of fundamental importance in photonic research. However, the bulky, passive phase modulators currently available remain a hurdle for photonic integration. Here we demonstrate full-range active phase modulations in the Tera-Hertz (THz) regime, realized by gate-tuned ultra-thin reflective metasurfaces based on graphene. A one-port resonator model, backed by our full-wave simulations, reveals the underlying mechanism of our extreme phase modulations, and points to general strategies for the design of tunable photonic devices. As a particular example, we demonstrate a gate-tunable THz polarization modulator based on our graphene metasurface. Our findings pave the road towards exciting photonic applications based on active phase manipulations.

preprint2014arXiv

Hierarchical analysis-suitable T-splines: Formulation, Bézier extraction, and application as an adaptive basis for isogeometric analysis

In this paper hierarchical analysis-suitable T-splines (HASTS) are developed. The resulting spaces are a superset of both analysis-suitable T-splines and hierarchical B-splines. The additional flexibility provided by the hierarchy of T-spline spaces results in simple, highly localized refinement algorithms which can be utilized in a design or analysis context. A detailed theoretical formulation is presented including a proof of local linear independence for analysis-suitable T-splines, a requisite theoretical ingredient for HASTS. Bézier extraction is extended to HASTS simplifying the implementation of HASTS in existing finite element codes. The behavior of a simple HASTS refinement algorithm is compared to the local refinement algorithm for analysis-suitable T-splines demonstrating the superior efficiency and locality of the HASTS algorithm. Finally, HASTS are utilized as a basis for adaptive isogeometric analysis.

preprint2014arXiv

High-resolution transcriptome analysis with long-read RNA sequencing

RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2x75 bp and 2x262 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.

preprint2014arXiv

Independent resolutions for totally disconnected dynamical systems I: Algebraic case

This is the first out of two papers on independent resolutions for totally disconnected dynamical systems. In the present paper, we discuss independent resolutions from an algebraic point of view. We also present applications to group homology and cohomology. This first paper sets the stage for our second paper, where we explain how to use independent resolutions in K-theory computations for crossed products attached to totally disconnected dynamical systems.

preprint2014arXiv

Independent resolutions for totally disconnected dynamical systems II: C*-algebraic case

We develop the notion of independent resolutions for crossed products attached to totally disconnected dynamical systems. If such a crossed product admits an independent resolution of finite length, then its K-theory can be computed (at least in principle) by analysing the corresponding six-term exact sequences. Building on our previous paper on algebraic independent resolutions, we give a criterion for the existence of finite length independent resolutions. Moreover, we illustrate our ideas in various concrete examples.

preprint2014arXiv

Influence of Molecular Solvation on the Conformation of Star Polymers

We have used neutron scattering to investigate the influence of concentration on the conformation of a star polymer. By varying the contrast between the solvent and isotopically labeled stars, we obtain the distributions of polymer and solvent within a star polymer from analysis of scattering data. A correlation between the local desolvation and the inward folding of star branches is discovered. From the perspective of thermodynamics, we find an analogy between the mechanism of polymer localization driven by solvent depletion and that of the hydrophobic collapse of polymers in solutions.

preprint2014arXiv

Interlayer Water Regulates the Bio-nano Interface of a \b{eta}-sheet Protein stacking on Graphene

Using molecular dynamics simulations, we investigated an integrated bio-nano interface consisting of a \b{eta}-sheet protein stacked onto graphene. We found that the stacking assembly of the model protein on graphene could be controlled by water molecules. The interlayer water filled within interstices of the bio-nano interface could suppress the molecular vibration of surface groups on protein, and could impair the CH...π interaction driving the attraction of the protein and graphene. The intermolecular coupling of interlayer water would be relaxed by the relative motion of protein upon graphene due to the interaction between water and protein surface. This effect reduced the hindrance of the interlayer water against the assembly of protein on graphene, resulting an appropriate adsorption status of protein on graphene with a deep free energy trap. Thereby, the confinement and the relative sliding between protein and graphene, the coupling of protein and water, and the interaction between graphene and water all have involved in the modulation of behaviors of water molecules within the bio-nano interface, governing the hindrance of interlayer water against the protein assembly on hydrophobic graphene. These results provide a deep insight into the fundamental mechanism of protein adsorption onto graphene surface in water.

preprint2014arXiv

Metastable vacuum decay in center-stabilized Yang-Mills theory at large N

We calculate the rate of the decay of a metastable vacuum state in $SU(N)$ gauge theory on ${\mathbb R}^3 \times {\mathbb S}^1$ space with a double trace deformation suggested by Ünsal and Yaffe. The derived analytical expression for the exponential factor in the decay rate, including the dependence on the parameter $θ$, gives the exact behavior in the leading power of $N$ in the limit of large $N$ and provides a better approximation than a recently found in the literature numerical result.

preprint2014arXiv

Multi-Source Randomness Extractors Against Quantum Side Information, and their Applications

We study the problem of constructing multi-source extractors in the quantum setting, which extract almost uniform random bits against quantum side information collected from several initially independent classical random sources. This is a natural generalization of seeded randomness extraction against quantum side information and classical independent source extraction. With new challenges such as potential entanglement in the side information, it is not a prior clear under what conditions do quantum multi-source extractors exist; the only previous work is [KK12], where the classical inner-product two-source extractors of [CG88] and [DEOR04] are shown to be quantum secure in the restricted Independent Adversary (IA) Model and entangled Bounded Storage (BS) Model. In this paper we propose a new model called General Entangled (GE) Adversary Model, which allows arbitrary entanglement in the side information and subsumes both the IA model and the BS model. We proceed to show how to construct GE-secure quantum multi-source extractors. To that end, we propose another model called One-sided Adversary (OA) Model, which is weaker than all the above models. Somewhat surprisingly, we establish equivalence between strong OA-security and strong GE-security. As a result, all classical multi-source extractors can either directly work, or be modified to work in the GE model at the cost of one extra random source. Thus, our constructions essentially match the best known constructions of classical multi-source extractors. We also apply our techniques to two important problems in cryptography and distributed computing --- privacy amplification and network extractor. We show that as long as the sources have certain amounts of conditional min-entropy in our GE model (even with entangled quantum side information), we can design very efficient privacy amplification protocols and network extractors.

preprint2014arXiv

Multiple-Population Moment Estimation: Exploiting Inter-Population Correlation for Efficient Moment Estimation in Analog/Mixed-Signal Validation

Moment estimation is an important problem during circuit validation, in both pre-Silicon and post-Silicon stages. From the estimated moments, the probability of failure and parametric yield can be estimated at each circuit configuration and corner, and these metrics are used for design optimization and making product qualification decisions. The problem is especially difficult if only a very small sample size is allowed for measurement or simulation, as is the case for complex analog/mixed-signal circuits. In this paper, we propose an efficient moment estimation method, called Multiple-Population Moment Estimation (MPME), that significantly improves estimation accuracy under small sample size. The key idea is to leverage the data collected under different corners/configurations to improve the accuracy of moment estimation at each individual corner/configuration. Mathematically, we employ the hierarchical Bayesian framework to exploit the underlying correlation in the data. We apply the proposed method to several datasets including post-silicon measurements of a commercial high-speed I/O link, and demonstrate an average error reduction of up to 2$\times$, which can be equivalently translated to significant reduction of validation time and cost.

preprint2014arXiv

Optimal Starting-Stopping and Switching of a CIR Process with Fixed Costs

This paper analyzes the problem of starting and stopping a Cox-Ingersoll-Ross (CIR) process with fixed costs. In addition, we also study a related optimal switching problem that involves an infinite sequence of starts and stops. We establish the conditions under which the starting-stopping and switching problems admit the same optimal starting and/or stopping strategies. We rigorously prove that the optimal starting and stopping strategies are of threshold type, and give the analytical expressions for the value functions in terms of confluent hypergeometric functions. Numerical examples are provided to illustrate the dependence of timing strategies on model parameters and transaction costs.

preprint2014arXiv

Perfect Valley Filter in Topological Domain Wall

We propose a realization of perfect valley filters based on the chiral domain-wall channels between a quantum anomalous Hall insulator and a quantum valley Hall insulator. Uniquely, all these channels reside in the same valley and propagate unidirectionally, $100\%$ valley-polarizing passing-by carriers without backscattering. The valley index, the chirality, and the number of the channels are protected by topological charges, controllable by external fields, and detectable by circular dichroism.

preprint2014arXiv

Phase behavior under a non-centrosymmetric interaction: shifted charge colloids investigated by Monte Carlo simulation

Using Monte Carlo simulations, we investigate the structural characteristics of an interacting hard sphere system with shifted charge to elucidate the effect of the non-centrosymmetric interaction on its phase behavior. Two different phase transitions are identified for this model system. Upon increasing the volume fraction, an abrupt liquid-to-crystal transition first occurs at a significantly lower volume fraction in comparison to that of the centro-charged system. This is due to the stronger effective inter-particle repulsion caused by the additional charge anisotropy. Moreover, within the crystal state at higher volume fraction, the system further undergoes a continuous disorder-to-order transition with respect to the charge orientation. Detailed analyses in this work disclose the nature of these transitions, and orientation fluctuation may cause non-centrosymmetric unit cells. The dependence of crystal formation and orientational ordering on temperature was also examined. These findings indicate that the non-centrosymmetric interaction in this work results in additional freedoms to fine-tune the phase diagram and increase the functionalities of materials. Moreover, these model studies are essential to advance our future understanding regarding the fundamental physiochemical properties of novel Janus colloidal particles and protein crystallization conditions.

preprint2014arXiv

Scattering from Colloid-Polymer Conjugates with Excluded Volume Effect

In this work we present scattering functions of conjugates consisting of a colloid particle and a self-avoiding polymer chain. This model is directly derived from the two point correlation function with the inclusion of excluded volume effects. The dependence of the calculated scattering function on the geometric shapes of the colloid and polymer stiffness is investigated. In comparison to existing experimental results, our model is found to be able to describe the scattering signature of the colloid-polymer conjugates and provide additional conformational information. This model explicitly elucidates the link between the global conformation of a conjugate and the microstructure of its constituent components.

preprint2014arXiv

Scattering from Star Polymers including Excluded Volume Effects

In this work we present a new model for the form factor of a star polymer consisting of self-avoiding branches. This new model incorporates excluded volume effects and is derived from the two point correlation function for a star polymer.. We compare this model to small angle neutron scattering (SANS) measurements from polystyrene (PS) stars immersed in a good solvent, tetrahydrofuran (THF). It is shown that this model provides a good description of the scattering signature originating from the excluded volume effect and it explicitly elucidates the connection between the global conformation of a star polymer and the local stiffness of its constituent branch.

preprint2014arXiv

The decays $Ξ_b \to Λ_b \, π$ and diquark correlations in hyperons

The decays $Ξ_b \to Λ_b π$ are strangeness changing weak transitions involving only the light diquark in the baryon. Thus these decays can test the properties of such diquarks, in particular the suggestions existing in the literature of enhanced correlations in $J^P=0^+$ light diquarks. We revisit the estimates of the rates of these decays and point out that with the enhanced correlation their branching fraction can reach a few percent and may become visible in the measurements of differences of the lifetimes of $b$ baryons.

preprint2014arXiv

The isomorphism problem for semigroup C*-algebras of right-angled Artin monoids

Semigroup C*-algebras for right-angled Artin monoids were introduced and studied by Crisp and Laca. In the paper at hand, we are able to present the complete answer to their question of when such C*-algebras are isomorphic. The answer to this question is presented both in terms of properties of the graph defining the Artin monoids as well as in terms of classification by K-theory, and is obtained using recent results from classification of non-simple C*-algebras. Moreover, we are able to answer another natural question: Which of these semigroup C*-algebras for right-angled Artin monoids are isomorphic to graph algebras? We give a complete answer, and note the consequence that many of the C*-algebras under study are semiprojective.

preprint2014arXiv

Topological metallic states in spin-orbit coupled bilayer systems

We investigate the influence of different spin-orbit couplings on topological phase transitions in the bilayer Kane-Mele model. We find that the competition between intrinsic spin-orbit coupling and Rashba spin-orbit coupling can lead to two dimensional topological metallic states with nontrivial topology. Such phases, although having a metallic bulk, still possess edge states with well-defined topological invariants. Specifically, we show that with preserved time reversal symmetry, the system can exhibit a $\mathbb{Z}_2$-metallic phase with spin helical edge states and a nontrivial $\mathbb{Z}_2$ invariant. When time reversal symmetry is broken, a Chern metallic phase could appear with chiral edge states and a nontrivial Chern invariant.

preprint2014arXiv

Valley-polarized quantum anomalous Hall phase and disorder induced valley-filtered chiral edge channels

We investigate the topological and transport properties of the recently discovered valley-polarized quantum anomalous Hall (VQAH) phase. In single layer, the phase is realized through the competition between two types of spin-orbit coupling, which breaks the symmetry between the two valleys. We show that the topological phase transition from conventional quantum anomalous Hall phase to the VQAH phase is due to the change of topological charges with the generation of additional skyrmions in the real spin texture, when the band gap closes and reopens at one of the valleys. In the presence of short range disorders, pairs of the gapless edge channels (one from each valley in a pair) would be destroyed due to intervalley scattering. However, we discover that in an extended range of moderate scattering strength, the transport through the system is quantized and fully valley-polarized, i.e. the system is equivalent to a quantum anomalous Hall system with valley-filtered chiral edge channels. We further show that with additional layer degree of freedom, much richer phase diagram could be realized with multiple VQAH phases. For a bilayer system, we demonstrate that topological phase transitions could be controlled by the interlayer bias potential.

preprint2014arXiv

Y(4260) and Y(4360) as mixed hadrocharmonium

Recent BESIII data indicate a significant rate of the process $e^+e^- \to h_c π^+ π^-$ at the Y(4260) and Y(4360) resonances, implying a substantial breaking of the heavy quark spin symmetry. We consider these resonances within the picture of hadrocharmonium, i.e. of (relatively) compact charmonium embedded in a light quark mesonic excitation. We suggest that the resonances Y(4260) and Y(4360) are a mixture, with mixing close to maximal, of two states of hadrochamonium, one containing a spin-triplet $c \bar c$ pair and the other containing a spin-singlet heavy quark pair. We argue that this model is in a reasonable agreement with the available data and produces distinctive and verifiable predictions for the energy dependence of the production rate in $e^+e^-$ annihilation of the final states $J/ψππ$, $ψ' ππ$ and $h_c ππ$, including the pattern of interference between the two resonances.

preprint2013arXiv

$Λ$CDM model with a scalar perturbation vs. preferred direction of the universe

We present a scalar perturbation for the $Λ$CDM model, which breaks the isotropic symmetry of the universe. Based on the Union2 data, the least-$χ^2$ fit of the scalar perturbed $Λ$CDM model shows that the universe has a preferred direction $(l,b)=(287^\circ\pm25^\circ,11^\circ\pm22^\circ)$. The magnitude of scalar perturbation is about $-2.3\times10^{-5}$. The scalar perturbation for the $Λ$CDM model implies a peculiar velocity, which is perpendicular to the radial direction. We show that the maximum peculiar velocities at redshift $z=0.15$ and $z=0.015$ equal to $73\pm28 \rm km\cdot s^{-1}$ and $1099\pm427 \rm km\cdot s^{-1}$, respectively. They are compatible with the constraints on peculiar velocity given by Planck Collaboration.

preprint2013arXiv

Chemical vapor deposition of carbon nanotubes on monolayer graphene substrates: reduced etching via suppressed catalytic hydrogenation using C2H4

In most envisioned applications, full utilization of a graphene-carbon nanotube (CNT) construct requires maintaining the integrity of the graphene layer during the CNT growth step. In this work, we exhibit an approach towards controlled CNT growth atop graphene substrates where the reaction equilibrium between source hydrocarbon decomposition and carbon saturation into and precipitation from the catalyst nanoparticles shifts towards CNT growth rather than graphene consumption. By utilizing C2H4 feedstock, we demonstrate that the low temperature growth permissible with this gas suppresses undesirable catalytic hydrogenation and dramatically reduces the etching of the graphene layer to exhibit graphene-CNT hybrids with continuous, undamaged structures.

preprint2013arXiv

Constraints from Type IA Supernovae on λ-CDM Model in Randers-Finsler Space

Gravitational field equations in Randers-Finsler space of approximate Berwald type are investigated. A modified Friedmann equation and a new luminosity distance-redshift relation is proposed. A best-fit to the Type Ia supernovae (SNe) observations yields that the $Ω_Λ$ in the $Λ$-CDM model is suppressed to almost zero. This fact indicates that the astronomical observations on the Type Ia SNe can be described well without invoking any form of dark energy. The best-fit age of the universe is given. It is in agreement with the age of our galaxy.

preprint2013arXiv

Cosmological model with local symmetry of very special relativity and constraints on it from supernovae

Based on Cohen \& Glashow's very special relativity [A. G. Cohen and S. L. Glashow, Phys. Rev. Lett. {\bf 97} (2006) 021601], we propose an anisotropic modification to the Friedmann-Robertson-Walker (FRW) line element. An arbitrarily oriented 1-form is introduced and the FRW spacetime becomes of the Randers-Finsler type. The 1-form picks out a privileged axis in the universe. Thus, the cosmological redshift as well as the Hubble diagram of the type Ia supernovae (SNe Ia) becomes anisotropic. By directly analyzing the Union2 compilation, we obtain the privileged axis pointing to $(l,b)=({304^\circ}\pm{43^\circ},{-27^\circ}\pm{13^\circ})$ ($68\%~\rm{C.L.}$). This privileged axis is close to those obtained by comparing the best-fit Hubble diagrams in pairs of hemispheres. It should be noticed that the result is consistent with isotropy at the $1σ$ level since the anisotropic magnitude is $D=0.03\pm 0.03$.

preprint2013arXiv

Decoherence of quantum gates based on Aharonov-Anandan phases in a multistep scheme

We study quantum decoherence of single-qubit and two-qubit Aharonov-Anandan (AA) geometric phase gates realized in a multistep scheme. Each AA gate is also compared with the dynamical phase gate performing the same unitary transformation within the same time period and coupled with the same environment, which is modeled as harmonic oscillators. It is found that the fidelities and the entanglement protection of the AA phase gates are enhanced by the states being superpositions of different eigenstates of the environmental coupling, and the noncommutativity between the qubit interaction and the environmental coupling.

preprint2013arXiv

Dynamics of the sub-Ohmic spin-boson model: a time-dependent variational study

The Dirac-Frenkel time-dependent variation is employed to probe the dynamics of the zero temperature sub-Ohmic spin-boson model with strong friction utilizing the Davydov D1 ansatz. It is shown that initial conditions of the phonon bath have considerable influence on the dynamics. Counterintuitively, even in the very strong coupling regime, quantum coherence features still manage to survive under the polarized bath initial condition, while such features are absent under the factorized bath initial condition. In addition, a coherent-incoherent transition is found at a critical coupling strength alpha ~ 0.1 for s=0.25 under the factorized bath initial condition. We quantify how faithfully our ansatz follows the Schrödinger equation, finding that the time-dependent variational approach is robust for strong dissipation and deep sub-Ohmic baths (s<<1).

preprint2013arXiv

Effect of decoherence on the Berry phase of a spin-half in a rotating magnetic field

We investigate the decoherence effect of a bosonic bath on the Berry phase of a spin-1/2 in a time-dependent magnetic field, without making the Markovian approximation. A two-cycle process resulting in a pure Berry phase is considered. The low-frequency quantum noise significantly affects the Berry phase. In the adiabatic limit, the high-frequency quantum noise only has a small effect. The result is also valid in some more general situations.

preprint2013arXiv

Effects of spacetime anisotropy on the galaxy rotation curves

The observations on galaxy rotation curves show significant discrepancies from the Newtonian theory. This issue could be explained by the effect of the anisotropy of the spacetime. Conversely, the spacetime anisotropy could also be constrained by the galaxy rotation curves. Finsler geometry is a kind of intrinsically anisotropic geometry. In this paper, we study the effect of the spacetime anisotropy at the galactic scales in the Finsler spacetime. It is found that the Finslerian model has close relations with the Milgrom's MOND. By performing the best-fit procedure to the galaxy rotation curves, we find that the anisotropic effects of the spacetime become significant when the Newtonian acceleration $GM/r^2$ is smaller than the critical acceleration $a_0$. Interestingly, the critical acceleration $a_0$, although varies between different galaxies, is in the order of magnitude $cH_0/2π\sim 10^{-10} \rm{m\,\, s^{-2}}$.

preprint2013arXiv

Electric discharge in vacuum by minicharged particles

We discuss the possibility of a laboratory search for light minicharged particles carrying electric charge that is a small fraction $ε$ of that of electron. We point out that the production of pairs of such particles in an electric field would result in a measurable discharge in vacuum of electrically charged objects. A realistic experiment may be sensitive to such particles at least down to $ε\sim 10^{-8}$ if their mass is below $\sim 10^{-4}$eV.

preprint2013arXiv

Extension of ITU IMT-A Channel Models for Elevation Domains and Line-of-Sight Scenarios

In this contribution, the 3-dimensional (3D) channel characteristics, particularly in the elevation domains, are extracted through measurements in typical urban macro and micro environments in Xi'an China. Stochastic channel model parameters are obtained based on the high-resolution multi-path parameter estimates. In addition, a modified spatial channel model (SCM) for the line-of-sight (LoS) scenario is proposed where the LoS polarization matrix is parameterized in accordance with the reality. Measurement results justify the reasonability of the proposed model. These works significantly improve the applicability of the ITU SCM models in realistic 3D channel simulations.

preprint2013arXiv

Finslerian MOND versus the Strong Gravitational Lensing of the Early-type Galaxies

The gravitational lensing of Bullet Clusters and early-type galaxies pose serious challenges on the validity of MOND. Recently, Finslerian MOND, a generalization of MOND in the framework of Finsler gravity, has been proposed to explain the mass discrepancy problem of Bullet Cluster 1E 0657\ 558. In this paper, we check the validity of the Finslerian MOND in describing the strong gravitational lensing of early-type galaxies. The investigation on ten strong lenses of the CASTLES samples shows that there is no strong evidence for the existence of dark matter.

preprint2013arXiv

Finslerian MOND vs. observations of Bullet Cluster 1E0657-558

It is known that theory of MOND with spherical symmetry cannot account for the convergence $κ$-map of Bullet Cluster 1E0657-558. In this paper, we try to set up a Finslerian MOND, a generalization of MOND in Finsler spacetime. We use $Ric=0$ to obtain the gravitational vacuum field equation in a four-dimensional Finsler spacetime. To leading order in the post-Newtonian approximation, we obtain the explicit form of the Finslerian line element. It is simply the Schwarzschild's metric except for the Finslerian rescaling coefficient $f(v)$ of the radial coordinate $r$, i.e. $R=f(v(r))r$. By setting $f(v(r))=(1-\sqrt{a_0r^2/GM})^{-1}$, we obtain the famous MOND in a Finslerian framework. Taking a dipole and a quadrupole term into consideration, we give the convergence $κ$ in gravitational lensing astrophysics in our model. Numerical analysis shows that our prediction is to a certain extent in agreement with the observations of Bullet Cluster 1E0657-558. With the theoretical temperature $T$ taking the observed value 14.8 keV, the mass density profile of the main cluster obtained in our model is the same order as that given by the best-fit King $β$-model.

preprint2013arXiv

Finslerian perturbation for the $Λ$CDM model

We present Finslerian perturbation for the $Λ$CDM model, which breaks the isotropic symmetry of the universe. The analysis on the Killing vectors shows that the Randers-Finsler spacetime breaks the isotropic symmetry even if the scalar perturbations of the FRW metric vanish. In Randers-Finsler spacetime, the modified geodesic equation deduces a modified Boltzmann equation. We propose a perturbational version of the gravitational field equation in Randers-Finsler spacetime, where we have omitted the curvature tensor that does not belong to the base space of the tangent bundle. The gravitational field equations for the gravitational wave are also presented. The primordial power spectrum of the gravitational wave is investigated. We show that the primordial power spectrum for super-horizon perturbations is unchanged. For sub-horizon perturbations, however, the power spectrum is modified.

preprint2013arXiv

Galaxies Rotation Curves in the Grumiller's Modified Gravity

The effective potential in the Grumiller's modified gravity [D. Grumiller, Phys. Rev. Lett {\bf 105}, 211303 (2010)] includes the Newtonian potential and a Rindler term. The fitting to the rotation curve data of eight galaxies suggests a universal Rindler acceleration $a\approx 0.30\times 10^{-10}$ m s$^{-2}$. We do a two-parameter fit first, with the mass-to-light ratio ($Υ_*$) and the Rindler acceleration ($a$) as free parameters. It is found that the data of six out of the eight galaxies fit well with the prediction of theory in the range $0\lesssim r \lesssim 40$ kpc, although the theoretical curves show a tendency of arising beyond this range. The Rindler accelerations of the six well-fitted galaxies have the same magnitude, with an average value $\bar{a}\approx0.30\times 10^{-10}$ m s$^{-2}$. Inspired by this fact, we then carry out a one-parameter ($Υ_*$) fit to the six galaxies, with $a$ fixed at $\bar{a}$, and find that the theory can still reproduce the observation. The value of Rindler acceleration we get here is a quarter of that of Milgrom's MOND. For the rest two galaxies, NGC5055 and DDO154, the fitting results are significantly improved if the photometric scale length ($h$) is included as another free parameter.

preprint2013arXiv

Generalization of Rindler Potential at Cluster Scales in Randers-Finslerian Spacetime: a Possible Explanation of the Bullet Cluster 1E0657-558?

The data of the Bullet Cluster 1E0657-558 released on November 15, 2006 reveal that the strong and weak gravitational lensing convergence $κ$-map has an $8σ$ offset from the $Σ$-map. The observed $Σ$-map is a direct measurement of the surface mass density of the Intracluster medium(ICM) gas. It accounts for 83% of the averaged mass-fraction of the system. This suggests a modified gravity theory at large distances different from Newton's inverse-square gravitational law. In this paper, as a cluster scale generalization of Grumiller's modified gravity model (D. Grumiller, Phys. Rev. Lett. 105, 211303 (2010)), we present a gravity model with a generalized linear Rindler potential in Randers-Finslerian spacetime without invoking any dark matter. The galactic limit of the model is qualitatively consistent with the MOND and Grumiller's. It yields approximately the flatness of the rotational velocity profile at the radial distance of several kpcs and gives the velocity scales for spiral galaxies at which the curves become flattened. Plots of convergence $κ$ for a galaxy cluster show that the peak of the gravitational potential has chances to lie on the outskirts of the baryonic mass center. Assuming an isotropic and isothermal ICM gas profile with temperature $T=14.8$ keV (which is the center value given by observations), we obtain a good match between the dynamical mass $M_\textmd{T}$ of the main cluster given by collisionless Boltzmann equation and that given by the King $β$-model. We also consider a Randers+dark matter scenario \textbf{and a $Λ$-CDM model} with the NFW dark matter distribution profile. We find that a mass ratio $η$ between dark matter and baryonic matter about 6 fails to reproduce the observed convergence $κ$-map for the isothermal temperature $T$ taking the observational center value.

preprint2013arXiv

Generating Stack-based Access Control Policies

The stack-based access control mechanism plays a fundamental role in the security architecture of Java and Microsoft CLR (common language runtime). It is enforced at runtime by inspecting methods in the current call stack for granted permissions before the program performs safety-critical operations. Although stack inspection is well studied, there is relatively little work on automated generation of access control policies, and most existing work on inferring security policies assume the permissions to be checked at stack inspection points are known beforehand. Practiced approaches to generating access control policies are still manually done by developers based on domain-specific knowledges and trial-and-error testing. In this paper, we present a systematic approach to automated generation of access control policies for Java programs that necessarily ensure the program to pass stack inspection. The techniques are abstract interpretation based context-sensitive static program analyses. Our analysis models the program by combining a context-sensitive call graph with a dependency graph. We are hereby able to precisely identify permission requirements at stack inspection points, which are usually ignored in previous study.

preprint2013arXiv

Isospin violation in the yield of S-wave heavy meson pairs near threshold

We consider production of overall neutral pairs consisting of heavy $D^{(*)}$ or $B^{(*)}$ meson and antimeson in the processes, where the pair can be created in an $S$-wave, such as $e^+e^- \to π^0 \, D^* \bar D^{(*)}$ and $e^+e^- \to γ\, D^{(*)} \bar D^{(*)}$, and similar reactions with $B^{(*)}$ mesons. The ratio $R^{c/n}$ of the yield of pairs of charged mesons to that of neutral mesons near the threshold for the heavy pair is strongly affected by the isospin breaking due to the isotopic mass differences and due to the Coulomb interaction between the charged mesons. The actual behavior of the isospin breaking in $R^{c/n}$ is also sensitive to the strong interaction between the heavy mesons, so that experimental measurement of this ratio can be used as a probe of this strong interaction. We calculate $R^{c/n}$ at and very near the threshold in terms of the isotopic scattering lengths for the meson pairs. In particular we find that the yield of pairs of charged mesons does not go to zero at exactly the threshold, but rather starts with a finite step, whose height depends on the value of the scattering length in a particular channel.

preprint2013arXiv

Mixing of partial waves near $B^* \bar B^*$ threshold in $e^+e^-$ annihilation

We consider the production of $B^* \bar B^*$ meson pairs in $e^+e^-$ annihilation near the threshold. The rescattering due to pion exchange between the mesons results in a mixing between three partial wave amplitudes: two $P$-wave with the total spin of the meson pair S=0 and S=2 and an F-wave amplitude. The mixing due to pion exchange with a low momentum transfer is calculable up to c.m. energy $E \approx 15 ÷20$\,MeV above the threshold. We find that the $P-F$ mixing is numerically quite small in this energy range, while the mixing of the two P-wave amplitudes is rapidly changing with energy and can reach of order one at such low energies.

preprint2013arXiv

On the K-theory of the C*-algebra generated by the left regular representation of an Ore semigroup

We compute the K-theory of C*-algebras generated by the left regular representation of left Ore semigroups satisfying certain regularity conditions. Our result describes the K-theory of these semigroup C*-algebras in terms of the K-theory for the reduced group C*-algebras of certain groups which are typically easier to handle. Then we apply our result to specific semigroups from algebraic number theory.

preprint2013arXiv

Quadrupole--octopole alignment of CMB related to primordial power spectrum with dipolar modulation in anisotropic spacetime

The WMAP and Planck observations show that the quadrupole and octopole orientations of the CMB might align with each other. We reveal that the quadrupole--octopole alignment is a natural implication of the primordial power spectrum in an anisotropic spacetime. The primordial power spectrum is presented with a dipolar modulation. We obtain the privileged plane by employing the ``power tensor'' technique. At this plane, there is the maximum correlation between quadrupole and octopole. The probability for the alignment is much larger than what in the isotropic universe. We find that this model would lead to deviations from the statistical isotropy only for low--$\ell$ multipoles.

preprint2013arXiv

Quantum resonance scheme to determine the gravitational constant G

Building on the principles of quantum electrodynamics and special relativity, we develop a semi-classical QED theory of gravitation. The experiment model for theoretical analysis is the "hyperfine splitting" of the ground energy state of hydrogen atoms. As the verification and validation by Newton's final experiment, Newton's gravity constant G has been determined by this new approach, G = 6.67221937(40)*10^(-11) N m2 /kg2. The result shows that, the gravitational constant G is really a universal constant in free space, and can be determined just by the more fundamental constants in physics.

preprint2013arXiv

Remarks on double Higgs boson production by gluon fusion at threshold

The amplitude of double Higgs boson production by the gluon fusion, $gg \to hh$, is known to be small due to cancellation between the graphs with the boson trilinear coupling and those with the coupling to the top quark. For this reason a study of this process was suggested as a sensitive probe of the Higgs sector nonlinearity. We calculate in a closed analytical form this amplitude at the threshold of the two bosons, where the cancellation is the strongest, and discuss the origin of the small value of the amplitude. We also note that the cancellation in the double boson production is in fact a part of a more general phenomenon of suppression of similar threshold amplitudes for multiple boson production, which, although not directly relevant to the actual top quark and the Higgs boson, can be useful in other studies.

preprint2013arXiv

Semigroup C*-algebras of $ax+b$-semigroups

We study semigroup C*-algebras of $ax+b$-semigroups over integral domains. The goal is to generalize several results about C*-algebras of $ax+b$-semigroups over rings of algebraic integers. We prove results concerning K-theory and structural properties like the ideal structure or pure infiniteness. Our methods allow us to treat $ax+b$-semigroups over a large class of integral domains containing all noetherian, integrally closed domains and coordinate rings of affine varieties over infinite fields.

preprint2013arXiv

Sub-Ohmic spin-boson model with off-diagonal coupling: Ground state properties

We have carried out analytical and numerical studies of the spin-boson model in the sub-ohmic regime with the influence of both the diagonal and off-diagonal coupling accounted for via the Davydov D1 variational ansatz. While a second-order phase transition is known to be exhibited by this model in the presence of diagonal coupling only, we demonstrate the emergence of a discontinuous first order phase transition upon incorporation of the off-diagonal coupling. A plot of the ground state energy versus magnetization highlights the discontinuous nature of the transition between the isotropic (zero magnetization) state and nematic (finite magnetization) phases. We have also calculated the entanglement entropy and a discontinuity found at a critical coupling strength further supports the discontinuous crossover in the spin-boson model in the presence of off-diagonal coupling. It is further revealed via a canonical transformation approach that for the special case of identical exponents for the spectral densities of the diagonal and the off-diagonal coupling, there exists a continuous crossover from a single localized phase to doubly degenerate localized phase with differing magnetizations.

preprint2013arXiv

Suppression of the S-wave production of (3/2)^+ + (1/2)^- heavy meson pairs in e^+e^- annihilation

The heavy meson-antimeson pairs, where one is an excited $\bigl ({3 \over 2} \bigr) ^+ $ meson and the other is a ground state $\bigl ({1 \over 2} \bigr) ^-$ meson, namely the pairs ($D_1(2420) \bar D$ + c.c.), ($D_1(2420) \bar D^*$ + c.c.), ($D_2(2460) \bar D^*$ + c.c.) in the charm sector and ($B_1(5721) \bar B$ + c.c.), ($B_1(5721) \bar B^*$ + c.c.), ($B_2(5747) \bar B^*$ + c.c.) in the bottom sector, are allowed, by the quantum numbers, to be produced in the $S$ wave in $e^+e^-$ annihilation. We show, however, that such $S$-wave production is forbidden by the heavy quark spin symmetry. Thus the yield of the considered meson pairs in $e^+e^-$ annihilation should be significantly suppressed near the respective thresholds. In our view, this substantially weakens the arguments for considering the Y(4260) charmonium-like resonance as a $D_1 \bar D$ molecular state.

preprint2012arXiv

An Inflationary Scenario Taking into Account of Possible Dark Energy Effects in the Early Universe

We investigate the possible effect of cosmological-constant type dark energy during the inflation period of the early universe. This is accommodated by a new dispersion relation in de Sitter space. The modified inflation model of a minimally-coupled scalar field is still able to yield an observation-compatible scale-invariant primordial spectrum, simultaneously having potential to generate a spectrum with lower power at large scales. A qualitative match to the WMAP 7-year data is presented. We obtain an $Ω_Λ$ of the same order of that in the $Λ$-CDM model. Possible relations between the de Sitter scenario and the Doubly Special Relativity(DSR) are also discussed.

preprint2012arXiv

Analysis-suitable T-splines: characterization, refineability, and approximation

We establish several fundamental properties of analysis-suitable T-splines which are important for design and analysis. First, we characterize T-spline spaces and prove that the space of smooth bicubic polynomials, defined over the extended T-mesh of an analysis-suitable T-spline, is contained in the corresponding analysis-suitable T-spline space. This is accomplished through the theory of perturbed analysis-suitable T-spline spaces and a simple topological dimension formula. Second, we establish the theory of analysis-suitable local refinement and describe the conditions under which two analysis-suitable T-spline spaces are nested. Last, we demonstrate that these results can be used to establish basic approximation results which are critical for analysis.

preprint2012arXiv

Erratum to "C*-algebras associated with integral domains and crossed products by actions on adele spaces" by J. Cuntz and X. Li

We correct the K-theory computation for ring C*-algebras associated with rings of integers in number fields.

preprint2012arXiv

Gravitational wave in Lorentz violating gravity

By making use of the weak gravitational field approximation, we obtain a linearized solution of the gravitational vacuum field equation in an anisotropic spacetime. The plane-wave solution and dispersion relation of gravitational wave is presented explicitly. There is possibility that the speed of gravitational wave is larger than the speed of light and the casuality still holds. We show that the energy-momentum of gravitational wave in the ansiotropic spacetime is still well defined and conserved.

preprint2012arXiv

Joint Viterbi Decoding and Decision Feedback Equalization for Monobit Digital Receivers

In ultra-wideband (UWB) communication systems with impulse radio (IR) modulation, the bandwidth is usually 1GHz or more. To process the received signal digitally, high sampling rate analog-digital-converters (ADC) are required. Due to the high complexity and large power consumption, monobit ADC is appropriate. The optimal monobit receiver has been derived. But it is not efficient to combat intersymbol interference (ISI). Decision feedback equalization (DFE) is an effect way dealing with ISI. In this paper, we proposed a algorithm that combines Viterbi decoding and DFE together for monobit receivers. In this way, we suppress the impact of ISI effectively, thus improving the bit error rate (BER) performance. By state expansion, we achieve better performance. The simulation results show that the algorithm has about 1dB SNR gain compared to separate demodulation and decoding method and 1dB loss compared to the BER performance in the channel without ISI. Compare to the full resolution detection in fading channel without ISI, it has 3dB SNR loss after state expansion.

preprint2012arXiv

Neutrino superluminality without Cherenkov-like processes in Finslerian special relativity

Recently, Cohen and Glashow [A.G. Cohen, S.L. Glashow, Phys. Rev. Lett. {\bf 107}, 181803 (2011)] pointed out that the superluminal neutrinos reported by the OPERA would lose their energy rapidly via the Cherenkov-like process. The Cherenkov-like process for the superluminal particles would be forbidden if the principle of special relativity holds in any frame instead violated with a preferred frame. We have proposed that the Finslerian special relativity could account for the data of the neutrino superluminality (arXiv:1110.6673[hep-ph]). The Finslerian special relativity preserves the principle of special relativity and involves a preferred direction while consists with the causality. In this paper, we prove that the energy-momentum conservation is preserved and the energy-momentum is well defined in Finslerian special relativity. The Cherenkov-like process is forbidden in the Finslerian special relativity. Thus, the superluminal neutrinos would not lose energy in their distant propagation.

preprint2012arXiv

Non-Malleable Condensers for Arbitrary Min-Entropy, and Almost Optimal Protocols for Privacy Amplification

Recently, the problem of privacy amplification with an active adversary has received a lot of attention. Given a shared n-bit weak random source X with min-entropy k and a security parameter s, the main goal is to construct an explicit 2-round privacy amplification protocol that achieves entropy loss O(s). Dodis and Wichs \cite{DW09} showed that optimal protocols can be achieved by constructing explicit non-malleable extractors. However, the best known explicit non-malleable extractor only achieves k=0.49n \cite{Li12b} and evidence in \cite{Li12b} suggests that constructing explicit non-malleable extractors for smaller min-entropy may be hard. In an alternative approach, Li \cite{Li12} introduced the notion of a non-malleable condenser and showed that explicit non-malleable condensers also give optimal privacy amplification protocols. In this paper, we give the first construction of non-malleable condensers for arbitrary min-entropy. Using our construction, we obtain a 2-round privacy amplification protocol with optimal entropy loss for security parameter up to s=Ω(\sqrt{k}). This is the first protocol that simultaneously achieves optimal round complexity and optimal entropy loss for arbitrary min-entropy k. We also generalize this result to obtain a protocol that runs in O(s/\sqrt{k}) rounds with optimal entropy loss, for security parameter up to s=Ω(k). This significantly improves the protocol in \cite{ckor}. Finally, we give a better non-malleable condenser for linear min-entropy, and in this case obtain a 2-round protocol with optimal entropy loss for security parameter up to s=Ω(k), which improves the entropy loss and communication complexity of the protocol in \cite{Li12b}.

preprint2012arXiv

Non-Malleable Extractors, Two-Source Extractors and Privacy Amplification

Dodis and Wichs introduced the notion of a non-malleable extractor to study the problem of privacy amplification with an active adversary. A non-malleable extractor is a much stronger version of a strong extractor. Previously, there are only two known constructions of non-malleable extractors. Both constructions only work for (n, k)-sources with k>n/2. Interestingly, both constructions are also two-source extractors. In this paper, we present a strong connection between non-malleable extractors and two-source extractors. The first part of the connection shows that non-malleable extractors can be used to construct two-source extractors. With appropriate parameters the resulted two-source extractor beats the best known construction of two-source extractors. This partially explains why previous constructions of non-malleable extractors only work for sources with entropy rate >1/2, and why explicit non-malleable extractors for small min-entropy may be hard to get. The second part of the connection shows that certain two-source extractors can be used to construct non-malleable extractors. Using this connection, we obtain the first construction of non-malleable extractors for k < n/2. Specifically, we give an unconditional construction for min-entropy k=(1/2-δ)n for some constant δ>0, and a conditional (semi-explicit) construction that can potentially achieve k=αn for any constant α>0. Finally, despite the lack of explicit non-malleable extractors for arbitrarily linear entropy, we give the first 2-round privacy amplification protocol with asymptotically optimal entropy loss and communication complexity for (n, k) sources with k=αn for any constant α>0. This dramatically improves previous results and answers an open problem in \cite{DLWZ11}.

preprint2012arXiv

Nuclearity of semigroup C*-algebras and the connection to amenability

We study C*-algebras associated with subsemigroups of groups. For a large class of such semigroups including positive cones in quasi-lattice ordered groups and left Ore semigroups, we describe the corresponding semigroup C*-algebras as C*-algebras of inverse semigroups, groupoid C*-algebras and full corners in associated group crossed products. These descriptions allow us to characterize nuclearity of semigroup C*-algebras in terms of faithfulness of left regular representations and amenability of group actions. Moreover, we also determine when boundary quotients of semigroup C*-algebras are UCT Kirchberg algebras. This leads to a unified approach to Cuntz algebras and ring C*-algebras.

preprint2012arXiv

On K-theoretic invariants of semigroup C*-algebras attached to number fields

We show that semigroup C*-algebras attached to ax+b-semigroups over rings of integers determine number fields up to arithmetic equivalence, under the assumption that the number fields have the same number of roots of unity. For finite Galois extensions, this means that the semigroup C*-algebras are isomorphic if and only if the number fields are isomorphic.

preprint2012arXiv

On the dimension of splines spaces over T-meshes with smoothing cofactor-conformality method

The present paper provides a general formula for the dimension of spline space over T-meshes using smoothing cofactor-conformality method. And we introduce a new notion, Diagonalizable T-mesh, over which the dimension formula is only associated with the topological information of the T-mesh. A necessary and sufficient condition for characterization a diagonalizable T-mesh is also provided. Using this technique, we find that the dimension is possible instable under the condition of [1] and we also provide a new correction theorem.

preprint2012arXiv

On the K-theory of crossed products by automorphic semigroup actions

Let P be a semigroup that admits an embedding into a group G. Assume that the embedding satisfies a certain Toeplitz condition and that the Baum-Connes conjecture holds for G. We prove a formula describing the K- theory of the reduced crossed product A \rtimesα,r P by any automorphic action of P. This formula is obtained as a consequence of a result on the K-theory of crossed products for special actions of G on totally disconnected spaces. We apply our result to various examples including left Ore semigroups and quasi-lattice ordered semigroups. We also use the results to show that for certain semigroups P, including the ax + b-semigroup for a Dedekind domain R, the K-theory of the left and right regular semigroup C*-algebras of P coincide, although the structure of these algebras can be very different.

preprint2012arXiv

Plancherel_Rotach Asymptotics for q-Orthogonal Polynomials

We establish the Plancherel-Rotach-type asymptotics around the largest zero (the soft edge asymptotics) for some classes of polynomials satisfying three-term recurrence relations with exponentially increasing coefficients. As special cases, our results include this type of asymptotics for q^{-1}-Hermite polynomials of Askey, Ismail and Masson, q-Laguerre polynomials, and the Stieltjes-Wigert polynomials. We also introduce a one parameter family of solutions to the q-difference equation of the Ramanujan function.

preprint2012arXiv

Remarks on decays of h_b(2P)

We consider hadronic transitions from the $h_b(2P)$ bottomonium resonance to lower states of bottomonium with emission of either $ω$ meson, or two pions, or $η$ meson. For the former two transitions the branching ratios are related to similar transitions from $χ_{b1}(2P)$ and the recently measured by Belle fractions of the radiative decays of $h_b(2P)$. We argue that the fraction of the $h_b(2P)$ total decay rate remaining for the annihilation rate is on the verge of contradiction with the `parton' picture of bottomonium annihilation resulting in similarity between the decays of $h_b(1,2P)$ and $χ_{b1}(1,2P)$. The contradiction gets even stronger, if the transition $h_b(2P) \to Υ(1S) \, η$ has branching fraction of a few percent or more. We argue that, although quite uncertain, the latter fraction may indeed be that significant.

preprint2012arXiv

Semigroup C*-algebras and amenability of semigroups

We construct reduced and full semigroup C*-algebras for left cancellative semigroups. Our new construction covers particular cases already considered by A. Nica and also Toeplitz algebras attached to rings of integers in number fields due to J. Cuntz. Moreover, we show how (left) amenability of semigroups can be expressed in terms of these semigroup C*-algebras in analogy to the group case.

preprint2012arXiv

Symmetry, causal structure and superluminality in Finsler spacetime

The superluminal behaviors of neutrinos were reported by the OPERA collaboration recently. It was also noticed by Cohen and Glashow that, in standard quantum field theory, the superluminal neutrinos would lose their energy via the Cherenkov-like process rapidly. Finslerian special relativity may provide a framework to cooperate with the OPERA neutrino superluminality without Cherenkov-like process. We present clearly the symmetry, causal structure and superluminality in Finsler spacetime. The principle of relativity and the causal law are preserved. The energy and momentum are well defined and conserved in Finslerian special relativity. The Cherenkov-like process is proved to be forbidden kinematically and the superluminal neutrinos would not lose energy in their distant propagations from CERN to the Gran Sasso Laboratory. The energy dependence of neutrino superluminality is studied based on the reported data of the OPERA collaboration as well as other groups.

preprint2012arXiv

The 2-adic ring $C^*$-algebra of the integers and its representations

We study the 2-adic version of the ring $C^*$-algebra of the integers. First, we work out the precise relation between the Cuntz algebra $\cO_2$ and our 2-adic ring $C^*$-algebra in terms of representations. Secondly, we prove a 2-adic duality theorem identifying the crossed product arising from 2-adic affine transformations on the 2-adic numbers with the analogous crossed product algebra over the real numbers. And finally, as an outcome of this duality result, we construct an explicit imprimitivity bimodule and prove that it transports one canonical representation into the other.

preprint2012arXiv

The spacetime structure of MOND with Tully-Fisher relation and Lorentz invariance violation

It is believed that the modification of Newtonian dynamics (MOND) is possible alternate for dark matter hypothesis. Although Bekenstein's TeVeS supplies a relativistic version of MOND, one may still wish a more concise covariant formulism of MOND. In this paper, within covariant geometrical framwork, we present another version of MOND. We show the spacetime structure of MOND with properties of Tully-Fisher relation and Lorentz invariance violation.

preprint2012arXiv

Z_b(10610) and Z_b(10650) decays to bottomonium plus pion

We consider the transitions from the $Z_b(10610)$ and $Z_b(10650)$ resonances to the states of bottomonium with emission of a pion. The $Z_b$ resonances are viewed as `molecular' objects of a large spatial size made of heavy $B^{(*)}$ mesons, while the states of bottomonium are considered to be compact, so that an application of the leading (dipole) term in the QCD multipole expansion is assumed to be justified. In this way we calculate the ratios of the decay rates to the final states $Υ(nS)\, π$ with $n=1,2,3$, and the ratio of the decay rates to $h_b(kP)\, π$ with $k=1$ and 2. We find our estimates in a reasonable agreement with recent experimental data.

preprint2011arXiv

Apologizing Comment on `Quantum Quasi-Cyclic Low-Density Parity-Check codes$\,$"

In our recent paper entitled "Quantum Quasi-Cyclic Low-Density Parity-Check codes" [ICIC 2009. LNCS 5754], it was claimed that some new quantum codes can be constructed via the CSS encoding/decoding approach with various lengths and rates. However, the further investigation shows that the proposed construction may steal some ideas from the paper entitled "Quantum Quasi-Cyclic LDPC codes" [quant-ph/0701020v2]. We feel that the apologizing point of the original protocol is that some results are almost similar to that of construction methods with algebraic combinatorics although we suggest the different approach for improving them. Also, there is a weak point of the original coding approach while considering the application of codes in imperfect channels.

preprint2011arXiv

Atomic Fermi gas at the unitary limit by quantum Monte Carlo methods: Effects of the interaction range

We calculate the ground-state properties of unpolarized two-component Fermi gas by the diffusion quantum Monte Carlo (DMC) methods. Using an extrapolation to the zero effective range of the attractive two-particle interaction, we find $E/E_{\rm free}$ to be 0.212(2), 0.407(2), 0.409(3) and 0.398(3) for 4, 14, 38 and 66 atoms, respectively. Our results indicate that the dependence of the total energy on the effective range is sizable and the extrapolation is therefore quite important. In order to test the quality of nodal surfaces and to estimate the impact of the fixed-node approximation we perform released-node DMC calculations for 4 and 14 atoms. Analysis of the released-node and the fixed-node results suggests that the main sources of the fixed-node errors are long-range correlations which are difficult to sample in the released-node approaches due to the fast growth of the bosonic noise. Besides energies, we evaluate the two-body density matrix and the condensate fraction. We find that the condensate fraction for the 66 atom system converges to 0.56(1) after the extrapolation to the zero interaction range.

preprint2011arXiv

Coupling of actin hydrolysis and polymerization: Reduced description with two nucleotide states

The polymerization of actin filaments is coupled to the hydrolysis of adenosine triphosphate (ATP), which involves both the cleavage of ATP and the release of inorganic phosphate. We describe hydrolysis by a reduced two-state model with a cooperative cleavage mechanism, where the cleavage rate depends on the state of the neighboring actin protomer in a filament. We obtain theoretical predictions of experimentally accessible steady state quantities such as the size of the ATP-actin cap, the size distribution of ATP-actin islands, and the cleavage flux for cooperative cleavage mechanisms.

preprint2011arXiv

Fine structure constant variation or spacetime anisotropy?

Recent observations on the quasar absorption spectra supply evidence for the variation of the fine structure constant $α$. In this paper, we propose another interpretation of the observational data on the quasar absorption spectra: a scenario with spacetime inhomogeneity and anisotropy. Maybe the spacetime is characterized by the Finsler geometry instead of the Riemann one. The Finsler geometry admits fewer symmetries than the Riemann geometry does. We investigate the Finslerian geodesic equations in the Randers spacetime (a special Finsler spacetime). It is found that the cosmological redshift in this spacetime deviates from the one in general relativity. The modification term to the redshift could be generally revealed as a monopole plus dipole function of spacetime locations and directions. We suggest that this modification corresponds to the spatial monopole and dipole of $α$ variation in the quasar absorption spectra.

preprint2011arXiv

Gravitational deflection of light in Rindler-type potential as a possible resolution to the observations of Bullet Cluster 1E0657-558

The surface density $Σ$-map and the convergence $κ$-map of Bullet Cluster 1E0657-558 show that the center of baryonic matters separates from the center of gravitational force, and the distribution of gravitational force do not possess spherical symmetry. This hints that a modified gravity with difference to Newtonian inverse-square law at large scale, and less symmetry is worth investigating. In this paper, we study the dynamics in Randers-Finsler spacetime. The Newtonian limit and gravitational deflection of light in a Rindler-type potential is focused in particular. It is shown that the convergence in Finsler spacetime could account for the observations of Bullet Cluster.

preprint2011arXiv

K-theory for ring C*-algebras attached to function fields with only one infinite place

We study the K-theory of ring C*-algebras associated to rings of integers in global function fields with only one single infinite place. First, we compute the torsion-free part of the K-groups of these ring C*-algebras. Secondly, we show that, under a certain primeness condition, the torsion part of K-theory determines the inertia degrees at infinity of our function fields.

preprint2011arXiv

OPERA superluminal neutrinos and Kinematics in Finsler spacetime

The OPERA collaboration recently reported that muon neutrinos could be superluminal. More recently, Cohen and Glashow pointed that such superluminal neutrinos would be suppressed since they lose their energies rapidly via bremsstrahlung. In this Letter, we propose that Finslerian nature of spacetime could account for the superluminal phenomena of particles. The Finsler spacetime permits the existence of superluminal behavior of particles while the casuality still holds. A new dispersion relation is obtained in a class of Finsler spacetime. It is shown that the superluminal speed is linearly dependent on the energy per unit mass of the particle. We find that such a superluminal speed formula is consistent with data of OPERA, MINOS and Fermilab-1979 neutrino experiments as well as observations on neutrinos from SN1987a.

preprint2011arXiv

Precision benchmark calculations for four particles at unitarity

The unitarity limit describes interacting particles where the range of the interaction is zero and the scattering length is infinite. We present precision benchmark calculations for two-component fermions at unitarity using three different ab initio methods: Hamiltonian lattice formalism using iterated eigenvector methods, Euclidean lattice formalism with auxiliary-field projection Monte Carlo, and continuum diffusion Monte Carlo with fixed and released nodes. We have calculated the ground state energy of the unpolarized four-particle system in a periodic cube as a dimensionless fraction of the ground state energy for the non-interacting system. We obtain values 0.211(2) and 0.210(2) using two different Hamiltonian lattice representations, 0.206(9) using Euclidean lattice, and an upper bound of 0.212(2) from fixed-node diffusion Monte Carlo. Released-node calculations starting from the fixed-node result yield a decrease of less than 0.002 over a propagation of 0.4/E_F in Euclidean time, where E_F is the Fermi energy. We find good agreement among all three ab initio methods.

preprint2011arXiv

Privacy Amplification and Non-Malleable Extractors Via Character Sums

In studying how to communicate over a public channel with an active adversary, Dodis and Wichs introduced the notion of a non-malleable extractor. A non-malleable extractor dramatically strengthens the notion of a strong extractor. A strong extractor takes two inputs, a weakly-random x and a uniformly random seed y, and outputs a string which appears uniform, even given y. For a non-malleable extractor nmExt, the output nmExt(x,y) should appear uniform given y as well as nmExt(x,A(y)), where A is an arbitrary function with A(y) not equal to y. We show that an extractor introduced by Chor and Goldreich is non-malleable when the entropy rate is above half. It outputs a linear number of bits when the entropy rate is 1/2 + alpha, for any alpha>0. Previously, no nontrivial parameters were known for any non-malleable extractor. To achieve a polynomial running time when outputting many bits, we rely on a widely-believed conjecture about the distribution of prime numbers in arithmetic progressions. Our analysis involves a character sum estimate, which may be of independent interest. Using our non-malleable extractor, we obtain protocols for "privacy amplification": key agreement between two parties who share a weakly-random secret. Our protocols work in the presence of an active adversary with unlimited computational power, and have asymptotically optimal entropy loss. When the secret has entropy rate greater than 1/2, the protocol follows from a result of Dodis and Wichs, and takes two rounds. When the secret has entropy rate delta for any constant delta>0, our new protocol takes a constant (polynomial in 1/delta) number of rounds. Our protocols run in polynomial time under the above well-known conjecture about primes.

preprint2011arXiv

Unification of Dark Matter and Dark Energy in a Modified Entropic Force Model

In Verlinde's entropic force scenario of gravity, Newton's laws and Einstein equations can be obtained from the first pinciples and general assumptions. However, the equipartition law of energy is invalid at very low temperatures. We show clearly that the threshold of the equipartition law of energy is related with horizon of the universe. Thus, a one-dimension Debye (ODD) model in the direction of radius of the modified entropic force (MEF) maybe suitable in description of the accelerated expanding universe. We present a Friedmann cosmic dynamical model in the ODD-MEF framework. We examine carefully constraints on the ODD-MEF model from the Union2 compilation of the Supernova Cosmology Project (SCP) collaboration, the data from the observation of the large-scale structure (LSS) and the cosmic microwave background (CMB), i.e. SNe Ia+LSS+CMB. The combined numerical analysis gives the best-fit value of the model parameters $ζ\simeq10^{-9}$ and $Ω_{m0}=0.224$, with $χ_{min}^2=591.156$. The corresponding age of the universe agrees with the result of D. Spergel {\it et al.}\cite{Spergel2003} at 95% confidence level. The numerical result also yields an accelerated expanding universe without invoking any kind of dark energy. Taking $ζ(\equiv 2πω_D/H_0)$ as a running parameter associated with the structure scale $r$, we obtain a possible unified scenario of the asymptotic flatness of the radial velocity dispersion of spiral galaxies, the accelerated expanding universe and the Pioneer 10/11 anomaly in the entropic force framework of Verlinde.

preprint2010arXiv

A matter dominated navigation Universe in accordance with the Type Ia supernova data

We investigate a matter dominated navigation cosmological model. The influence of a possible drift (wind) in the navigation cosmological model makes the spacetime geometry change from Riemannian to Finslerian. The evolution of the Finslerian Universe is governed by the same gravitational field equation with the familiar Friedmann-Robertson-Walker one. However, the change of space geometry from Riemannian to Finslerian supplies us a new relation between the luminosity distant and redshift. It is shown that the Hubble diagram based on this new relation could account for the observations on distant Type Ia supernovae.

preprint2010arXiv

A possible scenario of the Pioneer anomaly in the framework of Finsler geometry

The weak field approximation of geodesics in Randers-Finsler space is investigated. We show that a Finsler structure of Randers space corresponds to the constant and sunward anomalous acceleration demonstrated by the Pioneer 10 and 11 data. The additional term in the geodesic equation acts as "electric force", which provides the anomalous acceleration.

preprint2010arXiv

Debye entropic force and modified Newtonian dynamics

Verlinde has suggested that the gravity has an entropic origin, and a gravitational system could be regarded as a thermodynamical system. It is well-known that the equipartition law of energy is invalid at very low temperature. Therefore, entropic force should be modified while the temperature of the holographic screen is very low. It is shown that the modified entropic force is proportional to the square of the acceleration, while the temperature of the holographic screen is much lower than the Debye temperature $T_D$. The modified entropic force returns to the Newton's law of gravitation while the temperature of the holographic screen is much higher than the Debye temperature. The modified entropic force is connected with modified Newtonian dynamics (MOND). The constant $a_0$ involved in MOND is linear in the Debye frequency $ω_D$, which can be regarded as the largest frequency of the bits in screen. We find that there do have a strong connection between MOND and cosmology in the framework of Verlinde's entropic force, if the holographic screen is taken to be bound of the Universe. The Debye frequency is linear in the Hubble constant $H_0$.

preprint2010arXiv

Dilations of semigroup crossed products as crossed products of dilations

Laca constructed a minimal automorphic dilation for every semigroup dynamical system arising from an action of an Ore semigroup by injective endomorphisms of a unital $C^*$-algebra. Here we show that the semigroup crossed product with its action by inner endomorphisms given by the implementing isometries has as minimal automorphic dilation the group crossed product of the original dilation. Applications include recent examples studied by Cuntz and the second named author.

preprint2010arXiv

Isometric group of $(α,β)$-type Finsler space and the symmetry of Very Special Relativity

The Killing equation for a general Finsler space is set up. It is showed that the Killing equation of $(α,β)$ space can be divided into two parts. One is the same with Killing equation of a Riemannian metric, another equation can be regarded as a constraint. The solutions of Killing equations present explicitly the isometric symmetry of Finsler space. We find that the isometric group of a special case of $(α,β)$ space is the same with the symmetry of Very Special Relativity (VSR). The Killing vectors of Finsler-Funk space are given. Unlike Riemannian constant curvature space, the 4 dimensional Funk space with constant curvature just have 6 independent Killing vectors.

preprint2010arXiv

On the Problem of Local Randomness in Privacy Amplification with an Active Adversary

We study the problem of privacy amplification with an active adversary in the information theoretic setting. In this setting, two parties Alice and Bob start out with a shared $n$-bit weak random string $W$, and try to agree on a secret random key $R$ over a public channel fully controlled by an active and unbounded adversary. Typical assumptions are that these two parties have access to local private uniform random bits. In this paper we seek to minimize the requirements on the local randomness used by the two parties. We make two improvements over previous results. First, we reduce the number of random bits needed for each party to $Θ(\ell+\log n)$, where $\ell$ is the security parameter, as long as $W$ has min-entropy $n^{Ω(1)}$. Previously, the best known result needs to use $Θ((\ell+\log n)\log n)$ bits. Our result is also asymptotically optimal. Second, we generalize the problem to the case where the two parties only have local weak random sources instead of truly uniform random bits. We show that when each party has a local weak random source with min-entropy $> n/2$, there is an efficient privacy amplification protocol that works nearly as good as if the two parties have access to local uniform random bits. Next, in the case where each party only has a weak random source with arbitrarily linear min-entropy, we give an efficient privacy amplification protocol where we can achieve security parameter up to $Ω(\log k)$. Our results give the first protocols that achieve privacy amplification when each party only has access to a local weak random source.

preprint2010arXiv

Possible quantum gravity effects on the gravitational deflection of light

We investigate possible quantum gravity (QG) effects on the gravitational deflection of light. Two forms of deformation of the Schwarzschild spacetime are proposed. The first ansatz is a given Finslerian line element, it could be regarded as a weak QG effect on the Schwarzschild spacetime. Starting from this ansatz, we deduce the deflection angle of the light ray which passes a weak gravitational source. The second ansatz could be regarded as a strong QG effect on the Schwarzschild spacetime. The deflection angle of the light ray which passes a weak gravitational source is deduced in this Riemannian spacetime. This QG effect may distinguish the mixed light rays in the absence of gravitational source by a "spectroscope" (the gravitational source). The solutions of gravitational field equation in this Riemannian spacetime indicate that the QG effect could be regarded as the vacuum energy and the energy density of vacuum is related to the spacetime deformation parameter.

preprint2010arXiv

Reply to Sergiu I. Vacaru's "Critical remarks on Finsler modifications of gravity and cosmology by Zhe Chang and Xin Li"

This is our reply to "Critical remarks on Finslerian modifications of gravity and cosmology by Zhe Chang and Xin Li", Sergiu I. Vacaru, Phys. Lett. B 690 (2010) 224. It is pointed out that the Finslerian modifications of gravity and cosmology (Zhe Chang and Xin Li, Phys. Lett. B 676 (2009) 173; {\it ibid} 668 (2008) 453) is a suggestion on the generalization of Einstein's gravity and cosmology, but not a proof for theorems in geometry. False or true of the theory should be tested by experiments or observations. We show that the arguments of Sergiu I. Vacaru were based a wrong logic. A personal claim can not be used to prove any other theory be wrong. To get the claim: {\it "we may construct more "standard" physical Finsler classical/quantum gravity theories for metric compatible connections like the Cartan d-connection"}, Sergiu I. Vacaru should complete a consistent presentation at least. We suggest Sergiu I. Vacaru to make some predictions on gravity and cosmoligy using his {\it "standard" physical Finsler classical/quantum gravity theories} as we did, and compare them with astronomical observations. By the way, we should say that it is still really far from a theory of quantum gravity.

preprint2010arXiv

Symmetry and special relativity in Finsler spacetime with constant curvature

Within the framework of projective geometry, we investigate kinematics and symmetry in $(α,β)$ spacetime-one special types of Finsler spacetime. The projectively flat $(α,β)$ spacetime with constant flag curvature is divided into four types. The symmetry in type A-Riemann spacetime with constant sectional curvature is just the one in de Sitter special relativity. The symmetry in type B-locally Minkowski spacetime is just the one in very special relativity. It is found that type C-Funk spacetime and type D-scaled Berwald's metric spacetime both possess the Lorentz group as its isometric group. The geodesic equation, algebra and dispersion relation in the $(α,β)$ spacetime are given. The corresponding invariant special relativity in the four types of $(α,β)$ spacetime contain two parameters-the speed of light and a geometrical parameter which may relate to the new physical scale. They all reduce to Einstein's special relativity while the geometrical parameter vanishes.

preprint2008arXiv

Ultra-high energy cosmic rays threshold in Randers-Finsler space

Kinematics in Finsler space is used to study the propagation of ultra high energy cosmic rays particles through the cosmic microwave background radiation. We find that the GZK threshold is lifted dramatically in Randers-Finsler space. A tiny deformation of spacetime from Minkowskian to Finslerian allows more ultra-high energy cosmic rays particles arrive at the earth. It is suggested that the lower bound of particle mass is related with the negative second invariant speed in Randers-Finsler space.

preprint2007arXiv

Hadron Scattering in an Asymmetric Box

We propose to study hadron-hadron scattering using lattice QCD in an asymmetric box which allows one to access more non-degenerate low-momentum modes for a given volume. The conventional Lüscher's formula applicable in a symmetric box is modified accordingly. To illustrate the feasibility of this approach, pion-pion elastic scattering phase shifts in the I=2, J=0 channel are calculated within quenched approximation using improved gauge and Wilson fermion actions on anisotropic lattices in an asymmetric box. After the chiral and continuum extrapolation, we find that our quenched results for the scattering phase shifts in this channel are consistent with the experimental data when the three-momentum of the pion is below 300MeV. Agreement is also found when compared with previous theoretical results from lattice and other means. Moreover, with the usage of asymmetric volume, we are able to compute the scattering phases in the low-momentum range (pion three momentum less than about 350MeV in the center of mass frame) for over a dozen values of the pion three-momenta, much more than using the conventional symmetric box with comparable volume.

preprint2007arXiv

Modeling Interconnect Variability Using Efficient Parametric Model Order Reduction

Assessing IC manufacturing process fluctuations and their impacts on IC interconnect performance has become unavoidable for modern DSM designs. However, the construction of parametric interconnect models is often hampered by the rapid increase in computational cost and model complexity. In this paper we present an efficient yet accurate parametric model order reduction algorithm for addressing the variability of IC interconnect performance. The efficiency of the approach lies in a novel combination of low-rank matrix approximation and multi-parameter moment matching. The complexity of the proposed parametric model order reduction is as low as that of a standard Krylov subspace method when applied to a nominal system. Under the projection-based framework, our algorithm also preserves the passivity of the resulting parametric models.

preprint2006arXiv

Massive Domain Wall Fermions on Four-dimensional Anisotropic Lattices

We formulate the massive domain wall fermions on anisotropic lattices. For the massive domain wall fermion, we find that the dispersion relation assumes the usual form in the low momentum region when the bare parameters are properly tuned. The quark self-energy and the quark field renormalization constants are calculated to one-loop in bare lattice perturbation theory. For light domain wall fermions, we verified that the chiral mode is stable against quantum fluctuations on anisotropic lattices. This calculation serves as a guidance for the tuning of the parameters in the quark action in future numerical simulations.

preprint2006arXiv

Massive Overlap Fermions on Anisotropic Lattices

We formulate the massive overlap fermions on anisotropic lattices. We find that the dispersion relation for the overlap fermion resembles the continuum form in the low-momentum region once the bare parameters are properly tuned. The quark self-energy and the quark field renormalization constants are calculated to one-loop in bare lattice perturbation theory. We argue that massive domain wall quarks might be helpful in lattice QCD studies on heavy-light hadron spectroscopy.

preprint2006arXiv

Static Quark Potential and the Renormalized Anisotropy on Tadpole Improved Anisotropic Lattices

Static quark potential is studied using a tadpole improved gauge lattice action. The scale is set using the potential for a wide range of bare parameters. The renormalized anisotropy of the lattice is also measured.

preprint2005arXiv

A Numerical Study of Improved Quark Actions on Anisotropic Lattices

Tadpole improved Wilson quark actions with clover terms on anisotropic lattices are studied numerically. Using asymmetric lattice volumes, the pseudo-scalar meson dispersion relations are measured for 8 lowest lattice momentum modes with quark mass values ranging from the strange to the charm quark with various values of the gauge coupling $β$ and 3 different values of the bare speed of light parameter $ν$. These results can be utilized to extrapolate or interpolate to obtain the optimal value for the bare speed of light parameter $ν_{opt}(m)$ at a given gauge coupling for all bare quark mass values $m$. In particular, the optimal values of $ν$ at the physical strange and charm quark mass are given for various gauge couplings. The lattice action with these optimized parameters can then be used to study physical properties of hadrons involving either light or heavy quarks.

preprint2005arXiv

Enhanced transmission of optically thick metallic films at infrared wavelengths

For an optically thick metallic film, the transmission for both s- and p-polarized waves is extremely low. If the metallic film is coated on both sides with a finite dielectric layer, light transmission for $p$-polarized waves can be enhanced considerably. This enhancement is not related to surface plasmon-polaritions. Instead, it is due to the interplay between Fabry-Perot interference in the coated dielectric layer and the existence of the Brewster angle at the dielectric/metallic interface. It is shown that the coated metallic films can act as excellent polarizers at infrared wavelengths.

preprint2004arXiv

The similarity metric

A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new ``normalized information distance'', based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the {\em similarity metric}. This theory forms the foundation for a new practical tool. To evidence generality and robustness we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.

Xin Li

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272 published item(s)

Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding

Observation of the $γ$-ray Emission from W43 with LHAASO

Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

Ultrahigh-Energy Gamma-ray Emission Associated with Black Hole-Jet Systems

Coordinating Multiple Intelligent Reflecting Surfaces without Channel Information

Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment

Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security

A discrete-module-finite-element hydroelasticity method in analyzing dynamic response of floating flexible structures

A Logarithm Depth Quantum Converter: From One-hot Encoding to Binary Encoding

A multi-domain virtual network embedding algorithm with delay prediction

A Survey on Applications of Digital Human Avatars toward Virtual Co-presence

Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs

Aggregate effects of advertising decisions: a complex systems look at search engine advertising via an experimental study

AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results

An Adaptive Rank Continuation Algorithm for General Weighted Low-rank Recovery

Asymmetry in the number of L4 and L5 Jupiter Trojans driven by jumping Jupiter

C*-envelopes for operator algebras with a coaction and co-universal C*-algebras for product systems

Context-aware Visual Tracking with Joint Meta-updating

DirecFormer: A Directed Attention in Transformer Approach to Robust Action Recognition

Domain-knowledge-aided machine learning method for properties prediction of soft magnetic metallic glasses

DR-GAN: Distribution Regularization for Text-to-Image Generation

Efficient Linear and Affine Codes for Correcting Insertions/Deletions

Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach

Global Mapping of Gene/Protein Interactions in PubMed Abstracts: A Framework and an Experiment with P53 Interactions

Graph-based Facial Affect Analysis: A Review

Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation

Improved Decoding of Expander Codes

Intrinsic new properties of a quantum spin liquid

Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression

Learning Optical Flow with Adaptive Graph Reasoning

Left regular representations of Garside categories I. C*-algebras and groupoids

Low-Degree Polynomials Extract from Local Sources

Low-Rank Phase Retrieval with Structured Tensor Models

Machine learning prediction for mean motion resonance behaviour -- The planar case

Mechanical feedback controls the emergence of dynamical memory in growing tissue monolayers

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER

MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification

Model Attribution of Face-swap Deepfake Videos

Multitude of exceptional points in van der Waals magnets

Neural Collaborative Graph Machines for Table Structure Recognition

NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

Observations of pores and surrounding regions with CO 4.66 μm lines by BBSO/CYRA

On Coupled Dirac Systems under Boundary Condition

Relational Representation Learning in Visually-Rich Documents

RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment

Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System

Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Search for the correlations between host properties and ${\rm DM_{host}}$ of fast radio bursts: constraints on the baryon mass fraction in IGM

Self-Supervised Tracking via Target-Aware Data Synthesis

SimSR: Simple Distance-based State Representation for Deep Reinforcement Learning

Source-free Unsupervised Domain Adaptation for Blind Image Quality Assessment

Spin interactions and topological magnonics in chromium trihalide CrClBrI

Stroke-Based Autoencoders: Self-Supervised Learners for Efficient Zero-Shot Chinese Character Recognition

StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment

SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training

The eigenvectors-eigenvalues identity and Sun's conjectures on determinants and permanents

The HI Gas Disk Thickness of the Ultra-diffuse Galaxy AGC 242019

Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence

Unsupervised Learning of Accurate Siamese Tracking

Vector-quantized Image Modeling with Improved VQGAN

WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning

Analysis-suitable unstructured T-splines: Multiple extraordinary points per face

Boundary quotient C*-algebras of semigroups

Configuring Intelligent Reflecting Surface with Performance Guarantees: Optimal Beamforming

DeepReduce: A Sparse-tensor Communication Framework for Distributed Deep Learning

Extreme Suppression of Antiferromagnetic Order and Critical Scaling in a Two-Dimensional Random Quantum Magnet

Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution

Leveraging Planar Regularities for Point Line Visual-Inertial Odometry

Machine learning prediction of magnetic properties of Fe-based metallic glasses considering local structures

Saxl Conjecture for triple hooks

5G Technologies Based Remote E-Health: Architecture, Applications, and Solutions

C-envelopes for operator algebras with a coaction and co-universal C-algebras for product systems

Constructing Menger manifold C-diagonals in classifiable C-algebras