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Yue Wang

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Robotics Machine Learning Computer Vision Artificial Intelligence cond-mat.mtrl-sci math.AP Computation and Language Information Theory math.IT eess.SP Genomics Networking and Internet Architecture astro-ph.GA astro-ph.SR astro-ph.EP cond-mat.supr-con Cryptography and Security Distributed, Parallel, and Cluster Computing math.CO Quantitative Methods astro-ph.IM Cell Behavior cond-mat.str-el eess.AS Molecular Networks Multimedia Sound Applications Biological Physics Computational Engineering, Finance, and Science cond-mat.stat-mech Data Structures and Algorithms Databases eess.IV Emerging Technologies Methodology Neural and Evolutionary Computing Performance physics.app-ph physics.comp-ph physics.med-ph physics.plasm-ph q-fin.ST Computation cond-mat.mes-hall Digital Libraries Discrete Mathematics gr-qc Graphics Hardware Architecture Human-Computer Interaction Information Retrieval math.DG math.GM math.NA math.PR physics.chem-ph physics.class-ph physics.flu-dyn physics.ins-det physics.space-ph Populations and Evolution quant-ph Social and Information Networks Software Engineering Tissues and Organs

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preprint2026arXiv

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depends not only on language reasoning, but also on the ability to perceive, interpret, and act over heterogeneous contexts such as images, videos, webpages, documents, GUIs. GLM-5V-Turbo is built around this objective: multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution, rather than as an auxiliary interface to a language model. This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments lead to strong performance in multimodal coding, visual tool use, and framework-based agentic tasks, while preserving competitive text-only coding capability. More importantly, our development process offers practical insights for building multimodal agents, highlighting the central role of multimodal perception, hierarchical optimization, and reliable end-to-end verification.

preprint2026arXiv

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training

Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate unnecessarily long reasoning traces. Existing methods mainly rely on length penalties or early-exit strategies; however, the former may degrade accuracy and induce underthinking, whereas the latter assumes that substantial portions of reasoning traces can be safely truncated. To obtain a compression signal without these limitations, we revisit the training dynamics of existing compression methods. We observe that the length--accuracy correlation is initially negative but continually increases during compression, indicating that shorter responses are initially more likely to be correct but gradually lose this property as the policy moves toward underthinking. Based on this observation, we formalize overthinking: a negative correlation indicates an overthinking regime, while a positive one indicates underthinking. When overthinking, the shortest correct responses are shorter than the group-average response length in expectation, making them natural compression targets already present in on-policy rollouts. We therefore propose \emph{Implicit Compression Regularization} (ICR), an on-policy regularization method whose compression signal comes from a virtual shorter distribution induced by the shortest correct responses in rollout groups, guiding the policy toward concise yet correct trajectories. Training dynamics show that ICR maintains a better length--accuracy correlation during compression, indicating that short responses remain better aligned with correctness instead of drifting toward underthinking. Experiments on three reasoning backbones and multiple mathematical and knowledge-intensive benchmarks show that ICR consistently shortens responses while preserving or improving accuracy, achieving a stronger accuracy--length Pareto frontier.

preprint2025arXiv

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments

Among existing online mobile-use benchmarks, AndroidWorld has emerged as the dominant benchmark due to its reproducible environment and deterministic evaluation; however, recent agents achieving over 90% success rates indicate its saturation and motivate the need for a more challenging benchmark. In addition, its environment lacks key application categories, such as e-commerce and enterprise communication, and does not reflect realistic mobile-use scenarios characterized by vague user instructions and hybrid tool usage. We introduce MobileWorld, a substantially more challenging benchmark designed to reflect real-world usage through 201 tasks across 20 applications. MobileWorld derives its difficulty from an emphasis on long-horizon, cross-application workflows, requiring nearly twice as many completion steps on average (27.8 vs. 14.3) and featuring a significantly higher proportion of multi-app tasks (62.2% vs. 9.5%) than AndroidWorld. To overcome the limitations of existing environments, MobileWorld achieves a balance between production-grade utility and reproducible evaluation by utilizing open-source alternatives to industry standards (e.g., Mattermost for Slack). This approach enables a fully observable and controlled environment through source code modification and direct backend database access for precise verification. MobileWorld also introduces novel task categories, including agent-user interaction and Model Context Protocol (MCP)-augmented tasks, for evaluating agents in user-aware, hybrid-tool scenarios. To facilitate evaluation, we develop a planner-executor agentic framework with extended action spaces to support user interactions and MCP calls. Our results reveal a sharp performance drop compared to AndroidWorld, with the best agentic framework and end-to-end model achieving 51.7% and 20.9% success rates, respectively, highlighting ample headroom for future research.

preprint2025arXiv

PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies

A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically challenging due to the stochasticity, reproducibility, and time-consuming nature of real-world rollouts. This challenge is exacerbated for recent generalist policies, which has to be evaluated across a wide variety of scenes and tasks. Evaluation in simulation offers a scalable complement to real world evaluations, but the visual and physical domain gap between existing simulation benchmarks and the real world has made them an unreliable signal for policy improvement. Furthermore, building realistic and diverse simulated environments has traditionally required significant human effort and expertise. To bridge the gap, we introduce Policy Evaluation and Environment Reconstruction in Simulation (PolaRiS), a scalable real-to-sim framework for high-fidelity simulated robot evaluation. PolaRiS utilizes neural reconstruction methods to turn short video scans of real-world scenes into interactive simulation environments. Additionally, we develop a simple simulation data co-training recipe that bridges remaining real-to-sim gaps and enables zero-shot evaluation in unseen simulation environments. Through extensive paired evaluations between simulation and the real world, we demonstrate that PolaRiS evaluations provide a much stronger correlation to real world generalist policy performance than existing simulated benchmarks. Its simplicity also enables rapid creation of diverse simulated environments. As such, this work takes a step towards distributed and democratized evaluation for the next generation of robotic foundation models.

preprint2024arXiv

Specific Emitter Identification Based on Joint Variational Mode Decomposition

Specific emitter identification (SEI) technology is significant in device administration scenarios, such as self-organized networking and spectrum management, owing to its high security. For nonlinear and non-stationary electromagnetic signals, SEI often employs variational modal decomposition (VMD) to decompose the signal in order to effectively characterize the distinct device fingerprint. However, the trade-off of VMD between the robustness to noise and the ability to preserve signal information has not been investigated in the current literature. Moreover, the existing VMD algorithm does not utilize the stability of the intrinsic distortion of emitters within a certain temporal span, consequently constraining its practical applicability in SEI. In this paper, we propose a joint variational modal decomposition (JVMD) algorithm, which is an improved version of VMD by simultaneously implementing modal decomposition on multi-frame signals. The consistency of multi-frame signals in terms of the central frequencies and the inherent modal functions (IMFs) is exploited, which effectively highlights the distinctive characteristics among emitters and reduces noise. Additionally, the complexity of JVMD is analyzed, which is proven to be more computational-friendly than VMD. Simulations of both modal decomposition and SEI that involve real-world datasets are presented to illustrate that when compared with VMD, the JVMD algorithm improves the accuracy of device classification and the robustness towards noise.

preprint2023arXiv

Finding meaningful and workable applied mathematics problems in science

In this short review, I will summarize my research experience in three fields in applied mathematics: mathematical biology, applied probability, and applied discrete mathematics. Specifically, I will show how each project was initiated, and what wrong approaches were applied. Such details are important in learning how to do research, but they cannot be read out from research papers. I wish that students and junior researchers in applied mathematics could learn a lesson from this summary.

preprint2023arXiv

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density.

preprint2023arXiv

Optimal subsampling algorithm for composite quantile regression with distributed data

For massive data stored at multiple machines, we propose a distributed subsampling procedure for the composite quantile regression. By establishing the consistency and asymptotic normality of the composite quantile regression estimator from a general subsampling algorithm, we derive the optimal subsampling probabilities and the optimal allocation sizes under the L-optimality criteria. A two-step algorithm to approximate the optimal subsampling procedure is developed. The proposed methods are illustrated through numerical experiments on simulated and real datasets.

preprint2023arXiv

Super-Resolution Harmonic Retrieval of Non-Circular Signals

This paper proposes a super-resolution harmonic retrieval method for uncorrelated strictly non-circular signals, whose covariance and pseudo-covariance present Toeplitz and Hankel structures, respectively. Accordingly, the augmented covariance matrix constructed by the covariance and pseudo-covariance matrices is not only low rank but also jointly Toeplitz-Hankel structured. To efficiently exploit such a desired structure for high estimation accuracy, we develop a low-rank Toeplitz-Hankel covariance reconstruction (LRTHCR) solution employed over the augmented covariance matrix. Further, we design a fitting error constraint to flexibly implement the LRTHCR algorithm without knowing the noise statistics. In addition, performance analysis is provided for the proposed LRTHCR in practical settings. Simulation results reveal that the LRTHCR outperforms the benchmark methods in terms of lower estimation errors.

preprint2022arXiv

Academic Resource Text Level Multi-label Classification based on Attention

Hierarchical multi-label academic text classification (HMTC) is to assign academic texts into a hierarchically structured labeling system. We propose an attention-based hierarchical multi-label classification algorithm of academic texts (AHMCA) by integrating features such as text, keywords, and hierarchical structure, the academic documents are classified into the most relevant categories. We utilize word2vec and BiLSTM to obtain embedding and latent vector representations of text, keywords, and hierarchies. We use hierarchical attention mechanism to capture the associations between keywords, label hierarchies, and text word vectors to generate hierarchical-specific document embedding vectors to replace the original text embeddings in HMCN-F. The experimental results on the academic text dataset demonstrate the effectiveness of the AHMCA algorithm.

preprint2022arXiv

Accurate Portraits of Scientific Resources and Knowledge Service Components

With the advent of the cloud computing era, the cost of creating, capturing and managing information has gradually decreased. The amount of data in the Internet is also showing explosive growth, and more and more scientific and technological resources are uploaded to the network. Different from news and social media data ubiquitous in the Internet, the main body of scientific and technological resources is composed of academic-style resources or entities such as papers, patents, authors, and research institutions. There is a rich relationship network between resources, from which a large amount of cutting-edge scientific and technological information can be mined. There are a large number of management and classification standards for existing scientific and technological resources, but these standards are difficult to completely cover all entities and associations of scientific and technological resources, and cannot accurately extract important information contained in scientific and technological resources. How to construct a complete and accurate representation of scientific and technological resources from structured and unstructured reports and texts in the network, and how to tap the potential value of scientific and technological resources is an urgent problem. The solution is to construct accurate portraits of scientific and technological resources in combination with knowledge graph related technologies.

preprint2022arXiv

Adaptive Algorithm for Quantum Amplitude Estimation

Quantum amplitude estimation is a key sub-routine of a number of quantum algorithms with various applications. We propose an adaptive algorithm for interval estimation of amplitudes. The quantum part of the algorithm is based only on Grover's algorithm. The key ingredient is the introduction of an adjustment factor, which adjusts the amplitude of good states such that the amplitude after the adjustment, and the original amplitude, can be estimated without ambiguity in the subsequent step. We show with numerical studies that the proposed algorithm uses a similar number of quantum queries to achieve the same level of precision $ε$ compared to state-of-the-art algorithms, but the classical part, i.e., the non-quantum part, has substantially lower computational complexity. We rigorously prove that the number of oracle queries achieves $O(1/ε)$, i.e., a quadratic speedup over classical Monte Carlo sampling, and the computational complexity of the classical part achieves $O(\log(1/ε))$, both up to a double-logarithmic factor.

preprint2022arXiv

Algorithms for determining transposons in gene sequences

Some genes can change their relative locations in a genome. Thus for different individuals of the same species, the orders of genes might be different. Such jumping genes are called transposons. A practical problem is to determine transposons in given gene sequences. Through an intuitive rule, we transform the biological problem of determining transposons into a rigorous mathematical problem of determining the longest common subsequence. Depending on whether the gene sequence is linear (each sequence has a fixed head and tail) or circular (we can choose any gene as the head, and the previous one is the tail), and whether genes have multiple copies, we classify the problem of determining transposons into four scenarios: (1) linear sequences without duplicated genes; (2) circular sequences without duplicated genes; (3) linear sequences with duplicated genes; (4) circular sequences with duplicated genes. With the help of graph theory, we design fast algorithms for different scenarios. We also derive some results that might be of theoretical interests in combinatorics.

preprint2022arXiv

Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch

In multi-robot system (MRS) bounding overwatch, it is crucial to determine which point to choose for overwatch at each step and whether the robots' positions are trustworthy so that the overwatch can be performed effectively. In this paper, we develop a Bayesian optimization based computational trustworthiness model (CTM) for the MRS to select overwatch points. The CTM can provide real-time trustworthiness evaluation for the MRS on the overwatch points by referring to the robots' situational awareness information, such as traversability and line of sight. The evaluation can quantify each robot's trustworthiness in protecting its robot team members during the bounding overwatch. The trustworthiness evaluation can generate a dynamic cost map for each robot in the workspace and help obtain the most trustworthy bounding overwatch path. Our proposed Bayesian based CTM and motion planning can reduce the number of explorations for the workspace in data collection and improve the CTM learning efficiency. It also enables the MRS to deal with the dynamic and uncertain environments for the multi-robot bounding overwatch task. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.

preprint2022arXiv

BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers

As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm. When all edge devices (workers) simultaneously upload their local updates to the parameter server (PS) through commonly shared time-frequency resources, the PS obtains the averaged update only rather than the individual local ones. While such a concurrent transmission and aggregation scheme reduces the latency and communication costs, it unfortunately renders FLOA vulnerable to Byzantine attacks. Aiming at Byzantine-resilient FLOA, this paper starts from analyzing the channel inversion (CI) mechanism that is widely used for power control in FLOA. Our theoretical analysis indicates that although CI can achieve good learning performance in the benign scenarios, it fails to work well with limited defensive capability against Byzantine attacks. Then, we propose a novel scheme called the best effort voting (BEV) power control policy that is integrated with stochastic gradient descent (SGD). Our BEV-SGD enhances the robustness of FLOA to Byzantine attacks, by allowing all the workers to send their local updates at their maximum transmit power. Under worst-case attacks, we derive the expected convergence rates of FLOA with CI and BEV power control policies, respectively. The rate comparison reveals that our BEV-SGD outperforms its counterpart with CI in terms of better convergence behavior, which is verified by experimental simulations.

preprint2022arXiv

Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations. It is challenging to enrich a KG with newly harvested triples while maintaining the quality of the knowledge representation. This paper proposes a system to refine a KG using information harvested from an additional corpus. To this end, we formulate our task as two coupled sub-tasks, namely join event extraction (JEE) and knowledge graph fusion (KGF). We then propose a Collaborative Knowledge Graph Fusion Framework to allow our sub-tasks to mutually assist one another in an alternating manner. More concretely, the explorer carries out the JEE supervised by both the ground-truth annotation and an existing KG provided by the supervisor. The supervisor then evaluates the triples extracted by the explorer and enriches the KG with those that are highly ranked. To implement this evaluation, we further propose a Translated Relation Alignment Scoring Mechanism to align and translate the extracted triples to the prior KG. Experiments verify that this collaboration can both improve the performance of the JEE and the KGF.

preprint2022arXiv

Context-Based MEC Platform for Augmented-Reality Services in 5G Networks

Augmented reality (AR) has drawn great attention in recent years. However, current AR devices have drawbacks, e.g., weak computation ability and large power consumption. To solve the problem, mobile edge computing (MEC) can be introduced as a key technology to offload data and computation from AR devices to MEC servers via 5th Generation Mobile Communication Technology (5G) networks. To this end, a context-based MEC platform for AR services in 5G networks is proposed in this paper. On the platform, MEC is employed as a data processing center while AR devices are simplified as universal input/output devices, which overcomes their limitations and achieves better user experience. Moreover, the proof-of-concept (PoC) hardware prototype of the platform, and two typical use cases providing AR services of navigation and face recognition respectively are implemented to demonstrate the feasibility and effectiveness of the platform. Finally, the performance of the platform is also numerically evaluated, and the results validate the system design and agree well with the design expectations.

preprint2022arXiv

Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations

Navier-Stokes equations are significant partial differential equations that describe the motion of fluids such as liquids and air. Due to the importance of Navier-Stokes equations, the development on efficient numerical schemes is important for both science and engineer. Recently, with the development of AI techniques, several approaches have been designed to integrate deep neural networks in simulating and inferring the fluid dynamics governed by incompressible Navier-Stokes equations, which can accelerate the simulation or inferring process in a mesh-free and differentiable way. In this paper, we point out that the capability of existing deep Navier-Stokes informed methods is limited to handle non-smooth or fractional equations, which are two critical situations in reality. To this end, we propose the \emph{Deep Random Vortex Method} (DRVM), which combines the neural network with a random vortex dynamics system equivalent to the Navier-Stokes equation. Specifically, the random vortex dynamics motivates a Monte Carlo based loss function for training the neural network, which avoids the calculation of derivatives through auto-differentiation. Therefore, DRVM not only can efficiently solve Navier-Stokes equations involving rough path, non-differentiable initial conditions and fractional operators, but also inherits the mesh-free and differentiable benefits of the deep-learning-based solver. We conduct experiments on the Cauchy problem, parametric solver learning, and the inverse problem of both 2-d and 3-d incompressible Navier-Stokes equations. The proposed method achieves accurate results for simulation and inference of Navier-Stokes equations. Especially for the cases that include singular initial conditions, DRVM significantly outperforms existing PINN method.

preprint2022arXiv

Depth-Independent Depth Completion via Least Square Estimation

The depth completion task aims to complete a per-pixel dense depth map from a sparse depth map. In this paper, we propose an efficient least square based depth-independent method to complete the sparse depth map utilizing the RGB image and the sparse depth map in two independent stages. In this way can we decouple the neural network and the sparse depth input, so that when some features of the sparse depth map change, such as the sparsity, our method can still produce a promising result. Moreover, due to the positional encoding and linear procession in our pipeline, we can easily produce a super-resolution dense depth map of high quality. We also test the generalization of our method on different datasets compared to some state-of-the-art algorithms. Experiments on the benchmark show that our method produces competitive performance.

preprint2022arXiv

DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow

Accurate LiDAR-camera extrinsic calibration is a precondition for many multi-sensor systems in mobile robots. Most calibration methods rely on laborious manual operations and calibration targets. While working online, the calibration methods should be able to extract information from the environment to construct the cross-modal data association. Convolutional neural networks (CNNs) have powerful feature extraction ability and have been used for calibration. However, most of the past methods solve the extrinsic as a regression task, without considering the geometric constraints involved. In this paper, we propose a novel end-to-end extrinsic calibration method named DXQ-Net, using a differentiable pose estimation module for generalization. We formulate a probabilistic model for LiDAR-camera calibration flow, yielding a prediction of uncertainty to measure the quality of LiDAR-camera data association. Testing experiments illustrate that our method achieves a competitive with other methods for the translation component and state-of-the-art performance for the rotation component. Generalization experiments illustrate that the generalization performance of our method is significantly better than other deep learning-based methods.

preprint2022arXiv

Dynamic stability for steady Prandtl solutions

By establishing an invariant set (1.11) for the Prandtl equation in Crocco transformation, we prove orbital and asymptotic stability of Blasius-like steady states against Oleinik's monotone solutions.

preprint2022arXiv

Efficient Distance-Optimal Tethered Path Planning in Planar Environments: The Workspace Convexity

The main contribution of this paper is the proof of the convexity of the omni-directional tethered robot workspace (namely, the set of all tether-length-admissible robot configurations), as well as a set of distance-optimal tethered path planning algorithms that leverage the workspace convexity. The workspace is proven to be topologically a simply-connected subset and geometrically a convex subset of the set of all configurations. As a direct result, the tether-length-admissible optimal path between two configurations is proven exactly the untethered collision-free locally shortest path in the homotopy specified by the concatenation of the tether curve of the given configurations, which can be simply constructed by performing an untethered path shortening process in the 2D environment instead of a path searching process in the pre-calculated workspace. The convexity is an intrinsic property to the tethered robot kinematics, thus has universal impacts on all high-level distance-optimal tethered path planning tasks: The most time-consuming workspace pre-calculation (WP) process is replaced with a goal configuration pre-calculation (GCP) process, and the homotopy-aware path searching process is replaced with untethered path shortening processes. Motivated by the workspace convexity, efficient algorithms to solve the following problems are naturally proposed: (a) The optimal tethered reconfiguration (TR) planning problem is solved by a locally untethered path shortening (UPS) process, (b) The classic optimal tethered path (TP) planning problem (from a starting configuration to a goal location whereby the target tether state is not assigned) is solved by a GCP process and $n$ UPS processes, where $n$ is the number of tether-length-admissible configurations that visit the goal location, (c) The optimal tethered motion to visit a sequence of multiple goal locations, referred to as

preprint2022arXiv

Efficient Object Manipulation to an Arbitrary Goal Pose: Learning-based Anytime Prioritized Planning

We focus on the task of object manipulation to an arbitrary goal pose, in which a robot is supposed to pick an assigned object to place at the goal position with a specific orientation. However, limited by the execution space of the manipulator with gripper, one-step picking, moving and releasing might be failed, where a reorientation object pose is required as a transition. In this paper, we propose a learning-driven anytime prioritized search-based solver to find a feasible solution with low path cost in a short time. In our work, the problem is formulated as a hierarchical learning problem, with the high level finding a reorientation object pose, and the low level planning paths between adjacent grasps. We learn an offline-training path cost estimator to predict approximate path planning costs, which serve as pseudo rewards to allow for pre-training the high-level planner without interacting with the simulator. To deal with the problem of distribution mismatch of the cost net and the actual execution cost space, a refined training stage is conducted with simulation interaction. A series of experiments carried out in simulation and real world indicate that our system can achieve better performances in the object manipulation task with less time and less cost.

preprint2022arXiv

Efficient Search of the k Shortest Non-Homotopic Paths by Eliminating Non-k-Optimal Topologies

An efficient algorithm to solve the $k$ shortest non-homotopic path planning ($k$-SNPP) problem in a 2D environment is proposed in this paper. Motivated by accelerating the inefficient exploration of the homotopy-augmented space of the 2D environment, our fundamental idea is to identify the non-$k$-optimal path topologies as early as possible and terminate the pathfinding along them. This is a non-trivial practice because it has to be done at an intermediate state of the path planning process when locally shortest paths have not been fully constructed. In other words, the paths to be compared have not rendezvoused at the goal location, which makes the homotopy theory, modelling the spatial relationship among the paths having the same endpoint, not applicable. This paper is the first work that develops a systematic distance-based topology simplification mechanism to solve the $k$-SNPP task, whose core contribution is to assert the distance-based order of non-homotopic locally shortest paths before constructing them. If the order can be predicted, then those path topologies having more than $k$ better topologies are proven free of the desired $k$ paths and thus can be safely discarded during the path planning process. To this end, a hierarchical topological tree is proposed as an implementation of the mechanism, whose nodes are proven to expand in non-homotopic directions and edges (collision-free path segments) are proven locally shortest. With efficient criteria that observe the order relations between partly constructed locally shortest paths being imparted into the tree, the tree nodes that expand in non-$k$-optimal topologies will not be expanded. As a result, the computational time for solving the $k$-SNPP problem is reduced by near two orders of magnitude.

preprint2022arXiv

Enhancing the Erdős-Lovász Tihany Conjecture for line graphs of multigraphs

In this paper, we prove an enhanced version of the Erdős-Lovász Tihany Conjecture for line graphs of multigraphs. That is, for every graph $G$ whose chromatic number $χ(G)$ is more than its clique number $ω(G)$ and for nonnegative integer $\ell$, any two integers $s,t \geq 3.5\ell+2$ with $s+t = χ(G)+1$, there is a partition $(S,T)$ of the vertex set $V(G)$ such that $χ(G[S])\geq s$ and $χ(G[T])\geq t+\ell$. In particular, when $\ell=1$, we can obtain the same result just for any $s,t\geq4$. The Erdős-Lovász Tihany conjecture is a special case when $\ell=0$.

preprint2022arXiv

EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation

We introduce EventNarrative, a knowledge graph-to-text dataset from publicly available open-world knowledge graphs. Given the recent advances in event-driven Information Extraction (IE), and that prior research on graph-to-text only focused on entity-driven KGs, this paper focuses on event-centric data. However, our data generation system can still be adapted to other other types of KG data. Existing large-scale datasets in the graph-to-text area are non-parallel, meaning there is a large disconnect between the KGs and text. The datasets that have a paired KG and text, are small scale and manually generated or generated without a rich ontology, making the corresponding graphs sparse. Furthermore, these datasets contain many unlinked entities between their KG and text pairs. EventNarrative consists of approximately 230,000 graphs and their corresponding natural language text, 6 times larger than the current largest parallel dataset. It makes use of a rich ontology, all of the KGs entities are linked to the text, and our manual annotations confirm a high data quality. Our aim is two-fold: help break new ground in event-centric research where data is lacking, and to give researchers a well-defined, large-scale dataset in order to better evaluate existing and future knowledge graph-to-text models. We also evaluate two types of baseline on EventNarrative: a graph-to-text specific model and two state-of-the-art language models, which previous work has shown to be adaptable to the knowledge graph-to-text domain.

preprint2022arXiv

Feature-based Style Randomization for Domain Generalization

As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target domain without any additional adaption. In previous DG models, by generating virtual data to supplement observed source domains, the data augmentation based methods have shown its effectiveness. To simulate the possible unseen domains, most of them enrich the diversity of original data via image-level style transformation. However, we argue that the potential styles are hard to be exhaustively illustrated and fully augmented due to the limited referred styles, leading the diversity could not be always guaranteed. Unlike image-level augmentation, we in this paper develop a simple yet effective feature-based style randomization module to achieve feature-level augmentation, which can produce random styles via integrating random noise into the original style. Compared with existing image-level augmentation, our feature-level augmentation favors a more goal-oriented and sample-diverse way. Furthermore, to sufficiently explore the efficacy of the proposed module, we design a novel progressive training strategy to enable all parameters of the network to be fully trained. Extensive experiments on three standard benchmark datasets, i.e., PACS, VLCS and Office-Home, highlight the superiority of our method compared to the state-of-the-art methods.

preprint2022arXiv

FEJ-VIRO: A Consistent First-Estimate Jacobian Visual-Inertial-Ranging Odometry

In recent years, Visual-Inertial Odometry (VIO) has achieved many significant progresses. However, VIO methods suffer from localization drift over long trajectories. In this paper, we propose a First-Estimates Jacobian Visual-Inertial-Ranging Odometry (FEJ-VIRO) to reduce the localization drifts of VIO by incorporating ultra-wideband (UWB) ranging measurements into the VIO framework \textit{consistently}. Considering that the initial positions of UWB anchors are usually unavailable, we propose a long-short window structure to initialize the UWB anchors' positions as well as the covariance for state augmentation. After initialization, the FEJ-VIRO estimates the UWB anchors' positions simultaneously along with the robot poses. We further analyze the observability of the visual-inertial-ranging estimators and proved that there are \textit{four} unobservable directions in the ideal case, while one of them vanishes in the actual case due to the gain of spurious information. Based on these analyses, we leverage the FEJ technique to enforce the unobservable directions, hence reducing inconsistency of the estimator. Finally, we validate our analysis and evaluate the proposed FEJ-VIRO with both simulation and real-world experiments.

preprint2022arXiv

HDMapNet: An Online HD Map Construction and Evaluation Framework

Constructing HD semantic maps is a central component of autonomous driving. However, traditional pipelines require a vast amount of human efforts and resources in annotating and maintaining the semantics in the map, which limits its scalability. In this paper, we introduce the problem of HD semantic map learning, which dynamically constructs the local semantics based on onboard sensor observations. Meanwhile, we introduce a semantic map learning method, dubbed HDMapNet. HDMapNet encodes image features from surrounding cameras and/or point clouds from LiDAR, and predicts vectorized map elements in the bird's-eye view. We benchmark HDMapNet on nuScenes dataset and show that in all settings, it performs better than baseline methods. Of note, our camera-LiDAR fusion-based HDMapNet outperforms existing methods by more than 50% in all metrics. In addition, we develop semantic-level and instance-level metrics to evaluate the map learning performance. Finally, we showcase our method is capable of predicting a locally consistent map. By introducing the method and metrics, we invite the community to study this novel map learning problem.

preprint2022arXiv

Inference on autoregulation in gene expression

Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.

preprint2022arXiv

Inference on the structure of gene regulatory networks

In this paper, we conduct theoretical analyses on inferring the structure of gene regulatory networks. Depending on the experimental method and data type, the inference problem is classified into 20 different scenarios. For each scenario, we discuss the problem that with enough data, under what assumptions, what can be inferred about the structure. For scenarios that have been covered in the literature, we provide a brief review. For scenarios that have not been covered in literature, if the structure can be inferred, we propose new mathematical inference methods and evaluate them on simulated data. Otherwise, we prove that the structure cannot be inferred.

preprint2022arXiv

Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language

Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges resulting from the differences between humans and robots. Traditional optimization-based methods are time-consuming and rely heavily on good initialization, while recent studies using feedforward neural networks suffer from poor generalization to unseen motions. Moreover, they neglect the topological information in human skeletons and robot structures. In this paper, we propose a novel neural latent optimization approach to address these problems. Latent optimization utilizes a decoder to establish a mapping between the latent space and the robot motion space. Afterward, the retargeting results that satisfy robot constraints can be obtained by searching for the optimal latent vector. Alongside with latent optimization, neural initialization exploits an encoder to provide a better initialization for faster and better convergence of optimization. Both the human skeleton and the robot structure are modeled as graphs to make better use of topological information. We perform experiments on retargeting Chinese sign language, which involves two arms and two hands, with additional requirements on the relative relationships among joints. Experiments include retargeting various human demonstrations to YuMi, NAO, and Pepper in the simulation environment and to YuMi in the real-world environment. Both efficiency and accuracy of the proposed method are verified.

preprint2022arXiv

Learning to Fill the Seam by Vision: Sub-millimeter Peg-in-hole on Unseen Shapes in Real World

In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human behavior, we design architectures with position and orientation estimators based on the seam representation for pose alignment, which proves to be general to the unseen peg geometries. By putting the estimators into the closed-loop control with reinforcement learning, we further achieve a higher or comparable success rate, efficiency, and robustness compared with the baseline methods. The policy is trained totally in simulation without any manual intervention. To achieve sim-to-real, a learnable segmentation module with automatic data collecting and labeling can be easily trained to decouple the perception and the policy, which helps the model trained in simulation quickly adapt to the real world with negligible effort. Results are presented in simulation and on a physical robot. Code, videos, and supplemental material are available at https://github.com/xieliang555/SFN.git

preprint2022arXiv

Map-based Visual-Inertial Localization: Consistency and Complexity

Drift-free localization is essential for autonomous vehicles. In this paper, we address the problem by proposing a filter-based framework, which integrates the visual-inertial odometry and the measurements of the features in the pre-built map. In this framework, the transformation between the odometry frame and the map frame is augmented into the state and estimated on the fly. Besides, we maintain only the keyframe poses in the map and employ Schmidt extended Kalman filter to update the state partially, so that the uncertainty of the map information can be consistently considered with low computational cost. Moreover, we theoretically demonstrate that the ever-changing linearization points of the estimated state can introduce spurious information to the augmented system and make the original four-dimensional unobservable subspace vanish, leading to inconsistent estimation in practice. To relieve this problem, we employ first-estimate Jacobian (FEJ) to maintain the correct observability properties of the augmented system. Furthermore, we introduce an observability-constrained updating method to compensate for the significant accumulated error after the long-term absence (can be 3 minutes and 1 km) of map-based measurements. Through simulations, the consistent estimation of our proposed algorithm is validated. Through real-world experiments, we demonstrate that our proposed algorithm runs successfully on four kinds of datasets with the lower computational cost (20% time-saving) and the better estimation accuracy (45% trajectory error reduction) compared with the baseline algorithm VINS-Fusion, whereas VINS-Fusion fails to give bounded localization performance on three of four datasets because of its inconsistent estimation.

preprint2022arXiv

MBGDT:Robust Mini-Batch Gradient Descent

In high dimensions, most machine learning method perform fragile even there are a little outliers. To address this, we hope to introduce a new method with the base learner, such as Bayesian regression or stochastic gradient descent to solve the problem of the vulnerability in the model. Because the mini-batch gradient descent allows for a more robust convergence than the batch gradient descent, we work a method with the mini-batch gradient descent, called Mini-Batch Gradient Descent with Trimming (MBGDT). Our method show state-of-art performance and have greater robustness than several baselines when we apply our method in designed dataset.

preprint2022arXiv

Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs

Stochastic partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics. Neural Operators, generations of neural networks with capability of learning maps between infinite-dimensional spaces, are strong tools for solving parametric PDEs. However, they lack the ability to modeling SPDEs which usually have poor regularity due to the driving noise. As the theory of regularity structure has achieved great successes in analyzing SPDEs and provides the concept model feature vectors that well-approximate SPDEs' solutions, we propose the Neural Operator with Regularity Structure (NORS) which incorporates the feature vectors for modeling dynamics driven by SPDEs. We conduct experiments on various of SPDEs including the dynamic Phi41 model and the 2d stochastic Navier-Stokes equation, and the results demonstrate that the NORS is resolution-invariant, efficient, and achieves one order of magnitude lower error with a modest amount of data.

preprint2022arXiv

Observation of one-dimensional Dirac fermions in silicon nanoribbons

Dirac materials, which feature Dirac cones in the reciprocal space, have been one of the hottest topics in condensed matter physics in the past decade. To date, 2D and 3D Dirac Fermions have been extensively studied, while their 1D counterparts are rare. Recently, Si nanoribbons (SiNRs), which are composed of alternating pentagonal Si rings, have attracted intensive attention. However, the electronic structure and topological properties of SiNRs are still elusive. Here, by angle-resolved photoemission spectroscopy, scanning tunneling microscopy/spectroscopy measurements, first-principles calculations, and tight-binding model analysis, we demonstrate the existence of 1D Dirac Fermions in SiNRs. Our theoretical analysis shows that the Dirac cones derive from the armchairlike Si chain in the center of the nanoribbon and can be described by the Su-Schrieffer-Heeger model. These results establish SiNRs as a platform for studying the novel physical properties in 1D Dirac materials.

preprint2022arXiv

Policy Gradient Method For Robust Reinforcement Learning

This paper develops the first policy gradient method with global optimality guarantee and complexity analysis for robust reinforcement learning under model mismatch. Robust reinforcement learning is to learn a policy robust to model mismatch between simulator and real environment. We first develop the robust policy (sub-)gradient, which is applicable for any differentiable parametric policy class. We show that the proposed robust policy gradient method converges to the global optimum asymptotically under direct policy parameterization. We further develop a smoothed robust policy gradient method and show that to achieve an $ε$-global optimum, the complexity is $\mathcal O(ε^{-3})$. We then extend our methodology to the general model-free setting and design the robust actor-critic method with differentiable parametric policy class and value function. We further characterize its asymptotic convergence and sample complexity under the tabular setting. Finally, we provide simulation results to demonstrate the robustness of our methods.

preprint2022arXiv

Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks

In the current stock market, computer science and technology are more and more widely used to analyse stocks. Not same as most related machine learning stock price prediction work, this work study the predicting the tendency of the stock price on the second day right after the disclosure of the companies' annual reports. We use a variety of different models, including decision tree, logistic regression, random forest, neural network, prototypical networks. We use two sets of financial indicators (key and expanded) to conduct experiments, these financial indicators are obtained from the EastMoney website disclosed by companies, and finally we find that these models are not well behaved to predict the tendency. In addition, we also filter stocks with ROE greater than 0.15 and net cash ratio greater than 0.9. We conclude that according to the financial indicators based on the just-released annual report of the company, the predictability of the stock price movement on the second day after disclosure is weak, with maximum accuracy about 59.6% and maximum precision about 0.56 on our test set by the random forest classifier, and the stock filtering does not improve the performance. And random forests perform best in general among all these models which conforms to some work's findings.

preprint2022arXiv

QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a nonlinear prediction function that is globally optimal in the reproducing kernel Hilbert space with respect to the total instantaneous costs of all agents. In order to circumvent the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the non-parametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem. To further improve the communication efficiency, we add the quantization and censoring strategies in the communication stage and develop the Quantized and Communication-censored ODKLA (QC-ODKLA) algorithm. We theoretically prove that both ODKLA and QC-ODKLA can achieve the optimal sublinear regret $\mathcal{O}(\sqrt{T})$ over $T$ time slots. Through numerical experiments, we evaluate the learning effectiveness, communication, and computation efficiencies of the proposed methods.

preprint2022arXiv

Recycling of Perovskite Substrate

The use of water-soluble sacrificial layer of Sr$_3$Al$_2$O$_6$ has tremendously boosted the research on freestanding functional oxide thin films, especially thanks to its ultimate capability to produce high-quality epitaxial perovskite thin films. However, the costly single-crystalline substrates, e.g. SrTiO$_3$, were generally discarded after obtaining the freestanding thin films. Here, we demonstrate that the SrTiO$_3$ substrates can be recycled to fabricate La$_{0.7}$Sr$_{0.3}$MnO$_3$ films with nearly identical structural and electrical properties. After attaining freestanding thin films, the residues on SrTiO$_3$ can be removed by 80 \degree C hot water soaking and rinsing treatments. Consequently, the surface of SrTiO$_3$ reverted to its original step-and-terrace structure.

preprint2022arXiv

Robust Constrained Reinforcement Learning

Constrained reinforcement learning is to maximize the expected reward subject to constraints on utilities/costs. However, the training environment may not be the same as the test one, due to, e.g., modeling error, adversarial attack, non-stationarity, resulting in severe performance degradation and more importantly constraint violation. We propose a framework of robust constrained reinforcement learning under model uncertainty, where the MDP is not fixed but lies in some uncertainty set, the goal is to guarantee that constraints on utilities/costs are satisfied for all MDPs in the uncertainty set, and to maximize the worst-case reward performance over the uncertainty set. We design a robust primal-dual approach, and further theoretically develop guarantee on its convergence, complexity and robust feasibility. We then investigate a concrete example of $δ$-contamination uncertainty set, design an online and model-free algorithm and theoretically characterize its sample complexity.

preprint2022arXiv

Searching for multiple populations in star clusters using the China Space Station Telescope

Multiple stellar populations (MPs) in most star clusters older than 2 Gyr, as seen by lots of spectroscopic and photometric studies, have led to a significant challenge to the traditional view of star formation. In this field, space-based instruments, in particular the Hubble Space Telescope (HST), have made a breakthrough as they significantly improved the efficiency of detecting MPs in crowding stellar fields by images. The China Space Station Telescope (CSST) and the HST are sensitive to a similar wavelength interval, but it covers a field of view which is about 5-8 times wider than that of HST. One of its instruments, the Multi-Channel Imager (MCI), will have multiple filters covering a wide wavelength range from NUV to NIR, making the CSST a potentially powerful tool for studying MPs in clusters. In this work, we evaluate the efficiency of the designed filters for the MCI/CSST in revealing MPs in different color-magnitude diagrams (CMDs). We find that CMDs made with MCI/CSST photometry in appropriate UV filters are powerful tools to disentangle stellar populations with different abundances of He, C, N, O and Mg. On the contrary, the traditional CMDs are blind to multiple populations in globular clusters (GCs). We show that CSST has the potential of being the spearhead instrument for investigating MPs in GCs in the next decades.

preprint2022arXiv

Tac3D: A Novel Vision-based Tactile Sensor for Measuring Forces Distribution and Estimating Friction Coefficient Distribution

The importance of force perception in interacting with the environment was proven years ago. However, it is still a challenge to measure the contact force distribution accurately in real-time. In order to break through this predicament, we propose a new vision-based tactile sensor, the Tac3D sensor, for measuring the three-dimensional contact surface shape and contact force distribution. In this work, virtual binocular vision is first applied to the tactile sensor, which allows the Tac3D sensor to measure the three-dimensional tactile information in a simple and efficient way and has the advantages of simple structure, low computational costs, and inexpensive. Then, we used contact surface shape and force distribution to estimate the friction coefficient distribution in contact region. Further, combined with the global position of the tactile sensor, the 3D model of the object with friction coefficient distribution is reconstructed. These reconstruction experiments not only demonstrate the excellent performance of the Tac3D sensor but also imply the possibility to optimize the action planning in grasping based on the friction coefficient distribution of the object.

preprint2022arXiv

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications.

preprint2022arXiv

Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design

This paper focuses on designing a consistent and efficient filter for map-based visual-inertial localization. First, we propose a new Lie group with its algebra, based on which a novel invariant extended Kalman filter (invariant EKF) is designed. We theoretically prove that, when we do not consider the uncertainty of the map information, the proposed invariant EKF can naturally maintain the correct observability properties of the system. To consider the uncertainty of the map information, we introduce a Schmidt filter. With the Schmidt filter, the uncertainty of the map information can be taken into consideration to avoid over-confident estimation while the computation cost only increases linearly with the size of the map keyframes. In addition, we introduce an easily implemented observability-constrained technique because directly combining the invariant EKF with the Schmidt filter cannot maintain the correct observability properties of the system that considers the uncertainty of the map information. Finally, we validate our proposed system's high consistency, accuracy, and efficiency via extensive simulations and real-world experiments.

preprint2022arXiv

Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy

Current RGB-based 6D object pose estimation methods have achieved noticeable performance on datasets and real world applications. However, predicting 6D pose from single 2D image features is susceptible to disturbance from changing of environment and textureless or resemblant object surfaces. Hence, RGB-based methods generally achieve less competitive results than RGBD-based methods, which deploy both image features and 3D structure features. To narrow down this performance gap, this paper proposes a framework for 6D object pose estimation that learns implicit 3D information from 2 RGB images. Combining the learned 3D information and 2D image features, we establish more stable correspondence between the scene and the object models. To seek for the methods best utilizing 3D information from RGB inputs, we conduct an investigation on three different approaches, including Early- Fusion, Mid-Fusion, and Late-Fusion. We ascertain the Mid- Fusion approach is the best approach to restore the most precise 3D keypoints useful for object pose estimation. The experiments show that our method outperforms state-of-the-art RGB-based methods, and achieves comparable results with RGBD-based methods.

preprint2022arXiv

Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud

Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value. With the aid of gravity alignment, the degree of freedom in point cloud registration could be reduced to 4DoF, in which only the heading angle is required for rotation estimation. In this paper, we propose a fast and accurate global heading angle estimation method for gravity-aligned point clouds. Our key idea is that we generate a translation invariant representation based on Radon Transform, allowing us to solve the decoupled heading angle globally with circular cross-correlation. Besides, for heading angle estimation between point clouds with different distributions, we implement this heading angle estimator as a differentiable module to train a feature extraction network end- to-end. The experimental results validate the effectiveness of the proposed method in heading angle estimation and show better performance compared with other methods.

preprint2022arXiv

Trust-based Symbolic Motion Planning for Multi-robot Bounding Overwatch

Multi-robot bounding overwatch requires timely coordination of robot team members. Symbolic motion planning (SMP) can provide provably correct solutions for robot motion planning with high-level temporal logic task requirements. This paper aims to develop a framework for safe and reliable SMP of multi-robot systems (MRS) to satisfy complex bounding overwatch tasks constrained by temporal logics. A decentralized SMP framework is first presented, which guarantees both correctness and parallel execution of the complex bounding overwatch tasks by the MRS. A computational trust model is then constructed by referring to the traversability and line of sight of robots in the terrain. The trust model predicts the trustworthiness of each robot team's potential behavior in executing a task plan. The most trustworthy task and motion plan is explored with a Dijkstra searching strategy to guarantee the reliability of MRS bounding overwatch. A robot simulation is implemented in ROS Gazebo to demonstrate the effectiveness of the proposed framework.

preprint2022arXiv

Two Metrics on Rooted Unordered Trees with Labels

The early development of a zygote can be mathematically described by a developmental tree. To compare developmental trees of different species, we need to define distances on trees. If children cells after a division are not distinguishable, developmental trees are represented by the space $\mathcal{T}$ of rooted trees with possibly repeated labels, where all vertices are unordered. If children cells after a division are partially distinguishable, developmental trees are represented by the space $\mathcal{P}$ of rooted trees with possibly repeated labels, where vertices can be ordered or unordered. On $\mathcal{T}$, the space of rooted unordered trees with possibly repeated labels, we define two metrics: the best-match metric and the left-regular metric, which show some advantages over existing methods. On $\mathcal{P}$, the space of rooted labeled trees with ordered or unordered vertices, there is no metric, and we define a semimetric, which is a variant of the best-match metric. To compute the best-match distance between two trees, the expected time complexity and worst-case time complexity are both $\mathcal{O}(n^2)$, where $n$ is the tree size. To compute the left-regular distance between two trees, the expected time complexity is $\mathcal{O}(n)$, and the worst-case time complexity is $\mathcal{O}(n\log n)$. For rooted labeled trees with (fully/partially) unordered vertices, we define metrics (semimetric) that have fast algorithms to compute and have advantages over existing methods. Such trees also appear outside of developmental biology, and such metrics can be applied to other types of trees which have more extensive applications, especially in molecular biology.

preprint2022arXiv

Writing Style Aware Document-level Event Extraction

Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works discuss this issue with the token-level multi-label classification framework by distinguishing the tokens as different roles while ignoring the writing styles of documents. The writing style is a special way of content organizing for documents and it is relative fixed in documents with a special field (e.g. financial, medical documents, etc.). We argue that the writing style contains important clues for judging the roles for tokens and the ignorance of such patterns might lead to the performance degradation for the existing works. To this end, we model the writing style in documents as a distribution of argument roles, i.e., Role-Rank Distribution, and propose an event extraction model with the Role-Rank Distribution based Supervision Mechanism to capture this pattern through the supervised training process of an event extraction task. We compare our model with state-of-the-art methods on several real-world datasets. The empirical results show that our approach outperforms other alternatives with the captured patterns. This verifies the writing style contains valuable information that could improve the performance of the event extraction task.

preprint2021arXiv

Deep Samplable Observation Model for Global Localization and Kidnapping

Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL [1]. However, traditional MCL can only sample from a uniform distribution over the state space. Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes. To better deal with these problems, we present a distribution proposal model, named Deep Samplable Observation Model (DSOM). DSOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood. With such samples, the convergence is expected to be more effective and efficient. Considering that the learning-based sampling model may fail to capture the true pose sometimes, we furthermore propose the Adaptive Mixture MCL (AdaM MCL), which deploys a trusty mechanism to adaptively select updating mode for each particle to tolerate this situation. Equipped with DSOM, AdaM MCL can achieve more accurate estimation, faster convergence and better scalability compared to previous methods in both synthetic and real scenes. Even in real environments with long-term changing, AdaM MCL is able to localize the robot using DSOM trained only by simulation observations from a SLAM map or a blueprint map.

preprint2021arXiv

DiSCO: Differentiable Scan Context with Orientation

Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the appearance change. In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation. The orientation can further be used as the initial value for the down-stream local optimal metric pose estimation, improving the pose estimation especially when a large orientation between the current scan and retrieved scan exists. Our key idea is to transform the feature into the frequency domain. We utilize the magnitude of the spectrum as the place signature, which is theoretically rotation-invariant. In addition, based on the differentiable phase correlation, we can efficiently estimate the global optimal relative orientation using the spectrum. With such structural constraints, the network can be learned in an end-to-end manner, and the backbone is fully shared by the two tasks, achieving interpretability and light weight. Finally, DiSCO is validated on three datasets with long-term outdoor conditions, showing better performance than the compared methods.

preprint2021arXiv

Dynamic Movement Primitive based Motion Retargeting for Dual-Arm Sign Language Motions

We aim to develop an efficient programming method for equipping service robots with the skill of performing sign language motions. This paper addresses the problem of transferring complex dual-arm sign language motions characterized by the coordination among arms and hands from human to robot, which is seldom considered in previous studies of motion retargeting techniques. In this paper, we propose a novel motion retargeting method that leverages graph optimization and Dynamic Movement Primitives (DMPs) for this problem. We employ DMPs in a leader-follower manner to parameterize the original trajectories while preserving motion rhythm and relative movements between human body parts, and adopt a three-step optimization procedure to find deformed trajectories for robot motion planning while ensuring feasibility for robot execution. Experimental results of several Chinese Sign Language (CSL) motions have been successfully performed on ABB's YuMi dual-arm collaborative robot (14-DOF) with two 6-DOF Inspire-Robotics' multi-fingered hands, a system with 26 DOFs in total.

preprint2021arXiv

Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory

One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics. In this paper, we propose a hierarchical driving model with explicit model of continuous intention and continuous dynamics, which decouples the complexity in the observation-to-action reasoning in the human driving data. Specifically, the continuous intention module takes the route planning map obtained by GPS and IMU, perception from a RGB camera and LiDAR as input to generate a potential map encoded with obstacles and intentions being expressed as grid based potentials. Then, the potential map is regarded as a condition, together with the current dynamics, to generate a continuous trajectory as output by a continuous function approximator network, whose derivatives can be used for supervision without additional parameters. Finally, we validate our method on both datasets and simulator, demonstrating that our method has higher prediction accuracy of displacement and velocity and generates smoother trajectories. The method is also deployed on the real vehicle with loop latency, validating its effectiveness. To the best of our knowledge, this is the first work to produce the driving trajectory using a continuous function approximator network.

preprint2021arXiv

Joint Optimization of Communications and Federated Learning Over the Air

Federated learning (FL) is an attractive paradigm for making use of rich distributed data while protecting data privacy. Nonetheless, nonideal communication links and limited transmission resources may hinder the implementation of fast and accurate FL. In this paper, we study joint optimization of communications and FL based on analog aggregation transmission in realistic wireless networks. We first derive closed-form expressions for the expected convergence rate of FL over the air, which theoretically quantify the impact of analog aggregation on FL. Based on the analytical results, we develop a joint optimization model for accurate FL implementation, which allows a parameter server to select a subset of workers and determine an appropriate power scaling factor. Since the practical setting of FL over the air encounters unobservable parameters, we reformulate the joint optimization of worker selection and power allocation using controlled approximation. Finally, we efficiently solve the resulting mixed-integer programming problem via a simple yet optimal finite-set search method by reducing the search space. Simulation results show that the proposed solutions developed for realistic wireless analog channels outperform a benchmark method, and achieve comparable performance of the ideal case where FL is implemented over noise-free wireless channels.

preprint2021arXiv

Learn to Differ: Sim2Real Small Defection Segmentation Network

Recent studies on deep-learning-based small defection segmentation approaches are trained in specific settings and tend to be limited by fixed context. Throughout the training, the network inevitably learns the representation of the background of the training data before figuring out the defection. They underperform in the inference stage once the context changed and can only be solved by training in every new setting. This eventually leads to the limitation in practical robotic applications where contexts keep varying. To cope with this, instead of training a network context by context and hoping it to generalize, why not stop misleading it with any limited context and start training it with pure simulation? In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all. A small defection detection layer utilizing the pose sensitivity of phase correlation between images is introduced and is followed by an outlier masking layer. The network is trained on randomly generated simulated data with simple shapes and is generalized across the real world. Finally, SSDS is validated on real-world collected data and demonstrates the ability that even when trained in cheap simulation, SSDS can still find small defections in the real world showing the effectiveness and its potential for practical applications.

preprint2021arXiv

Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis

Visual analytics systems enable highly interactive exploratory data analysis. Across a range of fields, these technologies have been successfully employed to help users learn from complex data. However, these same exploratory visualization techniques make it easy for users to discover spurious findings. This paper proposes new methods to monitor a user's analytic focus during visual analysis of structured datasets and use it to surface relevant articles that contextualize the visualized findings. Motivated by interactive analyses of electronic health data, this paper introduces a formal model of analytic focus, a computational approach to dynamically update the focus model at the time of user interaction, and a prototype application that leverages this model to surface relevant medical publications to users during visual analysis of a large corpus of medical records. Evaluation results with 24 users show that the modeling approach has high levels of accuracy and is able to surface highly relevant medical abstracts.

preprint2021arXiv

RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model

Compared to the onboard camera and laser scanner, radar sensor provides lighting and weather invariant sensing, which is naturally suitable for long-term localization under adverse conditions. However, radar data is sparse and noisy, resulting in challenges for radar mapping. On the other hand, the most popular available map currently is built by lidar. In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping. We first embed both sensor modals into a common feature space by a neural network. Then multiple offsets are added to the map modal for exhaustive similarity evaluation against the current radar modal, yielding the regression of the current pose. Finally, we apply this differentiable measurement model to a Kalman Filter (KF) to learn the whole sequential localization process in an end-to-end manner. \textit{The whole learning system is differentiable with the network based measurement model at the front-end and KF at the back-end.} To validate the feasibility and effectiveness, we employ multi-session multi-scene datasets collected from the real world, and the results demonstrate that our proposed system achieves superior performance over $90km$ driving, even in generalization scenarios where the model training is in UK, while testing in South Korea. We also release the source code publicly.

preprint2021arXiv

REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination

Object 6D pose estimation is a fundamental task in many applications. Conventional methods solve the task by detecting and matching the keypoints, then estimating the pose. Recent efforts bringing deep learning into the problem mainly overcome the vulnerability of conventional methods to environmental variation due to the hand-crafted feature design. However, these methods cannot achieve end-to-end learning and good interpretability at the same time. In this paper, we propose REDE, a novel end-to-end object pose estimator using RGB-D data, which utilizes network for keypoint regression, and a differentiable geometric pose estimator for pose error back-propagation. Besides, to achieve better robustness when outlier keypoint prediction occurs, we further propose a differentiable outliers elimination method that regresses the candidate result and the confidence simultaneously. Via confidence weighted aggregation of multiple candidates, we can reduce the effect from the outliers in the final estimation. Finally, following the conventional method, we apply a learnable refinement process to further improve the estimation. The experimental results on three benchmark datasets show that REDE slightly outperforms the state-of-the-art approaches and is more robust to object occlusion.

preprint2021arXiv

The collision frequencies in the plasmas with the power-law q-distributions in nonextensive statistics

We study the collision frequencies of particles in the weakly and highly ionized plasmas with the power-law q-distributions in nonextensive statistics. We derive the average collision frequencies of neutral-neutral particle, electron-neutral particle, ion-neutral particle, electron-electron, ion-ion and electron-ion, respectively, in the q-distributed plasmas. We show that the average collision frequencies depend strongly on the q-parameter in a complex form and thus their properties are significantly different from that in Maxwell-distributed plasmas. These new average collision frequencies are important for us to study accurately the transport property in the complex plasmas with non-Maxwell/power-law velocity distributions.

preprint2021arXiv

Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks

We study the problem of incorporating prior knowledge into a deep Transformer-based model,i.e.,Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks. By probing and analyzing what BERT has already known when solving this task, we obtain better understanding of what task-specific knowledge BERT needs the most and where it is most needed. The analysis further motivates us to take a different approach than most existing works. Instead of using prior knowledge to create a new training task for fine-tuning BERT, we directly inject knowledge into BERT's multi-head attention mechanism. This leads us to a simple yet effective approach that enjoys fast training stage as it saves the model from training on additional data or tasks other than the main task. Extensive experiments demonstrate that the proposed knowledge-enhanced BERT is able to consistently improve semantic textual matching performance over the original BERT model, and the performance benefit is most salient when training data is scarce.

preprint2020arXiv

2-Entity RANSAC for robust visual localization in changing environment

Visual localization has attracted considerable attention due to its low-cost and stable sensor, which is desired in many applications, such as autonomous driving, inspection robots and unmanned aerial vehicles. However, current visual localization methods still struggle with environmental changes across weathers and seasons, as there is significant appearance variation between the map and the query image. The crucial challenge in this situation is that the percentage of outliers, i.e. incorrect feature matches, is high. In this paper, we derive minimal closed form solutions for 3D-2D localization with the aid of inertial measurements, using only 2 pairs of point matches or 1 pair of point match and 1 pair of line match. These solutions are further utilized in the proposed 2-entity RANSAC, which is more robust to outliers as both line and point features can be used simultaneously and the number of matches required for pose calculation is reduced. Furthermore, we introduce three feature sampling strategies with different advantages, enabling an automatic selection mechanism. With the mechanism, our 2-entity RANSAC can be adaptive to the environments with different distribution of feature types in different segments. Finally, we evaluate the method on both synthetic and real-world datasets, validating its performance and effectiveness in inter-session scenarios.

preprint2020arXiv

A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision

The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various environmental concerns. To reduce the dominant data movement cost of training, process in-memory (PIM) has emerged as a promising solution as it alleviates the need to access DNN weights. However, state-of-the-art PIM DNN training accelerators employ either analog/mixed signal computing which has limited precision or digital computing based on a memory technology that supports limited logic functions and thus requires complicated procedure to realize floating point computation. In this paper, we propose a spin orbit torque magnetic random access memory (SOT-MRAM) based digital PIM accelerator that supports floating point precision. Specifically, this new accelerator features an innovative (1) SOT-MRAM cell, (2) full addition design, and (3) floating point computation. Experiment results show that the proposed SOT-MRAM PIM based DNN training accelerator can achieve 3.3$\times$, 1.8$\times$, and 2.5$\times$ improvement in terms of energy, latency, and area, respectively, compared with a state-of-the-art PIM based DNN training accelerator.

preprint2020arXiv

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same entities. In this work, we introduce a fresh perspective to revisit the relational triple extraction task and propose a novel cascade binary tagging framework (CasRel) derived from a principled problem formulation. Instead of treating relations as discrete labels as in previous works, our new framework models relations as functions that map subjects to objects in a sentence, which naturally handles the overlapping problem. Experiments show that the CasRel framework already outperforms state-of-the-art methods even when its encoder module uses a randomly initialized BERT encoder, showing the power of the new tagging framework. It enjoys further performance boost when employing a pre-trained BERT encoder, outperforming the strongest baseline by 17.5 and 30.2 absolute gain in F1-score on two public datasets NYT and WebNLG, respectively. In-depth analysis on different scenarios of overlapping triples shows that the method delivers consistent performance gain across all these scenarios. The source code and data are released online.

preprint2020arXiv

Attachment and antibiotic response of early-stage biofilms studied using resonant hyperspectral imaging

Many bacterial species readily develop biofilms that act as a protective matrix against external challenge, e.g. from antimicrobial treatment. Therefore, biofilms are often responsible for persistent and recurring infections. Established methods for studying biofilms are either destructive or they focus on the biofilm surface. A non-destructive method that is sensitive to the underside of the biofilm is highly desirable, as it allows studying the penetration of antibiotics through the film. Here, we demonstrate that the high surface sensitivity of resonant hyperspectral imaging provides this capability. The method allows us to monitor the early stages of Escherichia coli biofilm formation, cell attachment and microcolony formation, in-situ and in real time. We study the response of the biofilm to a number of different antibiotics and verify our observations using confocal microscopy. Based on this ability to closely monitor the surface-bound cells, resonant hyperspectral imaging gives new insights into the antimicrobial resistance of biofilms.

preprint2020arXiv

AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs

Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growing demand for DNN chips. However, designing DNN chips is non-trivial because: (1) mainstream DNNs have millions of parameters and operations; (2) the large design space due to the numerous design choices of dataflows, processing elements, memory hierarchy, etc.; and (3) an algorithm/hardware co-design is needed to allow the same DNN functionality to have a different decomposition, which would require different hardware IPs to meet the application specifications. Therefore, DNN chips take a long time to design and require cross-disciplinary experts. To enable fast and effective DNN chip design, we propose AutoDNNchip - a DNN chip generator that can automatically generate both FPGA- and ASIC-based DNN chip implementation given DNNs from machine learning frameworks (e.g., PyTorch) for a designated application and dataset. Specifically, AutoDNNchip consists of two integrated enablers: (1) a Chip Predictor, built on top of a graph-based accelerator representation, which can accurately and efficiently predict a DNN accelerator's energy, throughput, and area based on the DNN model parameters, hardware configuration, technology-based IPs, and platform constraints; and (2) a Chip Builder, which can automatically explore the design space of DNN chips (including IP selection, block configuration, resource balancing, etc.), optimize chip design via the Chip Predictor, and then generate optimized synthesizable RTL to achieve the target design metrics. Experimental results show that our Chip Predictor's predicted performance differs from real-measured ones by < 10% when validated using 15 DNN models and 4 platforms (edge-FPGA/TPU/GPU and ASIC). Furthermore, accelerators generated by our AutoDNNchip can achieve better (up to 3.86X improvement) performance than that of expert-crafted state-of-the-art accelerators.

preprint2020arXiv

Blow-up criterion for the 2-D Prandtl equation

In this paper, we consider the 2-D Prandtl equation with constant outer flow and monotonic data. We prove that if the curvature of the velocity distribution(i.e., $\partial_y^2u$) is bounded near the boundary, then the solution can not develop the singularity.

preprint2020arXiv

Cellular Decomposition for Non-repetitive Coverage Task with Minimum Discontinuities

A mechanism to derive non-repetitive coverage path solutions with a proven minimal number of discontinuities is proposed in this work, with the aim to avoid unnecessary, costly end effector lift-offs for manipulators. The problem is motivated by the automatic polishing of an object. Due to the non-bijective mapping between the workspace and the joint-space, a continuous coverage path in the workspace may easily be truncated in the joint-space, incuring undesirable end effector lift-offs. Inversely, there may be multiple configuration choices to cover the same point of a coverage path through the solution of the Inverse Kinematics. The solution departs from the conventional local optimisation of the coverage path shape in task space, or choosing appropriate but possibly disconnected configurations, to instead explicitly explore the leaast number of discontinuous motions through the analysis of the structure of valid configurations in joint-space. The two novel contributions of this paper include proof that the least number of path discontinuities is predicated on the surrounding environment, independent from the choice of the actual coverage path; thus has a minimum. And an efficient finite cellular decomposition method to optimally divide the workspace into the minimum number of cells, each traversable without discontinuties by any arbitrary coverage path within. Extensive simulation examples and real-world results on a 5 DoF manipulator are presented to prove the validity of the proposed strategy in realistic settings.

preprint2020arXiv

Constructing Basis Path Set by Eliminating Path Dependency

The way the basis path set works in neural network remains mysterious, and the generalization of newly appeared G-SGD algorithm to more practical network is hindered. The Basis Path Set Searching problem is formulated from the perspective of graph theory, to find the basis path set in a regular complicated neural network. Our paper aims to discover the underlying cause of path dependency between two independent substructures. Algorithm DEAH is designed to solve the Basis Path Set Searching problem by eliminating such path dependency. The path subdivision chain is proposed to effectively eliminate the path dependency inside the chain and between chains. The theoretical proofs and analysis of polynomial time complexity are presented. The paper therefore provides one methodology to find the basis path set in a more general neural network, which offers theoretical and algorithmic support for the application of G-SGD algorithm in more practical scenarios.

preprint2020arXiv

DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification

Fine-grained image classification is a challenging problem, since the difficulty of finding discriminative features. To handle this circumstance, basically, there are two ways to go. One is use attention based method to focus on informative areas, while the other one aims to find high order between features. Further, for attention based method there are two directions, activation based and detection based, which are proved effective by scholars. However ,rare work focus on fusing two types of attention with high order feature. In this paper, we propose a novel DAF method which fuse two types of attention and use them to as PAF(part attention filter) in deep bilinear transformation module to mine the relationship between separate parts of an object. Briefly, our network constructed by a student net who attempt to output two attention maps and a teacher net uses these two maps as empirical information to refine the result. The experiment result shows that only student net could get 87.6% accuracy in CUB dataset while cooperating with teacher net could achieve 89.1% accuracy.

preprint2020arXiv

DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs

Subgraph listing is a fundamental problem in graph theory and has wide applications in areas like sociology, chemistry, and social networks. Modern graphs can usually be large-scale as well as highly dynamic, which challenges the efficiency of existing subgraph listing algorithms. Recent works have shown the benefits of partitioning and processing big graphs in a distributed system, however, there is only few work targets subgraph listing on dynamic graphs in a distributed environment. In this paper, we propose an efficient approach, called Distributed and Dynamic Subgraph Listing (DDSL), which can incrementally update the results instead of running from scratch. DDSL follows a general distributed join framework. In this framework, we use a Neighbor-Preserved storage for data graphs, which takes bounded extra space and supports dynamic updating. After that, we propose a comprehensive cost model to estimate the I/O cost of listing subgraphs. Then based on this cost model, we develop an algorithm to find the optimal join tree for a given pattern. To handle dynamic graphs, we propose an efficient left-deep join algorithm to incrementally update the join results. Extensive experiments are conducted on real-world datasets. The results show that DDSL outperforms existing methods in dealing with both static dynamic graphs in terms of the responding time.

preprint2020arXiv

Direct Speech-to-image Translation

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method.

preprint2020arXiv

Effect of magnetic field on transports of charged particles in the weakly ionized plasma with power-law q-distributions in nonextensive statistics

By using the generalized Boltzmann equation of transport in nonextensive statistics, we study transport properties of the diffusion flux and the heat flux of charged particles in the weakly ionized plasma with the power-law q-distributions under the magnetic field. We derive the tensor expressions of diffusion coefficient, thermal diffusion coefficient, mobility and thermal conductivity of electrons and ions in the q-distributed plasma under magnetic field. We show that the tensors of the diffusion coefficient, the thermal diffusion coefficient and the thermal conductivity are strongly depend on the q-parameters in nonextensive statistics, and so they are generally not the same as those in the magnetized plasma with a Maxwell distribution.

preprint2020arXiv

Enhancing the Erdős-Lovász Tihany Conjecture for graphs with independence number two

Let $s\ge2$ and $t\ge2$ be integers. A graph $G$ is $(s,t)$-\emph{splittable} if $V(G)$ can be partitioned into two sets $S$ and $T$ such that $χ(G[S])\geq s$ and $χ(G[T])\geq t$. The well-known Erdős-Lovász Tihany Conjecture from 1968 states that every graph $G$ whose chromatic number $χ(G)=s+t-1$ is more than its clique number $ω(G)$ is $(s,t)$-splittable. In this paper, we prove an enhanced version of the Erdős-Lovász Tihany Conjecture for graphs with independence number two. That is, for every graph $G$ with $χ(G)=s+t-1>ω(G)+1$ is $(s,t+1)$-splittable. There are examples showing that this result is best possible.

preprint2020arXiv

Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise

Greedy-GQ is an off-policy two timescale algorithm for optimal control in reinforcement learning. This paper develops the first finite-sample analysis for the Greedy-GQ algorithm with linear function approximation under Markovian noise. Our finite-sample analysis provides theoretical justification for choosing stepsizes for this two timescale algorithm for faster convergence in practice, and suggests a trade-off between the convergence rate and the quality of the obtained policy. Our paper extends the finite-sample analyses of two timescale reinforcement learning algorithms from policy evaluation to optimal control, which is of more practical interest. Specifically, in contrast to existing finite-sample analyses for two timescale methods, e.g., GTD, GTD2 and TDC, where their objective functions are convex, the objective function of the Greedy-GQ algorithm is non-convex. Moreover, the Greedy-GQ algorithm is also not a linear two-timescale stochastic approximation algorithm. Our techniques in this paper provide a general framework for finite-sample analysis of non-convex value-based reinforcement learning algorithms for optimal control.

preprint2020arXiv

Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference

While increasingly deep networks are still in general desired for achieving state-of-the-art performance, for many specific inputs a simpler network might already suffice. Existing works exploited this observation by learning to skip convolutional layers in an input-dependent manner. However, we argue their binary decision scheme, i.e., either fully executing or completely bypassing one layer for a specific input, can be enhanced by introducing finer-grained, "softer" decisions. We therefore propose a Dynamic Fractional Skipping (DFS) framework. The core idea of DFS is to hypothesize layer-wise quantization (to different bitwidths) as intermediate "soft" choices to be made between fully utilizing and skipping a layer. For each input, DFS dynamically assigns a bitwidth to both weights and activations of each layer, where fully executing and skipping could be viewed as two "extremes" (i.e., full bitwidth and zero bitwidth). In this way, DFS can "fractionally" exploit a layer's expressive power during input-adaptive inference, enabling finer-grained accuracy-computational cost trade-offs. It presents a unified view to link input-adaptive layer skipping and input-adaptive hybrid quantization. Extensive experimental results demonstrate the superior tradeoff between computational cost and model expressive power (accuracy) achieved by DFS. More visualizations also indicate a smooth and consistent transition in the DFS behaviors, especially the learned choices between layer skipping and different quantizations when the total computational budgets vary, validating our hypothesis that layer quantization could be viewed as intermediate variants of layer skipping. Our source code and supplementary material are available at \link{https://github.com/Torment123/DFS}.

preprint2020arXiv

Generative Temporal Link Prediction via Self-tokenized Sequence Modeling

We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks. GLSM captures the temporal link formation patterns from the observed links with a sequence modeling framework and has the ability to generate the emerging links by inferring from the probability distribution on the potential future links. To avoid overfitting caused by treating each link as a unique token, we propose a self-tokenization mechanism to transform each raw link in the network to an abstract aggregation token automatically. The self-tokenization is seamlessly integrated into the sequence modeling framework, which allows the proposed GLSM model to have the generalization capability to discover link formation patterns beyond raw link sequences. We compare GLSM with the existing state-of-art methods on five real-world datasets. The experimental results demonstrate that GLSM obtains future positive links effectively in a generative fashion while achieving the best performance (2-10\% improvements on AUC) among other alternatives.

preprint2020arXiv

Globally optimal consensus maximization for robust visual inertial localization in point and line map

Map based visual inertial localization is a crucial step to reduce the drift in state estimation of mobile robots. The underlying problem for localization is to estimate the pose from a set of 3D-2D feature correspondences, of which the main challenge is the presence of outliers, especially in changing environment. In this paper, we propose a robust solution based on efficient global optimization of the consensus maximization problem, which is insensitive to high percentage of outliers. We first introduce translation invariant measurements (TIMs) for both points and lines to decouple the consensus maximization problem into rotation and translation subproblems, allowing for a two-stage solver with reduced solution dimensions. Then we show that (i) the rotation can be calculated by minimizing TIMs using only 1-dimensional branch-and-bound (BnB), (ii) the translation can be found by running 1-dimensional search for three times with prioritized progressive voting. Compared with the popular randomized solver, our solver achieves deterministic global convergence without depending on an initial value. While compared with existing BnB based methods, ours is exponentially faster. Finally, by evaluating the performance on both simulation and real-world datasets, our approach gives accurate pose even when there are 90\% outliers (only 2 inliers).

preprint2020arXiv

Learning hierarchical behavior and motion planning for autonomous driving

Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution. Due to the coupled action space of behavior and motion, it is challenging to solve HBMP problem using reinforcement learning (RL) for long-horizon driving tasks. We transform HBMP problem by integrating a classical sampling-based motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning. As a result, this formulation reduces action space and diversifies the rewards without losing the optimality of HBMP. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment, so that models trained in a fast event-based simulator, SUMO, can be used to initialize and accelerate the RL training in a dynamics based simulator, CARLA. Experimental results demonstrate the effectiveness of the method. Besides, the model is successfully transferred to the real-world, validating the generalization capability.

preprint2020arXiv

Learning to fool the speaker recognition

Due to the widespread deployment of fingerprint/face/speaker recognition systems, attacking deep learning based biometric systems has drawn more and more attention. Previous research mainly studied the attack to the vision-based system, such as fingerprint and face recognition. While the attack for speaker recognition has not been investigated yet, although it has been widely used in our daily life. In this paper, we attempt to fool the state-of-the-art speaker recognition model and present \textit{speaker recognition attacker}, a lightweight model to fool the deep speaker recognition model by adding imperceptible perturbations onto the raw speech waveform. We find that the speaker recognition system is also vulnerable to the attack, and we achieve a high success rate on the non-targeted attack. Besides, we also present an effective method to optimize the speaker recognition attacker to obtain a trade-off between the attack success rate with the perceptual quality. Experiments on the TIMIT dataset show that we can achieve a sentence error rate of $99.2\%$ with an average SNR $57.2\text{dB}$ and PESQ 4.2 with speed rather faster than real-time.

preprint2020arXiv

Low Complexity Trellis-Coded Quantization in Versatile Video Coding

The forthcoming Versatile Video Coding (VVC) standard adopts the trellis-coded quantization, which leverages the delicate trellis graph to map the quantization candidates within one block into the optimal path. Despite the high compression efficiency, the complex trellis search with soft decision quantization may hinder the applications due to high complexity and low throughput capacity. To reduce the complexity, in this paper, we propose a low complexity trellis-coded quantization scheme in a scientifically sound way with theoretical modeling of the rate and distortion. As such, the trellis departure point can be adaptively adjusted, and unnecessarily visited branches are accordingly pruned, leading to the shrink of total trellis stages and simplification of transition branches. Extensive experimental results on the VVC test model show that the proposed scheme is effective in reducing the encoding complexity by 11% and 5% with all intra and random access configurations, respectively, at the cost of only 0.11% and 0.05% BD-Rate increase. Meanwhile, on average 24% and 27% quantization time savings can be achieved under all intra and random access configurations. Due to the excellent performance, the VVC test model has adopted one implementation of the proposed scheme.

preprint2020arXiv

Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product

Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. In this paper, we propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images. We argue that product attributes and values are highly correlated, e.g., it will be easier to extract the values on condition that the product attributes are given. Thus, we jointly model the attribute prediction and value extraction tasks from multiple aspects towards the interactions between attributes and values. Moreover, product images have distinct effects on our tasks for different product attributes and values. Thus, we selectively draw useful visual information from product images to enhance our model. We annotate a multimodal product attribute value dataset that contains 87,194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task. Our code and dataset will be released to the public.

preprint2020arXiv

Multiple populations in low mass globular clusters: Palomar 13

Since the discovery of chemically peculiar stars in globular clusters in the last century, the study of multiple populations has become increasingly important, given that chemical inhomogeneity is found in almost all globular clusters. Despite various proposed theories attempting to explain this phenomenon, fitting all the observational evidence in globular clusters with one single theory remains notoriously difficult and currently unsuccessful. In order to improve existing models and motivate new ones, we are observing globular clusters at critical conditions, e.g., metal-rich end, metal-poor end, and low mass end. In this paper, we present our first attempt to investigate multiple populations in low mass globular clusters. We obtained low-resolution spectra around 4000 A of 30 members of the globular cluster Palomar 13 using OSIRIS/Multi-object spectrograph mounted at the Gran Telescopio Canarias. The membership of red giant branch stars is confirmed by the latest proper motions from Gaia DR2 and literature velocities. After comparing the measured CN and CH spectral indices with those of the stellar models, we found a clear sign of nitrogen variation among the red giant branch stars. Palomar 13 may be the lowest mass globular cluster showing multiple populations.

preprint2020arXiv

Multiple stellar populations at less evolved stages: detection of chemical variations among main-sequence dwarfs in NGC 1978

Multiple stellar populations (MPs) with different chemical compositions are not exclusive features of old GCs (older than 10 Gyr). Indeed, recent studies reveal that younger clusters ($\sim$2--6 Gyr-old) in the Magellanic Clouds also exhibit star-to-star chemical variations among evolved stars. However, whether MPs are present among less evolved dwarfs of these intermediate-age clusters is still unclear. In this work, we search for chemical variations among GK-type dwarfs in the $\sim$2 Gyr-old cluster NGC 1978, which is the youngest cluster with MPs. We exploit deep ultraviolet and visual observations from the Hubble Space Telescope to constrain the nitrogen (N) and oxygen (O) variations among MS stars. To do this, we compare appropriate photometric diagrams that are sensitive to N and O with synthetic diagrams of simple stellar populations and MPs. We conclude that the G- and K-type MS stars in NGC\,1978 host MPs. Our statistical analysis shows that the fraction of N-rich stars ranges from $\sim$40\% to $\sim$80\%, depending on the detailed distributions of nitrogen and oxygen.

preprint2020arXiv

NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search

Neural architecture search (NAS) is a promising method for automatically design neural architectures. NAS adopts a search strategy to explore the predefined search space to find outstanding performance architecture with the minimum searching costs. Bayesian optimization and evolutionary algorithms are two commonly used search strategies, but they suffer from computationally expensive, challenge to implement or inefficient exploration ability. In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is defined from Bayesian optimization and we propose a graph-based uncertainty estimation network as a surrogate model that is easy to implement and computationally efficient. The second predictor is a graph-based neural network that directly outputs the performance prediction of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Extensive experiments demonstrate the superiority of NPENAS. Quantitative results on three NAS search spaces indicate that both NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-BO achieving state-of-the-art performance on NASBench-201 and NPENAS-NP on NASBench-101 and DARTS, respectively.

preprint2020arXiv

Pillar-based Object Detection for Autonomous Driving

We present a simple and flexible object detection framework optimized for autonomous driving. Building on the observation that point clouds in this application are extremely sparse, we propose a practical pillar-based approach to fix the imbalance issue caused by anchors. In particular, our algorithm incorporates a cylindrical projection into multi-view feature learning, predicts bounding box parameters per pillar rather than per point or per anchor, and includes an aligned pillar-to-point projection module to improve the final prediction. Our anchor-free approach avoids hyperparameter search associated with past methods, simplifying 3D object detection while significantly improving upon state-of-the-art.

preprint2020arXiv

Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

The focus of recent meta-learning research has been on the development of learning algorithms that can quickly adapt to test time tasks with limited data and low computational cost. Few-shot learning is widely used as one of the standard benchmarks in meta-learning. In this work, we show that a simple baseline: learning a supervised or self-supervised representation on the meta-training set, followed by training a linear classifier on top of this representation, outperforms state-of-the-art few-shot learning methods. An additional boost can be achieved through the use of self-distillation. This demonstrates that using a good learned embedding model can be more effective than sophisticated meta-learning algorithms. We believe that our findings motivate a rethinking of few-shot image classification benchmarks and the associated role of meta-learning algorithms. Code is available at: http://github.com/WangYueFt/rfs/.

preprint2020arXiv

SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation

We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep neural networks (DNNs). We develop a novel algorithm to enforce a specially favorable DNN weight structure, where each layerwise weight matrix can be stored as the product of a small basis matrix and a large sparse coefficient matrix whose non-zero elements are all power-of-2. To our best knowledge, this algorithm is the first formulation that integrates three mainstream model compression ideas: sparsification or pruning, decomposition, and quantization, into one unified framework. The resulting sparse and readily-quantized DNN thus enjoys greatly reduced energy consumption in data movement as well as weight storage. On top of that, we further design a dedicated accelerator to fully utilize the SmartExchange-enforced weights to improve both energy efficiency and latency performance. Extensive experiments show that 1) on the algorithm level, SmartExchange outperforms state-of-the-art compression techniques, including merely sparsification or pruning, decomposition, and quantization, in various ablation studies based on nine DNN models and four datasets; and 2) on the hardware level, the proposed SmartExchange based accelerator can improve the energy efficiency by up to 6.7$\times$ and the speedup by up to 19.2$\times$ over four state-of-the-art DNN accelerators, when benchmarked on seven DNN models (including four standard DNNs, two compact DNN models, and one segmentation model) and three datasets.

preprint2020arXiv

Three solutions for a new Kirchhoff-type problem

This article concerns on the existence of multiple solutions for a new Kirchhoff-type problem with negative modulus. We prove that there exist three nontrivial solutions when the parameter is enough small via the variational methods and algebraic analysis. Moreover, our fundamental technique is one of the Mountain Pass Lemma, Ekeland variational principle, and Minimax principle.

preprint2020arXiv

Universal Adversarial Perturbations Generative Network for Speaker Recognition

Attacking deep learning based biometric systems has drawn more and more attention with the wide deployment of fingerprint/face/speaker recognition systems, given the fact that the neural networks are vulnerable to the adversarial examples, which have been intentionally perturbed to remain almost imperceptible for human. In this paper, we demonstrated the existence of the universal adversarial perturbations~(UAPs) for the speaker recognition systems. We proposed a generative network to learn the mapping from the low-dimensional normal distribution to the UAPs subspace, then synthesize the UAPs to perturbe any input signals to spoof the well-trained speaker recognition model with high probability. Experimental results on TIMIT and LibriSpeech datasets demonstrate the effectiveness of our model.

preprint2020arXiv

User-generated Video Quality Assessment: A Subjective and Objective Study

Recently, we have observed an exponential increase of user-generated content (UGC) videos. The distinguished characteristic of UGC videos originates from the video production and delivery chain, as they are usually acquired and processed by non-professional users before uploading to the hosting platforms for sharing. As such, these videos usually undergo multiple distortion stages that may affect visual quality before ultimately being viewed. Inspired by the increasing consensus that the optimization of the video coding and processing shall be fully driven by the perceptual quality, in this paper, we propose to study the quality of the UGC videos from both objective and subjective perspectives. We first construct a UGC video quality assessment (VQA) database, aiming to provide useful guidance for the UGC video coding and processing in the hosting platform. The database contains source UGC videos uploaded to the platform and their transcoded versions that are ultimately enjoyed by end-users, along with their subjective scores. Furthermore, we develop an objective quality assessment algorithm that automatically evaluates the quality of the transcoded videos based on the corrupted reference, which is in accordance with the application scenarios of UGC video sharing in the hosting platforms. The information from the corrupted reference is well leveraged and the quality is predicted based on the inferred quality maps with deep neural networks (DNN). Experimental results show that the proposed method yields superior performance. Both subjective and objective evaluations of the UGC videos also shed lights on the design of perceptual UGC video coding.

preprint2020arXiv

When does the onset of multiple stellar populations in star clusters occur-III: No evidence of significant chemical variations in main-sequence stars of NGC 419

Recent studies have revealed that the onset age for the presence of multiple stellar populations (MPs) in star clusters seems to correspond to the disappearance of the extended main-sequence turnoff (eMSTO) in young clusters, a pattern associated with stellar rotations. A speculative suggestion is that MPs might be caused by the magnetic brake, a stellar evolutionary effect linked to the rotation. In this work, we use the young massive cluster NGC 419 as a testbed. We examined if its magnetically baked MS stars would exhibit MPs. Using the deep ultraviolet and visible images observed through the Hubble Space Telescope, combined with a specific color index that is sensitive to the nitrogen (N) abundance, we examined if its late G- and K-type MS stars are affected by N variation. Our analysis reports that the morphology of its GK-type MS is most likely an SSP, and only a negligible probability that indicates a N variation up to 0.4 dex is present. We conclude that there is no significant N variation among its GK-type MS stars. The absence of a significant chemical variation among the late-type MS stars indicates that MPs might not be a specific pattern of magnetically braked stars.

preprint2020arXiv

When Relation Networks meet GANs: Relation GANs with Triplet Loss

Though recent research has achieved remarkable progress in generating realistic images with generative adversarial networks (GANs), the lack of training stability is still a lingering concern of most GANs, especially on high-resolution inputs and complex datasets. Since the randomly generated distribution can hardly overlap with the real distribution, training GANs often suffers from the gradient vanishing problem. A number of approaches have been proposed to address this issue by constraining the discriminator's capabilities using empirical techniques, like weight clipping, gradient penalty, spectral normalization etc. In this paper, we provide a more principled approach as an alternative solution to this issue. Instead of training the discriminator to distinguish real and fake input samples, we investigate the relationship between paired samples by training the discriminator to separate paired samples from the same distribution and those from different distributions. To this end, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Extensive experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks including unconditional and conditional image generation and image translation.

preprint2019arXiv

Cross interface model for the thermal transport across interface between overlapped boron nitride nanoribbons

The application of low-dimensional materials for heat dissipation requires a comprehensive understanding of the thermal transport at the cross interface, which widely exists in various composite materials and electronic devices. In this work, we proposed an analytical model, named as cross interface model (CIM), to accurately reveal the essential mechanism of the two-dimensional thermal transport at the cross interface. The applicability of CIM is validated through the comparison of the analytical results with molecular dynamics simulations for a typical cross interface of two overlapped boron nitride nanoribbons. Besides, it is figured out that the factor (η) has important influence on the thermal transport besides the thermal resistance inside and between the materials, which is found to be determined by two dimensionless parameters from its expression. Our investigations deepen the understanding of the thermal transport at the cross interface and also facilitate to guide the applications of low-dimensional materials in thermal management.

preprint2019arXiv

Entropic Dynamic Time Warping Kernels for Co-evolving Financial Time Series Analysis

In this work, we develop a novel framework to measure the similarity between dynamic financial networks, i.e., time-varying financial networks. Particularly, we explore whether the proposed similarity measure can be employed to understand the structural evolution of the financial networks with time. For a set of time-varying financial networks with each vertex representing the individual time series of a different stock and each edge between a pair of time series representing the absolute value of their Pearson correlation, our start point is to compute the commute time matrix associated with the weighted adjacency matrix of the network structures, where each element of the matrix can be seen as the enhanced correlation value between pairwise stocks. For each network, we show how the commute time matrix allows us to identify a reliable set of dominant correlated time series as well as an associated dominant probability distribution of the stock belonging to this set. Furthermore, we represent each original network as a discrete dominant Shannon entropy time series computed from the dominant probability distribution. With the dominant entropy time series for each pair of financial networks to hand, we develop a similarity measure based on the classical dynamic time warping framework, for analyzing the financial time-varying networks. We show that the proposed similarity measure is positive definite and thus corresponds to a kernel measure on graphs. The proposed kernel bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks extracted through New York Stock Exchange (NYSE) database demonstrate the effectiveness of the proposed approach.

preprint2019arXiv

Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

Geospatial object detection of remote sensing imagery has been attracting an increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of previously proposed object detectors are very sensitive to object deformations, such as scaling and rotation. To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB). A Fourier-based rotation-invariant feature is first generated in polar coordinate. Then, the extracted features can be further structurally refined using aggregate channel features. This leads to a faster feature computation and more robust feature representation, which is good fitting for the coming boosting learning. Finally, in the test phase, we achieve a fast pyramid feature extraction by estimating a scale factor instead of directly collecting all features from image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness of the FRIFB compared to previous state-of-the-art methods.

preprint2019arXiv

Intrinsic 2D Ferromagnetism in V5Se8 Epitaxial Thin Films

The discoveries of intrinsic ferromagnetism in atomically-thin van der Waals crystals have opened up a new research field enabling fundamental studies on magnetism at two-dimensional (2D) limit as well as development of magnetic van der Waals heterostructures. To date, a variety of 2D ferromagnetism has been explored mainly by mechanically exfoliating 'originally ferromagnetic (FM)' van der Waals crystals, while bottom-up approach by thin film growth technique has demonstrated emergent 2D ferromagnetism in a variety of 'originally non-FM' van der Waals materials. Here we demonstrate that V5Se8 epitaxial thin films grown by molecular-beam epitaxy (MBE) exhibit emergent 2D ferromagnetism with intrinsic spin polarization of the V 3d electrons despite that the bulk counterpart is 'originally antiferromagnetic (AFM)'. Moreover, thickness-dependence measurements reveal that this newly-developed 2D ferromagnet could be classified as an itinerant 2D Heisenberg ferromagnet with weak magnetic anisotropy, broadening a lineup of 2D magnets to those potentially beneficial for future spintronics applications.

preprint2018arXiv

Efficient Two-Dimensional Line Spectrum Estimation Based on Decoupled Atomic Norm Minimization

This paper presents an efficient optimization technique for gridless {2-D} line spectrum estimation, named decoupled atomic norm minimization (D-ANM). The framework of atomic norm minimization (ANM) is considered, which has been successfully applied in 1-D problems to allow super-resolution frequency estimation for correlated sources even when the number of snapshots is highly limited. The state-of-the-art 2-D ANM approach vectorizes the 2-D measurements to their 1-D equivalence, which incurs huge computational cost and may become too costly for practical applications. We develop a novel decoupled approach of 2-D ANM via semi-definite programming (SDP), which introduces a new matrix-form atom set to naturally decouple the joint observations in both dimensions without loss of optimality. Accordingly, the original large-scale 2-D problem is equivalently reformulated via two decoupled one-level Toeplitz matrices, which can be solved by simple 1-D frequency estimation with pairing. Compared with the conventional vectorized approach, the proposed D-ANM technique reduces the computational complexity by several orders of magnitude with respect to the problem size. It also retains the benefits of ANM in terms of precise signal recovery, small number of required measurements, and robustness to source correlation. The complexity benefits are particularly attractive for large-scale antenna systems such as massive MIMO, radar signal processing and radio astronomy.

preprint2018arXiv

Low-complexity optimization for Two-Dimensional Direction-of-arrival Estimation via Decoupled Atomic Norm Minimization

This paper presents an efficient optimization technique for super-resolution two-dimensional (2D) direction of arrival (DOA) estimation by introducing a new formulation of atomic norm minimization (ANM). ANM allows gridless angle estimation for correlated sources even when the number of snapshots is far less than the antenna size, yet it incurs huge computational cost in 2D processing. This paper introduces a novel formulation of ANM via semi-definite programming, which expresses the original high-dimensional problem by two decoupled Toeplitz matrices in one dimension, followed by 1D angle estimation with automatic angle pairing. Compared with the state-of-the-art 2D ANM, the proposed technique reduces the computational complexity by several orders of magnitude with respect to the antenna size, while retaining the benefits of ANM in terms of super-resolution performance with use of a small number of measurements, and robustness to source correlation and noise. The complexity benefits are particularly attractive for large-scale antenna systems such as massive MIMO and radio astronomy.

preprint2016arXiv

A centrosymmetric hexagonal magnet with superstable biskyrmion magnetic nanodomains in a wide temperature range of 100K to 340K

Here, we report on the experimental discovery of biskyrmion magnetic nanodomains at RT and the observation of a biskyrmion-derived topological Hall effect (THE) in the centrosymmetric hexagonal MnNiGa magnet. Using a phase reconstruction technique based on a transport-of-intensity equation (TIE), we established the texture of the biskyrmion spin. Results from Lorentz transmission electron microscopy (TEM) and the topological Hall effect revealed that the biskyrmion phase is stable over a much wider temperature range (100 K to ~340K) and a larger magnetic field range in our material than in skyrmion-hosting bulk crystals reported previously[1-3,6]. The super-wide operating temperature and the broad range of material options indicate important progress toward the realization of skyrmion-based spintronic devices.

preprint2016arXiv

Boundary Expansions for Constant Mean Curvature Surfaces in the Hyperbolic Space

We study expansions near the boundary of solutions to the Dirichlet problem for the constant mean curvature equation in the hyperbolic space. With a characterization of remainders of the expansion by multiple integrals, we establish optimal asymptotic expansions of solutions with boundary values of finite regularity and demonstrate a slight loss of regularity for coefficients.

preprint2016arXiv

Chernoff Information of Bottleneck Gaussian Trees

In this paper, our objective is to find out the determining factors of Chernoff information in distinguishing a set of Gaussian trees. In this set, each tree can be attained via an edge removal and grafting operation from another tree. This is equivalent to asking for the Chernoff information between the most-likely confused, i.e. "bottleneck", Gaussian trees, as shown to be the case in ML estimated Gaussian tree graphs lately. We prove that the Chernoff information between two Gaussian trees related through an edge removal and a grafting operation is the same as that between two three-node Gaussian trees, whose topologies and edge weights are subject to the underlying graph operation. In addition, such Chernoff information is shown to be determined only by the maximum generalized eigenvalue of the two Gaussian covariance matrices. The Chernoff information of scalar Gaussian variables as a result of linear transformation (LT) of the original Gaussian vectors is also uniquely determined by the same maximum generalized eigenvalue. What is even more interesting is that after incorporating the cost of measurements into a normalized Chernoff information, Gaussian variables from LT have larger normalized Chernoff information than the one based on the original Gaussian vectors, as shown in our proved bounds

preprint2016arXiv

Distributed Beam Scheduling for Multi-RAT Coexistence in mm-Wave 5G Networks

Millimetre-wave communication (licensed or unlicensed) is envisaged to be an important part of the fifth generation (5G) multi-RAT ecosystem. In this paper, we consider the spectrum bands shared by 5G cellular base stations and some existing networks, such as WiGig. Sharing the same band among such multiple radio access technologies (RATs) is very challenging due to the lack of centralized coordination and demands novel and efficient interference mitigation and coexistence mechanisms to reduce the mutual interference. To address this important challenge, we propose in this paper a novel multi-RAT coexistence mechanism where neighbouring 5G and WiGig base stations, each serving their own associated UEs, schedule their beam configurations in a distributed manner such that their own utility function, e.g. spectral efficiency, is maximized. We formulate the problem as a combinatorial optimization problem and show via simulations that our proposed distributed algorithms yield a comparable spectral efficiency for the entire networks as that using an exhaustive search, which requires global coordination among coexisting RATs and also has a much higher algorithmic complexity.

preprint2016arXiv

Generalization Error Bounds for Optimization Algorithms via Stability

Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG). Conventional analysis on these optimization algorithms focuses on their convergence rates during the training process, however, people in the machine learning community may care more about the generalization performance of the learned model on unseen test data. In this paper, we investigate on this issue, by using stability as a tool. In particular, we decompose the generalization error for R-ERM, and derive its upper bound for both convex and non-convex cases. In convex cases, we prove that the generalization error can be bounded by the convergence rate of the optimization algorithm and the stability of the R-ERM process, both in expectation (in the order of $\mathcal{O}((1/n)+\mathbb{E}ρ(T))$, where $ρ(T)$ is the convergence error and $T$ is the number of iterations) and in high probability (in the order of $\mathcal{O}\left(\frac{\log{1/δ}}{\sqrt{n}}+ρ(T)\right)$ with probability $1-δ$). For non-convex cases, we can also obtain a similar expected generalization error bound. Our theorems indicate that 1) along with the training process, the generalization error will decrease for all the optimization algorithms under our investigation; 2) Comparatively speaking, SVRG has better generalization ability than GD and SGD. We have conducted experiments on both convex and non-convex problems, and the experimental results verify our theoretical findings.

preprint2016arXiv

Gravitational wave astronomy: the current status

In the centenary year of Einstein's General Theory of Relativity, this paper reviews the current status of gravitational wave astronomy across a spectrum which stretches from attohertz to kilohertz frequencies. Sect. 1 of this paper reviews the historical development of gravitational wave astronomy from Einstein's first prediction to our current understanding the spectrum. It is shown that detection of signals in the audio frequency spectrum can be expected very soon, and that a north-south pair of next generation detectors would provide large scientific benefits. Sect. 2 reviews the theory of gravitational waves and the principles of detection using laser interferometry. The state of the art Advanced LIGO detectors are then described. These detectors have a high chance of detecting the first events in the near future. Sect. 3 reviews the KAGRA detector currently under development in Japan, which will be the first laser interferometer detector to use cryogenic test masses. Sect. 4 of this paper reviews gravitational wave detection in the nanohertz frequency band using the technique of pulsar timing. Sect. 5 reviews the status of gravitational wave detection in the attohertz frequency band, detectable in the polarisation of the cosmic microwave background, and discusses the prospects for detection of primordial waves from the big bang. The techniques described in sects. 1-5 have already placed significant limits on the strength of gravitational wave sources. Sects. 6 and 7 review ambitious plans for future space based gravitational wave detectors in the millihertz frequency band. Sect. 6 presents a roadmap for development of space based gravitational wave detectors by China while sect. 7 discusses a key enabling technology for space interferometry known as time delay interferometry.

preprint2016arXiv

Minimal Graphs in the Hyperbolic Space with Singular Asymptotic Boundaries

We study asymptotic behaviors of solutions $f$ to the Dirichlet problem for minimal graphs in the hyperbolic space with singular asymptotic boundaries under the assumption that the boundaries are piecewise regular with positive curvatures. We derive an estimate of such solutions by the corresponding solutions in the intersections of interior tangent balls. The positivity of curvatures plays an important role.

preprint2016arXiv

NMR Evidence for the Topologically Nontrivial Nature in a Family of Half-Heusler Compounds

Spin-orbit coupling (SOC) is expected to partly determine the topologically nontrivial electronic structure of heavy half-Heusler ternary compounds. However, to date, attempts to experimentally observe either the strength of SOC or how it modifies the bulk band structure have been unsuccessful. By using bulk-sensitive nuclear magnetic resonance (NMR) spectroscopy combined with first-principles calculations, we reveal that 209Bi NMR isotropic shifts scale with relativity in terms of the strength of SOC and average atomic numbers, indicating strong relativistic effects on NMR parameters. According to first-principles calculations, we further claim that nuclear magnetic shieldings from relativistic p1/2 states and paramagnetic contributions from low-lying unoccupied p3/2 states are both sensitive to the details of band structures tuned by relativity, which explains why the hidden relativistic effects on band structure can be revealed by 209Bi NMR isotropic shifts in topologically nontrivial half-Heusler compounds. Used in complement to surface-sensitive methods, such as angle resolved photon electron spectroscopy and scanning tunneling spectroscopy, NMR can provide valuable information on bulk electronic states.

preprint2016arXiv

Nonvolatile Multi-level Memory and Boolean Logic Gates Based on a Single Memtranstor

Memtranstor that correlates charge and magnetic flux via nonlinear magnetoelectric effects has a great potential in developing next-generation nonvolatile devices. In addition to multi-level nonvolatile memory, we demonstrate here that nonvolatile logic gates such as NOR and NAND can be implemented in a single memtranstor made of the Ni/PMN-PT/Ni heterostructure. After applying two sequent voltage pulses (X1, X2) as the logic inputs on the memtranstor, the output magnetoelectric voltage can be positive high (logic "1"), positive low (logic "0"), or negative (logic "0"), depending on the levels of X1 and X2. The underlying physical mechanism is related to the complete or partial reversal of ferroelectric polarization controlled by inputting selective voltage pulses, which determines the magnitude and sign of the magnetoelectric voltage coefficient. The combined functions of both memory and logic could enable the memtranstor as a promising candidate for future computing systems beyond von Neumann architecture.

preprint2016arXiv

Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation

Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera. Although those platforms do not have sensors for 3D depth sensing capability, knowledge of depth is an essential part in many robotics activities. Therefore, recently, there is an increasing interest in depth estimation using monocular images. As this task is inherently ambiguous, the data-driven estimated depth might be unreliable in robotics applications. In this paper, we have attempted to improve the precision of monocular depth estimation by introducing 2D planar observation from the remaining laser range finder without extra cost. Specifically, we construct a dense reference map from the sparse laser range data, redefining the depth estimation task as estimating the distance between the real and the reference depth. To solve the problem, we construct a novel residual of residual neural network, and tightly combine the classification and regression losses for continuous depth estimation. Experimental results suggest that our method achieves considerable promotion compared to the state-of-the-art methods on both NYUD2 and KITTI, validating the effectiveness of our method on leveraging the additional sensory information. We further demonstrate the potential usage of our method in obstacle avoidance where our methodology provides comprehensive depth information compared to the solution using monocular camera or 2D laser range finder alone.

preprint2016arXiv

Roadmap for gravitational wave detection in space - a preliminary study

Part of a review paper entitled "Gravitational wave astronomy: the current status.", appeared in " Science China Physics, Mechanics & Astronomy 58.12 (2015): 1-41.

preprint2016arXiv

Service Intelligence Oriented Distributed Data Stream Integration

Software as a service (SaaS) has recently enjoyed much attention as it makes the use of software more convenient and cost-effective. At the same time, the arising of users' expectation for high quality service such as real-time information or functionality provisioning brings about new challenges: to satisfy such (near) real-time requirements, real-time monitoring and effective processing of streaming data is necessary. However, due to the composition structure and multi-instance property of services, service data streams are often distributed, hard to synchronize and aggregate. We tackles these challenges by (1) proposing systematic associate strategies for relating distributed data; (2) introducing a new small window array mechanism for aggregating distributed data; (3) setting window parameters based on the cumulative distribution function (CDF) method; and (4) modeling streaming operators with queuing models for performance evaluation and prediction. Experiments show that our approach has good accuracy, completeness and acceptable performance measurement in processing distributed service data streams.

preprint2015arXiv

Asymmetric Independence Model for Detecting Interactions between Variables

Detecting complex interactions among risk factors in case-control studies is a fundamental task in clinical and population research. However, though hypothesis testing using logistic regression (LR) is a convenient solution, the LR framework is poorly powered and ill-suited under several common circumstances in practice including missing or unmeasured risk factors, imperfectly correlated "surrogates", and multiple disease sub-types. The weakness of LR in these settings is related to the way in which the null hypothesis is defined. Here we propose the Asymmetric Independence Model (AIM) as a biologically-inspired alternative to LR, based on the key observation that the mechanisms associated with acquiring a "disease" versus maintaining "health" are asymmetric. We prove mathematically that, unlike LR, AIM is a robust model under the abovementioned confounding scenarios. Further, we provide a mathematical definition of a "synergistic" interaction, and prove that theoretically AIM has better power than LR for such interactions. We then experimentally show the superior performance of AIM as compared to LR on both simulations and four real datasets. While the principal application here involves genetic or environmental variables in the life sciences, our methodology is readily applied to other types of measurements and inferences, e.g. in the social sciences.

preprint2015arXiv

Asymptotic Error Free Partitioning over Noisy Boolean Multiaccess Channels

In this paper, we consider the problem of partitioning active users in a manner that facilitates multi-access without collision. The setting is of a noisy, synchronous, Boolean, multi-access channel where $K$ active users (out of a total of $N$ users) seek to access. A solution to the partition problem places each of the $N$ users in one of $K$ groups (or blocks) such that no two active nodes are in the same block. We consider a simple, but non-trivial and illustrative case of $K=2$ active users and study the number of steps $T$ used to solve the partition problem. By random coding and a suboptimal decoding scheme, we show that for any $T\geq (C_1 +ξ_1)\log N$, where $C_1$ and $ξ_1$ are positive constants (independent of $N$), and $ξ_1$ can be arbitrary small, the partition problem can be solved with error probability $P_e^{(N)} \to 0$, for large $N$. Under the same scheme, we also bound $T$ from the other direction, establishing that, for any $T \leq (C_2 - ξ_2) \log N$, the error probability $P_e^{(N)} \to 1$ for large $N$; again $C_2$ and $ξ_2$ are constants and $ξ_2$ can be arbitrarily small. These bounds on the number of steps are lower than the tight achievable lower-bound in terms of $T \geq (C_g +ξ)\log N $ for group testing (in which all active users are identified, rather than just partitioned). Thus, partitioning may prove to be a more efficient approach for multi-access than group testing.

preprint2015arXiv

BACOM2: a Java tool for detecting normal cell contamination of copy number in heterogeneous tumor

We develop a cross-platform open-source Java application (BACOM2) with graphic user interface (GUI), and users also can use a XML file to set the parameters of algorithm model, file paths and the dataset of paired samples. BACOM2 implements the new entire pipeline of copy number change analysis for heterogeneous cancer tissues, including extraction of raw copy number signals from CEL files of paired samples, attenuation correction, identification of balanced AB-genotype loci, copy number detection and segmentation, global baseline calculation and absolute normalization, differentiation of deletion types, estimation of the normal tissue fraction and correction of normal tissue contamination. BACOM2 focuses on the common tools for data preparation and absolute normalization for copy number analysis of heterogeneous cancer tissues. The software provides an additional choice for scientists who require a user-friendly, high-speed processing, cross-platform computing environment for large copy number data analysis.

preprint2015arXiv

Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources

Blind Source Separation (BSS) has proven to be a powerful tool for the analysis of composite patterns in engineering and science. We introduce Convex Analysis of Mixtures (CAM) for separating non-negative well-grounded sources, which learns the mixing matrix by identifying the lateral edges of the convex data scatter plot. We prove a sufficient and necessary condition for identifying the mixing matrix through edge detection, which also serves as the foundation for CAM to be applied not only to the exact-determined and over-determined cases, but also to the under-determined case. We show the optimality of the edge detection strategy, even for cases where source well-groundedness is not strictly satisfied. The CAM algorithm integrates plug-in noise filtering using sector-based clustering, an efficient geometric convex analysis scheme, and stability-based model order selection. We demonstrate the principle of CAM on simulated data and numerically mixed natural images. The superior performance of CAM against a panel of benchmark BSS techniques is demonstrated on numerically mixed gene expression data. We then apply CAM to dissect dynamic contrast-enhanced magnetic resonance imaging data taken from breast tumors and time-course microarray gene expression data derived from in-vivo muscle regeneration in mice, both producing biologically plausible decomposition results.

preprint2015arXiv

Large low-field positive magnetoresistance in nonmagnetic half-Heusler ScPtBi single crystal

High-quality nonmagnetic half-Heusler ScPtBi single crystals were synthesized by a Bi self-flux method. This compound was revealed to be a hole-dominated semimetal with a large low-field magnetoresistance up to 240% at 2K in a magnetic field of 1T. Magneto-transport measurements demonstrated that the large low-field magnetoresistance effect resulted from the coexistence of field-induced metal-semiconductor transition and weak-antilocalization effect. Moreover, Hall measurements indicated that ScPtBi single crystal showed a high mobility over a wide temperature region even up to room temperature (4050 cm2V-1s-1 at 2K - 2016 cm2V-1s-1 at 300K). These findings not only suggest the nonmagnetic ScPtBi semimetal a potential material candidate for applications in high-sensitivity magnetic sensors, but also are of great significance to comprehensively understand the rare-earth based half-Heusler compounds.

preprint2015arXiv

Optimal regularity of minimal graphs in the hyperbolic space

We discuss the global regularity of solutions $f$ to the Dirichlet problem for minimal graphs in the hyperbolic space when the boundary of the domain $Ω\subset\mathbb R^n$ has a nonnegative mean curvature and prove an optimal regularity $f\in C^{\frac{1}{n+1}}(\barΩ)$. We can improve the Hölder exponent for $f$ if certain combinations of principal curvatures of the boundary do not vanish, a phenomenon observed by F.-H. Lin.

preprint2015arXiv

Place classification with a graph regularized deep neural network model

Place classification is a fundamental ability that a robot should possess to carry out effective human-robot interactions. It is a nontrivial classification problem which has attracted many research. In recent years, there is a high exploitation of Artificial Intelligent algorithms in robotics applications. Inspired by the recent successes of deep learning methods, we propose an end-to-end learning approach for the place classification problem. With the deep architectures, this methodology automatically discovers features and contributes in general to higher classification accuracies. The pipeline of our approach is composed of three parts. Firstly, we construct multiple layers of laser range data to represent the environment information in different levels of granularity. Secondly, each layer of data is fed into a deep neural network model for classification, where a graph regularization is imposed to the deep architecture for keeping local consistency between adjacent samples. Finally, the predicted labels obtained from all the layers are fused based on confidence trees to maximize the overall confidence. Experimental results validate the effective- ness of our end-to-end place classification framework in which both the multi-layer structure and the graph regularization promote the classification performance. Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.

preprint2015arXiv

The overshoot and phenotypic equilibrium in characterizing cancer dynamics of reversible phenotypic plasticity

The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which extends the cellular hierarchy proposed by the classical cancer stem cell (CSC) theory. Since it is still question able if the phenotypic plasticity is a crucial improvement to the hierarchical model or just a minor extension to it, it is worthwhile to explore the dynamic behavior characterizing the reversible phenotypic plasticity. In this study we compare the hierarchical model and the reversible model in predicting the cell-state dynamics observed in biological experiments. Our results show that the hierarchical model shows significant disadvantages over the reversible model in describing both long-term stability (phenotypic equilibrium) and short-term transient dynamics (overshoot) of cancer cells. In a very specific case in which the total growth of population due to each cell type is identical, the hierarchical model predicts neither phenotypic equilibrium nor overshoot, whereas thereversible model succeeds in predicting both of them. Even though the performance of the hierarchical model can be improved by relaxing the specific assumption, its prediction to the phenotypic equilibrium strongly depends on a precondition that may be unrealistic in biological experiments, and it also fails to capture the overshoot of CSCs. By comparison, it is more likely for the reversible model to correctly describe the stability of the phenotypic mixture and various types of overshoot behavior.

preprint2015arXiv

The phenotypic equilibrium of cancer cells: From average-level stability to path-wise convergence

The phenotypic equilibrium, i.e. heterogeneous population of cancer cells tending to a fixed equilibrium of phenotypic proportions, has received much attention in cancer biology very recently. In previous literature, some theoretical models were used to predict the experimental phenomena of the phenotypic equilibrium, which were often explained by different concepts of stabilities of the models. Here we present a stochastic multi-phenotype branching model by integrating conventional cellular hierarchy with phenotypic plasticity mechanisms of cancer cells. Based on our model, it is shown that: (i) our model can serve as a framework to unify the previous models for the phenotypic equilibrium, and then harmonizes the different kinds of average-level stabilities proposed in these models; and (ii) path-wise convergence of our model provides a deeper understanding to the phenotypic equilibrium from stochastic point of view. That is, the emergence of the phenotypic equilibrium is rooted in the stochastic nature of (almost) every sample path, the average-level stability just follows from it by averaging stochastic samples.

preprint2015arXiv

Transition from semiconducting to metallic-like conducting and weak antilocalization effect in single crystals of LuPtSb

High quality half-Heusler single crystals of LuPtSb have been synthesized by a Pb flux method. The temperature dependent resistivity and Hall effects indicate that the LuPtSb crystal is a p-type gapless semiconductor showing a transition from semiconducting to metallic conducting at 150 K. Moreover, a weakly temperature-dependent positive magnetoresistance (MR) as large as 109 % and high carrier mobility up to 2950 cm2/Vs are experimentally observed at temperatures below 150 K. The low-field MR data shows evidence for weak antilocalization (WAL) effect at temperatures even up to 150 K. Analysis of the temperature and angle dependent magnetoconductance manifests that the WAL effect originates from the bulk contribution owing to the strong spin-orbital coupling.

preprint2015arXiv

Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks

Scene classification is a fundamental perception task for environmental understanding in today's robotics. In this paper, we have attempted to exploit the use of popular machine learning technique of deep learning to enhance scene understanding, particularly in robotics applications. As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples. Inspired by human scene understanding based on object knowledge, we address the problem of scene classification by encouraging deep neural networks to incorporate object-level information. This is implemented with a regularization of semantic segmentation. With only 5 thousand training images, as opposed to 2.5 million images, we show the proposed deep architecture achieves superior scene classification results to the state-of-the-art on a publicly available SUN RGB-D dataset. In addition, performance of semantic segmentation, the regularizer, also reaches a new record with refinement derived from predicted scene labels. Finally, we apply our SUN RGB-D dataset trained model to a mobile robot captured images to classify scenes in our university demonstrating the generalization ability of the proposed algorithm.

preprint2014arXiv

A Data- and Workload-Aware Algorithm for Range Queries Under Differential Privacy

We describe a new algorithm for answering a given set of range queries under $ε$-differential privacy which often achieves substantially lower error than competing methods. Our algorithm satisfies differential privacy by adding noise that is adapted to the input data and to the given query set. We first privately learn a partitioning of the domain into buckets that suit the input data well. Then we privately estimate counts for each bucket, doing so in a manner well-suited for the given query set. Since the performance of the algorithm depends on the input database, we evaluate it on a wide range of real datasets, showing that we can achieve the benefits of data-dependence on both "easy" and "hard" databases.

preprint2014arXiv

A feasible roadmap for developing volumetric probability atlas of localized prostate cancer

A statistical volumetric model, showing the probability map of localized prostate cancer within the host anatomical structure, has been developed from 90 optically-imaged surgical specimens. This master model permits an accurate characterization of prostate cancer distribution patterns and an atlas-informed biopsy sampling strategy. The model is constructed by mapping individual prostate models onto a site model, together with localized tumors. An accurate multi-object non-rigid warping scheme is developed based on a mixture of principal-axis registrations. We report our evaluation and pilot studies on the effectiveness of the method and its application to optimizing needle biopsy strategies.

preprint2014arXiv

A feasible roadmap to identifying significant intercellular genomic heterogeneity in deep sequencing data

Intercellular heterogeneity serves as both a confounding factor in studying individual clones and an information source in characterizing any heterogeneous tissues, such as blood, tumor systems. Due to inevitable sequencing errors and other sample preparation artifacts such as PCR errors, systematic efforts to characterize intercellular genomic heterogeneity must effectively distinguish genuine clonal sequences from fake derivatives. We developed a novel approach (SIGH) for identifying significant genuine clonal sequences directly from mixed sequencing reads that can improve genomic analyses in many biological contexts. This method offers several attractive features: (1) it automatically estimates the error rate from raw sequence reads and identifies genuine clonal sequences; (2) it is robust to the large variety of error rate due to the various experimental conditions; (3) it is supported by a well grounded statistical framework that exploits probabilistic characteristics of sequencing errors; (4) its unbiased strategy allows detecting rare clone(s) despite that clone relative abundance; and (5) it estimates constituent proportions in each sample. Extensive realistic simulation studies show that our method can reliably estimate the error rates and faithfully distinguish the genuine clones from fake derivatives, paving the way for follow up analysis that is otherwise ruined by the often dominant fake clones.

preprint2014arXiv

A multi-phenotypic cancer model with cell plasticity

The conventional cancer stem cell (CSC) theory indicates a hierarchy of CSCs and non-stem cancer cells (NSCCs), that is, CSCs can differentiate into NSCCs but not vice versa. However, an alternative paradigm of CSC theory with reversible cell plasticity among cancer cells has received much attention very recently. Here we present a generalized multi-phenotypic cancer model by integrating cell plasticity with the conventional hierarchical structure of cancer cells. We prove that under very weak assumption, the nonlinear dynamics of multi-phenotypic proportions in our model has only one stable steady state and no stable limit cycle. This result theoretically explains the phenotypic equilibrium phenomena reported in various cancer cell lines. Furthermore, according to the transient analysis of our model, it is found that cancer cell plasticity plays an essential role in maintaining the phenotypic diversity in cancer especially during the transient dynamics. Two biological examples with experimental data show that the phenotypic conversions from NCSSs to CSCs greatly contribute to the transient growth of CSCs proportion shortly after the drastic reduction of it. In particular, an interesting overshooting phenomenon of CSCs proportion arises in three-phenotypic example. Our work may pave the way for modeling and analyzing the multi-phenotypic cell population dynamics with cell plasticity.

preprint2014arXiv

A Statistical Approach to Identifying Significant Transgenerational Methylation Changes

Epigenetic aberrations have profound effects on phenotypic output. Genome wide methylation alterations are inheritable to pass down the aberrations through multiple generations. We developed a statistical method, Genome-wide Identification of Significant Methylation Alteration, GISAIM, to study the significant transgenerational methylation changes. GISAIM finds the significant methylation aberrations that are inherited through multiple generations. In a concrete biological study, we investigated whether exposing pregnant rats (F0) to a high fat (HF) diet throughout pregnancy or ethinyl estradiol (EE2)-supplemented diet during gestation days 14 20 affects carcinogen-induced mammary cancer risk in daughters (F1), granddaughters (F2) and great-granddaughters (F3). Mammary tumorigenesis was higher in daughters and granddaughters of HF rat dams, and in daughters, granddaughters and great-granddaughters of EE2 rat dams. Outcross experiments showed that increased mammary cancer risk was transmitted to HF granddaughters equally through the female or male germlines, but is only transmitted to EE2 granddaughters through the female germline. Transgenerational effect on mammary cancer risk was associated with increased expression of DNA methyltransferases, and across all three EE2 generations hypo or hyper methylation of the same 375 gene promoter regions in their mammary glands. Our study shows that maternal dietary estrogenic exposures during pregnancy can increase breast cancer risk in multiple generations of offspring, and the increase in risk may be inherited through non-genetic means, possibly involving DNA methylation.

preprint2014arXiv

Are there any first-generation stars in globular clusters today?

Several models compete to explain the abundance properties of stellar populations in globular clusters. One of the main constraints is the present-day ratio of first- and second-generation stars that are currently identified based on their sodium content. We propose an alternative interpretation of the observed sodium distribution, and suggest that stars with low sodium abundance that are counted as members of the first stellar generation could actually be second-generation stars. We compute the number ratio of second-generation stars along the Na distribution following the fast rotating massive star model using the same constraints from the well-documented case of NGC 6752 as in our previous developments. We reproduce the typical percentage of low-sodium stars usually classified as first-generation stars by invoking only secondary star formation from material ejected by massive stars and mixed with original globular cluster material in proportions that account for the Li-Na anti-correlation in this cluster. Globular clusters could be totally devoid of first-generation low-mass stars today. This can be tested with the determination of the carbon isotopic ratio and nitrogen abundance in turn-off globular cluster stars. Consequences and related issues are briefly discussed.

preprint2014arXiv

BACOM 2.0 facilitates absolute normalization and quantification of somatic copy number alterations in heterogeneous tumor

BACOM is a statistically principled and unsupervised method that detects copy number deletion types (homozygous versus heterozygous), estimates normal cell fraction, and recovers cancer specific copy number profiles, using allele specific copy number signals. In a subsequent analysis of TCGA ovarian cancer dataset, the average normal cell fraction estimated by BACOM was found higher than expected. In this letter, we first discuss the advantages of the BACOM in relation to alternative approaches. Then, we show that this elevated estimate of normal cell fraction is the combined result of inaccurate signal modeling and normalization. Lastly, we describe an allele specific signal modeling and normalization scheme that can enhance BACOM applications in many biological contexts. An open source MATLAB program was developed to implement our extended method and it is publically available.

preprint2014arXiv

Image Representation Learning Using Graph Regularized Auto-Encoders

We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning. In those learning tasks, the raw image vectors may not provide enough representation for their intrinsic structures due to their highly dense feature space. To overcome this problem, the raw image vectors should be mapped to a proper representation space which can capture the latent structure of the original data and represent the data explicitly for further learning tasks such as clustering. Inspired by the recent research works on deep neural network and representation learning, in this paper, we introduce the multiple-layer auto-encoder into image representation, we also apply the locally invariant ideal to our image representation with auto-encoders and propose a novel method, called Graph regularized Auto-Encoder (GAE). GAE can provide a compact representation which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. Extensive experiments on image clustering show encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-word cases.

preprint2014arXiv

Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results.

preprint2014arXiv

Liouville type theorems for the p-harmonic functions

We show that the Dirichlet problem at infinity is unsolvable for the p-Laplace equation for any nonconstant continuous boundary data, for certain range of p>n, on an n-dimensional Cartan-Hadamard manifold constructed from a complete noncompact shrinking gradient Ricci soliton. Using the steady gradient Ricci soliton, we find an incomplete Riemannian metric on ${\mathbb R}^2$ with positive Gauss curvature such that every positive p-harmonic function must be constant for $p\geq 4$.

preprint2014arXiv

Nonlinear attitude stability of a spacecraft on a stationary orbit around an asteroid subjected to gravity gradient torque

The classical problem of attitude stability in a central gravity field is generalized to that on a stationary orbit around a uniformly-rotating asteroid. This generalized problem is studied in the framework of geometric mechanics. Based on the natural symplectic structure, the non-canonical Hamiltonian structure of the problem is derived. The Poisson tensor, Casimir functions and equations of motion are obtained in a differential geometric method. The equilibrium of the equations of motion, i.e. the equilibrium attitude of the spacecraft, is determined from a global point of view. Nonlinear stability conditions of the equilibrium attitude are obtained with the energy-Casimir method. The nonlinear attitude stability is then investigated versus three parameters of the asteroid, including the ratio of the mean radius to the stationary orbital radius, the harmonic coefficients C20 and C22. It is found that when the spacecraft is located on the intermediate-moment principal axis of the asteroid, the nonlinear stability domain can be totally different from the classical Lagrange region on a circular orbit in a central gravity field.

preprint2014arXiv

Partition Information and its Transmission over Boolean Multi-Access Channels

In this paper, we propose a novel partition reservation system to study the partition information and its transmission over a noise-free Boolean multi-access channel. The objective of transmission is not message restoration, but to partition active users into distinct groups so that they can, subsequently, transmit their messages without collision. We first calculate (by mutual information) the amount of information needed for the partitioning without channel effects, and then propose two different coding schemes to obtain achievable transmission rates over the channel. The first one is the brute force method, where the codebook design is based on centralized source coding; the second method uses random coding where the codebook is generated randomly and optimal Bayesian decoding is employed to reconstruct the partition. Both methods shed light on the internal structure of the partition problem. A novel hypergraph formulation is proposed for the random coding scheme, which intuitively describes the information in terms of a strong coloring of a hypergraph induced by a sequence of channel operations and interactions between active users. An extended Fibonacci structure is found for a simple, but non-trivial, case with two active users. A comparison between these methods and group testing is conducted to demonstrate the uniqueness of our problem.

preprint2014arXiv

Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

preprint2013arXiv

A feasible roadmap for unsupervised deconvolution of two-source mixed gene expressions

Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we ask whether it is possible to deconvolute two-source mixed expressions (estimating both proportions and cell-specific profiles) from two or more heterogeneous samples without requiring any prior knowledge. Supported by a well-grounded mathematical framework, we argue that both constituent proportions and cell-specific expressions can be estimated in a completely unsupervised mode when cell-specific marker genes exist, which do not have to be known a priori, for each of constituent cell types. We demonstrate the performance of unsupervised deconvolution on both simulation and real gene expression data, together with perspective discussions.

preprint2013arXiv

Building the Chessboard-like Supramolecular Structure on Au (111) Surfaces

We investigate an anthracene derivative, 3(5)-(9-anthryl) pyrazole (ANP), self-assembled on the Au (111) surface by means of scanning tunneling microscopy (STM) and density functional theory (DFT) calculations. A chessboard-like network structure composed of ANP molecules is found, covering the whole Au (111) substrate. Our STM results and DFT calculations reveal that the formation of chessboard-like networks originates from a basic unit cell, a tetramer structure, which is formed by four ANP molecules connected through C-H-N hydrogen-bonds. The hydrogen bonds inside each tetramer and the molecular adsorption interaction are fundamentally important in providing a driving force for formation of the supramolecular networks.

preprint2013arXiv

Gravity gradient torque of spacecraft orbiting asteroids

Purpose: This paper presents a full fourth-order model of the gravity gradient torque of spacecraft around asteroids by taking into consideration of the inertia integrals of the spacecraft up to the fourth order, which is an improvement of the previous fourth-order model of the gravity gradient torque. Design, methodology and approach: The fourth-order gravitational potential of the spacecraft is derived based on Taylor expansion. Then the expression of the gravity gradient torque in terms of gravitational potential derivatives is derived. By using the formulation of the gravitational potential, explicit formulations of the full fourth-order gravity gradient torque are obtained. Then a numerical simulation is carried out to verify our model. Findings: We find that our model is more sound and precise than the previous fourth-order model due to the consideration of higher-order inertia integrals of the spacecraft. Numerical simulation results show that the motion of the previous fourth-order model is quite different from the exact motion, while our full fourth-order model fits the exact motion very well. Our full fourth-order model is precise enough for high-precision attitude dynamics and control around asteroids. Practical implications: This high-precision model is of importance for the future asteroids missions for scientific explorations and near-Earth objects mitigation. Originality and value: In comparison with the previous model, a gravity gradient torque model around asteroids that is more sound and precise is established. This model is valuable for high-precision attitude dynamics and control around asteroids.

preprint2013arXiv

On the existence of the relative equilibria of a rigid body in the J2 problem

The motion of a point mass in the J2 problem has been generalized to that of a rigid body in a J2 gravity field for new high-precision applications in the celestial mechanics and astrodynamics. Unlike the original J2 problem, the gravitational orbit-rotation coupling of the rigid body is considered in the generalized problem. The existence and properties of both the classical and non-classical relative equilibria of the rigid body are investigated in more details in the present paper based on our previous results. We nondimensionalize the system by the characteristic time and length to make the study more general. Through the study, it is found that the classical relative equilibria can always exist in the real physical situation. Numerical results suggest that the non-classical relative equilibria only can exist in the case of a negative J2, i.e., the central body is elongated; they cannot exist in the case of a positive J2 when the central body is oblate. In the case of a negative J2, the effect of the orbit-rotation coupling of the rigid body on the existence of the non-classical relative equilibria can be positive or negative, which depends on the values of J2 and the angular velocity. The bifurcation from the classical relative equilibria, at which the non-classical relative equilibria appear, has been shown with different parameters of the system. Our results here have given more details of the relative equilibria than our previous paper, in which the existence conditions of the relative equilibria are derived and primarily studied. Our results have also extended the previous results on the relative equilibria of a rigid body in a central gravity field by taking into account the oblateness of the central body.

preprint2013arXiv

Stability of the classical type of relative equilibria of a rigid body in the J2 problem

The motion of a point mass in the J2 problem is generalized to that of a rigid body in a J2 gravity field. The linear and nonlinear stability of the classical type of relative equilibria of the rigid body, which have been obtained in our previous paper, are studied in the framework of geometric mechanics with the second-order gravitational potential. Non-canonical Hamiltonian structure of the problem, i.e., Poisson tensor, Casimir functions and equations of motion, are obtained through a Poisson reduction process by means of the symmetry of the problem. The linear system matrix at the relative equilibria is given through the multiplication of the Poisson tensor and Hessian matrix of the variational Lagrangian. Based on the characteristic equation of the linear system matrix, the conditions of linear stability of the relative equilibria are obtained. The conditions of nonlinear stability of the relative equilibria are derived with the energy-Casimir method through the projected Hessian matrix of the variational Lagrangian. With the stability conditions obtained, both the linear and nonlinear stability of the relative equilibria are investigated in details in a wide range of the parameters of the gravity field and the rigid body. We find that both the zonal harmonic J2 and the characteristic dimension of the rigid body have significant effects on the linear and nonlinear stability. Similar to the classical attitude stability in a central gravity field, the linear stability region is also consisted of two regions that are analogues of the Lagrange region and the DeBra-Delp region respectively. The nonlinear stability region is the subset of the linear stability region in the first quadrant that is the analogue of the Lagrange region. Our results are very useful for the studies on the motion of natural satellites in our solar system.

preprint2013arXiv

The Influence of Radial Stellar Migration on the Chemical Evolution of the Milky Way

Stellar migration is an important dynamical process in Galactic disk. Here we model the radial stellar migration in the Galactic disk with an analytical method, then add it to detailed Galactic chemical evolution model to study the influence of radial stellar migration on the chemical evolution of the Milky Way, especially for the abundance gradients. We found that the radial stellar migration in the Galactic disk can make the profile of the G-dwarf metallicity distribution of the solar neighborhood taller and narrower, thus it becomes another solution to the "G-dwarf problem". It can also scatter the age-metallicity relation. However, after the migration, the abundance distributions along the Galactic radius don't change much, namely the abundance gradients would not be flattened by the radial stellar migration, which is different from the predictions of many theoretical works. But it can flatten the radial gradients of the mean chemical abundance of stars, and older stars possess flatter abundance gradients than younger stars. The most significant effect of the radial stellar migration on the chemical abundance is that at a position it scatters the abundance of stars there from a relatively concentrated value to a range.

preprint2012arXiv

A Tutorial of 802.11 Implementation in NS-2

By analyzing the source codes of ns-2, we discuss the simulated implementations of wireless channels, network interfaces and mostly the 802.11 MAC protocol in ns-2. We also notice the "bugs" of the 802.11 simulation compared with the reality, and present an extension to fading channels as well.

preprint2012arXiv

Asymptotic Outage Probability Analysis for General Fixed-Gain Amplify-and-Forward Multihop Relay Systems

In this paper, we present an analysis of the outage probability for fixed-gain amplify-and-forward (AF) multihop relay links operating in the high SNR regime. Our analysis exploits properties of Mellin transforms to derive an asymptotic approximation that is accurate even when the per-hop channel gains adhere to completely different fading models. The main result contained in the paper is a general expression for the outage probability, which is a functional of the Mellin transforms of the per-hop channel gains. Furthermore, we explicitly calculate the asymptotic outage probability for four different systems, whereby in each system the per-hop channels adhere to either a Nakagami-m, Weibull, Rician, or Hoyt fading profile, but where the distributional parameters may differ from hop to hop. This analysis leads to our second main result, which is a semi-general closed-form formula for the outage probability of general fixed-gain AF multihop systems. We exploit this formula to analyze an example scenario for a four-hop system where the per-hop channels follow the four aforementioned fading models, i.e., the first channel is Nakagami-m fading, the second is Weibull fading, and so on. Finally, we provide simulation results to corroborate our analysis.

preprint2012arXiv

Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments

Graphical models are widely used in scienti fic and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce significant structural changes in the corresponding graphical models. Therefore, it is of great importance to be able to detect such structural changes from data, so as to gain novel insights into where and how the structural changes take place and help the system adapt to the new environment. Here we report an effective learning strategy to extract structural changes in Gaussian graphical model using l1-regularization based convex optimization. We discuss the properties of the problem formulation and introduce an efficient implementation by the block coordinate descent algorithm. We demonstrate the principle of the approach on a numerical simulation experiment, and we then apply the algorithm to the modeling of gene regulatory networks under different conditions and obtain promising yet biologically plausible results.

preprint2012arXiv

Omni-tomography: Next-generation Biomedical Imaging

Omni-tomography is enabled by interior tomography that has been developed over the past five years. By omni-tomography, we envision that the next stage of biomedical imaging will be the grand fusion of many tomographic modalities into a single gantry (all in one) for simultaneous data acquisition of numerous complementary features (all at once). This integration has great synergistic potential for development of systems biology, personalized and preventive medicine, because many physiological processes are dynamic and complicated, and must be observed promptly, comprehensively, sensitively, specifically, and non-invasively. In this perspective, we first present the background for and power of omni-tomography, then discuss its important applications in vulnerable plaque characterization and intratumor heterogeneity evaluation, review its enabling theory and technology, explain for the first time the feasibility of the CT-MRI scanner as an example, and finally suggest exciting research opportunities.

preprint2012arXiv

On Effectiveness of Backlog Bounds Using Stochastic Network Calculus in 802.11

Network calculus is a powerful methodology of characterizing queueing processes and has wide applications, but few works on applying it to 802.11 by far. In this paper, we take one of the first steps to analyze the backlog bounds of an 802.11 wireless LAN using stochastic network calculus. In particular, we want to address its effectiveness on bounding backlogs. We model a wireless node as a single server with impairment service based on two best-known models in stochastic network calculus: Jiang's and Ciucu's. Interestingly, we find that the two models can derive equivalent stochastic service curves and backlog bounds in our studied case. We prove that the network-calculus bounds imply stable backlogs as long as the average rate of traffic arrival is less than that of service, indicating the theoretical effectiveness of stochastic network calculus in bounding backlogs. From A. Kumar's 802.11 model, we derive the concrete stochastic service curve of an 802.11 node and its backlog bounds. We compare the derived bounds with ns-2 simulations and find that the former are very loose and we discuss the reasons. And we show that the martingale and independent case analysis techniques can improve the bounds significantly. Our work offers a good reference to applying stochastic network calculus to practical scenarios.

preprint2011arXiv

A sol-gel method for growing superconducting MgB2 films

In this paper we report a new sol-gel method for the fabrication of MgB2 films. Polycrystalline MgB2 films were prepared by spin-coating a precursor solution of Mg(BH_4)_2 diethyl ether on (001)Al2O3 substrates followed with annealing in Mg vapor. In comparison with the MgB2 films grown by other techniques, our films show medium qualities including a superconducting transition temperature of Tc ~ 37 K, a critical current density of Jc(5 K, 0 T) ~ 5 {\times} 10^6 A cm^{-2}, and a critical field of H_{c2}(0) ~ 19 T. Such a sol-gel technique shows potential in the commercial fabrication of practically used MgB2 films as well as MgB2 wires and tapes.

preprint2011arXiv

Anisotropic magnetotransport of superconducting and normal state in an electron-doped Nd_{1.85}Ce_{0.15}CuO_{4-δ} single crystal

The anisotropic properties of an optimally doped Nd_{1.85}Ce_{0.15}CuO_{4-δ} single crystal have been studied both below and above the critical temperature Tc via the resistivity measurement in magnetic field H up to 12 T. By scaling the conductivity fluctuation around the superconducting transition, the upper critical field H_{c2}(T) has been determined for field parallel to the c-axis or to the basal ab-plane. The anisotropy factor γ={H_c2||ab}/{H_c2||c} is estimated to be about 8. In the normal state (50=<T=<180 K), the magnetoresistance (MR) basically follows an H^2 dependence and for H||c it is almost 10 times larger than that for H||ab. Comparing with hole-doped cuprates it suggests that the optimally doped Nd_{1.85}Ce_{0.15}CuO_{4-δ} cuprate superconductor has a moderate anisotropy.

preprint2011arXiv

Controlling Communication Field of Complex Networks by Transformation Method

Controlling the global statuses of a network by its local dynamic parameters is an important issue, and it is difficult to obtain the direct solution for. The transformation method, which is originally used to control physical field by designing material parameters, is proposed to obtain the necessary local dynamic parameters when the global statuses of a network system are prescribed in a space. The feasibility of this transformation method is demonstrated and verified by two examples (a communication field cloak and a communication field bender) in the network system. It is shown that the global system state can be controlled by adjusting the local nodes dynamics with the transformation method. Simulation results also show that the transformation method provides a powerful, intuitive and direct way for the global statuses controlling of network systems.

preprint2011arXiv

Determination of the superconducting gap in near optimally doped Bi_2Sr_{2-x}La_xCuO_{6+δ} (x ~ 0.4) from low-temperature specific heat

Low-temperature specific heat of the monolayer high-Tc superconductor Bi_2Sr_{2-x}La_xCuO_{6+δ} has been measured close to the optimal doping point (x ~ 0.4) in different magnetic fields. The identification of both a T^2 term in zero field and a \sqrt{H} dependence of the specific heat in fields is shown to follow the theoretical prediction for d-wave pairing, which enables us to extract the slope of the superconducting gap in the vicinity of the nodes (v_Δ, which is proportional to the superconducting gap Δ_0 at the antinodes according to the standard d_{x^2-y^2} gap function). The v_Δ or Δ_0 (~ 12 meV) determined from this bulk measurement shows close agreement with that obtained from spectroscopy or tunneling measurements, which confirms the simple d-wave form of the superconducting gap.

preprint2011arXiv

Omni-tomography/Multi-tomography -- Integrating Multiple Modalities for Simultaneous Imaging

Current tomographic imaging systems need major improvements, especially when multi-dimensional, multi-scale, multi-temporal and multi-parametric phenomena are under investigation. Both preclinical and clinical imaging now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities to define morphologic details, delineate interval changes due to disease or interventions, and study physiological functions that have interconnected aspects. Over the past decade, fusion of multimodality images has emerged with two different approaches: post-hoc image registration and combined acquisition on PET-CT, PET-MRI and other hybrid scanners. There are intrinsic limitations for both the post-hoc image analysis and dual/triple modality approaches defined by registration errors and physical constraints in the acquisition chain. We envision that tomography will evolve beyond current modality fusion and towards grand fusion, a large scale fusion of all or many imaging modalities, which may be referred to as omni-tomography or multi-tomography. Unlike modality fusion, grand fusion is here proposed for truly simultaneous but often localized reconstruction in terms of all or many relevant imaging mechanisms such as CT, MRI, PET, SPECT, US, optical, and possibly more. In this paper, the technical basis for omni-tomography is introduced and illustrated with a top-level design of a next generation scanner, interior tomographic reconstructions of representative modalities, and anticipated applications of omni-tomography.

preprint2011arXiv

The nullity of unicyclic signed graphs

In this paper we introduce the nullity of signed graphs, and give some results on the nullity of signed graphs with pendant trees. We characterize the unicyclic signed graphs of order n with nullity n-2; n-3; n-4; n-5 respectively.

preprint2011arXiv

Ultrathin MgB2 films fabricated on Al2O3 substrate by hybrid physical-chemical vapor deposition with high Tc and Jc

Ultrathin MgB2 superconducting films with a thickness down to 7.5 nm are epitaxially grown on (0001) Al2O3 substrate by hybrid physical-chemical vapor deposition method. The films are phase-pure, oxidation-free and continuous. The 7.5 nm thin film shows a Tc(0) of 34 K, which is so far the highest Tc(0) reported in MgB2 with the same thickness. The critical current density of ultrathin MgB2 films below 10 nm is demonstrated for the first time as Jc ~ 10^6 A cm^{-2} for the above 7.5 nm sample at 16 K. Our results reveal the excellent superconducting properties of ultrathin MgB2 films with thicknesses between 7.5 and 40 nm on Al2O3 substrate.

preprint2010arXiv

Applying Stochastic Network Calculus to 802.11 Backlog and Delay Analysis

Stochastic network calculus provides an elegant way to characterize traffic and service processes. However, little effort has been made on applying it to multi-access communication systems such as 802.11. In this paper, we take the first step to apply it to the backlog and delay analysis of an 802.11 wireless local network. In particular, we address the following questions: In applying stochastic network calculus, under what situations can we derive stable backlog and delay bounds? How to derive the backlog and delay bounds of an 802.11 wireless node? And how tight are these bounds when compared with simulations? To answer these questions, we first derive the general stability condition of a wireless node (not restricted to 802.11). From this, we give the specific stability condition of an 802.11 wireless node. Then we derive the backlog and delay bounds of an 802.11 node based on an existing model of 802.11. We observe that the derived bounds are loose when compared with ns-2 simulations, indicating that improvements are needed in the current version of stochastic network calculus.

preprint2010arXiv

Controlling elastic wave with isotropic transformation materials

There are great demands to design functional devices with isotropic materials, however the transformation method usually leads to anisotropic material parameters difficult to be realized in practice. In this letter, we derive the isotropic transformed material parameters in case of elastodynamic under local conformal transformation, they are subsequently used to design a beam bender, a four-beam antenna and an approximate carpet cloak for elastic wave with isotropic materials, the simulation results validate the derived transformed material parameters. The obtained materials are isotropic and greatly simplify subsequent experimental implementation.

preprint2010arXiv

Difference Antenna Selection and Power Allocation for Wireless Cognitive Systems

In this paper, we propose an antenna selection method in a wireless cognitive radio (CR) system, namely difference selection, whereby a single transmit antenna is selected at the secondary transmitter out of $M$ possible antennas such that the weighted difference between the channel gains of the data link and the interference link is maximized. We analyze mutual information and outage probability of the secondary transmission in a CR system with difference antenna selection, and propose a method of optimizing these performance metrics of the secondary data link subject to practical constraints on the peak secondary transmit power and the average interference power as seen by the primary receiver. The optimization is performed over two parameters: the peak secondary transmit power and the difference selection weight $δ\in [0, 1]$. We show that, difference selection using the optimized parameters determined by the proposed method can be, in many cases of interest, superior to a so called ratio selection method disclosed in the literature, although ratio selection has been shown to be optimal, when impractically, the secondary transmission power constraint is not applied. We address the effects that the constraints have on mutual information and outage probability, and discuss the practical implications of the results.

preprint2010arXiv

TCP Reno over Adaptive CSMA

An interesting distributed adaptive CSMA MAC protocol, called adaptive CSMA, was proposed recently to schedule any strictly feasible achievable rates inside the capacity region. Of particular interest is the fact that the adaptive CSMA can achieve a system utility arbitrarily close to that is achievable under a central scheduler. However, a specially designed transport-layer rate controller is needed for this result. An outstanding question is whether the widely-installed TCP Reno is compatible with adaptive CSMA and can achieve the same result. The answer to this question will determine how close to practical deployment adaptive CSMA is. Our answer is yes and no. First, we observe that running TCP Reno directly over adaptive CSMA results in severe starvation problems. Effectively, its performance is no better than that of TCP Reno over legacy CSMA (IEEE 802.11), and the potentials of adaptive CSMA cannot be realized. Fortunately, we find that multi-connection TCP Reno over adaptive CSMA with active queue management can materialize the advantages of adaptive CSMA. NS-2 simulations demonstrate that our solution can alleviate starvation and achieve fair and efficient rate allocation. Multi-connection TCP can be implemented at either application or transport layer. Application-layer implementation requires no kernel modification, making the solution readily deployable in networks running adaptive CSMA.

preprint2008arXiv

Understanding the Paradoxical Effects of Power Control on the Capacity of Wireless Networks

Recent works show conflicting results: network capacity may increase or decrease with higher transmission power under different scenarios. In this work, we want to understand this paradox. Specifically, we address the following questions: (1)Theoretically, should we increase or decrease transmission power to maximize network capacity? (2) Theoretically, how much network capacity gain can we achieve by power control? (3) Under realistic situations, how do power control, link scheduling and routing interact with each other? Under which scenarios can we expect a large capacity gain by using higher transmission power? To answer these questions, firstly, we prove that the optimal network capacity is a non-decreasing function of transmission power. Secondly, we prove that the optimal network capacity can be increased unlimitedly by higher transmission power in some network configurations. However, when nodes are distributed uniformly, the gain of optimal network capacity by higher transmission power is upper-bounded by a positive constant. Thirdly, we discuss why network capacity in practice may increase or decrease with higher transmission power under different scenarios using carrier sensing and the minimum hop-count routing. Extensive simulations are carried out to verify our analysis.

Yue Wang

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159 published item(s)

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments

PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies

Specific Emitter Identification Based on Joint Variational Mode Decomposition

Finding meaningful and workable applied mathematics problems in science

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

Optimal subsampling algorithm for composite quantile regression with distributed data

Super-Resolution Harmonic Retrieval of Non-Circular Signals

Academic Resource Text Level Multi-label Classification based on Attention

Accurate Portraits of Scientific Resources and Knowledge Service Components

Adaptive Algorithm for Quantum Amplitude Estimation

Algorithms for determining transposons in gene sequences

Bayesian Optimization Based Trustworthiness Model for Multi-robot Bounding Overwatch

BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackers

Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

Context-Based MEC Platform for Augmented-Reality Services in 5G Networks

Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations

Depth-Independent Depth Completion via Least Square Estimation

DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow

Dynamic stability for steady Prandtl solutions

Efficient Distance-Optimal Tethered Path Planning in Planar Environments: The Workspace Convexity

Efficient Object Manipulation to an Arbitrary Goal Pose: Learning-based Anytime Prioritized Planning

Efficient Search of the k Shortest Non-Homotopic Paths by Eliminating Non-k-Optimal Topologies

Enhancing the Erdős-Lovász Tihany Conjecture for line graphs of multigraphs

EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation

Feature-based Style Randomization for Domain Generalization

FEJ-VIRO: A Consistent First-Estimate Jacobian Visual-Inertial-Ranging Odometry

HDMapNet: An Online HD Map Construction and Evaluation Framework

Inference on autoregulation in gene expression

Inference on the structure of gene regulatory networks

Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language

Learning to Fill the Seam by Vision: Sub-millimeter Peg-in-hole on Unseen Shapes in Real World

Map-based Visual-Inertial Localization: Consistency and Complexity

MBGDT:Robust Mini-Batch Gradient Descent

Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs

Observation of one-dimensional Dirac fermions in silicon nanoribbons

Policy Gradient Method For Robust Reinforcement Learning

Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks

QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

Recycling of Perovskite Substrate

Robust Constrained Reinforcement Learning

Searching for multiple populations in star clusters using the China Space Station Telescope

Tac3D: A Novel Vision-based Tactile Sensor for Measuring Forces Distribution and Estimating Friction Coefficient Distribution

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

Toward Consistent and Efficient Map-based Visual-inertial Localization: Theory Framework and Filter Design

Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy

Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud

Trust-based Symbolic Motion Planning for Multi-robot Bounding Overwatch

Two Metrics on Rooted Unordered Trees with Labels

Writing Style Aware Document-level Event Extraction

Deep Samplable Observation Model for Global Localization and Kidnapping

DiSCO: Differentiable Scan Context with Orientation

Dynamic Movement Primitive based Motion Retargeting for Dual-Arm Sign Language Motions

Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory

Joint Optimization of Communications and Federated Learning Over the Air

Learn to Differ: Sim2Real Small Defection Segmentation Network

Modeling and Leveraging Analytic Focus During Exploratory Visual Analysis

RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model

REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination

The collision frequencies in the plasmas with the power-law q-distributions in nonextensive statistics

Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks

2-Entity RANSAC for robust visual localization in changing environment

A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision

A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

Attachment and antibiotic response of early-stage biofilms studied using resonant hyperspectral imaging

AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs

Blow-up criterion for the 2-D Prandtl equation

Cellular Decomposition for Non-repetitive Coverage Task with Minimum Discontinuities

Constructing Basis Path Set by Eliminating Path Dependency

DAF-NET: a saliency based weakly supervised method of dual attention fusion for fine-grained image classification

DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs

Direct Speech-to-image Translation

Effect of magnetic field on transports of charged particles in the weakly ionized plasma with power-law q-distributions in nonextensive statistics