BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Lei Liu

Lei Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Information Theorymath.ITeess.SPMachine LearningComputer Visioncond-mat.mtrl-sciArtificial Intelligenceeess.IVmath.STNetworking and Internet Architecturephysics.chem-phStatistics Theorycond-mat.mes-hallDistributed, Parallel, and Cluster ComputingComputation and Languagequant-phastro-ph.IMBiological Physicscond-mat.dis-nncond-mat.softCryptography and SecurityData Structures and AlgorithmsFormal Languages and Automata TheoryHardware Architecturemath.APmath.COmath.LONeural and Evolutionary ComputingOperating Systemsphysics.comp-phphysics.flu-dyn

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
49works
0followers
31topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

49 published item(s)

preprint2026arXiv

Diffusion-OAMP for Joint Image Compression and Wireless Transmission

Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in practical communication systems. We formulate this problem under an equivalent linear model, and propose Diffusion-OAMP, a training-free reconstruction framework that embeds a pre-trained diffusion model into the OAMP algorithm. In Diffusion-OAMP, the OAMP linear estimator produces pseudo-AWGN observations, while the diffusion model serves as a nonlinear estimator under an SNR-matching rule. This framework offers a way to incorporate multiple generative priors into OAMP. Experiments with varying compression ratios and noise levels show that Diffusion-OAMP performs favorably against classic methods in the evaluated settings.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Random Modulation: Achieving Asymptotic Replica Optimality over Arbitrary Norm-Bounded and Spectrally Convergent Channel Matrices

This paper introduces a random modulation technique that is decoupled from the channel matrix, allowing it to be applied to arbitrary norm-bounded and spectrally convergent channel matrices. The proposed random modulation constructs an equivalent dense and random channel matrix, ensuring that the signals undergo sufficient statistical channel fading. It also guarantees the asymptotic replica maximum a posteriori (MAP) bit-error rate (BER) optimality of approximate message passing (AMP)-type detectors for linear systems with arbitrary norm-bounded and spectrally convergent channel matrices when their state evolution has a unique fixed point. Then, a low-complexity cross-domain memory approximate message passing (CD-MAMP) detector is proposed for random modulation, leveraging the sparsity of the time-domain channel and the randomness of the random transform-domain channel. Furthermore, the optimal power allocation schemes are derived to minimize the replica MAP BER and maximize the replica constrained capacity of random-modulated linear systems, assuming the availability of channel state information (CSI) at the transceiver. Numerical results show that the proposed random modulation can achieve BER and block-error rate (BLER) performance gains of up to 2 - 3 dB compared to existing OFDM/OTFS/AFDM with 5G-NR LDPC codes, under both average and optimized power allocation.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer

In multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, representing the whole channel only based on partial subchannels will significantly reduce the channel acquisition overhead. For such a channel mapping task, inspired by the intrinsic coupling across the space and frequency domains, this letter proposes to use interleaved learning with partial antenna and subcarrier characteristics to represent the whole MIMO-OFDM channel. Specifically, we design a complex-domain multilayer perceptron (MLP)-Mixer (CMixer), which utilizes two kinds of complex-domain MLP modules to learn the space and frequency characteristics respectively and then interleaves them to couple the learned properties. The complex-domain computation facilitates the learning on the complex-valued channel data, while the interleaving tightens the coupling of space and frequency domains. These two designs jointly reduce the learning burden, making the physics-inspired CMixer more effective on channel representation learning than existing data-driven approaches. Simulation shows that the proposed scheme brings 4.6~10dB gains in mapping accuracy compared to existing schemes under different settings. Besides, ablation studies show the necessity of complex-domain computation as well as the extent to which the interleaved learning matches the channel properties.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

From Beginner to Expert: Modeling Medical Knowledge into General LLMs

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However, these models face a significant challenge when it comes to sensitive applications, such as reasoning over medical knowledge and answering medical questions in a physician-like manner. Prior studies attempted to overcome this challenge by increasing the model size (>100B) to learn more general medical knowledge, while there is still room for improvement in LLMs with smaller-scale model sizes (<100B). In this work, we start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a medical beginner towards a medical expert (called AntGLM-Med-10B), which leverages a 3-stage optimization procedure, i.e., general medical knowledge injection, medical domain instruction tuning, and specific medical task adaptation. Our contributions are threefold: (1) We specifically investigate how to adapt a pre-trained general LLM in medical domain, especially for a specific medical task. (2) We collect and construct large-scale medical datasets for each stage of the optimization process. These datasets encompass various data types and tasks, such as question-answering, medical reasoning, multi-choice questions, and medical conversations. (3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs. Remarkably, by combining the above approaches, our AntGLM-Med-10B model can outperform the most of LLMs on PubMedQA, including both general and medical LLMs, even when these LLMs have larger model size.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Overflow-Avoiding Memory AMP

Approximate Message Passing (AMP) type algorithms are widely used for signal recovery in high-dimensional noisy linear systems. Recently, a principle called Memory AMP (MAMP) was proposed. Leveraging this principle, the gradient descent MAMP (GD-MAMP) algorithm was designed, inheriting the strengths of AMP and OAMP/VAMP. In this paper, we first provide an overflow-avoiding GD-MAMP (OA-GD-MAMP) to address the overflow problem that arises from some intermediate variables exceeding the range of floating point numbers. Second, we develop a complexity-reduced GD-MAMP (CR-GD-MAMP) to reduce the number of matrix-vector products per iteration by 1/3 (from 3 to 2) with little to no impact on the convergence speed.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

RJUA-QA: A Comprehensive QA Dataset for Urology

We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models (LLMs) and medical-specific LLM applications. RJUA-QA is derived from realistic clinical scenarios and aims to facilitate LLMs in generating reliable diagnostic and advice. The dataset contains 2,132 curated Question-Context-Answer pairs, corresponding about 25,000 diagnostic records and clinical cases. The dataset covers 67 common urological disease categories, where the disease coverage exceeds 97.6\% of the population seeking medical services in urology. Each data instance in RJUA-QA comprises: (1) a question mirroring real patient to inquiry about clinical symptoms and medical conditions, (2) a context including comprehensive expert knowledge, serving as a reference for medical examination and diagnosis, (3) a doctor response offering the diagnostic conclusion and suggested examination guidance, (4) a diagnosed clinical disease as the recommended diagnostic outcome, and (5) clinical advice providing recommendations for medical examination. RJUA-QA is the first medical QA dataset for clinical reasoning over the patient inquiries, where expert-level knowledge and experience are required for yielding diagnostic conclusions and medical examination advice. A comprehensive evaluation is conducted to evaluate the performance of both medical-specific and general LLMs on the RJUA-QA dataset. Our data is are publicly available at \url{https://github.com/alipay/RJU_Ant_QA}.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

On Orthogonal Approximate Message Passing

Approximate Message Passing (AMP) is an efficient iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions, such as sparse systems. In AMP, a so-called Onsager term is added to keep estimation errors approximately Gaussian. Orthogonal AMP (OAMP) does not require this Onsager term, relying instead on an orthogonalization procedure to keep the current errors uncorrelated with (i.e., orthogonal to) past errors. \LL{In this paper, we show the generality and significance of the orthogonality in ensuring that errors are &#34;asymptotically independently and identically distributed Gaussian&#34; (AIIDG).} This AIIDG property, which is essential for the attractive performance of OAMP, holds for separable functions. \LL{We present a simple and versatile procedure to establish the orthogonality through Gram-Schmidt (GS) orthogonalization, which is applicable to any prototype. We show that different AMP-type algorithms, such as expectation propagation (EP), turbo, AMP and OAMP, can be unified under the orthogonal principle.} The simplicity and generality of OAMP provide efficient solutions for estimation problems beyond the classical linear models. \LL{As an example, we study the optimization of OAMP via the GS model and GS orthogonalization.} More related applications will be discussed in a companion paper where new algorithms are developed for problems with multiple constraints and multiple measurement variables.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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

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

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks

In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist. In the proposed LSTMH-RA scheme, mMTC devices access the network via a timing advance (TA)-aided four-step procedure to meet massive access requirement, while the access procedure of the URLLC devices is completed in two steps coupled with the mMTC devices&#39; access procedure to reduce latency. Furthermore, we propose an attention-based LSTM prediction model to predict the number of active URLLC devices, thereby determining the parameters of the multi-user detection algorithm to guarantee the latency and reliability access requirements of URLLC devices. We analyze the successful access probability of the LSTMH-RA scheme. Numerical results show that, compared with the benchmark schemes, the proposed LSTMH-RA scheme can significantly improve the successful access probability, and thus satisfy the diverse QoS requirements of URLLC and mMTC devices.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Capacity Optimal Coded Generalized MU-MIMO

With the complication of future communication scenarios, most conventional signal processing technologies of multi-user multiple-input multiple-output (MU-MIMO) become unreliable, which are designed based on ideal assumptions, such as Gaussian signaling and independent identically distributed (IID) channel matrices. As a result, this paper considers a generalized MU-MIMO (GMU-MIMO) system with more general assumptions, i.e., arbitrarily fixed input distributions, and general unitarily-invariant channel matrices. However, there is still no accurate capacity analysis and capacity optimal transceiver with practical complexity for GMU-MIMO under the constraint of coding. To address these issues, inspired by the replica method, the constrained sum capacity of coded GMU-MIMO with fixed input distribution is calculated by using the celebrated mutual information and minimum mean-square error (MMSE) lemma and the MMSE optimality of orthogonal/vector approximate message passing (OAMP/VAMP). Then, a capacity optimal multiuser OAMP/VAMP receiver is proposed, whose achievable rate is proved to be equal to the constrained sum capacity. Moreover, a design principle of multi-user codes is presented for the multiuser OAMP/VAMP, based on which a kind of practical multi-user low-density parity-check (MU-LDPC) code is designed. Numerical results show that finite-length performances of the proposed MU-LDPC codes with multi-user OAMP/VAMP are about 2 dB away from the constrained sum capacity and outperform those of the existing state-of-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Capacity Optimality of OAMP in Coded Large Unitarily Invariant Systems

This paper investigates a large unitarily invariant system (LUIS) involving a unitarily invariant sensing matrix, an arbitrary fixed signal distribution, and forward error control (FEC) coding. Several area properties are established based on the state evolution of orthogonal approximate message passing (OAMP) in an un-coded LUIS. Under the assumptions that the state evolution for joint OAMP and FEC decoding is correct and the replica method is reliable, we analyze the achievable rate of OAMP. We prove that OAMP reaches the constrained capacity predicted by the replica method of the LUIS with an arbitrary signal distribution based on matched FEC coding. Meanwhile, we elaborate a constrained capacity-achieving coding principle for LUIS, based on which irregular low-density parity-check (LDPC) codes are optimized for binary signaling in the simulation results. We show that OAMP with the optimized codes has significant performance improvement over the un-optimized ones and the well-known Turbo linear MMSE algorithm. For quadrature phase-shift keying (QPSK) modulation, constrained capacity-approaching bit error rate (BER) performances are observed under various channel conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Incorporating Distributed DRL into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network

Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The effective management of SAGIN resources is a prerequisite for high-reliability communication. However, the storage capacity of space-air network segment is extremely limited. The air servers also do not have sufficient storage resources to centrally accommodate the information uploaded by each edge server. So the problem of how to coordinate the storage resources of SAGIN has arisen. This paper proposes a SAGIN storage resource management algorithm based on distributed deep reinforcement learning (DRL). The resource management process is modeled as a Markov decision model. In each edge physical domain, we extract the network attributes represented by storage resources for the agent to build a training environment, so as to realize the distributed training. In addition, we propose a SAGIN resource management framework based on distributed DRL. Simulation results show that the agent has an ideal training effect. Compared with other algorithms, the resource allocation revenue and user request acceptance rate of the proposed algorithm are increased by about 18.15\% and 8.35\% respectively. Besides, the proposed algorithm has good flexibility in dealing with the changes of resource conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Intelligent Resource Scheduling for Co-located Latency-critical Services: A Multi-Model Collaborative Learning Approach

Latency-critical services have been widely deployed in cloud environments. For cost-efficiency, multiple services are usually co-located on a server. Thus, run-time resource scheduling becomes the pivot for QoS control in these complicated co-location cases. However, the scheduling exploration space enlarges rapidly with the increasing server resources, making the schedulers hardly provide ideal solutions quickly. More importantly, we observe that there are &#34;resource cliffs&#34; in the scheduling exploration space. They affect the exploration efficiency and always lead to severe QoS fluctuations. Resource cliffs cannot be easily avoided in previous schedulers. To address these problems, we propose a novel ML-based intelligent scheduler - OSML. It learns the correlation between architectural hints (e.g., IPC, cache misses, memory footprint, etc.), scheduling solutions and the QoS demands based on a data set we collected from 11 widely deployed services running on off-the-shelf servers. OSML employs multiple ML models to work collaboratively to predict QoS variations, shepherd the scheduling, and recover from QoS violations in complicated co-location cases. OSML can intelligently avoid resource cliffs during scheduling and reach an optimal solution much faster than previous approaches for co-located LC services. Experimental results show that OSML supports higher loads and meets QoS targets with lower scheduling overheads and shorter convergence time than previous studies.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Investigate the Essence of Long-Tailed Recognition from a Unified Perspective

As the data scale grows, deep recognition models often suffer from long-tailed data distributions due to the heavy imbalanced sample number across categories. Indeed, real-world data usually exhibit some similarity relation among different categories (e.g., pigeons and sparrows), called category similarity in this work. It is doubly difficult when the imbalance occurs between such categories with similar appearances. However, existing solutions mainly focus on the sample number to re-balance data distribution. In this work, we systematically investigate the essence of the long-tailed problem from a unified perspective. Specifically, we demonstrate that long-tailed recognition suffers from both sample number and category similarity. Intuitively, using a toy example, we first show that sample number is not the unique influence factor for performance dropping of long-tailed recognition. Theoretically, we demonstrate that (1) category similarity, as an inevitable factor, would also influence the model learning under long-tailed distribution via similar samples, (2) using more discriminative representation methods (e.g., self-supervised learning) for similarity reduction, the classifier bias can be further alleviated with greatly improved performance. Extensive experiments on several long-tailed datasets verify the rationality of our theoretical analysis, and show that based on existing state-of-the-arts (SOTAs), the performance could be further improved by similarity reduction. Our investigations highlight the essence behind the long-tailed problem, and claim several feasible directions for future work.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Massive Unsourced Random Access over Rician Fading Channels: Design, Analysis, and Optimization

In this paper, we investigate an unsourced random access scheme for massive machine-type communications (mMTC) in the sixth-generation (6G) wireless networks with sporadic data traffic. Firstly, we establish a general framework for massive unsourced random access based on a two-layer signal coding, i.e., an outer code and an inner code. In particular, considering Rician fading in the scenario of mMTC, we design a novel codeword activity detection algorithm for the inner code of unsourced random access based on the distribution of received signals by exploiting the maximum likelihood (ML) method. Then, we analyze the performance of the proposed codeword activity detection algorithm exploiting Fisher Information Matrix, which facilitates the derivative of the approximated distribution of the estimation error of the codeword activity vector when the number of base station (BS) antennas is sufficiently large. Furthermore, for the outer code, we propose an optimization algorithm to allocate the lengths of message bits and parity check bits, so as to strike a balance between the error probability and the complexity required for outer decoding. Finally, extensive simulation results validate the effectiveness of the proposed detection algorithm and the optimized length allocation scheme compared with an existing detection algorithm and a fixed length allocation scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Memory AMP

Approximate message passing (AMP) is a low-cost iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions. AMP only applies to independent identically distributed (IID) transform matrices, but may become unreliable (e.g., perform poorly or even diverge) for other matrix ensembles, especially for ill-conditioned ones. To solve this issue, orthogonal/vector AMP (OAMP/VAMP) was proposed for general right-unitarily-invariant matrices. However, the Bayes-optimal OAMP/VAMP (BO-OAMP/VAMP) requires a high-complexity linear minimum mean square error (MMSE) estimator. This prevents OAMP/VAMP from being used in large-scale systems. To address the drawbacks of AMP and BO-OAMP/VAMP, this paper offers a memory AMP (MAMP) framework based on the orthogonality principle, which ensures that estimation errors in MAMP are asymptotically IID Gaussian. To realize the required orthogonality for MAMP, we provide an orthogonalization procedure for the local memory estimators. In addition, we propose a Bayes-optimal MAMP (BO-MAMP), in which a long-memory matched filter is used for interference suppression. The complexity of BO-MAMP is comparable to AMP. To asymptotically characterize the performance of BO-MAMP, a state evolution is derived. The relaxation parameters and damping vector in BO-MAMP are optimized based on state evolution. Most crucially, the state evolution of the optimized BO-MAMP converges to the same fixed point as that of the high-complexity BO-OAMP/VAMP for all right-unitarily-invariant matrices, and achieves the Bayes optimal MSE predicted by the replica method if its state evolution has a unique fixed point. Finally, simulations are provided to verify the theoretical results&#39; validity and accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

OmniUV: A Multi-Purpose Simulation Toolkit for VLBI Observation

We present OmniUV, a multi-purpose simulation toolkit for space and ground VLBI observations. It supports various kinds of VLBI stations, including Earth (ground) fixed, Earth orbit, Lunar fixed, Lunar orbit, Moon-Earth and Earth-Sun Lagrange 1 and 2 points, etc. The main functionalities of this toolkit are: (1) Trajectory calculation; (2) Baseline uv calculation, by taking the vailability of each station into account; (3) Visibility simulation for the given uv distribution, source structure and system noise; (4) Image and beam reconstruction. Two scenarios, namely space VLBI network and wide field array, are presented as demonstrations of the toolkit applications in completely different scales. OmniUV is the acronym of &#34;Omnipotent UV&#34;. We hope it could work as a general framework, in which various kinds of stations could be easily incorporated and the functionalities could be further extended. The toolkit has been made publicly available.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

QuCloud+: A Holistic Qubit Mapping Scheme for Single/Multi-programming on 2D/3D NISQ Quantum Computers

Qubit mapping is essential to quantum computing&#39;s fidelity and quantum computers&#39; resource utilization. Yet, the existing qubit mapping schemes meet some challenges (e.g., crosstalk, SWAP overheads, diverse device topologies, etc.), leading to qubit resource under-utilization, high error rate, and low fidelity in computing results. This paper presents QuCloud+, a new qubit mapping scheme capable of handling these challenges. QuCloud+ has several new designs. (1) QuCloud+ enables multi-programming quantum computing on quantum chips with 2D/3D topology. (2) It partitions physical qubits for concurrent quantum programs with the crosstalk-aware community detection technique and further allocates qubits according to qubit degree, improving fidelity and resource utilization. (3) QuCloud+ includes an X-SWAP mechanism that avoids SWAPs with high crosstalk errors and enables inter-program SWAPs to reduce the SWAP overheads. (4) QuCloud+ schedules concurrent quantum programs to be mapped and executed based on estimated fidelity for the best practice. QuCloud+ outperforms the previous multi-programming work on various devices by 6.84% on fidelity and saves 40.9% additional gates required during mapping transition.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RIS-Aided Multiuser MIMO-OFDM with Linear Precoding and Iterative Detection: Analysis and Optimization

In this paper, we consider a reconfigurable intelligence surface (RIS) aided uplink multiuser multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, where the receiver is assumed to conduct low-complexity iterative detection. We aim to minimize the total transmit power by jointly designing the precoder of the transmitter and the passive beamforming of the RIS. This problem can be tackled from the perspective of information theory. But this information-theoretic approach may involve prohibitively high complexity since the number of rate constraints that specify the capacity region of the uplink multiuser channel is exponential in the number of users. To avoid this difficulty, we formulate the design problem of the iterative receiver under the constraints of a maximal iteration number and target bit error rates of users. To tackle this challenging problem, we propose a groupwise successive interference cancellation (SIC) optimization approach, where the signals of users are decoded and cancelled in a group-by-group manner. We present a heuristic user grouping strategy, and resort to the alternating optimization technique to iteratively solve the precoding and passive beamforming sub-problems. Specifically, for the precoding sub-problem, we employ fractional programming to convert it to a convex problem; for the passive beamforming sub-problem, we adopt successive convex approximation to deal with the unit-modulus constraints of the RIS. We show that the proposed groupwise SIC approach has significant advantages in both performance and computational complexity, as compared with the counterpart approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN&#39;s heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Sufficient Statistic Memory Approximate Message Passing

Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems. A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolution. However, state evolution does not necessarily guarantee the convergence of iterative algorithms. To solve the convergence problem of AMP-type algorithms in principle, this paper proposes a memory AMP (MAMP) under a sufficient statistic condition, named sufficient statistic MAMP (SS-MAMP). We show that the covariance matrices of SS-MAMP are L-banded and convergent. Given an arbitrary MAMP, we can construct the SS-MAMP by damping, which not only ensures the convergence, but also preserves the orthogonality, i.e., its dynamics can be correctly described by state evolution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The $C^0$-convergence at the Neumann boundary for Liouville equations

In this paper, we study the blow-up analysis for a sequence of solutions to the Liouville type equation with exponential Neumann boundary condition. For interior case, i.e. the blow-up point is an interior point, Li \cite{Li} gave a uniform asymptotic estimate. Later, Zhang \cite{Zhang} and Gluck \cite{Gluck} improved Li&#39;s estimate in the sense of $C^0$-convergence by using the method of moving planes or classification of solutions of the linearized version of Liouville equation. If the sequence blows up at a boundary point, Bao-Wang-Zhou \cite{Bao-Wang-Zhou} proved a similar asymptotic estimate of Li \cite{Li}. In this paper, we will prove a $C^0$-convergence result in this boundary blow-up process. Our method is different from \cite{Zhang,Gluck}.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A molecular dynamics simulation study on the frustrated Lewis pairs in ionic liquids

Steric hindered frustrated Lewis pairs (FLPs) have been shown to activate hydrogen molecules, and their reactivity is strongly determined by the geometric parameters of the Lewis acid s and bases. A recent experimental study showed that ionic liquids (ILs) could largely improve the effective configuration of FLPs. However, the detailed mechanistic profile is still unclear. Herein, we performed a molecular dynamics (MD) simulations, aimi ng to reveal the effects of ILs on the structures of FLPs, and to present a rule for selecting more efficient reaction media. For this purpose, mixture systems were adopt consisting of the ILs [Cnmim][NTf2] (n= 6, 10, 14), and the typical FLP (tBu)3P/B(C6F5)3 . Radial distribution function (RDF) results show that toluene competes with (tBu)3P to interact with B(C6F5)3 , resulting in a relatively low effective (tBu)3P/B(C6F5)3 complex. [Cnmim][NTf2] is more intended to form a solvated shell surrounding the (tBu)3P/B(C6F5)3 , which increases the amount of effective FLPs. Spatial distribution function (SDF) results show that toluene formed a continuum solvation shell, which hinders the interactions of (tBu)3P and B(C6F5)3 , while [Cnmim][NTf2] leave a relatively large empty space, which is accessible by (tBu3)P molecules, resulting in a higher probability of Lewis acids and bases interactions. Lastly, we find that the longer alkyl chain length of[Cnmim] cations, the higher probability of effective FLPs.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Efficient electrochemical reduction of CO2 to CO by soft functional materials

Electrochemical reduction of CO2 to CO is a promising strategy. However, achieving high Faradaic efficiency with high current density using ILs electrolyte remains a challenge. In this study, the IL N octyltrimethyl 1,2,4 triazole ammonium shows outstanding performance for electrochemical reduction of CO2 to CO on the commercial Ag electrode, and the current density can be up to 50.8 mA cm-2 with a Faradaic efficiency of 90.6%. The current density of CO is much higher than those reported in the ILs electrolyte. In addition, the density functional theory calculation further proved that IL interacts with CO2 to form IL CO2 complex which played a key role in reducing the activation energy of CO2. According to the molecular orbital theory, the electrons obtained from ILs was filled in the anti bonding orbit of the CO2, resulting in reducing the C=O bond energy. This work provides a new strategy to design novel ILs for high efficiency electrochemical reduction of CO2 to CO.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Mechanisms behind high CO2/CH4 selectivity using ZIF-8 metal organic frameworks with encapsulated ionic liquids: a computational study

CO2/CH4 separation using ionic liquids (ILs) encapsulated metal-organic frameworks (MOFs), especially ZIF-8, has shown promise as a new technique for separating CO2 from CH4. However, the mechanisms behind the high CO2/CH4 selectivity of the method remains indistinct. Here we report the progress of understanding the mechanisms from examining the ZIF-8 aperture configuration variation using DFT and MD simulations. The results indicate that the pristine aperture configuration exhibits the best separation performance, and the addition of ILs prevents the apertures from large swing (i.e. configuration variation). Subsequently, the effect of IL viscosity on the layout variation was investigated. MD simulations also show that the pristine aperture configuration is more stabilized by ILs with large viscosity (0-87Cp). Further increase of IL viscosity above 87Cp did not result in noticeable changes in the aperture stability.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification

Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for lung&#39;s multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative procedure. The core component of TSAL is the multi-label learning mechanism, in which label correlations information is used to design multi-label margin (MLM) strategy and confidence validation for automatically selecting informative samples and confident labels. On this basis, a multi-symptom multi-label (MSML) classification network is proposed to learn discriminative features of lung symptoms, and a human-machine interaction is exploited to confirm the final annotations that are used to fine-tune MSML with progressively labeled data. Moreover, a novel lung US dataset named COVID19-LUSMS is built, currently containing 71 clinical patients with 6,836 images sampled from 678 videos. Experimental evaluations show that TSAL using only 20% data can achieve superior performance to the baseline and the state-of-the-art. Qualitatively, visualization of both attention map and sample distribution confirms the good consistency with the clinic knowledge.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The effects of ionic liquids on the thermodynamics of H2 activation by frustrated Lewis pairs: a density functional theory study

Nowadays, hydrogen activation by frustrated Lewis pairs (FLPs) and their applications have been demonstrated to be one of emerge research topics in the field of catalysis. Previous studies have shown that the thermodynamics of these reaction is determined by electronic structures of FLPs and solvents. Herein, we investigated the systems consisting of typical FLPs and ionic liquids (ILs), which are well known by their large number of types and excellent solvent effects. The density functional theory (DFT) calculations were performed to study the thermodynamics for H2 activation by both inter- and intra-molecular FLPs, as well as the individual components. The results show that the computed overall Gibbs free energies in ILs are more negative than that computed in toluene. Through the thermodynamics partitioning, we find that ILs favor the H-H cleavage elemental step, while disfavored the elemental steps of proton attachment, hydride attachment and zwitterionic stabilization. Moreover, the results show that these effects are strongly dependent on the type of FLPs, where intra-molecular FLPs are more effected compared to the inter-molecular FLPs.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A New Qubits Mapping Mechanism for Multi-programming Quantum Computing

For a specific quantum chip, multi-programming helps to improve overall throughput and resource utilization. However, the previous solutions for mapping multiple programs onto a quantum chip often lead to resource under-utilization, high error rate and low fidelity. In this paper, we propose a new approach to map concurrent quantum programs. Our approach has three critical components. The first one is the Community Detection Assisted Partition (CDAP) algorithm, which partitions physical qubits for concurrent quantum programs by considering both physical typology and the error rates, avoiding the waste of robust resources. The second one is the X-SWAP scheme that enables inter-program SWAP operations to reduce the SWAP overheads. Finally, we propose a compilation task scheduling framework, which dynamically selects concurrent quantum programs to be executed based on estimated fidelity, increasing the throughput of the quantum computer. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ20. Our work outperforms the previous solution on multi-programming in both fidelity and SWAP overheads by 12.0% and 11.1%, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs

Accelerating the deep learning inference is very important for real-time applications. In this paper, we propose a novel method to fuse the layers of convolutional neural networks (CNNs) on Graphics Processing Units (GPUs), which applies data reuse analysis and access optimization in different levels of the memory hierarchy. To achieve the balance between computation and memory access, we explore the fusion opportunities in the CNN computation graph and propose three fusion modes of convolutional neural networks: straight, merge and split. Then, an approach for generating efficient fused code is designed, which goes deeper in multi-level memory usage for cross-layer data reuse. The effectiveness of our method is evaluated with the network layers from state-of-the-art CNNs on two different GPU platforms, NVIDIA TITAN Xp and Tesla P4. The experiments show that the average speedup is 2.02x on representative structures of CNNs, and 1.57x on end-to-end inference of SqueezeNet.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Challenge-Aware RGBT Tracking

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking. In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking. In particular, we design several parameter-shared branches in each layer to model the target appearance under the modality-shared challenges, and several parameterindependent branches under the modality-specific ones. Based on the observation that the modality-specific cues of different modalities usually contains the complementary advantages, we propose a guidance module to transfer discriminative features from one modality to another one, which could enhance the discriminative ability of some weak modality. Moreover, all branches are aggregated together in an adaptive manner and parallel embedded in the backbone network to efficiently form more discriminative target representations. These challenge-aware branches are able to model the target appearance under certain challenges so that the target representations can be learnt by a few parameters even in the situation of insufficient training data. From the experimental results we will show that our method operates at a real-time speed while performing well against the state-of-the-art methods on three benchmark datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Chebyshev Polynomial Method to Landauer-Büttiker Formula of Quantum Transport in Nanostructures

Landauer-Büttiker formula describes the electronic quantum transports in nanostructures and molecules. It will be numerically demanding for simulations of complex or large size systems due to, for example, matrix inversion calculations. Recently, Chebyshev polynomial method has attracted intense interests in numerical simulations of quantum systems due to the high efficiency in parallelization, because the only matrix operation it involves is just the product of sparse matrices and vectors. Many progresses have been made on the Chebyshev polynomial representations of physical quantities for isolated or bulk quantum structures. Here we present the Chebyshev polynomial method to the typical electronic scattering problem, the Landauer-Büttiker formula for the conductance of quantum transports in nanostructures. We first describe the full algorithm based on the standard bath kernel polynomial method (KPM). Then, we present two simple butefficient improvements. One of them has a time consumption remarkably less than the direct matrix calculation without KPM. Some typical examples are also presented to illustrate the numerical effectiveness.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Electrical and thermal transport properties of medium-entropy SiyGeySnx alloys

Electrical and thermal transport properties of disordered materials have long been of both theoretical interest and engineering importance. As a new class of materials with an intrinsic compositional disorder, high/medium-entropy alloys (HEAs/MEAs) are being immensely studied mainly for their excellent mechanical properties. By contrast, electrical and thermal transport properties of HEAs/MEAs are less well studied. Here we investigate these two properties of silicon (Si)-germanium (Ge)-tin (Sn) MEAs, where we keep the same content of Si and Ge while increasing the content of Sn from 0 to 1/3 to tune the configurational entropy and thus the degree of compositional disorder. We predict all SiyGeySnx MEAs to be semiconductors with a wide range of bandgaps from near-infrared (0.28 eV) to visible (1.11 eV) in the light spectrum. We find that the bandgaps and effective carrier masses decrease with increasing Sn content. As a result, increasing the compositional disorder in SiyGeySnx MEAs enhances their electrical conductivity. For the thermal transport properties of SiyGeySnx MEAs, our molecular dynamics simulations show an opposite trend in the thermal conductivity of these MEAs at room temperature, which decreases with increasing compositional disorder, owing to enhanced Anderson localization and strong phonon-phonon anharmonic interactions. The enhanced electrical conductivity and weakened thermal conductivity make SiyGeySnx MEAs with high Sn content promising functional materials for thermoelectric applications. Our work demonstrates that HEAs/MEAs not only represent a new class of structural alloys but also a novel category of functional alloys with unique electrical and thermal transport properties.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Four-dimensional Vibrational Spectroscopy for Nanoscale Mapping of Phonon Dispersion in BN Nanotubes

Direct measurement of local phonon dispersion in individual nanostructures can greatly advance our understanding of their electrical, thermal, and mechanical properties. However, such experimental measurements require extremely high detection sensitivity and combined spatial, energy and momentum resolutions, thus has been elusive. Here, we develop a four-dimensional electron energy loss spectroscopy (4D-EELS) technique based a monochromated scanning transmission electron microscope (STEM), and present the position-dependent phonon dispersion measurement in individual boron nitride nanotubes (BNNTs). Our measurement shows that the unfolded phonon dispersion of multi-walled BNNTs is close to hexagonal-boron nitride (h-BN) crystals, suggesting that interlayer coupling and curved geometry have no substantial impacts on phonon dispersion. We also find that the acoustic phonons are extremely sensitive to momentum-dependent defect scattering, while optical phonons are much less susceptible. This work not only provides useful insights into vibrational properties of BNNTs, but also demonstrates huge prospects of the developed 4D-EELS technique in nanoscale phonon dispersion measurements.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Identification of splicing edges in tampered image based on Dichromatic Reflection Model

Imaging is a sophisticated process combining a plenty of photovoltaic conversions, which lead to some spectral signatures beyond visual perception in the final images. Any manipulation against an original image will destroy these signatures and inevitably leave some traces in the final forgery. Therefore we present a novel optic-physical method to discriminate splicing edges from natural edges in a tampered image. First, we transform the forensic image from RGB into color space of S and o1o2. Then on the assumption of Dichromatic Reflection Model, edges in the image are discovered by composite gradient and classified into different types based on their different photometric properties. Finally, splicing edges are reserved against natural ones by a simple logical algorithm. Experiment results show the efficacy of the proposed method.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

In Situ Epitaxy of Pure Phase Ultra-Thin InAs-Al Nanowires for Quantum Devices

Hybrid semiconductor-superconductor InAs-Al nanowires with uniform and defect-free crystal interfaces are one of the most promising candidates used in the quest for Majorana zero modes (MZMs). However, InAs nanowires often exhibit a high density of randomly distributed twin defects and stacking faults, which result in an uncontrolled and non-uniform InAs-Al interface. Furthermore, this type of disorder can create potential inhomogeneity in the wire, destroy the topological gap, and form trivial sub-gap states mimicking MZM in transport experiments. Further study shows that reducing the InAs nanowire diameter from growth can significantly suppress the formation of these defects and stacking faults. Here, we demonstrate the in situ growth of ultra-thin InAs nanowires with epitaxial Al film by molecular-beam epitaxy. Our InAs diameter (~ 30 nm) is only one-third of the diameters (~ 100 nm) commonly used in literatures. The ultra-thin InAs nanowires are pure phase crystals for various different growth directions, suggesting a low level of disorder. Transmission electron microscopy confirms an atomically sharp and uniform interface between the Al shell and the InAs wire. Quantum transport study on these devices resolves a hard induced superconducting gap and $2e^-$ periodic Coulomb blockade at zero magnetic field, a necessary step for future MZM experiments. A large zero bias conductance peak with a peak height reaching 80% of $2e^2/h$ is observed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units

Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision quantized Winograd convolution algorithm, called LANCE, which combines the advantages of fast convolution and quantization techniques. By embedding linear quantization operations into the Winograd-domain, the fast convolution can be performed efficiently under low-precision computation on graphics processing units. We test neural network models with LANCE on representative image classification datasets, including SVHN, CIFAR, and ImageNet. The experimental results show that our 8-bit quantized Winograd convolution improves the performance by up to 2.40x over the full-precision convolution with trivial accuracy loss.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting

Crowd counting, i.e., estimating the number of people in a crowded area, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast scale variations in crowd density within the interested area, and severe occlusion among the crowd. In this paper, we propose a novel Pyramid Density-Aware Attention-based network, abbreviated as PDANet, that leverages the attention, pyramid scale feature and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract different scale features, focus on the relevant information, and suppress the misleading ones. We also address the variation of crowdedness levels among different images with an exclusive Density-Aware Decoder (DAD). For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowded density maps as spatial attention. Meanwhile, we employ two losses to create a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state of the arts.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Person Re-Identification via Active Hard Sample Mining

Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification models. To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts. Considering that hard samples can provide informative patterns, we first formulate an uncertainty estimation to actively select hard samples to iteratively train a re-ID model from scratch. Then, intra-diversity estimation is designed to reduce the redundant hard samples by maximizing their diversity. Moreover, we propose a computer-assisted identity recommendation module embedded in the active hard sample mining framework to help human annotators to rapidly and accurately label the selected samples. Extensive experiments were carried out to demonstrate the effectiveness of our method on several public datasets. Experimental results indicate that our method can reduce 57%, 63%, and 49% annotation efforts on the Market1501, MSMT17, and CUHK03, respectively, while maximizing the performance of the re-ID model.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Stone-Wales Defects Preserve Hyperuniformity in Amorphous Two-Dimensional Materials

Crystalline two-dimensional (2D) materials such as graphene possess unique physical properties absent in their bulk form, enabling many novel device applications. Yet, little is known about their amorphous counterparts, which can be obtained by introducing the Stone-Wales (SW) topological defects via proton radiation. Here we provide strong numerical evidence that SW defects preserve hyperuniformity in hexagonal 2D materials, a recently discovered new state of matter characterized by vanishing normalized infinite-wavelength density fluctuations, which implies that all amorphous states of these materials are hyperuniform. Specifically, the static structure factor S(k) of these materials possesses the scaling S(k) ~ k^α for small wave number k, where 1<=α(p)<=2 is monotonically decreasing as the SW defect concentration p increases, indicating a transition from type-I to type-II hyperuniformity at p ~= 0.12 induced by the saturation of the SW defects. This hyperuniformity transition marks a structural transition from perturbed lattice structures to truly amorphous structures, and underlies the onset of strong correlation among the SW defects as well as a transition between distinct electronic transport mechanisms associated with different hyperuniformity classes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Testing the Agreement of Trees with Internal Labels

The input to the agreement problem is a collection $P = \{T_1, T_2, \dots , T_k\}$ of phylogenetic trees, called input trees, over partially overlapping sets of taxa. The question is whether there exists a tree $T$, called an agreement tree, whose taxon set is the union of the taxon sets of the input trees, such that for each $i \in \{1, 2, \dots , k\}$, the restriction of $T$ to the taxon set of $T_i$ is isomorphic to $T_i$. We give a $O(n k (\sum_{i \in [k]} d_i + \log^2(nk)))$ algorithm for a generalization of the agreement problem in which the input trees may have internal labels, where $n$ is the total number of distinct taxa in $P$, $k$ is the number of trees in $P$, and $d_i$ is the maximum number of children of a node in $T_i$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Theoretical evidence for new adsorption sites of CO$_2$ on the Ag electrode surface

Nowadays, electrochemical reduction of CO$_2$ has been considered as an effective method to solve the problem of global warming. The primary challenge in studying the mechanism is to determine the adsorption states of CO$_2$, since complicated metal surfaces often result in many different adsorption sites. Based on the density functional theory (DFT) calculations, we performed a theoretical study on the adsorption of CO$_2$ on the Ag electrode surface. The results show that the adsorption populations of CO$_2$ are extremely sensitive to the adsorption sites. Importantly, we found that the preferable adsorption positions are the terrace sites, rather than the previous reported step sites. The adsorption populations were found with the order of (211) > (110) > (111) > (100). Subsequently, the adsorption characteristics were correlated with the d-band theory and the charge transfers between Ag surfaces and CO$_2$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Towards Distributed Privacy-Preserving Prediction

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models&#39; predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Universal mechanical exfoliation of large-area 2D crystals

Two-dimensional (2D) materials provide extraordinary opportunities for exploring phenomena arising in atomically thin crystals. Beginning with the first isolation of graphene, mechanical exfoliation has been a key to provide high-quality 2D materials but despite improvements it is still limited in yield, lateral size and contamination. Here we introduce a contamination-free, one-step and universal Au-assisted mechanical exfoliation method and demonstrate its effectiveness by isolating 40 types of single-crystalline monolayers, including elemental 2D crystals, metal-dichalcogenides, magnets and superconductors. Most of them are of millimeter-size and high-quality, as shown by transfer-free measurements of electron microscopy, photo spectroscopies and electrical transport. Large suspended 2D crystals and heterojunctions were also prepared with high-yield. Enhanced adhesion between the crystals and the substrates enables such efficient exfoliation, for which we identify a common rule that underpins a universal route for producing large-area monolayers and thus supports studies of fundamental properties and potential application of 2D materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet-of-Things

With recent advances on the dense low-earth orbit (LEO) constellation, LEO satellite network has become one promising solution to providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism, and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli-Rician message passing with expectation maximization (BR-MP-EM) algorithm is proposed for this terrestrial-satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation maximization (EM) method is employed in the outer iterations to update the hyper-parameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network

Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a $\mathcal{W}$-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the $\mathcal{W}$-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e\_ophtha\_EX and DiaRetDb1. We achieved F1-score of 94.76\% and 95.73\% for OD segmentation, and 92.80\% and 94.14\% for exudates segmentation. To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation. Our results demonstrated that by choosing the optimal weights of each task, the MTL based $\mathcal{W}$-net outperformed separate models trained individually on each task. Code and pre-trained models will be available at: \url{https://github.com/FundusResearch/MTL_for_OD_and_exudates.git}.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Compressing $Θ$-chain in slit geometry

When compressed in a slit of width $D$, a $Θ$-chain that displays the scaling of size $R_0$ (diameter) with respect to the number of monomers $N$, $R_0\sim aN^{1/2}$, expands in the lateral direction as $R_{\parallel}\sim a N^ν(a/D)^{2ν-1}$. Provided that the $Θ$ condition is strictly maintained throughout the compression, the well-known scaling exponent of $Θ$-chain in 2 dimensions, $ν=4/7$, is anticipated in a perfect confinement. However, numerics shows that upon increasing compression from $R_0/D<1$ to $R_0/D\gg 1$, $ν$ gradually deviates from $ν=1/2$ and plateaus at $ν=3/4$, the exponent associated with the self-avoiding walk in two dimensions. Using both theoretical considerations and numerics, we argue that it is highly nontrivial to maintain the $Θ$ condition under confinement because of two major effects. First, as the dimension is reduced from 3 to 2 dimensions, the contributions of higher order virial terms, which can be ignored in 3 dimensions at large $N$, become significant. Second and more importantly, the geometrical confinement, which is regarded as an applied external field, alters the second virial coefficient ($B_2$) changes from $B_2=0$ ($Θ$ condition) in free space to $B_2>0$ (good-solvent condition) in confinement. Our study provides practical insight into how confinement affects the conformation of a single polymer chain.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Iterative Channel Estimation Using LSE and Sparse Message Passing for MmWave MIMO Systems

We propose an iterative channel estimation algorithm based on the Least Square Estimation (LSE) and Sparse Message Passing (SMP) algorithm for the Millimeter Wave (mmWave) MIMO systems. The channel coefficients of the mmWave MIMO are approximately modeled as a Bernoulli-Gaussian distribution and the channel matrix is sparse with only a few non-zero entries. By leveraging the advantage of sparseness, we propose an algorithm that iteratively detects the exact locations and values of non-zero entries of the sparse channel matrix. At each iteration, the locations are detected by the SMP, and values are estimated with the LSE. We also analyze the Cramér-Rao Lower Bound (CLRB), and show that the proposed algorithm is a minimum variance unbiased estimator under the assumption that we have the partial priori knowledge of the channel. Furthermore, we employ the Gaussian approximation for message densities under density evolution to simplify the analysis of the algorithm, which provides a simple method to predict the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm has much better performance than the existing sparse estimators, especially when the channel is sparse. In addition, our proposed algorithm converges to the CRLB of the genie-aided estimation of sparse channels with only five turbo iterations.

0 citations0 reviewsNo code linkedOpen access