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Tao Jiang

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preprint2026arXiv

Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction

Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for parallel window decoding. While it still outputs a single bit, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we overcome the throughput bottleneck that previously precluded the use of AlphaQubit-type decoders in FTQC. Our work presents the first scalable, neural-network-based parallel decoding framework that simultaneously achieves SOTA accuracy and the stringent throughput required for real-time quantum error correction. Using an end-to-end experimental workflow, we benchmark our decoder on the Zuchongzhi 3.2 superconducting quantum processor on surface codes with distances up to 7, demonstrating its superior accuracy. Moreover, we demonstrate that, using our approach, a single TPU v6e is capable of decoding surface codes with distances up to 25 within 1us per decoding round.

preprint2026arXiv

SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution

Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills as an experience schema that couples executable scripts, with non-executable guidance on procedures. Yet open skill ecosystems contain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, a lifecycle-governance framework for Agent Skills from collection and recommendation to evolution. SkillsVote profiles a million-scale open-source corpus for environment requirements, quality, and verifiability, then synthesizes tasks for verifiable skills. Before execution, SkillsVote performs agentic library search over structured skill library to expose instructional skill context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill use, agent exploration, environment, and result signals, and admits only successful reusable discoveries to evidence-gated updates. In our evaluation, offline evolution improves GPT-5.2 on Terminal-Bench 2.0 by up to 7.9 pp, while online evolution improves SWE-Bench Pro by up to 2.6 pp. Overall, governed external skill libraries can improve frozen agents without model updates when systems control exposure, credit, and preservation.

preprint2026arXiv

Soliton Thouless pumping engineered by inter-site nonlinearities

We study soliton Thouless pumping in an extended diagonal Aubry-André-Harper model with on-site nonlinearities and inter-site nonlinearities. We show that the inter-site nonlinearities can make solitons acquire anomalous transport distances far beyond the ones predicted by the linear bands, and the quantized displacements can be engineered well. We uncover that nonlinear instabilities require lower limits on sweeping rates for soliton pumping, challenging the common notion that slower modulation enables a more favorable realization of topological transport. The nonlinear interactions between solitons make multi-soliton pumping generally lack the robustness characteristic of Thouless pumping as linear systems. Our results provide many possibilities to engineer topological pumping by nonlinearities, and further make a step for applications of soliton pumping.

preprint2025arXiv

Hear: Hierarchically Enhanced Aesthetic Representations For Multidimensional Music Evaluation

Evaluating song aesthetics is challenging due to the multidimensional nature of musical perception and the scarcity of labeled data. We propose HEAR, a robust music aesthetic evaluation framework that combines: (1) a multi-source multi-scale representations module to obtain complementary segment- and track-level features, (2) a hierarchical augmentation strategy to mitigate overfitting, and (3) a hybrid training objective that integrates regression and ranking losses for accurate scoring and reliable top-tier song identification. Experiments demonstrate that HEAR consistently outperforms the baseline across all metrics on both tracks of the ICASSP 2026 SongEval benchmark. The code and trained model weights are available at https://github.com/Eps-Acoustic-Revolution-Lab/EAR_HEAR.

preprint2025arXiv

Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking

Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrotor systems. To address these challenges, we propose HBO-PID, a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization (HBO) framework with the classical PID controller to achieve accurate and robust trajectory tracking. By explicitly modeling input-dependent noise variance, the proposed method can better adapt to dynamic and complex environments, and therefore improve the accuracy and robustness of trajectory tracking. To accelerate the convergence of optimization, we adopt a two-stage optimization strategy that allow us to more efficiently find the optimal controller parameters. Through experiments in both simulation and real-world scenarios, we demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) methods. Compared to SOTA methods, it improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.

preprint2024arXiv

Synthetic Data in AI: Challenges, Applications, and Ethical Implications

In the rapidly evolving field of artificial intelligence, the creation and utilization of synthetic datasets have become increasingly significant. This report delves into the multifaceted aspects of synthetic data, particularly emphasizing the challenges and potential biases these datasets may harbor. It explores the methodologies behind synthetic data generation, spanning traditional statistical models to advanced deep learning techniques, and examines their applications across diverse domains. The report also critically addresses the ethical considerations and legal implications associated with synthetic datasets, highlighting the urgent need for mechanisms to ensure fairness, mitigate biases, and uphold ethical standards in AI development.

preprint2023arXiv

Bipartite-ness under smooth conditions

Given a family $\mathcal{F}$ of bipartite graphs, the {\it Zarankiewicz number} $z(m,n,\mathcal{F})$ is the maximum number of edges in an $m$ by $n$ bipartite graph $G$ that does not contain any member of $\mathcal{F}$ as a subgraph (such $G$ is called {\it $\mathcal{F}$-free}). For $1\leq β<α<2$, a family $\mathcal{F}$ of bipartite graphs is $(α,β)$-{\it smooth} if for some $ρ>0$ and every $m\leq n$, $z(m,n,\mathcal{F})=ρm n^{α-1}+O(n^β)$. Motivated by their work on a conjecture of Erdős and Simonovits on compactness and a classic result of Andrásfai, Erdős and Sós, in \cite{AKSV} Allen, Keevash, Sudakov and Verstraëte proved that for any $(α,β)$-smooth family $\mathcal{F}$, there exists $k_0$ such that for all odd $k\geq k_0$ and sufficiently large $n$, any $n$-vertex $\mathcal{F}\cup\{C_k\}$-free graph with minimum degree at least $ρ(\frac{2n}{5}+o(n))^{α-1}$ is bipartite. In this paper, we strengthen their result by showing that for every real $δ>0$, there exists $k_0$ such that for all odd $k\geq k_0$ and sufficiently large $n$, any $n$-vertex $\mathcal{F}\cup\{C_k\}$-free graph with minimum degree at least $δn^{α-1}$ is bipartite. Furthermore, our result holds under a more relaxed notion of smoothness, which include the families $\mathcal{F}$ consisting of the single graph $K_{s,t}$ when $t\gg s$. We also prove an analogous result for $C_{2\ell}$-free graphs for every $\ell\geq 2$, which complements a result of Keevash, Sudakov and Verstraëte in \cite{KSV}.

preprint2023arXiv

Rainbow Turán number of clique subdivisions

We show that for any integer $t\geq 2$, every properly edge-coloured graph on $n$ vertices with more than $n^{1+o(1)}$ edges contains a rainbow subdivision of $K_t$. Note that this bound on the number of edges is sharp up to the $o(1)$ error term. This is a rainbow analogue of some classical results on clique subdivisions and extends some results on rainbow Turán numbers. Our method relies on the framework introduced by Sudakov and Tomon[2020] which we adapt to find robust expanders in the coloured setting.

preprint2023arXiv

Towards A Unified Conformer Structure: from ASR to ASV Task

Transformer has achieved extraordinary performance in Natural Language Processing and Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant Conformer has become a state-of-the-art architecture in the field of Automatic Speech Recognition (ASR). However, the main-stream architecture for Automatic Speaker Verification (ASV) is convolutional Neural Networks, and there is still much room for research on the Conformer based ASV. In this paper, firstly, we modify the Conformer architecture from ASR to ASV with very minor changes. Length-Scaled Attention (LSA) method and Sharpness-Aware Minimizationis (SAM) are adopted to improve model generalization. Experiments conducted on VoxCeleb and CN-Celeb show that our Conformer based ASV achieves competitive performance compared with the popular ECAPA-TDNN. Secondly, inspired by the transfer learning strategy, ASV Conformer is natural to be initialized from the pretrained ASR model. Via parameter transferring, self-attention mechanism could better focus on the relationship between sequence features, brings about 11% relative improvement in EER on test set of VoxCeleb and CN-Celeb, which reveals the potential of Conformer to unify ASV and ASR task. Finally, we provide a runtime in ASV-Subtools to evaluate its inference speed in production scenario. Our code is released at https://github.com/Snowdar/asv-subtools/tree/master/doc/papers/conformer.md.

preprint2022arXiv

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyperparameter tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the ViT model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.

preprint2022arXiv

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

With the acceleration of urbanization and living standards, microorganisms play increasingly important roles in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The changing trend can be adjusted in time and provided a basis for the improvement. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image segmentation methods with periods. This study has high research significance and application value, which can be referred to microbial researchers to have a comprehensive understanding of microorganism biovolume measurements using digital image analysis methods and potential applications.

preprint2022arXiv

A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers

To increase the objectivity and accuracy of pathologists' work, artificial neural network(ANN) methods have been generally needed in the segmentation, classification, and detection of histopathological WSI. In this paper, WSI analysis methods based on ANN are reviewed. Firstly, the development status of WSI and ANN methods is introduced. Secondly, we summarize the common ANN methods. Next, we discuss publicly available WSI datasets and evaluation metrics. These ANN architectures for WSI processing are divided into classical neural networks and deep neural networks(DNNs) and then analyzed. Finally, the application prospect of the analytical method in this field is discussed. The important potential method is Visual Transformers.

preprint2022arXiv

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis. An important step in detection, tracking, monitoring, feature extraction, modeling and analysis is image segmentation, in which U-Net has increasingly applied in microscopic image segmentation. This paper comprehensively reviews the development history of U-Net, and analyzes various research results of various segmentation methods since the emergence of U-Net and conducts a comprehensive review of related papers. First, this paper has summarized the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements that has introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can clearly see the dynamics of transmission of technological development and keep up with future trends in this interdisciplinary field.

preprint2022arXiv

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

The Computer Assisted Sperm Analysis (CASA) plays a crucial role in male reproductive health diagnosis and Infertility treatment. With the development of the computer industry in recent years, a great of accurate algorithms are proposed. With the assistance of those novel algorithms, it is possible for CASA to achieve a faster and higher quality result. Since image processing is the technical basis of CASA, including pre-processing,feature extraction, target detection and tracking, these methods are important technical steps in dealing with CASA. The various works related to Computer Assisted Sperm Analysis methods in the last 30 years (since 1988) are comprehensively introduced and analysed in this survey. To facilitate understanding, the methods involved are analysed in the sequence of general steps in sperm analysis. In other words, the methods related to sperm detection (localization) are first analysed, and then the methods of sperm tracking are analysed. Beside this, we analyse and prospect the present situation and future of CASA. According to our work, the feasible for applying in sperm microscopic video of methods mentioned in this review is explained. Moreover, existing challenges of object detection and tracking in microscope video are potential to be solved inspired by this survey.

preprint2022arXiv

Acceleration of Federated Learning with Alleviated Forgetting in Local Training

Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, their training efficiency remains low when the data are not independently and identically distributed (non-i.i.d.) across different clients. We observe that the slow convergence rates of the existing methods are (at least partially) caused by the catastrophic forgetting issue during the local training stage on each individual client, which leads to a large increase in the loss function concerning the previous training data at the other clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage by regularizing locally trained parameters with the loss on generated pseudo data, which encode the knowledge of previous training data learned by the global model. Our comprehensive experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep and the clients' data are extremely non-i.i.d., but is also able to protect privacy better in classification problems and more robust against gradient inversion attacks. The code is available at: https://github.com/Zoesgithub/FedReg.

preprint2022arXiv

Active Sensing for Communications by Learning

This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function. In such an active learning setting, the agent needs to design an adaptive sensing strategy sequentially based on the observations made so far. To tackle such a challenging problem in which the dimension of historical observations increases over time, we propose to use a long short-term memory (LSTM) network to exploit the temporal correlations in the sequence of observations and to map each observation to a fixed-size state information vector. We then use a deep neural network (DNN) to map the LSTM state at each time frame to the design of the next measurement step. Finally, we employ another DNN to map the final LSTM state to the desired solution. We investigate the performance of the proposed framework for adaptive channel sensing problems in wireless communications. In particular, we consider the adaptive beamforming problem for mmWave beam alignment and the adaptive reconfigurable intelligent surface sensing problem for reflection alignment. Numerical results demonstrate that the proposed deep active sensing strategy outperforms the existing adaptive or nonadaptive sensing schemes.

preprint2022arXiv

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder--decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97\% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting.

preprint2022arXiv

Cache-Aided Massive MIMO with Linear Precoding in Multi-cell Systems

In this paper, we propose a novel joint caching and massive multiple-input multiple-output (MIMO) transmission scheme, referred to as \emph{cache-aided massive MIMO}, for multi-cell downlink transmission to multiple cache-enabled receivers. With the proposed scheme, users who have cached (a portion of) the files that they request are offloaded and, hence, (partially) inactive during downlink transmission. The other users either benefit from the cache-enabled offloading for mitigating pilot contamination or exploit the cached but unrequested files to cancel interference during uplink channel estimation and downlink file reception. Moreover, by redesigning the transmit precoders based on the cache status of the users and channel state information, we gain additional degrees of freedom for massive MIMO transmission. For a given cache status, we analyze the equivalent content delivery rates (ECDRs), i.e., the average rates of delivering a requested file via both caching and massive MIMO transmission to the requesting user, for cache-aided massive MIMO employing re-designed maximum ratio transmission (MRT), zero-forcing (ZF) precoding, and regularized zero-forcing (RZF) precoding. Based on the derived results, the impact of (random) uncoded caching and coded caching on the performance of the re-designed precoding schemes is investigated. Simulation results validate our derivations and show that caching is beneficial for precoded downlink transmission as it enhances the transmit power allocation, mitigates intra- and inter-cell interference, and reduces the impairment caused by pilot contamination. Compared with conventional massive MIMO without caching and with cache-oblivious precoding, the proposed cache-aided massive MIMO scheme achieves a significantly higher ECDR even when the number of users approaches the number of transmit antennas.

preprint2022arXiv

CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. Manual screening of cytopathology images is time-consuming and error-prone. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model.

preprint2022arXiv

Efficient Distinction between Quantum Direct and Common Causes and its Experimental Verification

Identifying the causal structures between two statistically correlated events has been widely investigated in many fields of science. While some of the well-studied classical methods are carefully generalized to quantum version of causal inference for certain cases, an effective and efficient way to detect the more general quantum causal structures is still lacking. Here, we introduce a quantity named `Causal Determinant' to efficiently identify the quantum causal structures between two quantum systems and experimentally verify the validity of the method. According to the causal determinant, the quantum direct cause imposed by an arbitrary unitary operator can be perfectly discriminated with the quantum common cause, in which the two quantum systems share a joint quantum state. In addition, the causal determinant has the capability to discriminate between more general causal structures and predict the range of their parameters. The ability to detect more general quantum causal structures of our method can shed new light on the field of quantum causal inference.

preprint2022arXiv

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related databases, not to mention the databases with GT images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and target detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods.

preprint2022arXiv

Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

The cybersecurity breaches expose surveillance video streams to forgery attacks, under which authentic streams are falsified to hide unauthorized activities. Traditional video forensics approaches can localize forgery traces using spatial-temporal analysis on relatively long video clips, while falling short in real-time forgery detection. The recent work correlates time-series camera and wireless signals to detect looped videos but cannot realize fine-grained forgery localization. To overcome these limitations, we propose Secure-Pose, which exploits the pervasive coexistence of surveillance and Wi-Fi infrastructures to defend against video forgery attacks in a real-time and fine-grained manner. We observe that coexisting camera and Wi-Fi signals convey common human semantic information and forgery attacks on video streams will decouple such information correspondence. Particularly, retrievable human pose features are first extracted from concurrent video and Wi-Fi channel state information (CSI) streams. Then, a lightweight detection network is developed to accurately discover forgery attacks and an efficient localization algorithm is devised to seamlessly track forgery traces in video streams. We implement Secure-Pose using one Logitech camera and two Intel 5300 NICs and evaluate it in different environments. Secure-Pose achieves a high detection accuracy of 98.7% and localizes abnormal objects under playback and tampering attacks.

preprint2022arXiv

Interference Nulling Using Reconfigurable Intelligent Surface

This paper investigates the interference nulling capability of reconfigurable intelligent surface (RIS) in a multiuser environment where multiple single-antenna transceivers communicate simultaneously in a shared spectrum. From a theoretical perspective, we show that when the channels between the RIS and the transceivers have line-of-sight and the direct paths are blocked, it is possible to adjust the phases of the RIS elements to null out all the interference completely and to achieve the maximum $K$ degrees-of-freedom (DoF) in the overall $K$-user interference channel, provided that the number of RIS elements exceeds some finite value that depends on $K$. Algorithmically, for any fixed channel realization we formulate the interference nulling problem as a feasibility problem, and propose an alternating projection algorithm to efficiently solve the resulting nonconvex problem with local convergence guarantee. Numerical results show that the proposed alternating projection algorithm can null all the interference if the number of RIS elements is only slightly larger than a threshold of $2K(K-1)$. For the practical sum-rate maximization objective, this paper proposes to use the zero-forcing solution obtained from alternating projection as an initial point for subsequent Riemannian conjugate gradient optimization and shows that it has a significant performance advantage over random initializations. For the objective of maximizing the minimum rate, this paper proposes a subgradient projection method which is capable of achieving excellent performance at low complexity.

preprint2022arXiv

KaraTuner: Towards end to end natural pitch correction for singing voice in karaoke

An automatic pitch correction system typically includes several stages, such as pitch extraction, deviation estimation, pitch shift processing, and cross-fade smoothing. However, designing these components with strategies often requires domain expertise and they are likely to fail on corner cases. In this paper, we present KaraTuner, an end-to-end neural architecture that predicts pitch curve and resynthesizes the singing voice directly from the tuned pitch and vocal spectrum extracted from the original recordings. Several vital technical points have been introduced in KaraTuner to ensure pitch accuracy, pitch naturalness, timbre consistency, and sound quality. A feed-forward Transformer is employed in the pitch predictor to capture longterm dependencies in the vocal spectrum and musical note. We also develop a pitch-controllable vocoder based on a novel source-filter block and the Fre-GAN architecture. KaraTuner obtains a higher preference than the rule-based pitch correction approach through A/B tests, and perceptual experiments show that the proposed vocoder achieves significant advantages in timbre consistency and sound quality compared with the parametric WORLD vocoder, phase vocoder and CLPC vocoder.

preprint2022arXiv

Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Reconfigurable intelligent surface (RIS) is capable of intelligently manipulating the phases of the incident electromagnetic wave to improve the wireless propagation environment between the base-station (BS) and the users. This paper addresses the joint user scheduling, RIS configuration, and BS beamforming problem in an RIS-assisted downlink network with limited pilot overhead. We show that graph neural networks (GNN) with permutation invariant and equivariant properties can be used to appropriately schedule users and to design RIS configurations to achieve high overall throughput while accounting for fairness among the users. As compared to the conventional methodology of first estimating the channels then optimizing the user schedule, RIS configuration and the beamformers, this paper shows that an optimized user schedule can be obtained directly from a very short set of pilots using a GNN, then the RIS configuration can be optimized using a second GNN, and finally the BS beamformers can be designed based on the overall effective channel. Numerical results show that the proposed approach can utilize the received pilots more efficiently than the conventional channel estimation based approach, and can generalize to systems with an arbitrary number of users.

preprint2022arXiv

Learning Progressive Distributed Compression Strategies from Local Channel State Information

This paper proposes a deep learning framework to design distributed compression strategies in which distributed agents need to compress high-dimensional observations of a source, then send the compressed bits via bandwidth limited links to a fusion center for source reconstruction. Further, we require the compression strategy to be progressive so that it can adapt to the varying link bandwidths between the agents and the fusion center. Moreover, to ensure scalability, we investigate strategies that depend only on the local channel state information (CSI) at each agent. Toward this end, we use a data-driven approach in which the progressive linear combination and uniform quantization strategy at each agent are trained as a function of its local CSI. To deal with the challenges of modeling the quantization operations (which always produce zero gradients in the training of neural networks), we propose a novel approach of exploiting the statistics of the batch training data to set the dynamic ranges of the uniform quantizers. Numerically, we show that the proposed distributed estimation strategy designed with only local CSI can significantly reduce the signaling overhead and can achieve a lower mean-squared error distortion for source reconstruction than state-of-the-art designs that require global CSI at comparable overall communication cost.

preprint2022arXiv

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent onboard sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of 1.24\times and 1.28\times compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively

preprint2022arXiv

Stochastic Variational Methods in Generalized Hidden Semi-Markov Models to Characterize Functionality in Random Heteropolymers

Recent years have seen substantial advances in the development of biofunctional materials using synthetic polymers. The growing problem of elusive sequence-functionality relations for most biomaterials has driven researchers to seek more effective tools and analysis methods. In this study, statistical models are used to study sequence features of the recently reported random heteropolymers (RHP), which transport protons across lipid bilayers selectively and rapidly like natural proton channels. We utilized the probabilistic graphical model framework and developed a generalized hidden semi-Markov model (GHSMM-RHP) to extract the function-determining sequence features, including the transmembrane segments within a chain and the sequence heterogeneity among different chains. We developed stochastic variational methods for efficient inference on parameter estimation and predictions, and empirically studied their computational performance from a comparative perspective on Bayesian (i.e., stochastic variational Bayes) versus frequentist (i.e., stochastic variational expectation-maximization) frameworks that have been studied separately before. The real data results agree well with the laboratory experiments, and suggest GHSMM-RHP's potential in predicting protein-like behavior at the polymer-chain level.

preprint2022arXiv

Tree-degenerate graphs and nested dependent random choice

The celebrated dependent random choice lemma states that in a bipartite graph an average vertex (weighted by its degree) has the property that almost all small subsets $S$ in its neighborhood has common neighborhood almost as large as in the random graph of the same edge-density. Two well-known applications of the lemma are as follows. The first is a theorem of Füredi and of Alon, Krivelevich, and Sudakov showing that the maximum number of edges in an $n$-vertex graph not containing a fixed bipartite graph with maximum degree at most $r$ on one side is $O(n^{2-1/r})$. This was recently extended by Grzesik, Janzer and Nagy to the family of so-called $(r,t)$-blowups of a tree. A second application is a theorem of Conlon, Fox, and Sudakov, confirming a special case of a conjecture of Erdős and Simonovits and of Sidorenko, showing that if $H$ is a bipartite graph that contains a vertex complete to the other part and $G$ is a graph then the probability that the uniform random mapping from $V(H)$ to $V(G)$ is a homomorphismis at least $\left[\frac{2|E(G)|}{|V(G)|^2}\right]^{|E(H)|}$. In this note, we introduce a nested variant of the dependent random choice lemma, which might be of independent interest. We then apply it to obtain a common extension of the theorem of Conlon, Fox, and Sudakov and the theorem of Grzesik, Janzer, and Nagy, regarding Turán and Sidorenko properties of so-called tree-degenerate graphs.

preprint2022arXiv

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision-support systems for diagnosis, surgery planning, and population-based analysis on spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms towards labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The content and code concerning VerSe can be accessed at: https://github.com/anjany/verse.

preprint2021arXiv

A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

Environmental microorganism (EM) offers a high-efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, monitoring, and decomposition of environmental pollutants. However, this depends on the proper identification of suitable microorganisms. In order to fasten, low the cost, increase consistency and accuracy of identification, we propose the novel pairwise deep learning features to analyze microorganisms. The pairwise deep learning features technique combines the capability of handcrafted and deep learning features. In this technique we, leverage the Shi and Tomasi interest points by extracting deep learning features from patches which are centered at interest points locations. Then, to increase the number of potential features that have intermediate spatial characteristics between nearby interest points, we use Delaunay triangulation theorem and straight-line geometric theorem to pair the nearby deep learning features. The potential of pairwise features is justified on the classification of EMs using SVMs, k-NN, and Random Forest classifier. The pairwise features obtain outstanding results of 99.17%, 91.34%, 91.32%, 91.48%, and 99.56%, which are the increase of about 5.95%, 62.40%, 62.37%, 61.84%, and 3.23% in accuracy, F1-score, recall, precision, and specificity respectively, compared to non-paired deep learning features.

preprint2021arXiv

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

The advances in agile micro aerial vehicles (MAVs) have shown great potential in replacing humans for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight poses fundamental challenges in such dim or smoky environments. Current dominated computer-vision based solutions only work in well-lighted texture-rich environments. This paper addresses the challenge by proposing Marvel, an RF backscatter-based state estimation system with online initialization and calibration. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion with an automatic initialization module. Marvel is enabled by three new designs, a backscatter-based pose sensing module, an online initialization and calibration module, and a backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and 4.99 degrees for orientation estimation. We further demonstrate our online initialization and calibration by comparing to the perfect initial parameter measurements from burdensome manual operations.

preprint2021arXiv

Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities

With the continued spread of coronavirus, the task of forecasting distinctive COVID-19 growth curves in different cities, which remain inadequately explained by standard epidemiological models, is critical for medical supply and treatment. Predictions must take into account non-pharmaceutical interventions to slow the spread of coronavirus, including stay-at-home orders, social distancing, quarantine and compulsory mask-wearing, leading to reductions in intra-city mobility and viral transmission. Moreover, recent work associating coronavirus with human mobility and detailed movement data suggest the need to consider urban mobility in disease forecasts. Here we show that by incorporating intra-city mobility and policy adoption into a novel metapopulation SEIR model, we can accurately predict complex COVID-19 growth patterns in U.S. cities ($R^2$ = 0.990). Estimated mobility change due to policy interventions is consistent with empirical observation from Apple Mobility Trends Reports (Pearson's R = 0.872), suggesting the utility of model-based predictions where data are limited. Our model also reproduces urban "superspreading", where a few neighborhoods account for most secondary infections across urban space, arising from uneven neighborhood populations and heightened intra-city churn in popular neighborhoods. Therefore, our model can facilitate location-aware mobility reduction policy that more effectively mitigates disease transmission at similar social cost. Finally, we demonstrate our model can serve as a fine-grained analytic and simulation framework that informs the design of rational non-pharmaceutical interventions policies.

preprint2021arXiv

Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

The cybersecurity breaches render surveillance systems vulnerable to video forgery attacks, under which authentic live video streams are tampered to conceal illegal human activities under surveillance cameras. Traditional video forensics approaches can detect and localize forgery traces in each video frame using computationally-expensive spatial-temporal analysis, while falling short in real-time verification of live video feeds. The recent work correlates time-series camera and wireless signals to recognize replayed surveillance videos using event-level timing information but it cannot realize fine-grained forgery detection and localization on each frame. To fill this gap, this paper proposes Secure-Pose, a novel cross-modal forgery detection and localization system for live surveillance videos using WiFi signals near the camera spot. We observe that coexisting camera and WiFi signals convey common human semantic information and the presence of forgery attacks on video frames will decouple such information correspondence. Secure-Pose extracts effective human pose features from synchronized multi-modal signals and detects and localizes forgery traces under both inter-frame and intra-frame attacks in each frame. We implement Secure-Pose using a commercial camera and two Intel 5300 NICs and evaluate it in real-world environments. Secure-Pose achieves a high detection accuracy of 95.1% and can effectively localize tampered objects under different forgery attacks.

preprint2021arXiv

Two-dimensional charge density wave TaX$_2$ (X=S, Se, Te) from first principles

Transition metal dichalcogenides are rich in their structural phases, e.g. 1T-TaS2 and 1T-TaSe2 form charge density wave (CDW) under low temperature with interesting and exotic properties. Here, we present a systematic study of different structures in two-dimensional TaX2 (X=S, Se, Te) using density functional theory calculations with consideration of van der Waals interaction. All the normal phases present metal characteristics with various ground state and magnetic properties. The lattice reconstruction of CDW drastically affects the electronic and structural characteristics of 1T-TaS2 and 1T-TaSe2, leading to a transition from metal to insulator and an emergence of magnetic moment within periodic atomic clusters called the Star of David. The evaluated Heisenberg couplings indicate the weak ferromagnetic coupling between the clusters in monolayer. Furthermore, in bilayer commensurate CDW cases, we find intriguing phenomenon of the varying magnetic properties with different stacking orders. The magnetic moment in each layer disappears when two layers are coupled, but may sustain in certain stackings of interlayer antiferromagnetic configurations.

preprint2020arXiv

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.

preprint2020arXiv

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, "mU-Net-B3", with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel "buffer" strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

preprint2020arXiv

Achieving 50 femtosecond resolution in MeV ultrafast electron diffraction with a double bend achromat compressor

We propose and demonstrate a novel scheme to produce ultrashort and ultrastable MeV electron beam. In this scheme, the electron beam produced in a photocathode radio-frequency (rf) gun first expands under its own Coulomb force with which a positive energy chirp is imprinted in the beam longitudinal phase space. The beam is then sent through a double bend achromat with positive longitudinal dispersion where electrons at the bunch tail with lower energies follow shorter paths and thus catch up with the bunch head, leading to longitudinal bunch compression. We show that with optimized parameter sets, the whole beam path from the electron source to the compression point can be made isochronous such that the time of flight for the electron beam is immune to the fluctuations of rf amplitude. With a laser-driven THz deflector, the bunch length and arrival time jitter for a 20 fC beam after bunch compression are measured to be about 29 fs (FWHM) and 22 fs (FWHM), respectively. Such an ultrashort and ultrastable electron beam allows us to achieve 50 femtosecond (FWHM) resolution in MeV ultrafast electron diffraction where lattice oscillation at 2.6 THz corresponding to Bismuth A1g mode is clearly observed without correcting both the short-term timing jitter and long-term timing drift. Furthermore, oscillating weak diffuse scattering signal related to phonon coupling and decay is also clearly resolved thanks to the improved temporal resolution and increased electron flux. We expect that this technique will have a strong impact in emerging ultrashort electron beam based facilities and applications.

preprint2020arXiv

Authenticating On-Body IoT Devices: An Adversarial Learning Approach

By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining their widespread acceptance. This paper overcomes these limitations with a general authentication solution by integrating wireless physical layer (PHY) signatures with upper-layer protocols. The key enabling techniques are constructing representative radio propagation profiles from received signals, and developing an adversarial multi-player neural network to accurately recognize underlying radio propagation patterns and facilitate on-body device authentication. Once hearing a suspicious transmission, our system triggers a PHY-based challenge-response protocol to defend in depth against active attacks. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances and environment changes. We build a prototype of our system using Universal Software Radio Peripheral (USRP) devices and conduct extensive experiments with various static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 91.6%, with a high area under the receiver operating characteristic curve (AUROC) of 0.96 and a better generalization performance compared with the conventional non-adversarial approach.

preprint2020arXiv

Edge Intelligence: Architectures, Challenges, and Applications

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

preprint2020arXiv

Enabling Low-Power OFDM for IoT by Exploiting Asymmetric Clock Rates

The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications. OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity. While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY. In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT. To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side. Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices. The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability. We provide an implementation of our design and show that it can reduce up to 88% of the IoT's TX power when the AP sets $8\times$ clock rate.

preprint2020arXiv

Extremal problems for hypergraph blowups of trees

In this paper we present a novel approach in extremal set theory which may be viewed as an asymmetric version of Katona's permutation method. We use it to find more Turán numbers of hypergraphs in the Erdős--Ko--Rado range. An $(a,b)$-path $P$ of length $2k-1$ consists of $2k-1$ sets of size $r=a+b$ as follows. Take $k$ pairwise disjoint $a$-element sets $A_0, A_2, \dots, A_{2k-2}$ and other $k$ pairwise disjoint $b$-element sets $B_1, B_3, \dots, B_{2k-1}$ and order them linearly as $A_0, B_1, A_2, B_3, A_4\dots$. Define the (hyper)edges of $P_{2k-1}(a,b)$ as the sets of the form $A_i\cup B_{i+1}$ and $B_j\cup A_{j+1}$. The members of $P$ can be represented as $r$-element intervals of the $ak+bk$ element underlying set. Our main result is about hypergraphs that are blowups of trees, and implies that for fixed $k,a,b$, as $n\to \infty$ \[ {\rm ex}_r(n,P_{2k-1}(a,b)) = (k - 1){n \choose r - 1} + o(n^{r - 1}).\] This generalizes the Erdős--Gallai theorem for graphs which is the case of $a=b=1$. We also determine the asymptotics when $a+b$ is even; the remaining cases are still open.

preprint2020arXiv

Higher Criticism Tuned Regression For Weak And Sparse Signals

Here we propose a novel searching scheme for a tuning parameter in high-dimensional penalized regression methods to address variable selection and modeling when sample sizes are limited compared to the data dimensions. Our method is motivated by high-throughput biological data such as genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS). We propose a new estimate of the regularization parameter $λ$ in penalized regression methods based on an estimated lower bound of the proportion of false null hypotheses with confidence $(1 - α)$. The bound is estimated by applying the empirical null distribution of the higher criticism statistic, a second-level significance test constructed by dependent $p$-values using a multi-split regression and aggregation method. A tuning parameter estimate in penalized regression, $λ$, corresponds with the lower bound of the proportion of false null hypotheses. Different penalized regression methods with varied signal sparsity and strength are compared in the multi-split method setting. We demonstrate the performance of our method using both simulation experiments and the applications of real data on (1) lipid-trait genetics from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial and (2) epigenetic analysis evaluating smoking's influence in differential methylation in the Agricultural Lung Health Study. The proposed algorithm is included in the HCTR package, available at https://cran.r-project.org/web/packages/HCTR/index.html.

preprint2020arXiv

Knockoff Boosted Tree for Model-Free Variable Selection

In this article, we propose a novel strategy for conducting variable selection without prior model topology knowledge using the knockoff method with boosted tree models. Our method is inspired by the original knockoff method, where the differences between original and knockoff variables are used for variable selection with false discovery rate control. The original method uses Lasso for regression models and assumes there are more samples than variables. We extend this method to both model-free and high-dimensional variable selection. We propose two new sampling methods for generating knockoffs, namely the sparse covariance and principal component knockoff methods. We test these methods and compare them with the original knockoff method in terms of their ability to control type I errors and power. The boosted tree model is a complex system and has more hyperparameters than models with simpler assumptions. In our framework, these hyperparameters are either tuned through Bayesian optimization or fixed at multiple levels for trend detection. In simulation tests, we also compare the properties and performance of importance test statistics of tree models. The results include combinations of different knockoffs and importance test statistics. We also consider scenarios that include main-effect, interaction, exponential, and second-order models while assuming the true model structures are unknown. We apply our algorithm for tumor purity estimation and tumor classification using the Cancer Genome Atlas (TCGA) gene expression data. The proposed algorithm is included in the KOBT package, available at \url{https://cran.r-project.org/web/packages/KOBT/index.html}.

preprint2020arXiv

Large-Scale Beamforming for Massive MIMO via Randomized Sketching

Massive MIMO system yields significant improvements in spectral and energy efficiency for future wireless communication systems. The regularized zero-forcing (RZF) beamforming is able to provide good performance with the capability of achieving numerical stability and robustness to the channel uncertainty. However, in massive MIMO systems, the matrix inversion operation in RZF beamforming becomes computationally expensive. To address this computational issue, we shall propose a novel randomized sketching based RZF beamforming approach with low computational complexity. This is achieved by solving a linear system via randomized sketching based on the preconditioned Richard iteration, which guarantees high quality approximations to the optimal solution. We theoretically prove that the sequence of approximations obtained iteratively converges to the exact RZF beamforming matrix linearly fast as the number of iterations increases. Also, it turns out that the system sum-rate for such sequence of approximations converges to the exact one at a linear convergence rate. Our simulation results verify our theoretical findings.

preprint2020arXiv

Linear cycles of consecutive lengths

A well-known result of Verstraëte \cite{V00} shows that for each integer $k\geq 2$ every graph $G$ with average degree at least $8k$ contains cycles of $k$ consecutive even lengths, the shortest of which is at most twice the radius of $G$. We establish two extensions of Verstraëte's result for linear cycles in linear $r$-uniform hypergraphs. We show that for any fixed integers $r\geq 3,k\geq 2$, there exist constants $c_1=c_1(r)$ and $c_2=c_2(r,k)$, such that every linear $r$-uniform hypergraph $G$ with average degree $d(G)\geq c_1 k$ contains linear cycles of $k$ consecutive even lengths, the shortest of which is at most $2\lceil \frac{ \log n}{\log (d(G)/k)-c_2}\rceil$. In particular, as an immediate corollary, we retrieve the current best known upper bound on the linear Turán number of $C^r_{2k}$ with improved coefficients. Furthermore, we show that for any fixed integers $r\geq 3,k\geq 2$, there exist constants $c_3=c_3(r)$ and $c_4=c_4(r)$ such that every $n$-vertex linear $r$-uniform graph with average degree $d(G)\geq c_3k$, contains linear cycles of $k$ consecutive lengths, the shortest of which has length at most $6\lceil \frac{\log n}{\log (d(G)/k)-c_4} \rceil +6$. Both the degree condition and the shortest length among the cycles guaranteed are best possible up to a constant factor.

preprint2020arXiv

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm.

preprint2020arXiv

Partitioning ordered hypergraphs

An {\em ordered $r$-graph} is an $r$-uniform hypergraph whose vertex set is linearly ordered. Given $2\leq k\leq r$, an ordered $r$-graph $H$ is {\em interval} $k$-{\em partite} if there exist at least $k$ disjoint intervals in the ordering such that every edge of $H$ has nonempty intersection with each of the intervals and is contained in their union. Our main result implies that for each $α> k - 1$ and $d>0$, every $n$-vertex ordered $r$-graph with $d \,n^α$ edges has for some $m\leq n$ an $m$-vertex interval $k$-partite subgraph with $Ω(d\, m^α)$ edges. This is an extension to ordered $r$-graphs of the observation by Erd\H os and Kleitman that every $r$-graph contains an $r$-partite subgraph with a constant proportion of the edges. The restriction $α> k-1$ is sharp. We also present applications of the main result to several extremal problems for ordered hypergraphs.

preprint2020arXiv

Robot-assisted Backscatter Localization for IoT Applications

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

preprint2020arXiv

Tight paths in convex geometric hypergraphs

In this paper, we prove a theorem on tight paths in convex geometric hypergraphs, which is asymptotically sharp in infinitely many cases. Our geometric theorem is a common generalization of early results of Hopf and Pannwitz [12], Sutherland [19], Kupitz and Perles [16] for convex geometric graphs, as well as the classical Erdős-Gallai Theorem [6] for graphs. As a consequence, we obtain the first substantial improvement on the Turán problem for tight paths in uniform hypergraphs.

preprint2020arXiv

Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference

Aspect based sentiment analysis aims to identify the sentimental tendency towards a given aspect in text. Fine-tuning of pretrained BERT performs excellent on this task and achieves state-of-the-art performances. Existing BERT-based works only utilize the last output layer of BERT and ignore the semantic knowledge in the intermediate layers. This paper explores the potential of utilizing BERT intermediate layers to enhance the performance of fine-tuning of BERT. To the best of our knowledge, no existing work has been done on this research. To show the generality, we also apply this approach to a natural language inference task. Experimental results demonstrate the effectiveness and generality of the proposed approach.

preprint2020arXiv

Variance of lattice point counting in some special shells in $\mathbb{R}^d$

We study the variance of the random variable that counts the number of lattice points in some shells generated by a special class of finite type domains in $\mathbb R^d$. The proof relies on estimates of the Fourier transform of indicator functions of convex domains.

preprint2020arXiv

WiFi-Inertial Indoor Pose Estimation for Micro Aerial Vehicles

This paper presents an indoor pose estimation system for micro aerial vehicles (MAVs) with a single WiFi access point. Conventional approaches based on computer vision are limited by illumination conditions and environmental texture. Our system is free of visual limitations and instantly deployable, working upon existing WiFi infrastructure without any deployment cost. Our system consists of two coupled modules. First, we propose an angle-of-arrival (AoA) estimation algorithm to estimate MAV attitudes and disentangle the AoA for positioning. Second, we formulate a WiFi-inertial sensor fusion model that fuses the AoA and the odometry measured by inertial sensors to optimize MAV poses. Considering the practicality of MAVs, our system is designed to be real-time and initialization-free for the need of agile flight in unknown environments. The indoor experiments show that our system achieves the accuracy of pose estimation with the position error of $61.7$ cm and the attitude error of $0.92^\circ$.

preprint2019arXiv

Femtosecond relativistic electron beam with reduced timing jitter from THz-driven beam compression

We propose and demonstrate a novel method to reduce the pulse width and timing jitter of a relativistic electron beam through THz-driven beam compression. In this method the longitudinal phase space of a relativistic electron beam is manipulated by a linearly polarized THz pulse in a dielectric tube such that the bunch tail has a higher velocity than the bunch head, which allows simultaneous reduction of both pulse width and timing jitter after passing through a drift. In this experiment, the beam is compressed by more than a factor of four from 130 fs to 28 fs with the arrival time jitter also reduced from 97 fs to 36 fs, opening up new opportunities in using pulsed electron beams for studies of ultrafast dynamics. This technique extends the well known rf buncher to the THz frequency and may have a strong impact in accelerator and ultrafast science facilities that require femtosecond electron beams with tight synchronization to external lasers.

preprint2017arXiv

Supersaturation of Even Linear Cycles in Linear Hypergraphs

A classic result of Erdős and, independently, of Bondy and Simonovits says that the maximum number of edges in an $n$-vertex graph not containing $C_{2k}$, the cycle of length $2k$, is $O( n^{1+1/k})$. Simonovits established a corresponding supersaturation result for $C_{2k}$'s, showing that there exist positive constants $C,c$ depending only on $k$ such that every $n$-vertex graph $G$ with $e(G)\geq Cn^{1+1/k}$ contains at least $c\left(\frac{e(G)}{v(G)}\right)^{2k}$ many copies of $C_{2k}$, this number of copies tightly achieved by the random graph (up to a multiplicative constant). In this paper, we extend Simonovits' result to a supersaturation result of $r$-uniform linear cycles of even length in $r$-uniform linear hypergraphs. Our proof is self-contained and includes the $r=2$ case. As an auxiliary tool, we develop a reduction lemma from general host graphs to almost-regular host graphs that can be used for other supersaturation problems, and may therefore be of independent interest.

preprint2016arXiv

BOOST: Base station on-off switching strategy for energy efficient massive MIMO HetNets

In this paper, we investigate the problem of optimal base station (BS) ON-OFF switching and user association in a heterogeneous network (HetNet) with massive MIMO, with the objective to maximize the system energy efficiency (EE). The joint BS ON-OFF switching and user association problem is formulated as an integer programming problem. We first develop a centralized scheme, in which we relax the integer constraints and employ a series of Lagrangian dual methods that transform the original problem into a standard linear programming (LP) problem. Due to the special structure of the LP, we prove that the optimal solution to the relaxed LP is also feasible and optimal to the original problem. We then propose a distributed scheme by formulating a repeated bidding game for users and BS's, and prove that the game converges to a Nash Equilibrium (NE). Simulation studies demonstrate that the proposed schemes can achieve considerable gains in EE over several benchmark schemes in all the scenarios considered.

preprint2016arXiv

Cycles of given lengths in hypergraphs

In this paper, we develop a method for studying cycle lengths in hypergraphs. Our method is built on earlier ones used in [21,22,18]. However, instead of utilizing the well-known lemma of Bondy and Simonovits [4] that most existing methods do, we develop a new and very simple lemma in its place. One useful feature of the new lemma is its adaptiveness for the hypergraph setting. Using this new method, we prove a conjecture of Verstraëte [37] that for $r\ge 3$, every $r$-uniform hypergraph with average degree $Ω(k^{r-1})$ contains Berge cycles of $k$ consecutive lengths. This is sharp up to the constant factor. As a key step and a result of independent interest, we prove that every $r$-uniform linear hypergraph with average degree at least $7r(k+1)$ contains Berge cycles of $k$ consecutive lengths. In both of these results, we have additional control on the lengths of the cycles, which therefore also gives us bounds on the Turán numbers of Berge cycles (for even and odd cycles simultaneously). In relation to our main results, we obtain further improvements on the Turán numbers of Berge cycles and the Zarankiewicz numbers of even cycles. We will also discuss some potential further applications of our method.

preprint2016arXiv

Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.

preprint2016arXiv

Improving Bandwidth Efficiency of FBMC-OQAM Through Virtual Symbols

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

preprint2016arXiv

Novel 16-QAM and 64-QAM Near-Complementary Sequences with Low PMEPR in OFDM Systems

In this paper, we firstly propose a novel construction of $16$-quadrature amplitude modulation (QAM) near-complementary sequences with low peak-to-mean envelope power ratio (PMEPR) in orthogonal frequency division multiplexing (OFDM) systems. The proposed $16$-QAM near-complementary sequences can be constructed by utilizing novel nonlinear offsets, where the length of the sequences is $n=2^m$. The family size of the newly constructed $16$-QAM near-complementary sequences is $8\times (\frac{m!}{2})\times 4^{m+1}$, and the PMEPR of these sequences is proven to satisfy ${\textrm{PMEPR}}\leq 2.4$. Thus, the proposed construction can generate a number of $16$-QAM near-complementary sequences with low PMEPR, resulting in the improvement of the code rate in OFDM systems. Furthermore, we also propose a novel construction of $64$-QAM near-complementary sequences with low PMEPR, which is the first proven construction of $64$-QAM near-complementary sequences. The PMEPRs of two types of the proposed $64$-QAM near-complementary sequences are proven to satisfy that ${\textrm{PMEPR}}\leq 3.62$ or ${\textrm{PMEPR}}\leq 2.48$, respectively. The family size of the newly constructed $64$-QAM near-complementary sequences is $64\times (\frac{m!}{2})\times 4^{m+1}$.

preprint2016arXiv

Perturbation-Assisted PAPR Reduction for Large-Scale MIMO-OFDM Systems via ADMM

We consider the problem of peak-to-average power ratio (PAPR) reduction for orthogonal frequency-division multiplexing (OFDM) based large-scale multiple-input multipleoutput (MIMO) systems. A novel perturbation-assisted scheme is developed to reduce the PAPRs of the transmitted signals by exploiting the redundant degrees-of-freedom (DoFs) inherent in the large-scale antenna array. Specifically, we introduce artificial perturbation signals to the frequency-domain precoded signals, with the aim of reducing the PAPRs of their time-domain counterpart signals. Meanwhile, the additive perturbation signal associated with each tone is constrained to lie in the null-space of its associated channel matrix, such that it does not cause any multi-user inference or out-of-band radiations. Such a problem is formulated as a convex optimization problem, and an efficient algorithm is developed by resorting to the variable splitting and alterative direction method of multipliers (ADMM) techniques. Simulation results show that the proposed method has a fast convergence rate and achieves substantial PAPR reduction within only tens of iterations. In addition, unlike other precoding-based PAPR reduction methods, our proposed method which introduces perturbation signals to the precoded signals is independent of the precoding stage and thus could be more suitable for practical systems.

preprint2016arXiv

Time-resolved Measurement of Quadrupole Wakefields in Corrugated Structures

Corrugated structures have recently been widely used for manipulating electron beam longitudinal phase space and for producing THz radiation. Here we report on time-resolved measurements of the quadrupole wakefields in planar corrugated structures. It is shown that while the time- dependent quadrupole wakefield produced by a planar corrugated structure causes significant growth in beam transverse emittance, it can be effectively canceled with a second corrugated structure with orthogonal orientation. The strengths of the time-dependent quadrupole wakefields for various corrugated structure gaps are also measured and found to be in good agreement with theories. Our work should forward the applications of corrugated structures in many accelerator based scientific facilities.

preprint2016arXiv

Towards Optimal Adaptive Wireless Communications in Unknown Environments

Designing efficient channel access schemes for wireless communications without any prior knowledge about the nature of environments has been a very challenging issue, especially when the channel states distribution of all spectrum resources could be entirely or partially stochastic and/or adversarial at different time and locations. In this paper, we propose an adaptive channel access algorithm for wireless communications in unknown environments based on the theory of multi-armed bandits (MAB) problems. By automatically tuning two control parameters, i.e., learning rate and exploration probability, our algorithms are capable of finding the optimal channel access strategies and achieving the almost optimal learning performance over time under our defined four typical regimes for general unknown environments, e.g., the stochastic regime where channels follow some unknown i.i.d process, the adversarial regime where all channels are suffered by adversarial jamming attack, the mixed stochastic and adversarial regime where a subset of channels are attacked and the contaminated stochastic regime where occasionally adversarial events contaminate the stochastic channel process, etc. To reduce the implementation time and space complexity, we further develop an enhanced algorithm by exploiting the internal structure of the selection of channel access strategy. We conduct extensive simulations in all these regimes to validate our theoretical analysis. The quantitative performance studies indicate the superior throughput gain and the flexibility of our algorithm in practice, which is resilient to both oblivious and adaptive jamming attacks with different intelligence and any attacking strength that ranges from no-attack to the full-attack of all spectrum resources.

preprint2016arXiv

Trust-Aware Network Utility Optimization in Multihop Wireless Networks with Delay Constraints

Many resource allocation problems can be formulated as a constrained maximization of a utility function. Network Utility Maximization (NUM) applies optimization techniques to achieve decomposition by duality or the primal-dual method. Several important problems, for example joint source rate control, routing, and scheduling design, can be optimized by using this framework. In this work, we introduce an important network security concept, "trust", into the NUM formulation and we integrate nodes' trust values in the optimization framework. These trust values are based on the interaction history between network entities and community based monitoring. Our objective is to avoid routing packets though paths with large percentage of malicious nodes. We also add end-to-end delay constraints for each of the traffic flows. The delay constraints are introduced to capture the quality of service (QoS) requirements imposed to each traffic flow.

preprint2016arXiv

Turan numbers of extensions of some sparse hypergraphs via Lagrangians

Given a positive integer $n$ and an $r$-uniform hypergraph (or $r$-graph for short) $F$, the Turan number $ex(n,F)$ of $F$ is the maximum number of edges in an $r$-graph on $n$ vertices that does not contain $F$ as a subgraph. The extension $H^F $ of $F$ is obtained as follows: For each pair of vertices $v_i,v_j$ in $F$ not contained in an edge of $F$, we add a set $B_{ij}$ of $r-2$ new vertices and the edge $\{v_i,v_j\} \cup B_{ij}$, where the $B_{ij}$ 's are pairwise disjoint over all such pairs $\{i,j\}$. Let $K^r_p$ denote the complete $r$-graph on $p$ vertices. For all sufficiently large $n$, we determine the Turan numbers of the extensions of a $3$-uniform $t$-matching, a $3$-uniform linear star of size $t$, and a $4$-uniform linear star of size $t$, respectively. We also show that the unique extremal hypergraphs are balanced blowups of $K^3_{3t-1}, K^3_{2t}$, and $K^4_{3t}$, respectively. Our results generalize the recent result of Hefetz and Keevash [7].

preprint2016arXiv

Uplink Spectral Efficiency Analysis of Decoupled Access in Multiuser MIMO Communications

In a heterogeneous network consisting of macro base stations (MBSs) and small base stations (SBSs), the traditional cell association policy, i.e., coupled access (CA), is far from optimal, due to the significant difference between the coverage and transmit powers of MBSs and SBSs. Hence, users may choose to associate with different types of BSs in downlink (DL) and uplink (UL), i.e., decoupled access (DA), to enhance spectral efficiency. In this paper, DA in multiuser MIMO communications is investigated in terms of UL spectral efficiency. Firstly, we obtain the UL association probabilities. In contrast to the CA scenario, association probabilities for DA scenario only depend on the densities of BSs. Hence, DA allows UL and DL to be totally independent. Secondly, we derive lower bounds on the spectral efficiency. The lower bounds show that, different from CA, the UL spectral efficiency for DA scenario is irrelative with the transmit powers of BSs, which implies DA allows users to associate with any BSs that can achieve the highest UL spectral efficiency. Finally, the spectral efficiencies for DA and CA scenarios are compared via simulation results, where it can be concluded that the spectral efficiency in multiuser MIMO systems is improved by DA.

preprint2015arXiv

A Lower Bound on the Average-Case Complexity of Shellsort

We prove a general lower bound on the average-case complexity of Shellsort: the average number of data-movements (and comparisons) made by a $p$-pass Shellsort for any incremental sequence is $Ω(pn^{1 + 1/p})$ for every $p$. The proof method is an incompressibility argument based on Kolmogorov complexity. Using similar techniques, the average-case complexity of several other sorting algorithms is analyzed.

preprint2015arXiv

Almost Optimal Energy-Efficient Cognitive Communications in Unknown Environments

Cognitive (Radio) (CR) Communications (CC) are mainly deployed within the environments of primary (user) communications, where the channel states and accessibility are usually stochastically distributed (benign or IID). However, many practical CC are also exposed to disturbing events (contaminated) and vulnerable jamming attacks (adversarial or non-IID). Thus, the channel state distribution of spectrum could be stochastic, contaminated or adversarial at different temporal and spatial locations. Without any a priori, facilitating optimal CC is a very challenging issue. In this paper, we propose an online learning algorithm that performs the joint channel sensing, probing and adaptive channel access for multi-channel CC in general unknown environments. We take energy-efficient CC (EECC) into our special attention, which is highly desirable for green wireless communications and demanding to combat with potential jamming attack who could greatly mar the energy and spectrum efficiency of CC. The EECC is formulated as a constrained regret minimization problem with power budget constraints. By tuning a novel exploration parameter, our algorithms could adaptively find the optimal channel access strategies and achieve the almost optimal learning performance of EECC in different scenarios provided with the vanishing long-term power budget violations. We also consider the important scenario that cooperative learning and information sharing among multiple CR users to see further performance improvements. The proposed algorithms are resilient to both oblivious and adaptive jamming attacks with different intelligence and attacking strength. Extensive numerical results are conducted to validate our theory.

preprint2015arXiv

Demonstration of nonlinear-energy-spread compensation in relativistic electron bunches with corrugated structures

High quality electron beams with flat distributions in both energy and current are critical for many accelerator-based scientific facilities such as free-electron lasers and MeV ultrafast electron diffraction and microscopes. In this Letter we report on using corrugated structures to compensate for the beam nonlinear energy chirp imprinted by the curvature of the radio-frequency field, leading to a significant reduction in beam energy spread. By using a pair of corrugated structures with orthogonal orientations, we show that the quadrupole wake fields which otherwise increase beam emittance can be effectively canceled. This work also extends the applications of corrugated structures to the low beam charge (a few pC) and low beam energy (a few MeV) regime and may have a strong impact in many accelerator-based facilities.

preprint2015arXiv

Mixed-ADC Massive MIMO Detectors: Performance Analysis and Design Optimization

Using a very low-resolution analog-to-digital convertor (ADC) unit at each antenna can remarkably reduce the hardware cost and power consumption of a massive multiple-input multiple-output (MIMO) system. However, such a pure low-resolution ADC architecture also complicates parameter estimation problems such as time/frequency synchronization and channel estimation. A mixed-ADC architecture, where most of the antennas are equipped with low-precision ADCs while a few antennas have full-precision ADCs, can solve these issues and actualize the potential of the pure low-resolution ADC architecture. In this paper, we present a unified framework to develop a family of detectors over the massive MIMO uplink system with the mixed-ADC receiver architecture by exploiting probabilistic Bayesian inference. As a basic setup, an optimal detector is developed to provide a minimum mean-squared-error (MMSE) estimate on data symbols. Considering the highly nonlinear steps involved in the quantization process, we also investigate the potential for complexity reduction on the optimal detector by postulating the common \emph{pseudo-quantization noise} (PQN) model. In particular, we provide asymptotic performance expressions including the MSE and bit error rate for the optimal and suboptimal MIMO detectors. The asymptotic performance expressions can be evaluated quickly and efficiently; thus, they are useful in system design optimization. We show that in the low signal-to-noise ratio (SNR) regime, the distortion caused by the PQN model can be ignored, whereas in the high-SNR regime, such distortion may cause 1-bit detection performance loss. The performance gap resulting from the PQN model can be narrowed by a small fraction of high-precision ADCs in the mixed-ADC architecture.

preprint2015arXiv

Small dense subgraphs of a graph

Given a family ${\cal F}$ of graphs, and a positive integer $n$, the Turán number $ex(n,{\cal F})$ of ${\cal F}$ is the maximum number of edges in an $n$-vertex graph that does not contain any member of ${\cal F}$ as a subgraph. The order of a graph is the number of vertices in it. In this paper, we study the Turán number of the family of graphs with bounded order and high average degree. For every real $d\geq 2$ and positive integer $m\geq 2$, let ${\cal F}_{d,m}$ denote the family of graphs on at most $m$ vertices that have average degree at least $d$. It follows from the Erdős-Rényi bound that $ex(n,{\cal F}_{d,m})=Ω(n^{2-\frac{2}{d}+\frac{c}{dm}})$, for some positive constant $c$. Verstraëte asked if it is true that for each fixed $d$ there exists a function $ε_d(m)$ that tends to $0$ as $m\to \infty$ such that $ex(n,{\cal F}_{d,m})=O(n^{2-\frac{2}{d}+ε_d(m)})$. We answer Verstraëte's question in the affirmative whenever $d$ is an integer. We also prove an extension of the cube theorem on the Turán number of the cube $Q_3$, which partially answers a question of Pinchasi and Sharir.

preprint2015arXiv

Stability and Turán numbers of a class of hypergraphs via Lagrangians

Given a family of $r$-uniform hypergraphs ${\cal F}$ (or $r$-graphs for brevity), the Turán number $ex(n,{\cal F})$ of ${\cal F}$ is the maximum number of edges in an $r$-graph on $n$ vertices that does not contain any member of ${\cal F}$. A pair $\{u,v\}$ is covered in a hypergraph $G$ if some edge of $G$ contains $\{u,v\}$. Given an $r$-graph $F$ and a positive integer $p\geq n(F)$, let $H^F_p$ denote the $r$-graph obtained as follows. Label the vertices of $F$ as $v_1,\ldots, v_{n(F)}$. Add new vertices $v_{n(F)+1},\ldots, v_p$. For each pair of vertices $v_i,v_j$ not covered in $F$, add a set $B_{i,j}$ of $r-2$ new vertices and the edge $\{v_i,v_j\}\cup B_{i,j}$, where the $B_{i,j}$'s are pairwise disjoint over all such pairs $\{i,j\}$. We call $H^F_p$ the expanded $p$-clique with an embedded $F$. For a relatively large family of $F$, we show that for all sufficiently large $n$, $ex(n,H^F_p)=|T_r(n,p-1)|$, where $T_r(n,p-1)$ is the balanced complete $(p-1)$-partite $r$-graph on $n$ vertices. We also establish structural stability of near extremal graphs. Our results generalize or strengthen several earlier results and provide a class of hypergraphs for which the Turán number is exactly determined (for large $n$).

preprint2015arXiv

Turán numbers of hypergraph trees

An $r$-graph is an $r$-uniform hypergraph tree (or $r$-tree) if its edges can be ordered as $E_1,\ldots, E_m$ such that $\forall i>1 \, \exists α(i)<i$ such that $E_i\cap (\bigcup_{j=1}^{i-1} E_j)\subseteq E_{α(i)}$. The Turán number $ex(n,{\cal H})$ of an $r$-graph ${\cal H}$ is the largest size of an $n$-vertex $r$-graph that does not contain ${\cal H}$. A cross-cut of ${\cal H}$ is a set of vertices in ${\cal H}$ that contains exactly one vertex of each edge of ${\cal H}$. The cross-cut number $σ({\cal H})$ of ${\cal H}$ is the minimum size of a cross-cut of ${\cal H}$. We show that for a large family of $r$-graphs (largest within a certain scope) that are embeddable in $r$-trees, $ex(n,{\cal H})=(σ-1)\binom{n}{r-1}+o(n^{r-1})$ holds, and we establish structural stability of near extremal graphs. From stability, we establish exact results for some subfamilies.

preprint2014arXiv

Linear Turan numbers of r-uniform linear cycles and related Ramsey numbers

An $r$-uniform hypergraph is called an $r$-graph. A hypergraph is linear if every two edges intersect in at most one vertex. Given a linear $r$-graph $H$ and a positive integer $n$, the linear Turán number $ex_L(n,H)$ is the maximum number of edges in a linear $r$-graph $G$ that does not contain $H$ as a subgraph. For each $\ell\geq 3$, let $C^r_\ell$ denote the $r$-uniform linear cycle of length $\ell$, which is an $r$-graph with edges $e_1,\ldots, e_\ell$ such that $\forall i\in [\ell-1]$, $|e_i\cap e_{i+1}|=1$, $|e_\ell\cap e_1|=1$ and $e_i\cap e_j=\emptyset$ for all other pairs $\{i,j\}, i\neq j$. For all $r\geq 3$ and $\ell\geq 3$, we show that there exist positive constants $c_{m,r}$ and $c'_{m,r}$, depending only $m$ and $r$, such that $ex_L(n,C^r_{2m})\leq c_{m,r} n^{1+\frac{1}{m}}$ and $ex_L(n,C^r_{2m+1})\leq c'_{m,r} n^{1+\frac{1}{m}}$. This answers a question of Kostochka, Mubayi, and Verstraëte. For even cycles, our result extends the result of Bondy and Simonovits on the Turán numbers of even cycles to linear hypergraphs. Using our results on linear Turán numbers we also obtain bounds on the cycle-complete hypergraph Ramsey numbers. We show that there are positive constants $a_{m,r}$ and $b_{m,r}$, depending only on $m$ and $r$, such that $R(C^r_{2m}, K^r_t)\leq a_{m,r} (\frac{t}{\ln t})^\frac{m}{m-1}$ and $R(C^r_{2m+1}, K^r_t)\leq b_{m,r} t^\frac{m}{m-1}$.

preprint2013arXiv

Feasibility Study on SRF Cavity Improvement by Doping Method

In this article a new idea is proposed by PKU group to improve the RF superconductive property of niobium cavity. Rare earth elements like scandium and yttrium are doped into ingot niobium during the smelting processes. A series of experiments have been done since 2010, and the preliminary testing results show that the magnetic property of niobium materials has been changed with different doping elements and proportions while the superconductive transition temperature almost does not change. Then a large ingot niobium doped with scandium was melted. RRR and mechanical properties were measured. This method may increase the superheating magnetic field and decrease surface resistance of niobium so as to improve the performance of niobium cavity which is a key component of SRF accelerators. A single-cell cavity made of Sc-doped niobium has been fabricated.

preprint2013arXiv

Hypergraph Turan numbers of linear cycles

A k-uniform linear cycle of length s is a cyclic list of k-sets A_1,..., A_s such that consecutive sets intersect in exactly one element and nonconsecutive sets are disjoint. For all k at least 5 and s at least 3 and sufficiently large n we determine the largest size of a k-uniform set family on [n] not containing a linear cycle of length s. For odd s=2t+1 the unique extremal family F_S consists of all k-sets in [n] intersecting a fixed t-set S in [n]. For even s=2t+2, the unique extremal family consists of F_S plus all the k-sets outside S containing some fixed two elements. For k at least 4 and large n we also establish an exact result for so-called minimal cycles. For all k at least 4 our results substantially extend Erdos' result on largest k-uniform families without t+1 pairwise disjoint members and confirm, in a stronger form, a conjecture of Mubayi and Verstraete. Our main method is the delta system method.

preprint2013arXiv

On the co-degree threshold for the Fano plane

Given a 3-graph H, let \ex_2(n, H) denote the maximum value of the minimum codegree of a 3-graph on n vertices which does not contain a copy of H. Let F denote the Fano plane, which is the 3-graph \{axx',ayy',azz',xyz',xy'z,x'yz,x'y'z'\}. Mubayi proved that \ex_2(n,F)=(1/2+o(1))n and conjectured that \ex_2(n, F)=\floor{n/2} for sufficiently large n. Using a very sophisticated quasi-randomness argument, Keevash proved Mubayi's conjecture. Here we give a simple proof of Mubayi's conjecture by using a class of 3-graphs that we call rings. We also determine the Turán density of the family of rings.

preprint2013arXiv

Sensing and Recognition When Primary User Has Multiple Power Levels

In this paper, we present a new cognitive radio (CR) scenario when the primary user (PU) operates under more than one transmit power levels. Different from the existing studies where PU is assumed to have only one constant transmit power, the new consideration well matches the practical standards, i.e., IEEE 802.11 Series, GSM, LTE, LTE-A, etc., as well as the adaptive power concept that has been studied over the past decades. The primary target in this new CR scenario is, of course, still to detect the presence of PU. However, there appears a secondary target as to identify the PU's transmit power level. Compared to the existing works where the secondary user (SU) only senses the ``on-off'' status of PU, recognizing the power level of PU achieves more ``cognition", and could be utilized to protect different powered PU with different interference levels. We derived quite many closed-form results for either the threshold expressions or the performance analysis, from which many interesting points and discussions are raised. We then further study the cooperative sensing strategy in this new cognitive scenario and show its significant difference from traditional algorithms. Numerical examples are provided to corroborate the proposed studies.

preprint2012arXiv

Galaxy growth by merging in the nearby universe

We measure the mass growth rate by merging for a wide range of galaxy types. We present the small-scale (0.014 < r < 11 h70^{-1} Mpc) projected cross-correlation functions w(rp) of galaxy subsamples from the spectroscopic sample of the NYU VAGC (5 \times 10^5 galaxies of redshifts 0.03 < z < 0.15) with galaxy subsamples from the SDSS imaging (4 \times 10^7 galaxies). We use smooth fits to de-project the two-dimensional functions w(rp) to obtain smooth three-dimensional real-space cross-correlation functions ξ(r) for each of several spectroscopic subsamples with each of several imaging subsamples. Because close pairs are expected to merge, the three-space functions and dynamical evolution time estimates provide galaxy accretion rates. We find that the accretion onto massive blue galaxies and onto red galaxies is dominated by red companions, and that onto small-mass blue galaxies, red and blue galaxies make comparable contributions. We integrate over all types of companions and find that at fixed stellar mass, the total fractional accretion rates onto red galaxies (\sim 1.5 h70 percent per Gyr) is greater than that onto blue galaxies (\sim 0.5 h70 percent per Gyr). Although these rates are very low, they are almost certainly over-estimates because we have assumed that all close pairs merge as quickly as dynamical friction permits.

preprint2012arXiv

The Extreme Small Scales: Do Satellite Galaxies Trace Dark Matter?

We investigate the radial distribution of galaxies within their host dark matter halos by modeling their small-scale clustering, as measured in the Sloan Digital Sky Survey. Specifically, we model the Jiang et al. (2011) measurements of the galaxy two-point correlation function down to very small projected separations (10 < r < 400 kpc/h), in a wide range of luminosity threshold samples (absolute r-band magnitudes of -18 up to -23). We use a halo occupation distribution (HOD) framework with free parameters that specify both the number and spatial distribution of galaxies within their host dark matter halos. We assume that the first galaxy in each halo lives at the halo center and that additional satellite galaxies follow a radial density profile similar to the dark matter Navarro-Frenk-White (NFW) profile, except that the concentration and inner slope are allowed to vary. We find that in low luminosity samples, satellite galaxies have radial profiles that are consistent with NFW. M_r < -20 and brighter satellite galaxies have radial profiles with significantly steeper inner slopes than NFW (we find inner logarithmic slopes ranging from -1.6 to -2.1, as opposed to -1 for NFW). We define a useful metric of concentration, M_(1/10), which is the fraction of satellite galaxies (or mass) that are enclosed within one tenth of the virial radius of a halo. We find that M_(1/10) for low luminosity satellite galaxies agrees with NFW, whereas for luminous galaxies it is 2.5-4 times higher, demonstrating that these galaxies are substantially more centrally concentrated within their dark matter halos than the dark matter itself. Our results therefore suggest that the processes that govern the spatial distribution of galaxies, once they have merged into larger halos, must be luminosity dependent, such that luminous galaxies become poor tracers of the underlying dark matter.

preprint2011arXiv

Exact solution of the hypergraph Turán problem for $k$-uniform linear paths

A $k$-uniform linear path of length $\ell$, denoted by $P^{(k)}_\ell$, is a family of $k$-sets $\{F_1,..., F_\ell\}$ such that $|F_i\cap F_{i+1}|=1$ for each $i$ and $F_i\cap F_j=\emptyset$ whenever $|i-j|>1$. Given a $k$-uniform hypergraph $H$ and a positive integer $n$, the {\it $k$-uniform hypergraph Turán number} of $H$, denoted by $\ex_k(n,H)$, is the maximum number of edges in a $k$-uniform hypergraph $\cF$ on $n$ vertices that does not contain $H$ as a subhypergraph. With an intensive use of the delta-system method, we determine $\ex_k(n,P^{(k)}_\ell)$ exactly for all fixed $\ell\geq 1, k\geq 4$, and sufficiently large $n$. We show that $$\ex_k(n,P^{(k)}_{2t+1})={n-1\choose k-1}+{n-2\choose k-1}+...+{n-t\choose k-1}.$$ The only extremal family consists of all the $k$-sets in $[n]$ that meet some fixed set of $t$ vertices. We also show that $$\ex(n, P^{(k)}_{2t+2})={n-1\choose k-1}+{n-2\choose k-1}+...+{n-t\choose k-1}+{n-t-2\choose k-2},$$ and describe the unique extremal family. Stability results on these bounds and some related results are also established.

preprint2011arXiv

Set families with a forbidden induced subposet

For each poset $H$ whose Hasse diagram is a tree of height $k$, we show that the largest size of a family $\cF$ of subsets of $[n]=\{1,..., n\}$ not containing $H$ as an induced subposet is asymptotic to $(k-1){n\choose \fl{n/2}}$. This extends the result of Bukh \cite{bukh}, which in turn generalizes several known results including Sperner's theorem.

Tao Jiang

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82 published item(s)

Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction

SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution

Soliton Thouless pumping engineered by inter-site nonlinearities

Hear: Hierarchically Enhanced Aesthetic Representations For Multidimensional Music Evaluation

Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking

Synthetic Data in AI: Challenges, Applications, and Ethical Implications

Bipartite-ness under smooth conditions

Rainbow Turán number of clique subdivisions

Towards A Unified Conformer Structure: from ASR to ASV Task

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis

Acceleration of Federated Learning with Alleviated Forgetting in Local Training

Active Sensing for Communications by Learning

An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

Cache-Aided Massive MIMO with Linear Precoding in Multi-cell Systems

CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron

Efficient Distinction between Quantum Direct and Common Causes and its Experimental Verification

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information

Interference Nulling Using Reconfigurable Intelligent Surface

KaraTuner: Towards end to end natural pitch correction for singing voice in karaoke

Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

Learning Progressive Distributed Compression Strategies from Local Channel State Information

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Stochastic Variational Methods in Generalized Hidden Semi-Markov Models to Characterize Functionality in Random Heteropolymers

Tree-degenerate graphs and nested dependent random choice

VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

Policy-Aware Mobility Model Explains the Growth of COVID-19 in Cities

Towards Cross-Modal Forgery Detection and Localization on Live Surveillance Videos

Two-dimensional charge density wave TaX$_2$ (X=S, Se, Te) from first principles

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

Achieving 50 femtosecond resolution in MeV ultrafast electron diffraction with a double bend achromat compressor

Authenticating On-Body IoT Devices: An Adversarial Learning Approach

Edge Intelligence: Architectures, Challenges, and Applications

Enabling Low-Power OFDM for IoT by Exploiting Asymmetric Clock Rates

Extremal problems for hypergraph blowups of trees

Higher Criticism Tuned Regression For Weak And Sparse Signals

Knockoff Boosted Tree for Model-Free Variable Selection

Large-Scale Beamforming for Massive MIMO via Randomized Sketching

Linear cycles of consecutive lengths

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

Partitioning ordered hypergraphs

Robot-assisted Backscatter Localization for IoT Applications

Tight paths in convex geometric hypergraphs

Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference

Variance of lattice point counting in some special shells in $\mathbb{R}^d$

WiFi-Inertial Indoor Pose Estimation for Micro Aerial Vehicles

Femtosecond relativistic electron beam with reduced timing jitter from THz-driven beam compression

Supersaturation of Even Linear Cycles in Linear Hypergraphs

BOOST: Base station on-off switching strategy for energy efficient massive MIMO HetNets

Cycles of given lengths in hypergraphs

Distributed Private Online Learning for Social Big Data Computing over Data Center Networks

Improving Bandwidth Efficiency of FBMC-OQAM Through Virtual Symbols

Novel 16-QAM and 64-QAM Near-Complementary Sequences with Low PMEPR in OFDM Systems

Perturbation-Assisted PAPR Reduction for Large-Scale MIMO-OFDM Systems via ADMM

Time-resolved Measurement of Quadrupole Wakefields in Corrugated Structures

Towards Optimal Adaptive Wireless Communications in Unknown Environments

Trust-Aware Network Utility Optimization in Multihop Wireless Networks with Delay Constraints

Turan numbers of extensions of some sparse hypergraphs via Lagrangians

Uplink Spectral Efficiency Analysis of Decoupled Access in Multiuser MIMO Communications

A Lower Bound on the Average-Case Complexity of Shellsort

Almost Optimal Energy-Efficient Cognitive Communications in Unknown Environments

Demonstration of nonlinear-energy-spread compensation in relativistic electron bunches with corrugated structures

Mixed-ADC Massive MIMO Detectors: Performance Analysis and Design Optimization

Small dense subgraphs of a graph

Stability and Turán numbers of a class of hypergraphs via Lagrangians

Turán numbers of hypergraph trees

Linear Turan numbers of r-uniform linear cycles and related Ramsey numbers