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Qin Zhang

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

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Data Structures and Algorithmscond-mat.mes-hallcond-mat.mtrl-sciMachine LearningComputer Visioncond-mat.otherDistributed, Parallel, and Cluster Computingphysics.ins-detArtificial IntelligenceBiological PhysicsComputational Complexitycond-mat.supr-conDatabaseseess.ASeess.IVeess.SPInformation Theorymath.GMmath.ITmath.OAMultimediaphysics.flu-dynphysics.med-phphysics.opticsphysics.pop-phquant-phRobotics

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Published work

33 published item(s)

preprint2026arXiv

A Pilot Kinematic Study on the Forehand Reverse Flick: Feasibility of a Novel Short Return Technique in Table Tennis

Background Following changes in table tennis ball materials, offensive returns have become more important for initiating sustained topspin offense. However, using the backhand flick (BF) to return forehand short balls often increases the difficulty of recovery and continuity, revealing a technical gap. This study preliminarily verified a novel forehand short return technique, the forehand reverse flick (FRF), and analyzed its similarities and differences with the BF. Methods Four elite athletes completed seven consecutive days of FRF specific training. Infrared motion capture and ultra-high-speed cameras were used to collect data on racket kinematics, movement duration, and ball performance. Results The success rate of the FRF increased steadily, reaching 86%. Racket trajectories of the two techniques were highly similar along the X (r = 1) and Y (r = 0.99) axes but differed along the Z (r = -0.04) axis. Racket and ball velocities were comparable between techniques, whereas the FRF showed lower resultant acceleration (approximately 265.57 m/s) and required about 0.03 s more for movement duration. Ball velocity was comparable between techniques, for the ball spin, the FRF generated lower spin (approximately 76.61 r/s) about 64% of the BF value (approximately 120.13 r/s). The highest participant mean spin rate reached 93 r/s, about 77% of the BF mean. Conclusion Overall, the FRF was found to have favorable learnability and training value, with potential for further optimization and competitive application.

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preprint2026arXiv

Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation

Accurate skin lesion segmentation is vital for dermoscopic Computer-Aided Diagnosis. However, visual ambiguity and morphological irregularity often defeat spatial modeling, necessitating multi-domain architectures. Existing paradigms frequently overlook the active use of prediction uncertainty, leading to deterministic frameworks that suffer from blind cross-domain fusion and overfit to label noise. To address these issues, we propose the Uncertainty-Guided Dual-Domain Network (UGDD-Net). UGDD-Net introduces a novel "Glance-and-Gaze" mechanism to transform uncertainty into an active guiding signal. Specifically, the Uncertainty-Guided Bi-directional Feature Fusion (UGBFF) module uses pixel-level uncertainty to modulate spatial-spectral interactions. The Uncertainty-Guided Graph Refinement (UGGR) module constructs a topology-aware graph to propagate reliable semantic consensus and refine uncertain nodes. Finally, the Uncertainty-Guided Margin-Adaptive Loss (UGML) enforces strict constraints on confident pixels while relaxing penalties on uncertain ones to improve statistical calibration. Extensive experiments on ISIC2017, ISIC2018, PH2, and HAM10000 datasets demonstrate that UGDD-Net achieves state-of-the-art performance, especially on "Hard Samples". Our uncertainty maps align with expert inter-observer variability, providing robust interpretability for human-machine collaborative diagnosis.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Unsupervised Hashing with Latent Semantic Components

Deep unsupervised hashing has been appreciated in the regime of image retrieval. However, most prior arts failed to detect the semantic components and their relationships behind the images, which makes them lack discriminative power. To make up the defect, we propose a novel Deep Semantic Components Hashing (DSCH), which involves a common sense that an image normally contains a bunch of semantic components with homology and co-occurrence relationships. Based on this prior, DSCH regards the semantic components as latent variables under the Expectation-Maximization framework and designs a two-step iterative algorithm with the objective of maximum likelihood of training data. Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal~(GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited. Besides, coarse-grained semantics components, are discovered by considering the homology relationships between fine-grained components, and the hierarchy organization is then constructed. Secondly, DSCH makes the images close to their semantic component centers at both fine-grained and coarse-grained levels, and also makes the images share similar semantic components close to each other. Extensive experiments on three benchmark datasets demonstrate that the proposed hierarchical semantic components indeed facilitate the hashing model to achieve superior performance.

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preprint2022arXiv

Joint Optimization of STAR-RIS Assisted UAV Communication Systems

In this letter, we study the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) communications. Our goal is to maximize the sum rate of all users by jointly optimizing the STAR-RIS's beamforming vectors, the UAV's trajectory and power allocation. We decompose the formulated non-convex problem into three subproblems and solve them alternately to obtain the solution. Simulations show that: 1) the STAR-RIS achieves a higher sum rate than traditional RIS; 2) to exploit the benefits of STAR-RIS, the UAV's trajectory is closer to STAR-RIS than that of RIS; 3) the energy splitting for reflection and transmission highly depends on the real-time trajectory of UAV.

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preprint2022arXiv

SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving

Safe reinforcement learning (RL) has achieved significant success on risk-sensitive tasks and shown promise in autonomous driving (AD) as well. Considering the distinctiveness of this community, efficient and reproducible baselines are still lacking for safe AD. In this paper, we release SafeRL-Kit to benchmark safe RL methods for AD-oriented tasks. Concretely, SafeRL-Kit contains several latest algorithms specific to zero-constraint-violation tasks, including Safety Layer, Recovery RL, off-policy Lagrangian method, and Feasible Actor-Critic. In addition to existing approaches, we propose a novel first-order method named Exact Penalty Optimization (EPO) and sufficiently demonstrate its capability in safe AD. All algorithms in SafeRL-Kit are implemented (i) under the off-policy setting, which improves sample efficiency and can better leverage past logs; (ii) with a unified learning framework, providing off-the-shelf interfaces for researchers to incorporate their domain-specific knowledge into fundamental safe RL methods. Conclusively, we conduct a comparative evaluation of the above algorithms in SafeRL-Kit and shed light on their efficacy for safe autonomous driving. The source code is available at \href{ https://github.com/zlr20/saferl_kit}{this https URL}.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Unsupervised Quantized Prosody Representation for Controllable Speech Synthesis

In this paper, we propose a novel prosody disentangle method for prosodic Text-to-Speech (TTS) model, which introduces the vector quantization (VQ) method to the auxiliary prosody encoder to obtain the decomposed prosody representations in an unsupervised manner. Rely on its advantages, the speaking styles, such as pitch, speaking velocity, local pitch variance, etc., are decomposed automatically into the latent quantize vectors. We also investigate the internal mechanism of VQ disentangle process by means of a latent variables counter and find that higher value dimensions usually represent prosody information. Experiments show that our model can control the speaking styles of synthesis results by directly manipulating the latent variables. The objective and subjective evaluations illustrated that our model outperforms the popular models.

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preprint2021arXiv

Facile preparation of CuCo$_2$S$_4$/Cu$_7$S$_4$ nanocomposites as high-performance cathode materials for rechargeable magnesium batteries

Searching for efficient and high-performance cathode materials for rechargeable magnesium ion batteries (RMBs) is urgent for exploring sustainable energy technologies. However, the majority of cathode materials for RMBs usually suffer from low rate capacity and inferior cycle performance caused by structure collapse. Herein, ternary CuCo$_2$S$_4$/Cu$_7$S$_4$ composites with nano-sized are first synthesized by a facile solvothermal method in this work and the magnesium ion storage behavior is discussed when applied in the cathode for RMBs. Electrochemical results demonstrate that the nanosphere-like CuCo$_2$S$_4$/Cu$_7$S$_4$ composites exhibit a high initial discharge capacity of 256 mAh/g at 10 mA/g and 123 mAh/g at 300 mA/g at room temperature. Furthermore, an outstanding long-term cyclic stability with a reversible capacity of 106 mA/g after 300 cycles and the coulombic efficiency of about 99% are achieved at 300 mA/g. Such remarkable electrochemical performance is owing to the nanosphere-like structure, ternary composition and pseudocapacitive storage behavior of CuCo$_2$S$_4$/Cu$_7$S$_4$. The results of this work indicate that the CuCo$_2$S$_4$/Cu$_7$S$_4$ nanocomposites synthesized by a facile and efficient method are promising cathodes for RMBs in energy storage/conversion application.

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preprint2021arXiv

Ordinal sum of two binary operations being a t-norm on bounded lattice

The ordinal sum of t-norms on a bounded lattice has been used to construct other t-norms. However, an ordinal sum of binary operations (not necessarily t-norms) defined on the fixed subintervals of a bounded lattice may not be a t-norm. Some necessary and sufficient conditions are presented in this paper for ensuring that an ordinal sum on a bounded lattice of two binary operations is, in fact, a t-norm. In particular, the results presented here provide an answer to an open problem put forward by Ertuğrul and Yeşilyurt [Ordinal sums of triangular norms on bounded lattices, Inf. Sci., 517 (2020) 198-216].

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preprint2020arXiv

Collaborative Top Distribution Identifications with Limited Interaction

We consider the following problem in this paper: given a set of $n$ distributions, find the top-$m$ ones with the largest means. This problem is also called {\em top-$m$ arm identifications} in the literature of reinforcement learning, and has numerous applications. We study the problem in the collaborative learning model where we have multiple agents who can draw samples from the $n$ distributions in parallel. Our goal is to characterize the tradeoffs between the running time of learning process and the number of rounds of interaction between agents, which is very expensive in various scenarios. We give optimal time-round tradeoffs, as well as demonstrate complexity separations between top-$1$ arm identification and top-$m$ arm identifications for general $m$ and between fixed-time and fixed-confidence variants. As a byproduct, we also give an algorithm for selecting the distribution with the $m$-th largest mean in the collaborative learning model.

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preprint2020arXiv

Multinomial Logit Bandit with Low Switching Cost

We study multinomial logit bandit with limited adaptivity, where the algorithms change their exploration actions as infrequently as possible when achieving almost optimal minimax regret. We propose two measures of adaptivity: the assortment switching cost and the more fine-grained item switching cost. We present an anytime algorithm (AT-DUCB) with $O(N \log T)$ assortment switches, almost matching the lower bound $Ω(\frac{N \log T}{ \log \log T})$. In the fixed-horizon setting, our algorithm FH-DUCB incurs $O(N \log \log T)$ assortment switches, matching the asymptotic lower bound. We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.

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preprint2016arXiv

Computing Skylines on Distributed Data

In this paper we study skyline queries in the distributed computational model, where we have $s$ remote sites and a central coordinator (the query node); each site holds a piece of data, and the coordinator wants to compute the skyline of the union of the $s$ datasets. The computation is in terms of rounds, and the goal is to minimize both the total communication cost and the round cost. Viewing data objects as points in the Euclidean space, we consider both the horizontal data partition case where each site holds a subset of points, and the vertical data partition case where each site holds one coordinate of all the points. We give a set of algorithms that have provable theoretical guarantees, and complement them with information theoretical lower bounds. We also demonstrate the superiority of our algorithms over existing heuristics by an extensive set of experiments on both synthetic and real world datasets.

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preprint2016arXiv

Edit Distance: Sketching, Streaming and Document Exchange

We show that in the document exchange problem, where Alice holds $x \in \{0,1\}^n$ and Bob holds $y \in \{0,1\}^n$, Alice can send Bob a message of size $O(K(\log^2 K+\log n))$ bits such that Bob can recover $x$ using the message and his input $y$ if the edit distance between $x$ and $y$ is no more than $K$, and output "error" otherwise. Both the encoding and decoding can be done in time $\tilde{O}(n+\mathsf{poly}(K))$. This result significantly improves the previous communication bounds under polynomial encoding/decoding time. We also show that in the referee model, where Alice and Bob hold $x$ and $y$ respectively, they can compute sketches of $x$ and $y$ of sizes $\mathsf{poly}(K \log n)$ bits (the encoding), and send to the referee, who can then compute the edit distance between $x$ and $y$ together with all the edit operations if the edit distance is no more than $K$, and output "error" otherwise (the decoding). To the best of our knowledge, this is the first result for sketching edit distance using $\mathsf{poly}(K \log n)$ bits. Moreover, the encoding phase of our sketching algorithm can be performed by scanning the input string in one pass. Thus our sketching algorithm also implies the first streaming algorithm for computing edit distance and all the edits exactly using $\mathsf{poly}(K \log n)$ bits of space.

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preprint2016arXiv

Improved Algorithms for Distributed Entropy Monitoring

Modern data management systems often need to deal with massive, dynamic and inherently distributed data sources. We collect the data using a distributed network, and at the same time try to maintain a global view of the data at a central coordinator using a minimal amount of communication. Such applications have been captured by the distributed monitoring model which has attracted a lot of attention in recent years. In this paper we investigate the monitoring of the entropy functions, which are very useful in network monitoring applications such as detecting distributed denial-of-service attacks. Our results improve the previous best results by Arackaparambil et al. [2]. Our technical contribution also includes implementing the celebrated AMS sampling method (by Alon et al. [1]) in the distributed monitoring model, which could be of independent interest.

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preprint2016arXiv

Submodular Maximization over Sliding Windows

In this paper we study the extraction of representative elements in the data stream model in the form of submodular maximization. Different from the previous work on streaming submodular maximization, we are interested only in the recent data, and study the maximization problem over sliding windows. We provide a general reduction from the sliding window model to the standard streaming model, and thus our approach works for general constraints as long as there is a corresponding streaming algorithm in the standard streaming model. As a consequence, we obtain the first algorithms in the sliding window model for maximizing a monotone/non-monotone submodular function under cardinality and matroid constraints. We also propose several heuristics and show their efficiency in real-world datasets.

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preprint2015arXiv

Lower Bounds for Number-in-Hand Multiparty Communication Complexity, Made Easy

In this paper we prove lower bounds on randomized multiparty communication complexity, both in the \emph{blackboard model} (where each message is written on a blackboard for all players to see) and (mainly) in the \emph{message-passing model}, where messages are sent player-to-player. We introduce a new technique for proving such bounds, called \emph{symmetrization}, which is natural, intuitive, and often easy to use. For example, for the problem where each of $k$ players gets a bit-vector of length $n$, and the goal is to compute the coordinate-wise XOR of these vectors, we prove a tight lower bounds of $Ω(nk)$ in the blackboard model. For the same problem with AND instead of XOR, we prove a lower bounds of roughly $Ω(nk)$ in the message-passing model (assuming $k \le n/3200$) and $Ω(n \log k)$ in the blackboard model. We also prove lower bounds for bit-wise majority, for a graph-connectivity problem, and for other problems; the technique seems applicable to a wide range of other problems as well. All of our lower bounds allow randomized communication protocols with two-sided error. We also use the symmetrization technique to prove several direct-sum-like results for multiparty communication.

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preprint2014arXiv

A Sketching Algorithm for Spectral Graph Sparsification

We study the problem of compressing a weighted graph $G$ on $n$ vertices, building a "sketch" $H$ of $G$, so that given any vector $x \in \mathbb{R}^n$, the value $x^T L_G x$ can be approximated up to a multiplicative $1+ε$ factor from only $H$ and $x$, where $L_G$ denotes the Laplacian of $G$. One solution to this problem is to build a spectral sparsifier $H$ of $G$, which, using the result of Batson, Spielman, and Srivastava, consists of $O(n ε^{-2})$ reweighted edges of $G$ and has the property that simultaneously for all $x \in \mathbb{R}^n$, $x^T L_H x = (1 \pm ε) x^T L_G x$. The $O(n ε^{-2})$ bound is optimal for spectral sparsifiers. We show that if one is interested in only preserving the value of $x^T L_G x$ for a {\it fixed} $x \in \mathbb{R}^n$ (specified at query time) with high probability, then there is a sketch $H$ using only $\tilde{O}(n ε^{-1.6})$ bits of space. This is the first data structure achieving a sub-quadratic dependence on $ε$. Our work builds upon recent work of Andoni, Krauthgamer, and Woodruff who showed that $\tilde{O}(n ε^{-1})$ bits of space is possible for preserving a fixed {\it cut query} (i.e., $x\in \{0,1\}^n$) with high probability; here we show that even for a general query vector $x \in \mathbb{R}^n$, a sub-quadratic dependence on $ε$ is possible. Our result for Laplacians is in sharp contrast to sketches for general $n \times n$ positive semidefinite matrices $A$ with $O(\log n)$ bit entries, for which even to preserve the value of $x^T A x$ for a fixed $x \in \mathbb{R}^n$ (specified at query time) up to a $1+ε$ factor with constant probability, we show an $Ω(n ε^{-2})$ lower bound.

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preprint2014arXiv

Electron and Hole Photoemission Detection for Band Offset Determination of Tunnel Field-Effect Transistor Heterojunctions

The electrical performance of a tunnel field-effect transistor depends critically on the band offset at their semiconductor heterojunction interface. Historically, it has been difficult to experimentally determine how the electronic bands align at the heterojunction interface. We report here on experimental methods to ascertain a complete energy band alignment of a broken-gap tunnel field-effect transistor based on an InAs/GaSb hetero-junction. By using graphene as an optically transparent electrode in a traditional internal photoemission measurement, both the electron and hole barrier heights at the InAs/GaSb interface can be quantified. For a Al2O3/InAs/GaSb layer structure, the barrier height from the top of InAs and GaSb valence band to the bottom of Al2O3 conduction band is inferred from electron emission whereas hole emissions reveal the barrier height from the top of Al2O3 valence band to the bottom of InAs and GaSb conduction band. Subsequently, the offset parameter at the broken gap InAs/GaSb interface is extracted and thus can be used to facilitate the development of predicted model of electron quantum tunneling efficiency and transistor performance.

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preprint2014arXiv

Subspace Embeddings and $\ell_p$-Regression Using Exponential Random Variables

Oblivious low-distortion subspace embeddings are a crucial building block for numerical linear algebra problems. We show for any real $p, 1 \leq p < \infty$, given a matrix $M \in \mathbb{R}^{n \times d}$ with $n \gg d$, with constant probability we can choose a matrix $Π$ with $\max(1, n^{1-2/p}) \poly(d)$ rows and $n$ columns so that simultaneously for all $x \in \mathbb{R}^d$, $\|Mx\|_p \leq \|ΠMx\|_{\infty} \leq \poly(d) \|Mx\|_p.$ Importantly, $ΠM$ can be computed in the optimal $O(\nnz(M))$ time, where $\nnz(M)$ is the number of non-zero entries of $M$. This generalizes all previous oblivious subspace embeddings which required $p \in [1,2]$ due to their use of $p$-stable random variables. Using our matrices $Π$, we also improve the best known distortion of oblivious subspace embeddings of $\ell_1$ into $\ell_1$ with $\tilde{O}(d)$ target dimension in $O(\nnz(M))$ time from $\tilde{O}(d^3)$ to $\tilde{O}(d^2)$, which can further be improved to $\tilde{O}(d^{3/2}) \log^{1/2} n$ if $d = Ω(\log n)$, answering a question of Meng and Mahoney (STOC, 2013). We apply our results to $\ell_p$-regression, obtaining a $(1+\eps)$-approximation in $O(\nnz(M)\log n) + \poly(d/\eps)$ time, improving the best known $\poly(d/\eps)$ factors for every $p \in [1, \infty) \setminus \{2\}$. If one is just interested in a $\poly(d)$ rather than a $(1+\eps)$-approximation to $\ell_p$-regression, a corollary of our results is that for all $p \in [1, \infty)$ we can solve the $\ell_p$-regression problem without using general convex programming, that is, since our subspace embeds into $\ell_{\infty}$ it suffices to solve a linear programming problem. Finally, we give the first protocols for the distributed $\ell_p$-regression problem for every $p \geq 1$ which are nearly optimal in communication and computation.

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preprint2013arXiv

Atomized Spraying of Liquid Metal Droplets on Desired Substrate Surfaces as a Generalized Way for Ubiquitous Printed Electronics

A direct electronics printing technique through atomized spraying for patterning room temperature liquid metal droplets on desired substrate surfaces is proposed and experimentally demonstrated for the first time. This method has generalized purpose and is highly flexible and capable of fabricating electronic components on any desired target objects, with either flat or rough surfaces, made of different materials, or different orientations from 1-D to 3-D geometrical configurations. With a pre-designed mask, the liquid metal ink can be directly deposited on the substrate to form various specific patterns which lead to the rapid prototyping of electronic devices. Further, extended printing strategies were also suggested to illustrate the adaptability of the method such that the natural porous structure can be adopted to offer an alternative way of making transparent conductive film with an optical transmittance of 47% and a sheet resistance of 5.167Ω/O. Different from the former direct writing technology where large surface tension and poor adhesion between the liquid metal and the substrate often impede the flexible printing process, the liquid metal here no longer needs to be pre-oxidized to guarantee its applicability on target substrates. One critical mechanism was found as that the atomized liquid metal microdroplets can be quickly oxidized in the air due to its large specific surface area, resulting in a significant increase of the adhesive capacity and thus firm deposition of the ink to the substrate. This study established a generalized way for pervasively and directly printing electronics on various substrates which are expected to be significant in a wide spectrum of electrical engineering areas.

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preprint2013arXiv

Graphene as Transparent Electrode for Direct Observation of Hole Photoemission from Silicon to Oxide

The outstanding electrical and optical properties of graphene make it an excellent alternative as a transparent electrode. Here we demonstrate the application of graphene as collector material in internal photoemission (IPE) spectroscopy; enabling the direct observation of both electron and hole injections at a Si/Al2O3 interface and successfully overcoming the long-standing difficulty of detecting holes injected from a semiconductor emitter in IPE measurements. The observed electron and hole barrier heights are 3.5 eV and 4.1 eV, respectively. Thus the bandgap of Al2O3 can be further deduced to be 6.5 eV, in close agreement with the valued obtained by vacuum ultraviolet spectroscopic ellipsometry analysis. The detailed optical modeling of a graphene/Al2O3/Si stack reveals that by using graphene in IPE measurements the carrier injection from the emitter is significantly enhanced and the contribution of carrier injection from the collector electrode is minimal. The method can be readily extended to various IPE test structures for a complete band alignment analysis and interface characterization.

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preprint2013arXiv

Pervasive liquid metal direct writing electronics with roller-ball pen

A roller-ball pen enabled direct writing electronics via room temperature liquid metal ink was proposed. With the rolling to print mechanism, the metallic inks were smoothly written on flexible polymer substrate to form conductive tracks and electronic devices. The contact angle analyzer and scanning electron microscope were implemented to probe the inner property of the obtained electronics. An ever high writing resolution with line width and thickness as 200μm and 80μm, respectively was realized. Further, with the administration of external writing pressure, GaIn24.5 droplets embody increasing wettability on polymer which demonstrates the pervasive adaptability of the roller-ball pen electronics.

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preprint2013arXiv

Tight Bounds for Distributed Functional Monitoring

We resolve several fundamental questions in the area of distributed functional monitoring, initiated by Cormode, Muthukrishnan, and Yi (SODA, 2008). In this model there are $k$ sites each tracking their input and communicating with a central coordinator that continuously maintain an approximate output to a function $f$ computed over the union of the inputs. The goal is to minimize the communication. We show the randomized communication complexity of estimating the number of distinct elements up to a $1+\eps$ factor is $\tildeΩ(k/\eps^2)$, improving the previous $Ω(k + 1/\eps^2)$ bound and matching known upper bounds up to a logarithmic factor. For the $p$-th frequency moment $F_p$, $p > 1$, we improve the previous $Ω(k + 1/\eps^2)$ communication bound to $\tildeΩ(k^{p-1}/\eps^2)$. We obtain similar improvements for heavy hitters, empirical entropy, and other problems. We also show that we can estimate $F_p$, for any $p > 1$, using $\tilde{O}(k^{p-1}\poly(\eps^{-1}))$ communication. This greatly improves upon the previous $\tilde{O}(k^{2p+1}N^{1-2/p} \poly(\eps^{-1}))$ bound of Cormode, Muthukrishnan, and Yi for general $p$, and their $\tilde{O}(k^2/\eps + k^{1.5}/\eps^3)$ bound for $p = 2$. For $p = 2$, our bound resolves their main open question. Our lower bounds are based on new direct sum theorems for approximate majority, and yield significant improvements to problems in the data stream model, improving the bound for estimating $F_p, p > 2,$ in $t$ passes from $\tildeΩ(n^{1-2/p}/(\eps^{2/p} t))$ to $\tildeΩ(n^{1-2/p}/(\eps^{4/p} t))$, giving the first bound for estimating $F_0$ in $t$ passes of $Ω(1/(\eps^2 t))$ bits of space that does not use the gap-hamming problem.

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preprint2013arXiv

When Distributed Computation is Communication Expensive

We consider a number of fundamental statistical and graph problems in the message-passing model, where we have $k$ machines (sites), each holding a piece of data, and the machines want to jointly solve a problem defined on the union of the $k$ data sets. The communication is point-to-point, and the goal is to minimize the total communication among the $k$ machines. This model captures all point-to-point distributed computational models with respect to minimizing communication costs. Our analysis shows that exact computation of many statistical and graph problems in this distributed setting requires a prohibitively large amount of communication, and often one cannot improve upon the communication of the simple protocol in which all machines send their data to a centralized server. Thus, in order to obtain protocols that are communication-efficient, one has to allow approximation, or investigate the distribution or layout of the data sets.

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preprint2011arXiv

Edit Distance to Monotonicity in Sliding Windows

Given a stream of items each associated with a numerical value, its edit distance to monotonicity is the minimum number of items to remove so that the remaining items are non-decreasing with respect to the numerical value. The space complexity of estimating the edit distance to monotonicity of a data stream is becoming well-understood over the past few years. Motivated by applications on network quality monitoring, we extend the study to estimating the edit distance to monotonicity of a sliding window covering the $w$ most recent items in the stream for any $w \ge 1$. We give a deterministic algorithm which can return an estimate within a factor of $(4+\eps)$ using $O(\frac{1}{\eps^2} \log^2(\eps w))$ space. We also extend the study in two directions. First, we consider a stream where each item is associated with a value from a partial ordered set. We give a randomized $(4+ε)$-approximate algorithm using $O(\frac{1}{ε^2} \log ε^2 w \log w)$ space. Second, we consider an out-of-order stream where each item is associated with a creation time and a numerical value, and items may be out of order with respect to their creation times. The goal is to estimate the edit distance to monotonicity with respect to the numerical value of items arranged in the order of creation times. We show that any randomized constant-approximate algorithm requires linear space.

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preprint2011arXiv

Influence of Metal-Graphene Contact on the Operation and Scalability of Graphene Field-Effect-Transistors

We explore the effects of metal contacts on the operation and scalability of 2D Graphene Field-Effect-Transistors (GFETs) using detailed numerical device simulations based on the non-equilibrium Green's function formalism self-consistently solved with the Poisson equation at the ballistic limit. Our treatment of metal-graphene (M-G) contacts captures: (1) the doping effect due to the shift of the Fermi level in graphene contacts, (2) the density-of-states (DOS) broadening effect inside graphene contacts due to Metal-Induced-States (MIS). Our results confirm the asymmetric transfer characteristics in GFETs due to the doping effect by metal contacts. Furthermore, at higher M-G coupling strengths the contact DOS broadening effect increases the on-current, while the impact on the minimum current (Imin) in the off-state depends on the source to drain bias voltage and the work-function difference between graphene and the contact metal. Interestingly, with scaling of the channel length, the MIS inside the channel has a weak influence on Imin even at large M-G coupling strengths, while direct source-to-drain (S -> D) tunneling has a stronger influence. Therefore, channel length scalability of GFETs with sufficient gate control will be mainly limited by direct S -> D tunneling, and not by the MIS.

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preprint2011arXiv

Noncommutative maximal ergodic inequality for non-tracial L1-spaces

We extend the noncommutative L1-maximal ergodic inequality for semifinite von Neumann algebras established by Yeadon in 1977 to the framework of noncommutative L1-spaces associated with sigma-finite von Neumann algebras. Since the semifnite case of this result is one of the two essential parts in the proof of noncommutative maximal ergodic inequality for tracial Lp-spaces (1<p<infinity) by Junge-Xu in 2007, we hope our result will be helpful to establish a complete noncommutative maximal ergodic inequality for non-tracial Lp-spaces in the future.

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preprint2011arXiv

Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks

We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the {\em count-tracking} problem, where there are $k$ players, each holding a counter $n_i$ that gets incremented over time, and the goal is to track an $\eps$-approximation of their sum $n=\sum_i n_i$ continuously at all times, using minimum communication. While the deterministic communication complexity of the problem is $Θ(k/\eps \cdot \log N)$, where $N$ is the final value of $n$ when the tracking finishes, we show that with randomization, the communication cost can be reduced to $Θ(\sqrt{k}/\eps \cdot \log N)$. Our algorithm is simple and uses only O(1) space at each player, while the lower bound holds even assuming each player has infinite computing power. Then, we extend our techniques to two related distributed tracking problems: {\em frequency-tracking} and {\em rank-tracking}, and obtain similar improvements over previous deterministic algorithms. Both problems are of central importance in large data monitoring and analysis, and have been extensively studied in the literature.

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preprint2011arXiv

Sorting, Searching, and Simulation in the MapReduce Framework

In this paper, we study the MapReduce framework from an algorithmic standpoint and demonstrate the usefulness of our approach by designing and analyzing efficient MapReduce algorithms for fundamental sorting, searching, and simulation problems. This study is motivated by a goal of ultimately putting the MapReduce framework on an equal theoretical footing with the well-known PRAM and BSP parallel models, which would benefit both the theory and practice of MapReduce algorithms. We describe efficient MapReduce algorithms for sorting, multi-searching, and simulations of parallel algorithms specified in the BSP and CRCW PRAM models. We also provide some applications of these results to problems in parallel computational geometry for the MapReduce framework, which result in efficient MapReduce algorithms for sorting, 2- and 3-dimensional convex hulls, and fixed-dimensional linear programming. For the case when mappers and reducers have a memory/message-I/O size of $M=Θ(N^ε)$, for a small constant $ε>0$, all of our MapReduce algorithms for these applications run in a constant number of rounds.

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preprint2010arXiv

Clustering with diversity

We consider the {\em clustering with diversity} problem: given a set of colored points in a metric space, partition them into clusters such that each cluster has at least $\ell$ points, all of which have distinct colors. We give a 2-approximation to this problem for any $\ell$ when the objective is to minimize the maximum radius of any cluster. We show that the approximation ratio is optimal unless $\mathbf{P=NP}$, by providing a matching lower bound. Several extensions to our algorithm have also been developed for handling outliers. This problem is mainly motivated by applications in privacy-preserving data publication.

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preprint2010arXiv

Scalability of Atomic-Thin-Body (ATB) Transistors Based on Graphene Nanoribbons

A general solution for the electrostatic potential in an atomic-thin-body (ATB) field-effect transistor geometry is presented. The effective electrostatic scaling length, λeff, is extracted from the analytical model, which cannot be approximated by the lowest order eigenmode as traditionally done in SOI-MOSFETs. An empirical equation for the scaling length that depends on the geometry parameters is proposed. It is shown that even for a thick SiO2 back oxide λeff can be improved efficiently by thinner top oxide thickness, and to some extent, with high-k dielectrics. The model is then applied to self-consistent simulation of graphene nanoribbon (GNR) Schottky-barrier field-effect transistors (SB-FETs) at the ballistic limit. In the case of GNR SB-FETs, for large λeff, the scaling is limited by the conventional electrostatic short channel effects (SCEs). On the other hand, for small λeff, the scaling is limited by direct source-to-drain tunneling. A subthreshold swing below 100mV/dec is still possible with a sub-10nm gate length in GNR SB-FETs.

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preprint2006arXiv

Analysis of measurement errors for a superconducting phase qubit

We analyze several mechanisms leading to errors in a course of measurement of a superconducting flux-biased phase qubit. Insufficiently long measurement pulse may lead to nonadiabatic transitions between qubit states $|1>$ and $|0>$, before tunneling through a reduced barrier is supposed to distinguish the qubit states. Finite (though large) ratio of tunneling rates for these states leads to incomplete discrimination between $|1>$ and $|0>$. Insufficiently fast energy relaxation after the tunneling of state $|1>$ may cause the repopulation of the quantum well in which only the state $|0>$ is supposed to remain. We analyze these types of measurement errors using analytical approaches as well as numerical solution of the time-dependent Schrödinger equation.

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preprint2005arXiv

Continuous quantum feedback of coherent oscillations in a solid-state qubit

We have analyzed theoretically the operation of the Bayesian quantum feedback of a solid-state qubit, designed to maintain perfect coherent oscillations in the qubit for arbitrarily long time. In particular, we have studied the feedback efficiency in presence of dephasing environment and detector nonideality. Also, we have analyzed the effect of qubit parameter deviations and studied the quantum feedback control of an energy-asymmetric qubit.

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preprint2002arXiv

A Codebook Generation Algorithm for Document Image Compression

Pattern-matching-based document-compression systems (e.g. for faxing) rely on finding a small set of patterns that can be used to represent all of the ink in the document. Finding an optimal set of patterns is NP-hard; previous compression schemes have resorted to heuristics. This paper describes an extension of the cross-entropy approach, used previously for measuring pattern similarity, to this problem. This approach reduces the problem to a k-medians problem, for which the paper gives a new algorithm with a provably good performance guarantee. In comparison to previous heuristics (First Fit, with and without generalized Lloyd's/k-means postprocessing steps), the new algorithm generates a better codebook, resulting in an overall improvement in compression performance of almost 17%.

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