arXiv subjects

Hang Li

Publications and source records attributed to Hang Li.

359 records · Page 20Linked to original sources

arXiv2015

A General Utility Optimization Framework for Energy Harvesting Based Wireless Communications

In recent years, wireless communication systems are expected to achieve more cost-efficient and sustainable operations by replacing conventional fixed power supplies such as batteries with energy harvesting (EH) devices, which could provide electric energy from renewable energy sources (e.g., solar and wind). Such EH power supplies, however, are random and instable in nature, and as a result impose new challenges on reliable communication design and have triggered substantial research interests in EH based wireless communications. Building upon existing works, in this article, we develop a general optimization framework to maximize the utility of EH wireless communication systems. Our framework encapsulates a variety of design problems, such as throughput maximization and outage probability minimization in single-user and multiuser setups, and provides useful guidelines to the practical design of general EH based communication systems with different assumptions over the knowledge of time-varying wireless channels and EH rates at the transmitters.

cs.IT↗

arXiv2014

Experimental entanglement-assisted quantum delayed-choice experiment

The puzzling properties of quantum mechanics, wave-particle duality, entanglement and superposition, were dissected experimentally at past decades. However, hidden-variable (HV) models, based on three classical assumptions of wave-particle objectivity, determinism and independence, strive to explain or even defeat them. The development of quantum technologies enabled us to test experimentally the predictions of quantum mechanics and HV theories. Here, we report an experimental demonstration of an entanglement-assisted quantum delayed-choice scheme using a liquid nuclear magnetic resonance quantum information processor. This scheme we realized is based on the recently proposed scheme [Nat. Comms. 5:4997(2014)], which gave different results for quantum mechanics and HV theories. In our experiments, the intensities and the visibilities of the interference are in consistent the theoretical prediction of quantum mechanics. The results imply that a contradiction is appearing when all three assumptions of HV models are combined, though any two of the above assumptions are compatible with it.

quant-ph↗

arXiv2014

Experimental Estimation of Average Fidelity of a Clifford Gate on a 7-qubit Quantum Processor

Quantum gates in experiment are inherently prone to errors that need to be characterized before they can be corrected. Full characterization via quantum process tomography is impractical and often unnecessary. For most practical purposes, it is enough to estimate more general quantities such as the average fidelity. Here we use a unitary 2-design and twirling protocol for efficiently estimating the average fidelity of Clifford gates, to certify a 7-qubit entangling gate in a nuclear magnetic resonance quantum processor. Compared with more than $10^8$ experiments required by full process tomography, we conducted 1656 experiments to satisfy a statistical confidence level of 99%. The average fidelity of this Clifford gate in experiment is 55.1%, and rises to 87.5% if the infidelity due to decoherence is removed. The entire protocol of certifying Clifford gates is efficient and scalable, and can easily be extended to any general quantum information processor with minor modifications.

quant-ph↗

arXiv2014

A Parallel and Efficient Algorithm for Learning to Match

Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix factorization, formalize the task as an extension to matrix factorization by incorporating auxiliary features into the model. Unfortunately, making those algorithms scale to real world problems is challenging, and simple parallelization strategies fail due to the complex cross talking patterns between sub-tasks. In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization. Our algorithm, based on coordinate descent, can easily handle hundreds of millions of instances and features on a single machine. The key recipe of this algorithm is an iterative relaxation of the objective to facilitate parallel updates of parameters, with guaranteed convergence on minimizing the original objective function. Experimental results demonstrate that the proposed method is effective on a wide range of matching problems, with efficiency significantly improved upon the baselines while accuracy retained unchanged.

cs.LG↗

arXiv2014

Experimental Quantum Hamiltonian Identification from Measurement Time Traces

Identifying Hamiltonian of a quantum system is of vital importance for quantum information processing. In this Letter, we realized and benchmarked a quantum Hamiltonian identification algorithm recently proposed [Phys. Rev. Lett. \textbf{113}, 080401 (2014)]. we realized the algorithm on liquid nuclear magnetic resonance quantum information processor using two different working media with different forms of Hamiltonian. Our experiment realized the quantum identification algorithm based on free induction decay signals. We also showed how to process data obtained in practical experiment. We studied the influence of decoherence by numerical simulations. Our experiments and simulations demonstrate that the algorithm is effective and robust.

quant-ph↗

arXiv2014

An Information Retrieval Approach to Short Text Conversation

Human computer conversation is regarded as one of the most difficult problems in artificial intelligence. In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message. We leverage the vast amount of short conversation data available on social media to study the issue. We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task. We investigate the significance as well as the limitation of the IR approach. Our experiments demonstrate that the retrieval-based model can make the system behave rather "intelligently", when combined with a huge repository of conversation data from social media.

cs.IR↗

arXiv2014

Experimental realization of post-selected weak measurements on an NMR quantum processor

The ability to post-select the outcomes of an experiment is a useful theoretical concept and experimental tool. In the context of weak measurements post-selection can lead to surprising results such as complex weak values outside the range of eigenvalues. Usually post-selection is realized by a projective measurement, which is hard to implement in ensemble systems such as NMR. We demonstrate the first experiment of a weak measurement with post-selection on an NMR quantum information processor. Our setup is used for measuring complex weak values and weak values outside the range of eigenvalues. The scheme for overcoming the problem of post-selection in an ensemble quantum computer is general and can be applied to any circuit-based implementation. This experiment paves the way for studying and exploiting post-selection and weak measurements in systems where projective measurements are hard to realize experimentally.

quant-ph↗

arXiv2013

Query Segmentation for Relevance Ranking in Web Search

In this paper, we try to answer the question of how to improve the state-of-the-art methods for relevance ranking in web search by query segmentation. Here, by query segmentation it is meant to segment the input query into segments, typically natural language phrases, so that the performance of relevance ranking in search is increased. We propose employing the re-ranking approach in query segmentation, which first employs a generative model to create top $k$ candidates and then employs a discriminative model to re-rank the candidates to obtain the final segmentation result. The method has been widely utilized for structure prediction in natural language processing, but has not been applied to query segmentation, as far as we know. Furthermore, we propose a new method for using the result of query segmentation in relevance ranking, which takes both the original query words and the segmented query phrases as units of query representation. We investigate whether our method can improve three relevance models, namely BM25, key n-gram model, and dependency model. Our experimental results on three large scale web search datasets show that our method can indeed significantly improve relevance ranking in all the three cases.

cs.IR↗

arXiv2013

Tailoring spin-orbit torque in diluted magnetic semiconductors

We study the spin orbit torque arising from an intrinsic linear Dresselhaus spin-orbit coupling in a single layer III-V diluted magnetic semiconductor. We investigate the transport properties and spin torque using the linear response theory and we report here : (1) a strong correlation exists between the angular dependence of the torque and the anisotropy of the Fermi surface; (2) the spin orbit torque depends nonlinearly on the exchange coupling. Our findings suggest the possibility to tailor the spin orbit torque magnitude and angular dependence by structural design.

cond-mat.mes-hall↗

arXiv1998

Word Clustering and Disambiguation Based on Co-occurrence Data

We address the problem of clustering words (or constructing a thesaurus) based on co-occurrence data, and using the acquired word classes to improve the accuracy of syntactic disambiguation. We view this problem as that of estimating a joint probability distribution specifying the joint probabilities of word pairs, such as noun verb pairs. We propose an efficient algorithm based on the Minimum Description Length (MDL) principle for estimating such a probability distribution. Our method is a natural extension of those proposed in (Brown et al 92) and (Li & Abe 96), and overcomes their drawbacks while retaining their advantages. We then combined this clustering method with the disambiguation method of (Li & Abe 95) to derive a disambiguation method that makes use of both automatically constructed thesauruses and a hand-made thesaurus. The overall disambiguation accuracy achieved by our method is 85.2%, which compares favorably against the accuracy (82.4%) obtained by the state-of-the-art disambiguation method of (Brill & Resnik 94).

cmp-lg↗

arXiv1997

Document Classification Using a Finite Mixture Model

We propose a new method of classifying documents into categories. The simple method of conducting hypothesis testing over word-based distributions in categories suffers from the data sparseness problem. In order to address this difficulty, Guthrie et.al. have developed a method using distributions based on hard clustering of words, i.e., in which a word is assigned to a single cluster and words in the same cluster are treated uniformly. This method might, however, degrade classification results, since the distributions it employs are not always precise enough for representing the differences between categories. We propose here the use of soft clustering of words, i.e., in which a word can be assigned to several different clusters and each cluster is characterized by a specific word probability distribution. We define for each document category a finite mixture model, which is a linear combination of the probability distributions of the clusters. We thereby treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models. In order to accomplish this testing, we employ the EM algorithm which helps efficiently estimate parameters in a finite mixture model. Experimental results indicate that our method outperforms not only the method using distributions based on hard clustering, but also the method using word-based distributions and the method based on cosine-similarity.

cmp-lg↗

arXiv1996

Learning Dependencies between Case Frame Slots

We address the problem of automatically acquiring case frame patterns (selectional patterns) from large corpus data. In particular, we propose a method of learning dependencies between case frame slots. We view the problem of learning case frame patterns as that of learning multi-dimensional discrete joint distributions, where random variables represent case slots. We then formalize the dependencies between case slots as the probabilistic dependencies between these random variables. Since the number of parameters in a multi-dimensional joint distribution is exponential, it is infeasible to accurately estimate them in practice. To overcome this difficulty, we settle with approximating the target joint distribution by the product of low order component distributions, based on corpus data. In particular we propose to employ an efficient learning algorithm based on the MDL principle to realize this task. Our experimental results indicate that for certain classes of verbs, the accuracy achieved in a disambiguation experiment is improved by using the acquired knowledge of dependencies.

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arXiv1996

Clustering Words with the MDL Principle

We address the problem of automatically constructing a thesaurus (hierarchically clustering words) based on corpus data. We view the problem of clustering words as that of estimating a joint distribution over the Cartesian product of a partition of a set of nouns and a partition of a set of verbs, and propose an estimation algorithm using simulated annealing with an energy function based on the Minimum Description Length (MDL) Principle. We empirically compared the performance of our method based on the MDL Principle against that of one based on the Maximum Likelihood Estimator, and found that the former outperforms the latter. We also evaluated the method by conducting pp-attachment disambiguation experiments using an automatically constructed thesaurus. Our experimental results indicate that we can improve accuracy in disambiguation by using such a thesaurus.

cmp-lg↗

arXiv1996

A Probabilistic Disambiguation Method Based on Psycholinguistic Principles

We address the problem of structural disambiguation in syntactic parsing. In psycholinguistics, a number of principles of disambiguation have been proposed, notably the Lexical Preference Rule (LPR), the Right Association Principle (RAP), and the Attach Low and Parallel Principle (ALPP) (an extension of RAP). We argue that in order to improve disambiguation results it is necessary to implement these principles on the basis of a probabilistic methodology. We define a `three-word probability' for implementing LPR, and a `length probability' for implementing RAP and ALPP. Furthermore, we adopt the `back-off' method to combine these two types of probabilities. Our experimental results indicate our method to be effective, attaining an accuracy of 89.2%.

cmp-lg↗

arXiv1996

Learning Word Association Norms Using Tree Cut Pair Models

We consider the problem of learning co-occurrence information between two word categories, or more in general between two discrete random variables taking values in a hierarchically classified domain. In particular, we consider the problem of learning the `association norm' defined by A(x,y)=p(x, y)/(p(x)*p(y)), where p(x, y) is the joint distribution for x and y and p(x) and p(y) are marginal distributions induced by p(x, y). We formulate this problem as a sub-task of learning the conditional distribution p(x|y), by exploiting the identity p(x|y) = A(x,y)*p(x). We propose a two-step estimation method based on the MDL principle, which works as follows: It first estimates p(x) as p1 using MDL, and then estimates p(x|y) for a fixed y by applying MDL on the hypothesis class of {A * p1 | A \in B} for some given class B of representations for association norm. The estimation of A is therefore obtained as a side-effect of a near optimal estimation of p(x|y). We then apply this general framework to the problem of acquiring case-frame patterns. We assume that both p(x) and A(x, y) for given y are representable by a model based on a classification that exists within an existing thesaurus tree as a `cut,' and hence p(x|y) is represented as the product of a pair of `tree cut models.' We then devise an efficient algorithm that implements our general strategy. We tested our method by using it to actually acquire case-frame patterns and conducted disambiguation experiments using the acquired knowledge. The experimental results show that our method improves upon existing methods.

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arXiv1996

Generalizing Case Frames Using a Thesaurus and the MDL Principle

We address the problem of automatically acquiring case-frame patterns from large corpus data. In particular, we view this problem as the problem of estimating a (conditional) distribution over a partition of words, and propose a new generalization method based on the MDL (Minimum Description Length) principle. In order to assist with the efficiency, our method makes use of an existing thesaurus and restricts its attention on those partitions that are present as `cuts' in the thesaurus tree, thus reducing the generalization problem to that of estimating the `tree cut models' of the thesaurus. We then give an efficient algorithm which provably obtains the optimal tree cut model for the given frequency data, in the sense of MDL. We have used the case-frame patterns obtained using our method to resolve pp-attachment ambiguity.Our experimental results indicate that our method improves upon or is at least as effective as existing methods.

cmp-lg↗

arXiv1995

On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms

We consider the problem of learning a certain type of lexical semantic knowledge that can be expressed as a binary relation between words, such as the so-called sub-categorization of verbs (a verb-noun relation) and the compound noun phrase relation (a noun-noun relation). Specifically, we view this problem as an on-line learning problem in the sense of Littlestone's learning model in which the learner's goal is to minimize the total number of prediction mistakes. In the computational learning theory literature, Goldman, Rivest and Schapire and subsequently Goldman and Warmuth have considered the on-line learning problem for binary relations R : X * Y -> {0, 1} in which one of the domain sets X can be partitioned into a relatively small number of types, namely clusters consisting of behaviorally indistinguishable members of X. In this paper, we extend this model and suppose that both of the sets X, Y can be partitioned into a small number of types, and propose a host of prediction algorithms which are two-dimensional extensions of Goldman and Warmuth's weighted majority type algorithm proposed for the original model. We apply these algorithms to the learning problem for the `compound noun phrase' relation, in which a noun is related to another just in case they can form a noun phrase together. Our experimental results show that all of our algorithms out-perform Goldman and Warmuth's algorithm. We also theoretically analyze the performance of one of our algorithms, in the form of an upper bound on the worst case number of prediction mistakes it makes.

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