arXiv subjects

Liu Yang

Publications and source records attributed to Liu Yang.

At least 235 records · Page 13Linked to original sources

arXiv2018

Analyzing and Characterizing User Intent in Information-seeking Conversations

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intent on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models.

cs.IR↗

arXiv2018

Risk-Averse Matchings over Uncertain Graph Databases

A large number of applications such as querying sensor networks, and analyzing protein-protein interaction (PPI) networks, rely on mining uncertain graph and hypergraph databases. In this work we study the following problem: given an uncertain, weighted (hyper)graph, how can we efficiently find a (hyper)matching with high expected reward, and low risk? This problem naturally arises in the context of several important applications, such as online dating, kidney exchanges, and team formation. We introduce a novel formulation for finding matchings with maximum expected reward and bounded risk under a general model of uncertain weighted (hyper)graphs that we introduce in this work. Our model generalizes probabilistic models used in prior work, and captures both continuous and discrete probability distributions, thus allowing to handle privacy related applications that inject appropriately distributed noise to (hyper)edge weights. Given that our optimization problem is NP-hard, we turn our attention to designing efficient approximation algorithms. For the case of uncertain weighted graphs, we provide a $\frac{1}{3}$-approximation algorithm, and a $\frac{1}{5}$-approximation algorithm with near optimal run time. For the case of uncertain weighted hypergraphs, we provide a $Ω(\frac{1}{k})$-approximation algorithm, where $k$ is the rank of the hypergraph (i.e., any hyperedge includes at most $k$ nodes), that runs in almost (modulo log factors) linear time. We complement our theoretical results by testing our approximation algorithms on a wide variety of synthetic experiments, where we observe in a controlled setting interesting findings on the trade-off between reward, and risk. We also provide an application of our formulation for providing recommendations of teams that are likely to collaborate, and have high impact.

cs.DS↗

arXiv2017

Parity-time-symmetric optical lattice with alternating gain and loss atomic configurations

Since the spatially extended periodic parity-time (PT) symmetric potential can possess certain unique properties compared to a single PT cell (with only a pair of coupled gain-loss components), various schemes have been proposed to realize periodic PT-symmetric potentials based on optical lattices. Here, we experimentally construct a spatially periodic PT-symmetric optical potential based on gain-loss arrays induced in a coherently-prepared atomic medium. The gain and loss arrays are generated in alternating four-level N-type and three-level $Λ$-type configurations in the same atomic medium, respectively, which do not require discrete diffractions as demonstrated in the previous work [Phys. Rev. Lett. 117, 123601(2016)] and can be easier to realize with more relaxed operating conditions. The dynamical behaviors of the system are investigated by measuring the phase difference between two adjacent gain and loss channels. The demonstrated PT-symmetric optical lattice with easy accessibility and better tunability sets a new stage for further exploiting the peculiar physical properties in periodic non-Hermitian systems.

physics.optics↗

arXiv2017

A Prediction Model of the Project Life-span in Open Source Software Ecosystem

In nature ecosystems, animal life-spans are determined by genes and some other biological characteristics. Similarly, the software project life-spans are related to some internal or external characteristics. Analyzing the relations between these characteristics and the project life-span, may help developers, investors, and contributors to control the development cycle of the software project. The paper provides an insight on the project life-span for a free open source software ecosystem. The statistical analysis of some project characteristics in GitHub is presented, and we find that the choices of programming languages, the number of files, the label format of the project, and the relevant membership expressions can impact the life-span of a project. Based on these discovered characteristics, we also propose a prediction model to estimate the project life-span in open source software ecosystems. These results may help developers reschedule the project in open source software ecosystem.

cs.SE↗

arXiv2017

A Note on Property Testing Sum of Squares and Multivariate Polynomial Interpolation

In this paper, we investigate property testing whether or not a degree d multivariate poly- nomial is a sum of squares or is far from a sum of squares. We show that if we require that the property tester always accepts YES instances and uses random samples, $n^{Ω(d)}$ samples are required, which is not much fewer than it would take to completely determine the polynomial. To prove this lower bound, we show that with high probability, multivariate polynomial in- terpolation matches arbitrary values on random points and the resulting polynomial has small norm. We then consider a particular polynomial which is non-negative yet not a sum of squares and use pseudo-expectation values to prove it is far from being a sum of squares.

cs.CC↗

arXiv2017

Neural Matching Models for Question Retrieval and Next Question Prediction in Conversation

The recent boom of AI has seen the emergence of many human-computer conversation systems such as Google Assistant, Microsoft Cortana, Amazon Echo and Apple Siri. We introduce and formalize the task of predicting questions in conversations, where the goal is to predict the new question that the user will ask, given the past conversational context. This task can be modeled as a "sequence matching" problem, where two sequences are given and the aim is to learn a model that maps any pair of sequences to a matching probability. Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have attracted immense research interests of information retrieval and natural language processing communities. In this paper, we first study neural matching models for the question retrieval task that has been widely explored in the literature, whereas the effectiveness of neural models for this task is relatively unstudied. We further evaluate the neural matching models in the next question prediction task in conversations. We have used the publicly available Quora data and Ubuntu chat logs in our experiments. Our evaluations investigate the potential of neural matching models with representation learning for question retrieval and next question prediction in conversations. Experimental results show that neural matching models perform well for both tasks.

cs.IR↗

arXiv2017

Radiation Pressure Cooling as a Quantum Dynamical Process

One of the most fundamental problems in optomechanical cooling is how small the thermal phonon number of a mechanical oscillator can be achieved under the radiation pressure of a proper cavity field. Different from previous theoretical predictions, which were based on an optomechanical system's time-independent steady states, we treat such cooling as a dynamical process of driving the mechanical oscillator from its initial thermal state, due to its thermal equilibrium with the environment, to a stabilized quantum state of higher purity. We find that the stabilized thermal phonon number left in the end actually depends on how fast the cooling process could be. The cooling speed is decided by an effective optomechanical coupling intensity, which constitutes an essential parameter for cooling, in addition to the sideband resolution parameter that has been considered in other theoretical studies. The limiting thermal phonon number that any cooling process cannot surpass exhibits a discontinuous jump across a certain value of the parameter.

quant-ph↗

arXiv2017

Learning with Changing Features

In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. In particular, we propose an approach to provably determine the time instant from which the new/changed features start becoming relevant with respect to an output variable in an agnostic (supervised) learning setting. We also suggest an efficient version of our approach which has the same asymptotic performance. Moreover, our theory also applies when we have more than one such change point. Independent post analysis of a change point identified by our method for a large retailer revealed that it corresponded in time with certain unflattering news stories about a brand that resulted in the change in customer behavior. We also applied our method to data from an advanced manufacturing plant identifying the time instant from which downstream features became relevant. To the best of our knowledge this is the first work that formally studies change point detection in a distribution independent agnostic setting, where the change point is based on the changing relationship between input and output.

cs.LG↗

arXiv2017

Entangling light field with mechanical resonator at high temperature

We present a study on how to realize the widely interested optomechanical entanglement at high temperature. Unlike the majority of the previous experimental and theoretical researches that consider the entanglement of a mechanical resonator with a cavity field created by red-detuned continuous-wave or blue-detuned pulsed driving field, we find that applying blue-detuned continuous-wave pump field to cavity optomechanical systems can achieve considerable degrees of quantum entanglement, which is generally challenging to obtain at high temperature for the known physical systems. The competition between the induced squeezing-type interaction and the existing decoherence leads to stable entanglement in dynamically unstable regime. There is a much more relaxed condition for the existence of entanglement, as compared with the well-known criterion for neglecting the thermal decoherence on optomechanically coupled systems. A simple relation about a boundary in the parameter space, across which the entanglement can exist or not, is found with an analytical expression for the degree of the achieved entanglement at any temperature, which is derived for the systems of highly resolved sideband. The studied scenario with blue-detuned continuous-wave driving field can greatly simplify the generation of the widely interested optomechanical entanglement of macroscopic quantum states. Our study also provides the answers to two fundamentally meaningful open problems: (1) what is the condition for a system to avoid its loss of quantum entanglement under thermal decoherence? (2) is it possible to preserve the entanglement in a thermal environment by increasing the interaction that entangles the subsystems?

quant-ph↗

arXiv2017

Constraint on cosmological parameters by Hubble parameter from gravitational wave standard sirens of neutron star binary system

In this paper, we present a new method of measuring Hubble parameter($H(z)$), making use of the anisotropy of luminosity distance($d_{L}$), and the analysis of gravitational wave(GW) of neutron star(NS) binary system. The method has never been put into practice before due to the lack of the ability of detecting GW. LIGO's success in detecting GW of black hole(BH) binary system merger announced the possibility of this new method. We apply this method to several GW detecting projects, including Advanced LIGO(Adv-LIGO), Einstein Telescope(ET) and DECIGO, finding that the $H(z)$ by Adv-LIGO and ET is of bad accuracy, while the $H(z)$ by DECIGO shows a good accuracy. We use the error information of $H(z)$ by DECIGO to simulate $H(z)$ data at every 0.1 redshift span, and put the mock data into the forecasting of cosmological parameters. Compared with the available 38 observed $H(z)$ data(OHD), mock data shows an obviously tighter constraint on cosmological parameters, and a concomitantly higher value of Figure of Merit(FoM). For a 3-year-observation by standard sirens of DECIGO, the FoM value is as high as 834.9. If a 10-year-observation is launched, the FoM could reach 2783.1. For comparison, the FoM of 38 actual observed $H(z)$ data is 9.3. These improvement indicates that the new method has great potential in further cosmological constraints.

astro-ph.CO↗

arXiv2017

The inexact residual iteration method for quadratic eigenvalue problem and the analysis of convergence

In this paper, we first establish the convergence criteria of the residual iteration method for solving quadratic eigenvalue problem- s. We analyze the impact of shift point and the subspace expansion on the convergence of this method. In the process of expanding subspace, this method needs to solve a linear system at every step. For large scale problems in which the equations cannot be solved directly, we propose an inner and outer iteration version of the residual iteration method. The new method uses the iterative method to solve the equations and uses the approximate solution to expand the subspace. We analyze the relationship between inner and outer iterations and provide a quantita- tive criterion for the inner iteration which can ensure the convergence of the outer iteration. Finally, our numerical experiments provide proof of our analysis and demonstrate the effectiveness of the inexact residual iteration method.

math.NA↗

arXiv2016

Observation of Parity-Time Symmetry in Optically Induced Atomic Lattices

We experimentally demonstrate PT-symmetric optical lattices with periodical gain and loss profiles in a coherently-prepared four-level N-type atomic system. By appropriately tuning the pertinent atomic parameters, the onset of PT-symmetry breaking is observed through measuring an abrupt phase-shift jump between adjacent gain and loss waveguides. The experimental realization of such readily reconfigurable and effectively controllable PT-symmetric waveguide array structure sets a new stage for further exploiting and better understanding the peculiar physical properties of these non-Hermitian systems in atomic settings.

physics.optics↗

arXiv2016

Adaptive and Scalable Android Malware Detection through Online Learning

It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) do not change over time. In this work, we address the problem of malware population drift and propose a novel online machine learning based framework, named DroidOL to handle it and effectively detect malware. In order to perform accurate detection, security-sensitive behaviors are captured from apps in the form of inter-procedural control-flow sub-graph features using a state-of-the-art graph kernel. In order to perform scalable detection and to adapt to the drift and evolution in malware population, an online passive-aggressive classifier is used. In a large-scale comparative analysis with more than 87,000 apps, DroidOL achieves 84.29% accuracy outperforming two state-of-the-art malware techniques by more than 20% in their typical batch learning setting and more than 3% when they are continuously re-trained. Our experimental findings strongly indicate that online learning based approaches are highly suitable for real-world malware detection.

cs.CR↗

arXiv2016

Interacting photon pulses in Rydberg medium

The understanding of dynamical evolutions of interacting photon pulses in Rydberg atomic ensemble is the prerequisite for realizing quantum devices with such system. We present an approach that efficiently simulates the dynamical processes, using a set of local functions we construct to reflect the profiles of narrowband pulses. For two counter-propagating photon pulses, our approach predicts the distinct phenomena from the widely concerned Rydberg blockade to the previously less noticed significant absorption in the anomalous dispersion regime, which can occur by respectively setting the pulse frequency to the appropriate values. Our numerical simulations also demonstrate how spatially extending photon pulses become deformed under realistic non-uniform interaction over their distributions.

quant-ph↗

arXiv2016

Dynamical Phonon Laser in Coupled Active-Passive Microresonators

Effective transition between the population-inverted optical eigenmodes of two coupled microcavities carrying mechanical oscillation realizes a phonon analogue of optical two-level laser. By providing an approach that linearizes the dynamical equations of weak nonlinear systems without relying on their steady states, we study such phonon laser action as a realistic dynamical process, which exhibits time-dependent stimulated phonon field amplification especially when one of the cavities is added with optical gain medium. The approach we present explicitly gives the conditions for the optimum phonon lasing, and thermal noise is found to be capable of facilitating the phonon laser action significantly.

quant-ph↗

arXiv2016

Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection

In this paper, we propose a novel graph kernel specifically to address a challenging problem in the field of cyber-security, namely, malware detection. Previous research has revealed the following: (1) Graph representations of programs are ideally suited for malware detection as they are robust against several attacks, (2) Besides capturing topological neighbourhoods (i.e., structural information) from these graphs it is important to capture the context under which the neighbourhoods are reachable to accurately detect malicious neighbourhoods. We observe that state-of-the-art graph kernels, such as Weisfeiler-Lehman kernel (WLK) capture the structural information well but fail to capture contextual information. To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information. We show that for the malware detection problem, CWLK is more expressive and hence more accurate than WLK while maintaining comparable efficiency. Through our large-scale experiments with more than 50,000 real-world Android apps, we demonstrate that CWLK outperforms two state-of-the-art graph kernels (including WLK) and three malware detection techniques by more than 5.27% and 4.87% F-measure, respectively, while maintaining high efficiency. This high accuracy and efficiency make CWLK suitable for large-scale real-world malware detection.

cs.CR↗

arXiv2016

Dynamic matrix factorization with social influence

Matrix factorization is a key component of collaborative filtering-based recommendation systems because it allows us to complete sparse user-by-item ratings matrices under a low-rank assumption that encodes the belief that similar users give similar ratings and that similar items garner similar ratings. This paradigm has had immeasurable practical success, but it is not the complete story for understanding and inferring the preferences of people. First, peoples' preferences and their observable manifestations as ratings evolve over time along general patterns of trajectories. Second, an individual person's preferences evolve over time through influence of their social connections. In this paper, we develop a unified process model for both types of dynamics within a state space approach, together with an efficient optimization scheme for estimation within that model. The model combines elements from recent developments in dynamic matrix factorization, opinion dynamics and social learning, and trust-based recommendation. The estimation builds upon recent advances in numerical nonlinear optimization. Empirical results on a large-scale data set from the Epinions website demonstrate consistent reduction in root mean squared error by consideration of the two types of dynamics.

stat.ML↗

arXiv2015

Storage and retrieval of photons under their mutual interaction in Rydberg medium

Stopping and regenerating a pair of single-photon pulses at adjacent locations in coherently prepared Rydberg atomic ensembles are significantly affected by their effective interaction mediated by Rydberg excitations, and the similar processes can differ notably from the one exhibiting the common Rydberg blockade as with the stationary propagation of multi-photon light beams in the same medium. Based on the complete dynamics, we reveal the detailed features in such processes by finding how the profiles of the involved quantum fields evolve in various situations. The findings help to determine the proper regimes for implementing photonic gates and transistors. In addition, we discuss the non-adiabatic corrections associated with quickly changing control fields, and illustrate a method that restores the photon pulses' original amplitude during their retrieval unless they are heavily damped before storage.

quant-ph↗