arXiv subjects

Hui Wang

Publications and source records attributed to Hui Wang.

At least 415 records · Page 23Linked to original sources

arXiv2020

A Group Norm Regularized Factorization Model for Subspace Segmentation

Subspace segmentation assumes that data comes from the union of different subspaces and the purpose of segmentation is to partition the data into the corresponding subspace. Low-rank representation (LRR) is a classic spectral-type method for solving subspace segmentation problems, that is, one first obtains an affinity matrix by solving a LRR model and then performs spectral clustering for segmentation. This paper proposes a group norm regularized factorization model (GNRFM) inspired by the LRR model for subspace segmentation and then designs an Accelerated Augmented Lagrangian Method (AALM) algorithm to solve this model. Specifically, we adopt group norm regularization to make the columns of the factor matrix sparse, thereby achieving a purpose of low rank, which means no Singular Value Decompositions (SVD) are required and the computational complexity of each step is greatly reduced. We obtain affinity matrices by using different LRR models and then performing cluster testing on different sets of synthetic noisy data and real data, respectively. Compared with traditional models and algorithms, the proposed method is faster and more robust to noise, so the final clustering results are better. Moreover, the numerical results show that our algorithm converges fast and only requires approximately ten iterations.

cs.LG↗

arXiv2020

Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning

Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to its large state space (on the order of the game of Go) traditional search algorithms, such as MCTS, have not been able to find good solutions. A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of 82 steps, albeit with large computational resources. After achieving this record, to the best of our knowledge, there has been no further progress reported, for about a decade. In this paper we take the recent impressive performance of deep self-learning reinforcement learning approaches from AlphaGo/AlphaZero as inspiration to design a searcher for Morpion Solitaire. A challenge of Morpion Solitaire is that the state space is sparse, there are few win/loss signals. Instead, we use an approach known as ranked reward to create a reinforcement learning self-play framework for Morpion Solitaire. This enables us to find medium-quality solutions with reasonable computational effort. Our record is a 67 steps solution, which is very close to the human best (68) without any other adaptation to the problem than using ranked reward. We list many further avenues for potential improvement.

cs.AI↗

arXiv2020

Sequential Recommendation with Self-Attentive Multi-Adversarial Network

Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained with Maximum Likelihood Estimation (MLE). When context information (called factor) is involved, it is difficult to analyze when and how each individual factor would affect the final recommendation performance. For this purpose, we take a new perspective and introduce adversarial learning to sequential recommendation. In this paper, we present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Specifically, our proposed MFGAN has two kinds of modules: a Transformer-based generator taking user behavior sequences as input to recommend the possible next items, and multiple factor-specific discriminators to evaluate the generated sub-sequence from the perspectives of different factors. To learn the parameters, we adopt the classic policy gradient method, and utilize the reward signal of discriminators for guiding the learning of the generator. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods, in terms of effectiveness and interpretability.

cs.IR↗

arXiv2020

A subconvex bound for twisted $L$-functions

Let $\mathfrak{q}>2$ be a prime number, $χ$ a primitive Dirichlet character modulo $\mathfrak{q}$ and $f$ a primitive holomorphic cusp form or a Hecke-Maass cusp form of level $\mathfrak{q}$ and trivial nebentypus. We prove the subconvex bound $$ L(1/2,f\otimes χ)\ll \mathfrak{q}^{1/2-1/12+\varepsilon}, $$ where the implicit constant depends only on the archimedean parameter of $f$ and $\varepsilon$. The main input is a modifying trivial delta method developed in [1].

math.NT↗

arXiv2020

Analogue Gravity on a Superconducting Chip

We describe how analogues of a Hawking evaporating black hole as well as the Unruh effect for an oscillatory, accelerating photodetector in vacuum may be realized using superconducting, microwave circuits that are fashioned out of Josephson tunnel junction and film bulk acoustic resonator elements.

quant-ph↗

arXiv2020

Computational neurology: Computational modeling approaches in dementia

Dementia is a collection of symptoms associated with impaired cognition and impedes everyday normal functioning. Dementia, with Alzheimer's disease constituting its most common type, is highly complex in terms of etiology and pathophysiology. A more quantitative or computational attitude towards dementia research, or more generally in neurology, is becoming necessary - Computational Neurology. We provide a focused review of some computational approaches that have been developed and applied to the study of dementia, particularly Alzheimer's disease. Both mechanistic modeling and data-drive, including AI or machine learning, approaches are discussed. Linkage to clinical decision support systems for dementia diagnosis will also be discussed.

q-bio.NC↗

arXiv2020

Warm-Start AlphaZero Self-Play Search Enhancements

Recently, AlphaZero has achieved landmark results in deep reinforcement learning, by providing a single self-play architecture that learned three different games at super human level. AlphaZero is a large and complicated system with many parameters, and success requires much compute power and fine-tuning. Reproducing results in other games is a challenge, and many researchers are looking for ways to improve results while reducing computational demands. AlphaZero's design is purely based on self-play and makes no use of labeled expert data ordomain specific enhancements; it is designed to learn from scratch. We propose a novel approach to deal with this cold-start problem by employing simple search enhancements at the beginning phase of self-play training, namely Rollout, Rapid Action Value Estimate (RAVE) and dynamically weighted combinations of these with the neural network, and Rolling Horizon Evolutionary Algorithms (RHEA). Our experiments indicate that most of these enhancements improve the performance of their baseline player in three different (small) board games, with especially RAVE based variants playing strongly.

cs.AI↗

arXiv2020

Iterative Context-Aware Graph Inference for Visual Dialog

Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inference in a graphical model with sparse contexts and unknown graph structure (relation descriptor), and how to model the underlying context-aware relation inference is critical. To this end, we propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. The graph structure (relations in dialog) is iteratively updated using an adaptive top-$K$ message passing mechanism. Specifically, in every message passing step, each node selects the most $K$ relevant nodes, and only receives messages from them. Then, after the update, we impose graph attention on all the nodes to get the final graph embedding and infer the answer. In CAG, each node has dynamic relations in the graph (different related $K$ neighbor nodes), and only the most relevant nodes are attributive to the context-aware relational graph inference. Experimental results on VisDial v0.9 and v1.0 datasets show that CAG outperforms comparative methods. Visualization results further validate the interpretability of our method.

cs.CV↗

arXiv2020

Vortex-Meissner phase transition induced by two-tone-drive-engineered artificial gauge potential in the fermionic ladder constructed by superconducting qubit circuits

We propose to periodically modulate the onsite energy via two-tone drives, which can be furthermore used to engineer artificial gauge potential. As an example, we show that the fermionic ladder model penetrated with effective magnetic flux can be constructed by superconducting flux qubits using such two-tone-drive-engineered artificial gauge potential. In this superconducting system, the single-particle ground state can range from vortex phase to Meissner phase due to the competition between the interleg coupling strength and the effective magnetic flux. We also present the method to experimentally measure the chiral currents by the single-particle Rabi oscillations between adjacent qubits. In contrast to previous methods of generating artifical gauge potential, our proposal does not need the aid of auxiliary couplers and in principle remains valid only if the qubit circuit maintains enough anharmonicity. The fermionic ladder model with effective magnetic flux can also be interpreted as one-dimensional spin-orbit-coupled model, which thus lay a foundation towards the realization of quantum spin Hall effect.

quant-ph↗

arXiv2020

Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?

The landmark achievements of AlphaGo Zero have created great research interest into self-play in reinforcement learning. In self-play, Monte Carlo Tree Search is used to train a deep neural network, that is then used in tree searches. Training itself is governed by many hyperparameters.There has been surprisingly little research on design choices for hyper-parameter values and loss-functions, presumably because of the prohibitive computational cost to explore the parameter space. In this paper, we investigate 12 hyper-parameters in an AlphaZero-like self-play algorithm and evaluate how these parameters contribute to training. We use small games, to achieve meaningful exploration with moderate computational effort. The experimental results show that training is highly sensitive to hyper-parameter choices. Through multi-objective analysis we identify 4 important hyper-parameters to further assess. To start, we find surprising results where too much training can sometimes lead to lower performance. Our main result is that the number of self-play iterations subsumes MCTS-search simulations, game-episodes, and training epochs. The intuition is that these three increase together as self-play iterations increase, and that increasing them individually is sub-optimal. A consequence of our experiments is a direct recommendation for setting hyper-parameter values in self-play: the overarching outer-loop of self-play iterations should be maximized, in favor of the three inner-loop hyper-parameters, which should be set at lower values. A secondary result of our experiments concerns the choice of optimization goals, for which we also provide recommendations.

cs.LG↗

arXiv2020

Computation Rate Maximization in Wireless Powered MEC with Spread Spectrum Multiple Access

The integration of mobile edge computing (MEC) and wireless power transfer (WPT) technologies has recently emerged as an effective solution for extending battery life and increasing the computing power of wireless devices. In this paper, we study the resource allocation problem of a multi-user wireless powered MEC system, where the users share the wireless channel via direct sequence code division multiple access (DS-CDMA). In particular, we are interested in jointly optimizing the task offloading decisions and resource allocation, to maximize the weighted sum computation rate of all the users in the network. The optimization problem is formulated as a mixed integer non-linear programming (MINLP). For a given offloading user set, we implement an efficient Fractional Programming (FP) approach to mitigate the multi-user interference in the uplink task offloading. On top of that, we then propose a Stochastic Local Search algorithm to optimize the offloading decisions. Simulation results show that the proposed method can effectively enhance the computing performance of a wireless powered MEC with spread spectrum multiple access compared to other representative benchmark methods.

cs.NI↗

arXiv2020

Development of Hyperthermia Measurable Fiber Radiometric Thermometer for Monitoring Tissue Temperature during Thermo-therapy

ABSTRACT: Temperature monitoring is extremely im-portant during thermotherapy. Fiber-optic temperature sensors are preferred because of their flexibility and im-munity to electromagnetic interference. Although many types of fiber-optic sensors have been developed, it re-mains challenging for clinically adopting them. Here, we report a silica fiber-based radiometric thermometer using a low-cost extended InGaAs detector to detect black body radiation between 1.7um to 2.4 um. For the first time, this silica fiber-based thermometer is capable of measuring temperature down to 35°C, making it suitable for seamless integration with current silica fiber catheters used in laser interstitial thermotherapy to monitor hyperthermia during a surgery. The feasibility, capability, and sensitivity of track-ing tissue temperature variation were proved through ex vivo and in vivo tissue studies. The technology is promising for being translated into clinics after further improving the signal to noise ratio.

physics.med-ph↗

arXiv2020

Abnormally low thermal conductivity of 2D selenene: An ab initio study

The lattice thermal conductivity and thermal transport properties of 2D $α$-selenene are investigated based on the first-principles calculations. The isotropic in-plane thermal conductivity is as low as 3.04 W m$^{-1}$ K$^{-1}$ at room temperature, even abnormally lower than $α$-tellurene which processes analogous configuration and lower Debye temperature. We find this abnormal phenomenon reasonably stems from the larger anharmonicity of the acoustic phonon branch. Moreover, the phonon spectra, elastic properties, and related thermal properties are also exhibited. Acoustic phonons contribute mainly to the total thermal conductivity. Furthermore, size effect, boundary effect, the total phase space for three-phonon processes, phonon group velocity and relaxation time are further investigated, and the last one is unveiled to be the key ingredient of thermal transport in 2D selenene.

physics.comp-ph↗

arXiv2020

Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack

Machine learning algorithms, when applied to sensitive data, pose a potential threat to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose specific private information in the training data to an attacker. Meanwhile, the algorithmic fairness of machine learning has increasingly caught attention from both academia and industry. Algorithmic fairness ensures that the machine learning models do not discriminate a particular demographic group of individuals (e.g., black and female people). Given that MIA is indeed a learning model, it raises a serious concern if MIA ``fairly'' treats all groups of individuals equally. In other words, whether a particular group is more vulnerable against MIA than the other groups. This paper examines the algorithmic fairness issue in the context of MIA and its defenses. First, for fairness evaluation, it formalizes the notation of vulnerability disparity (VD) to quantify the difference of MIA treatment on different demographic groups. Second, it evaluates VD on four real-world datasets, and shows that VD indeed exists in these datasets. Third, it examines the impacts of differential privacy, as a defense mechanism of MIA, on VD. The results show that although DP brings significant change on VD, it cannot eliminate VD completely. Therefore, fourth, it designs a new mitigation algorithm named FAIRPICK to reduce VD. An extensive set of experimental results demonstrate that FAIRPICK can effectively reduce VD for both with and without the DP deployment.

cs.CR↗

arXiv2020

Reusing Wireless Power Transfer for Backscatter-assisted Relaying in WPCNs

User cooperation is an effective technique to tackle the severe near-far user unfairness problem in wireless powered communication networks (WPCNs). In this paper, we consider a WPCN where two collaborating wireless devices (WDs) first harvest wireless energy from a hybrid access point (HAP) and then transmit their information to the HAP. The WD with the stronger WD-to-HAP channel helps relay the message of the other weaker user. In particular, we exploit the use of ambient backscatter communication during the wireless energy transfer phase, where the weaker user backscatters the received energy signal to transmit its information to the relay user in a passive manner. By doing so, the relay user can reuse the energy signal for simultaneous energy harvesting and information decoding (e.g., using an energy detector). Compared to active information transmission in conventional WPCNs, the proposed method effectively saves the energy and time consumed by the weaker user on information transmission during cooperation. With the proposed backscatter-assisted relaying scheme, we jointly optimize the time and power allocations on wireless energy and information transmissions to maximize the common throughput. Specifically, we derive the semi-closed-form expressions of the optimal solution and propose a low-complexity optimal algorithm to solve the joint optimization problem. By comparing with some representative benchmark methods, we simulate under extensive network setups and demonstrate that the proposed cooperation method effectively improves the throughput performance in WPCNs.

cs.NI↗

arXiv2020

Residual Block-based Multi-Label Classification and Localization Network with Integral Regression for Vertebrae Labeling

Accurate identification and localization of the vertebrae in CT scans is a critical and standard preprocessing step for clinical spinal diagnosis and treatment. Existing methods are mainly based on the integration of multiple neural networks, and most of them use the Gaussian heat map to locate the vertebrae's centroid. However, the process of obtaining the vertebrae's centroid coordinates using heat maps is non-differentiable, so it is impossible to train the network to label the vertebrae directly. Therefore, for end-to-end differential training of vertebra coordinates on CT scans, a robust and accurate automatic vertebral labeling algorithm is proposed in this study. Firstly, a novel residual-based multi-label classification and localization network is developed, which can capture multi-scale features, but also utilize the residual module and skip connection to fuse the multi-level features. Secondly, to solve the problem that the process of finding coordinates is non-differentiable and the spatial structure is not destructible, integral regression module is used in the localization network. It combines the advantages of heat map representation and direct regression coordinates to achieve end-to-end training, and can be compatible with any key point detection methods of medical image based on heat map. Finally, multi-label classification of vertebrae is carried out, which use bidirectional long short term memory (Bi-LSTM) to enhance the learning of long contextual information to improve the classification performance. The proposed method is evaluated on a challenging dataset and the results are significantly better than the state-of-the-art methods (mean localization error <3mm).

eess.IV↗

arXiv2019

Strong local moment antiferromagnetic spin fluctuations in V-doped LiFeAs

We use neutron scattering to study vanadium (hole)-doped LiFe$_{1-x}$V$_x$As. In the undoped state, LiFeAs exhibits superconductivity at $T_c=18$ K and transverse incommensurate spin excitations similar to electron overdoped iron pnictides. Upon vanadium-doping to form LiFe$_{0.955}$V$_{0.045}$, the transverse incommensurate spin excitations in LiFeAs transform into longitudinally elongated in a similar fashion as that of potassium (hole) doped Ba$_{0.7}$K$_{0.3}$Fe$_2$As$_2$, but with dramatically enhanced magnetic scattering and elimination of superconductivity. This is different from the suppression of the overall magnetic excitations in hole doped BaFe$_2$As$_2$ and the enhancement of superconductivity near optimal hole doping. These results are consistent with density function theory plus dynamic mean field theory calculations, suggesting that vanadium-doping in LiFeAs may induce an enlarged effective magnetic moment $S_{eff}$ with a spin crossover ground state arising from the inter-orbital scattering of itinerant electrons.

cond-mat.supr-con↗

arXiv2019

Experimental investigation of the uncertainty relations with coherent light

Taking advantage of coherent light beams, we experimentally investigate the variancebased uncertainty relations and the optimal majorization uncertainty relation for the two-dimensional quantum mechanical system.Different from most of the experiments which devoted to record each individual quantum, we examine the uncertainty relations by measuring an ensemble of photons with two polarization degree of freedom characterized by the Stokes parameters which allow us to determine the polarization density matrix with high precision. The optimality of the recently proposed direct-sum majorization uncertainty relation is verified by measuring the Lorenz curves. Results show that the Lorenz curve method represents a faithful verification of the majorization uncertainty relation and the uncertainty relation is indeed an ensemble property of quantum system.

quant-ph↗