arXiv subjects

Lei Chen

Publications and source records attributed to Lei Chen.

At least 505 records · Page 28Linked to original sources

arXiv2021

Dual on-chip SQUID measurement protocol for flux detection in large magnetic fields

Sensitive magnetometers that can operate in high magnetic fields are essential for detecting magnetic resonance signals originating from small ensembles of quantum spins. Such devices have potential applications in quantum technologies, in particular quantum computing. We present a novel experimental setup implementing a differential flux measurement using two DC-SQUID magnetometers. The differential measurement allows for cancellation of background flux signals while enhancing sample signal. The developed protocol uses pulsed readout which minimizes on-chip heating since sub-Kelvin temperatures are needed to preserve quantum spin coherence. Results of a proof of concept experiment are shown as well.

cond-mat.mes-hall↗

arXiv2021

Certification of Genuine Multipartite Entanglement with General and Robust Device-independent Witnesses

Genuine multipartite entanglement represents the strongest type of entanglement, which is an essential resource for quantum information processing. Standard methods to detect genuine multipartite entanglement, e.g., entanglement witnesses, state tomography, or quantum state verification, require full knowledge of the Hilbert space dimension and precise calibration of measurement devices, which are usually difficult to acquire in an experiment. The most radical way to overcome these problems is to detect entanglement solely based on the Bell-like correlations of measurement outcomes collected in the experiment, namely, device-independently (DI). However, it is difficult to certify genuine entanglement of practical multipartite states in this way, and even more difficult to quantify it, due to the difficulty to identify optimal multipartite Bell inequalities and protocols tolerant to state impurity. In this work, we explore a general and robust DI method which can be applied to various realistic multipartite quantum state in arbitrary finite dimension, while merely relying on bipartite Bell inequalities. Our method allows us both to certify the presence of genuine multipartite entanglement and to quantify it. Several important classes of entangled states are tested with this method, leading to the detection of genuinely entangled states. We also certify genuine multipartite entanglement in weakly-entangled GHZ states, thus showing that the method applies equally well to less standard states.

quant-ph↗

arXiv2021

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report the connection states between mobile devices and Telco networks, e.g., received signal strength. MR data have been widely used to localize outdoor mobile devices for human mobility analysis, urban planning, and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture spatio-temporal locality in underlying mobility patterns for lower localization errors. The HMM approaches typically assume stable mobility patterns of the underlying mobile devices. Yet real MR datasets exhibit heterogeneous mobility patterns due to mixed transportation modes of the underlying mobile devices and uneven distribution of the positions associated with MR samples. Thus, the existing solutions cannot handle these heterogeneous mobility patterns. we propose a multi-task learning-based deep neural network (DNN) framework, namely PRNet+, to incorporate outdoor position recovery and transportation mode detection. To make sure the framework work, PRNet+ develops a feature extraction module to precisely learn local-, short- and long-term spatio-temporal locality from heterogeneous MR samples. Extensive evaluation on eight datasets collected at three representative areas in Shanghai indicates that PRNet+ greatly outperforms state-of-the-arts.

cs.NI↗

arXiv2021

Privacy-Preserving Batch-based Task Assignment in Spatial Crowdsourcing with Untrusted Server

In this paper, we study the privacy-preserving task assignment in spatial crowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability (a differential privacy notion for location-based systems). Different from the previously studied online setting, where each task is assigned immediately upon arrival, we target the batch-based setting, where the server maximizes the number of successfully assigned tasks after a batch of tasks arrive. To achieve this goal, we propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhance the task assignment. Furthermore, we expedite HE-based computation by limiting the size of the small groups under k. Extensive experiments demonstrate that, in terms of the number of successfully assigned tasks, the k-Switch solution improves batch-based baselines by 5.9X and the existing online solution by 1.74X, with no privacy leak.

cs.CR↗

arXiv2021

MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions

Spatio-temporal action detection is an important and challenging problem in video understanding. The existing action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. This paper aims to present a new multi-person dataset of spatio-temporal localized sports actions, coined as MultiSports. We first analyze the important ingredients of constructing a realistic and challenging dataset for spatio-temporal action detection by proposing three criteria: (1) multi-person scenes and motion dependent identification, (2) with well-defined boundaries, (3) relatively fine-grained classes of high complexity. Based on these guide-lines, we build the dataset of MultiSports v1.0 by selecting 4 sports classes, collecting 3200 video clips, and annotating 37701 action instances with 902k bounding boxes. Our datasets are characterized with important properties of high diversity, dense annotation, and high quality. Our Multi-Sports, with its realistic setting and detailed annotations, exposes the intrinsic challenges of spatio-temporal action detection. To benchmark this, we adapt several baseline methods to our dataset and give an in-depth analysis on the action detection results in our dataset. We hope our MultiSports can serve as a standard benchmark for spatio-temporal action detection in the future. Our dataset website is at https://deeperaction.github.io/multisports/.

cs.CV↗

arXiv2021

A Queueing-Theoretic Framework for Vehicle Dispatching in Dynamic Car-Hailing [technical report]

With the rapid development of smart mobile devices, the car-hailing platforms (e.g., Uber or Lyft) have attracted much attention from both the academia and the industry. In this paper, we consider an important dynamic car-hailing problem, namely \textit{maximum revenue vehicle dispatching} (MRVD), in which rider requests dynamically arrive and drivers need to serve as many riders as possible such that the entire revenue of the platform is maximized. We prove that the MRVD problem is NP-hard and intractable. In addition, the dynamic car-hailing platforms have no information of the future riders, which makes the problem even harder. To handle the MRVD problem, we propose a queueing-based vehicle dispatching framework, which first uses existing machine learning algorithms to predict the future vehicle demand of each region, then estimates the idle time periods of drivers through a queueing model for each region. With the information of the predicted vehicle demands and estimated idle time periods of drivers, we propose two batch-based vehicle dispatching algorithms to efficiently assign suitable drivers to riders such that the expected overall revenue of the platform is maximized during each batch processing. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.

cs.LG↗

arXiv2021

Resisting Out-of-Distribution Data Problem in Perturbation of XAI

With the rapid development of eXplainable Artificial Intelligence (XAI), perturbation-based XAI algorithms have become quite popular due to their effectiveness and ease of implementation. The vast majority of perturbation-based XAI techniques face the challenge of Out-of-Distribution (OoD) data -- an artifact of randomly perturbed data becoming inconsistent with the original dataset. OoD data leads to the over-confidence problem in model predictions, making the existing XAI approaches unreliable. To our best knowledge, the OoD data problem in perturbation-based XAI algorithms has not been adequately addressed in the literature. In this work, we address this OoD data problem by designing an additional module quantifying the affinity between the perturbed data and the original dataset distribution, which is integrated into the process of aggregation. Our solution is shown to be compatible with the most popular perturbation-based XAI algorithms, such as RISE, OCCLUSION, and LIME. Experiments have confirmed that our methods demonstrate a significant improvement in general cases using both computational and cognitive metrics. Especially in the case of degradation, our proposed approach demonstrates outstanding performance comparing to baselines. Besides, our solution also resolves a fundamental problem with the faithfulness indicator, a commonly used evaluation metric of XAI algorithms that appears to be sensitive to the OoD issue.

cs.AI↗

arXiv2021

Proposal for constraining non-Newtonian gravity at nm range via criticality enhanced measurement of resonance frequency shift

We propose a quantum mechanical method of constraining non-Newtonian gravity at the nanometer range. In this method, a hybrid electro-optomechanical system is employed. Applying a strong driving field, we can obtain normal mode splitting of the electromechanical subsystem which is related to the resonance frequency of the mechanical oscillator. Moreover, we investigate the relationship between the variance of normal mode splitting and the resonance frequency shift induced by the gradient of exotic forces provided that our system is operated at critical points. Furthermore, via suppressing the Casimir background, we set a constraint on the non-Newtonian gravity which improves the previous bounds by about a factor of 7 at 1 nanometer range. Our results indicate that our method could be put into consideration in relevant experimental searches.

quant-ph↗

arXiv2021

Proposal for measuring Newtonian constant of gravitation at an exceptional point in an optomechanical system

We develop a quantum mechanical method of measuring the Newtonian constant of gravitation, G. In this method, an optomechanical system consisting of two cavities and two membrane resonators is used. The added source mass would induce the shifts of the eigenfrequencies of the supermodes. Via detecting the shifts, we can perform our measurement of G. Furthermore, our system can features exceptional point (EP) which are branch point singularities of the spectrum and eigenfunctions. In the paper, we demonstrate that operating the system at EP can enhance our measurement of G. In addition, we derive the relationship between EP enlarged eigenfrequency shift and the Newtonian constant. This work provides a way to engineer EP-assisted optomechanical devices for applications in the field of precision measurement of G

quant-ph↗

arXiv2021

Constraining Axion-to-Nucleon interaction via ultranarrow linewidth in the Casimir-less regime

In this paper we develop a quantum optical method to detect the axion-nucleon interaction. We ultilize a levitated optomechanical system consisting of a silica nanosphere and an optical cavity here. We translate the trapping positions of the nanosphere, resulting the shift of its resonance frequency, which can be determined from measuring the resulting resonance shift in the transmission spectrum. Furthermore, The frequency shift can be related to the additional forces due to two-axion exchange via substraction. Based on noise ananlysis, estimation and calculation, we set the stringent prospective constraints for the coupling constants of axion-neucleon interaction $g_{an}$ and $g_{ap}$. In the case of $g_{an}^2 = g_{ap}^2$ , our constraints are most stringent at an ultrawide axion mass range approximately from $10^{-4}μeV$ to $10$ $eV$ .

quant-ph↗

arXiv2021

Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding

Negative sampling, which samples negative triplets from non-observed ones in knowledge graph (KG), is an essential step in KG embedding. Recently, generative adversarial network (GAN), has been introduced in negative sampling. By sampling negative triplets with large gradients, these methods avoid the problem of vanishing gradient and thus obtain better performance. However, they make the original model more complex and harder to train. In this paper, motivated by the observation that negative triplets with large gradients are important but rare, we propose to directly keep track of them with the cache. In this way, our method acts as a "distilled" version of previous GAN-based methods, which does not waste training time on additional parameters to fit the full distribution of negative triplets. However, how to sample from and update the cache are two critical questions. We propose to solve these issues by automated machine learning techniques. The automated version also covers GAN-based methods as special cases. Theoretical explanation of NSCaching is also provided, justifying the superior over fixed sampling scheme. Besides, we further extend NSCaching with skip-gram model for graph embedding. Finally, extensive experiments show that our method can gain significant improvements on various KG embedding models and the skip-gram model, and outperforms the state-of-the-art negative sampling methods.

cs.LG↗

arXiv2021

Emerging Oscillating Reactions at the Insulator/Semiconductor Solid/Solid Interface via Proton Implantation

Most oscillating reactions (ORs) happen in solutions. Few existing solid-based ORs either happen on solid/gas (e.g., oxidation or corrosion) or solid/liquid interfaces, or at the all-solid interfaces neighboring to metals or ionic conductors (e.g., electrolysis or electroplate). We report in this paper a new type of all-solid based OR that happens at the insulator (amorphous SiO$_2$)/semiconductor (Si) interface with the interfacial point defects as the oscillating species. This OR is the first example of the point-defect coupled ORs (PDC-ORs) proposed by H. Schmalzried et al. and J. Janek et al. decades ago. We use proton implantation as the driving force of the oscillation, and employ techniques common in semiconductor device characterization to monitor the oscillation in situ. This approach not only overcomes the difficulties associated with detecting reactions in solids, but also accurately measure the oscillating ultra-low concentration ($10^{10}\sim10^{11}$ cm$^{-2}$) of the interfacial charged point-defects. We propose a mechanism for the reported PDC-OR based on the Brusselator model by identifying the interfacial reactions.

physics.chem-ph↗

arXiv2021

Variational Information Bottleneck for Effective Low-resource Audio Classification

Large-scale deep neural networks (DNNs) such as convolutional neural networks (CNNs) have achieved impressive performance in audio classification for their powerful capacity and strong generalization ability. However, when training a DNN model on low-resource tasks, it is usually prone to overfitting the small data and learning too much redundant information. To address this issue, we propose to use variational information bottleneck (VIB) to mitigate overfitting and suppress irrelevant information. In this work, we conduct experiments ona 4-layer CNN. However, the VIB framework is ready-to-use and could be easily utilized with many other state-of-the-art network architectures. Evaluation on a few audio datasets shows that our approach significantly outperforms baseline methods, yielding more than 5.0% improvement in terms of classification accuracy in some low-source settings.

cs.SD↗

arXiv2021

Statistics of Complex Wigner Time Delays as a counter of S-matrix poles: Theory and Experiment

We study the statistical properties of the complex generalization of Wigner time delay $τ_\text{W}$ for sub-unitary wave chaotic scattering systems. We first demonstrate theoretically that the mean value of the $\text{Re}[τ_\text{W}]$ distribution function for a system with uniform absorption strength $η$ is equal to the fraction of scattering matrix poles with imaginary parts exceeding $η$. The theory is tested experimentally with an ensemble of microwave graphs with either one or two scattering channels, and showing broken time-reversal invariance and variable uniform attenuation. The experimental results are in excellent agreement with the developed theory. The tails of the distributions of both real and imaginary time delay are measured and are also found to agree with theory. The results are applicable to any practical realization of a wave chaotic scattering system in the short-wavelength limit.

cond-mat.dis-nn↗

arXiv2021

User-specific Adaptive Fine-tuning for Cross-domain Recommendations

Making accurate recommendations for cold-start users has been a longstanding and critical challenge for recommender systems (RS). Cross-domain recommendations (CDR) offer a solution to tackle such a cold-start problem when there is no sufficient data for the users who have rarely used the system. An effective approach in CDR is to leverage the knowledge (e.g., user representations) learned from a related but different domain and transfer it to the target domain. Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain. However, current methods are mainly based on the global fine-tuning strategy: the decision of which layers of the pre-trained model to freeze or fine-tune is taken for all users in the target domain. In this paper, we argue that users in RS are personalized and should have their own fine-tuning policies for better preference transfer learning. As such, we propose a novel User-specific Adaptive Fine-tuning method (UAF), selecting which layers of the pre-trained network to fine-tune, on a per-user basis. Specifically, we devise a policy network with three alternative strategies to automatically decide which layers to be fine-tuned and which layers to have their parameters frozen for each user. Extensive experiments show that the proposed UAF exhibits significantly better and more robust performance for user cold-start recommendation.

cs.IR↗

arXiv2021

Learning-Aided Heuristics Design for Storage System

Computer systems such as storage systems normally require transparent white-box algorithms that are interpretable for human experts. In this work, we propose a learning-aided heuristic design method, which automatically generates human-readable strategies from Deep Reinforcement Learning (DRL) agents. This method benefits from the power of deep learning but avoids the shortcoming of its black-box property. Besides the white-box advantage, experiments in our storage productions resource allocation scenario also show that this solution outperforms the systems default settings and the elaborately handcrafted strategy by human experts.

cs.AI↗

arXiv2021

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard" unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.

cs.IR↗

arXiv2021

Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding

Knowledge graph (KG) embedding is well-known in learning representations of KGs. Many models have been proposed to learn the interactions between entities and relations of the triplets. However, long-term information among multiple triplets is also important to KG. In this work, based on the relational paths, which are composed of a sequence of triplets, we define the Interstellar as a recurrent neural architecture search problem for the short-term and long-term information along the paths. First, we analyze the difficulty of using a unified model to work as the Interstellar. Then, we propose to search for recurrent architecture as the Interstellar for different KG tasks. A case study on synthetic data illustrates the importance of the defined search problem. Experiments on real datasets demonstrate the effectiveness of the searched models and the efficiency of the proposed hybrid-search algorithm.

cs.LG↗