Reviewerly/exHarmony huggingface dataset:
Variational quantum eigensolver with reduced circuit complexity

The variational quantum eigensolver (VQE) is one of the most promising algorithms to find eigenvalues and eigenvectors of a given Hamiltonian on noisy intermediate-scale quantum (NISQ) devices. A particular application is to obtain ground or excited states of molecules. The practical realization is currently limited by the complexity of quantum circuits. Here we present a novel approach to reduce quantum circuit complexity in VQE for electronic structure calculations. Our algorithm, called ClusterVQE, splits the initial qubit space into subspaces (qubit clusters) which are further distributed on individual (shallower) quantum circuits. The clusters are obtained based on quantum mutual information reflecting maximal entanglement between qubits, whereas entanglement between different clusters is taken into account via a new "dressed" Hamiltonian. ClusterVQE therefore allows exact simulation of the problem by using fewer qubits and shallower circuit depth compared to standard VQE at the cost of additional classical resources. In addition, a new gradient measurement method without using an ancillary qubit is also developed in this work. Proof-of-principle demonstrations are presented for several molecular systems based on quantum simulators as well as an IBM quantum device with accompanying error mitigation. The efficiency of the new algorithm is comparable to or even improved over qubit-ADAPT-VQE and iterative Qubit Coupled Cluster (iQCC), state-of-the-art circuit-efficient VQE methods to obtain variational ground state energies of molecules on NISQ hardware. Above all, the new ClusterVQE algorithm simultaneously reduces the number of qubits and circuit depth, making it a potential leader for quantum chemistry simulations on NISQ devices.
Meta-FDMixup

A recent study [4] finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. This motivates the task of Cross-Domain Few-Shot Learning (CD-FSL). In this paper, we realize that the labeled target data in CD-FSL has not been leveraged in any way to help the learning process. Thus, we advocate utilizing few labeled target data to guide the model learning. Technically, a novel meta-FDMixup network is proposed. We tackle this problem mainly from two aspects. Firstly, to utilize the source and the newly introduced target data of two different class sets, a mixup module is re-proposed and integrated into the meta-learning mechanism. Secondly, a novel disentangle module together with a domain classifier is proposed to extract the disentangled domain-irrelevant and domain-specific features. These two modules together enable our model to narrow the domain gap thus generalizing well to the target datasets. Additionally, a detailed feasibility and pilot study is conducted to reflect the intuitive understanding of CD-FSL under our new setting. Experimental results show the effectiveness of our new setting and the proposed method. Codes and models are available at https://github.com/lovelyqian/Meta-FDMixup.
Relevance Modeling for Microblog Summarization

This paper introduces a new type of summarization task, known as microblog summarization, which aims to synthesize content from multiple microblog posts on the same topic into a human-readable prose description of fixed length. Our approach leverages (1) a generative model which induces event structures from text and (2) a user behavior model which captures how users convey relevant content.
Sample Bounded Distributed Reinforcement Learning for Decentralized POMDPs

Decentralized partially observable Markov decision processes (Dec-POMDPs) offer a powerful modeling technique for realistic multi-agent coordination problems under uncertainty. Prevalent solution techniques are centralized and assume prior knowledge of the model. We propose a distributed reinforcement learning approach, where agents take turns to learn best responses to each other’s policies. This promotes decentralization of the policy computation problem, and relaxes reliance on the full knowledge of the problem parameters. We derive the relation between the sample complexity of best response learning and error tolerance. Our key contribution is to show that sample complexity could grow exponentially with the problem horizon. We show empirically that even if the sample requirement is set lower than what theory demands, our learning approach can produce (near) optimal policies in some benchmark Dec-POMDP problems.
Quantum correlations and global coherence in distributed quantum computing

Deviations from classical physics when distant quantum systems become correlated are interesting both fundamentally and operationally. There exist situations where the correlations enable collaborative tasks that are impossible within the classical formalism. Here, we consider the efficiency of quantum computation protocols compared to classical ones as a benchmark for separating quantum and classical resources and argue that the computational advantage of collaborative quantum protocols in the discrete variable domain implies the nonclassicality of correlations. By analyzing a toy model, it turns out that this argument implies the existence of quantum correlations distinct from entanglement and discord. We characterize such quantum correlations in terms of the net global coherence resources inherent within quantum states and show that entanglement and discord can be understood as special cases of our general framework. Finally, we provide an operational interpretation of such correlations as those allowing two distant parties to increase their respective local quantum computational resources only using locally incoherent operations and classical communication.
Flexible Wildcard Searchable Encryption System

Searchable encryption is an important technique for public cloud storage service to provide user data confidentiality protection and at the same time allow users performing keyword search over their encrypted data. Previous schemes only deal with exact or fuzzy keyword search to correct some spelling errors. In this paper, we propose a new wildcard searchable encryption system to support wildcard keyword queries which has several highly desirable features. First, our system allows multiple keywords search in which any queried keyword may contain zero, one or two wildcards, and a wildcard may appear in any position of a keyword and represent any number of symbols. Second, it supports simultaneous search on multiple data owner's data using only one trapdoor. Third, it provides flexible user authorization and revocation to effectively manage search and decryption privileges. Fourth, it is constructed based on homomorphic encryption rather than Bloom filter and hence completely eliminates the false probability caused by Bloom filter. Finally, it achieves a high level of privacy protection since matching results are unknown to the cloud server in the test phase. The proposed system is thoroughly analyzed and is proved secure. Extensive experimental results indicate that our system is efficient compared with other existing wildcard searchable encryption schemes in the public key setting.
Variational Inference for Stochastic Block Models From Sampled Data

This article deals with nonobserved dyads during the sampling of a network and consecutive issues in the inference of the stochastic block model (SBM). We review sampling designs and recover missing at random (MAR) and not missing at random (NMAR) conditions for the SBM. We introduce variants of the variational EM algorithm for inferring the SBM under various sampling designs (MAR and NMAR) all available as an R package. Model selection criteria based on integrated classification likelihood are derived for selecting both the number of blocks and the sampling design. We investigate the accuracy and the range of applicability of these algorithms with simulations. We explore two real-world networks from ethnology (seed circulation network) and biology (protein–protein interaction network), where the interpretations considerably depend on the sampling designs considered. Supplementary materials for this article are available online.
Entanglement and secret-key-agreement capacities of bipartite quantum interactions and read-only memory devices

A bipartite quantum interaction corresponds to the most general quantum interaction that can occur between two quantum systems in the presence of a bath. In this work, we determine bounds on the capacities of bipartite interactions for entanglement generation and secret-key agreement between two quantum systems. Our upper bound on the entanglement generation capacity of a bipartite quantum interaction is given by a quantity called the bidirectional max-Rains information. Our upper bound on the secret-key-agreement capacity of a bipartite quantum interaction is given by a related quantity called the bidirectional max-relative entropy of entanglement. We also derive tighter upper bounds on the capacities of bipartite interactions obeying certain symmetries. Observing that reading of a memory device is a particular kind of bipartite quantum interaction, we leverage our bounds from the bidirectional setting to deliver bounds on the capacity of a task that we introduce, called private reading of a wiretap memory cell. Given a set of point-to-point quantum wiretap channels, the goal of private reading is for an encoder to form codewords from these channels, in order to establish a secret key with a party who controls one input and one output of the channels, while a passive eavesdropper has access to one output of the channels. We derive both lower and upper bounds on the private reading capacities of a wiretap memory cell. We then extend these results to determine achievable rates for the generation of entanglement between two distant parties who have coherent access to a controlled point-to-point channel, which is a particular kind of bipartite interaction.
Memory-Efficient Implementation of Elliptic Curve Cryptography for the Internet-of-Things

In this paper, we present memory-efficient and scalable implementations of NIST standardized elliptic curves P-256, P-384 and P-521 on three ARMv6-M processors (i.e. Cortex-M0, M0+, and M1). Specifically, we propose a refined approach to perform the Multiply-ACcumulate (MAC) operation using hardware multiplier provided by ARMv6-M processor, and a compact doubling routine for multi-precision squaring that executes both doubling and partial product operations in an efficient way. We demonstrate that the proposed squaring implementation achieves a speed up of 28 percent compared to the same operation employed in Micro-ECC. Then, we reduce one modular reduction in co-Z conjugate point addition by using lazy reduction and special form representation (CD-AB, EF-AB), which further reduces the execution time of both P-256 and P-384 implementations. Finally, we propose scalable implementations of ECC scalar multiplication on ARMv6-M processors that are widely used for Internet of Things applications.
Learning Logistic Circuits

This paper proposes a new classification model called logistic circuits. On MNIST and Fashion datasets, our learning algorithm outperforms neural networks that have an order of magnitude more parameters. Yet, logistic circuits have a distinct origin in symbolic AI, forming a discriminative counterpart to probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that parameter learning for logistic circuits is convex optimization, and that a simple local search algorithm can induce strong model structures from data.
Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data

When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way.This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme.However, it may import large errors and even yield negative results.Besides, SWHE would be too inefficient for normal clients.In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries.GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work.As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index.We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model.Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.
Image and Sentence Matching via Semantic Concepts and Order Learning

Image and sentence matching has made great progress recently, but it remains challenging due to the existing large visual-semantic discrepancy. This mainly arises from two aspects: 1) images consist of unstructured content which is not semantically abstract as the words in the sentences, so they are not directly comparable, and 2) arranging semantic concepts in different semantic order could lead to quite diverse meanings. The words in the sentences are sequentially arranged in a grammatical manner, while the semantic concepts in the images are usually unorganized. In this work, we propose a semantic concepts and order learning framework for image and sentence matching, which can improve the image representation by first predicting semantic concepts and then organizing them in a correct semantic order. Given an image, we first use a multi-regional multi-label CNN to predict its included semantic concepts in terms of object, property and action. These word-level semantic concepts are directly comparable with the words of noun, adjective and verb in the matched sentence. Then, to organize these concepts and make them express similar meanings as the matched sentence, we use a context-modulated attentional LSTM to learn the semantic order. It regards the predicted semantic concepts and image global scene as context at each timestep, and selectively attends to concept-related image regions by referring to the context in a sequential order. To further enhance the semantic order, we perform additional sentence generation on the image representation, by using the groundtruth order in the matched sentence as supervision. After obtaining the improved image representation, we learn the sentence representation with a conventional LSTM, and then jointly perform image and sentence matching and sentence generation for model learning. Extensive experiments demonstrate the effectiveness of our learned semantic concepts and order, by achieving the state-of-the-art results on two public benchmark datasets.
A Resource-Efficient and Side-Channel Secure Hardware Implementation of Ring-LWE Cryptographic Processor

Lattice-based cryptography has shown great potential due to its resistance against quantum attacks. With the security requirements for high-precision Gaussian sampling and complex polynomial multiplication over rings, as well as storage of large public-keys, it is extremely challengeable but important to implement lattice-based schemes on resources constrained devices. In this paper, a resource-efficient and side-channel secure Ring-LWE cryptographic processor is presented. A discrete Gaussian sampler with constant response time, high precision, and large distribution tails is designed. The proposed Gaussian sampler is proven to be secure against side-channel timing attack according to the timing analysis attack results on a FPGA-based testing platform. A universal module MPE (Modular Processing Element) is designed to carry out all basic modular operations for Ring-LWE cryptography with high speed. The prototype verification is performed on the Xilinx Spartan-6 FPGA platform. The processor can execute an encryption/decryption operation on a 256-bit message in 4.5/0.9 ms whilst it consumes only 1307 LUTs, 889 FFs, 4 BRAMs, and none DSP module. Compared with other related hardware implementations, the Ring-LWE processor is advantageous not only in hardware efficiency but also in secure protection against side-channel attacks.
RGBD Based Gaze Estimation via Multi-Task CNN

This paper tackles RGBD based gaze estimation with Convolutional Neural Networks (CNNs). Specifically, we propose to decompose gaze point estimation into eyeball pose, head pose, and 3D eye position estimation. Compared with RGB image-based gaze tracking, having depth modality helps to facilitate head pose estimation and 3D eye position estimation. The captured depth image, however, usually contains noise and black holes which noticeably hamper gaze tracking. Thus we propose a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points. We utilize a generator network for depth image generation with a Generative Neural Network (GAN), where the generator network is partially shared by both the gaze tracking network and GAN-based depth synthesizing. By optimizing the whole network simultaneously, depth image synthesis improves gaze point estimation and vice versa. Since the only existing RGBD dataset (EYEDIAP) is too small, we build a large-scale RGBD gaze tracking dataset for performance evaluation. As far as we know, it is the largest RGBD gaze dataset in terms of the number of participants. Comprehensive experiments demonstrate that our method outperforms existing methods by a large margin on both our dataset and the EYEDIAP dataset.
On Data Sovereignty in Cloud-Based Computation Offloading for Smart Cities Applications

Smart city applications are increasingly popular due to their potential to improve quality of life in an urbanized society, and such applications typically leverage on cloud computing for data and computation offloading from the sensing infrastructure. Despite the capability of achieving scalability and flexibility, the use of cloud computing imposes inherent security and privacy concerns regarding data analysis and exchange, as well as legal implications. For example, data in a smart city application being outsourced to the cloud and/or exchanged among sensing devices may be accessible to users located in a different jurisdiction or subsequently reside in a data center in a different jurisdiction. There may be conflicting privacy protection and disclosure laws among these jurisdictions/locations; thus, limiting the widespread adoption of smart city applications. Restricting the data flow for such applications is not viable since it may cause inefficiencies, although there are situations requiring to limit the data access to selected geographical locations. Therefore, we propose addressing this issue by using a location-dependent cryptographic approach, and we integrate such an approach within the context of cloud-based smart city applications.
Microscopic 3D measurement of shiny surfaces based on a multi-frequency phase-shifting scheme

Microscopic fringe projection profilometry is a powerful 3D measurement technique with a theoretical measurement accuracy better than one micron. However, the defocus of the dense fringe and complex surface reflexivity characteristics usually cause intensity saturation and decrease the fringe quality, which makes the complete 3D reconstruction difficult. To address this problem, we calculate the phase of the highlighted areas from a subset of the phase-shifted fringe images which are not subjected to intensity saturation. A multi-frequency phase-shifting scheme is proposed to improve the integrity of the final phase map of the shiny surface, based on which we can achieve a complete and high-accuracy 3D reconstruction combined with a microscopic telecentric stereo system. Experimental results of different highlight surfaces demonstrate that our approach can retrieve the complete morphology of shiny surfaces with high accuracy and reliability.
Bayesian Smoothing for the Extended Object Random Matrix Model

The random matrix model is popular in extended object tracking, due to its relative simplicity and versatility. In this model, the extended object state consists of a kinematic vector for the position and motion parameters (velocity, etc.), and an extent matrix. Two versions of the model can be found in the literature, one where the state density is modeled by a conditional density, and one where the state density is modeled by a factorized density. In this paper, we present closed-form Bayesian smoothing expressions for both the conditional and the factorized model. In a simulation study, we compare the performance of different versions of the smoother. Code is published on GitHub.
Using FTOC to track shuttlecock for the badminton robot

Sport video analysis is gaining popularity recently owing to its importance in understanding sports and improving the performance of athletes. In this paper we focus on shuttlecock tracking algorithm. Particularly, a novel fast tracking based on object center (i.e., FTOC) method by fusing heterogeneous cues and AdaBoost algorithm are proposed to improve the tracking performance for a robot. Experimental results show that the proposed FTOC tracking method performs favorably against many other popular tracking approaches, such as TLD, MIL, KCF, DCF_CA, SMAF_CA, KCC, DSN, COKCF, etc., in term of speed, accuracy, and robustness, especially in challenging scenarios such as scale variations and background clutter. We further demonstrate the feasibility of the FTOC algorithm in a real-time ZED binocular camera based 3D shuttlecock tracking system for a robot.
Online Tuning of a PID Controller with a Fuzzy Reinforcement Learning MAS for Flow Rate Control of a Desalination Unit

This paper proposes a hybrid Zeigler-Nichols (Z-N) fuzzy reinforcement learning MAS (Multi-Agent System) approach for online tuning of a Proportional Integral Derivative (PID) controller in order to control the flow rate of a desalination unit. The PID gains are set by the Z-N method and then are adapted online through the fuzzy Q-learning MAS. The fuzzy Q-learning is introduced in each agent in order to confront with the continuous state-action space. The global state of the MAS is defined by the value of the error and the derivative of error. The MAS consists of three agents and the output signal of each agent defines the percentage change of each gain. The increment or the reduction of each gain can be in the range of 0% to 100% of its initial value. The simulation results highlight the performance of the suggested hybrid control strategy through comparison with the conventional PID controller tuned by Z-N.
A differential evolution based method for tuning concept drift detectors in data streams

Predicting online on data streams, with data flowing continuously, quickly, and in large quantities, is becoming increasingly more important in tackling real-world problems. In such scenarios, data distribution usually evolves over time, a situation known as concept drift. This article presents an empirical method, based on a differential evolution, aimed at guiding users on how to tune concept drift detectors to improve their accuracies in data streams. It also suggests the best detectors, strategies and/or parameterization to use in different data stream scenarios. The most time-consuming part of the method was preprocessed and is based on experiments using eight artificial dataset generators, each of them with five abrupt, fast gradual, and slow gradual concept drift versions (120 dataset versions in all), as well as six real-world datasets, for 11 different drift detectors and two different base learners. The use of the proposed method is illustrated mostly with Drift Detection Method (DDM) together with Naive Bayes, but we compared the performances of all 11 detectors using their default parameter values and the several parameter sets prescribed by the method using both base classifiers—Naive Bayes and Hoeffding Trees. Results indicate that the predictive accuracies of the detectors tuned using the method increased considerably in many situations.