Reviewerly/exHarmony huggingface dataset:
Quantum correlation alignment for unsupervised domain adaptation

Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines (QBLAS) to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely the synthetic data, the synthetic-Iris data, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.
Effects of partial measurements on quantum resources and quantum Fisher information of a teleported state in a relativistic scenario

We address the teleportation of single- and two-qubit quantum states, parametrized by weight θ and phase ϕ parameters, in the presence of the Unruh effect experienced by a mode of a free Dirac field. We investigate the effects of the partial measurement (PM) and partial measurement reversal (PMR) on the quantum resources and quantum Fisher information (QFI) of the teleported states. In particular, we discuss the optimal behaviour of the QFI, quantum coherence (QC) as well as fidelity with respect to the PM and PMR strength and examine the effect of the Unruh noise on optimal estimation. It is found that, in the single-qubit scenario, the PM (PMR) strength at which the optimal estimation of the phase parameter occurs is the same as the PM (PMR) strength with which the teleportation fidelity and the QC of the teleported single-qubit state reaches its maximum value. On the other hand, generalizing the results to two-qubit teleportation, we find that the encoded information in the weight parameter is better protected against the Unruh noise in two-qubit teleportation than in the one-qubit scenario. However, extraction of information encoded in the phase parameter is more efficient in single-qubit teleportation than in the two-qubit version.
Dynamic term-modal logics for first-order epistemic planning

Many classical planning frameworks are built on first-order languages. The first-order expressive power is desirable for compactly representing actions via schemas, and for specifying quantified conditions such as ¬∃xblocks_door(x). In contrast, several recent epistemic planning frameworks are built on propositional epistemic logic. The epistemic language is useful to describe planning problems involving higher-order reasoning or epistemic goals such as Ka¬problem. This paper develops a first-order version of Dynamic Epistemic Logic (DEL). In this framework, for example, ∃xKx∃yblocks_door(y) is a formula. The formalism combines the strengths of DEL (higher-order reasoning) with those of first-order logic (lifted representation) to model multi-agent epistemic planning. The paper introduces an epistemic language with a possible-worlds semantics, followed by novel dynamics given by first-order action models and their execution via product updates. Taking advantage of the first-order machinery, epistemic action schemas are defined to provide compact, problem-independent domain descriptions, in the spirit of PDDL. Concerning metatheory, the paper defines axiomatic normal term-modal logics, shows a Canonical Model Theorem-like result which allows establishing completeness through frame characterization formulas, shows decidability for the finite agent case, and shows a general completeness result for the dynamic extension by reduction axioms.
General upper bound for conferencing keys in arbitrary quantum networks

Secure quantum conferencing refers to a protocol where a number of trusted users generate exactly the same secret key to confidentially broadcast private messages. By a modification of the techniques first introduced in (arXiv:1601.00966), the author derives a single-letter upper bound for the maximal rates of secure conferencing in a quantum network with arbitrary topology, where the users are allowed to perform the most powerful local operations assisted by two-way classical communications, and the quantum systems are routed according to the most efficient multipath flooding strategies. More precisely, the author bounds the ultimate rates that are achievable by single-message multiple-multicast protocols, where N senders distribute N independent secret keys, and each key is to be shared with an ensemble of M receivers.
Slowing Down the Weight Norm Increase in Momentum-based Optimizers

Normalization techniques, such as batch normalization (BN), have led to significant improvements in deep neural network performances. Prior studies have analyzed the benefits of the resulting scale invariance of the weights for the gradient descent (GD) optimizers: it leads to a stabilized training due to the auto-tuning of step sizes. However, we show that, combined with the momentum-based algorithms, the scale invariance tends to induce an excessive growth of the weight norms. This in turn overly suppresses the effective step sizes during training, potentially leading to sub-optimal performances in deep neural networks. We analyze this phenomenon both theoretically and empirically. We propose a simple and effective solution: at each iteration of momentum-based GD optimizers (e.g. SGD or Adam) applied on scale-invariant weights (e.g. Conv weights preceding a BN layer), we remove the radial component (i.e. parallel to the weight vector) from the update vector. Intuitively, this operation prevents the unnecessary update along the radial direction that only increases the weight norm without contributing to the loss minimization. We verify that the modified optimizers SGDP and AdamP successfully regularize the norm growth and improve the performance of a broad set of models. Our experiments cover tasks including image classification and retrieval, object detection, robustness benchmarks, and audio classification. Source code is available at this https URL.
Differential privacy of hierarchical Census data: An optimization approach

This paper is motivated by applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual. The released information can be the number of individuals living alone, the number of cars they own, or their salary brackets. Recent events have identified some of the privacy challenges faced by these organizations [1]. To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals. The counts are reported at multiple granularities (e.g., the national, state, and county levels) and must be consistent across all levels. The core of the mechanism is an optimization model that redistributes the noise introduced to achieve differential privacy in order to meet the consistency constraints between the hierarchical levels. The key technical contribution of the paper shows that this optimization problem can be solved in polynomial time by exploiting the structure of its cost functions. Experimental results on very large, real datasets show that the proposed mechanism provides improvements of up to two orders of magnitude in terms of computational efficiency and accuracy with respect to other state-of-the-art techniques.
Unsupervised Quantum Gate Control for Gate-Model Quantum Computers

Abstract In near-term quantum computers, the operations are realized by unitary quantum gates. The precise and stable working mechanism of quantum gates is essential for the implementation of any complex quantum computations. Here, we define a method for the unsupervised control of quantum gates in near-term quantum computers. We model a scenario in which a tensor product structure of non-stable quantum gates is not controllable in terms of control theory. We prove that the non-stable quantum gate becomes controllable via a machine learning method if the quantum gates formulate an entangled gate structure.
Distributed Heterogeneous N-Variant Execution

N-Variant Execution (NVX) systems utilize artificial diversity techniques to enhance software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their diverging behavior on a malicious input. Existing NVX systems execute diversified program variants on a single host. This means the level of inter-variant diversity will be limited to what a single platform can offer, without costly emulation. This paper presents DMON , a novel distributed NVX design that executes native program variants across multiple heterogeneous hosts. Our approach greatly increases the level of diversity between the simultaneously running variants that can be supported, encompassing different ISAs and ABIs. Our evaluation shows that DMON can provide comparable performance to traditional, non-distributed NVX systems, while enhancing security.
Batch Prioritization in Multigoal Reinforcement Learning

In multigoal reinforcement learning, an agent interacts with an environment and learns to achieve multiple goals. The goal-conditioned policy is trained to effectively generalize its behavior for multiple goals. During training, the experiences collected by the agent are randomly sampled from a replay buffer. Because biased sampling of achieved goals affects the success rate of a given task, it should be avoided by considering the valid goal space, introduced here as the set of goals to achieve, and the current competence of the policy. To this end, a novel prioritization method for creation of batches, e.g., collections of samples, is proposed. Candidate batches are sampled and associated with costs; in each iteration the batch with the minimum cost is chosen to train the policy. The cost function is modeled by an intended goal, which is proposed as a hypothetical goal that the policy is trying to learn in each cycle, and the information of the valid goal space. The minimum cost of the batch selected for each iteration decreases throughout training as the policy learns to achieve goals near the center of the valid goal space. The proposed batch prioritization method is combined with hindsight experience replay (HER) for experiments in robotic control tasks presented in the OpenAI gym suite to demonstrate learning performance comparable to that of other state-of-the-art prioritization methods. As a result, the proposed batch prioritization method can achieve improved learning performance in 4 out of 5 tasks, particularly for harder tasks. The experimental results suggest that the proposed method for the creation of training batches, using the valid goal space information and current competence of the policy, can enhance learning performance in multigoal tasks with high-dimensional goal space.
Deep Face Rectification for 360° Dual-Fisheye Cameras

Rectilinear face recognition models suffer from severe performance degradation when applied to fisheye images captured by 360° back-to-back dual fisheye cameras. We propose a novel face rectification method to combat the effect of fisheye image distortion on face recognition. The method consists of a classification network and a restoration network specifically designed to handle the non-linear property of fisheye projection. The classification network classifies an input fisheye image according to its distortion level. The restoration network takes a distorted image as input and restores the rectilinear geometric structure of the face. The performance of the proposed method is tested on an end-to-end face recognition system constructed by integrating the proposed rectification method with a conventional rectilinear face recognition system. The face verification accuracy of the integrated system is 99.18% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 95.70% for images in a real image dataset, resulting in an average accuracy improvement of 6.57% over the conventional face recognition system. For face identification, the average improvement over the conventional face recognition system is 4.51%.
Bounds on amplitude-damping-channel discrimination

Amplitude-damping (AD) channels are good models for many physical scenarios, and so the development of protocols to discriminate between them is an important task in quantum information science. It is therefore important to bound the performance of such protocols. Since adaptivity has been shown to improve the performance of discrimination protocols, bounds on the distinguishability of AD channels must take this into account. In this paper, we use both channel simulation and a bound based on the diamond norm to significantly tighten the upper bound on the trace norm between the possible outputs of binary channel discrimination protocols acting on AD channels (and hence the lower bound on the error probability of such protocols). The diamond norm between any two AD channels is found analytically, giving the optimal error probability for a one-shot discrimination protocol. We also present a tighter lower bound on the achievable trace norm between protocol outputs (and a corresponding upper bound on the achievable error probability). The upper and lower bounds are compared with existing bounds and then applied to quantum hacking and biological quantum sensing scenarios.
The Interaction Effect of Comprehensiveness Between Social Media and Online Purchasing Intention in Jordanian Pharmacies

&lt;p&gt;This empirical paper examined the moderating role of comprehensiveness between social media and online shopping intention among customers of Jordanian pharmacies. Technology acceptance model (TAM) and the theory of planned behaviour (TPB) were the applied theories in this study. Data were collected from potential customers using a survey questionnaire. A total of 198 usable questionnaires were obtained and the data were analyzed. Partial least squares structural equation modeling (PLS-SEM) was used in the outcome examination. The results show a positive impact of social media on online shopping intention. Further, comprehensiveness moderated the relationship strengths between social media and online shopping intention. This paper finds that focusing on social media strategies such as adopting intimacy, decreasing the perceived risk, and increasing trust could motivate online shopping among customers. Pharmacies in Jordan may consider these findings and achieve them by providing more comprehensive information in their advertisement and announcement.&lt;/p&gt;
Isosceles Constraints for Person Re-Identification

In the existing works of person re-identification (ReID), batch hard triplet loss has achieved great success. However, it only cares about the hardest samples within the batch. For any probe, there are massive mismatched samples (crucial samples) outside the batch which are closer than the matched samples. To reduce the disruptive influence of crucial samples, we propose a novel isosceles contraint for triplet. Theoretically, we show that if a matched pair has equal distance to any one of mismatched sample, the matched pair should be infinitely close. Motivated by this, the isosceles constraint is designed for the two mismatched pairs of each triplet, to restrict some matched pairs with equal distance to different mismatched samples. Meanwhile, to ensure that the distance of mismatched pairs are larger than the matched pairs, margin constraints are necessary. Minimizing the isosceles and margin constraints with respect to the feature extraction network makes the matched pairs closer and the mismatched pairs farther away than the matched ones. By this way, crucial samples are effectively reduced and the performance on ReID is improved greatly. Likewise, our isosceles contraint can be applied to quadruplet as well. Comprehensive experimental evaluations on Market-1501, DukeMTMC-reID and CUHK03 datasets demonstrate the advantages of our isosceles constraint over the related state-of-the-art approaches.
Tensor-Network Codes

We introduce tensor-network stabilizer codes which come with a natural tensor-network decoder. These codes can correspond to any geometry, but, as a special case, we generalize holographic codes beyond those constructed from perfect or block-perfect isometries, and we give an example that corresponds to neither. Using the tensor-network decoder, we find a threshold of 18.8% for this code under depolarizing noise. We show that, for holographic codes, the exact tensor-network decoder (with no bond-dimension truncation) has polynomial complexity in the number of physical qubits, even for locally correlated noise, making this the first efficient decoder for holographic codes against Pauli noise and, also, a rare example of a decoder that is both efficient and exact.
Review and Critical Analysis of Privacy-Preserving Infection Tracking and Contact Tracing

The outbreak of viruses have necessitated contact tracing and infection tracking methods. Despite various efforts, there is currently no standard scheme for the tracing and tracking. Therefore, many nations of the world have developed their own ways where carriers of disease could be tracked and their contacts traced. These are generalized methods developed either in a distributed manner giving citizens control of their identity or in a centralized manner where a health authority gathers data on those who are carriers. This paper outlines some of the most significant approaches that have been established for contact tracing around the world. A comprehensive review on the key enabling methods used to realize the infrastructure around these infection tracking and contact tracing methods is also presented, and recommendations are made for the most effective way to develop such a practice.
Beneficial Perturbation Network for Designing General Adaptive Artificial Intelligence Systems

The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs. This limits their applicability to more dynamic situations, where the input to output mapping may change with different contexts. A salient example is continual learning-learning new independent tasks sequentially without forgetting previous tasks. Continual learning of multiple tasks in artificial neural networks using gradient descent leads to catastrophic forgetting, whereby a previously learned mapping of an old task is erased when learning new mappings for new tasks. Herein, we propose a new biologically plausible type of deep neural network with extra, out-of-network, task-dependent biasing units to accommodate these dynamic situations. This allows, for the first time, a single network to learn potentially unlimited parallel input to output mappings, and to switch on the fly between them at runtime. Biasing units are programed by leveraging beneficial perturbations (opposite to well-known adversarial perturbations) for each task. Beneficial perturbations for a given task bias the network toward that task, essentially switching the network into a different mode to process that task. This largely eliminates catastrophic interference between tasks. Our approach is memory-efficient and parameter-efficient, can accommodate many tasks, and achieves the state-of-the-art performance across different tasks and domains.
Inhomogeneous Image Segmentation Using Hybrid Active Contours Model With Application to Breast Tumor Detection

The most fatal and frequent cancer amongst women is breast cancer.Mammography provides timely detection of lumps and masses in breast tissue, but effective diagnosis requires accurately identifying malignant tumor boundaries, which remains challenging, particularly for images with inhomogeneous regions.Therefore, we propose an active contour method based on a reformed combined local and global fitted function to address breast tumor segmentation.This combined function is strengthened by a proposed average energy driving function to capture obscure boundaries for regions of interest more precisely from inhomogeneous images.Including a p-Laplace term eliminates reinitialization requirements and suppresses false contours in the segmentation.Bias field signal, which causes image homogeneity corruption, is estimated by bias field initialization to ensure independence from the initial contour position.Local and global fitted models are incorporated by introducing bias fields within them.The proposed method was tested on the MIAS MiniMammographic Database, with quantitative analysis to calculate its accuracy, effectiveness, and efficiency.Experimentation confirmed the proposed method provided superior results compared with previous state-of-the-art methods.
Boundary-Aware RGBD Salient Object Detection With Cross-Modal Feature Sampling

Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper, we propose a boundary-aware cross-modal fusion network for RGBD salient object detection. In particular, to enhance the fusion of color and depth features, we present a cross-modal feature sampling module to balance the contribution of the RGB and depth features based on the statistics of their channel values. In addition, in our multi-scale dense fusion network architecture, we not only incorporate edge-sensitive losses to preserve the boundary of the detected salient region, but also refine its structure by merging the estimated saliency maps of different scales. We accomplish the multi-scale saliency map merging using two alternative methods which produce refined saliency maps via per-pixel weighted combination and an encoder-decoder network. Extensive experimental evaluations demonstrate that our proposed framework can achieve the state-of-the-art performance on several public RGBD-based datasets.