Reviewerly/exHarmony huggingface dataset:
Hardware error correction for programmable photonics

Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, providing a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence. A major obstacle to scaling up these systems is static fabrication error, where small component errors within each device accrue to produce significant errors within the circuit computation. Mitigating this error usually requires numerical optimization dependent on real-time feedback from the circuit, which can greatly limit the scalability of the hardware. Here we present a deterministic approach to correcting circuit errors by locally correcting hardware errors within individual optical gates. We apply our approach to simulations of large scale optical neural networks and infinite impulse response filters implemented in programmable photonics, finding that they remain resilient to component error well beyond modern day process tolerances. Our results highlight a potential way to scale up programmable photonics to hundreds of modes with current fabrication processes.
A task scheduling algorithm considering game theory designed for energy management in cloud computing

With the increasing popularity of cloud computing products, task scheduling problem has become a hot research topic in this field. The task scheduling problem of cloud computing system is more complex than the traditional distributed system. Based on the analysis of cloud computing in related literature, we established a simplified model for task scheduling system in cloud computing.Different from the previous research of cloud computing task scheduling algorithm, the simplified model in this paper is based on game theory as a mathematical tool. Based on game theory, the task scheduling algorithm considering the reliability of the balanced task is proposed. Based on the balanced scheduling algorithm, the task scheduling model for computing nodes is proposed. In the cooperative game model, game strategy is used for the task in the calculation of rate allocation strategy on the node. Through analysis of experimental results, it is shown that the proposed algorithm has better optimization effect.
Toward an artificial intelligence physicist for unsupervised learning

We investigate opportunities and challenges for improving unsupervised machine learning using four common strategies with a long history in physics: divide and conquer, Occam's razor, unification, and lifelong learning. Instead of using one model to learn everything, we propose a paradigm centered around the learning and manipulation of theories, which parsimoniously predict both aspects of the future (from past observations) and the domain in which these predictions are accurate. Specifically, we propose a generalized mean loss to encourage each theory to specialize in its comparatively advantageous domain, and a differentiable description length objective to downweight bad data and ``snap'' learned theories into simple symbolic formulas. Theories are stored in a ``theory hub,'' which continuously unifies learned theories and can propose theories when encountering new environments. We test our implementation, the toy ``artificial intelligence physicist'' learning agent, on a suite of increasingly complex physics environments. From unsupervised observation of trajectories through worlds involving random combinations of gravity, electromagnetism, harmonic motion, and elastic bounces, our agent typically learns faster and produces mean-squared prediction errors about a billion times smaller than a standard feedforward neural net of comparable complexity, typically recovering integer and rational theory parameters exactly. Our agent successfully identifies domains with different laws of motion also for a nonlinear chaotic double pendulum in a piecewise constant force field.
The Born supremacy: quantum advantage and training of an Ising Born machine

The search for an application of near-term quantum devices is widespread. Quantum Machine Learning is touted as a potential utilisation of such devices, particularly those which are out of the reach of the simulation capabilities of classical computers. In this work, we propose a generative Quantum Machine Learning Model, called the Ising Born Machine (IBM), which we show cannot, in the worst case, and up to suitable notions of error, be simulated efficiently by a classical device. We also show this holds for all the circuit families encountered during training. In particular, we explore quantum circuit learning using non-universal circuits derived from Ising Model Hamiltonians, which are implementable on near term quantum devices. We propose two novel training methods for the IBM by utilising the Stein Discrepancy and the Sinkhorn Divergence cost functions. We show numerically, both using a simulator within Rigetti's Forest platform and on the Aspen-1 16Q chip, that the cost functions we suggest outperform the more commonly used Maximum Mean Discrepancy (MMD) for differentiable training. We also propose an improvement to the MMD by proposing a novel utilisation of quantum kernels which we demonstrate provides improvements over its classical counterpart. We discuss the potential of these methods to learn `hard' quantum distributions, a feat which would demonstrate the advantage of quantum over classical computers, and provide the first formal definitions for what we call `Quantum Learning Supremacy'. Finally, we propose a novel view on the area of quantum circuit compilation by using the IBM to `mimic' target quantum circuits using classical output data only.
Track to Reconstruct and Reconstruct to Track

Object tracking and 3D reconstruction are often performed together, with tracking used as input for reconstruction. However, the obtained reconstructions also provide useful information for improving tracking. We propose a novel method that closes this loop, first tracking to reconstruct, and then reconstructing to track. Our approach, MOTSFusion (Multi-Object Tracking, Segmentation and dynamic object Fusion), exploits the 3D motion extracted from dynamic object reconstructions to track objects through long periods of complete occlusion and to recover missing detections. Our approach first builds up short tracklets using 2D optical flow, and then fuses these into dynamic 3D object reconstructions. The precise 3D object motion of these reconstructions is used to merge tracklets through occlusion into long-term tracks, and to locate objects when detections are missing. On KITTI, our reconstruction-based tracking reduces the number of ID switches of the initial tracklets by more than 50%, and outperforms all previous approaches for both bounding box and segmentation tracking. Code available: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tobiasfshr/MOTSFusion</uri> .
Deep embedded multi-view clustering with collaborative training

Multi-view clustering has attracted increasing attentions recently by utilizing information from multiple views. However, existing multi-view clustering methods are either with high computation and space complexities, or lack of representation capability. To address these issues, we propose deep embedded multi-view clustering with collaborative training (DEMVC) in this paper. Firstly, the embedded representations of multiple views are learned individually by deep autoencoders. Then, both consensus and complementary of multiple views are taken into account and a novel collaborative training scheme is proposed. Concretely, the feature representations and cluster assignments of all views are learned collaboratively. A new consistency strategy for cluster centers initialization is further developed to improve the multi-view clustering performance with collaborative training. Experimental results on several popular multi-view datasets show that DEMVC achieves significant improvements over state-of-the-art methods.
From distributed machine learning to federated learning: a survey

In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distributed data and computing resources cannot be directly shared among different regions or organizations for machine learning tasks. Federated learning emerges as an efficient approach to exploit distributed data and computing resources, so as to collaboratively train machine learning models, while obeying the laws and regulations and ensuring data security and data privacy. In this paper, we provide a comprehensive survey of existing works for federated learning. We propose a functional architecture of federated learning systems and a taxonomy of related techniques. Furthermore, we present the distributed training, data communication, and security of FL systems. Finally, we analyze their limitations and propose future research directions.
FiberClay: Sculpting Three Dimensional Trajectories to Reveal Structural Insights

Visualizing 3D trajectories to extract insights about their similarities and spatial configuration is a critical task in several domains. Air traffic controllers for example deal with large quantities of aircrafts routes to optimize safety in airspace and neuroscientists attempt to understand neuronal pathways in the human brain by visualizing bundles of fibers from DTI images. Extracting insights from masses of 3D trajectories is challenging as the multiple three dimensional lines have complex geometries, may overlap, cross or even merge with each other, making it impossible to follow individual ones in dense areas. As trajectories are inherently spatial and three dimensional, we propose FiberClay: a system to display and interact with 3D trajectories in immersive environments. FiberClay renders a large quantity of trajectories in real time using GP-GPU techniques. FiberClay also introduces a new set of interactive techniques for composing complex queries in 3D space leveraging immersive environment controllers and user position. These techniques enable an analyst to select and compare sets of trajectories with specific geometries and data properties. We conclude by discussing insights found using FiberClay with domain experts in air traffic control and neurology.
Light Field Saliency Detection With Deep Convolutional Networks

Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light.The detection of salient regions in a light field image benefits from the additional modeling of angular patterns.For RGB imaging, methods using CNNs have achieved excellent results on a range of tasks, including saliency detection.However, it is not trivial to use CNN-based methods for saliency detection on light field images because these methods are not specifically designed for processing light field inputs.In addition, current light field datasets are not sufficiently large to train CNNs.To overcome these issues, we present a new Lytro Illum dataset, which contains 640 light fields and their corresponding ground-truth saliency maps.Compared to current publicly available light field saliency datasets [1], [2], our new dataset is larger, of higher quality, contains more variation and more types of light field inputs.This makes our dataset suitable for training deeper networks and benchmarking.Furthermore, we propose a novel end-to-end CNN-based framework for light field saliency detection.Specifically, we propose three novel MAC (Model Angular Changes) blocks to process light field micro-lens images.We systematically study the impact of different architecture variants and compare light field saliency with regular 2D saliency.Our extensive comparisons indicate that our novel network significantly outperforms state-of-the-art methods on the proposed dataset and has desired generalization abilities on other existing datasets.
Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks

Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data. However, most of the works published in the literature focused on the Single-image Super-resolution problem so far. At present, satellite-based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution. In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the Multi-image Super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images. We introduce the mechanism of visual feature attention with 3D convolutions in order to obtain an aware data fusion and information extraction of the multiple low-resolution images, transcending limitations of the local region of convolutional operations. Moreover, having multiple inputs with the same scene, our representation learning network makes extensive use of nestled residual connections to let flow redundant low-frequency signals and focus the computation on more important high-frequency components. Extensive experimentation and evaluations against other available solutions, either for Single or Multi-image Super-resolution, demonstrated that the proposed deep learning-based solution can be considered state-of-the-art for Multi-image Super-resolution for remote sensing applications.
PaDNet: Pan-Density Crowd Counting

Crowd counting is a highly challenging problem in computer vision and machine learning. Most previous methods have focused on consistent density crowds, i.e., either a sparse or a dense crowd, meaning they performed well in global estimation while neglecting local accuracy. To make crowd counting more useful in the real world, we propose a new perspective, named pan-density crowd counting, which aims to count people in varying density crowds. Specifically, we propose the Pan-Density Network (PaDNet) which is composed of the following critical components. First, the Density-Aware Network (DAN) contains multiple subnetworks pretrained on scenarios with different densities. This module is capable of capturing pan-density information. Second, the Feature Enhancement Layer (FEL) effectively captures the global and local contextual features and generates a weight for each density-specific feature. Third, the Feature Fusion Network (FFN) embeds spatial context and fuses these density-specific features. Further, the metrics Patch MAE (PMAE) and Patch RMSE (PRMSE) are proposed to better evaluate the performance on the global and local estimations. Extensive experiments on four crowd counting benchmark datasets, the ShanghaiTech, the UCF_CC_50, the UCSD, and the UCF-QNRF, indicate that PaDNet achieves state-of-the-art recognition performance and high robustness in pan-density crowd counting.
Quantum circuit optimizations for NISQ architectures

Currently available quantum computing hardware platforms have limited 2-qubit connectivity among their addressable qubits. In order to run a generic quantum algorithm on such a platform, one has to transform the initial logical quantum circuit describing the algorithm into an equivalent one that obeys the connectivity restrictions. In this work we construct a circuit synthesis scheme that takes as input the qubit connectivity graph and a quantum circuit over the gate set generated by and outputs a circuit that respects the connectivity of the device. As a concrete application, we apply our techniques to Google's Bristlecone 72-qubit quantum chip connectivity, IBM's Tokyo 20-qubit quantum chip connectivity, and Rigetti's Acorn 19-qubit quantum chip connectivity. In addition, we also compare the performance of our scheme as a function of sparseness of randomly generated quantum circuits, and discuss how to apply our techniques as a subroutine for the more general mapping problem over universal set of gates (Clifford + T).
An ecologically motivated image dataset for deep learning yields better models of human vision

Significance Inspired by core principles of information processing in the brain, deep neural networks (DNNs) have demonstrated remarkable success in computer vision applications. At the same time, networks trained on the task of object classification exhibit similarities to representations found in the primate visual system. This result is surprising because the datasets commonly used for training are designed to be engineering challenges. Here, we use linguistic corpus statistics and human concreteness ratings as guiding principles to design a resource that more closely mirrors categories that are relevant to humans. The result is ecoset, a collection of 1.5 million images from 565 basic-level categories. We show that ecoset-trained DNNs yield better models of human higher-level visual cortex and human behavior.
Incentive and Unconditionally Anonymous Identity-Based Public Provable Data Possession

When the data is stored in public clouds, provable data possession (for short, PDP) is of crucial importance in cloud storage. PDP can make the users verify whether their outsourced data is kept intact without downloading the whole data. In some application scenarios, anonymity is very important in order to protect the user identity privacy. In order to encourage users to disclose bad event, the government or organization or individual may pay for the user who provides the precious data. Thus, incentive and unconditionally anonymous identity-based public PDP (for short, IAID-PDP) is a very important security concept. From the above requirements, for the first time, we propose the concept of IAID-PDP. We formalize its system model and security model. Based on the bilinear pairings, a concrete IAID-PDP protocol is presented. Based on the standard hard problems, the proposed IAID-PDP protocol is provably secure. IAID-PDP protocol eliminates the complex certificate management since it is designed in the identity-based public key cryptography. Through the performance analysis and security analysis, our IAID-PDP protocol satisfies the following properties: certification elimination, incentive, unconditional anonymity and remote data integrity checking.
An imperceptible spatial domain color image watermarking scheme

The paper proposes a novel scheme for color image watermarking. Spatial domain techniques are used here for embedding the watermark information to generate high quality watermarked image. Spatial domain methods are popular with fragile watermarking techniques that mostly use two or three least significant image bits for storing the recovery information. Here spatial domain methods are further explored to develop a robust mechanism for copyright protection. The method presented gradually spreads the watermark information over a region of pixels as implemented by the transform domain techniques. Thus the method is designed to deliver the two essential features required of watermarking systems namely, high image quality and high robustness to attacks. Also after watermark embedding it is ensured that the alterations in one color component are well compensated and no color difference or variations is visually evident. For watermark embedding and color compensation two masks are proposed. The algorithm is experimentally analyzed using various quality metrics and watermark removal attacks. The results prove the model support imperceptible watermarking and high resilience to attacks.
Magic State Distillation: Not as Costly as You Think

Despite significant overhead reductions since its first proposal, magic state distillation is often considered to be a very costly procedure that dominates the resource cost of fault-tolerant quantum computers. The goal of this work is to demonstrate that this is not true. By writing distillation circuits in a form that separates qubits that are capable of error detection from those that are not, most logical qubits used for distillation can be encoded at a very low code distance. This significantly reduces the space-time cost of distillation, as well as the number of qubits. In extreme cases, it can cost less to distill a magic state than to perform a logical Clifford gate on full-distance logical qubits.
Context-aware rule learning from smartphone data: survey, challenges and future directions

Abstract Smartphones are considered as one of the most essential and highly personal devices of individuals in our current world. Due to the popularity of context-aware technology and recent developments in smartphones, these devices can collect and process raw contextual data about users’ surrounding environment and their corresponding behavioral activities with their phones. Thus, smartphone data analytics and building data-driven context-aware systems have gained wide attention from both academia and industry in recent days. In order to build intelligent context-aware applications on smartphones, effectively learning a set of context-aware rules from smartphone data is the key. This requires advanced data analytical techniques with high precision and intelligent decision making strategies based on contexts. In comparison to traditional approaches, machine learning based techniques provide more effective and efficient results for smartphone data analytics and corresponding context-aware rule learning. Thus, this article first makes a survey on previous work in the area of contextual smartphone data analytics and then presents a discussion of challenges and future directions for effectively learning context-aware rules from smartphone data, in order to build rule-based automated and intelligent systems.
Cybersecurity Risks in a Pandemic

Cybersecurity threats are estimated to cost the world US $6 trillion a year by 2021, and the number of attacks has increased five-fold after COVID-19. Although there is substantial literature on the threats technological vulnerabilities have on the health care industry, less research exists on how pandemics like COVID-19 are opportunistic for cybercriminals. This paper outlines why cyberattacks have been particularly problematic during COVID-19 and ways that health care industries can better protect patient data. The Office for Civil Rights has loosened enforcement of the Health Insurance Portability and Accountability Act, which, although useful in using new platforms like Zoom, has also loosened physical and technical safeguards to cyberattacks. This is especially problematic given that 90% of health care providers had already encountered data breaches. Companies must implement well-defined software upgrade procedures, should use secure networks like virtual local area networks, and conduct regular penetration tests of their systems. By understanding factors that make individuals, health care organizations, and employers more susceptible to cyberattacks, we can better prepare for the next pandemic.