Reviewerly/exHarmony huggingface dataset:
An optimized quantum minimum searching algorithm with sure-success probability and its experiment simulation with Cirq

Finding a minimum is an essential part of mathematical models, and it plays an important role in some optimization problems. Durr and Hoyer proposed a quantum searching algorithm (DHA), with a certain probability of success, to achieve quadratic speed than classical ones. In this paper, we propose an optimized quantum minimum searching algorithm with sure-success probability, which utilizes Grover-Long searching to implement the optimal exact searching, and the dynamic strategy to reduce the iterations of our algorithm. Besides, we optimize the oracle circuit to reduce the number of gates by the simplified rules. The performance evaluation including the theoretical success rate and computational complexity shows that our algorithm has higher accuracy and efficiency than DHA algorithm. Finally, a simulation experiment based on Cirq is performed to verify its feasibility.
New Results on the Computation-Communication Tradeoff for Heterogeneous Coded Distributed Computing

Coded distributed computing (CDC) can alleviate the communication load in distributed computing systems by leveraging coding opportunities via redundant computation. While the optimal computation-communication tradeoff has been well studied for homogeneous systems, it remains largely unknown for heterogeneous systems where workers have different computation capabilities. This paper characterizes the upper and lower bounds of the optimal communication load as two linear programming problems for a general heterogeneous CDC system using the MapReduce framework. Our achievable scheme first designs a parametric data shuffling strategy for any given mapping strategy, and then jointly optimizes the mapping strategy and the data shuffling strategy to obtain the upper bound. The parametric data shuffling strategy allows adjusting the size of the multicast message intended for each worker set, so that it can largely decrease the number of unicast messages and hence increase the communication efficiency. Numerical results show that our achievable communication load is lower than those achieved in existing works. Our lower bound is established by unifying an improved cut-set bound and a peeling method. The obtained upper and lower bounds degenerate to the existing result in homogeneous systems, and coincide with each other when the system is approximately homogeneous or grouped homogeneous.
Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition

Training deep learning models for accurate spatiotemporal recognition of facial expressions in videos requires significant computational resources. For practical reasons, 3D Convolutional Neural Networks (3D CNNs) are usually trained with relatively short clips randomly extracted from videos. However, such uniform sampling is generally sub-optimal because equal importance is assigned to each temporal clip. In this paper, we present a strategy for efficient video-based training of 3D CNNs. It relies on softmax temporal pooling and a weighted sampling mechanism to select the most relevant training clips. The proposed softmax strategy provides several advantages – a reduced computational complexity due to efficient clip sampling, and an improved accuracy since temporal weighting focuses on more relevant clips during both training and inference. Experimental results obtained with the proposed method on several facial expression recognition benchmarks show the benefits of focusing on more informative clips in training videos. In particular, our approach improves performance and computational cost by reducing the impact of inaccurate trimming and coarse annotation of videos, and heterogeneous distribution of visual information across time.
Deep Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles

This paper presents a deep reinforcement learning-based system for goal-oriented mapless navigation for Unmanned Aerial Vehicles (UAVs). In this context, image-based sensing approaches are the most common. However, they demand high processing power hardware which are heavy and difficult to embed into a small-autonomous UAV. Our approach is based on localization data and simple sparse range data to train the intelligent agent. We based our approach in two state-of-the-art Deep- Rl techniques for terrestrial robot: Deep Deterministic Policy Gradient (DDPG) and Soft Actor Critic (SAC). We compare the performance with a classic geometric-based tracking controller for mapless navigation of UAVs. Based on experimental results, we conclude that Deep- Rl algorithms are effective to perform mapless navigation and obstacle avoidance for UAVs. Our vehicle successfully performed two proposed tasks, reaching the desired goal and outperforming the geometric-based tracking controller on the obstacle avoiding capability.
Robustifying models against adversarial attacks by Langevin dynamics

Adversarial attacks on deep learning models have compromised their performance considerably. As remedies, a number of defense methods were proposed, which however, have been circumvented by newer and more sophisticated attacking strategies. In the midst of this ensuing arms race, the problem of robustness against adversarial attacks still remains a challenging task. This paper proposes a novel, simple yet effective defense strategy where off-manifold adversarial samples are driven towards high density regions of the data generating distribution of the (unknown) target class by the Metropolis-adjusted Langevin algorithm (MALA) with perceptual boundary taken into account. To achieve this task, we introduce a generative model of the conditional distribution of the inputs given labels that can be learned through a supervised Denoising Autoencoder (sDAE) in alignment with a discriminative classifier. Our algorithm, called MALA for DEfense (MALADE), is equipped with significant dispersion—projection is distributed broadly. This prevents white box attacks from accurately aligning the input to create an adversarial sample effectively. MALADE is applicable to any existing classifier, providing robust defense as well as off-manifold sample detection. In our experiments, MALADE exhibited state-of-the-art performance against various elaborate attacking strategies.
Privacy-Preserving Policy Synthesis in Markov Decision Processes

In decision-making problems, the actions of an agent may reveal sensitive information that drives its decisions. For instance, a corporation's investment decisions may reveal its sensitive knowledge about market dynamics. To prevent this type of information leakage, we introduce a policy synthesis algorithm that protects the privacy of the transition probabilities in a Markov decision process. We use differential privacy as the mathematical definition of privacy. The algorithm first perturbs the transition probabilities using a mechanism that provides differential privacy. Then, based on the privatized transition probabilities, we synthesize a policy using dynamic programming. Our main contribution is to bound the "cost of privacy," i.e., the difference between the expected total rewards with privacy and the expected total rewards without privacy. We also show that computing the cost of privacy has time complexity that is polynomial in the parameters of the problem. Moreover, we establish that the cost of privacy increases with the strength of differential privacy protections, and we quantify this increase. Finally, numerical experiments on two example environments validate the established relationship between the cost of privacy and the strength of data privacy protections.
Adaptive Multiplane Image Generation from a Single Internet Picture

In the last few years, several works have tackled the problem of novel view synthesis from stereo images or even from a single picture. However, previous methods are computationally expensive, specially for high-resolution images. In this paper, we address the problem of generating a multiplane image (MPI) from a single high-resolution picture. We present the adaptive-MPI representation, which allows rendering novel views with low computational requirements. To this end, we propose an adaptive slicing algorithm that produces an MPI with a variable number of image planes. We present a new lightweight CNN for depth estimation, which is learned by knowledge distillation from a larger network. Occluded regions in the adaptive-MPI are inpainted also by a lightweight CNN. We show that our method is capable of producing high-quality predictions with one order of magnitude less parameters compared to previous approaches. The robustness of our method is evidenced on challenging pictures from the Internet.
A Scalable Platform for Distributed Object Tracking Across a Many-Camera Network

Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking platforms have two key limitations: 1) They are monolithic, proprietary and lack the ability to rapidly incorporate sophisticated tracking models, and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog, and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance, and the active camera-set size. We illustrate the concise expressiveness of the programming model for four tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance, and quality trade-offs enabled by our dynamic tracking, batching, and dropping strategies.
Upperbounds on the probability of finding marked connected components using quantum walks

Quantum walk search may exhibit phenomena beyond the intuition from a conventional random walk theory. One of such examples is exceptional configuration phenomenon -- it appears that it may be much harder to find any of two or more marked vertices, that if only one of them is marked. In this paper, we analyze the probability of finding any of marked vertices in such scenarios and prove upper bounds for various sets of marked vertices. We apply the upper bounds to large collection of graphs and show that the quantum search may be slow even when taking real-world networks.
Encrypted Physical Layer Communications Using Synchronized Hyperchaotic Maps

With the proliferation of Internet-of-Things (IoT) devices at public facilities, work places, homes, and beyond, securing data communications is becoming increasingly challenging.This paper describes the analysis and practical implementation of synchronized hyperchaotic maps for securing short-range data links in IoT devices.The data is stream-encrypted in the physical layer using chaotic masking and decrypted using a synchronized chaotic map.Two different energy-efficient chaotic encryption schemes are proposed: 1) direct sampling and masking of analog sensor outputs, thus avoiding the need for analog-to-digital (ADC) conversion; and 2) bit scrambling and masking of digital data.Both schemes were initially investigated and simulated in MATLAB.The effective number of encryption keys when using such hyperchaotic maps was also studied.Simulation results were validated by implementing two maps on circuit boards using high-speed discrete components.Experimental results for digital communication show a bit error rate (BER) of ≈ 2 × 10 -6 at a bit rate of 10 kbps and a clock frequency of 0.5 MHz, making the approach feasible for high-fidelity real-time speech and image transmission without additional error control coding.An energy-efficient on-chip chaotic encryption system was developed using analog current-mode circuits and has a simulated power consumption of <100 µW in the TSMC 180 nm CMOS process, which is sufficiently low for use in many battery-less miniaturized IoT sensor nodes.
AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks

The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.
PAS Tracker: Position-, Appearance- and Size-Aware Multi-object Tracking in Drone Videos

While most multi-object tracking methods based on tracking-by-detection use either spatial or appearance cues for associating detections or apply one cue after another, our proposed PAS tracker employs a novel similarity measure that combines position, appearance and size information jointly to make full use of object representations. We further extend the PAS tracker by introducing a filtering technique to remove false positive detections, particularly in crowded scenarios, and apply a camera motion compensation model to align track positions across frames. To provide high quality detections as input for the proposed tracker, the performance of eight state-of-the-art object detectors is compared on the VisDrone MOT dataset, on which the PAS tracker achieves state-of-the-art performance and ranks 3rd in the VisDrone2020 MOT challenge. In an ablation study, we demonstrate the effectiveness of the introduced tracking components and the impact of the employed detections on the tracking performance is analyzed in detail.
Scenario optimization with relaxation: a new tool for design and application to machine learning problems

Scenario optimization is by now a well established technique to perform designs in the presence of uncertainty. It relies on domain knowledge integrated with first-hand information that comes from data and generates solutions that are also accompanied by precise statements of reliability. In this paper, following recent developments in [22], we venture beyond the traditional set-up of scenario optimization by analyzing the concept of constraints relaxation. By a solid theoretical underpinning, this new paradigm furnishes fundamental tools to perform designs that meet a proper compromise between robustness and performance. After suitably expanding the scope of constraints relaxation as proposed in [22], we focus on various classical Support Vector methods in machine learning - including SVM (Support Vector Machine), SVR (Support Vector Regression) and SVDD (Support Vector Data Description) - and derive new results that attest the ability of these methods to generalize.
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning

Existing few-shot learning (FSL) methods assume that there exist sufficient training samples from source classes for knowledge transfer to target classes with few training samples. However, this assumption is often invalid, especially when it comes to fine-grained recognition. In this work, we define a new FSL setting termed scarce-source few-shot learning (SSFSL), under which both the source and target classes have limited training samples. To overcome the source class data scarcity problem, a natural option is to crawl images from the web with class names as search keywords. However, the crawled images are inevitably corrupted by large amount of noise (irrelevant images) and thus may harm the performance. To address this problem, we propose a graph convolutional network (GCN)-based label denoising (LDN) method to remove the irrelevant images. Further, with the cleaned web images as well as the original clean training images, we propose a GCN-based FSL method. For both the LDN and FSL tasks, a novel adaptive aggregation GCN (AdarGCN) model is proposed, which differs from existing GCN models in that adaptive aggregation is performed based on a multi-head multi-level aggregation module. With AdarGCN, how much and how far information carried by each graph node is propagated in the graph structure can be determined automatically, therefore alleviating the effects of both noisy and outlying training samples. Extensive experiments demonstrate the superior performance of our AdarGCN under both the new SSFSL and the conventional FSL settings.
Chiral Floquet Systems and Quantum Walks at Half-Period

We classify chiral symmetric periodically driven quantum systems on a one-dimensional lattice. The driving process is local, can be continuous or discrete in time, and we assume a gap condition for the corresponding Floquet operator. The analysis is in terms of the unitary operator at a half-period, the half-step operator. We give a complete classification of the connected classes of half-step operators in terms of five integer indices. On the basis of these indices it can be decided whether the half-step operator can be obtained from a continuous Hamiltonian driving, or not. The half-step operator determines two Floquet operators, obtained by starting the driving at zero or at half period, respectively. These are called timeframes and are chiral symmetric quantum walks. Conversely, we show under which conditions two chiral symmetric walks determine a common half-step operator. Moreover, we clarify the connection between the classification of half-step operators and the corresponding quantum walks. Within this theory we prove bulk-edge correspondence and show that a second timeframe allows to distinguish between symmetry protected edge states at $+1$ and $-1$ which is not possible for a single timeframe.
What Is the Model in Model‐Based Planning?

Flexibility is one of the hallmarks of human problem-solving. In everyday life, people adapt to changes in common tasks with little to no additional training. Much of the existing work on flexibility in human problem-solving has focused on how people adapt to tasks in new domains by drawing on solutions from previously learned domains. In real-world tasks, however, humans must generalize across a wide range of within-domain variation. In this work we argue that representational abstraction plays an important role in such within-domain generalization. We then explore the nature of this representational abstraction in realistically complex tasks like video games by demonstrating how the same model-based planning framework produces distinct generalization behaviors under different classes of task representation. Finally, we compare the behavior of agents with these task representations to humans in a series of novel grid-based video game tasks. Our results provide evidence for the claim that within-domain flexibility in humans derives from task representations composed of propositional rules written in terms of objects and relational categories.
HIDMS-PSO: A New Heterogeneous Improved Dynamic Multi-Swarm PSO Algorithm

In this paper, a variant of the particle swarm optimisation (PSO) algorithm is introduced with heterogeneous behaviour and a new dynamic multi-swarm topological structure. The new topological structure enables the algorithm to have more control over the interaction and information exchange between the particles to reduce the loss of diversity and avoid premature convergence. In the new algorithm, the population is initially divided into two sub-populations, first sub-population is further divided into sub-swarms that are formed using the introduced topological structure. The particles of sub-swarms are guided using heterogeneous behaviour by selecting various exemplars. The second sub-population employs the classical PSO search with local and global information to simulate a homogenous behaviour. There is information flow between the two subpopulations. The algorithm was tested on the CEC2005 and CEC2017 test suites with comparison against various state-of the-art PSO variants and other state-of-the-art meta-heuristics. The experimental results show that for the two test suites, the proposed algorithm outperformed the majority of the state-of the-art algorithms on most problems.
Adaptiope: A Modern Benchmark for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) deals with the adaptation process of a given source domain with labeled training data to a target domain for which only unannotated data is available. This is a challenging task as the domain shift leads to degraded performance on the target domain data if not addressed. In this paper, we analyze commonly used UDA classification datasets and discover systematic problems with regard to dataset setup, ground truth ambiguity and annotation quality. We manually clean the most popular UDA dataset in the research area (Office-31) and quantify the negative effects of inaccurate annotations through thorough experiments. Based on these insights, we collect the Adaptiope dataset - a large scale, diverse UDA dataset with synthetic, product and real world data - and show that its transfer tasks provide a challenge even when considering recent UDA algorithms. Our datasets are available at https://gitlab.com/tringwald/adaptiope.
Hybrid quantum-classical approach to enhanced quantum metrology

Quantum metrology plays a fundamental role in many scientific areas. However, the complexity of engineering entangled probes and the external noise raise technological barriers for realizing the expected precision of the to-be-estimated parameter with given resources. Here, we address this problem by introducing adjustable controls into the encoding process and then utilizing a hybrid quantum-classical approach to automatically optimize the controls online. Our scheme does not require any complex or intractable off-line design, and it can inherently correct certain unitary errors during the learning procedure. We also report the first experimental demonstration of this promising scheme for the task of finding optimal probes for frequency estimation on a nuclear magnetic resonance (NMR) processor. The proposed scheme paves the way to experimentally auto-search optimal protocol for improving the metrology precision.