Reviewerly/exHarmony huggingface dataset:
Unsupervised Classification of Quantum Data

We introduce the problem of unsupervised classification of quantum data, namely, of systems whose quantum states are unknown. We derive the optimal single-shot protocol for the binary case, where the states in a disordered input array are of two types. Our protocol is universal and able to automatically sort the input under minimal assumptions, yet partially preserving information contained in the states. We quantify analytically its performance for arbitrary size and dimension of the data. We contrast it with the performance of its classical counterpart, which clusters data that has been sampled from two unknown probability distributions. We find that the quantum protocol fully exploits the dimensionality of the quantum data to achieve a much higher performance, provided data is at least three-dimensional. For the sake of comparison, we discuss the optimal protocol when the classical and quantum states are known.
Comparing Relaxation Mechanisms in Quantum and Classical Transverse-Field Annealing

Annealing schedule control provides opportunities to better understand the manner and mechanisms by which putative quantum annealers operate. By appropriately modifying the annealing schedule to include a pause (keeping the Hamiltonian fixed) for a period of time, we show that it is possible to more directly probe the dissipative dynamics of the system at intermediate points along the anneal and examine thermal relaxation rates, for example, by observing the repopulation of the ground state after the minimum spectral gap. We provide a detailed comparison of experiments from a D-Wave device, simulations of the quantum adiabatic master equation, and a classical analogue of quantum annealing, spin-vector Monte Carlo, and we observe qualitative agreement, showing that the characteristic increase in success probability when pausing is not a uniquely quantum phenomena. We find that the relaxation in our system is dominated by a single timescale, which allows us to give a simple condition for when we can expect pausing to improve the time to solution, the relevant metric for classical optimization. Finally, we also explore in simulation the role of temperature whilst pausing as a means to better distinguish quantum and classical models of quantum annealers.
Efficient Stabilized Two-Qubit Gates on a Trapped-Ion Quantum Computer

In order to scale up quantum processors and achieve a quantum advantage, it is crucial to economize on the power requirement of two-qubit gates, make them robust to drift in experimental parameters, and shorten the gate times. Applicable to all quantum computer architectures whose two-qubit gates rely on phase-space closure, we present here a new gate-optimizing principle according to which negligible amounts of gate fidelity are traded for substantial savings in power, which, in turn, can be traded for substantial increases in gate speed and/or qubit connectivity. As a concrete example, we illustrate the method by constructing optimal pulses for entangling gates on a pair of ions within a trapped-ion chain, one of the leading quantum computing architectures. Our method is direct, noniterative, and linear, and, in some parameter regimes, constructs gate-steering pulses requiring up to an order of magnitude less power than the standard method. Additionally, our method provides increased robustness to mode drift. We verify the new trade-off principle experimentally on our trapped-ion quantum computer.
Deterministic spin-photon entanglement from a trapped ion in a fiber Fabry–Perot cavity

Abstract The development of efficient network nodes is a key element for the realization of quantum networks which promise great capabilities as distributed quantum computing or provable secure communication. We report the realization of a quantum network node using a trapped ion inside a fiber-based Fabry–Perot cavity. We show the generation of deterministic entanglement at a high fidelity of 90.1(17)% between a trapped Yb ion and a photon emitted into the resonator mode. We achieve a success probability for generation and detection of entanglement for a single shot of 2.5 × 10 −3 resulting in 62 Hz entanglement rate.
Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork

AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In such AI-advised decision making, humans and machines form a team, where the human is responsible for making final decisions. But is the most accurate AI the best teammate? We argue "not necessarily" --- predictable performance may be worth a slight sacrifice in AI accuracy. Instead, we argue that AI systems should be trained in a human-centered manner, directly optimized for team performance. We study this proposal for a specific type of human-AI teaming, where the human overseer chooses to either accept the AI recommendation or solve the task themselves. To optimize the team performance for this setting we maximize the team's expected utility, expressed in terms of the quality of the final decision, cost of verifying, and individual accuracies of people and machines. Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance and show the benefit of modeling teamwork during training through improvements in expected team utility across datasets, considering parameters such as human skill and the cost of mistakes. We discuss the shortcoming of current optimization approaches beyond well-studied loss functions such as log-loss, and encourage future work on AI optimization problems motivated by human-AI collaboration.
T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks

The objective of this study was to propose an optimal input image quality for a conditional generative adversarial network (GAN) in T1-weighted and T2-weighted magnetic resonance imaging (MRI) images.A total of 2,024 images scanned from 2017 to 2018 in 104 patients were used. The prediction framework of T1-weighted to T2-weighted MRI images and T2-weighted to T1-weighted MRI images were created with GAN. Two image sizes (512 × 512 and 256 × 256) and two grayscale level conversion method (simple and adaptive) were used for the input images. The images were converted from 16-bit to 8-bit by dividing with 256 levels in a simple conversion method. For the adaptive conversion method, the unused levels were eliminated in 16-bit images, which were converted to 8-bit images by dividing with the value obtained after dividing the maximum pixel value with 256.The relative mean absolute error (rMAE ) was 0.15 for T1-weighted to T2-weighted MRI images and 0.17 for T2-weighted to T1-weighted MRI images with an adaptive conversion method, which was the smallest. Moreover, the adaptive conversion method has a smallest mean square error (rMSE) and root mean square error (rRMSE), and the largest peak signal-to-noise ratio (PSNR) and mutual information (MI). The computation time depended on the image size.Input resolution and image size affect the accuracy of prediction. The proposed model and approach of prediction framework can help improve the versatility and quality of multi-contrast MRI tests without the need for prolonged examinations.
The controlled SWAP test for determining quantum entanglement

Abstract Quantum entanglement is essential to the development of quantum computation, communications, and technology. The controlled SWAP test, widely used for state comparison, can be adapted to an efficient and useful test for entanglement of a pure state. Here we show that the test can evidence the presence of entanglement (and further, genuine n -qubit entanglement), can distinguish entanglement classes, and that the concurrence of a two-qubit state is related to the test’s output probabilities. We also propose a multipartite measure of entanglement that acts similarly for n -qubit states. The average number of copies of the test state required to detect entanglement decreases for larger systems, to four on average for many ( n ≳ 8) qubits for maximally entangled states. For non-maximally entangled states, the number of copies required to detect entanglement increases with decreasing entanglement. Furthermore, the results are robust to second order when typical small errors are introduced to the state under investigation.
Continual Learning for Blind Image Quality Assessment

The explosive growth of image data facilitates the fast development of image processing and computer vision methods for emerging visual applications, meanwhile introducing novel distortions to the processed images. This poses a grand challenge to existing blind image quality assessment (BIQA) models, which are weak at adapting to subpopulation shift. In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data. We first identify five desiderata in the continual setting with three criteria to quantify the prediction accuracy, the plasticity, and the stability, respectively. We then propose a simple yet effective continual learning method for BIQA. Specifically, based on a shared backbone network, we add a prediction head for a new dataset, and enforce a regularizer to allow all prediction heads to evolve with new data while being resistant to catastrophic forgetting of old data. We compute the overall quality score by a weighted summation of predictions from all heads. Extensive experiments demonstrate the promise of the proposed continual learning method in comparison to standard training techniques for BIQA, with and without experience replay.
A Trust-Evaluation-Enhanced Blockchain-Secured Industrial IoT System

The Industrial Internet of Things (IIoT), allowing direct wireless communications between industrial machines and people, is the key component of Industry 4.0. By grouping several IIoT devices (IIoT-Ds) into IIoT groups (IIoT-Gs), the IIoT-Ds within one group can share information and deter malicious users from accessing the network by modifying the access control list (ACL) in IIoT-Ds. In an IIoT-G, several IIoT-Ds jointly manage the access control of IIoT sensors. The security of ACL is protected by blockchains deployed in IIoT-Ds. Even secured by blockchains, a blockchain-based IIoT-G (B-IIoT-G) still cannot secure correct access control in a system with more than half of the blockchain-based IIoT-Ds (B-IIoT-Ds) from spreading wrong authorization information. In this article, we propose a B-IIoT-G with trust evaluation for each B-IIoT-D to evaluate the weight in voting for final decisions of authorization. By designing a new voting mechanism with trust evaluation for the access control in B-IIoT-G, the system has a higher probability of making correct authorization even with malicious B-IIoT-Ds. Simulation results have demonstrated that the mechanism with trust evaluation can still make, with a high probability, correct authorization even with more than half of the B-IIoT-Ds in B-IIoT-G being malicious.
Scalable Privacy-Preserving Distributed Learning

Abstract In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstraction that we extend with privacy constraints. We design spindle (Scalable Privacy-preservINg Distributed LEarning), the first distributed and privacy-preserving system that covers the complete ML workflow by enabling the execution of a cooperative gradient-descent and the evaluation of the obtained model and by preserving data and model confidentiality in a passive-adversary model with up to N −1 colluding parties. spindle uses multiparty homomorphic encryption to execute parallel high-depth computations on encrypted data without significant overhead. We instantiate spindle for the training and evaluation of generalized linear models on distributed datasets and show that it is able to accurately (on par with non-secure centrally-trained models) and efficiently (due to a multi-level parallelization of the computations) train models that require a high number of iterations on large input data with thousands of features, distributed among hundreds of data providers. For instance, it trains a logistic-regression model on a dataset of one million samples with 32 features distributed among 160 data providers in less than three minutes.
DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing

For the last few years, Cloud computing has been considered an attractive high-performance computing platform for individuals as well as organizations. The Cloud service providers (CSPs) are setting up data centers with high performance computing resources to accommodate the needs of Cloud users. The users are mainly interested in the response time, whereas the Cloud service providers are more concerned about the revenue generation. Concerning these requirements, the task scheduling for the users’ applications in Cloud computing attained focus from the research community. Various task scheduling heuristics have been proposed that are available in the literature. However, the task scheduling problem is NP-hard in nature and thus finding optimal scheduling is always challenging. In this research, a resource-aware dynamic task scheduling approach is proposed and implemented. The simulation experiments have been performed on the Cloudsim simulation tool considering three renowned datasets, namely HCSP, GoCJ, and Synthetic workload. The obtained results of the proposed approach are then compared against RALBA, Dynamic MaxMin, DLBA, and PSSELB scheduling approaches concerning average resource utilization (ARUR), Makespan, Throughput, and average response time (ART). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DRALBA</i> approach has revealed significant improvements in terms of attained ARUR, Throughput, and Makespan.This fact is endorsed by the average resource utilization results (i.e., 98 % for HCSP dataset, 75 % for Synthetic workload (improve ARUR by 72.00 %, 77.33 %, 78.67 %, and 13.33 % as compared to RALBA, Dynamic MaxMin, DLBA and PSSELB respectively), and 77 % for GoCJ (i.e., the second best attained ARUR)).
CRaDLe: Deep code retrieval based on semantic Dependency Learning

Code retrieval is a common practice for programmers to reuse existing code snippets in the open-source repositories. Given a user query (i.e., a natural language description), code retrieval aims at searching the most relevant ones from a set of code snippets. The main challenge of effective code retrieval lies in mitigating the semantic gap between natural language descriptions and code snippets. With the ever-increasing amount of available open-source code, recent studies resort to neural networks to learn the semantic matching relationships between the two sources. The statement-level dependency information, which highlights the dependency relations among the program statements during the execution, reflects the structural importance of one statement in the code, which is favorable for accurately capturing the code semantics but has never been explored for the code retrieval task. In this paper, we propose CRaDLe, a novel approach for Code Retrieval based on statement-level semantic Dependency Learning. Specifically, CRaDLe distills code representations through fusing both the dependency and semantic information at the statement level, and then learns a unified vector representation for each code and description pair for modeling the matching relationship. Comprehensive experiments and analysis on real-world datasets show that the proposed approach can accurately retrieve code snippets for a given query and significantly outperform the state-of-the-art approaches on the task.
Contrastive Out-of-Distribution Detection for Pretrained Transformers

Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution.However, in realworld scenarios, the model often faces out-ofdistribution (OOD) instances that can cause severe semantic shift problems at inference time.Therefore, in practice, a reliable model should identify such instances, and then either reject them during inference or pass them over to models that handle another distribution.In this paper, we develop an unsupervised OOD detection method, in which only the indistribution (ID) data are used in training.We propose to fine-tune the Transformers with a contrastive loss, which improves the compactness of representations, such that OOD instances can be better differentiated from ID ones.These OOD instances can then be accurately detected using the Mahalanobis distance in the model's penultimate layer.We experiment with comprehensive settings and achieve near-perfect OOD detection performance, outperforming baselines drastically.We further investigate the rationales behind the improvement, finding that more compact representations through margin-based contrastive learning bring the improvement.We release our code to the community for future research 1 .
Digitization and the evolution of money as a social technology of account

Abstract Throughout the history of monetary thought, economists have predominantly emphasised the function of money as a medium of exchange along with the intrinsic properties that enhance its salability and credibility as the most liquid store of value. But the social institution of money co-evolves with technology. It is significant that the advent of digital crypto-currencies was initiated by computer scientists and has taken economists completely by surprise. As a consequence, it also forces our profession to rethink the basic phenomenology of money. In accordance with the views of Wieser and Schumpeter, digitization brings to the fore the immaterial function of money as a standard of value and social technology of account, which increasingly absorbs its function as a medium of exchange. The potential impact of this on economic policy is huge. The variety of different crypto coins has proven the technical feasibility of competing private currencies as proposed by Hayek. In the long term, however, there is reason to doubt the persistence of intense competition. One must fear that major digital platforms will extend their current dominance in multisided virtual market places to include digital payments and money. Central banks are increasingly anxious to preserve public sovereignty over the common unit of account and are considering issuing their own digital fiat money. After the current era of intense creative experimentation, the potentially new spontaneous order of private crypto-currencies is likely to be supplanted by central bank digital currencies (CBDCs), the design of which will depend on deliberate public choices and policies.
Meta-Learning for Domain Generalization in Semantic Parsing

The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing outof-domain performance are becoming increasingly available.However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning.In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing.We apply a modelagnostic training algorithm that simulates zeroshot parsing by constructing virtual train and test sets from disjoint domains.The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains.Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.
Time Series Anomaly Detection with Multiresolution Ensemble Decoding

Recurrent autoencoder is a popular model for time series anomaly detection, in which outliers or abnormal segments are identified by their high reconstruction errors. However, existing recurrent autoencoders can easily suffer from overfitting and error accumulation due to sequential decoding. In this paper, we propose a simple yet efficient recurrent network ensemble called Recurrent Autoencoder with Multiresolution Ensemble Decoding (RAMED). By using decoders with different decoding lengths and a new coarse-to-fine fusion mechanism, lower-resolution information can help long-range decoding for decoders with higher-resolution outputs. A multiresolution shape-forcing loss is further introduced to encourage decoders' outputs at multiple resolutions to match the input's global temporal shape. Finally, the output from the decoder with the highest resolution is used to obtain an anomaly score at each time step. Extensive empirical studies on real-world benchmark data sets demonstrate that the proposed RAMED model outperforms recent strong baselines on time series anomaly detection.
Verisig 2.0: Verification of Neural Network Controllers Using Taylor Model Preconditioning

Abstract This paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2.0 to efficiently handle larger NNs than existing tools can. We provide an extensive evaluation over 10 benchmarks and compare Verisig 2.0 against three state-of-the-art verification tools. We show that Verisig 2.0 is both more accurate and faster, achieving speed-ups of up to 21x and 268x against different tools, respectively.
Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup

Contrastive learning has been applied successfully to learn vector representations of text.Previous research demonstrated that learning high-quality representations benefits from batch-wise contrastive loss with a large number of negatives.In practice, the technique of in-batch negative is used, where for each example in a batch, other batch examples' positives will be taken as its negatives, avoiding encoding extra negatives.This, however, still conditions each example's loss on all batch examples and requires fitting the entire large batch into GPU memory.This paper introduces a gradient caching technique that decouples backpropagation between contrastive loss and the encoder, removing encoder backward pass data dependency along the batch dimension.As a result, gradients can be computed for one subset of the batch at a time, leading to almost constant memory usage. 1