Optical tweezers throw and catch single atoms

Single atoms movable from one place to another would enable a flying quantum memory that can be used for quantum communication and quantum computing at the same time. Guided atoms, e.g., by optical tweezers, provide a partial solution, but the benefit of flying qubits could be lost if they still interact with the guiding means. Here we propose and experimentally demonstrate freely-flying atoms that are not guided but are instead thrown and caught by optical tweezers. In experiments, cold atoms at 40 micro Kelvin temperature are thrown up to a free-flying speed of 0.65 m/s over a travel distance of 12.6 micrometer at a transportation efficiency of 94(3)%, even in the presence of other optical tweezers or atoms en route. This performance is not fundamentally limited but by current settings of optical tweezers with limited potential depth and width. We provide a set of proof-of-principle flying atom demonstrations, which include atom transport through optical tweezers, atom arrangements by flying atoms, and atom scattering off optical tweezers. Our study suggests possible applications of flying atoms, not only in fundamental studies such as single-atom low-energy collisions, but also non-photon quantum communication and flying-qubit-based quantum computing.Comment: 8 pages, 5 figure

Rydberg-atom graphs for quadratic unconstrained binary optimization problems

There is a growing interest in harnessing the potential of the Rydberg-atom system to address complex combinatorial optimization challenges. Here we present an experimental demonstration of how the quadratic unconstrained binary optimization (QUBO) problem can be effectively addressed using Rydberg-atom graphs. The Rydberg-atom graphs are configurations of neutral atoms organized into mathematical graphs, facilitated by programmable optical tweezers, and designed to exhibit many-body ground states that correspond to the maximum independent set (MIS) of their respective graphs. We have developed four elementary Rydberg-atom subgraph components, not only to eliminate the need of local control but also to be robust against interatomic distance errors, while serving as the building blocks sufficient for formulating generic QUBO graphs. To validate the feasibility of our approach, we have conducted a series of Rydberg-atom experiments selected to demonstrate proof-of-concept operations of these building blocks. These experiments illustrate how these components can be used to programmatically encode the QUBO problems to Rydberg-atom graphs and, by measuring their many-body ground states, how their QUBO solutions are determined subsequently.Comment: 13 pages, 6 figure

Estimating dietary intake from grocery shopping data – a comparative validation of relevant indicators in Switzerland

In light of the globally increasing prevalence of diet-related chronic diseases, new scalable and non-invasive dietary monitoring techniques are urgently needed. Automatically collected digital receipts from loyalty cards hereby promise to serve as an objective and automatically traceable digital marker for individual food choice behavior and do not require users to manually log individual meal items. With the introduction of the General Data Privacy Regulation in the European Union, millions of consumers gained the right to access their shopping data in a machine-readable form, representing a historic chance to leverage shopping data for scalable monitoring of food choices. Multiple quantitative indicators for evaluating the nutritional quality of food shopping have been suggested, but so far, no comparison has validated the potential of these alternative indicators within a comparative setting. This manuscript thus represents the first study to compare the calibration capacity and to validate the discrimination potential of previously suggested food shopping quality indicators for the nutritional quality of shopped groceries, including the Food Standards Agency Nutrient Profiling System Dietary Index (FSA-NPS DI), Grocery Purchase Quality Index-2016 (GPQI), Healthy Eating Index-2015 (HEI-2015), Healthy Trolley Index (HETI) and Healthy Purchase Index (HPI), checking if any of them performs differently from the others. The hypothesis is that some food shopping quality indicators outperform the others in calibrating and discriminating individual actual dietary intake. To assess the indicators’ potentials, 89 eligible participants completed a validated food frequency questionnaire (FFQ) and donated their digital receipts from the loyalty card programs of the two leading Swiss grocery retailers, which represent 70% of the national grocery market. Compared to absolute food and nutrient intake, correlations between density based relative food and nutrient intake and food shopping data are stronger. The FSA-NPS DI has the best calibration and discrimination performance in classifying participants’ consumption of nutrients and food groups, and seems to be a superior indicator to estimate nutritional quality of a user’s diet based on digital receipts from grocery shopping in Switzerland

Efficient homomorphic encryption framework for privacy-preserving regression

Homomorphic encryption (HE) has recently attracted considerable attention as a key solution for privacy-preserving machine learning because HE can apply to various areas that require to delegate outsourcing computations of user???s data. Nevertheless, its computational inefficiency still hinders its wider application. In this study, we propose an alternative to bridge the gap between the privacy and efficiency of HE by encrypting only a small amount of private information. We first derive an exact solution to HE-friendly ridge regression with multiple private variables, while linearly reducing the computational complexity of this algorithm over the number of variables. The proposed method has the advantage that it can be implemented using any HE scheme. Moreover, we propose an adversarial perturbation method that can prevent potential attacks on private variables, which have rarely been explored in HE-based machine learning studies. An extensive experiment on real-world benchmarking datasets supports the effectiveness of our method

Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis

Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and adjusts the ensemble weight based on loss value in this study. We theoretically demonstrate that the ensemble weight converges to the limiting distribution, and, thus, minimizes the average total loss from a new regret measure based on adversarial assumption. We also present an overall framework that can be applied to analyze time series. In the experiments, we focused on the on-line phase, in which the ensemble models predict the binary class for the simulated data and the financial and non-financial real data. The proposed method outperformed other ensemble approaches. Moreover, our method was not only robust to the intentional attacks but also sustainable in data distribution changes. In the future, our algorithm can be extended to regression and multiclass classification problems

Efficient differentially private kernel support vector classifier for multi-class classification

In this paper, we propose a multi-class classification method using kernel supports and a dynamical system under differential privacy. For small datasets, kernel methods, such as kernel support vector machines (SVMs), show good generalization performance with high-dimensional feature mapping. However, kernel SVMs have a fundamental weakness in achieving differential privacy because they construct decision functions based on a subset of the training data called support vectors. Furthermore, multi-class SVMs must decompose the training data into a binary class, which requires multiple accesses to the same training data. To address these limitations, we develop a two-phase classification algorithm based on support vector data description (SVDD). We first generate and prove a differentially private SVDD (DP-SVDD) by perturbing the sphere center in a high-dimensional feature space. Next, we partition the input space using a dynamical system and classify the partitioned regions using a noisy count. The proposed method results in robust, fast, and user-friendly multi-class classification, even on small-sized datasets, where differential privacy performs poorly

Oliot EPCIS: An open-source EPCIS 2.0 system for supply chain transparency

An international standard, GS1 EPCIS, has given data transparency on supply chains and logistics and taken a new turn in the era of the Internet of Things by ratifying the major release of v2.0 in July 2022 with the official support for sensor data and Semantic Web. Oliot EPCIS is an open-source Web information system pursuing full compliance with the data format and service interface requirements ratified in the standard. The paper presents challenges towards a highly scalable EPCIS system and how the proposed system resolves the challenges. We share the quantitative and qualitative evaluation compared to existing open sources

Variational cycle-consistent imputation adversarial networks for general missing patterns

Imputation of missing data is an important but challenging issue because we do not know the underlying distribution of the missing data. Previous imputation models have addressed this problem by assuming specific kinds of missing distributions. However, in practice, the mechanism of the missing data is un-known, so the most general case of missing pattern needs to be considered for successful imputation. In this paper, we present cycle-consistent imputation adversarial networks to discover the underlying distribution of missing patterns closely under some relaxations. Using adversarial training, our model successfully learns the most general case of missing patterns. Therefore our method can be applied to a wide variety of imputation problems. We empirically evaluated the proposed method with numerical and image data. The result shows that our method yields the state-of-the-art performance quantitatively and qualitatively on standard datasets. (c) 2022 Elsevier Ltd. All rights reserved.N

HE-Friendly Algorithm for Privacy-Preserving SVM Training

Support vector machine (SVM) is one of the most popular machine learning algorithms. It predicts a pre-defined output variable in real-world applications. Machine learning on encrypted data is becoming more and more important to protect both model information and data against various adversaries. While some studies have been proposed on inference or prediction phases, few have been reported on the training phase. Homomorphic encryption (HE) for the arithmetic of approximate numbers scheme enables efficient arithmetic evaluations of encrypted data of real numbers, which encourages to develop privacy-preserving machine learning training algorithm. In this study, we propose an HE-friendly algorithm for the SVM training phase which avoids inefficient operations and numerical instability on an encrypted domain. The inference phase is also implemented on the encrypted domain with fully-homomorphic encryption which enables real-time prediction. Our experiment showed that our HE-friendly algorithm outperformed the state-of-the-art logistic regression classifier with fully homomorphic encryption on toy and real-world datasets. To the best of our knowledge, this study is the first practical algorithm for training an SVM model with fully homomorphic encryption. Therefore, our result supports the development of practical applications of the privacy-preserving SVM model

Security-preserving support vector machine with fully homomorphic encryption

Recently, security issues have become more and more important to apply machine learning models to a real-world problem. It is necessary to preserve the data privacy for using sensitive data and to protect the information of a trained model for defending the intentional attacks. In this paper, we want to propose a security-preserving learning framework using fully homomorphic encryption for support vector machine model. Our approach aims to train the model on encrypted domain to preserve data and model privacy with the reduced communication between the servers. The proposed procedure includes our protocol, data structure and homomorphic evaluation