Reviewerly/exHarmony huggingface dataset:
Optimizing Resource Efficiencies for Scalable Full-Stack Quantum Computers

In the race to build scalable quantum computers, minimizing the resource consumption of their full stack to achieve a target performance becomes crucial. It mandates a synergy of fundamental physics and engineering: the former for the microscopic aspects of computing performance and the latter for the macroscopic resource consumption. For this, we propose a holistic methodology dubbed metric noise resource (MNR) that is able to quantify and optimize all aspects of the full-stack quantum computer, bringing together concepts from quantum physics (e.g., noise on the qubits), quantum information (e.g., computing architecture and type of error correction), and enabling technologies (e.g., cryogenics, control electronics, and wiring). This holistic approach allows us to define and study resource efficiencies as ratios between performance and resource cost. As a proof of concept, we use MNR to minimize the power consumption of a full-stack quantum computer, performing noisy or fault-tolerant computing with a target performance for the task of interest. Comparing this with a classical processor performing the same task, we identify a quantum energy advantage in regimes of parameters distinct from the commonly considered quantum computational advantage. This provides a previously overlooked practical argument for building quantum computers. While our illustration uses highly idealized parameters inspired by superconducting qubits with concatenated error correction, the methodology is universal—it applies to other qubits and error-correcting codes—and it provides experimenters with guidelines to build energy-efficient quantum computers. In some regimes of high energy consumption, it can reduce this consumption by orders of magnitude. Overall, our methodology lays the theoretical foundation for resource-efficient quantum technologies.7 MoreReceived 29 November 2022Accepted 31 July 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040319Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEnergy-efficient infrastructureQuantum algorithmsQuantum benchmarkingQuantum computationQuantum engineeringQuantum error correctionQuantum gatesQuantum information architectures & platformsQuantum information processingQuantum softwareQuantum Information, Science & TechnologyInterdisciplinary Physics
An illustration of model agnostic explainability methods applied to environmental data

Abstract Historically, two primary criticisms statisticians have of machine learning and deep neural models is their lack of uncertainty quantification and the inability to do inference (i.e., to explain what inputs are important). Explainable AI has developed in the last few years as a sub‐discipline of computer science and machine learning to mitigate these concerns (as well as concerns of fairness and transparency in deep modeling). In this article, our focus is on explaining which inputs are important in models for predicting environmental data. In particular, we focus on three general methods for explainability that are model agnostic and thus applicable across a breadth of models without internal explainability: “feature shuffling”, “interpretable local surrogates”, and “occlusion analysis”. We describe particular implementations of each of these and illustrate their use with a variety of models, all applied to the problem of long‐lead forecasting monthly soil moisture in the North American corn belt given sea surface temperature anomalies in the Pacific Ocean.
APF-IRRT*: An Improved Informed Rapidly-Exploring Random Trees-Star Algorithm by Introducing Artificial Potential Field Method for Mobile Robot Path Planning

An Informed RRT* (IRRT*) algorithm is one of the optimized versions of a Rapidly-exploring Random Trees (RRT) algorithm which finds near-optimal solutions faster than RRT and RRT* algorithms by restricting the search area to an ellipsoidal subset of the state space. However, IRRT* algorithm has the disadvantage of randomness of sampling and a non-real time process, which has a negative impact on the convergence rate and search efficiency in path planning applications. In this paper, we report a hybrid algorithm by combining the Artificial Potential Field Method (APF) with an IRRT* algorithm for mobile robot path planning. By introducing the virtual force field of APF into the search tree expansion stage of the IRRT* algorithm, the guidance of the algorithm increases, which greatly improves the convergence rate and search efficiency of the IRRT* algorithm. The proposed algorithm was validated in simulations and proven to be superior to some other RRT-based algorithms in search time and path length. It also was performed in a real robotic platform, which shows that the proposed algorithm can be well executed in real scenarios.
Preparing Precollege Students for the Second Quantum Revolution with Core Concepts in Quantum Information Science

After the passage of the U.S. National Quantum Initiative Act in December 2018, the National Science Foundation (NSF) and the Office of Science and Technology Policy (OSTP) recently assembled an interagency working group and conducted a workshop titled “Key Concepts for Future Quantum Information Science Learners” that focused on identifying core concepts for future curricular and educator activities to help precollege students engage with quantum information science (QIS). Helping precollege students learn these key concepts in QIS is an effective approach to introducing them to the second quantum revolution and inspiring them to become future contributors in the growing field of quantum information science and technology as leaders in areas related to quantum computing, communication, and sensing. This paper is a call to precollege educators to contemplate including QIS concepts into their existing courses at appropriate levels and get involved in the development of curricular materials suitable for their students. Also, research shows that compare-and-contrast activities can provide an effective approach to helping students learn. Therefore, we illustrate a pedagogical approach that contrasts the classical and quantum concepts so that educators can adapt them for their students in their lesson plans to help them learn the differences between key concepts in quantum and classical contexts.
Monitoring and processing of physiological and domotics parameters in an Internet of Things (IoT) assistive living environment

In relation to an ever changing epidemiological world context, a category of people that is more subject to be impacted consists of the elderly. Certain steps can be taken in order to improve their quality of life especially in case of illness. One way of achieving this is to have a smart assistive living environment, which includes home automation and medical monitoring. The proposed system expands on an IoT solution for assisted living and introduces a highly flexible rules engine for processing physiological and domotics data obtained from the home environment, and for interacting with the system actuators. As proof-of-concept, there are several use-cases that are discussed depending on the type of patient: diabetic, cardiac, hypertensive, obese, COVID or Alzheimer. These scenarios emphasize the efficiency of the proposed solution and offer an insight on the high degree of abstraction and extensibility of the system.
Accurate method and variable tracking in commit history

Tracking program elements in the commit history of a project is essential for supporting various software maintenance, comprehension and evolution tasks. Accuracy is of paramount importance for the adoption of program element tracking tools by developers and researchers. To this end, we propose CodeTracker, a refactoring-aware tool that can generate the commit change history for method and variable declarations with a very high accuracy. More specifically, CodeTracker has 99.9% precision and recall in method tracking, surpassing the previous state-of-the-art tool, CodeShovel, with a comparable execution time. CodeTracker is the first tool of its kind that can track the change history of variables with 99.7% precision and 99.8% recall. To evaluate its accuracy in variable tracking, we extended the oracle created by Grund et al. for the evaluation of CodeShovel, with the complete change history of all 1345 variables and parameters declared in the 200 methods comprising the Grund et al. oracle. We make our tool and extended oracle publicly available to enable the replication of our experiments and facilitate future research on program element tracking techniques.
Scalable Multi-Class Sampling via Filtered Sliced Optimal Transport

We propose a multi-class point optimization formulation based on continuous Wasserstein barycenters. Our formulation is designed to handle hundreds to thousands of optimization objectives and comes with a practical optimization scheme. We demonstrate the effectiveness of our framework on various sampling applications like stippling, object placement, and Monte-Carlo integration. We a derive multi-class error bound for perceptual rendering error which can be minimized using our optimization. We provide source code at https://github.com/iribis/filtered-sliced-optimal-transport.
Technical Analysis of Contact Tracing Platform Developed by Google–Apple for Constraining the Spread of COVID-19

Amid the ongoing COVID-19 pandemic, technical solutions (e.g., smartphone apps, web-based platforms, digital surveillance platforms, etc.) have played a vital role in constraining the spread of COVID-19. The major aspects in which technical solutions have helped the general public (or health officials) are contact tracing, spread prediction, trend forecasting, infection risk estimation, hotspot identification, alerting people to stay away from contaminated places, hospitalization length estimation, clinical severity analysis, and quarantine monitoring, to name a few. Apart from other services, contact tracing has been extensively performed with the help of Bluetooth and GPS-powered smartphone applications when vaccines were unavailable. In this article, we technically analyze the contact tracing platform developed by Google–Apple for constraining the spread of COVID-19. We suggest unexplored technical functionalities that can further strengthen the platform from privacy preservation, service scenarios, and robustness point of view. Lastly, some AI-based and privacy-assured services that can be integrated with the platform to control the pandemic adequately are suggested. The technical analysis demonstrates that while the Google–Apple platform is well-engineered, it is not free of vulnerabilities, weaknesses, and misconfigurations that may lead to its poor adoption in real-life scenarios. This work can serve as a guideline for further enhancing the practicality of contact tracing platform to effectively handle future infectious diseases.
Personalized Federated Multi-Task Learning over Wireless Fading Channels

Multi-task learning (MTL) is a paradigm to learn multiple tasks simultaneously by utilizing a shared network, in which a distinct header network is further tailored for fine-tuning for each distinct task. Personalized federated learning (PFL) can be achieved through MTL in the context of federated learning (FL) where tasks are distributed across clients, referred to as personalized federated MTL (PF-MTL). Statistical heterogeneity caused by differences in the task complexities across clients and the non-identically independently distributed (non-i.i.d.) characteristics of local datasets degrades the system performance. To overcome this degradation, we propose FedGradNorm, a distributed dynamic weighting algorithm that balances learning speeds across tasks by normalizing the corresponding gradient norms in PF-MTL. We prove an exponential convergence rate for FedGradNorm. Further, we propose HOTA-FedGradNorm by utilizing over-the-air aggregation (OTA) with FedGradNorm in a hierarchical FL (HFL) setting. HOTA-FedGradNorm is designed to have efficient communication between the parameter server (PS) and clients in the power- and bandwidth-limited regime. We conduct experiments with both FedGradNorm and HOTA-FedGradNorm using MT facial landmark (MTFL) and wireless communication system (RadComDynamic) datasets. The results indicate that both frameworks are capable of achieving a faster training performance compared to equal-weighting strategies. In addition, FedGradNorm and HOTA-FedGradNorm compensate for imbalanced datasets across clients and adverse channel effects.
Efficient Informative Path Planning via Normalized Utility in Unknown Environments Exploration

Exploration is an important aspect of autonomous robotics, whether it is for target searching, rescue missions, or reconnaissance in an unknown environment. In this paper, we propose a solution to efficiently explore the unknown environment by unmanned aerial vehicles (UAV). Innovatively, a topological road map is incrementally built based on Rapidly-exploring Random Tree (RRT) and maintained along with the whole exploration process. The topological structure can provide a set of waypoints for searching an optimal informative path. To evaluate the path, we consider the information measurement based on prior map uncertainty and the distance cost of the path, and formulate a normalized utility to describe information-richness along the path. The informative path is determined in every period by a local planner, and the robot executes the planned path to collect measurements of the unknown environment and restructure a map. The proposed framework and its composed modules are verified in two 3-D environments, which exhibit better performance in improving the exploration efficiency than other methods.
Improved Management of Issue Dependencies in Issue Trackers of Large Collaborative Projects

Issue trackers, such as Jira, have become the prevalent collaborative tools in software engineering for managing issues, such as requirements, development tasks, and software bugs. However, issue trackers inherently focus on the lifecycle of single issues, although issues have and express dependencies on other issues that constitute issue dependency networks in large complex collaborative projects. The objective of this study is to develop supportive solutions for the improved management of dependent issues in an issue tracker. This study follows the Design Science methodology, consisting of eliciting drawbacks and constructing and evaluating a solution and system. The study was carried out in the context of The Qt Company's Jira, which exemplifies an actively used, almost two-decade-old issue tracker with over 100,000 issues. The drawbacks capture how users operate with issue trackers to handle issue information in large, collaborative, and long-lived projects. The basis of the solution is to keep issues and dependencies as separate objects and automatically construct an issue graph. Dependency detections complement the issue graph by proposing missing dependencies, while consistency checks and diagnoses identify conflicting issue priorities and release assignments. Jira's plugin and service-based system architecture realize the functional and quality concerns of the system implementation. We show how to adopt the intelligent supporting techniques of an issue tracker in a complex use context and a large data-set. The solution considers an integrated and holistic system view, practical applicability and utility, and the practical characteristics of issue data, such as inherent incompleteness.
The Ball is in Our Court: Conducting Visualization Research With Sports Experts

Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric data, making sports a challenging target for visualization. Emerging technologies, such as augmented and mixed reality (AR/XR), have brought exciting opportunities along with new challenges for sports visualization. We share our experience working with sports domain experts and present lessons learned from conducting visualization research in SportsXR. In our previous work, we have targeted different types of users in sports, including athletes, game analysts, and fans. Each user group has unique design constraints and requirements, such as obtaining real-time visual feedback in training, automating the low-level video analysis workflow, or personalizing embedded visualizations for live game data analysis. In this paper, we synthesize our best practices and pitfalls we identified while working on SportsXR. We highlight lessons learned in working with sports domain experts in designing and evaluating sports visualizations and in working with emerging AR/XR technologies. We envision that sports visualization research will benefit the larger visualization community through its unique challenges and opportunities for immersive and situated analytics.
Online Phase Reconstruction via DNN-Based Phase Differences Estimation

This paper presents a two-stage online phase reconstruction framework using causal deep neural networks (DNNs). Phase reconstruction is a task of recovering phase of the short-time Fourier transform (STFT) coefficients only from the corresponding magnitude. However, phase is sensitive to waveform shifts and not easy to estimate from the magnitude even with a DNN. To overcome this problem, we propose to use DNNs for estimating differences of phase between adjacent time-frequency bins. We show that convolutional neural networks are suitable for phase difference estimation, according to the theoretical relation between partial derivatives of STFT phase and magnitude. The estimated phase differences are used for reconstructing phase by solving a weighted least squares problem in a frame-by-frame manner. In contrast to existing DNN-based phase reconstruction methods, the proposed framework is causal and does not require any iterative procedure. The experiments showed that the proposed method outperforms existing online methods and a DNN-based method for phase reconstruction.
Breaking stereotypes

The Interaction s website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Visual attention during seeing for speaking in healthy aging.

As we age, we accumulate a wealth of information about the surrounding world. Evidence from visual search suggests that older adults retain intact knowledge for where objects tend to occur in everyday environments (semantic information) that allows them to successfully locate objects in scenes, but may overrely on semantic guidance. We investigated age differences in the allocation of attention to semantically informative and visually salient information in a task in which the eye movements of younger (N = 30, aged 18-24) and older (N = 30, aged 66-82) adults were tracked as they described real-world scenes. We measured the semantic information in scenes based on "meaning map" ratings from a norming sample of young and older adults, and image salience as graph-based visual saliency. Logistic mixed-effects modeling was used to determine whether, controlling for center bias, fixated scene locations differed in semantic informativeness and visual salience from locations that were not fixated, and whether these effects differed for young and older adults. Semantic informativeness predicted fixated locations well overall, as did image salience, although unique variance in the model was better explained by semantic informativeness than image salience. Older adults were less likely to fixate informative locations in scenes than young adults were, though the locations older adults' fixated were independently predicted well by informativeness. These results suggest young and older adults both use semantic information to guide attention in scenes and that older adults do not overrely on semantic information across the board. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
A generalized approach for the determination of optimum tilt angle for solar photovoltaic modules with selected locations in Ethiopia as illustration examples

The amount of total solar energy that strikes the photovoltaic module's surface directly affects how much power is generated. On the other hand, solar energy is influenced by the solar module's location, tilt angle, and orientation. The models presented in this study can be used to determine the seasonal and annual optimal tilt angles of photovoltaic modules at any location in Ethiopia as a function of the location's latitude and elevation without the need for solar radiation data. Both isotropic and anisotropic diffuse solar radiation models were used to determine the monthly, seasonal, and annual optimal tilt angles. The monthly average daily global horizontal solar radiation for a total of 44 study sites, 32 for training and 12 for testing, were retrieved from the National Aeronautical and Space Administration database. The optimal tilt angle and regression models were determined by developing and applying algorithms in the programming languages MATLAB and R. The proposed model estimates the optimal tilt angle with the fewest statistical validation errors. By using the proposed monthly optimal tilt angles, solar radiation gains of 6.76% - 8.48% (for isotropic models) and 10.16% - 26.97% (anisotropic models) were obtained for Bahir Dar city. The solar radiation gains for the cities of Adigrat, Butajira, and Gode were found to be 7.18–9.02% (isotropic) and 10.90%–28.06% (anisotropic), 5.43%–7.05% (isotropic) and 8.48%–25.99% (anisotropic), and 4.33%–5.79% (isotropic) and 6.96%–23.03% (anisotropic), respectively. The results of this study were further confirmed by comparing them to previously published papers and online PVGIS and NREL's PVWatt software tools.
Deep networks on toroids: removing symmetries reveals the structure of flat regions in the landscape geometry*

Abstract We systematize the approach to the investigation of deep neural network landscapes by basing it on the geometry of the space of implemented functions rather than the space of parameters. Grouping classifiers into equivalence classes, we develop a standardized parameterization in which all symmetries are removed, resulting in a toroidal topology. On this space, we explore the error landscape rather than the loss. This lets us derive a meaningful notion of the flatness of minimizers and of the geodesic paths connecting them. Using different optimization algorithms that sample minimizers with different flatness we study the mode connectivity and relative distances. Testing a variety of state-of-the-art architectures and benchmark datasets, we confirm the correlation between flatness and generalization performance; we further show that in function space flatter minima are closer to each other and that the barriers along the geodesics connecting them are small. We also find that minimizers found by variants of gradient descent can be connected by zero-error paths composed of two straight lines in parameter space, i.e. polygonal chains with a single bend. We observe similar qualitative results in neural networks with binary weights and activations, providing one of the first results concerning the connectivity in this setting. Our results hinge on symmetry removal, and are in remarkable agreement with the rich phenomenology described by some recent analytical studies performed on simple shallow models.