Reviewerly/exHarmony huggingface dataset:
Three-dimensional multimodal medical imaging system based on freehand ultrasound and structured light

We propose a three-dimensional (3D) multimodal medical imaging system that combines freehand ultrasound and structured light 3D reconstruction in a single coordinate system without requiring registration. To the best of our knowledge, these techniques have not been combined as a multimodal imaging technique. The system complements the internal 3D information acquired with ultrasound with the external surface measured with the structured light technique. Moreover, the ultrasound probe's optical tracking for pose estimation was implemented based on a convolutional neural network. Experimental results show the system's high accuracy and reproducibility, as well as its potential for preoperative and intraoperative applications. The experimental multimodal error, or the distance from two surfaces obtained with different modalities, was 0.12 mm. The code is available in a Github repository.
Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.
Improving the Design of Ambient Intelligence Systems: Guidelines Based on a Systematic Review

Ambient intelligence (AmI) emerged in Europe with the idea that the computational support available in a living environment can help people's lives.AmI introduces new challenges and critical issues because this technology differs from the traditional form of interaction that is centered on a device or system.In this work, we aim to contribute to this topic by conducting a systematic literature review in order to identify Human-Computer Interaction guidelines for the design of ambient intelligence systems.We found a total of 120 guidelines from 27 papers, and we grouped similar guidelines creating different categories.These categories of guidelines later became a unified guideline incorporating also some of our own ideas.As a result, this paper identifies ten categories and guidelines to improve user interaction with ambient intelligence systems.We believe that these guidelines significantly contribute to designing more intuitive AmI systems for users, including those with disabilities.
Mining Non-Redundant Co-Location Patterns

Co-location pattern mining refers to discovering neighboring relationships of spatial features distributed in geographic space. With the rapid growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the large number of discovered patterns containing multiple redundancies. To address this problem, in this article, we propose a novel approach for discovering the super participation index-closed (SPI-closed) co-location patterns which are a newly proposed lossless condensed representation of co-location patterns by considering distributions of the spatial instances. In the proposed approach, first, a linear-time method is designed to generate complete and correct neighboring cliques using extended neighboring relationships. Based on these cliques, a hash structure is then constructed to store the distributions of the co-location patterns in a condensed way. Finally, using this hash structure, the SPI-closed co-location patterns (SCPs) are efficiently discovered even if the prevalence threshold is changed, while similar approaches have to restart their mining processes. To confirm the efficiency of the proposed method, we compared its performance with similar approaches in the literature on multiple real and synthetic spatial datasets. The experiments confirm that our new approach is more efficient, effective, and flexible than similar approaches.
Health improvement framework for actionable treatment planning using a surrogate Bayesian model

Abstract Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. A prominent issue is the development of objective treatment processes in clinical situations. This study proposes a framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the actionability for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model. We first evaluate the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework is applied to an actual health checkup dataset comprising data from 3132 participants, to lower systolic blood pressure and risk of chronic kidney disease at the individual level. We confirm that the computed treatment processes are actionable and consistent with clinical knowledge for improving these values. We also show that the improvement processes presented by the framework can be clinically informative. These results demonstrate that our framework can contribute toward decision-making in the medical field, providing clinicians with deeper insights.
Hyperparameter Optimization Using Genetic Algorithms to Detect Frauds Transactions

Today, online services have evolved at a large scale which has made our life very easy, but there are many problems and challenges to make these services more secure for users. For instance, every day, many transactions are made by customers, and much private information is posted and shared on E-commerce and social media websites which makes privacy, safety and reliability a trough challenge to defy. Credit card fraud detection is one of these problems because fraudsters try to make every transaction legitimate by stealing the information related to the credit card. Hence, easy methods and other less complex techniques are not going to detect this type of fraud. Having an effective fraud detection technique has become a requirement for all banks to minimize chaos and maintain some order in place. In this paper, we use machine learning to detect fraudulent transactions by applying a genetic algorithm (GA) to optimize the hyperparameter and compare it with grid search (GS) methods. The used algorithms are random forest (RF), AdaBoost (AB), logistic regression (LR), decision tree (DT), and support vector machine (SVM) classifier. As the credit card fraud dataset is highly skewed (imbalanced data set) and the performance of fraud detection is greatly affected by the sampling approach, so we use undersampling to handle this issue. The obtained results in terms of accuracy, precision, recall, and F1_score have shown that the genetic algorithm can generate better performances in a short-time in comparison with the GS algorithm.
One-particle and two-particle visibilities in bipartite entangled Gaussian states

Complementarity between one-particle visibility and two-particle visibility in discrete systems can be extended to bipartite quantum-entangled Gaussian states implemented with continuous-variable quantum optics. The meaning of the two-particle visibility originally defined by Jaeger, Horne, Shimony, and Vaidman with the use of an indirect method that first corrects the two-particle probability distribution by adding and subtracting other distributions with varying degree of entanglement, however, deserves further analysis. Furthermore, the origin of complementarity between one-particle visibility and two-particle visibility is somewhat elusive and it is not entirely clear what is the best way to associate particular two-particle quantum observables with the two-particle visibility. Here, we develop a direct method for quantifying the two-particle visibility based on measurement of a pair of two-particle observables that are compatible with the measured pair of single-particle observables. For each of the two-particle observables from the pair is computed corresponding visibility, after which the absolute difference of the latter pair of visibilities is considered as a redefinition of the two-particle visibility. Our approach reveals an underlying mathematical symmetry as it treats the two pairs of one-particle or two-particle observables on equal footing by formally identifying all four observable distributions as rotated marginal distributions of the original two-particle probability distribution. The complementarity relation between one-particle visibility and two-particle visibility obtained with the direct method is exact in the limit of infinite Gaussian precision where the entangled Gaussian state approaches an ideal Einstein-Podolsky-Rosen state. The presented results demonstrate the theoretical utility of rotated marginal distributions for elucidating the nature of two-particle visibility and provide tools for the development of quantum applications employing continuous variables.
MemX

This work presents MemX: a biologically-inspired attention-aware eyewear system developed with the goal of pursuing the long-awaited vision of a personalized visual Memex. MemX captures human visual attention on the fly, analyzes the salient visual content, and records moments of personal interest in the form of compact video snippets. Accurate attentive scene detection and analysis on resource-constrained platforms is challenging because these tasks are computation and energy intensive. We propose a new temporal visual attention network that unifies human visual attention tracking and salient visual content analysis. Attention tracking focuses computation-intensive video analysis on salient regions, while video analysis makes human attention detection and tracking more accurate. Using the YouTube-VIS dataset and 30 participants, we experimentally show that MemX significantly improves the attention tracking accuracy over the eye-tracking-alone method, while maintaining high system energy efficiency. We have also conducted 11 in-field pilot studies across a range of daily usage scenarios, which demonstrate the feasibility and potential benefits of MemX.
RMP2: A Structured Composable Policy Class for Robot Learning

We consider the problem of learning motion policies for acceleration-based robotics systems with a structured policy class.We leverage a multi-task control framework called RMPflow which has been successfully applied in many robotics problems.Using RMPflow as a structured policy class in learning has several benefits, such as sufficient expressiveness, the flexibility to inject different levels of prior knowledge as well as the ability to transfer policies between robots.However, implementing a system for end-to-end learning of RMPflow policies faces several computational challenges.In this work, we re-examine the RMPflow algorithm and propose a more practical alternative, called RMP 2 , that uses modern automatic differentiation tools (such as TensorFlow and PyTorch) to compute RMPflow policies.Our new design retains the strengths of RMPflow while bringing in advantages from automatic differentiation, including 1) simple programming interfaces to designing complex transformations; 2) support of general directed acyclic graph (DAG) transformation structures; 3) end-to-end differentiability for policy learning; 4) improved computational efficiency.Because of these features, RMP 2 can be treated as a structured policy class for efficient robot learning that is suitable for encoding domain knowledge.Our experiments show that using the structured policy class given by RMP 2 can improve policy performance and safety in reinforcement learning tasks for goal reaching in cluttered space.The video for our experimental results can be found at https://youtu.
Deep Multiview Clustering via Iteratively Self-Supervised Universal and Specific Space Learning

Multiview clustering seeks to partition objects via leveraging cross-view relations to provide a comprehensive description of the same objects. Most existing methods assume that different views are linear transformable or merely sampling from a common latent space. Such rigid assumptions betray reality, thus leading to unsatisfactory performance. To tackle the issue, we propose to learn both common and specific sampling spaces for each view to fully exploit their collaborative representations. The common space corresponds to the universal self-representation basis for all views, while the specific spaces are the view-specific basis accordingly. An iterative self-supervision scheme is conducted to strengthen the learned affinity matrix. The clustering is modeled by a convex optimization. We first solve its linear formulation by the popular scheme. Then, we employ the deep autoencoder structure to exploit its deep nonlinear formulation. The extensive experimental results on six real-world datasets demonstrate that the proposed model achieves uniform superiority over the benchmark methods.
Evaluating refactorings for disciplining #ifdef annotations: An eye tracking study with novices

The C preprocessor is widely used in practice. Conditional compilation with #ifdef annotations allows developers to flexibly introduce variability in their programs. Developers can use disciplined annotations, entirely enclosing full statements with preprocessor directives, or undisciplined ones, enclosing only parts of the statements. Despite some debate, there is no consensus on whether a developer should use exclusively disciplined annotations. While one prior study found undisciplined annotations more time-consuming and error-prone, another study found no difference between disciplined and undisciplined annotations regarding task completion time and accuracy. In this article, we evaluate whether three fine-grained refactorings to discipline #ifdef annotations correlate with improvements in code comprehension and visual effort with an eye tracker. We conduct a controlled experiment with 64 human subjects who were majoritarily novices in the C programming language. We observed statistically significant differences for two refactorings to discipline annotations with respect to the analyzed metrics (time, fixation duration, fixation count, and regressions count) in the code regions changed by each refactoring.
Realistic medical image super-resolution with pyramidal feature multi-distillation networks for intelligent healthcare systems

There are two key requirements for medical lesion image super-resolution reconstruction in intelligent healthcare systems: clarity and reality. Because only clear and real super-resolution medical images can effectively help doctors observe the lesions of the disease. The existing super-resolution methods based on pixel space optimization often lack high-frequency details which result in blurred detail features and unclear visual perception. Also, the super-resolution methods based on feature space optimization usually have artifacts or structural deformation in the generated image. This paper proposes a novel pyramidal feature multi-distillation network for super-resolution reconstruction of medical images in intelligent healthcare systems. Firstly, we design a multi-distillation block that combines pyramidal convolution and shallow residual block. Secondly, we construct a two-branch super-resolution network to optimize the visual perception quality of the super-resolution branch by fusing the information of the gradient map branch. Finally, we combine contextual loss and L1 loss in the gradient map branch to optimize the quality of visual perception and design the information entropy contrast-aware channel attention to give different weights to the feature map. Besides, we use an arbitrary scale upsampler to achieve super-resolution reconstruction at any scale factor. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance compared to other methods in this work.
Accurate Methods for the Analysis of Strong-Drive Effects in Parametric Gates

The ability to perform fast, high-fidelity entangling gates is a requirement for a viable quantum processor. In practice, achieving fast gates often comes with the penalty of strong-drive effects that are not captured by the rotating-wave approximation. These effects can be analyzed in simulations of the gate protocol, but those are computationally costly and often hide the physics at play. Here, we show how to efficiently extract gate parameters by directly solving a Floquet eigenproblem using exact numerics and a perturbative analytical approach. As an example application of this toolkit, we study the space of parametric gates generated between two fixed-frequency transmon qubits connected by a parametrically driven coupler. Our analytical treatment, based on time-dependent Schrieffer-Wolff perturbation theory, yields closed-form expressions for gate frequencies and spurious interactions, and is valid for strong drives. From these calculations, we identify optimal regimes of operation for different types of gates including $i$swap, controlled-Z, and cnot. These analytical results are supplemented by numerical Floquet computations from which we directly extract drive-dependent gate parameters. This approach has a considerable computational advantage over full simulations of time evolutions. More generally, our combined analytical and numerical strategy allows us to characterize two-qubit gates involving parametrically driven interactions, and can be applied to gate optimization and cross-talk mitigation such as the cancelation of unwanted $ZZ$ interactions in multiqubit architectures.
ARCH-Elites

This paper introduces ARCH-Elites, a MAP-Elites implementation that can reconfigure large-scale urban layouts at real-world locations via a pre-trained surrogate model instead of costly simulations. In a series of experiments, we generate novel urban designs for two real-world locations in Boston, Massachusetts. Combining the exploration of a possibility space with real-time performance evaluation creates a powerful new paradigm for architectural generative design that can extract and articulate design intelligence.
AutoBERT-Zero: Evolving BERT Backbone from Scratch

Transformer-based pre-trained language models like BERT and its variants have recently achieved promising performance in various natural language processing (NLP) tasks. However, the conventional paradigm constructs the backbone by purely stacking the manually designed global self-attention layers, introducing inductive bias and thus leads to sub-optimal. In this work, we make the first attempt to automatically discover novel pre-trained language model (PLM) backbone on a flexible search space containing the most fundamental operations from scratch. Specifically, we propose a well-designed search space which (i) contains primitive math operations in the intra-layer level to explore novel attention structures, and (ii) leverages convolution blocks to be the supplementary for attentions in the inter-layer level to better learn local dependency. To enhance the efficiency for finding promising architectures, we propose an Operation-Priority Neural Architecture Search (OP-NAS) algorithm, which optimizes both the search algorithm and evaluation of candidate models. Specifically, we propose Operation-Priority (OP) evolution strategy to facilitate model search via balancing exploration and exploitation. Furthermore, we design a Bi-branch Weight-Sharing (BIWS) training strategy for fast model evaluation. Extensive experiments show that the searched architecture (named AutoBERT-Zero) significantly outperforms BERT and its variants of different model capacities in various downstream tasks, proving the architecture's transfer and scaling abilities. Remarkably, AutoBERT-Zero-base outperforms RoBERTa-base (using much more data) and BERT-large (with much larger model size) by 2.4 and 1.4 higher score on GLUE test set.
Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving

In recent years, deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks. However, current deep neural networks are easily deceived by adversarial attacks. This vulnerability raises significant concerns, particularly in safety-critical applications. As a result, research into attacking and defending DNNs has gained much coverage. In this work, detailed adversarial attacks are applied on a diverse multi-task visual perception deep network across distance estimation, semantic segmentation, motion detection, and object detection. The experiments consider both white and black box attacks for targeted and un-targeted cases, while attacking a task and inspecting the effect on all others, in addition to inspecting the effect of applying a simple defense method. We conclude this paper by comparing and discussing the experimental results, proposing insights and future work. The visualizations of the attacks are available at &lt;uri&gt;https://youtu.be/6AixN90budY&lt;/uri&gt;.
Tag-based regulation of modules in genetic programming improves context-dependent problem solving

We introduce and experimentally demonstrate the utility of tag-based genetic regulation, a new genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express.Tags are evolvable labels that provide a flexible mechanism for referencing code modules. Tag-based genetic regulation extends existing tag-based naming schemes to allow programs to “promote” and “repress” code modules in order to alter expression patterns. This extension allows evolution to structure a program as a gene regulatory network where modules are regulated based on instruction executions. We demonstrate the functionality of tag-based regulation on a range of program synthesis problems. We find that tag-based regulation improves problem-solving performance on context-dependent problems; that is, problems where programs must adjust how they respond to current inputs based on prior inputs. Indeed, the system could not evolve solutions to some context-dependent problems until regulation was added. Our implementation of tag-based genetic regulation is not universally beneficial, however. We identify scenarios where the correct response to a particular input never changes, rendering tag-based regulation an unneeded functionality that can sometimes impede adaptive evolution. Tag-based genetic regulation broadens our repertoire of techniques for evolving more dynamic genetic programs and can easily be incorporated into existing tag-enabled GP systems.