Reviewerly/exHarmony huggingface dataset:
Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review

Abstract Background Predicting morphological changes to anatomical structures from 3D shapes such as blood vessels or appearance of the face is a growing interest to clinicians. Machine learning (ML) has had great success driving predictions in 2D, however, methods suitable for 3D shapes are unclear and the use cases unknown. Objective and methods This systematic review aims to identify the clinical implementation of 3D shape prediction and ML workflows. Ovid-MEDLINE, Embase, Scopus and Web of Science were searched until 28th March 2022. Results 13,754 articles were identified, with 12 studies meeting final inclusion criteria. These studies involved prediction of the face, head, aorta, forearm, and breast, with most aiming to visualize shape changes after surgical interventions. ML algorithms identified were regressions (67%), artificial neural networks (25%), and principal component analysis (8%). Meta-analysis was not feasible due to the heterogeneity of the outcomes. Conclusion 3D shape prediction is a nascent but growing area of research in medicine. This review revealed the feasibility of predicting 3D shapes using ML clinically, which could play an important role for clinician-patient visualization and communication. However, all studies were early phase and there were inconsistent language and reporting. Future work could develop guidelines for publication and promote open sharing of source code.
Intent-driven network and service management: Definitions, modeling and implementation

Cognitive autonomous networks imply that networks will independently derive and execute intelligent decisions, and thereby elevating the human operator's role to a higher level of abstraction. At that level, the operator only specifies the desired outcomes, called intents, which must then be supported by corresponding intent-driven capabilities in the network or its management functions. Although, Intent-Driven Management (IDM) has been published in multiple works, there is still no globally agreed end-to-end view of such IDM systems, let alone a globally agreed definition of intents. This paper provides a comprehensive discussion on the core aspects of IDM systems and combines them into an end-to-end system view with the related example solutions. Contrasting against a short review of related scientific and standards literature, the paper introduces a flexible, generic definition of intents and an end-to-end IDM system architecture as well as the related modeling of intents to support their standardization. The paper also introduces implementation examples fitting the architecture and discusses advanced IDM features that need to be provided, including the ability to detect and resolve conflicts between intents.
Partition and Learned Clustering with joined-training: Active learning of GNNs on large-scale graph

Graph neural networks (GNNs) have recently achieved impressive progress on graph-based semi-supervised learning. The active learning of GNNs aims to select a small number of representative nodes to train a high-performance GNN. However, existing active learning methods of GNNs have the following unsolved problems: (1) only consider nodes distribution in embedding space, without considering the distribution on graph structure; (2) difficult to be applied to large-scale graphs; (3) cluster-based methods do not consider topological structure during clustering; (4) the search space is inconsistent with the classification space. To address these problems, we propose a novel cluster-based active learning method of GNNs on large-scale graphs, called Partition and Learned Clustering with Joined-training (PLCJ). PLCJ first partitions the graph into several subgraphs, then clusters in each subgraph, and the cluster centers are selected. The partition makes the selected nodes evenly distributed on the structure and makes PLCJ can be applied to a large-scale graph. Moreover, PLCJ uses Learned Clustering, which clusters nodes considering both embedding and structural information. In the classification stage, PLCJ employs a joined-training skill to ensure the closeness of search and classification space. Experiments show that PLCJ can achieve better performance than state of the art active learning methods on graphs with only a few percent of the running time.
Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis

Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount importance for an efficient treatment. Therefore, this work aims to propose a robust platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of-the-art techniques by classifying them as benign, malignant and normal. Investigations were performed on a dataset containing a total of 780 images of tumor-affected persons, divided into benign, malignant and normal. A data augmentation technique was used to scale up the corpus of images available in the chosen dataset. For this, novel image enhancement techniques were used and the Multilayer Perceptrons, k-Nearest Neighbor and Support Vector Machines algorithms were used for classification. From the promising outcomes of the conducted experiments, it was observed that the bilateral algorithm together with the SVM classifier achieved the best result for the classification of breast cancer, with an overall accuracy of 96.69% and an accuracy for the detection of malignant nodules of 95.11%. Therefore, it was found that the application of image enhancement methods can help in the detection of breast cancer at a much earlier stage with better accuracy in detection.
Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals

Human Activity Recognition (HAR) is one of the fundamental building blocks of human assistive devices like orthoses and exoskeletons. There are different approaches to HAR depending on the application. Numerous studies have been focused on improving them by optimising input data or classification algorithms. However, most of these studies have been focused on applications like security and monitoring, smart devices, the internet of things, etc. On the other hand, HAR can help adjust and control wearable assistive devices, yet there has not been enough research facilitating its implementation. In this study, we propose several models to predict four activities from inertial sensors located in the ankle area of a lower-leg assistive device user. This choice is because they do not need to be attached to the user’s skin and can be directly implemented inside the control unit of the device. The proposed models are based on Artificial Neural Networks and could achieve up to 92.8% average classification accuracy.
Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

Much of recent progress in geophysics can be attributed to the adaptation of heterogeneous high-performance computing architectures. It is projected that the next major leap in many areas of science, and hence hopefully in geophysics too, will be due to the emergence of quantum computers. Finding a right combination of hardware, algorithms, and a use case, however, proves to be a very challenging task — especially when looking for a relevant application that scales efficiently on a quantum computer and is difficult to solve using classical means. We find that maximizing stack power for residual statics correction, an NP-hard combinatorial optimization problem, appears to naturally fit a particular type of quantum computing known as quantum annealing. We express the underlying objective function as a quadratic unconstrained binary optimization, which is a quantum-native formulation of the problem. We choose some solution space and define a proper encoding to translate the problem variables into qubit states. We find that these choices can have a significant impact on the maximum problem size that can fit on the quantum annealer and on the fidelity of the final result. To improve the latter, we embed the quantum optimization step in a hybrid classical-quantum workflow, which aims to increase the frequency of finding the global, rather than some local, optimum of the objective function. Finally, we find that a generic, black-box, hybrid classical-quantum solver also could be used to solve stack-power maximization problems proximal to industrial relevance and capable of surpassing deterministic solvers prone to cycle skipping. A custom-built workflow capable of solving larger problems with an even higher robustness and greater control of the user appears to be within reach in the very near future.
A graph neural network framework for mapping histological topology in oral mucosal tissue

Histological feature representation is advantageous for computer aided diagnosis (CAD) and disease classification when using predictive techniques based on machine learning. Explicit feature representations in computer tissue models can assist explainability of machine learning predictions. Different approaches to feature representation within digital tissue images have been proposed. Cell-graphs have been demonstrated to provide precise and general constructs that can model both low- and high-level features. The basement membrane is high-level tissue architecture, and interactions across the basement membrane are involved in multiple disease processes. Thus, the basement membrane is an important histological feature to study from a cell-graph and machine learning perspective.We present a two stage machine learning pipeline for generating a cell-graph from a digital H &E stained tissue image. Using a combination of convolutional neural networks for visual analysis and graph neural networks exploiting node and edge labels for topological analysis, the pipeline is shown to predict both low- and high-level histological features in oral mucosal tissue with good accuracy.Convolutional and graph neural networks are complementary technologies for learning, representing and predicting local and global histological features employing node and edge labels. Their combination is potentially widely applicable in histopathology image analysis and can enhance explainability in CAD tools for disease prediction.
Underwater Polarization Imaging Recovery Based on Polarimetric Residual Dense Network

Application of deep-learning to polarization imaging technology for image restoration has led to many technological breakthroughs, especially in underwater image recovery and recognition.In this work, a four-input deep learning model with the Polarimetric Residual Dense Network is proposed for underwater image recovery.The diverse polarization component images are trained and tested in different processes in the network for the recognition and dehazing by considering the physical model of polarization dehazing imaging.Our study reveals that the proposed method can efficiently recover the hazed images, and provide good performance for improving the quality of image restoration even in a high-turbidity complex underwater environment.
Can language models automate data wrangling?

Abstract The automation of data science and other data manipulation processes depend on the integration and formatting of ‘messy’ data. Data wrangling is an umbrella term for these tedious and time-consuming tasks. Tasks such as transforming dates, units or names expressed in different formats have been challenging for machine learning because (1) users expect to solve them with short cues or few examples, and (2) the problems depend heavily on domain knowledge. Interestingly, large language models today (1) can infer from very few examples or even a short clue in natural language, and (2) can integrate vast amounts of domain knowledge. It is then an important research question to analyse whether language models are a promising approach for data wrangling, especially as their capabilities continue growing. In this paper we apply different variants of the language model Generative Pre-trained Transformer (GPT) to five batteries covering a wide range of data wrangling problems. We compare the effect of prompts and few-shot regimes on their results and how they compare with specialised data wrangling systems and other tools. Our major finding is that they appear as a powerful tool for a wide range of data wrangling tasks. We provide some guidelines about how they can be integrated into data processing pipelines, provided the users can take advantage of their flexibility and the diversity of tasks to be addressed. However, reliability is still an important issue to overcome.
MOSP: Multi-Objective Sensitivity Pruning of Deep Neural Networks

Deep neural networks (DNNs) are computationally intensive, making them difficult to deploy on resource-constrained embedded systems. Model compression is a set of techniques that removes redundancies from a neural network with affordable degradation in task performance. Most compression methods do not target hardware-based objectives such as latency directly; however, few methods approximate latency with floating-point operations (FLOPs) or multiply-accumulate operations (MACs). Using these indirect metrics cannot directly translate to the relevant performance metric on the hardware, i.e., latency and throughput. To address this limitation, we introduce Multi-Objective Sensitivity Pruning, “MOSP,” a three-stage pipeline for filter pruning: hardware-aware sensitivity analysis, Criteria-optimal configuration selection, and pruning based on explainable AI (XAI). Our pipeline is compatible with a single or combination of target objectives such as latency, energy consumption, and accuracy. Our method first formulates the sensitivity of layers of a model against the target objectives as a classical machine learning problem. Next, we choose a Criteria-optimal configuration controlled by hyperparameters specific to each objective of choice. Finally, we apply XAI-based filter ranking to select filters to be pruned. The pipeline follows an iterative pruning methodology to recover any loss in degradation in task performance (e.g., accuracy). We allow the user to prefer one objective function over the other. Our method outperforms the selected baseline method across different neural networks and datasets in both accuracy and latency reductions and is competitive with state-of-the-art approaches.
NGIoU Loss: Generalized Intersection over Union Loss Based on a New Bounding Box Regression

Loss functions, such as the IoU Loss function and the GIoU (Generalized Intersection over Union) Loss function have been put forward to replace regression loss functions commonly used in regression loss calculation. GIoU Loss alleviates the vanishing gradient in the case of the non-overlapping, but it will completely degenerate into the IoU Loss function when bounding boxes overlap totally, which fails to achieve the optimization effect. To solve this problem, some improvements are proposed in this paper on the basis of the GIoU Loss function, taking into account the overlap rate of complete overlap of bounding boxes. In PASCAL VOC data, the experimental results demonstrate that the AP of NGIoU Loss function in the YOLOv4 model is 47.68%, 1.15% higher than that of the GIoU Loss function, and the highest map value is 86.79% in the YOLOv5 model.
CapsulePose: A variational CapsNet for real-time end-to-end 3D human pose estimation

Estimating 3D human poses from images is an ill-posed regression problem, which is usually tackled by viewpoint-invariant convolutional neural networks (CNNs). Recently, capsule networks (CapsNets) have been introduced as a viable alternative to CNNs, ensuring viewpoint-equivariance and drastically reducing both the dataset size and the network complexity, while retaining high output accuracy. We propose a real-time end-to-end human pose estimation (HPE) network which employs state-of-the-art matrix capsules [1] and a fast variational Bayesian capsule routing, without relying on pre-training, complex data augmentation or multiple datasets. We achieve comparable results to the HPE state-of-the-art, and the lowest error among methods using CapsNets, while at the same time achieving other desirable properties, namely greater generalization capabilities, stronger viewpoint equivariance and highly decreased data dependency, allowing for our network to be trained with only a fraction of the available datasets and without any data augmentation.
Multisensor Suboptimal Fusion Student's $t$ Filter

A multi-sensor fusion Student's t filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises.Driven from an informationtheoretic optimization, the approach extends the single sensor Student's t Kalman filter based on the suboptimal arithmetic average (AA) fusion approach.To ensure computationally efficient, closed-form t density recursion, reasonable approximation has been used in both local-sensor filtering and inter-sensor fusion calculation.The overall framework accommodates any Gaussianoriented fusion approach such as the covariance intersection (CI).Simulation demonstrates the effectiveness of the proposed multi-sensor AA fusion-based t filter in dealing with outliers as compared with the classic Gaussian estimator, and the advantage of the AA fusion in comparison with the CI approach and the augmented measurement fusion.
Solar energy digitalization at high latitudes: A model chain combining solar irradiation models, a LiDAR scanner, and high-detail 3D building model

Solar mapping can contribute to exploiting more efficiently the solar energy potential in cities. Solar maps and 3D solar cadasters consist of visualization tools for solar irradiation analysis on urban surfaces (i.e., orography, roofs, and façades). Recent advancements in solar decomposition and transposition modeling and Light Detection and Ranging (LiDAR) scanning enable high levels of detail in 3D solar cadasters, in which the façade domain is considered beside the roof. In this study, a model chain to estimate solar irradiation impinging on surfaces with different orientations at high latitudes is developed and validated against experimental data. The case study is the Zero Emission Building Laboratory in Trondheim (Norway). The main stages of the workflow concern (1) data acquisition, (2) geometry detection, (3) solar radiation modeling, (4) data quality check, and (5) experimental validation. Data are recorded from seven pyranometers installed on the façades (4), roof (2), and pergola (1) and used to validate the Radiance-based numerical model over the period between June 21st and September 21st. This study investigates to which extent high-resolution data sources for both solar radiation and geometry are suitable to estimate global tilted irradiation at high latitudes. In general, the Radiance-based model is found to overestimate solar irradiation. Nonetheless, the hourly solar irradiation modeled for the two pyranometers installed on the roof has been experimentally validated in accordance with ASHRAE Guideline 14. When monthly outcomes are considered for validation, the east and the south pyranometers are validated as well. The achieved results build the ground for the further development of the 3D solar cadaster of Trondheim.
Effect of Augmented Reality Technology on Spatial Intelligence among High School Students

Spatial Intelligence is a mental ability to understand and solve real-world problems. These visual-spatial representations are fundamental in learning various "STEM" topics, like digital drawing, art presentations, creating graphical representations, 2D designs. Opportunity to interact with real and/or virtual objects. It is a good opportunity in applying new techniques such as the augmenter, which is able to clarify mathematical tables, concepts and generalizations greatly to the visualization, understanding and mastery of concepts mathematically. The purpose of the research is to investigate impact of using AR technology in developing spatial intelligence for secondary school students, Baghdad. The quasi-experimental design was used, the participants consisted of (60) students of 4th class scientific in mathematics. Tools used where a spatial intelligence scale was prepared and contains three skills that include (20) questions. Research findings show that AR technology has a positive on spatial intelligence in mathematics, and concluded with a set of recommendations and proposals.
QPU-System Co-design for Quantum HPC Accelerators

The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, but the quantum devices currently available possess only a very limited number of qubits and suffer from considerable imperfections. One possibility to progress towards practical utility is to use a co-design approach: Problem formulation and algorithm, but also the physical QPU properties are tailored to the specific application. Since QPUs will likely be used as accelerators for classical computers, details of systemic integration into existing architectures are another lever to influence and improve the practical utility of QPUs. In this work, we investigate the influence of different parameters on the runtime of quantum programs on tailored hybrid CPU-QPU-systems. We study the influence of communication times between CPU and QPU, how adapting QPU designs influences quantum and overall execution performance, and how these factors interact. Using a simple model that allows for estimating which design choices should be subjected to optimisation for a given task, we provide an intuition to the HPC community on potentials and limitations of co-design approaches. We also discuss physical limitations for implementing the proposed changes on real quantum hardware devices.
Design and Implementation of Proximal Planning and Control of an Unmanned Ground Vehicle to Operate in Dynamic Environments

This paper presents a novel proximal planning and control (PPC) formulation for an unmanned ground vehicle (UGV) which is affected by skidding and slip disturbances. The control approach also considers the presence of moving and static obstacles in the context of operation. The PPC technique is divided into three parts; first, a nonlinear model predictive control (NMPC) based path-planner is designed to periodically generate an updated feasible trajectory for reaching the goal pose, under the constraint of avoiding collisions with dynamic and static objects which are present in the context. In particular, a proximal averaged Newton-type method for optimal control (PANOC) is used to implement NMPC. Second, the velocity commands are produced via evolutionary programming (EP) based on kinematic control (KC). Third, a dynamic control process with an extended state observer (ESO) is introduced to estimate disturbances whose magnitudes are unknown but bounded. Finally, to verify the performance of the proposed scheme, simulations are performed for the platform operating in the presence of static obstacles (SO) and moving obstacles (MO), whose trajectories may be nonlinear and difficult to be accurately predicted. Additionally, we have investigated and confirmed that the proposed PPC is able to operate in real time under limited CPU processing resources.