Reviewerly/exHarmony huggingface dataset:
SlideGraph<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"><mml:msup><mml:mrow /><mml:mo>+</mml:mo></mml:msup></mml:math>: Whole slide image level graphs to predict HER2 status in breast cancer

Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive factor which is overexpressed in 15-20% of breast cancer (BCa). The determination of its status is a key clinical decision making step for selection of treatment regimen and prognostication. HER2 status is evaluated using transcriptomics or immunohistochemistry (IHC) through in-situ hybridisation (ISH) which incurs additional costs and tissue burden and is prone to analytical variabilities in terms of manual observational biases in scoring. In this study, we propose a novel graph neural network (GNN) based model (SlideGraph+) to predict HER2 status directly from whole-slide images of routine Haematoxylin and Eosin (H&E) stained slides. The network was trained and tested on slides from The Cancer Genome Atlas (TCGA) in addition to two independent test datasets. We demonstrate that the proposed model outperforms the state-of-the-art methods with area under the ROC curve (AUC) values > 0.75 on TCGA and 0.80 on independent test sets. Our experiments show that the proposed approach can be utilised for case triaging as well as pre-ordering diagnostic tests in a diagnostic setting. It can also be used for other weakly supervised prediction problems in computational pathology. The SlideGraph+ code repository is available at https://github.com/wenqi006/SlideGraph along with an IPython notebook showing an end-to-end use case at https://github.com/TissueImageAnalytics/tiatoolbox/blob/develop/examples/full-pipelines/slide-graph.ipynb.
Reconstruction, optimization, and design of heterogeneous materials and media: Basic principles, computational algorithms, and applications

Modeling of heterogeneous materials and media is a problem of fundamental importance to a wide variety of phenomena with applications to many disciplines, ranging from condensed and soft materials, fuel cells, alloys, and composite media, to biological materials such as proteins, and even such large-scale structure as field-scale porous media and clusters of galaxies. While several approaches have been developed over the past several decades to address this class of problems, it has become increasingly clear that a most fruitful approach to the problem is by reconstruction: given a certain amount of data for a given medium, how should one develop a model for the medium that not only honors (reproduces) the data, but also provides accurate predictions for those properties of the medium for which no data are available, or are hard to measure. Although deterministic approaches were developed to address the problem, given that the amount of available data is typically limited, the uncertainty in the models is large and, at the same time the deterministic approaches cannot provide any quantitative measure of the uncertainty in the models. Thus, it became clear that the best that one can hope for is developing stochastic methods of reconstruction that not only can generate plausible realizations of the media of interest, but also provide quantitative measures of the uncertainty in the realizations. The literature on the subject is rich. Some have formulated the problem as one of optimization: one begins with a “guess” for the structure of a medium, and defines an “energy” or “cost” function that represents a quantitative measure of the difference between the data and the corresponding “predictions” of the model. There are many optimization techniques, ranging from simulated annealing and its variants, to Nature-inspired methods, such as the genetic algorithm. Through optimization one varies systematically the initial guess for the morphology of the medium in order to finally identify a structure for the medium that minimizes the energy in a global sense. the cost or energy function may be based on direct measurements of some properties, or based on various two- or three-point statistical correlation functions. A second approach attempts to mimic the physical process that gave rise to the medium. The third approach uses a global cross-correlation function, together with some concepts from graph theory in order to achieve the goal of reconstruction. This Review describes and discusses the current important methods of reconstruction. After describing the relevant theoretical and computational aspects of reconstruction and the various approaches to the problem, a wide variety of applications are described, and comparison is made between the reconstructed systems and their properties and the existing data. Finally, the problem of developing new classes of materials with specific properties, based on reconstruction techniques is discussed.
Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces \texttt{scikit-dimension}, an open-source Python package for intrinsic dimension estimation. \texttt{scikit-dimension} package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface to evaluate global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data. The source code is available from https://github.com/j-bac/scikit-dimension , the documentation is available from https://scikit-dimension.readthedocs.io .
Toward simulating quantum field theories with controlled phonon-ion dynamics: A hybrid analog-digital approach

Quantum field theories are the cornerstones of modern physics, providing relativistic and quantum mechanical descriptions of physical systems at the most fundamental level. Simulating real-time dynamics within these theories remains elusive in classical computing. This provides a unique opportunity for quantum simulators, which hold the promise of revolutionizing our simulation capabilities. Trapped-ion systems are successful quantum-simulator platforms for quantum many-body physics and can operate in digital, or gate-based, and analog modes. Inspired by the progress in proposing and realizing quantum simulations of a number of relativistic quantum field theories using trapped-ion systems, and by the hybrid analog-digital proposals for simulating interacting boson-fermion models, we propose hybrid analog-digital quantum simulations of selected quantum field theories, taking recent developments to the next level. On one hand, the semi-digital nature of this proposal offers more flexibility in engineering generic model interactions compared with a fully-analog approach. On the other hand, encoding the bosonic fields onto the phonon degrees of freedom of the trapped-ion system allows a more efficient usage of simulator resources, and a more natural implementation of intrinsic quantum operations in such platforms. This opens up ways for simulating complex dynamics of, e.g., Abelian and non-Abelian gauge theories, by combining the benefits of digital and analog schemes.
SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection

Existing point-cloud based 3D object detectors use convolution-like operators to process information in a local neighbourhood with fixed-weight kernels and aggregate global context hierarchically. However, non-local neural networks and self-attention for 2D vision have shown that explicitly modeling long-range interactions can lead to more robust and competitive models. In this paper, we propose two variants of self-attention for contextual modeling in 3D object detection by augmenting convolutional features with self-attention features. We first incorporate the pairwise self-attention mechanism into the current state-of-the-art BEV, voxel and point-based detectors and show consistent improvement over strong baseline models of up to 1.5 3D AP while simultaneously reducing their parameter footprint and computational cost by 15-80% and 30-50%, respectively, on the KITTI validation set. We next propose a self-attention variant that samples a sub-set of the most representative features by learning deformations over randomly sampled locations. This not only allows us to scale explicit global contextual modeling to larger point-clouds, but also leads to more discriminative and informative feature descriptors. Our method can be flexibly applied to most state-of-the-art detectors with in-creased accuracy and parameter and compute efficiency. We show our proposed method improves 3D object detection performance on KITTI, nuScenes and Waymo Open datasets. Code is available at https://github.com/AutoVision-cloud/SA-Det3D.
A survey on deep learning based Point-of-Interest (POI) recommendations

Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. A huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users’ historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focus on traditional machine learning techniques that use hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.
Dynamic Multi-Key FHE in Asymmetric Key Setting From LWE

Multi-key Fully homomorphic encryption (MFHE) schemes allow computation on the encrypted data under different keys. However, traditional multi-key FHE schemes based on Learning with errors (LWE) have the undesirable property that is the number of keys has to be fixed in advance. A dynamic multi-key FHE scheme is the most versatile variant which the information about the participants is not required before key generation. To support further homomorphic computation on extended ciphertexts and ciphertexts encrypted under additional keys, Peikert and Shiehian (TCC ’16) proposed a leveled dynamic multi-key FHE scheme. Nevertheless, it introduces the circular-security assumption for the LWE parameters to ensure its security, which provides weaker security to the scheme. The problem of how to construct a LWE-based dynamic multi-key FHE scheme is still open. To address the above problem, in this work, we present a dynamic multi-key FHE scheme based on the LWE assumption in public key setting. The ciphertext can be extended and performed homomorphic evaluation with the ciphertexts encrypted under additional keys. Compared with current constructions, our proposed method requires fewer “local” memory and the process of ciphertext extension is distributed. Our proposed method provides a new way to extend the ciphertext such that the ciphertext homomorphism computation is more efficient. Our scheme is proven to be secure under standard LWE assumptions without using the circular-security assumption.
<scp>SummaC</scp>: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization

Abstract In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) models do not perform competitively when applied to inconsistency detection. In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level). We provide a highly effective and light-weight method called SummaCConv that enables NLI models to be successfully used for this task by segmenting documents into sentence units and aggregating scores between pairs of sentences. We furthermore introduce a new benchmark called SummaC (Summary Consistency) which consists of six large inconsistency detection datasets. On this dataset, SummaCConv obtains state-of-the-art results with a balanced accuracy of 74.4%, a 5% improvement compared with prior work.
FENeRF: Face Editing in Neural Radiance Fields

Previous portrait image generation methods roughly fall into two categories: 2D GANs and 3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency. 3D-aware GAN methods can maintain view consistency but their generated images are not locally editable. To overcome these limitations, we propose FENeRF, a 3D-aware generator that can produce view-consistent and locally-editable portrait images. Our method uses two decoupled latent codes to generate corresponding facial semantics and texture in a spatial-aligned 3D volume with shared geometry. Benefiting from such underlying 3D representation, FENeRF can Jointly render the boundary-aligned image and semantic mask and use the semantic mask to edit the 3D volume via GAN inversion. We further show such 3D representation can be learned from widely available monocular image and semantic mask pairs. Moreover, we reveal that Joint learning semantics and texture helps to generate finer geometry. Our experiments demonstrate that FENeRF outperforms state-of-the-art methods in various face editing tasks. Code is available at https://github.com/MrTornado24/FENeRF.
Pose with style

We present an algorithm for re-rendering a person from a single image under arbitrary poses. Existing methods often have difficulties in hallucinating occluded contents photo-realistically while preserving the identity and fine details in the source image. We first learn to inpaint the correspondence field between the body surface texture and the source image with a human body symmetry prior. The inpainted correspondence field allows us to transfer/warp local features extracted from the source to the target view even under large pose changes. Directly mapping the warped local features to an RGB image using a simple CNN decoder often leads to visible artifacts. Thus, we extend the StyleGAN generator so that it takes pose as input (for controlling poses) and introduces a spatially varying modulation for the latent space using the warped local features (for controlling appearances). We show that our method compares favorably against the state-of-the-art algorithms in both quantitative evaluation and visual comparison.
Enhanced ant colony algorithm with communication mechanism for mobile robot path planning

In mobile robot path planning, the ant colony algorithm has the problem that the historical paths explored by ants cannot be fully utilized. With this in mind, in this paper an enhanced ant colony algorithm with a communication mechanism is proposed. The communication mechanism is inspired by the contact of ant tentacles in nature, which can integrate historical paths to obtain a better composite path. To further improve the algorithm, an enlarged roulette method is presented to accelerate the convergence. Subsequently, an adaptive sigmoid attenuation function is designed to optimize the heuristic information at different stages. The various forms of the deadlock problem are analyzed and specific strategies formulated. Finally, parameter determination and comparison experiments are carried out. The experimental results demonstrate the efficiency of the proposed method and its considerable advantages in enhancing the performance of the ant colony algorithm.
Digital Content Creation Tools: American University Teachers’ Perception

This paper studies the perception of Latin American university teachers about the effectiveness of digital content creation (DCC) tools for the creation of e-learning training actions. For this purpose, the opinions of a group of 564 teachers from different universities in 16 Latin American countries have been collected and their answers have been analyzed according to different sociological and academic characteristics (gender, age, teaching experience, area of knowledge, and academic degree). The results indicate that Latin American university teachers express high levels of digital training and highly value the didactic effectiveness of DCC tools to develop e-learning training actions. This valuation is significantly higher among females than males. Gender is also revealed as the most influential characteristic in the perception of teachers about the use of digital resources in the classroom, above any other aspect.
Physical Implementation of Reservoir Computing through Electrochemical Reaction

Nonlinear dynamical systems serving reservoir computing enrich the physical implementation of computing systems. A method for building physical reservoirs from electrochemical reactions is provided, and the potential of chemical dynamics as computing resources is shown. The essence of signal processing in such systems includes various degrees of ionic currents which pass through the solution as well as the electrochemical current detected based on a multiway data acquisition system to achieve switchable and parallel testing. The results show that they have respective advantages in periodic signals and temporal dynamic signals. Polyoxometalate molecule in the solution increases the diversity of the response current and thus improves their abilities to predict periodic signals. Conversely, distilled water exhibits great computing power in solving a second-order nonlinear problem. It is expected that these results will lead to further exploration of ionic conductance as a nonlinear dynamical system and provide more support for novel devices as computing resources.
Learning to Fly—a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control

Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments— simple simulations coupled with a problem specification in the form of a reward function—are also important to standardize the development (and benchmarking) of learning algorithms. Yet, full-scale simulators typically lack portability and paral-lelizability. Vice versa, many reinforcement learning environments trade-off realism for high sample throughputs in toy-like problems. While public data sets have greatly benefited deep learning and computer vision, we still lack the software tools to simultaneously develop—and fairly compare—control theory and reinforcement learning approaches. In this paper, we propose an open-source OpenAI Gym-like environment for multiple quadcopters based on the Bullet physics engine. Its multi-agent and vision-based reinforcement learning interfaces, as well as the support of realistic collisions and aerodynamic effects, make it, to the best of our knowledge, a first of its kind. We demonstrate its use through several examples, either for control (trajectory tracking with PID control, multi-robot flight with downwash, etc.) or reinforcement learning (single and multi-agent stabilization tasks), hoping to inspire future research that combines control theory and machine learning.
Price explosiveness in cryptocurrencies and Elon Musk's tweets

We detect episodes of price explosivity and collapse in Bitcoin and its contender Dogecoin using four-hourly data. The results show multiple bubble episodes in both cryptocurrencies, with a more frequent occurrence in Bitcoin. Collapse episodes are only observed in Bitcoin. We relate price explosivity to Elon Musk's tweets. His cryptocurrency-related general tweets have contributed to the price explosivity of Bitcoin, whereas his Dogecoin-specific tweets have contributed to price explosivity in Dogecoin. Our findings highlight the influential role of key persons through social media on the formation of bubbles, which matters to the decision-making of cryptotraders and market efficiency.
A decision model based on decision tree and particle swarm optimization algorithms to identify optimal locations for solar power plants construction in Iran

Sustainable energy sources such as solar energy are the most common renewable alternatives to fossil fuels. The first step in expanding the use of solar energy is to identify high-potential locations for the construction of solar power plants. This study aims to assess and compare the performance of decision tree and particle swarm optimization (PSO) algorithms in identifying optimal solar power locations. High-potential regions are extracted using multiple-criteria decision making (MCDM), which are used in the decision tree and PSO algorithms for recognizing optimal locations. The results indicate the potential of the study area for establishing solar power plants based on three-level of suitability. The comparison of optimization methods showed that the prediction rate for the very high potential class belonged to the decision tree (0.29) and PSO (0.13), respectively. Therefore, the decision tree method offers a better solution for detecting potential areas for solar energy development in the study area. The research results provide a useful resource for planning and decision-making concerning the development of renewable energy in the future. The used decision tree in the present study can also be used in other parts of the world to select optimal locations for constructing renewable energy plants.
Multi-label feature selection based on label correlations and feature redundancy

The task of multi-label feature selection (MLFS) is to reduce redundant information and generate the optimal feature subset from the original multi-label data. A variety of MLFS methods utilize pseudo-label matrix to explore label correlations for identifying the most informative features. Moreover, some methods consider feature redundancy by virtue of information theory technique, but no prior literature unites them in a framework to perform feature selection. To remedy the deficiency, we propose a novel MLFS method based on label correlations and feature redundancy, namely LFFS. To be specific, we first utilize the ridge regression to create a feature selection matrix and a low dimensional embedding, and impose ℓ2,1-norm on the feature selection matrix. Then, the low-dimensional embedding is devoted to mine label correlations, which can keep the global and local structure of original label space. Finally, cosine similarity is employed to analyze feature redundancy, so as to generate a low redundancy feature subset. By virtue of the above process, we design an objective function followed with an optimization solution. Comprehensive experiments results demonstrate the effectiveness and superiority of the proposed method LFFS among ten competition methods.