Miracle-dz/UniFAR-FTUs huggingface dataset:
Active learning using adaptable task-based prioritisation

Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert annotation, for label-efficient model training. We develop a controller neural network that measures priority of images in a sequence of batches, as in batch-mode active learning, for multi-class segmentation tasks. The controller is optimised by rewarding positive task-specific performance gain, within a Markov decision process (MDP) environment that also optimises the task predictor. In this work, the task predictor is a segmentation network. A meta-reinforcement learning algorithm is proposed with multiple MDPs, such that the pre-trained controller can be adapted to a new MDP that contains data from different institutes and/or requires segmentation of different organs or structures within the abdomen. We present experimental results using multiple CT datasets from more than one thousand patients, with segmentation tasks of nine different abdominal organs, to demonstrate the efficacy of the learnt prioritisation controller function and its cross-institute and cross-organ adaptability. We show that the proposed adaptable prioritisation metric yields converging segmentation accuracy for the novel class of kidney, unseen in training, using between approximately 40\% to 60\% of labels otherwise required with other heuristic or random prioritisation metrics. For clinical datasets of limited size, the proposed adaptable prioritisation offers a performance improvement of 22.6\% and 10.2\% in Dice score, for tasks of kidney and liver vessel segmentation, respectively, compared to random prioritisation and alternative active sampling strategies.
High-Dimensional Yield Estimation using Shrinkage Deep Features and Maximization of Integral Entropy Reduction

Despite the fast advances in high-sigma yield analysis with the help of machine learning techniques in the past decade, one of the main challenges, the curse of “dimensionality”, which is inevitable when dealing with modern large-scale circuits, remains unsolved. To resolve this challenge, we propose an absolute shrinkage deep kernel learning, ASDK, which automatically identifies the dominant process variation parameters in a nonlinear-correlated deep kernel and acts as a surrogate model to emulate the expensive SPICE simulation. To further improve the yield estimation efficiency, we propose a novel maximization of approximated entropy reduction for an efficient model update, which is also enhanced with parallel batch sampling for parallel computing, making it ready for practical deployment. Experiments on SRAM column circuits demonstrate the superiority of ASDK over the state-of-the-art (SOTA) approaches in terms of accuracy and efficiency with up to 11.1x speedup over SOTA methods.
The non-commuting, non-generating graph of a finite simple group

Let $G$ be a group such that $G/Z(G)$ is finite and simple. The non-commuting, non-generating graph $\Xi(G)$ of $G$ has vertex set $G \setminus Z(G)$, with edges corresponding to pairs of elements that do not commute and do not generate $G$. We show that $\Xi(G)$ is connected with diameter at most $5$, with smaller upper bounds for certain families of groups. When $G$ itself is simple, we prove that the diameter of the complement of the generating graph of $G$ has a tight upper bound of $4$. In the companion paper arXiv:2211.08869, we consider $\Xi(G)$ when $G/Z(G)$ is not simple.
Multi-Agent Reinforcement Learning with Reward Delays

This paper considers multi-agent reinforcement learning (MARL) where the rewards are received after delays and the delay time varies across agents and across time steps. Based on the V-learning framework, this paper proposes MARL algorithms that efficiently deal with reward delays. When the delays are finite, our algorithm reaches a coarse correlated equilibrium (CCE) with rate $\tilde{\mathcal{O}}(\frac{H^3\sqrt{S\mathcal{T}_K}}{K}+\frac{H^3\sqrt{SA}}{\sqrt{K}})$ where $K$ is the number of episodes, $H$ is the planning horizon, $S$ is the size of the state space, $A$ is the size of the largest action space, and $\mathcal{T}_K$ is the measure of total delay formally defined in the paper. Moreover, our algorithm is extended to cases with infinite delays through a reward skipping scheme. It achieves convergence rate similar to the finite delay case.
Learning Bifunctional Push-Grasping Synergistic Strategy for Goal-Agnostic and Goal-Oriented Tasks

Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at grasping pre-assigned goal objects. However, most current grasping methods are only better at coping with one task. In this work, we propose a bifunctional push-grasping synergistic strategy for goal-agnostic and goal-oriented grasping tasks. Our method integrates pushing along with grasping to pick up all objects or pre-assigned goal objects with high action efficiency depending on the task requirement. We introduce a bifunctional network, which takes in visual observations and outputs dense pixel-wise maps of $Q$ values for pushing and grasping primitive actions, to increase the available samples in the action space. Then we propose a hierarchical reinforcement learning framework to coordinate the two tasks by considering the goal-agnostic task as a combination of multiple goal-oriented tasks. To reduce the training difficulty of the hierarchical framework, we design a two-stage training method to train the two types of tasks separately. We perform pre-training of the model in simulation, and then transfer the learned model to the real world without any additional real-world fine-tuning. Experimental results show that the proposed approach outperforms existing methods in task completion rate and grasp success rate with less motion number. Supplementary material is available at https://github.com/DafaRen/Learning_Bifunctional_Push-grasping_Synergistic_Strategy_for_Goal-agnostic_and_Goal-oriented_Tasks.
Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning

Extreme wildfires continue to be a significant cause of human death and biodiversity destruction within countries that encompass the Mediterranean Basin. Recent worrying trends in wildfire activity (i.e., occurrence and spread) suggest that wildfires are likely to be highly impacted by climate change. In order to facilitate appropriate risk mitigation, it is imperative to identify the main drivers of extreme wildfires and assess their spatio-temporal trends, with a view to understanding the impacts of the changing climate on fire activity. To this end, we analyse the monthly burnt area due to wildfires over a region encompassing most of Europe and the Mediterranean Basin from 2001 to 2020, and identify high fire activity during this period in eastern Europe, Algeria, Italy and Portugal. We build an extreme quantile regression model with a high-dimensional predictor set describing meteorological conditions, land cover usage, and orography, for the domain. To model the complex relationships between the predictor variables and wildfires, we make use of a hybrid statistical deep-learning framework that allows us to disentangle the effects of vapour-pressure deficit (VPD), air temperature, and drought on wildfire activity. Our results highlight that whilst VPD, air temperature, and drought significantly affect wildfire occurrence, only VPD affects wildfire spread. Furthermore, to gain insights into the effect of climate trends on wildfires in the near future, we focus on the extreme wildfires in August 2001 and perturb VPD and temperature according to their observed trends. We find that, on average over Europe, trends in temperature (median over Europe: +0.04K per year) lead to a relative increase of 17.1% and 1.6% in the expected frequency and severity, respectively, of wildfires in August 2001; similar analyses using VPD (median over Europe: +4.82Pa per year) give respective increases of 1.2% and 3.6%. Our analysis finds evidence suggesting that global warming can lead to spatially non-uniform changes in wildfire activity.
TMSTC*: A Turn-minimizing Algorithm For Multi-robot Coverage Path Planning

Coverage path planning is a major application for mobile robots, which requires robots to move along a planned path to cover the entire map. For large-scale tasks, coverage path planning benefits greatly from multiple robots. In this paper, we describe Turn-minimizing Multirobot Spanning Tree Coverage Star(TMSTC*), an improved multirobot coverage path planning (mCPP) algorithm based on the MSTC*. Our algorithm partitions the map into minimum bricks as tree's branches and thereby transforms the problem into finding the maximum independent set of bipartite graph. We then connect bricks with greedy strategy to form a tree, aiming to reduce the number of turns of corresponding circumnavigating coverage path. Our experimental results show that our approach enables multiple robots to make fewer turns and thus complete terrain coverage tasks faster than other popular algorithms.
Detection of brain activations induced by naturalistic stimuli in a pseudo model-driven way

Naturalistic fMRI has been suggested to be a powerful alternative for investigations of human brain function. Stimulus-induced activation has been playing an essential role in fMRI-based brain function analyses. Due to the complexity of the stimuli, however, detection of activations induced by naturalistic stimuli (AINSs) has been a tricky problem, as AINS cannot be detected simply in a model-driven way. In this study, we proposed a method to detect AINS in a pseudo model-driven way. Inspired by the strategy of utilizing the commonalities among the brains exposed to the same stimuli for inter-subject correlation analysis, we established response models for one subject by averaging the fMRI signals across several other subjects, and then detected AINSs of the subject using general linear model. We evaluated the effectiveness of AINS with both statistical and predictive analyses on individual differences in sex and intelligence quotient (IQ), based on the four movie fMRI runs included in the Human Connectome Project dataset. The results indicate that AINS is not only sensitive to sex- and IQ-related differences, but also specific enough to decode individuals' sex and IQ. Specifically, activations in brain regions associated with visual-spatial processing were observed to be consistently stronger in the males, and individuals with higher IQ exhibited consistently stronger activations in regions within the visual and the default mode networks. Predictions of individuals' sex and IQ were significantly better than those based on random labels (P<0.005). Taken together, AINS advanced in this study can be an effective evaluation of human brain function. The conceptual simplicity and easy application of its detection may make AINS a favorable choice for future brain function analyses and personalized medicine based on naturalistic fMRI.
Meta-Learning Fast Weight Language Models

Dynamic evaluation of language models (LMs) adapts model parameters at test time using gradient information from previous tokens and substantially improves LM performance. However, it requires over 3x more compute than standard inference. We present Fast Weight Layers (FWLs), a neural component that provides the benefits of dynamic evaluation much more efficiently by expressing gradient updates as linear attention. A key improvement over dynamic evaluation is that FWLs can also be applied at training time, so the model learns to make good use of gradient updates. FWLs can easily be added on top of existing transformer models, require relatively little extra compute or memory to run, and significantly improve language modeling perplexity.
L2SR: Learning to Sample and Reconstruct for Accelerated MRI

Accelerated MRI aims to ﬁnd a pair of samplers and reconstructors to reduce acquisition time while maintaining the reconstruction quality. Most of the existing works focus on ﬁnding either sparse samplers with a ﬁxed reconstructor or ﬁnding reconstructors with a ﬁxed sampler. Recently, people have begun to consider learning samplers and reconstructors jointly. In this paper, we propose an alternating training framework for ﬁnding a good pair of samplers and reconstructors via deep reinforcement learning (RL). In particular, we propose a novel sparse-reward Partially Observed Markov Decision Process (POMDP) to formulate the MRI sampling trajectory. Compared to the existing works that utilize dense-reward POMDPs, the proposed sparse-reward POMDP is more computationally efﬁcient and has a provable advantage over dense-reward POMDPs. We evaluate our method on fastMRI, a public benchmark MRI dataset, and it achieves state-of-the-art reconstruction performances.
RGB-L: Enhancing Indirect Visual SLAM Using LiDAR-Based Dense Depth Maps

In this paper, we present a novel method for integrating 3D LiDAR depth measurements into the existing ORB-SLAM3 by building upon the RGB-D mode. We propose and compare two methods of depth map generation: conventional computer vision methods, namely an inverse dilation operation, and a supervised deep learning-based approach. We integrate the former directly into the ORB-SLAM3 framework by adding a so-called RGB-L (LiDAR) mode that directly reads LiDAR point clouds. The proposed methods are evaluated on the KITTI Odometry dataset and compared to each other and the standard ORB-SLAM3 stereo method. We demonstrate that, depending on the environment, advantages in trajectory accuracy and robustness can be achieved. Furthermore, we demonstrate that the runtime of the ORB-SLAM3 algorithm can be reduced by more than 40 % compared to the stereo mode. The related code for the ORB-SLAM3 RGB-L mode is available as open-source software under https://github.com/TUMFTM/ORB_SLAM3_RGBL.
Identifying Discrete Breathers Using Convolutional Neural Networks

Artificial intelligence in the form of deep learning is now very popular and directly implemented in many areas of science and technology. In the present work we study time evolution of Discrete Breathers in one-dimensional nonlinear chains using the framework of Convolutional Neural Networks. We focus on differentiating discrete breathers which are localized nonlinear modes from linearized phonon modes. The breathers are localized in space and time-periodic solutions of non-linear discrete lattices while phonons are the linear collective oscillations of interacting atoms and molecules. We show that deep learning neural networks are indeed able not only to distinguish breather from phonon modes but also determine with high accuracy the underlying nonlinear on-site potentials that generate breathers. This work can have extensions to more complex natural systems.
Semiconductor Nanocrystals: A Powerful Visual Aid for Introducing the Particle in a Box

For most undergraduate students, introductory quantum chemistry is a challenging subject with many uncommon, if not bizarre, ideas. It is important to relate quantum concepts to the "real" world. Semiconductor quantum dots can be used to introduce and reinforce important points of quantum mechanics and provide real-world applications that exploit quantum phenomena. Most stunning is the colorful demonstration of quantum confinement; few other instances allow direct observation of a quantum effect. Semiconductor nanocrystals can be qualitatively modeled as a particle in a box. This paper presents methods of fabricating nanocrystals, focusing on the pyrolysis of organometallic precursors. The relationship between energy and nanocrystal size is detailed, and the paper closes with an overview of applications of semiconductor nanocrystals.
Prediction of topological phases in metastable ferromagnetic 
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 monolayers

Density functional theory calculations are carried out to study the electronic and topological properties of $M$P$X_3$ ($M$ = Mn, Fe, Co, Ni, and $X$ = S, Se) monolayers in the ferromagnetic (FM) metastable magnetic state. We find that FM MnPSe$_3$ monolayers host topological semimetal signatures that are gapped out when spin-orbit coupling (SOC) is included. These findings are supported by explicit calculations of the Berry curvature and the Chern number. The choice of the Hubbard-$U$ parameter to describe the $d$-electrons is thoroughly discussed, as well as the influence of using a hybrid-functional approach. The presence of band inversions and the associated topological features are found to be formalism-dependent. Nevertheless, routes to achieve the topological phase via the application of external biaxial strain are demonstrated. Within the hybrid-functional picture, topological band structures are recovered under a pressure of 15% (17 GPa). The present work provides a potential avenue for uncovering new topological phases in metastable ferromagnetic phases.
Real-time Bidding Strategy in Display Advertising: An Empirical Analysis

Bidding strategies that help advertisers determine bidding prices are receiving increasing attention as more and more ad impressions are sold through real-time bidding systems. This paper first describes the problem and challenges of optimizing bidding strategies for individual advertisers in real-time bidding display advertising. Then, several representative bidding strategies are introduced, especially the research advances and challenges of reinforcement learning-based bidding strategies. Further, we quantitatively evaluate the performance of several representative bidding strategies on the iPinYou dataset. Specifically, we examine the effects of state, action, and reward function on the performance of reinforcement learning-based bidding strategies. Finally, we summarize the general steps for optimizing bidding strategies using reinforcement learning algorithms and present our suggestions.
Interaction models and configurational entropies of binary MoTa and the MoNbTaW high entropy alloy

We introduce a simplified method to model the interatomic interactions of high entropy alloys based on a lookup table of cluster energies. These interactions are employed in replica exchange Monte Carlo simulations with histogram analysis to obtain thermodynamic properties across a broad temperature range. Kikuchi's Cluster Variation Method entropy formalism and high temperature series expansions are applied to directly calculate entropy from statistics on short- and long-range chemical order, and we discuss the convergence of the entropy as clusters of differing size are included.
Democratizing Machine Translation with OPUS-MT

This paper presents the OPUS ecosystem with a focus on the development of open machine translation models and tools, and their integration into end-user applications, development platforms and professional work-ﬂows. We discuss our on-going mission of increasing language coverage and translation quality, and also describe on-going work on the development of modular translation models and speed-optimized compact solutions for real-time translation on regular desktops and small devices
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation

—Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI). Here, we develop CloudBrain-ReconAI, an online cloud computing platform, for algorithm deployment, fast and blind reader study. This platform supports online image reconstruction using state-of-the-art artificial intelligence and compressed sensing algorithms with applications to fast imaging and high-resolution diffusion imaging. Through visiting the website, radiologists can easily score and mark the images. Then, automatic statistical analysis will be provided. CloudBrain-ReconAI is now open accessed at https://csrc.xmu. edu.cn/CloudBrain.html and will be continually improved to serve the MRI research community.
Modelling of Quantum Dots with the Finite Element Method

: Considering the increasing number of experimental results in the manufacturing process of quantum dots (QDs) with different geometries, and the fact that most numerical methods that can be used to investigate quantum dots with non-trivial geometries require large computational capacities the finite element method (FEM) becomes an incredibly attractive tool for modeling semiconductor QDs. In the current article, the authors have used the FEM to obtain the first twenty-six probability densities and energy values for the following GaAs structures: rectangular, spherical, cylindrical, ellipsoidal, spheroidal, and conical QDs, quantum rings, nanotadpoles, and nanostars. The results of the numerical calculations were compared with the exact analytical solutions and a good deviation was obtained. The ground states energies dependence on the element size was obtained to find the optimal parameter for the investigated structures. The abovementioned calculation results were used to obtain valuable insight into the effects of the size quantization’s dependence on the shape of the QDs. Additionally, the wavefunctions and energies of spherical CdSe/CdS quantum dots were obtained while taking into account the diffusion effects on the potential depth with the use of a piecewise Woods-Saxon potential. The diffusion of the effective mass and the dielectric permittivity is obtained with the use of a normal Woods-Saxon potential. A structure with a quasi type-II band alignment was obtained at the core size of 2.2 nm  . This result is consistent with the experimental data.
Search for ultralight axion dark matter in a side-band analysis of a ${}^{199}$Hg free-spin precession signal

Ultra-low-mass axions are a viable dark matter candidate and may form a coherently oscillating classical field. Nuclear spins in experiments on Earth might couple to this oscillating axion dark-matter field, when propagating on Earth’s trajectory through our Galaxy. This spin coupling resembles an oscillating pseudo-magnetic field which modulates the spin precession of nuclear spins. Here we report on the null result of a demonstration experiment searching for a frequency modulation of the free spin-precession signal of 199Hg in a magnetic field. Our search covers the axion mass range 10^{-16} \textrm{eV} \lesssim m_a \lesssim 10^{-13} \textrm{eV}10−16eV≲ma≲10−13eV and achieves a peak sensitivity to the axion-nucleon coupling of g_{aNN} \approx 3.5 \times 10^{-6} \textrm{GeV}^{-1}gaNN≈3.5×10−6GeV−1.
Satellite Navigation and Coordination with Limited Information Sharing

We explore space traffic management as an application of collision-free navigation in multi-agent systems where vehicles have limited observation and communication ranges. We investigate the effectiveness of transferring a collision avoidance multi-agent reinforcement (MARL) model trained on a ground environment to a space one. We demonstrate that the transfer learning model outperforms a model that is trained directly on the space environment. Furthermore, we find that our approach works well even when we consider the perturbations to satellite dynamics caused by the Earth's oblateness. Finally, we show how our methods can be used to evaluate the benefits of information-sharing between satellite operators in order to improve coordination.