Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust FWER control in Neuroimaging using Random Field Theory: Riding the SuRF to Continuous Land Part 2

Historically, applications of RFT in fMRI have relied on assumptions of smoothness, stationarity and Gaussianity. The first two assumptions have been addressed in Part 1 of this article series. Here we address the severe non-Gaussianity of (real) fMRI data to greatly improve the performance of voxelwise RFT in fMRI group analysis. In particular, we introduce a transformation which accelerates the convergence of the Central Limit Theorem allowing us to rely on limiting Gaussianity of the test-statistic. We shall show that, when the GKF is combined with the Gaussianization transformation, we are able to accurately estimate the EEC of the excursion set of the transformed test-statistic even when the data is non-Gaussian. This allows us to drop the key assumptions of RFT inference and enables us to provide a fast approach which correctly controls the voxelwise false positive rate in fMRI. We employ a big data \cite{Eklund2016} style validation in which we process resting state data from 7000 subjects from the UK BioBank with fake task designs. We resample from this data to create realistic noise and use this to demonstrate that the error rate is correctly controlled.
Bifurcation tracking on moving meshes and with consideration of azimuthal symmetry breaking instabilities

We present a black-box method to numerically investigate the linear stability of arbitrary multi-physics problems. While the user just has to enter the system's residual in weak formulation, i.e. by a finite element method, all required discretized matrices are automatically assembled based on just-in-time generated and compiled highly performant C code. Based on this method, entire phase diagrams in the parameter space can be obtained by bifurcation tracking and continuation within minutes. Particular focus is put on problems with moving domains, e.g. free surface problems in fluid dynamics, since a moving mesh introduces a plethora of complicated nonlinearities to the system. By symbolic differentiation before the code generation, however, these moving mesh problems are made accessible to bifurcation tracking methods. In a second step, our method is generalized to investigate symmetry-breaking instabilities of axisymmetric stationary solutions by effectively utilizing the symmetry of the base state. Each bifurcation type is validated on the basis of results reported in the literature on versatile fluid dynamics problems.
Bilayer crystals of trapped ions for quantum information processing

Trapped ion systems are a leading platform for quantum information processing, but they are currently limited to 1D and 2D arrays, which imposes restrictions on both their scalability and their range of applications. Here, we propose a path to overcome this limitation by demonstrating that Penning traps can be used to realize remarkably clean bilayer crystals, wherein hundreds of ions self-organize into two well-defined layers. These bilayer crystals are made possible by the inclusion of an anharmonic trapping potential, which is readily implementable with current technology. We study the normal modes of this system and discover salient differences compared to the modes of single-plane crystals. The bilayer geometry and the unique properties of the normal modes open new opportunities, in particular in quantum sensing and quantum simulation, that are not straightforward in single-plane crystals. Furthermore, we illustrate that it may be possible to extend the ideas presented here to realize multilayer crystals with more than two layers. Our work increases the dimensionality of trapped ion systems by efficiently utilizing all three spatial dimensions and lays the foundation for a new generation of quantum information processing experiments with multilayer 3D crystals of trapped ions.
Secure Full Duplex Integrated Sensing and Communications

The following paper models a secure full duplex (FD) integrated sensing and communication (ISAC) scenario, where malicious eavesdroppers aim at intercepting the downlink (DL) as well as the uplink (UL) information exchanged between the dual functional radar and communication (DFRC) base station (BS) and a set of communication users. The DFRC BS, on the other hand, aims at illuminating radar beams at the eavesdroppers in order to sense their physical parameters, while maintaining high UL/DL secrecy rates. Based on the proposed model, we formulate a power efficient secure ISAC optimization framework design, which is intended to guarantee both UL and DL secrecy rates requirements, while illuminating radar beams towards eavesdroppers. The framework exploits artificial noise (AN) generation at the DFRC BS, along with UL/DL beamforming design and UL power allocation. We propose a beamforming design solution to the secure ISAC optimization problem. Finally, we corroborate our findings via simulation results and demonstrate the feasibility, as well as the superiority of the proposed algorithm, under different situations. We also reveal insightful trade-offs achieved by our approach.
UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification

Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition of natural domains and shown promise in medical imaging diagnosis for the Chest X-Rays (CXRs). However, current works mainly pay attention to the exploration on single dataset of CXRs, which locks the potential of this powerful paradigm on larger hybrid of multi-source CXRs datasets. We identify that although blending samples from the diverse sources offers the advantages to improve the model generalization, it is still challenging to maintain the consistent superiority for the task of each source due to the existing heterogeneity among sources. To handle this dilemma, we design a Conquer-and-Divide pre-training framework, termed as UniChest, aiming to make full use of the collaboration benefit of multiple sources of CXRs while reducing the negative influence of the source heterogeneity. Specially, the "Conquer" stage in UniChest encourages the model to sufficiently capture multi-source common patterns, and the "Divide" stage helps squeeze personalized patterns into different small experts (query networks). We conduct thorough experiments on many benchmarks, e.g., ChestX-ray14, CheXpert, Vindr-CXR, Shenzhen, Open-I and SIIM-ACR Pneumothorax, verifying the effectiveness of UniChest over a range of baselines, and release our codes and pre-training models at https://github.com/Elfenreigen/UniChest.
On the properties of falling and rising factorial transforms

In this paper, we will extend the falling and rising factorial transforms \cite{ref. 1} which in this case every arbitrary function can be applied. Then, the properties of these transforms will be investigated and some corollaries will be shown. These transforms have interesting properties that led to new series expansions, representation of functions in terms of operators, connection between different series and operators, etc. By converting the power series into the Newton series, useful identities can be derived.
Colored Noise in PPO: Improved Exploration and Performance Through Correlated Action Sampling

Proximal Policy Optimization (PPO), a popular on-policy deep reinforcement learning method, employs a stochastic policy for exploration. In this paper, we propose a colored noise-based stochastic policy variant of PPO. Previous research highlighted the importance of temporal correlation in action noise for effective exploration in off-policy reinforcement learning. Building on this, we investigate whether correlated noise can also enhance exploration in on-policy methods like PPO. We discovered that correlated noise for action selection improves learning performance and outperforms the currently popular uncorrelated white noise approach in on-policy methods. Unlike off-policy learning, where pink noise was found to be highly effective, we found that a colored noise, intermediate between white and pink, performed best for on-policy learning in PPO. We examined the impact of varying the amount of data collected for each update by modifying the number of parallel simulation environments for data collection and observed that with a larger number of parallel environments, more strongly correlated noise is beneficial. Due to the significant impact and ease of implementation, we recommend switching to correlated noise as the default noise source in PPO.
Validation of Rigorous Requirements Specifications and Document Automation with the ITLingo RSL Language

Despite being an essential step in software development, writing requirements specifications is frequently performed in natural language, leading to issues like inconsistency, incompleteness, or ambiguity. The ITLingo initiative has introduced a requirements specification language named RSL to enhance the rigor and consistency of technical documentation. On the other hand, natural language processing (NLP) is a field that has been supporting the automatic analysis of requirements by helping to detect issues that may be difficult to see during a manual review. Once the requirements specifications are validated, it is important to automate the generation of documents for these specifications to reduce manual work, reduce errors, and to produce documentation in multiple formats that are more easily reusable or recognized by the different stakeholders. This paper reviews existing research and tools in the fields of requirements validation and document automation. We propose to extend RSL with validation of specifications based on customized checks, and on linguistic rules dynamically defined in the RSL itself. In addition, we also propose the automatic generation of documents from these specifications to JSON, TXT, or other file formats using template files. We use a fictitious business information system to support the explanation and to demonstrate how these validation checks can assist in writing better requirements specifications and then generate documents in multiple formats based on them. Finally, we evaluate the usability of the proposed validation and document automation approach through a user session.
Self-Supervised Learning for Image Super-Resolution and Deblurring

Self-supervised methods have recently proved to be nearly as effective as supervised methods in various imaging inverse problems, paving the way for learning-based methods in scientific and medical imaging applications where ground truth data is hard or expensive to obtain. This is the case in magnetic resonance imaging and computed tomography. These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data alone. However, existing approaches fail to obtain competitive performances in the problems of image super-resolution and deblurring, which play a key role in most imaging systems. In this work, we show that invariance to translations and rotations is insufficient to learn from measurements that only contain low-frequency information. Instead, we propose a new self-supervised approach that leverages the fact that many image distributions are approximately scale-invariant, and that enables recovering high-frequency information lost in the measurement process. We demonstrate throughout a series of experiments on real datasets that the proposed method outperforms other self-supervised approaches, and obtains performances on par with fully supervised learning.
Calabi-Yau meets Gravity: A Calabi-Yau three-fold at fifth post-Minkowskian order

We study geometries occurring in Feynman integrals that contribute to the scattering of black holes in the post-Minkowskian expansion. These geometries become relevant to gravitational-wave production during the inspiralling phase of binary black hole mergers through the classical conservative potential. At fourth post-Minkowskian order, a K3 surface is known to occur in a three-loop integral, leading to elliptic integrals in the result. In this letter, we identify a Calabi-Yau three-fold in a four-loop integral, contributing at fifth post-Minkowskian order. The presence of this Calabi-Yau geometry indicates that completely new functions occur in the full analytical results at this order.
Animal-AI 3: What's New & Why You Should Care

The Animal-AI Environment is a unique game-based research platform designed to serve both the artificial intelligence and cognitive science research communities. In this paper, we present Animal-AI 3, the latest version of the environment, outlining several major new features that make the game more engaging for humans and more complex for AI systems. New features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant increases in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with Animal-AI 3. We present results from a series of agents, including the state-of-the-art Deep Reinforcement Learning agent (dreamer-v3), on newly designed tests and the Animal-AI Testbed of 900 tasks inspired by research in comparative psychology. Animal-AI 3 is designed to facilitate collaboration between the cognitive sciences and artificial intelligence. This paper serves as a stand-alone document that motivates, describes, and demonstrates Animal-AI 3 for the end user.
Language-conditioned Learning for Robotic Manipulation: A Survey

Language-conditioned robotic manipulation represents a cutting-edge area of research, enabling seamless communication and cooperation between humans and robotic agents. This field focuses on teaching robotic systems to comprehend and execute instructions conveyed in natural language. To achieve this, the development of robust language understanding models capable of extracting actionable insights from textual input is essential. In this comprehensive survey, we systematically explore recent advancements in language-conditioned approaches within the context of robotic manipulation. We analyze these approaches based on their learning paradigms, which encompass reinforcement learning, imitation learning, and the integration of foundational models, such as large language models and vision-language models. Furthermore, we conduct an in-depth comparative analysis, considering aspects like semantic information extraction, environment&evaluation, auxiliary tasks, and task representation. Finally, we outline potential future research directions in the realm of language-conditioned learning for robotic manipulation, with the topic of generalization capabilities and safety issues. The GitHub repository of this paper can be found at https://github.com/hk-zh/language-conditioned-robot-manipulation-models
Monte Carlo Tree Search in the Presence of Transition Uncertainty

Monte Carlo Tree Search (MCTS) is an immensely popular search-based framework used for decision making. It is traditionally applied to domains where a perfect simulation model of the environment is available. We study and improve MCTS in the context where the environment model is given but imperfect. We show that the discrepancy between the model and the actual environment can lead to significant performance degradation with standard MCTS. We therefore develop Uncertainty Adapted MCTS (UA-MCTS), a more robust algorithm within the MCTS framework. We estimate the transition uncertainty in the given model, and direct the search towards more certain transitions in the state space. We modify all four MCTS phases to improve the search behavior by considering these estimates. We prove, in the corrupted bandit case, that adding uncertainty information to adapt UCB leads to tighter regret bound than standard UCB. Empirically, we evaluate UA-MCTS and its individual components on the deterministic domains from the MinAtar test suite. Our results demonstrate that UA-MCTS strongly improves MCTS in the presence of model transition errors.
Energy-Aware Hierarchical Control of Joint Velocities

Nowadays, robots are applied in dynamic environments. For a robust operation, the motion planning module must consider other tasks besides reaching a specified pose: (self) collision avoidance, joint limit avoidance, keeping an advantageous configuration, etc. Each task demands different joint control commands, which may counteract each other. We present a hierarchical control that, depending on the robot and environment state, determines online a suitable priority among those tasks. Thereby, the control command of a lower-prioritized task never hinders the control command of a higher-prioritized task. We ensure smooth control signals also during priority rearrangement. Our hierarchical control computes reference joint velocities. However, the underlying concepts of hierarchical control differ when using joint accelerations or joint torques as control signals instead. So, as a further contribution, we provide a comprehensive discussion on how joint velocity control, joint acceleration control, and joint torque control differ in hierarchical task control. We validate our formulation in an experiment on hardware.
Open X-Embodiment: Robotic Learning Datasets and RT-X Models

different robots collected through a collaboration between 21 institutions, demonstrating 527 skills ( 160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website robotics-transformer-x.github. io
A comparison of indicators reflecting the status of the North Sea benthos

Interest in the use of summary measures (indicators) to quantify the responses of benthic communities to natural and human-induced changes has increased in recent years, especially to meet the requirements of initiatives such as the United Nations Convention on Biological Diversity (1992), the EU Water Framework Directive (European Communities, 2000), the European Marine Strategy (Commission of the European Communities, 2002), and OSPAR (see Lanters et al., 1999 and ICES, 2004). There exists a wide variety of measures to describe the biological status or changes (Diaz et al., 2004). In their simplest form, the primary variables (typically the total numbers of individuals, species, or biomass) are employed as stand-alone summary measures. Derived univariate measures such as the Hill diversity series (Whittaker, 1972; Hill, 1973; Magurran, 1988) add an additional layer of complexity and typically combine the numerical dominance and species richness components of diversity. Finally, the multimetric methodologies (below) combine a variety of primary or derived univariate measures in one index or, in a more complex form, employ outputs from multivariate analysis.
Contribution of EARLINET/ACTRIS to the summer 2013 Special Observing Period of the ChArMEx project: monitoring of a Saharan dust event over the western and central Mediterranean

ABSTRACT In the framework of the Chemistry-Aerosol Mediterranean Experiment (ChArMEx; http://charmex.lsce.ipsl.fr/) initiative, a field campaign took place in the western Mediterranean Basin between 10 June and 5 July 2013 within the ADRIMED (Aerosol Direct Radiative Impact on the regional climate in the MEDiterranean region) project. The scientific objectives of ADRIMED are the characterization of the most common ‘Mediterranean aerosols’ and their direct radiative forcing (column closure and regional scale). During 15–24 June a multi-intrusion dust event took place over the western and central Mediterranean Basin. Extra measurements were carried out by some EARLINET/ACTRIS (European Aerosol Research Lidar Network /Aerosols, Clouds, and Trace gases Research InfraStructure Network, http://www.actris.net/) lidar stations in Spain and Italy, in particular on 22 June in support to the flight over southern Italy of the Falcon 20 aircraft involved in the campaign. This article describes the physical and optical properties of dust observed at the different lidar stations in terms of dust plume centre of mass, optical depth, lidar ratio, and particle depolarization ratio. To link the differences found in the origin of dust plumes, the results are discussed on the basis of back-trajectories and air- and space-borne lidars. This work puts forward the collaboration between a European research infrastructure (ACTRIS) and an international project (ChArMEx) on topics of interest for both parties, and more generally for the atmospheric community.
Amidst an amygdala renaissance in Alzheimer’s disease

Abstract The amygdala was highlighted as an early site for neurofibrillary tau tangle pathology in Alzheimer’s disease in the seminal 1991 article by Braak and Braak. This knowledge has, however, only received traction recently with advances in imaging and image analysis techniques. Here, we provide a cross-disciplinary overview of pathology and neuroimaging studies on the amygdala. These studies provide strong support for an early role of the amygdala in Alzheimer’s disease and the utility of imaging biomarkers of the amygdala in detecting early changes and predicting decline in cognitive functions and neuropsychiatric symptoms in early stages. We summarize the animal literature on connectivity of the amygdala, demonstrating that amygdala nuclei that show the earliest and strongest accumulation of neurofibrillary tangle pathology are those that are connected to brain regions that also show early neurofibrillary tangle accumulation. Additionally, we propose an alternative pathway of neurofibrillary tangle spreading within the medial temporal lobe between the amygdala and the anterior hippocampus. The proposed existence of this pathway is strengthened by novel experimental data on human functional connectivity. Finally, we summarize the functional roles of the amygdala, highlighting the correspondence between neurofibrillary tangle accumulation and symptomatic profiles in Alzheimer’s disease. In summary, these findings provide a new impetus for studying the amygdala in Alzheimer’s disease and a unique perspective to guide further study on neurofibrillary tangle spreading and the occurrence of neuropsychiatric symptoms in Alzheimer’s disease.