Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusion

Time Series Classification (TSC) is an important and challenging task for many visual computing applications. Despite the extensive range of methods developed for TSC, relatively few utilized Deep Neural Networks (DNNs). In this paper, we propose two novel attention blocks (Global Temporal Attention and Temporal Pseudo-Gaussian augmented Self-Attention) that can enhance deep learning-based TSC approaches, even when such approaches are designed and optimized for a specific dataset or task. We validate this claim by evaluating multiple state-of-the-art deep learning-based TSC models on the University of East Anglia (UEA) benchmark, a standardized collection of 30 Multivariate Time Series Classification (MTSC) datasets. We show that adding the proposed attention blocks improves base models' average accuracy by up to 3.6%. Additionally, the proposed TPS block uses a new injection module to include the relative positional information in transformers. As a standalone unit with less computational complexity, it enables TPS to perform better than most of the state-of-the-art DNN-based TSC methods. The source codes for our experimental setups and proposed attention blocks are made publicly available.
Multilevel Objective-Function-Free Optimization with an Application to Neural Networks Training

A class of multi-level algorithms for unconstrained nonlinear optimization is presented which does not require the evaluation of the objective function. The class contains the momentum-less AdaGrad method as a particular (single-level) instance. The choice of avoiding the evaluation of the objective function is intended to make the algorithms of the class less sensitive to noise, while the multi-level feature aims at reducing their computational cost. The evaluation complexity of these algorithms is analyzed and their behaviour in the presence of noise is then illustrated in the context of training deep neural networks for supervised learning applications.
Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomography

Background Several studies indicate that the anterior visual pathway provides information about the dynamics of axonal degeneration in Multiple Sclerosis (MS). Current research in the field is focused on the quest for the most discriminative features among patients and controls and the development of machine learning models that yield computer-aided solutions widely usable in clinical practice. However, most studies are conducted with small samples and the models are used as black boxes. Clinicians should not trust machine learning decisions unless they come with comprehensive and easily understandable explanations. Materials and methods A total of 216 eyes from 111 healthy controls and 100 eyes from 59 patients with relapsing-remitting MS were enrolled. The feature set was obtained from the thickness of the ganglion cell layer (GCL) and the retinal nerve fiber layer (RNFL). Measurements were acquired by the novel Posterior Pole protocol from Spectralis Optical Coherence Tomography (OCT) device. We compared two black-box methods (gradient boosting and random forests) with a glass-box method (explainable boosting machine). Explainability was studied using SHAP for the black-box methods and the scores of the glass-box method. Results The best-performing models were obtained for the GCL layer. Explainability pointed out to the temporal location of the GCL layer that is usually broken or thinning in MS and the relationship between low thickness values and high probability of MS, which is coherent with clinical knowledge. Conclusions The insights on how to use explainability shown in this work represent a first important step toward a trustworthy computer-aided solution for the diagnosis of MS with OCT.
Morphology of the gas-rich debris disk around HD121617 with SPHERE observations in polarized light

Debris disks are the signposts of collisionally eroding planetesimal circumstellar belts, whose study can put important constraints on the structure of extrasolar planetary systems. The best constraints on the morphology of disks are often obtained from spatially resolved observations in scattered light. Here, we investigate the young (~16 Myr) bright gas-rich debris disk around HD121617. We use new scattered-light observations with VLT/SPHERE to characterize the morphology and the dust properties of this disk. From these properties we can then derive constraints on the physical and dynamical environment of this system, for which significant amounts of gas have been detected. The disk morphology is constrained by linear-polarimetric observations in the J band. Based on our modeling results and archival photometry, we also model the SED to put constraints on the total dust mass and the dust size distribution. We explore different scenarios that could explain these new constraints. We present the first resolved image in scattered light of the debris disk HD121617. We fit the morphology of the disk, finding a semi-major axis of 78.3$\pm$0.2 au, an inclination of 43.1$\pm$0.2{\deg} and a position angle of the major axis with respect to north, of 239.8$\pm$0.3{\deg}, compatible with the previous continuum and CO detection with ALMA. Our analysis shows that the disk has a very sharp inner edge, possibly sculpted by a yet-undetected planet or gas drag. While less sharp, its outer edge is steeper than expected for unperturbed disks, which could also be due to a planet or gas drag, but future observations probing the system farther from the main belt would help explore this further. The SED analysis leads to a dust mass of 0.21$\pm$0.02 M$_{\oplus}$ and a minimum grain size of 0.87$\pm$0.12 $\mu$m, smaller than the blowout size by radiation pressure, which is not unexpected for very bright col.
Environments of active galactic nuclei at z < 1.5 in the UKIDSS Ultra-Deep Survey

We investigate the environments of both X-ray and radio-loud active galactic nuclei (AGN) within the UK Infrared Telescope (UKIRT) Infrared Deep Sky Survey (UKIDSS) Ultra-deep Survey (UDS) using deep infrared selection to sample the galaxy density field in the redshift range 1.0 ≤ z ≤ 1.5. Using angular cross-correlation techniques we find that both X-ray and radio-loud AGN preferentially reside in overdense environments. We also find that both types of AGN cluster more strongly with those galaxies classified as ‘passive’ rather than those that are actively star forming. We infer clustering scalelengths comparable to those of passive red galaxies, suggesting that typical AGN at these epochs reside in dark matter haloes of mass M 10 13 M� . A closer look at the small-scale environments of the AGN reveals that the neighbouring galaxies of radio-loud AGN haveU −B colours more skewed towards the ‘green valley’ and the red sequence, whereas the neighbours of X-ray AGN show no difference to the general galaxy population. This suggests that although both AGN types live in overdense environments, the radio-loud AGN may be preferentially located in more evolved cluster cores, in a similar environment to low-powered radio AGN in the local Universe.
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) neglect the non-Euclidean topology and causal dynamics of brain connectivity across time. In this paper, a deep probabilistic spatiotemporal framework developed based on variational Bayes (DSVB) is proposed to learn time-varying topological structures in dynamic brain FC networks for autism spectrum disorder (ASD) identification. The proposed framework incorporates a spatial-aware recurrent neural network to capture rich spatiotemporal patterns across dynamic FC networks, followed by a fully-connected neural network to exploit these learned patterns for subject-level classification. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework significantly outperformed state-of-the-art methods in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states.
Detection of corruption in Gaussian processes with application to noise thermometry

Statistical tests are given for the detection of corruption in the Gaussian process underlying the measurement of temperature with a Johnson noise thermometer. Principles of hypothesis testing are discussed, with emphasis being placed on the importance of utilizing prior knowledge in the design of a powerful test. Tests are presented for corruption affecting the time signal and frequency spectrum as spikes or as localized deviations. Calculations of the statistical power of the tests are described, and example values given. The statistical power is sufficient for corruption at practical levels of interest to be detected with high probability.
Energy response and spatial alignment of the perturbed electron gas.

We study the linear energy response of the uniform electron gas to an external harmonic perturbation with a focus on resolving different contributions to the total energy. This has been achieved by carrying out highly accurate ab initio path integral Monte Carlo (PIMC) calculations for a variety of densities and temperatures. We report a number of physical insights into effects such as screening and the relative importance of kinetic and potential energies for different wave numbers. A particularly interesting finding is obtained from the observed non-monotonic behavior of the induced change in the interaction energy, which becomes negative for intermediate wave numbers. This effect is strongly dependent on the coupling strength and constitutes further direct evidence for the spatial alignment of electrons introduced in earlier works [T. Dornheim et al., Commun. Phys. 5, 304 (2022)]. The observed quadratic dependence on the perturbation amplitude in the limit of weak perturbations and the quartic dependence of perturbation amplitude corrections are consistent with linear and nonlinear versions of the density stiffness theorem. All PIMC simulation results are freely available online and can be used to benchmark new methods or as input for other calculations.
Tetris-inspired detector with neural network for radiation mapping

In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their configurations, radiation detectors have been developed to locate the directions and positions of the radiation sources. In this process, algorithm is essential in converting detector signals to radiation source information. However, due to the complex mechanisms of radiation-matter interaction and the current limitation of data collection, high-performance, low-cost radiation mapping is still challenging. Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. By further imposing Maximum a Posteriori (MAP) with a moving detector, further radiation position localization is achieved. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector. Our framework offers a new avenue for high quality radiation mapping with least number of detector pixels possible, and is anticipated to be capable to deploy for real-world radiation detection with moderate validation.
Quantizing graphs, one way or two?

Quantum graphs were introduced to model free electrons in organic molecules using a self-adjoint Hamiltonian on a network of intervals. A second graph quantization describes wave propagation on a graph by specifying scattering matrices at the vertices. A question that is frequently raised is the extent to which these models are the same or complementary. In particular, are all energy independent unitary vertex scattering matrices associated with a self-adjoint Hamiltonian? Here we review results related to this issue. In addition, we observe that a self-adjoint Dirac operator with four component spinors produces a secular equation for the graph spectrum that matches the secular equation associated with wave propagation on the graph when the Dirac operator describes particles with zero mass and the vertex conditions do not allow spin rotation at the vertices.
Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems

As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to the superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering the low complexity of the AMP algorithm and adaptability of GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding

Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties? Although target-aware generative models have made significant advances in drug design, they do not consider chemistry conditions and cannot guarantee the desired chemical properties. Unfortunately, merging the target-aware and chemical-aware models into a unified model to meet customized requirements may lead to the problem of negative transfer. Inspired by the success of multi-task learning in the NLP area, we use prefix embeddings to provide a novel generative model that considers both the targeted pocket's circumstances and a variety of chemical properties. All conditional information is represented as learnable features, which the generative model subsequently employs as a contextual prompt. Experiments show that our model exhibits good controllability in both single and multi-conditional molecular generation. The controllability enables us to outperform previous structure-based drug design methods. More interestingly, we open up the attention mechanism and reveal coupling relationships between conditions, providing guidance for multi-conditional molecule generation.
Progress in the Concentrator Initiative Program

Progress made by the eight participating solar cell and solar collector manufacturers since the beginning of the US Department of Energy's multi-year photovoltaic Concentrator Initiative Program is reviewed. Point and line-focus cell manufacturers achieved peak efficiencies between 19% and 25% with designs that could be manufactured commercially. Collector manufacturers delivered prototype hardware of point and line-focus collectors representing commercially producible technology. <<ETX>>
A Norm-Minimization Algorithm for Solving the Cold-Start Problem with XNAV

An algorithm is presented for solving the cold-start problem using observations of X-ray pulsars. Using a norm-minimization-based approach, the algorithm extends Lohan's banded-error intersection model to 3-dimensional space while reducing compute time by an order of magnitude. Higher-fidelity X-ray pulsar signal models, including the parallax effect, Shapiro delay, time dilation, and higher-order pulsar timing models, are considered. The feasibility of solving the cold-start problem using X-ray pulsar navigation is revisited with the improved models and prior knowledge requirements are discussed. Monte Carlo simulations are used to establish upper bounds on uncertainty and determine the accuracy of the algorithm. Results indicate that it is necessary to account for the parallax effect, time dilation, and higher-order pulsar timing models in order to successfully determine the position of the spacecraft in a cold-start scenario. The algorithm can uniquely identify a candidate spacecraft position within a 10 AU $\times$ 10 AU $\times$ 0.01 AU spheroid domain by observing eight to nine pulsars. The median position error of the algorithm is on the order of 15 km. Prior knowledge of spacecraft position is technically required, but only to an accuracy of 100 AU, making it practically unnecessary for navigation within the Solar System. Results further indicate that choosing lower-frequency pulsars increases the maximum domain size but also increases position error.
Unified description of the Aharonov-Bohm effect in isotropic multiband electronic systems

We present a unified treatment of the Aharonov--Bohm (AB) effect for two-dimensional multiband electronic systems possessing isotropic band structures. We propose an integral representation of the AB scattering state of an electron scattered by an infinitely thin solenoid. Moreover, we derive the asymptotic form of the AB scattering state and obtain the differential cross section from that. We found a remarkable result, namely that this cross section is {\it the same for all isotropic systems} and agrees with that obtained first by Aharonov and Bohm for spinless free particle systems. To demonstrate the generality of our theory, we consider several specific multiband systems relevant to condensed matter physics.
K\"{a}hler structures for holomorphic submersions

In this short paper, for any holomorphic submersion $\pi: X\rightarrow B$, we derive a criterion for $X$ to have K\"{a}hler structures. This criterion generalizes Blanchard's criterion for a special class of isotrivial holomorphic submersions. We use this criterion to answer a question of Harvey-Lawson in the case of fiber dimension one. As the main application, we prove that the existence of Hermitian-Symplectic structures on certain class of holomorphic submersions with K\"{a}hler fibers and K\"{a}hler bases implies that the total spaces are K\"{a}hler. This class includes isotrivial submersions and torus fibrations.
Quantum-fluctuation-driven dynamics of droplet splashing, recoiling, and deposition in ultracold binary Bose gases

Droplet impact on a surface is practically relevant to a variety of fields in nature and industry, while a complete control of its outcomes remains challenging due to various unmanageable factors. In this work, we propose the quantum simulation of droplet impact outcomes in the platform of ultracold atoms. Specifically, we study the quantum-fluctuation-driven dynamics (QFDD) of two-dimensional Bose-Bose mixtures from an initial Townes soliton towards the formation of a quantum droplet. By tuning the fluctuation energy of the initial Townes state through its size and number, the subsequent QFDD can produce various outcomes including splashing, recoiling, and deposition, similar to those in droplet impact dynamics. We have utilized the Weber number to identify the thresholds of splashing and recoiling, and further established a universal scaling law between the maximum spreading factor and the Weber number in the recoiling regime. In addition, we show that the residual QFDD in the deposition regime can be used to probe the collective breathing modes of a quantum droplet. Our results reveal a mechanism for the droplet impact outcomes, which can be directly tested in cold-atom experiments and can pave the way for exploring intriguing droplet dynamics in a clean and fully controlled quantum setting.
Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?

According to the Stimulus Organism Response (SOR) theory, all human behavioral reactions are stimulated by context, where people will process the received stimulus and produce an appropriate reaction. This implies that in a specific context for a given input stimulus, a person can react differently according to their internal state and other contextual factors. Analogously, in dyadic interactions, humans communicate using verbal and nonverbal cues, where a broad spectrum of listeners' non-verbal reactions might be appropriate for responding to a specific speaker behaviour. There already exists a body of work that investigated the problem of automatically generating an appropriate reaction for a given input. However, none attempted to automatically generate multiple appropriate reactions in the context of dyadic interactions and evaluate the appropriateness of those reactions using objective measures. This paper starts by defining the facial Multiple Appropriate Reaction Generation (fMARG) task for the first time in the literature and proposes a new set of objective evaluation metrics to evaluate the appropriateness of the generated reactions. The paper subsequently introduces a framework to predict, generate, and evaluate multiple appropriate facial reactions.