Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting New Visual Binaries in Gaia DR3 with Gaia and Two Micron All Sky Survey (2MASS) Photometry. I. New Candidate Binaries within 200 pc of the Sun

We present a method to identify likely visual binaries in Gaia eDR3 that does not rely on parallax or proper motion. This method utilizes the various point-spread function sizes of Two Micron All Sky Survey (2MASS)/Gaia, where at <2. ″5 two stars may be unresolved in 2MASS but resolved by Gaia. Due to this, if close neighbors listed in Gaia are a resolved pair, the associated 2MASS source will have a predictable excess in the J band that depends on the ΔG of the pair. We demonstrate that the expected relationship between 2MASS excess and ΔG differs for chance alignments, as compared to true binary systems, when parameters like magnitude and location on the sky are also considered. Using these multidimensional distributions, we compute the likelihood of a close pair of stars to be a chance alignment, resulting in a total(clean) catalog of 68,725(50,230) likely binaries within 200 pc with a completeness rate of ∼75%(∼64%) and contamination rate of ∼14%(∼0.4%). Within this, we find 590 previously unidentified binaries from Gaia eDR3 with projected physical separations <30 au, where 138 systems were previously identified, and for s < 10 au we find that 4 out of 15 new likely binaries have not yet been observed with high-resolution imaging. We also demonstrate the potential of our catalog to determine physical separation distributions and binary fraction estimates, from this increase in low-separation binaries. Overall, this catalog provides a good complement for the study of local binary populations by probing smaller physical separations and mass ratios, and provides prime targets for speckle monitoring.
Zero Modes of Massive Fermions Delocalize from Axion Strings

Massless chiral excitations can arise from the interactions between a fermion and an axion string, propagating along the string and allowing it to superconduct. The properties of these excitations, or zero modes, dictate how the string interacts with light and can thus have important phenomenological consequences. In this paper, we add a nowhere-vanishing Dirac mass for the fermion in the usual model of axion electrodynamics. We find that the zero modes exhibit an interesting phase structure in which they delocalize from the string's core as the mass increases, up until a critical value past which they disappear. We study this structure from an analytic perspective, with explicit numerical solutions, and via anomaly inflow arguments. Finally, we derive the two-dimensional effective theory of the zero mode and its interactions with the four-dimensional gauge field and show how this effective theory breaks down as the zero modes delocalize.
A Scaling Approach to Stochastic Chemical Reaction Networks

We investigate the asymptotic properties of Markov processes associated to stochastic chemical reaction networks (CRNs) driven by the kinetics of the law of mass action. Their transition rates exhibit a polynomial dependence on the state variable, with possible discontinuities of the dynamics along the boundary of the state space. As a natural choice to study stability properties of CRNs, the scaling parameter considered in this paper is the norm of the initial state. Compared to existing scalings of the literature, this scaling does not change neither the topology of a CRN, nor its reactions constants. Functional limit theorems with this scaling parameter can be used to prove positive recurrence of the Markov process. This scaling approach also gives interesting insights on the transient behavior of these networks, to describe how multiple time scales drive the time evolution of their sample paths for example. General stability criteria are presented as well as a possible framework for scaling analyses. Several simple examples of CRNs are investigated with this approach. A detailed stability and scaling analyses of a CRN with slow and fast timescales is worked out.
CODA: Temporal Domain Generalization via Concept Drift Simulator

In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the"concept drift". Existing works propose model-specific strategies to achieve temporal generalization in the near-future domain. However, the diverse characteristics of real-world datasets necessitate customized prediction model architectures. To this end, there is an urgent demand for a model-agnostic temporal domain generalization approach that maintains generality across diverse data modalities and architectures. In this work, we aim to address the concept drift problem from a data-centric perspective to bypass considering the interaction between data and model. Developing such a framework presents non-trivial challenges: (i) existing generative models struggle to generate out-of-distribution future data, and (ii) precisely capturing the temporal trends of joint distribution along chronological source domains is computationally infeasible. To tackle the challenges, we propose the COncept Drift simulAtor (CODA) framework incorporating a predicted feature correlation matrix to simulate future data for model training. Specifically, CODA leverages feature correlations to represent data characteristics at specific time points, thereby circumventing the daunting computational costs. Experimental results demonstrate that using CODA-generated data as training input effectively achieves temporal domain generalization across different model architectures.
Sex-specific DNA methylation and transcription of zbtb38 and effects of gene–environment interactions on its natural antisense transcript in zebrafish

ABSTRACT There is increasing evidence for the involvement of epigenetics in sex determination, maintenance, and plasticity, from plants to humans. In our previous work, we reported a transgenerational feminization of a zebrafish population for which the first generation was exposed to cadmium, a metal with endocrine disrupting effects. In this study, starting from the previously performed whole methylome analysis, we focused on the zbtb38 gene and hypothesized that it could be involved in sex differentiation and Cd-induced offspring feminization. We observed sex-specific patterns of both DNA methylation and RNA transcription levels of zbtb38. We also discovered that the non-coding exon 3 of zbtb38 encodes for a natural antisense transcript (NAT). The activity of this NAT was found to be influenced by both genetic and environmental factors. Furthermore, increasing transcription levels of this NAT in parental gametes was highly correlated with offspring sex ratios. Since zbtb38 itself encodes for a transcription factor that binds methylated DNA, our results support a non-negligible role of zbtb38 not only in orchestrating the sex-specific transcriptome (i.e., sex differentiation) but also, via its NAT, offspring sex ratios.
Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning

Perceptive deep reinforcement learning (DRL) has lead to many recent breakthroughs for complex AI systems leveraging image-based input data. Applications of these results range from super-human level video game agents to dexterous, physically intelligent robots. However, training these perceptive DRL-enabled systems remains incredibly compute and memory intensive, often requiring huge training datasets and large experience replay buffers. This poses a challenge for the next generation of field robots that will need to be able to learn on the edge in order to adapt to their environments. In this paper, we begin to address this issue through differentially encoded observation spaces. By reinterpreting stored image-based observations as a video, we leverage lossless differential video encoding schemes to compress the replay buffer without impacting training performance. We evaluate our approach with three state-of-the-art DRL algorithms and find that differential image encoding reduces the memory footprint by as much as 14.2x and 16.7x across tasks from the Atari 2600 benchmark and the DeepMind Control Suite (DMC) respectively. These savings also enable large-scale perceptive DRL that previously required paging between flash and RAM to be run entirely in RAM, improving the latency of DMC tasks by as much as 32%.
Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods

Deep neural networks, despite their success in numerous applications, often function without established theoretical foundations. In this paper, we bridge this gap by drawing parallels between deep learning and classical numerical analysis. By framing neural networks as operators with fixed points representing desired solutions, we develop a theoretical framework grounded in iterative methods for operator equations. Under defined conditions, we present convergence proofs based on fixed point theory. We demonstrate that popular architectures, such as diffusion models and AlphaFold, inherently employ iterative operator learning. Empirical assessments highlight that performing iterations through network operators improves performance. We also introduce an iterative graph neural network, PIGN, that further demonstrates benefits of iterations. Our work aims to enhance the understanding of deep learning by merging insights from numerical analysis, potentially guiding the design of future networks with clearer theoretical underpinnings and improved performance.
Solving the Quadratic Assignment Problem using Deep Reinforcement Learning

The Quadratic Assignment Problem (QAP) is an NP-hard problem which has proven particularly challenging to solve: unlike other combinatorial problems like the traveling salesman problem (TSP), which can be solved to optimality for instances with hundreds or even thousands of locations using advanced integer programming techniques, no methods are known to exactly solve QAP instances of size greater than 30. Solving the QAP is nevertheless important because of its many critical applications, such as electronic wiring design and facility layout selection. We propose a method to solve the original Koopmans-Beckman formulation of the QAP using deep reinforcement learning. Our approach relies on a novel double pointer network, which alternates between selecting a location in which to place the next facility and a facility to place in the previous location. We train our model using A2C on a large dataset of synthetic instances, producing solutions with no instance-specific retraining necessary. Out of sample, our solutions are on average within 7.5% of a high-quality local search baseline, and even outperform it on 1.2% of instances.
Quality of bread made from rye grown with conventional and Ecological methods.

There were checked the baking proprieties of six rye cultivars coming from conventional and ecological tillages. Bread made from rye grown with conventional method characterized with higher volume and imperceptibly lower baking loss. The way of tillage had no essential influence on porosity. Bread made from cultivar Conduct grown with ecological way received the highest notes in the sensory assessment (31.5 points). The addition of 19moll lactic acid to the dough enlarged bread volume, bread porosity and the total baking loss, not influencing on the sensory assessment of breads. In dependence of analyzed feature of texture the factor of changeability (the way of tillage, cultivar and the method of baking) influenced on the reological properties of breads from rye flour in different degree. The hardness, gumminess and chewiness of crumb depended on way of tillage, then on cultivar as well as the addition of souring agent. The cohesiveness of crumb depended on the way of tillage mainly. However there was affirmed no influence of the studied variables on bread springiness.
Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification

To leverage peer influence and increase population behavioral changes, behavioral interventions often rely on peer-based strategies. A common study design that assesses such strategies is the egocentric-network randomized trial (ENRT), in which those receiving the intervention are encouraged to disseminate information to their peers. The Average Spillover Effect (ASpE) measures the impact of the intervention on participants who do not receive it, but whose outcomes may be affected by others who do. The assessment of the ASpE relies on assumptions about, and correct measurement of, interference sets within which individuals may influence one another's outcomes. It can be challenging to properly specify interference sets, such as networks in ENRTs, and when mismeasured, intervention effects estimated by existing methods will be biased. In HIV prevention studies where social networks play an important role in disease transmission, correcting ASpE estimates for bias due to network misclassification is critical for accurately evaluating the full impact of interventions. We combined measurement error and causal inference methods to bias-correct the ASpE estimate for network misclassification in ENRTs, when surrogate networks are recorded in place of true ones, and validation data that relate the misclassified to the true networks are available. We investigated finite sample properties of our methods in an extensive simulation study, and illustrated our methods in the HIV Prevention Trials Network (HPTN) 037 study.
Scalar Relativistic Effects with Multiwavelets: Implementation and Benchmark

The importance of relativistic effects in quantum chemistry is widely recognized, not only for heavier elements but throughout the periodic table. At the same time, relativistic effects are strongest in the nuclear region, where the description of electrons through a linear combination of atomic orbitals becomes more challenging. Furthermore, the choice of basis sets for heavier elements is limited compared with lighter elements where precise basis sets are available. Thanks to the framework of multiresolution analysis, multiwavelets provide an appealing alternative to overcoming this challenge: they lead to robust error control and adaptive algorithms that automatically refine the basis set description until the desired precision is reached. This allows one to achieve a proper description of the nuclear region. In this work, we extended the multiwavelet-based code MRChem to the scalar zero-order regular approximation framework. We validated our implementation by comparing the total energies for a small set of elements and molecules. To confirm the validity of our implementation, we compared both against a radial numerical code for atoms and the plane-wave-based code EXCITING.
Predicting Future Spatiotemporal Occupancy Grids with Semantics for Autonomous Driving

For autonomous vehicles to proactively plan safe trajectories and make informed decisions, they must be able to predict the future occupancy states of the local environment. However, common issues with occupancy prediction include predictions where moving objects vanish or become blurred, particularly at longer time horizons. We propose an environment prediction framework that incorporates environment semantics for future occupancy prediction. Our method first semantically segments the environment and uses this information along with the occupancy information to predict the spatiotemporal evolution of the environment. We validate our approach on the real-world Waymo Open Dataset. Compared to baseline methods, our model has higher prediction accuracy and is capable of maintaining moving object appearances in the predictions for longer prediction time horizons.
Adaptive Gait Modeling and Optimization for Principally Kinematic Systems

Robotic adaptation to unanticipated operating conditions is crucial to achieving persistence and robustness in complex real world settings. For a wide range of cutting-edge robotic systems, such as micro- and nano-scale robots, soft robots, medical robots, and bio-hybrid robots, it is infeasible to anticipate the operating environment a priori due to complexities that arise from numerous factors including imprecision in manufacturing, chemo-mechanical forces, and poorly understood contact mechanics. Drawing inspiration from data-driven modeling, geometric mechanics (or gauge theory), and adaptive control, we employ an adaptive system identification framework and demonstrate its efficacy in enhancing the performance of principally kinematic locomotors (those governed by Rayleigh dissipation or zero momentum conservation). We showcase the capability of the adaptive model to efficiently accommodate varying terrains and iteratively modified behaviors within a behavior optimization framework. This provides both the ability to improve fundamental behaviors and perform motion tracking to precision. Notably, we are capable of optimizing the gaits of the Purcell swimmer using approximately 10 cycles per link, which for the nine-link Purcell swimmer provides a factor of ten improvement in optimization speed over the state of the art. Beyond simply a computational speed up, this ten-fold improvement may enable this method to be successfully deployed for in-situ behavior refinement, injury recovery, and terrain adaptation, particularly in domains where simulations provide poor guides for the real world.
Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems

Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction. They are known to promote high levels of cognitive engagement and benefit learning outcomes, particularly in reasoning tasks. Nonetheless, the time and cost required to author CTS content is a major obstacle to widespread adoption. In this paper, we introduce a novel type of CTS that leverages the recent advances in large language models (LLMs) in two ways: First, the system induces a tutoring script automatically from a lesson text. Second, the system automates the script orchestration via two LLM-based agents (Ruffle&Riley) with the roles of a student and a professor in a learning-by-teaching format. The system allows a free-form conversation that follows the ITS-typical inner and outer loop structure. In an initial between-subject online user study (N = 100) comparing Ruffle&Riley to simpler QA chatbots and reading activity, we found no significant differences in post-test scores. Nonetheless, in the learning experience survey, Ruffle&Riley users expressed higher ratings of understanding and remembering and further perceived the offered support as more helpful and the conversation as coherent. Our study provides insights for a new generation of scalable CTS technologies.
Content Bias in Deep Learning Image Age Approximation: A new Approach Towards better Explainability

In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features. Usually, images taken in close temporal proximity (e.g., belonging to the same age class) share some common content properties. Such content bias can be exploited by a neural network. In this work, a novel approach is proposed that evaluates the influence of image content. This approach is verified using synthetic images (where content bias can be ruled out) with an age signal embedded. Based on the proposed approach, it is shown that a deep learning approach proposed in the context of age classification is most likely highly dependent on the image content. As a possible countermeasure, two different models from the field of image steganalysis, along with three different preprocessing techniques to increase the signal-to-noise ratio (age signal to image content), are evaluated using the proposed method.
Paediatric diabetes‐related presentations to emergency departments in Victoria, Australia from 2008 to 2018

Despite significant treatment advances in paediatric diabetes management, ED presentations for potentially preventable (PP) complications such as diabetic ketoacidosis (DKA) remains a major issue. We aimed to examine the characteristics, rates and trends of diabetes‐related ED presentations and subsequent admissions in youth aged 0–19 years from 2008 to 2018.
Permanent Magnets Based on Hard Ferrite Ceramics

Permanent magnets are integral components in many of the modern technologies that are critical for the transition to a sustainable society. However, most of the high-performance (BHmax>100 kJ/m3) permanent magnets that are currently employed contain rare earth elements (REE), which have long been classified as critical materials with a high supply risk and concerns regarding pollution in their mining. Therefore, suitable REE-lean/free magnets must be developed in order to ensure the sustainability of clean energy generation and electric mobility. The REE-free hexagonal ferrites (or hexaferrites) are the most used permanent magnets across all applications, with an 85 wt. % pie of the permanent magnet market. They are the dominant lower-grade option (BHmax<25 kJ/m3) due to their relatively good hard magnetic properties, high Curie temperature (>700 K), low cost and good chemical stability. In recent years, the hexaferrites have also emerged as candidates for substituting REE-based permanent magnets in applications requiring intermediate magnetic performance (25-100 kJ/m3), due to considerable performance improvements achieved through chemical tuning, nanostructuring and compaction/sintering optimization. This chapter reviews the state-of-the-art sintering strategies being investigated with the aim of manufacturing hexaferrite magnets with optimized magnetic properties, identifying key challenges and highlighting the natural future steps to be followed.
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things

The Internet of Federated Things (IoFT) represents a network of interconnected systems with federated learning as the backbone, facilitating collaborative knowledge acquisition while ensuring data privacy for individual systems. The wide adoption of IoFT, however, is hindered by security concerns, particularly the susceptibility of federated learning networks to adversarial attacks. In this paper, we propose an effective non-parametric approach FedRR, which leverages the low-rank features of the transmitted parameter updates generated by federated learning to address the adversarial attack problem. Besides, our proposed method is capable of accurately detecting adversarial clients and controlling the false alarm rate under the scenario with no attack occurring. Experiments based on digit recognition using the MNIST datasets validated the advantages of our approach.