Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Results from GLASS-JWST. VII. Evidence for Lensed, Gravitationally Bound Protoglobular Clusters at z = 4 in the Hubble Frontier Field A2744

We investigate the blue and optical rest-frame sizes (λ ≃ 2300–4000 Å) of three compact star-forming regions in a galaxy at z = 4 strongly lensed (×30, ×45, and ×100) by the Hubble Frontier Field galaxy cluster A2744 using GLASS-ERS James Webb Space Telescope (JWST)/NIRISS imaging at 1.15 μm, 1.50 μm, and 2.0 μm with a point-spread function ≲0. ″1. In particular, the Balmer break is probed in detail for all multiply imaged sources of the system. With ages of a few tens of Myr, stellar masses in the range (0.7–4.0) ×106 M ⊙ and optical/ultraviolet effective radii spanning the interval 3 < R eff < 20 pc, such objects are currently the highest-redshift (spectroscopically confirmed) gravitationally bound young massive star clusters (YMCs), with stellar mass surface densities resembling those of local globular clusters. Optical (4000 Å, JWST-based) and ultraviolet (1600 Å, Hubble Space Telescope–based) sizes are fully compatible. The contribution to the ultraviolet underlying continuum emission (1600 Å) is ∼30%, which decreases by a factor of 2 in the optical for two of the YMCs (∼4000 Å rest-frame), reflecting the young ages (<30 Myr) inferred from the spectral energy distribution fitting and supported by the presence of high-ionization lines secured with the Very Large Telescope/MUSE. Such bursty forming regions enhance the specific star formation rate of the galaxy, which is ≃10 Gyr−1. This galaxy would be among the extreme analogs observed in the local universe having a high star formation rate surface density and a high occurrence of massive stellar clusters in formation.
A Novel Optimized Decomposition Method for Smoluchowski's Aggregation Equation

The Smoluchowski's aggregation equation has applications in the field of bio-pharmaceuticals \cite{zidar2018characterisation}, financial sector \cite{PUSHKIN2004571}, aerosol science \cite{shen2020efficient} and many others. Several analytical, numerical and semi-analytical approaches have been devised to calculate the solutions of this equation. Semi-analytical methods are commonly employed since they do not require discretization of the space variable. The article deals with the introduction of a novel semi-analytical technique called the optimized decomposition method (ODM) (see \cite{odibat2020optimized}) to compute solutions of this relevant integro-partial differential equation. The series solution computed using ODM is shown to converge to the exact solution. The theoretical results are validated using numerical examples for scientifically relevant aggregation kernels for which the exact solutions are available. Additionally, the ODM approximated results are compared with the solutions obtained using the Adomian decomposition method (ADM) in \cite{singh2015adomian}. The novel method is shown to be superior to ADM for the examples considered and thus establishes as an improved and efficient method for solving the Smoluchowski's equation.
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation

Despite the progresses on pre-trained language models, there is a lack of unified frameworks for pre-trained sentence representation. As such, it calls for different pre-training methods for specific scenarios, and the pre-trained models are likely to be limited by their universality and representation quality. In this work, we extend the recently proposed MAE style pre-training strategy, RetroMAE, such that it may effectively support a wide variety of sentence representation tasks. The extended framework consists of two stages, with RetroMAE conducted throughout the process. The first stage performs RetroMAE over generic corpora, like Wikipedia, BookCorpus, etc., from which the base model is learned. The second stage takes place on domain-specific data, e.g., MS MARCO and NLI, where the base model is continuingly trained based on RetroMAE and contrastive learning. The pre-training outputs at the two stages may serve different applications, whose effectiveness are verified with comprehensive experiments. Concretely, the base model are proved to be effective for zero-shot retrieval, with remarkable performances achieved on BEIR benchmark. The continuingly pre-trained models further benefit more downstream tasks, including the domain-specific dense retrieval on MS MARCO, Natural Questions, and the sentence embeddings' quality for standard STS and transfer tasks in SentEval. The empirical insights of this work may inspire the future design of sentence representation pre-training. Our pre-trained models and source code will be released to the public communities.
An Approximate Generalization of the Okamura-Seymour Theorem

We consider the problem of multi-commodity flows in planar graphs. Okamura and Seymour showed that if all the demands are incident on one face, then the cut-condition is sufficient for routing demands. We consider the following generalization of this setting and prove an approximate max flow-min cut theorem: for every demand edge, there exists a face containing both its end points. We show that the cut-condition is sufficient for routing $\Omega(1)$-fraction of all the demands. To prove this, we give a $L_{1} $-embedding of the planar metric which approximately preserves distance between all pair of points on the same face.
Deep Multi-Object Symbol Learning with Self-Attention Based Predictors

This paper proposes an architecture that can learn symbolic representations from the continuous sensorimotor experience of a robot interacting with a varying number of objects. Unlike previous works, this work aims to remove constraints on the learned symbols such as a fixed number of interacted objects or pre-defined symbolic structures. The proposed architecture can learn symbols for single objects and relations between them in a unified manner. The architecture is an encoder-decoder network with a binary activation layer followed by self-attention layers. Experiments are conducted in a robotic manipulation setup with a varying number of objects. Results showed that the robot successfully encodes the interaction dynamics between a varying number of objects using the discovered symbols. We also showed that the discovered symbols can be used for planning to reach symbolic goal states by training a higher-level neural network.
Momentum gauge fields from curved momentum space through Kaluza–Klein reduction

In this work we investigate the relation between curved momentum space and momentum-dependent gauge fields. While the former is a classic idea that has been shown to be tied to minimal-length models, the latter constitutes a relatively recent development in quantum gravity phenomenology. In particular, the gauge principle in momentum space amounts to a modification of the position operator of the form Xˆμ→Xˆμ−gAμ(Pˆ) akin to a gauge-covariant derivative in momentum space according to the minimal coupling prescription. Here, we derive both effects from a Kaluza–Klein reduction of a higher-dimensional geometry exhibiting curvature in momentum space. The interplay of the emerging gauge fields as well as the remaining curved momentum space lead to modifications of the Heisenberg algebra. While the gauge fields imply Moyal-type noncommutativity dependent on the analogue field strength tensor, the dimensionally reduced curved momentum space geometry translates to a Snyder-type noncommutative geometry.
Input-Output Tables and Some Theory of Defective Matrices

Recent developments in the theory of production networks offer interesting applications and revival of input-output analysis. Some recent papers have studied the propagation of a temporary, negative shock through an input-output network. Such analyses of shock propagation relies on eigendecomposition of relevant input-output matrices. It is well known that only diagonalizable matrices can be eigendecomposed; those that are not diagonalizable, are known as defective matrices. In this paper, we provide necessary and sufficient conditions for diagonalizability of any square matrix using its rank and eigenvalues. To apply our results, we offer examples of input-output tables from India in the 1950s that were not diagonalizable and were hence, defective.
Wigner Analysis of Operators. Part II: Schr\"odinger equations

We study the phase-space concentration of the so-called generalized metaplectic operators whose main examples are Schr¨odinger equations with bounded perturbations. To reach this goal, we perform a so-called A -Wigner analysis of the previous equations, as started in Part I, cf. [14]. Namely, the classical Wigner distribution is extended by considering a class of time-frequency representations constructed as images of metaplectic operators acting on symplectic matrices A ∈ Sp (2 d, R ). Sub-classes of these representations, related to covariant symplectic matrices, reveal to be particularly suited for the time-frequency study of the Schr¨odinger evolution. This testiﬁes the eﬀectiveness of this approach for such equations, highlighted by the development of a related wave front set. We ﬁrst study the properties of A -Wigner representations and related pseudodiﬀerential operators needed for our goal. This approach paves the way to new quantization procedures. As a byproduct, we introduce new quasi-algebras of generalized metaplectic operators containing Schr¨odinger equations with more general potentials, extend-ing the results contained in the previous works [8, 9].
Energy Demand Unawareness and the Popularity of Bitcoin: Evidence from Nigeria

Decentralised cryptocurrency networks, notably those with high energy demand, have faced significant criticism and subsequent regulatory scrutiny. Despite these concerns, policy interventions targeting cryptocurrency operations in the pursuit of sustainability have largely been ineffective. Some were abandoned for fear of jeopardising innovation, while others failed due to the highly globalised nature of blockchain systems. In search of a more effective angle for energy policy measures, this study adopts a consumer-centric perspective, examining the sentiments of Nigerian cryptocurrency users (N = 158) towards Bitcoin’s sustainability, a representative cryptocurrency known for its high electricity demand. Three main findings emerged: 1) Even among those self-identifying as highly knowledgeable, the majority considerably underestimated Bitcoin’s electricity consumption. 2) Participants with a more accurate understanding of Bitcoin’s energy demand were more inclined to support sustainability measures. 3) The majority of this supportive cohort viewed private entities as the primary stakeholders for implementing such measures. Given these findings, we suggest that consumer education should be at the forefront of policy initiatives aimed at cryptocurrency sustainability.
Modeling Human Response to Robot Errors for Timely Error Detection

In human-robot collaboration, robot errors are inevitable—damaging user trust, willingness to work together, and task performance. Prior work has shown that people naturally respond to robot errors socially and that in social interactions it is possible to use human responses to detect errors. However, there is little exploration in the domain of nonsocial, physical human-robot collaboration such as assembly and tool retrieval. In this work, we investigate how people's organic, social responses to robot errors may be used to enable timely automatic detection of errors in physical human-robot interactions. We conducted a data collection study to obtain facial responses to train a real-time detection algorithm and a case study to explore the generalizability of our method with different task settings and errors. Our results show that natural social responses are effective signals for timely detection and localization of robot errors even in nonsocial contexts and that our method is robust across a variety of task contexts, robot errors, and user responses. This work contributes to robust error detection without detailed task specifications.
Two New Characterizations of Path Graphs

Path graphs are intersection graphs of paths in a tree. We start from the characterization of path graphs by Monma and Wei [C.L. ~Monma,~and~V.K. ~Wei, Intersection Graphs of Paths in a Tree, J. Combin. Theory Ser. B, 41:2 (1986) 141--181] and we reduce it to some 2-colorings subproblems, obtaining the first characterization that directly leads to a polynomial recognition algorithm. Then we introduce the collection of the attachedness graphs of a graph and we exhibit a list of minimal forbidden 2-edge colored subgraphs in each of the attachedness graph.
DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal

OBJECTIVE Electrocardiogram (ECG) signals commonly suffer noise interference, such as baseline wander. High-quality and high-fidelity reconstruction of the ECG signals is of great significance to diagnosing cardiovascular diseases. Therefore, this paper proposes a novel ECG baseline wander and noise removal technology. METHODS We extended the diffusion model in a conditional manner that was specific to the ECG signals, namely the Deep Score-Based Diffusion model for Electrocardiogram baseline wander and noise removal (DeScoD-ECG). Moreover, we deployed a multi-shots averaging strategy that improved signal reconstructions. We conducted the experiments on the QT Database and the MIT-BIH Noise Stress Test Database to verify the feasibility of the proposed method. Baseline methods are adopted for comparison, including traditional digital filter-based and deep learning-based methods. RESULTS The quantities evaluation results show that the proposed method obtained outstanding performance on four distance-based similarity metrics with at least 20% overall improvement compared with the best baseline method. CONCLUSION This paper demonstrates the state-of-the-art performance of the DeScoD-ECG for ECG baseline wander and noise removal, which has better approximations of the true data distribution and higher stability under extreme noise corruptions. SIGNIFICANCE This study is one of the first to extend the conditional diffusion-based generative model for ECG noise removal, and the DeScoD-ECG has the potential to be widely used in biomedical applications.
Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship

Neural language models have the potential to support human writing. However, questions remain on their integration and influence on writing and output. To address this, we designed and compared two user interfaces for writing with AI on mobile devices, which manipulate levels of initiative and control: 1) Writing with continuously generated text, the AI adds text word-by-word and user steers. 2) Writing with suggestions, the AI suggests phrases and user selects from a list. In a supervised online study (N=18), participants used these prototypes and a baseline without AI. We collected touch interactions, ratings on inspiration and authorship, and interview data. With AI suggestions, people wrote less actively, yet felt they were the author. Continuously generated text reduced this perceived authorship, yet increased editing behavior. In both designs, AI increased text length and was perceived to influence wording. Our findings add new empirical evidence on the impact of UI design decisions on user experience and output with co-creative systems.
Basis light-front quantization approach to $\Lambda$ and $\Lambda_c$ and their isospin triplet baryons

We obtain the masses, the electromagnetic properties, and the parton distribution functions (PDFs) of $\Lambda$, $\Lambda_c$, and their isospin triplet baryons, i.e, $\Sigma^0$, $\Sigma^+$, $\Sigma^-$ and $\Sigma_c^0$, $\Sigma_c^+$, $\Sigma_c^{++}$ from a light-front effective Hamiltonian in the leading Fock sector in the basis light-front quantization framework. The light-front wave functions of these baryons are given by the eigenstates of the effective Hamiltonian consisting of a three-dimensional confinement potential and a one-gluon exchange interaction with fixed coupling. The masses of these baryons in our approach are in the experimental range while isospin-dependent mass differences are too small. Meanwhile, the electromagnetic properties are in agreement with the available experimental data, the lattice QCD simulations, and the other theoretical calculations. We also present the gluon and the sea quark PDFs, which we generate dynamically from the QCD evolution of the valence quark distributions.
H2S and SO2 detectability in hot Jupiters. Sulphur species as indicators of metallicity and C/O ratio

The high cosmic abundance and the intermediate volatility and chemical properties of sulfur allow the use of sulfur-bearing species as a tracer of the chemical processes in the atmospheres of hot Jupiter exoplanets. Nevertheless, despite its properties and relevance as a tracer of the giant planets' formation history, little attention has been paid to this species in the context of hot Jupiter atmospheres. Here we provide an overview of the abundances of sulfur-bearing species in hot Jupiter atmospheres under different conditions and explore their observability. We use the photochemical kinetics code VULCAN to model hot Jupiter atmospheric disequilibrium chemistry. Transmission spectra for these atmospheres are created using the modelling framework ARCiS. We vary model parameters such as the diffusion coefficient, and we study the importance of photochemistry on the resulting mixing ratios. Furthermore, we vary the chemical composition of the atmosphere by increasing the metallicity from solar to ~10 times solar. We also explore different C/O ratios. We find that H2S and SO2 are the best candidates for detection between 1 and 10 micron, using a spectral resolution that is representative of the instruments on board the JWST. H2S is easiest to detect at an equilibrium temperature of ~1500 K and C/O ratios between 0.7 and 0.9, with the ideal value increasing slightly for increasing metallicity. SO2 is most likely to be detected at an equilibrium temperature of ~1000 K at low C/O ratios and high metallicities. Nevertheless, among these two molecules, we expect SO2 detection to be more common, as it is detectable in scenarios more favoured by formation models. We conclude that H2S and SO2 will most likely be detected in the coming years with the JWST and that the detection of these species will provide information on atmospheric processes and planet formation scenarios.
Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi‐Hot Class Embedding

Synthesizing multi‐character interactions is a challenging task due to the complex and varied interactions between the characters. In particular, precise spatiotemporal alignment between characters is required in generating close interactions such as dancing and fighting. Existing work in generating multi‐character interactions focuses on generating a single type of reactive motion for a given sequence which results in a lack of variety of the resultant motions. In this paper, we propose a novel way to create realistic human reactive motions which are not presented in the given dataset by mixing and matching different types of close interactions. We propose a Conditional Hierarchical Generative Adversarial Network with Multi‐Hot Class Embedding to generate the Mix and Match reactive motions of the follower from a given motion sequence of the leader. Experiments are conducted on both noisy (depth‐based) and high‐quality (MoCap‐based) interaction datasets. The quantitative and qualitative results show that our approach outperforms the state‐of‐the‐art methods on the given datasets. We also provide an augmented dataset with realistic reactive motions to stimulate future research in this area.
Continuous locomotion mode recognition and gait phase estimation based on a shank-mounted IMU with artificial neural networks

To improve the control of wearable robotics for gait assistance, we present an approach for continuous locomotion mode recognition as well as gait phase and stair slope estimation based on artificial neural networks that include time history information. The input features consist exclusively of processed variables that can be measured with a single shank-mounted inertial measurement unit. We introduce a wearable device to acquire real-world environment test data to demonstrate the performance and the robustness of the approach. Mean absolute error (gait phase, stair slope) and accuracy (locomotion mode) were determined for steady level walking and steady stair ambulation. Robustness was assessed using test data from different sensor hardware, sensor fixations, ambulation environments and subjects. The mean absolute error from the steady gait test data for the gait phase was 2.0-3.5 % for gait phase estimation and 3.3-3.8° for stair slope estimation. The accuracy of classifying the correct locomotion mode on the test data with the utilization of time history information was in between 98.51% and 99.67 %. Results show high performance and robustness for continuously predicting gait phase, stair slope and locomotion mode during steady gait. As hypothesized, time history information improves the locomotion mode recognition. However, while the gait phase estimation performed well for untrained transitions between locomotion modes, our qualitative analysis revealed that it may be beneficial to include transition data into the training of the neural network to improve the prediction of the slope and the locomotion mode. Our results suggest that artificial neural networks could be used for high level control of wearable lower limb robotics.
Identifying Influential Brokers on Social Media from Social Network Structure

Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marketing and preventing the spread of fake news and rumors. The literature contains a rich body of studies on identifying influential source spreaders who can spread their own messages to many other nodes. In contrast, the identification of influential brokers who can spread other nodes' messages to many nodes has not been fully explored. Theoretical and empirical studies suggest that involvement of both influential source spreaders and brokers is a key to facilitating large-scale information diffusion cascades. Therefore, this paper explores ways to identify influential brokers from a given social network. By using three social media datasets, we investigate the characteristics of influential brokers by comparing them with influential source spreaders and central nodes obtained from centrality measures. Our results show that (i) most of the influential source spreaders are not influential brokers (and vice versa) and (ii) the overlap between central nodes and influential brokers is small (less than 15%) in Twitter datasets. We also tackle the problem of identifying influential brokers from centrality measures and node embeddings, and we examine the effectiveness of social network features in the broker identification task. Our results show that (iii) although a single centrality measure cannot characterize influential brokers well, prediction models using node embedding features achieve F1 scores of 0.35--0.68, suggesting the effectiveness of social network features for identifying influential brokers.