Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical circular polarization of blazar S4 0954+65 during high linear polarized states

Optical circular polarization observations can directly test the particle composition in black hole jets. We report the first observations of the BL Lac-type object S4 0954+65 in high linear polarized states. While no circular polarization was detected, we were able to place upper limits of $<0.5$ at 99.7 confidence. Using a simple model and our novel optical circular polarization observations, we can constrain the allowed parameter space for the magnetic field strength and composition of the emitting particles. Our results favor models that require magnetic field strengths of only a few Gauss and models in which the jet composition is dominated by electron-positron pairs. We discuss our findings in the context of typical magnetic field strength requirements for blazar emission models.
Dynamics of subgroups of mapping class groups

Let $\rm{Mod(S)}$ be the mapping class group of a closed orientable surface $S$ of genus $g \geq 2$. Let $G$ be a non-elementary subgroup of $\rm{Mod(S)}$ so that the associated Bowen-Margulis measure is finite. In this paper, we give an asymptotic growth formula for $G$ with respect to the Teichm\"{u}ller metric.
Strong relaxation limit and uniform time asymptotics of the Jin-Xin model in the $L^{p}$ framework

We investigate the diffusive relaxation limit and the time-asymptotic stability of the Jin-Xin model toward viscous conservation laws in $\mathbb{R}^d$ with $d\geq1$. First, we establish uniform regularity estimates with respect to both the time and the relaxation parameter $\varepsilon>0$, for initial data in hybrid Besov spaces based on general $L^{p}$-norms. This uniformity enables us to derive $\mathcal{O}(\varepsilon)$ bounds on the difference between solutions of the viscous conservation law and its associated Jin-Xin approximation, thus justifying the strong convergence of the Jin-Xin hyperbolic relaxation. Furthermore, under an additional condition on the initial data, for instance, that the low frequencies belong to $L^{p/2}(\mathbb{R}^{d})$, we show that the $L^{p}(\mathbb{R}^d)$-norm of the solution to the Jin-Xin model decays at the optimal rate $(1+t)^{-d/{2p}}$ while the $L^{p}(\mathbb{R}^d)$-norm of its difference with the solution of the associated viscous conservation law decays at the enhanced rate $\varepsilon(1+t)^{-d/{2p}-1/2}$.
Active smectics on a sphere.

The dynamics of active smectic liquid crystals confined on a spherical surface is explored through an active phase field crystal model. Starting from an initially randomly perturbed isotropic phase, several types of topological defects are spontaneously formed, and then annihilate during a coarsening process until a steady state is achieved. The coarsening process is highly complex involving several scaling laws of defect densities as a function of time where different dynamical exponents can be identified. In general the exponent for the final stage towards the steady state is significantly larger than that in the passive and in the planar case, i.e., the coarsening is getting accelerated both by activity and by the topological and geometrical properties of the sphere. A defect type characteristic for this active system is a rotating spiral of evolving smectic layering lines. On a sphere this defect type also determines the steady state. Our results can in principle be confirmed by dense systems of synthetic or biological active particles.
Dynamics and Scaling of Internally Cooled Convection

Our goal is to investigate fundamental properties of the system of internally cooled convection. The system consists of an upward thermal flux at the lower boundary and a constant cooling term in the bulk. This simple model represents idealised dry convection in the atmospheric boundary layer, where the bulk cooling mimics the radiative cooling to space notably through longwave radiation. We introduce a lapse-rate dependent dimensionless Rayleigh-number that determines the behaviour of the system and the critical Rayleigh-number for transition to convection is obtained numerically. The dynamic behaviour of the fluid system is described and we also infer the scaling of various important measured quantities such as the total vertical convective heat flux and the upward mass flux. We conclude with a short discussion on the relevance to atmospheric convection and the scope for further investigations of atmospheric convection using similar simplified approaches.
The UMIST Database for Astrochemistry 2006

Aims. We present a new version of the UMIST Database for Astrochemistry, the fourth such version to be released to the public. The current version contains some 4573 binary gas-phase reactions, an increase of 10% from the previous (1999) version, among 420 species, of which 23 are new to the database. Methods. Major updates have been made to ion-neutral reactions, neutral-neutral reactions, particularly at low temperature, and dissociative recombination reactions. We have included for the first time the interstellar chemistry of fluorine. In addition to the usual database, we have also released a reaction set in which the effects of dipole-enhanced ion-neutral rate coefficients are included. Results. These two reactions sets have been used in a dark cloud model and the results of these models are presented and discussed briefly. The database and associated software are available on the World Wide Web at www.udfa. net.
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this paper, we present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized and total intelligence) using graph neural networks on rsfMRI derived static functional network connectivity matrices. Extending from the existing graph convolution networks, our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization and efficient network expression, in combination with TopK pooling and attention-based readout functions. We evaluated our proposed architecture on a large dataset, specifically the Adolescent Brain Cognitive Development Dataset, and demonstrated its effectiveness in predicting individual differences in intelligence. Our model achieved lower mean squared errors, and higher correlation scores than existing relevant graph architectures and other traditional machine learning models for all of the intelligence prediction tasks. The middle frontal gyrus exhibited a significant contribution to both fluid and crystallized intelligence, suggesting their pivotal role in these cognitive processes. Total composite scores identified a diverse set of brain regions to be relevant which underscores the complex nature of total intelligence.
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning

Open-world semi-supervised learning aims at inferring both known and novel classes in unlabeled data, by harnessing prior knowledge from a labeled set with known classes. Despite its importance, there is a lack of theoretical foundations for this problem. This paper bridges the gap by formalizing a graph-theoretic framework tailored for the open-world setting, where the clustering can be theoretically characterized by graph factorization. Our graph-theoretic framework illuminates practical algorithms and provides guarantees. In particular, based on our graph formulation, we apply the algorithm called Spectral Open-world Representation Learning (SORL), and show that minimizing our loss is equivalent to performing spectral decomposition on the graph. Such equivalence allows us to derive a provable error bound on the clustering performance for both known and novel classes, and analyze rigorously when labeled data helps. Empirically, SORL can match or outperform several strong baselines on common benchmark datasets, which is appealing for practical usage while enjoying theoretical guarantees.
Stable manifolds for periodically perturbed maps

We prove that if a certain entry in the map of the Hadamard-Perron theorem is $T$-periodic in one of the variables, then the stable manifold guaranteed by the Hadamard-Perron theorem is a graph of a $T$-periodic function. As an application, we extend the classical Levinson's result about the occurrence of an attracting closed invariant curve near a stable cycle of a system of autonomous equations under periodic perturbations to hybrid differential equations.
A proof system for certifying symmetry and optimality reasoning in integer programming

We present a proof system for establishing the correctness of results produced by optimization algorithms, with a focus on mixed-integer programming (MIP). Our system generalizes the seminal work of Bogaerts, Gocht, McCreesh, and Nordstr\"om (2022) for binary programs to handle any additional difficulties arising from unbounded and continuous variables, and covers a broad range of solving techniques, including symmetry handling, cutting planes, and presolving reductions. Consistency across all decisions that affect the feasible region is achieved by a pair of transitive relations on the set of solutions, which relies on the newly introduced notion of consistent branching trees. Combined with a series of machine-verifiable derivation rules, the resulting framework offers practical solutions to enhance the trust in integer programming as a methodology for applications where reliability and correctness are key.
Bandit Pareto Set Identification: the Fixed Budget Setting

We study a multi-objective pure exploration problem in a multi-armed bandit model. Each arm is associated to an unknown multi-variate distribution and the goal is to identify the distributions whose mean is not uniformly worse than that of another distribution: the Pareto optimal set. We propose and analyze the first algorithms for the \emph{fixed budget} Pareto Set Identification task. We propose Empirical Gap Elimination, a family of algorithms combining a careful estimation of the ``hardness to classify'' each arm in or out of the Pareto set with a generic elimination scheme. We prove that two particular instances, EGE-SR and EGE-SH, have a probability of error that decays exponentially fast with the budget, with an exponent supported by an information theoretic lower-bound. We complement these findings with an empirical study using real-world and synthetic datasets, which showcase the good performance of our algorithms.
Exact fluctuation and long-range correlations in a single-file model under resetting

Resetting is a renewal mechanism in which a process is intermittently repeated after a random or fixed time. This simple act of stop and repeat profoundly influences the behaviour of a system as exemplified by the emergence of non-equilibrium properties and expedition of search processes. Herein, we explore the ramifications of stochastic resetting in the context of a single-file system called random average process (RAP) in one dimension. In particular, we focus on the dynamics of tracer particles and analytically compute the variance, equal time correlation, autocorrelation and unequal time correlation between the positions of different tracer particles. Our study unveils that resetting gives rise to rather different behaviours depending on whether the particles move symmetrically or asymmetrically. For the asymmetric case, the system for instance exhibits a long-range correlation which is not seen in absence of the resetting. Similarly, in contrast to the reset-free RAP, the variance shows distinct scalings for symmetric and asymmetric cases. While for the symmetric case, it decays (towards its steady value) as $\sim e^{-r t} / \sqrt{t}$, we find $\sim t e^{-r t}$ decay for the asymmetric case ($r$ being the resetting rate). Finally, we examine the autocorrelation and unequal time correlation in the steady state and demonstrate that they obey interesting scaling forms at late times. All our analytical results are substantiated by extensive numerical simulations.
Enabling Dynamic Selection of Implementation Variants in Component-Based Parallel Programming for Heterogeneous Systems

Heterogeneous systems, consisting of CPUs and GPUs, offer the capability to address the demands of compute- and data-intensive applications. However, programming such systems is challenging, requiring knowledge of various parallel programming frameworks. This paper introduces COMPAR, a component-based parallel programming framework that enables the exposure and selection of multiple implementation variants of components at runtime. The framework leverages compiler directive-based language extensions to annotate the source code and generate the necessary glue code for the StarPU runtime system. COMPAR provides a unified view of implementation variants and allows for intelligent selection based on runtime context. Our evaluation demonstrates the effectiveness of COMPAR through benchmark applications. The proposed approach simplifies heterogeneous parallel programming and promotes code reuse while achieving optimal performance.
Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints

Macro placement is a critical phase in chip design, which becomes more intricate when involving general rectilinear macros and layout areas. Furthermore, macro placement that incorporates human-like constraints, such as design hierarchy and peripheral bias, has the potential to significantly reduce the amount of additional manual labor required from designers. This study proposes a methodology that leverages an approach suggested by Google's Circuit Training (G-CT) to provide a learning-based macro placer that not only supports placing rectilinear cases, but also adheres to crucial human-like design principles. Our experimental results demonstrate the effectiveness of our framework in achieving power-performance-area (PPA) metrics and in obtaining placements of high quality, comparable to those produced with human intervention. Additionally, our methodology shows potential as a generalized model to address diverse macro shapes and layout areas.
Stochastic Control with Distributionally Robust Constraints for Cyber-Physical Systems Vulnerable to Attacks

In this paper, we investigate the control of a cyber-physical system (CPS) while accounting for its vulnerability to external attacks. We formulate a constrained stochastic problem with a robust constraint to ensure robust operation against potential attacks. We seek to minimize the expected cost subject to a constraint limiting the worst-case expected damage an attacker can impose on the CPS. We present a dynamic programming decomposition to compute the optimal control strategy in this robust-constrained formulation and prove its recursive feasibility. We also illustrate the utility of our results by applying them to a numerical simulation.
deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning

Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regularized Ensemble-based Multi-task Learning with Asymmetric Loss for Probabilistic Inference ($\rm{deep-REMAP}$), a novel framework that utilizes the rich synthetic spectra from the PHOENIX library and observational data from the MARVELS survey to accurately predict stellar atmospheric parameters. By harnessing advanced machine learning techniques, including multi-task learning and an innovative asymmetric loss function, $\rm{deep-REMAP}$ demonstrates superior predictive capabilities in determining effective temperature, surface gravity, and metallicity from observed spectra. Our results reveal the framework's effectiveness in extending to other stellar libraries and properties, paving the way for more sophisticated and automated techniques in stellar characterization.
Improving Korean NLP Tasks with Linguistically Informed Subword Tokenization and Sub-character Decomposition

We introduce a morpheme-aware subword tokenization method that utilizes sub-character decomposition to address the challenges of applying Byte Pair Encoding (BPE) to Korean, a language characterized by its rich morphology and unique writing system. Our approach balances linguistic accuracy with computational efficiency in Pre-trained Language Models (PLMs). Our evaluations show that this technique achieves good performances overall, notably improving results in the syntactic task of NIKL-CoLA. This suggests that integrating morpheme type information can enhance language models' syntactic and semantic capabilities, indicating that adopting more linguistic insights can further improve performance beyond standard morphological analysis.
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase

In-Context Learning (ICL) combined with pre-trained large language models has achieved promising results on various NLP tasks. However, ICL requires high-quality annotated demonstrations which might not be available in real-world scenarios. To overcome this limitation, we propose \textbf{D}ata \textbf{A}ugmentation for \textbf{I}n-Context \textbf{L}earning (\textbf{DAIL}). DAIL leverages the intuition that large language models are more familiar with the content generated by themselves. It first utilizes the language model to generate paraphrases of the test sample and employs majority voting to determine the final result based on individual predictions. Our extensive empirical evaluation shows that DAIL outperforms the standard ICL method and other ensemble-based methods in the low-resource scenario. Additionally, we explore the use of voting consistency as a confidence score of the model when the logits of predictions are inaccessible. We believe our work will stimulate further research on ICL in low-resource settings.
Seeding Gaussian boson samplers with single photons for enhanced state generation

Non-Gaussian quantum states are crucial to fault-tolerant quantum computation with continuous-variable systems. Usually, generation of such states involves trade-offs between success probability and quality of the resultant state. For example, injecting squeezed light into a multimode interferometer and postselecting on certain patterns of photon-number outputs in all but one mode, a fundamentally probabilistic task, can herald the creation of cat states, Gottesman-Kitaev-Preskill (GKP) states, and more. We consider the addition of a non-Gaussian resource state, particularly single photons, to this configuration and show how it improves the qualities and generation probabilities of desired states. With only two modes, adding a single photon source improves GKP-state fidelity from 0.68 to 0.95 and adding a second then increases the success probability eightfold; for cat states with a fixed target fidelity, the probability of success can be improved by factors of up to 4 by adding single-photon sources. These demonstrate the usefulness of additional commonplace non-Gaussian resources for generating desirable states of light.
Cosmic Birefringence from CP-Violating Axion Interactions

We explore the cosmic birefringence signal produced by an ultralight axion field with a small CP-violating coupling to bulk SM matter in addition to the usual CP-preserving photon coupling. The change in the vacuum expectation value of the field between recombination and today results in a frequency-independent rotation of the plane of CMB linear polarization across the entire sky. While many previous approaches rely on the axion rolling from a large initial expectation value, the couplings considered in this work robustly generate the birefringence signal regardless of initial conditions, by sourcing the field from the cosmological nucleon density. We place bounds on such monopole-dipole interactions using measurements of the birefringence angle from Planck and WMAP data, which improve upon existing constraints by up to three orders of magnitude. We also discuss UV completions of this model, and possible strategies to avoid fine-tuning the axion mass.
Image change detection with only a few samples

This paper considers image change detection with only a small number of samples, which is a significant problem in terms of a few annotations available. A major impediment of image change detection task is the lack of large annotated datasets covering a wide variety of scenes. Change detection models trained on insufficient datasets have shown poor generalization capability. To address the poor generalization issue, we propose using simple image processing methods for generating synthetic but informative datasets, and design an early fusion network based on object detection which could outperform the siamese neural network. Our key insight is that the synthetic data enables the trained model to have good generalization ability for various scenarios. We compare the model trained on the synthetic data with that on the real-world data captured from a challenging dataset, CDNet, using six different test sets. The results demonstrate that the synthetic data is informative enough to achieve higher generalization ability than the insufficient real-world data. Besides, the experiment shows that utilizing a few (often tens of) samples to fine-tune the model trained on the synthetic data will achieve excellent results.
Ensembling Textual and Structure-Based Models for Knowledge Graph Completion

We consider two popular approaches to Knowledge Graph Completion (KGC): textual models that rely on textual entity descriptions, and structure-based models that exploit the connectivity structure of the Knowledge Graph (KG). Preliminary experiments show that these approaches have complementary strengths: structure-based models perform well when the gold answer is easily reachable from the query head in the KG, while textual models exploit descriptions to give good performance even when the gold answer is not reachable. In response, we explore ensembling as a way of combining the best of both approaches. We propose a novel method for learning query-dependent ensemble weights by using the distributions of scores assigned by individual models to all candidate entities. Our ensemble baseline achieves state-of-the-art results on three standard KGC datasets, with up to 6.8 pt MRR and 8.3 pt Hits@1 gains over best individual models.