Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiating particle in the vicinity of the weakly charged Schwarzschild black hole

It is well known that supermassive black holes in the centers of galaxies are capable of accelerating charged particles to very high energies. In many cases, the particle acceleration by black holes occurs electromagnetically through an electric field induced by the source. In such scenarios, the accelerated particles radiate electromagnetic waves, leading to the appearance of the backreaction force, which can considerably change the dynamics, especially, if the particles are relativistic. The effect of the radiation reaction force due to accelerating electric field of the central body in curved spacetime has not been considered previously. We study the dynamics of radiating charged particles in the field of the Schwarzschild black hole in the presence of an electric field associated with a small central charge of negligible gravitational influence. We start from the flat spacetime description, solving the Lorentz-Dirac equation reduced to the Landau-Lifshitz form. In curved spacetime, we use the DeWitt-Brehme equation and discuss the effect of the self-force, also known as the tail term, within the given approach. We also study the pure effect of the self-force to calculate the radiative deceleration of radially moving charged particles. In the case of bounded orbits, we find that the radiation reaction force can stabilize and circularize the orbits of oscillating charged particles by suppressing the oscillations or causing the particles to spiral down into the black hole depending on the sign of the electrostatic interaction. In all cases, we calculate the energy losses and exact trajectories of charged particles for different values and signs of electric charge.
On the limitations of non-geometric fluxes to realize dS vacua

In this paper, we perform a systematic and analytical exploration of de Sitter conditions in type IIA compactifications with (non-)geometric fluxes along with the standard NS-NS and RR $p$-form fluxes. Exploiting the fact that the F-term scalar potential can be written as a bilinear form, we start by studying the most generic case. We find four conditions that the scalar fields and fluxes must satisfy to achieve de Sitter vacua. Particularizing to different configurations, we recover and extend previous results in the literature. We then impose an Ansatz in which the F-terms are proportional to the respective K\"ahler derivatives. In this set-up we are able to derive additional constraints and to classify the possible dS no-go scenarios in terms of eight axionic fluxes. Individually considering that any of these fluxes can be vanishing or non-vanishing leads to a total of 256 flux configurations. We find that 227 of these 256 possibilities result in a dS no-go scenario. The remaining 29 flux configurations, a priori, do not lead to dS no-go cases and would deserve further investigation.
Double machine learning for causal hybrid modeling - applications in the Earth sciences

Hybrid modeling integrates machine learning with scientific knowledge with the goal of enhancing interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and regularization biases pose challenges in hybrid modeling to achieve these purposes. This paper introduces a novel approach to estimating hybrid models via a causal inference framework, specifically employing Double Machine Learning (DML) to estimate causal effects. We showcase its use for the Earth sciences on two problems related to carbon dioxide fluxes. In the $Q_{10}$ model, we demonstrate that DML-based hybrid modeling is superior in estimating causal parameters over end-to-end deep neural network (DNN) approaches, proving efficiency, robustness to bias from regularization methods, and circumventing equifinality. Our approach, applied to carbon flux partitioning, exhibits flexibility in accommodating heterogeneous causal effects. The study emphasizes the necessity of explicitly defining causal graphs and relationships, advocating for this as a general best practice. We encourage the continued exploration of causality in hybrid models for more interpretable and trustworthy results in knowledge-guided machine learning.
Sharpening the dark matter signature in gravitational waveforms II: Numerical simulations with the NbodyIMRI code

Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a detection hinges on the accurate modelling of the dynamical friction, induced by dark matter on the secondary compact object in intermediate and extreme mass ratio inspirals. In this paper, we introduce NbodyIMRI, a new publicly available code designed for simulating binary systems within cold dark matter `spikes'. Leveraging higher particle counts and finer timesteps, we validate the applicability of the standard dynamical friction formalism and provide an accurate determination of the maximum impact parameter of particles which can effectively scatter with a compact object, across various mass ratios. We also show that in addition to feedback due to dynamical friction, the dark matter also evolves through a `stirring' effect driven by the time-dependent potential of the binary. We introduce a simple semi-analytical scheme to account for this effect and demonstrate that including stirring tends to slow the rate of dark matter depletion and therefore enhances the impact of dark matter on the dynamics of the binary.
Computational unique continuation with finite dimensional Neumann trace

We consider finite element approximations of unique continuation problems subject to elliptic equations in the case where the normal derivative of the exact solution is known to reside in some finite dimensional space. To give quantitative error estimates we prove Lipschitz stability of the unique continuation problem in the global H1-norm. This stability is then leveraged to derive optimal a posteriori and a priori error estimates for a primal-dual stabilised finite method.
Multi-indice B-series

We propose a novel way to describe numerical methods for ordinary differential equations via the notion of multi-indice. The main idea is to replace rooted trees in Butcher's B-series by multi-indices. The latter were introduced recently in the context of describing solutions of singular stochastic partial differential equations. The combinatorial shift away from rooted trees allows for a compressed description of numerical schemes. Moreover, these multi-indices B-series characterise uniquely the Taylor development of local and affine equivariant maps.
Toward TransfORmers: Revolutionizing the Solution of Mixed Integer Programs with Transformers

In this study, we introduce an innovative deep learning framework that employs a transformer model to address the challenges of mixed-integer programs, specifically focusing on the Capacitated Lot Sizing Problem (CLSP). Our approach, to our knowledge, is the first to utilize transformers to predict the binary variables of a mixed-integer programming (MIP) problem. Specifically, our approach harnesses the encoder decoder transformer's ability to process sequential data, making it well-suited for predicting binary variables indicating production setup decisions in each period of the CLSP. This problem is inherently dynamic, and we need to handle sequential decision making under constraints. We present an efficient algorithm in which CLSP solutions are learned through a transformer neural network. The proposed post-processed transformer algorithm surpasses the state-of-the-art solver, CPLEX and Long Short-Term Memory (LSTM) in solution time, optimal gap, and percent infeasibility over 240K benchmark CLSP instances tested. After the ML model is trained, conducting inference on the model, including post-processing, reduces the MIP into a linear program (LP). This transforms the ML-based algorithm, combined with an LP solver, into a polynomial-time approximation algorithm to solve a well-known NP-Hard problem, with almost perfect solution quality.
Tunable topological phases in nanographene-based spin-1/2 alternating-exchange Heisenberg chains

Unlocking the potential of topological order within many-body spin systems has long been a central pursuit in the realm of quantum materials. Despite extensive efforts, the quest for a versatile platform enabling site-selective spin manipulation, essential for tuning and probing diverse topological phases, has persisted. Here, we utilize on-surface synthesis to construct spin-1/2 alternating-exchange Heisenberg (AH) chains[1] with antiferromagnetic couplings $J_1$ and $J_2$ by covalently linking Clar's goblets -- nanographenes each hosting two antiferromagnetically-coupled unpaired electrons[2]. Utilizing scanning tunneling microscopy, we exert atomic-scale control over the spin chain lengths, parities and exchange-coupling terminations, and probe their magnetic response by means of inelastic tunneling spectroscopy. Our investigation confirms the gapped nature of bulk excitations in the chains, known as triplons[3]. Besides, the triplon dispersion relation is successfully extracted from the spatial variation of tunneling spectral amplitudes. Furthermore, depending on the parity and termination of chains, we observe varying numbers of in-gap $S=1/2$ edge spins, enabling the determination of the degeneracy of distinct topological ground states in the thermodynamic limit-either 1, 2, or 4. By monitoring interactions between these edge spins, we identify the exponential decay of spin correlations. Our experimental findings, corroborated by theoretical calculations, present a phase-controlled many-body platform, opening promising avenues toward the development of spin-based quantum devices.
Neutrino Rate Predictions for FASER

The Forward Search Experiment (FASER) at CERN's Large Hadron Collider (LHC) has recently directly detected the first collider neutrinos. Neutrinos play an important role in all FASER analyses, either as signal or background, and it is therefore essential to understand the neutrino event rates. In this study, we update previous simulations and present prescriptions for theoretical predictions of neutrino fluxes and cross sections, together with their associated uncertainties. With these results, we discuss the potential for possible measurements that could be carried out in the coming years with the FASER neutrino data to be collected in LHC Run 3 and Run 4.
Allowing Growing Dimensional Binary Outcomes via the Multivariate Probit Indian Buffet Process

There is a rich literature on infinite latent feature models, with much of the focus being on the Indian Buffet Process (IBP). The current literature focuses on the case in which latent features are generated independently, so that there is no within-sample dependence in which features are selected. Motivated by ecology applications in which latent features correspond to which species are discovered in a sample, we propose a new class of dependent infinite latent feature models. Our construction starts with a probit Indian buffet process, and then incorporates dependence among features through a correlation matrix in a latent multivariate Gaussian random variable. We show that the proposed approach preserves many appealing theoretical properties of the IBP. To address the challenge of modeling and inference for a growing-dimensional correlation matrix, we propose Bayesian modeling approaches to reduce effective dimensionality. We additionally describe extensions to incorporate covariates and hierarchical structure to enable borrowing of information. For posterior inference, we describe Markov chain Monte Carlo sampling algorithms and efficient approximations. Simulation studies and applications to fungal biodiversity data provide compelling support for the new modeling class relative to competitors.
Thermal transport in a 2D amorphous material

Two-dimensional (2D) crystals proved revolutionary soon after graphene was discovered in 2004. However, 2D amorphous materials only became accessible in 2020 and remain largely unexplored. In particular, the thermophysical properties of amorphous materials are of great interest upon transition from 3D to 2D. Here, we probe thermal transport in 2D amorphous carbon. A cross-plane thermal conductivity ($\kappa$) down to 0.079 $\rm{Wm}^{-1}K^{-1}$ is measured for van der Waals stacked multilayers at room temperature, which is among the lowest reported to date. Meanwhile, an unexpectedly high in-plane $\kappa$ is obtained for freestanding monolayers which is a few times larger than what is predicted by conventional wisdom for 3D amorphous carbon with similar $\rm{sp}^{2}$ fraction. Our molecular dynamics simulations reveal the role of disorder and highlight the impact of dimensionality. Amorphous materials at the 2D limit open up new avenues for understanding and manipulating heat at the atomic scale.
Generalizable Semantic Vision Query Generation for Zero-shot Panoptic and Semantic Segmentation

Zero-shot Panoptic Segmentation (ZPS) aims to recognize foreground instances and background stuff without images containing unseen categories in training. Due to the visual data sparsity and the difficulty of generalizing from seen to unseen categories, this task remains challenging. To better generalize to unseen classes, we propose Conditional tOken aligNment and Cycle trAnsiTion (CONCAT), to produce generalizable semantic vision queries. First, a feature extractor is trained by CON to link the vision and semantics for providing target queries. Formally, CON is proposed to align the semantic queries with the CLIP visual CLS token extracted from complete and masked images. To address the lack of unseen categories, a generator is required. However, one of the gaps in synthesizing pseudo vision queries, ie, vision queries for unseen categories, is describing fine-grained visual details through semantic embeddings. Therefore, we approach CAT to train the generator in semantic-vision and vision-semantic manners. In semantic-vision, visual query contrast is proposed to model the high granularity of vision by pulling the pseudo vision queries with the corresponding targets containing segments while pushing those without segments away. To ensure the generated queries retain semantic information, in vision-semantic, the pseudo vision queries are mapped back to semantic and supervised by real semantic embeddings. Experiments on ZPS achieve a 5.2% hPQ increase surpassing SOTA. We also examine inductive ZPS and open-vocabulary semantic segmentation and obtain comparative results while being 2 times faster in testing.
Universal equation describes the shape of air bubbles trapped in ice

Water usually contains dissolved gases, and because freezing is a purifying process these gases must be expelled for ice to form. Bubbles appear at the freezing front and are trapped into ice, making pores. These pores come in a range of sizes from microns to millimeters and their shapes are peculiar; never spherical but elongated, and usually fore-aft asymmetric. We show that these remarkable shapes result of a delicate balance between freezing, capillarity and mass diffusion. A highly non-linear ordinary differential equation suffices to describe the bubbles, with only two non-dimensional parameters representing the supersaturation and the freezing rate. Our experiments provide us with a large variety of pictures of bubble shapes. We show that all of them have their rounded tip well described by an asymptotic regime of the differential equation, and that most of them can have their full shape quantitatively matched by a full solution. This enables the measurement of the freezing conditions of ice samples, and the design of freeze-cast porous materials. Furthermore, the equation exhibits a bifurcation that explains why some bubbles grow indefinitely and make long cylindrical ``ice worms''.
Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex

Classification models based on deep neural networks (DNNs) must be calibrated to measure the reliability of predictions. Some recent calibration methods have employed a probabilistic model on the probability simplex. However, these calibration methods cannot preserve the accuracy of pre-trained models, even those with a high classification accuracy. We propose an accuracy-preserving calibration method using the Concrete distribution as the probabilistic model on the probability simplex. We theoretically prove that a DNN model trained on cross-entropy loss has optimality as the parameter of the Concrete distribution. We also propose an efficient method that synthetically generates samples for training probabilistic models on the probability simplex. We demonstrate that the proposed method can outperform previous methods in accuracy-preserving calibration tasks using benchmarks.
Effect of diet supplementation with N-acetylcysteine on leucocyte functions in prematurely aging mice

According to the free radical theory of aging, oxidative stress plays a key role in the senescent decline of the immune system, and therefore antioxidants may provide an effective therapy in order to protect the immune function against oxidative damage. Accordingly, the purpose of this study was to investigate the favourable effect of a diet supplemented with a glutathione precursor – namely the antioxidant N-acetylcysteine (NAC, 0.1% w/w) – on several functions of the leucocytes from prematurely-aging mice (PAM). In adult PAM, the immune functions studied showed values similar to those of older mice, and worse than those found in adult non prematurely-aging mice (NPAM). A short-term ingestion (4 weeks) of NAC, although unable to influence the NK activity, the IL-2 release or the glutathione content of the peritoneal leucocytes of PAM, resulted in an improvement of other very important leucocyte functions such as the lymphoproliferative response to the mitogen Con A, and IL-1β and TNFα release.
Mutual information scrambling in Ising spin chain

We consider a chain of spin-half particles of a finite length, evolved with the mixed-field Ising Hamiltonian and impose open boundary condition. We simulate the time evolution of entanglement entropy and mutual information following quench from the N\'eel state in this system using tensor networks. We find that the entanglement entropy for non-integrable systems saturates to a constant value at late times, however it continues to oscillate for integrable systems. We also find that mutual information peaks as a function of distance between intervals decay faster for non-integrable systems compared to integrable systems, in agreement with the conclusion of \cite{Alba:2019ybw} for XXZ chains. We compare the oscillations in entanglement entropy evolution obtained from simulations in the integrable case with analytic results from quasi-particle picture and find agreement.
Multi-messenger Astrophysics of Black Holes and Neutron Stars as Probed by Ground-based Gravitational Wave Detectors: From Present to Future

The ground-based gravitational wave (GW) detectors LIGO and Virgo have enabled the birth of multi-messenger GW astronomy via the detection of GWs from merging stellar-mass black holes (BHs) and neutron stars (NSs). GW170817, the first binary NS merger detected in GWs and all bands of the electromagnetic spectrum, is an outstanding example of the impact that GW discoveries can have on multi-messenger astronomy. Yet, GW170817 is only one of the many and varied multi-messenger sources that can be unveiled using ground-based GW detectors. In this contribution, we summarize key open questions in the astrophysics of stellar-mass BHs and NSs that can be answered using current and future-generation ground-based GW detectors, and highlight the potential for new multi-messenger discoveries ahead.
Reconfigurable Intelligent Surfaces for THz: Hardware Impairments and Switching Technologies

The demand for unprecedented performance in the upcoming 6G wireless networks is fomenting the research on THz communications empowered by Reconfigurable Inteligent Surfaces (RISs). A wide range of use cases have been proposed, most of them, assuming high-level RIS models that overlook some of the hardware impairments that this technology faces. The expectation is that the emergent reconfigurable THz technologies will eventually overcome its current limitations. This disassociation from the hardware may mask nonphysical assumptions, perceived as hardware limitations. In this paper, a top-down approach bounded by physical constraints is presented, distilling from system-level specifications, hardware requirements, and upper bounds for the RIS-aided system performance. We consider D-band indoor and outdoor scenarios where a more realistic assessment of the state-of-the-art solution can be made. The goal is to highlight the intricacies of the design procedure based on sound assumptions for the RIS performance. For a given signal range and angular coverage, we quantify the required RIS size, number of switching elements, and maximum achievable bandwidth and capacity.
Lipocalin-2 expression identifies an intestinal regulatory neutrophil population during acute graft-versus-host disease

Acute graft-versus-host disease (aGVHD) is a life-threatening complication of allogeneic hematopoietic cell transplantation (allo-HCT), for which therapeutic options are limited. Strategies to promote intestinal tissue tolerance during aGVHD may improve patient outcomes. Using single-cell RNA sequencing, we identified a lipocalin-2 (LCN2)–expressing neutrophil population in mice with intestinal aGVHD. Transfer of LCN2-overexpressing neutrophils or treatment with recombinant LCN2 reduced aGVHD severity, whereas the lack of epithelial or hematopoietic LCN2 enhanced aGVHD severity and caused microbiome alterations. Mechanistically, LCN2 induced insulin-like growth factor 1 receptor (IGF-1R) signaling in macrophages through the LCN2 receptor SLC22A17, which increased interleukin-10 (IL-10) production and reduced major histocompatibility complex class II (MHCII) expression. Transfer of LCN2-pretreated macrophages reduced aGVHD severity but did not reduce graft-versus-leukemia effects. Furthermore, LCN2 expression correlated with IL-10 expression in intestinal biopsies in multiple cohorts of patients with aGVHD, and LCN2 induced IGF-1R signaling in human macrophages. Collectively, we identified a LCN2-expressing intestinal neutrophil population that reduced aGVHD severity by decreasing MHCII expression and increasing IL-10 production in macrophages. This work provides the foundation for administration of LCN2 as a therapeutic approach for aGVHD. LCN2+ neutrophils reduce severity of intestinal aGVHD by decreasing major histocompatibility complex class II expression and increasing IL-10 production in macrophages. Editor’s summary An approach to treating aGVHD. Acute graft-versus-host disease (aGVHD) is a major complication after allogeneic hematopoietic cell transplant (allo-HCT) for which there are limited treatment options. As aGVHD often presents with gastrointestinal (GI) tract inflammation, studying drivers of disease in the GI tract may identify new therapeutic avenues. Here, Czech et al. identified a population of lipocalin-2 (LCN2)–expressing neutrophils that were increased in the GI tract of mice with aGVHD and exhibited a tolerogenic effect by promoting IL-10 production and decreasing MHCII expression on macrophages; similar observations were made across multiple cohorts of patients who received allo-HCT. Transfer of LCN2-overexpressing neutrophils or administration of recombinant LCN2 reduced aGVHD severity across multiple murine allo-HCT models, supporting further development of LCN2 as a therapeutic for aGVHD. —Courtney Malo