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In this work, we present a novel method for extensive multi-scale generative terrain modeling. At the core of our model is a cascade of superresolution diffusion models that can be combined to produce consistent images across multiple resolutions. Pairing this concept with a tiled generation method yields a scalable system that can generate thousands of square kilometers of realistic Earth surfaces at high resolution. We evaluate our method on a dataset collected from Bing Maps and show that it outperforms super-resolution baselines on the extreme super-resolution task of 1024x zoom. We also demonstrate its ability to create diverse and coherent scenes via an interactive gigapixel-scale generated map. Finally, we demonstrate how our system can be extended to enable novel content creation applications including controllable world generation and 3D scene generation.

This paper presents a novel graphical approach that counts the number of global 8-cycles on the SCRAM three-layer Tanner graph. SCRAM, which stands for Slotted Coded Random Access Multiplexing, is a joint decoder that is meets challenging requirements of 6G. At the transmitter side, the data of the accommodated users is encoded by Low Density Parity Check (LDPC) codes, and the codewords are transmitted over the shared channel by means of Slotted ALOHA. Unlike the state-of-the-art sequential decoders, the SCRAM decoder jointly resolves collisions and decodes the LDPC codewords, in a similar analogy to Belief Propagation on a three-layer Tanner graph. By leveraging the analogy between the two-layer Tanner graph of conventional LDPC codes and the three-layer Tanner graph of SCRAM, the well-developed analysis tools of classical LDPC codes could be utilized to enhance the performance of SCRAM. In essence, the contribution of this paper is three-fold; First it proposes the methodology to utilize these tools to assess the performance of SCRAM. Second, it derives a lower bound on the shortest cycle length of an arbitrary SCRAM Tanner graph. Finally, the paper presents a novel graphical method that counts the number of cycles of length that corresponds to the girth., Comment: 12 pages, 10 figures, 3 tables, 1 Algorithm, Submitted to IEEE Internet of Things Journal

Many organizations depend on human decision-makers to make subjective decisions, especially in settings where information is scarce. Although workers are often viewed as interchangeable, the specific individual assigned to a task can significantly impact outcomes due to their unique decision-making processes and risk tolerance. In this paper, we introduce a novel framework that utilizes machine learning to predict worker behavior and employs integer optimization to strategically assign workers to tasks. Unlike traditional methods that treat machine learning predictions as static inputs for optimization, in our approach, the optimal predictive model used to represent a worker's behavior is determined by how that worker is allocated within the optimization process. We present a decision-aware optimization framework that integrates predictive model selection with worker allocation. Collaborating with an auto-insurance provider and using real-world data, we evaluate the effectiveness of our proposed method by applying three different techniques to predict worker behavior. Our findings show the proposed decision-aware framework outperforms traditional methods and offers context-sensitive and data-responsive strategies for workforce management.

This paper presents a novel scheme dubbed Collision Diversity (CoD) SCRAM, which is provisioned to meet the challenging requirements of the future 6G, portrayed in massive connectivity, reliability, and ultra-low latency. The conventional SCRAM mechanism, which stands for Slotted Coded Random Access Multiplexing, is a hybrid decoding scheme, that jointly resolves collisions and decodes the Low Density Parity Check (LDPC) codewords, in a similar analogy to Belief Propagation (BP) decoding on a joint three-layer Tanner graph. The CoD SCRAM proposed herein tends to enhance the performance of SCRAM by adopting an information-theoretic approach that tends to maximize the attainable Spectral Efficiency. Besides, due to the analogy between the two-layer Tanner graph of classical LDPC codes, and the three-layer Tanner graph of SCRAM, the CoD SCRAM adopts the well-developed tools utilized to design powerful LDPC codes. Finally, the proposed CoD scheme tends to leverage the collisions among the users in order to induce diversity. Results show that the proposed CoD SCRAM scheme surpasses the conventional SCRAM scheme, which is superior to the state-of-the-art Non-Orthogonal Multiple Access (NOMA) schemes. Additionally, by leveraging the collisions, the CoD SCRAM tends to surpass the Sphere-Packing Bound (SPB) at the respective information block length of the underlying LDPC codes of the accommodated users., Comment: 12 pages, 7 figures, 2 Tables, Submitted to IEEE Transactions on Wireless Communications

Semiconductor quantum dots in microcavities are an excellent platform for the efficient generation of indistinguishable single photons. However, their use in a wide range of quantum technologies requires their controlled fabrication and integration in compact closed-cycle cryocoolers, with a key challenge being the efficient and stable extraction of the single photons into a single-mode fiber. Here we report on a novel method for fiber-pigtailing of deterministically fabricated single-photon sources. Our technique allows for nanometer-scale alignment accuracy between the source and a fiber, alignment that persists all the way from room temperature to 2.4 K. We demonstrate high performance of the device under near-resonant optical excitation with g$^{(2)}$(0) = 1.3 %, a photon indistinguishability of 97.5 % and a fibered brightness of 20.8 %. We show that the indistinguishability and single-photon rate are stable for over ten hours of continuous operation in a single cooldown. We further confirm that the device performance is not degraded by nine successive cooldown-warmup cycles., Comment: 7 pages, 5 figures

We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models. Unlike many open-source models, Pixtral is also a cutting-edge text model for its size, and does not compromise on natural language performance to excel in multimodal tasks. Pixtral uses a new vision encoder trained from scratch, which allows it to ingest images at their natural resolution and aspect ratio. This gives users flexibility on the number of tokens used to process an image. Pixtral is also able to process any number of images in its long context window of 128K tokens. Pixtral 12B substanially outperforms other open models of similar sizes (Llama-3.2 11B \& Qwen-2-VL 7B). It also outperforms much larger open models like Llama-3.2 90B while being 7x smaller. We further contribute an open-source benchmark, MM-MT-Bench, for evaluating vision-language models in practical scenarios, and provide detailed analysis and code for standardized evaluation protocols for multimodal LLMs. Pixtral-12B is released under Apache 2.0 license.

In this study, we propose LLM agents as a novel approach in behavioral strategy research, complementing simulations and laboratory experiments to advance our understanding of cognitive processes in decision-making. Specifically, we reproduce a human laboratory experiment in behavioral strategy using large language model (LLM) generated agents and investigate how LLM agents compare to observed human behavior. Our results show that LLM agents effectively reproduce search behavior and decision-making comparable to humans. Extending our experiment, we analyze LLM agents' simulated "thoughts," discovering that more forward-looking thoughts correlate with favoring exploitation over exploration to maximize wealth. We show how this new approach can be leveraged in behavioral strategy research and address limitations.

To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different floors, and interacting with a wide and unseen range of everyday objects. We refer to these tasks as Building-wide Mobile Manipulation. To tackle these inherently long-horizon tasks, we introduce BUMBLE, a unified Vision-Language Model (VLM)-based framework integrating open-world RGBD perception, a wide spectrum of gross-to-fine motor skills, and dual-layered memory. Our extensive evaluation (90+ hours) indicates that BUMBLE outperforms multiple baselines in long-horizon building-wide tasks that require sequencing up to 12 ground truth skills spanning 15 minutes per trial. BUMBLE achieves 47.1% success rate averaged over 70 trials in different buildings, tasks, and scene layouts from different starting rooms and floors. Our user study demonstrates 22% higher satisfaction with our method than state-of-the-art mobile manipulation methods. Finally, we demonstrate the potential of using increasingly-capable foundation models to push performance further. For more information, see https://robin-lab.cs.utexas.edu/BUMBLE/, Comment: 7 Figures, 2 Tables, 11 Pages

Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities. In this study, we explore a surprising phenomenon related to ICL: LLMs can perform multiple, computationally distinct ICL tasks simultaneously, during a single inference call, a capability we term "task superposition". We provide empirical evidence of this phenomenon across various LLM families and scales and show that this phenomenon emerges even if we train the model to in-context learn one task at a time. We offer theoretical explanations that this capability is well within the expressive power of transformers. We also explore how LLMs internally compose task vectors during superposition. Furthermore, we show that larger models can solve more ICL tasks in parallel, and better calibrate their output distribution. Our findings offer insights into the latent capabilities of LLMs, further substantiate the perspective of "LLMs as superposition of simulators", and raise questions about the mechanisms enabling simultaneous task execution.

We show that the spatial part of the Klein-Gordon operator is an essentially self-adjoint operator on the Cauchy surfaces of various classes of spacetimes. Our proof employs the intricate connection between global hyperbolicity and geodesically complete Riemannian surfaces, and concludes by proving global hyperbolicity of the spacetimes under study., Comment: 16 pages

We propose a Truncated Gaussian Basis Approach (TGBA) for simulating the dynamics of quantum many-body systems. The approach constructs an effective Hamiltonian within a reduced subspace, spanned by fermionic Gaussian states, and diagonalizes it to obtain approximate eigenstates and eigenenergies. Symmetries can be exploited to perform parallel computation, enabling to simulate systems with much larger sizes. As an example, we compute the dynamic structure factor and study quench dynamics in a non-integrable quantum Ising chain, known as ``$E_8$ magnet''. The mass ratios calculated through the dynamic structure factor show excellent agreement with Zamolodchikov's analytical predictions. For quench dynamics we observe that time-evolving wave functions in the truncated subspace facilitates the simulation of long-time dynamics., Comment: 6+1 pages, 2 figures

We report the synthesis of a new functional molecule, a quater-tiophene based surfactant, which can both adsorb at the water / gas interface (surface active molecule) and aggregates through pi-pi stacking interactions. We assess then the ability of this molecule to create these functionalities at interfaces. This interfacial functional aggregation, characterized here in situ for the first time, is probed thanks to Langmuir trough experiments and spectrometric ellipsometry. These results open some new routes for the design of new water based opto-electronic devices., Comment: 17 pages, 14 figures

Asynchronous Boolean networks are a type of discrete dynamical system in which each variable can take one of two states, and a single variable state is updated in each time step according to pre-selected rules. Boolean networks are popular in systems biology due to their ability to model long-term biological phenotypes within a qualitative, predictive framework. Boolean networks model phenotypes as attractors, which are closely linked to minimal trap spaces (inescapable hypercubes in the system's state space). In biological applications, attractors and minimal trap spaces are typically in one-to-one correspondence. However, this correspondence is not guaranteed: motif-avoidant attractors (MAAs) that lie outside minimal trap spaces are possible. MAAs are rare and (despite recent efforts) poorly understood. Here we summarize the current state of knowledge regarding MAAs and present several novel observations regarding their response to node deletion reductions and linear extensions of edges. We conduct large-scale computational studies on an ensemble of 14,000 models derived from published Boolean models of biological systems, and more than 100 million Random Boolean Networks. Our findings quantify the rarity of MAAs (in particular, we found no MAAs in the biological models), but highlight the role of network reduction in introducing MAAs into the dynamics. We also show that MAAs are fragile to linear extensions: in sparse networks, even a single linear node can disrupt virtually all MAAs. Motivated by this observation, we improve the upper bound on the number of delays needed to disrupt a motif-avoidant attractor., Comment: 30 pages, 7 figures

Over the past several decades, time-series photometry of CSPNe has yielded significant results including, but not limited to, discoveries of nearly 100 binary systems, insights into pulsations and winds in young white dwarfs, and studies of stars undergoing very late thermal pulses. We have undertaken a systematic study of optical photometric variability of cataloged CSPNe, using the epochal photometric data from the Zwicky Transient Facility (ZTF). By applying appropriate variability metrics, we arrive at a list of 94 significantly variable CSPNe. Based on the timescales of the light-curve activity, we classify the variables broadly into short- and long-timescale variables. In this first paper in this series, we focus on the former, which is the majority class comprising 83 objects. We infer periods for six sources for the first time, and recover several known periodic variables. Among the aperiodic sources, most exhibit a jitter around a median flux with a stable amplitude, and a few show outbursts. We draw attention to WeSb 1, which shows a different kind of variability: prominent deep and aperiodic dips, resembling transits from a dust/debris disk. We find strong evidence for a binary nature of WeSb 1 (possibly an A- to G-type companion). The compactness of the emission lines and inferred high electron densities make WeSb 1 a candidate for either an EGB 6-type planetary nucleus, or a symbiotic system inside an evolved planetary nebula, both of which are rare objects. To demonstrate further promise with ZTF, we report three additional newly identified periodic sources that do not appear in the list of highly variable sources. Finally, we also introduce a two-dimensional metric space defined by the von Neumann statistics and Pearson Skew and demonstrate its effectiveness in identifying unique variables of astrophysical interest, like WeSb 1., Comment: 19 pages + 8 pages appendix, 5 tables, 17 figures; Submitted to PASP; Comments are welcome!

In this letter, we investigate the impact of non-spherical effects on the Primordial Black Hole mass function, based on the ellipticity-dependent threshold calculated by performing $3+1$ relativistic numerical simulations. We consider an equation of state of radiation $w:=P/\rho=1/3$ and a softer one $w=1/10$ with $P$ and $\rho$ begin the pressure and energy density, respectively. We suppose that the curvature perturbations obey Gaussian statistics with a monochromatic power spectrum and examine the most probable ellipsoidal configurations utilizing peak theory. We also suppose the critical scaling law of the PBH mass near the threshold following the known results. The simulations show that the non-sphericity can easily prevent the system from black hole formation when the initial fluctuation amplitude is near the threshold (critical scaling regime). Nevertheless, we show that the non-spherical effects make the mass function just a few times smaller and are insignificant on the mass function distribution, including the power-law scaling in the small mass region., Comment: 7 pages, 3 figures. Companion paper with the results of simulations submitted simultaneously

We perform $3+1$ relativistic numerical simulations to study primordial black hole (PBH) formation from the collapse of adiabatic super-horizon non-spherical perturbations generated from curvature fluctuations obeying random Gaussian statistics with a monochromatic power spectrum. The matter field is assumed to be a perfect fluid of an equation of state $w:=P/\rho={\rm const.}$ with $P$ and $\rho$ being the pressure and the energy density, respectively. The initial spatial profile of the curvature perturbation is modeled with the amplitude $\mu$ and non-spherical parameters $e$ (ellipticity) and $p$ (prolateness) according to peak theory. We focus on the dynamics and the threshold for PBH formation in terms of the non-spherical parameters $e$ and $p$. We find that the critical values ($e_c, p_c$) with a fixed value of $\mu$ closely follow a superellipse curve. With $p=0$, for the range of amplitudes considered, we find that the critical ellipticity for non-spherical collapse follows a decaying power law as a function of $(\mu-\mu_{\rm c,sp})$ with $\mu_{\rm c,sp}$ being the threshold for the spherical case. Our results also indicate that, for both cases of $w = 1/3$ and $w = 1/10$, small deviations from sphericity can avoid collapsing to a black hole when the amplitude is near its critical threshold. Finally we discuss the significance of the ellipticity on the rate of the PBH production., Comment: 31 pages, 24 figures

Spatial information from cell-surface receptors is crucial for processes that require signal processing and sensing of the environment. Here, we investigate the optimal placement of such receptors through a theoretical model that minimizes uncertainty in gradient estimation. Without requiring a priori knowledge of the physical limits of sensing or biochemical processes, we reproduce the emergence of clusters that closely resemble those observed in real cells. On perfect spherical surfaces, optimally placed receptors spread uniformly. When perturbations break their symmetry, receptors cluster in regions of high curvature, massively reducing estimation uncertainty. This agrees with mechanistic models that minimize elastic preference discrepancies between receptors and cell membranes. We further extend our model to motile receptors responding to cell-shape changes and external fluid flow, demonstrating the relevance of our model in realistic scenarios. Our findings provide a simple and utilitarian explanation for receptor clustering at high-curvature regions when high sensing accuracy is paramount.

1 - Spatial confounding is a phenomenon that has been studied extensively in recent years in the statistical literature to describe and mitigate apparent inconsistencies between the results obtained by regression models with and without random spatial effects. While the most common solutions target almost exclusively areal data or geostatistical data modelling by splines, we aim to extend some resolution methods in the context of geostatistical data modelling by Gaussian Markov Random Fields (GMRF) using R-INLA methodology. 2 - First, we present three approaches for alleviating spatial confounding: Restricted Spatial Regression (RSR), Spatial+, and its recent simplified version, called here Spatial+ 2.0. We show how each can be implemented from geostatistical data in a GMRF framework using R-inlabru. 3 - Next, a simulation study that reproduces a spatial confounding phenomenon is carried out to assess the coherence of the extensions with the expectations of these methods. Finally, we apply the expanded methods to a case study, linking cadmium (Cd) concentration in terrestrial mosses to Cd concentration in air. 4 - Our findings support the feasibility of our extended approach of spatial confounding resolution methods to geostatistical data using R-INLA in keeping with the previous contexts, although certain precautions and limitations must be considered., Comment: 39 pages, 5 figures

The equilibrium properties and interminiband transitions for Hartree-interacting two-dimensional electron gas in a one-dimensional chain of planar quantum rings subjected to a transverse homogeneous magnetic field are examined theoretically and numerically. The proposed analytical models for the external modulation potential and the basis wave-function reflect the symmetry and the topology of the system allowing to get high accuracy results in comparatively short computational times. The calculated dependencies of the electronic band structure versus magnetic field show two detached minibands in the region of low energies, while higher minibands manifest multiple crossings and anticrossings. The existence of highly degenerate energy levels (referred to as miniband nodes) for certain values of magnetic field is revealed. The miniband nodes are preserved when taking into account the Coulomb interaction of the electrons. The interaction leads to an up-shift and a broadening of the minibands, as well as to a shift of the nodes toward the larger values of the magnetic field. The miniband nodes have their signature in the magnetization of the electron gas and in the interminiband transitions. The number of electrons per a unite cell of the system has a strong impact on the current density distribution, magnetization and the oscillator strength. The obtained results open new opportunities for a flexible manipulation of magneto-optical properties of future devices operating in far-infrared and THz regimes.

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

Deep neural networks trained as image denoisers are widely used as priors for solving imaging inverse problems. While Gaussian denoising is thought sufficient for learning image priors, we show that priors from deep models pre-trained as more general restoration operators can perform better. We introduce Stochastic deep Restoration Priors (ShaRP), a novel method that leverages an ensemble of such restoration models to regularize inverse problems. ShaRP improves upon methods using Gaussian denoiser priors by better handling structured artifacts and enabling self-supervised training even without fully sampled data. We prove ShaRP minimizes an objective function involving a regularizer derived from the score functions of minimum mean square error (MMSE) restoration operators, and theoretically analyze its convergence. Empirically, ShaRP achieves state-of-the-art performance on tasks such as magnetic resonance imaging reconstruction and single-image super-resolution, surpassing both denoiser-and diffusion-model-based methods without requiring retraining.

In the context of braneworld scenarios, the real scalar field plays a crucial role by providing thickness for brane. In this work, we investigate a codimension-one thick brane within the framework of $f(R,Q,P)$ gravity, where $R$ represents the curvature scalar, while $Q=R_{\mu\nu}R^{\mu\nu}$ and $P=R_{\mu\nu\alpha\beta}R^{\mu\nu\alpha\beta}$ are quadratic geometric invariants. Our interest is to investigate the influence of these invariants on the scalar field solution and the energy density of the brane. Furthermore, we analyze the localization of spin $1/2$ fermions and the gravitino by employing a Yukawa-like coupling with the scalar field background. Such a coupling is able to produce a normalizable zero-mode for both fields., Comment: 26 pages, 11 figures

Real-time supercomputing performance analysis is a critical aspect of evaluating and optimizing computational systems in a dynamic user environment. The operation of supercomputers produce vast quantities of analytic data from multiple sources and of varying types so compiling this data in an efficient matter is critical to the process. MIT Lincoln Laboratory Supercomputing Center has been utilizing the Unity 3D game engine to create a Digital Twin of our supercomputing systems for several years to perform system monitoring. Unity offers robust visualization capabilities making it ideal for creating a sophisticated representation of the computational processes. As we scale the systems to include a diversity of resources such as accelerators and the addition of more users, we need to implement new analysis tools for the monitoring system. The workloads in research continuously change, as does the capability of Unity, and this allows us to adapt our monitoring tools to scale and incorporate features enabling efficient replay of system wide events, user isolation, and machine level granularity. Our system fully takes advantage of the modern capabilities of the Unity Engine in a way that intuitively represents the real time workload performed on a supercomputer. It allows HPC system engineers to quickly diagnose usage related errors with its responsive user interface which scales efficiently with large data sets.

We sometimes observe monotonically decreasing cross-attention weights in our Conformer-based global attention-based encoder-decoder (AED) models. Further investigation shows that the Conformer encoder internally reverses the sequence in the time dimension. We analyze the initial behavior of the decoder cross-attention mechanism and find that it encourages the Conformer encoder self-attention to build a connection between the initial frames and all other informative frames. Furthermore, we show that, at some point in training, the self-attention module of the Conformer starts dominating the output over the preceding feed-forward module, which then only allows the reversed information to pass through. We propose several methods and ideas of how this flipping can be avoided. Additionally, we investigate a novel method to obtain label-frame-position alignments by using the gradients of the label log probabilities w.r.t. the encoder input frames., Comment: Submitted to ICASSP 2025

We show that a subset of the basis for the irreducible representations of the total $SU(2)$ rotation forms a covariant approximate quantum error-correcting code with transversal $U(1)$ logical gates. Using only properties of the angular momentum algebra, we obtain bounds on the code inaccuracy against generic noise on any known $d$ sites and against heralded $d$-local erasures, generalizing and improving previous works on the ``thermodynamic code" to general local spin and different irreducible representations. We demonstrate that this family of codes can host and protect a probe state with quantum Fisher information surpassing the standard quantum limit when the sensing parameter couples to the generator of the $U(1)$ logical gate., Comment: 7+12 pages, 2 figures

Attempts to probe the atmospheres of rocky planets around M dwarfs present both promise and peril. While their favorable planet-to-star radius ratios enable searches for even thin secondary atmospheres, their high activity levels and high-energy outputs threaten atmosphere survival. Here, we present the 0.6--2.85$\mu$m transmission spectrum of the 1.1 Earth-radius, ~340K rocky planet TRAPPIST-1c obtained over two JWST NIRISS/SOSS transit observations. Each of the two spectra displays 100--500 ppm signatures of stellar contamination. Despite being separated by 367 days, the retrieved spot and faculae properties are consistent between the two visits, resulting in nearly identical transmission spectra. Jointly retrieving for stellar contamination and a planetary atmosphere rules out with high confidence (>3-$\sigma$) not only clear hydrogen-dominated atmospheres, but even thin, 1-bar high-mean molecular weight atmospheres rich in H$_2$O, NH$_3$, or CO (at the 2-$\sigma$ level). We find that the only atmosphere scenarios which our spectrum cannot rule out are CH$_4$- or CO$_2$-rich atmospheres, which are both unlikely to be retained when considering the photodestruction of CH$_4$ and the susceptibility of even a CO$_2$-rich atmosphere to escape given the cumulative high-energy irradiation experienced by the planet. Our results further stress the importance of robustly accounting for stellar contamination when analyzing JWST observations of exo-Earths around M dwarfs, as well as the need for high-fidelity stellar models to search for the potential signals of thin secondary atmospheres., Comment: Minor typo corrections and figure axis adjustments compared to previous version

Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate models. Here we introduce sbijax, a Python package that implements a wide variety of state-of-the-art methods in neural simulation-based inference using a user-friendly programming interface. sbijax offers high-level functionality to quickly construct SBI estimators, and compute and visualize posterior distributions with only a few lines of code. In addition, the package provides functionality for conventional approximate Bayesian computation, to compute model diagnostics, and to automatically estimate summary statistics. By virtue of being entirely written in JAX, sbijax is extremely computationally efficient, allowing rapid training of neural networks and executing code automatically in parallel on both CPU and GPU.

Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion Monte Carlo (DMC) and density functional theory (DFT) approach to accurately investigate the magnetic properties and response of strain of monolayer 1T-VSe$_2$. Our calculations show the delicate competition between various phases, revealing critical insights into the relationship between their energetic and structural properties. We went on to perform Classical Monte Carlo simulations informed by our DMC and DFT results, and found the magnetic transition temperature ($T_c$) of the undistorted (non-CDW) FM phase to be 228 K and the distorted (CDW) phase to be 68 K. Additionally, we studied the response of biaxial strain on the energetic stability and magnetic properties of various phases of 2D 1T-VSe$_2$ and found that small amounts of strain can enhance the $T_c$, suggesting a promising route for engineering and enhancing magnetic behavior. Finally, we synthesized 1T-VSe$_2$ and performed Raman spectroscopy measurements, which were in close agreement with our calculated results. Our work emphasizes the role of highly accurate DMC methods in advancing the understanding of monolayer 1T-VSe$_2$ and provides a robust framework for future studies of 2D magnetic materials.

For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare a wide range of Markov Chain Monte Carlo (MCMC) and nested sampling techniques to determine their relative efficacy on functions that resemble those encountered most frequently in the particle astrophysics literature. Our first series of tests explores a series of high-dimensional analytic test functions that exemplify particular challenges, for example highly multimodal posteriors or posteriors with curving degeneracies. We then investigate two real physics examples, the first being a global fit of the $\Lambda$CDM model using cosmic microwave background data from the Planck experiment, and the second being a global fit of the Minimal Supersymmetric Standard Model using a wide variety of collider and astrophysics data. We show that several examples widely thought to be most easily solved using nested sampling approaches can in fact be more efficiently solved using modern MCMC algorithms, but the details of the implementation matter. Furthermore, we also provide a series of useful insights for practitioners of particle astrophysics and cosmology., Comment: 45 pages, 8 figures, 20 tables

We experimentally show that the Inelastic Cooper-Pair Tunneling Amplifier (ICTA), implementing a DC-powered parametric amplification scheme, can achieve gain and noise performance similar to that of Josephson parametric amplifiers. Using experimental data and simulations, we show that the ICTA has near-quantum-limited noise as long as the integral voltage bias noise divided by the superconducting flux quantum is below the amplification bandwidth. We observe a gain of 20 dB with noise below 1.7 times the quantum limit when the full width at half maximum of the integral voltage noise, expressed as frequency, is 5.6 MHz., Comment: 14 pages, 5 figures

In Natural Language Generation (NLG), important information is sometimes omitted in the output text. To better understand and analyse how this type of mistake arises, we focus on RDF-to-Text generation and explore two methods of probing omissions in the encoder output of BART (Lewis et al, 2020) and of T5 (Raffel et al, 2019): (i) a novel parameter-free probing method based on the computation of cosine similarity between embeddings of RDF graphs and of RDF graphs in which we removed some entities and (ii) a parametric probe which performs binary classification on the encoder embeddings to detect omitted entities. We also extend our analysis to distorted entities, i.e. entities that are not fully correctly mentioned in the generated text (e.g. misspelling of entity, wrong units of measurement). We found that both omitted and distorted entities can be probed in the encoder's output embeddings. This suggests that the encoder emits a weaker signal for these entities and therefore is responsible for some loss of information. This also shows that probing methods can be used to detect mistakes in the output of NLG models., Comment: Accepted for publication in Transactions of the ACL (TACL)

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that brain connectivity undergoes frequency-dependent reorganization post-stroke, adapting to new conditions. Traditional methods often rely on handcrafted features that may not capture the complexities of clinical phenomena. In this study, we propose a novel approach using Graph Neural Networks (GNNs) to predict stroke severity, as measured by the NIH Stroke Scale (NIHSS). We analyzed electroencephalography (EEG) recordings from 71 patients at the time of hospitalization. For each patient, we generated five graphs weighted by Lagged Linear Coherence (LLC) between signals from distinct Brodmann Areas, covering $\delta$ (2-4 Hz), $\theta$ (4-8 Hz), $\alpha_1$ (8-10.5 Hz), $\alpha_2$ (10.5-13 Hz), and $\beta_1$ (13-20 Hz) frequency bands. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties. We then trained a graph attention model to predict the NIHSS. By examining its attention coefficients, our model reveals insights into brain reconfiguration, providing clinicians with a valuable tool for diagnosis, personalized treatment, and early intervention in neurorehabilitation., Comment: 7 pages, 7 figures

Modern scientific complementary metal-oxide semiconductor (sCMOS) detectors provide a highly competitive alternative to charge-coupled devices (CCDs), the latter of which have historically been dominant in optical imaging. sCMOS boast comparable performances to CCDs with faster frame rates, lower read noise, and a higher dynamic range. Furthermore, their lower production costs are shifting the industry to abandon CCD support and production in favour of CMOS, making their characterization urgent. In this work, we characterized a variety of high-end commercially available sCMOS detectors to gauge the state of this technology in the context of applications in optical astronomy. We evaluated a range of sCMOS detectors, including larger pixel models such as the Teledyne Prime 95B and the Andor Sona-11, which are similar to CCDs in pixel size and suitable for wide-field astronomy. Additionally, we assessed smaller pixel detectors like the Ximea xiJ and Andor Sona-6, which are better suited for deep-sky imaging. Furthermore, high-sensitivity quantitative sCMOS detectors such as the Hamamatsu Orca-Quest C15550-20UP, capable of resolving individual photoelectrons, were also tested. In-lab testing showed low levels of dark current, read noise, faulty pixels, and fixed pattern noise, as well as linearity levels above $98\%$ across all detectors. The Orca-Quest had particularly low noise levels with a dark current of $0.0067 \pm 0.0003$ e$^-$/s (at $-20^\circ$C with air cooling) and a read noise of $0.37 \pm 0.09$ e$^-$ using its standard readout mode. Our tests revealed that the latest generation of sCMOS detectors excels in optical imaging performance, offering a more accessible alternative to CCDs for future optical astronomy instruments., Comment: SPIE Astronomical Telescopes + Instrumentation, Proceedings Volume 13103, X-Ray, Optical, and Infrared Detectors for Astronomy XI; 131030R (2024)

The BICEP3 and BICEP Array polarimeters are small-aperture refracting telescopes located at the South Pole designed to measure primordial gravitational wave signatures in the Cosmic Microwave Background (CMB) polarization, predicted by inflation. Constraining the inflationary signal requires not only excellent sensitivity, but also careful control of instrumental systematics. Both instruments use antenna-coupled orthogonally polarized detector pairs, and the polarized sky signal is reconstructed by taking the difference in each detector pair. As a result, the differential response between detectors within a pair becomes an important systematic effect we must control. Additionally, mapping the intensity and polarization response in regions away from the main beam can inform how sidelobe levels affect CMB measurements. Extensive calibration measurements are taken in situ every austral summer for control of instrumental systematics and instrument characterisation. In this work, we detail the set of beam calibration measurements that we conduct on the BICEP receivers, from deep measurements of main beam response to polarized beam response and sidelobe mapping. We discuss the impact of these measurements for instrumental systematics studies and design choices for future CMB receivers., Comment: 13 pages, 7 figures, 1 table, Proceedings paper SPIE 2024

Infrared-faint white dwarfs are cool white dwarfs exhibiting significant infrared flux deficits, most often attributed to collision-induced absorption (CIA) from H$_2$-He in mixed hydrogen-helium atmospheres. We present James Webb Space Telescope (JWST) near- and mid-infrared spectra of three such objects using NIRSpec (0.6-5.3 $\mu$m) and MIRI (5-14 $\mu$m): LHS 3250, WD J1922+0233, and LHS 1126. Surprisingly, for LHS 3250, we detect no H$_2$-He CIA absorption at 2.4 $\mu$m, instead observing an unexpected small flux bump at this wavelength. WD J1922+0233 exhibits the anticipated strong absorption feature centered at 2.4 $\mu$m, but with an unexpected narrow emission-like feature inside this absorption band. LHS 1126 shows no CIA features and follows a $\lambda^{-2}$ power law in the mid-infrared. LHS 1126's lack of CIA features suggests a very low hydrogen abundance, with its infrared flux depletion likely caused by He-He-He CIA. For LHS 3250 and WD J1922+0233, the absence of a 1.2 $\mu$m CIA feature in both stars argues against ultracool temperatures, supporting recent suggestions that infrared-faint white dwarfs are warmer and more massive than previously thought. This conclusion is further solidified by Keck near-infrared spectroscopy of seven additional objects. We explore possible explanations for the unexpected emission-like features in both stars, and temperature inversions above the photosphere emerge as a promising hypothesis. Such inversions may be common among the infrared-faint population, and since they significantly affect the infrared spectral energy distribution, this would impact their photometric fits. Further JWST observations are needed to confirm the prevalence of this phenomenon and guide the development of improved atmospheric models., Comment: Submitted to ApJ on 2024-09-07

In ultra-fast astronomical observations featuring fast transients on sub-$\mu$s time scales, the conventional Signal-to-Noise Ratio (SNR) threshold, often fixed at $5\sigma$, becomes inadequate as observational window timescales shorten, leading to unsustainably high False Alarm Rates (FAR). We provide a basic statistical framework that captures the essential noise generation processes relevant to the analysis of time series data from photon-counting detectors. In particular, we establish a protocol of defining detection limits in astronomical photon-counting experiments, such that a FAR-based criterion is preferred over the traditional SNR-based threshold scheme. We develop statistical models that account for noise sources such as dark counts, sky background, and crosstalk, and establish a probabilistic detection criterion applicable to high-speed detectors. We compare the performance of several detector technologies, including photon-counting CMOS/CCDs, SPADs, SiPMs, and PMTs, in detecting faint astronomical signals. These findings offer insights into optimizing detector choice for future ultra-fast astronomical instruments and suggest pathways for improving detection fidelity under rapid observational conditions.

Picking diverse objects in the real world is a fundamental robotics skill. However, many objects in such settings are bulky, heavy, or irregularly shaped, making them ungraspable by conventional end effectors like suction grippers and parallel jaw grippers (PJGs). In this paper, we expand the range of pickable items without hardware modifications using bimanual nonprehensile manipulation. We focus on a grocery shopping scenario, where a bimanual mobile manipulator equipped with a suction gripper and a PJG is tasked with retrieving ungraspable items from tightly packed grocery shelves. From visual observations, our method first identifies optimal grasp points based on force closure and friction constraints. If the grasp points are occluded, a series of nonprehensile nudging motions are performed to clear the obstruction. A bimanual grasp utilizing contacts on the side of the end effectors is then executed to grasp the target item. In our replica grocery store, we achieved a 90% success rate over 102 trials in uncluttered scenes, and a 67% success rate over 45 trials in cluttered scenes. We also deployed our system to a real-world grocery store and successfully picked previously unseen items. Our results highlight the potential of bimanual nonprehensile manipulation for in-the-wild robotic picking tasks. A video summarizing this work can be found at youtu.be/g0hOrDuK8jM

Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks adopt the client-server model with a single-level aggregation (AGR) process, where the server builds the global model by aggregating all trained local models received from client devices. However, this conventional approach encounters challenges, including susceptibility to model/data poisoning attacks. In recent years, advancements in the Internet of Things (IoT) and edge computing have enabled the development of hierarchical FL systems with a two-level AGR process running at edge and cloud servers. In this paper, we propose a Secure Hierarchical FL (SHFL) framework to address poisoning attacks in hierarchical edge networks. By aggregating trained models at the edge, SHFL employs two novel methods to address model/data poisoning attacks in the presence of client adversaries: 1) a client selection algorithm running at the edge for choosing IoT devices to participate in training, and 2) a model AGR method designed based on convex optimization theory to reduce the impact of edge models from networks with adversaries in the process of computing the global model (at the cloud level). The evaluation results reveal that compared to state-of-the-art methods, SHFL significantly increases the maximum accuracy achieved by the global model in the presence of client adversaries applying model/data poisoning attacks.

We discuss in this article the creation and organization of a national core facility for bioimage analysis, based on a distributed team. F-BIAS federates bioimage analysts across France and relies on them to deliver services to the researchers of this territory. The main challenge in implementing this structure is to provide significant scientific value to its members, thereby encouraging their active participation and persuading their respective host teams to support their involvement. F-BIAS accomplished this by creating a professional network that mitigates the negative effects of isolation experienced by its members, who are often the sole bioimage analyst within their local teams, and fosters the development of their technical skills. In a second phase we capitalized on F-BIAS to create a virtual, remotely-operating core facility for bioimage analysis, offering consultations and collaborative project services to the scientific community of France. The services are organized so that they also contribute to the technical proficiency of the analysts. To promote the creation of similar structures, we present and discuss here the organization of this nationally distributed bioimage analysis service core, highlighting successes and challenges.

Recent work on the quantum mechanics of near-extremal non-supersymmetric black holes has identified a characteristic $T^{3/2}$ scaling of the low temperature black hole partition function. This result has only been derived using the path integral in the near-horizon region and relies on many assumptions. We discuss how to derive the $T^{3/2}$ scaling for the near-extremal rotating BTZ black hole from a calculation in the full black hole background using the Denef-Hartnoll-Sachdev (DHS) formula, which expresses the 1-loop determinant of a thermal geometry in terms of a product over the quasinormal mode spectrum. We also derive the spectral measure for fields of any spin in Euclidean BTZ and use it to provide a new proof of the DHS formula and a new, direct derivation of the BTZ heat kernel. The computations suggest a path to proving the $T^{3/2}$ scaling for the asymptotically flat 4d Kerr black hole., Comment: 32 pages

Background: Large language models (LLMs) have seen extraordinary advances with applications in clinical decision support. However, high-quality evidence is urgently needed on the potential and limitation of LLMs in providing accurate clinical decisions based on real-world medical data. Objective: To evaluate quantitatively whether universal state-of-the-art LLMs (ChatGPT and GPT-4) can predict the incidence risk of myocardial infarction (MI) with logical inference, and to further make comparison between various models to assess the performance of LLMs comprehensively. Methods: In this retrospective cohort study, 482,310 participants recruited from 2006 to 2010 were initially included in UK Biobank database and later on resampled into a final cohort of 690 participants. For each participant, tabular data of the risk factors of MI were transformed into standardized textual descriptions for ChatGPT recognition. Responses were generated by asking ChatGPT to select a score ranging from 0 to 10 representing the risk. Chain of Thought (CoT) questioning was used to evaluate whether LLMs make prediction logically. The predictive performance of ChatGPT was compared with published medical indices, traditional machine learning models and other large language models. Conclusions: Current LLMs are not ready to be applied in clinical medicine fields. Future medical LLMs are suggested to be expert in medical domain knowledge to understand both natural languages and quantified medical data, and further make logical inferences.

Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (RL), one popular approach has been to use massively-parallelized, domain-randomized simulations to learn a policy offline over a vast array of contact conditions, allowing robust sim-to-real transfer. Inspired by recent advances in real-time parallel simulation, this work considers instead the viability of online planning methods for contact-rich manipulation by studying the well-known in-hand cube reorientation task. We propose a simple architecture that employs a sampling-based predictive controller and vision-based pose estimator to search for contact-rich control actions online. We conduct thorough experiments to assess the real-world performance of our method, architectural design choices, and key factors for robustness, demonstrating that our simple sampling-based approach achieves performance comparable to prior RL-based works. Supplemental material: https://caltech-amber.github.io/drop., Comment: Extended version. Submitted to ICRA 2025

Optical spectra of the Very Late Thermal Pulse (VLTP) object V4334 Sgr have shown a rapidly changing spectrum resulting from shocks in the outflow, which created a new bipolar nebula inside the old nebula. We see C II and C III emission lines emerging typical of a [WC 11-10]-type star. The strong increase of [O III] and [S III] emission lines indicate the possible onset of photoionisation in the new ejecta., Comment: 2 pages, 3 figures, accepted for publication in the proceedings of IAU Symposium 384: Planetary Nebulae: a Universal Toolbox in the Era of Precision Astrophysics

Two-dimensional terahertz spectroscopy (2DTS) is a low-frequency analogue of two-dimensional optical spectroscopy that is rapidly maturing as a probe of a wide variety of condensed matter systems. However, a persistent problem of 2DTS is the long experimental acquisition times, preventing its broader adoption. A potential solution, requiring no increase in experimental complexity, is signal reconstruction via compressive sensing. In this work, we apply the sparse exponential mode analysis (SEMA) technique to 2DTS of a cuprate superconductor. We benchmark the performance of the algorithm in reconstructing the terahertz nonlinearities and find that SEMA reproduces the asymmetric photon echo lineshapes with as low as a 10% sampling rate and reaches the reconstruction noise floor with beyond 20-30% sampling rate. The success of SEMA in reproducing such subtle, asymmetric lineshapes confirms compressive sensing as a general method to accelerate 2DTS and multidimensional spectroscopies more broadly.

We propose a programming technology that bridges cross-platform compatibility and hardware acceleration in ray tracing applications. Our methodology enables developers to define algorithms while our translator manages implementation specifics for different hardware or APIs. Features include: generating hardware-accelerated code from hardware-agnostic, object-oriented C++ algorithm descriptions; enabling users to define software fallbacks for non-hardware-accelerated CPUs and GPUs; producing GPU programming API-based algorithm implementations resembling manually ported C++ versions. The generated code is editable and readable, allowing for additional hardware acceleration. Our translator supports single megakernel and multiple kernel path tracing implementations without altering the programming model or input source code. Wavefront mode is crucial for NeRF and SDF, ensuring efficient evaluation with multiple kernels. Validation on tasks such as BVH tree build/traversal, ray-surface intersection for SDF, ray-volume intersection for 3D Gaussian Splatting, and complex Path Tracing models showed comparable performance levels to expert-written implementations for GPUs. Our technology outperformed existing Path Tracing implementations.

We present ConvexECG, an explainable and resource-efficient method for reconstructing six-lead electrocardiograms (ECG) from single-lead data, aimed at advancing personalized and continuous cardiac monitoring. ConvexECG leverages a convex reformulation of a two-layer ReLU neural network, enabling the potential for efficient training and deployment in resource constrained environments, while also having deterministic and explainable behavior. Using data from 25 patients, we demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead, highlighting its potential for real-time, low-resource monitoring applications.

Blockchain integration in industries like online advertising is hindered by its connectivity limitations to off-chain data. These industries heavily rely on precise counting systems for collecting and analyzing off-chain data. This requires mechanisms, often called oracles, to feed off-chain data into smart contracts. However, current oracle solutions are ill-suited for counting systems since the oracles do not know when to expect the data, posing a significant challenge. To address this, we present CountChain, a decentralized oracle network for counting systems. In CountChain, data is received by all oracle nodes, and any node can submit a proposition request. Each proposition contains enough data to evaluate the occurrence of an event. Only randomly selected nodes participate in a game to evaluate the truthfulness of each proposition by providing proof and some stake. Finally, the propositions with the outcome of True increment the counter in a smart contract. Thus, instead of a contract calling oracles for data, in CountChain, the oracles call a smart contract when the data is available. Furthermore, we present a formal analysis and experimental evaluation of the system's parameters on over half a million data points to obtain optimal system parameters. In such conditions, our game-theoretical analysis demonstrates that a Nash equilibrium exists wherein all rational parties participate with honesty., Comment: being published at https://ieee-cybermatics.org/2024/blockchain/

Rb-82 is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of 82-Rb, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of 82-Rb results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, 82-Rb emits high-energy positrons. Compared with other tracers such as 18-F, 82-Rb travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for 82-Rb cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09% to 7.58% on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against 15-O-water scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner., Comment: 15 Pages, 10 Figures, 5 tables. Paper Under review. Oral Presentation at IEEE MIC 2023

In the realm of task-oriented dialogue systems, a robust intent detection mechanism must effectively handle malformed utterances encountered in real-world scenarios. This study presents a novel fine-tuning framework for large language models (LLMs) aimed at enhancing in-distribution (ID) intent classification and out-of-distribution (OOD) intent detection, which utilizes semantic matching with prototypes derived from ID class names. By harnessing the highly distinguishable representations of LLMs, we construct semantic prototypes for each ID class using a diversity-grounded prompt tuning approach. We rigorously test our framework in a challenging OOD context, where ID and OOD classes are semantically close yet distinct, referred to as \emph{near} OOD detection. For a thorough assessment, we benchmark our method against the prevalent fine-tuning approaches. The experimental findings reveal that our method demonstrates superior performance in both few-shot ID intent classification and near-OOD intent detection tasks., Comment: work in progress

HPC systems used for research run a wide variety of software and workflows. This software is often written or modified by users to meet the needs of their research projects, and rarely is built with security in mind. In this paper we explore several of the key techniques that MIT Lincoln Laboratory Supercomputing Center has deployed on its systems to manage the security implications of these workflows by providing enforced separation for processes, filesystem access, network traffic, and accelerators to make every user feel like they are running on a personal HPC.