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We show that whenever independent events are binned, there is no correlation between bins. In particular, neither the uncertainty in the energy resolution nor a systematic error in the energy response of a detector will lead to a correlation between bins. This is in contrast with a result that recently appeared in the literature, where it was claimed that the finite energy resolution could induce correlation between bins, changing the distribution of $\Delta\chi^2$ when attempting to determine the neutrino mass ordering using reactor neutrinos. We will compute the expression for the variance of $\Delta\chi^2$ in the case of non-nested hypotheses if correlations between bins are present, showing that they could indeed affect the distribution. We will also show, however, that the detector response cannot introduce statistical correlations between bins unless these are already present in the original spectrum. Both of these results are valid in general: the absence of correlation is true for any detector response and the distribution of $\Delta\chi^2$ in the presence of (intrinsic) correlations, while obtained having the mass ordering in mind, it is true for any non-nested hypotheses., Comment: 15 pages, 12 figures

The primary purpose of this paper is to present the concept of dichotomy in image illumination modeling based on the power function. In particular, we review several mathematical properties of the power function to identify the limitations and propose a new mathematical model capable of abstracting illumination dichotomy. The simplicity of the equation opens new avenues for classical and modern image analysis and processing. The article provides practical and illustrative image examples to explain how the new model manages dichotomy in image perception. The article shows dichotomy image space as a viable way to extract rich information from images despite poor contrast linked to tone, lightness, and color perception. Moreover, a comparison with state-of-the-art methods in image enhancement provides evidence of the method's value., Comment: 49 pages, 11 figures and 36 references

Full thickness crevasses can transport water from the glacier surface to the bedrock where high water pressures can open kilometre-long cracks along the basal interface, which can accelerate glacier flow. We present a first computational modelling study that describes time-dependent fracture propagation in an idealised glacier causing rapid supraglacial lake drainage. A novel two-scale numerical method is developed to capture the elastic and viscoelastic deformations of ice along with crevasse propagation. The fluid-conserving thermo-hydro-mechanical model incorporates turbulent fluid flow and accounts for melting/refreezing in fractures. Applying this model to observational data from a 2008 rapid lake drainage event indicates that viscous deformation exerts a much stronger control on hydrofracture propagation compared to thermal effects. This finding contradicts the conventional assumption that elastic deformation is adequate to describe fracture propagation in glaciers over short timescales (minutes to several hours) and instead demonstrates that viscous deformation must be considered to reproduce observations of lake drainage rate and local ice surface elevation change. As supraglacial lakes continue expanding inland and as Greenland Ice Sheet temperatures become warmer than -8 degree C, our results suggest rapid lake drainages are likely to occur without refreezing, which has implications for the rate of sea level rise.

We survey the distributional properties of progressively dilating sets under projection by covering maps, focusing on manifolds of constant sectional curvature. In the Euclidean case, we review previously known results and formulate some generalizations, derived as a direct byproduct of recent developments on the problem of Fourier decay of finite measures. In the hyperbolic setting, we consider a natural upgrade of the problem to unit tangent bundles; confining ourselves to compact hyperbolic surfaces, we discuss an extension of our recent result with Ravotti on expanding circle arcs, establishing a precise asymptotic expansion for averages along expanding translates of homogeneous curves., Comment: 30 pages, no figures

We extend a theorem of the second author on the $L^q$-dimensions of dynamically driven self-similar measures from the real line to arbitrary dimension. Our approach provides a novel, simpler proof even in the one-dimensional case. As consequences, we show that, under mild separation conditions, the $L^q$-dimensions of homogeneous self-similar measures in $\mathbb{R}^d$ take the expected values, and we derive higher rank slicing theorems in the spirit of Furstenberg's slicing conjecture., Comment: 43 pages, no figures

Populations very often self-organize into regular spatial patterns with important ecological and evolutionary consequences. Yet, most existing models neglect the effect that external biophysical drivers might have both on pattern formation and the spatiotemporal population dynamics once patterns form. Here, we investigate the effect of environmental flows on pattern formation and population dynamics using a spatially nonlocal logistic model (or Fisher-Kolmogorov equation) coupled to a simple shear and a Rankine vortex flow. We find that, whereas population abundance generally decreases with increasing flow intensity, the effect of the flow on the pattern instability depends on the spatial structure of the flow velocity field. This result shows that the velocity field interacts with the spatial feedbacks responsible for pattern formation in non-trivial ways, leading to a variety of spatiotemporal population dynamics regimes in which the total population abundance can exhibit either regular oscillations with a characteristic frequency or more erratic dynamics without a well-defined period. More generally, the diversity of spatiotemporal population dynamics caused by the interplay between self-organizing feedbacks and environmental flows highlights the importance of incorporating environmental and biophysical processes when studying both ecological pattern formation and its consequences., Comment: 27 pages, 10 figures

Historically, spectroscopic techniques have been essential for studying the optical properties of thin solid films. However, existing formulae for both normal transmission and reflection spectroscopy often rely on simplified theoretical assumptions, which may not accurately align with real-world conditions. For instance, it is common to assume (1) that the thin solid layers are deposited on completely transparent thick substrates and (2) that the film surface forms a specular plane with a relatively small wedge angle. While recent studies have addressed these assumptions separately, this work presents an integrated framework that eliminates both assumptions simultaneously. In addition, the current work presents a deep review of various formulae from the literature, each with their corresponding levels of complexity. Our review analysis highlights a critical trade-off between computational complexity and expression accuracy, where the newly developed formulae offer enhanced accuracy at the expense of increased computational time. Our user-friendly code, which includes several classical transmittance and reflectance formulae from the literature and our newly proposed expressions, is publicly available in both Python and Matlab at this link.

The fault tolerance method currently used in High Performance Computing (HPC) is the rollback-recovery method by using checkpoints. This, like any other fault tolerance method, adds an additional energy consumption to that of the execution of the application. The objective of this work is to determine the factors that affect the energy consumption of the computing nodes on homogeneous cluster, when performing checkpoint and restart operations, on SPMD (Single Program Multiple Data) applications. We have focused on the energetic study of compute nodes, contemplating different configurations of hardware and software parameters. We studied the effect of performance states (states P) and power states (states C) of processors, application problem size, checkpoint software (DMTCP) and distributed file system (NFS) configuration. The results analysis allowed to identify opportunities to reduce the energy consumption of checkpoint and restart operations., Comment: 15 pages, 20 figures

We prove that $\operatorname{vol}(S^{d})/8$ is the highest volume of a pair of $d$-dimensional isospectral and non-isometric spherical orbifolds for any $d\geq5$. Furthermore, we show that $\operatorname{vol}(S^{2n-1})/11$ is the highest volume of a pair of $(2n-1)$-dimensional isospectral and non-isometric spherical space forms if either $n\geq11$ and $n\equiv 1\pmod 5$, or $n\geq7$ and $n\equiv 2\pmod 5$, or $n\geq3$ and $n\equiv 3\pmod 5$.

Programs written in C/C++ often include inline assembly: a snippet of architecture-specific assembly code used to access low-level functionalities that are impossible or expensive to simulate in the source language. Although inline assembly is widely used, its semantics has not yet been formally studied. In this paper, we overcome this deficiency by investigating the effect of inline assembly on the consistency semantics of C/C++ programs. We propose the first memory model of the C++ Programming Language with support for inline assembly for Intel's x86 including non-temporal stores and store fences. We argue that previous provably correct compiler optimizations and correct compiler mappings should remain correct under such an extended model and we prove that this requirement is met by our proposed model.

We consider dynamic games with linear dynamics and quadratic objective functions. We observe that the unconstrained open-loop Nash equilibrium coincides with the LQR in an augmented space, thus deriving an explicit expression of the cost-to-go. With such cost-to-go as a terminal cost, we show asymptotic stability for the receding-horizon solution of the finite-horizon, constrained game. Furthermore, we show that the problem is equivalent to a non-symmetric variational inequality, which does not correspond to any Nash equilibrium problem. For unconstrained closed-loop Nash equilibria, we derive a receding-horizon controller that is equivalent to the infinite-horizon one and ensures asymptotic stability.

The nitrogen-vacancy (NV) center in diamond is a promising candidate for various quantum applications, such as quantum sensing. High sensitivity in NV-based magnetic sensing requires a diamond sample with a high density of NV centers and a long electron spin dephasing time. In this work, we propose a systematic measurement method for determining the electron spin dephasing time of NV center ensembles and analyze the contributions to the dephasing time from various sources, including NV-NV interactions, strain distribution, $^{13}C$ nuclear spin, and P1 electron spin. We demonstrate the effectiveness of our method on a series of high-performance diamond samples and provide a comprehensive understanding of dephasing sources, enabling the optimization of NV-based quantum sensing applications.

Silicon exhibits several metastable allotropes which recently attracted attention in the quest for materials with superior (e.g. optical) properties, compatible with Si technology. In this work we shed light on the atomic-scale mechanisms leading to phase transformations in Si under pressure. To do so, we synergically exploit different state-of-the-art approaches. In particular, we use the advanced GAP interatomic potential both in NPT molecular dynamics simulations and in solid-state nudged elastic band calculations, validating our predictions with ab initio DFT calculations. We provide a link between evidence reported in experimental nanoindentation literature and simulation results. Particular attention is dedicated to the investigation of atomistic transition paths allowing for the transformation between BC8/R8 phases to the hd one under pure annealing. In this case we show a direct simulation of the local nucleation of the hexagonal phase in a BC8/R8 matrix and its corresponding atomic-scale mechanism extracted by the use of SS-NEB. We extend our study investigating the effect of pressure on the nucleation barrier, providing an argument for explaining the heterogeneous nucleation of the hd phase observed in experiments reported in the literature.

Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valuable in computational pathology. These models hold the potential to advance critical tasks such as few-shot classification, slide retrieval, and patient stratification. Existing approaches for slide representation learning extend the principles of SSL from small images (e.g., 224 x 224 patches) to entire slides, usually by aligning two different augmentations (or views) of the slide. Yet the resulting representation remains constrained by the limited clinical and biological diversity of the views. Instead, we postulate that slides stained with multiple markers, such as immunohistochemistry, can be used as different views to form a rich task-agnostic training signal. To this end, we introduce Madeleine, a multimodal pretraining strategy for slide representation learning. Madeleine is trained with a dual global-local cross-stain alignment objective on large cohorts of breast cancer samples (N=4,211 WSIs across five stains) and kidney transplant samples (N=12,070 WSIs across four stains). We demonstrate the quality of slide representations learned by Madeleine on various downstream evaluations, ranging from morphological and molecular classification to prognostic prediction, comprising 21 tasks using 7,299 WSIs from multiple medical centers. Code is available at https://github.com/mahmoodlab/MADELEINE., Comment: ECCV'24

Model networks and their underlying topologies have been used as a reference for techno-economic studies for several decades. Existing reference topologies for optical networks may cover different network segments such as backbone, metro core, metro aggregation, access and/or data center. While telco operators work on the optimization of their own existing deployed optical networks, the availability of different topologies is useful for researchers and technology developers to test their solutions in a variety of scenarios and validate the performance in terms of energy efficiency or cost reduction. This paper presents an open-source tool, MoleNetwork, to generate graphs inspired by real network topologies of telecommunication operators that can be used as benchmarks for techno-economic studies.

This paper investigates the advantages of representing and processing semantic knowledge extracted into graphs within the emerging paradigm of semantic communications. The proposed approach leverages semantic and pragmatic aspects, incorporating recent advances on large language models (LLMs) to achieve compact representations of knowledge to be processed and exchanged between intelligent agents. This is accomplished by using the cascade of LLMs and graph neural networks (GNNs) as semantic encoders, where information to be shared is selected to be meaningful at the receiver. The embedding vectors produced by the proposed semantic encoder represent information in the form of triplets: nodes (semantic concepts entities), edges(relations between concepts), nodes. Thus, semantic information is associated with the representation of relationships among elements in the space of semantic concept abstractions. In this paper, we investigate the potential of achieving high compression rates in communication by incorporating relations that link elements within graph embeddings. We propose sending semantic symbols solely equivalent to node embeddings through the wireless channel and inferring the complete knowledge graph at the receiver. Numerical simulations illustrate the effectiveness of leveraging knowledge graphs to semantically compress and transmit information., Comment: Accepted for publication at the 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

The spread and rapid development of AI-related technologies are influencing many aspects of our daily lives, from social to educational, including the labour market. Many researchers have been highlighting the key role AI and technologies play in reshaping jobs and their related tasks, either by automating or enhancing human capabilities in the workplace. Can we estimate if, and to what extent, jobs and related tasks are exposed to the risk of being automatized by state-of-the-art AI-related technologies? Our work tackles this question through a data-driven approach: (i) developing a reproducible framework that exploits a battery of open-source Large Language Models to assess current AI and robotics' capabilities in performing job-related tasks; (ii) formalising and computing an AI exposure measure by occupation, namely the teai (Task Exposure to AI) index. Our results show that about one-third of U.S. employment is highly exposed to AI, primarily in high-skill jobs (aka, white collars). This exposure correlates positively with employment and wage growth from 2019 to 2023, indicating a beneficial impact of AI on productivity. The source codes and results are publicly available, enabling the whole community to benchmark and track AI and technology capabilities over time.

The $H_{0}$ tension problem is studied in the light of a matter creation mechanism (an effective approach to replacing dark energy), the way to define the matter creation rate being of pure phenomenological nature. Bayesian (probabilistic) Machine Learning is used to learn the constraints on the free parameters of the models, with the learning being based on the generated expansion rate, $H(z)$. Taking advantage of the method, the constraints for three redshift ranges are learned. Namely, for the two redshift ranges: $z\in [0,2]$~(cosmic chronometers) and $z\in [0,2.5]$~(cosmic chronometers + BAO), covering already available $H(z)$ data, to validate the learned results; and for a third redshift interval, $z\in[0,5]$, for forecasting purposes. It is learned that the $3\alpha H_{0}$ term in the creation rate provides options that have the potential to solve the $H_{0}$ tension problem., Comment: To appear in EPJC

The Low-Level RF (LLRF) control circuits of linear accelerators (LINACs) are conventionally realized with heterodyne based architectures, which have analog RF mixers for up and down conversion with discrete data converters. We have developed a new LLRF platform for C-band linear accelerator based on the Frequency System-on-Chip (RFSoC) device from AMD Xilinx. The integrated data converters in the RFSoC can directly sample the RF signals in C-band and perform the up and down mixing digitally. The programmable logic and processors required for signal processing for the LLRF control system are also included in a single RFSoC chip. With all the essential components integrated in a device, the RFSoC-based LLRF control platform can be implemented more cost-effectively and compactly, which can be applied to a broad range of accelerator applications. In this paper, the structure and configuration of the newly developed LLRF platform will be described. The LLRF prototype has been tested with high power test setup with a Cool Cooper Collider (C\(^3\)) accelerating structure. The LLRF and the solid state amplifier (SSA) loopback setup demonstrated phase jitter in 1 s as low as 115 fs, which is lower than the requirement of C\(^3\). The rf signals from the klystron forward and accelerating structure captured with peak power up to 16.45 MW will be presented and discussed.

Large-scale pre-training methodologies for chemical language models represent a breakthrough in cheminformatics. These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning on large unlabeled corpora. Typically, this involves pre-training on unlabeled data followed by fine-tuning on specific tasks, reducing dependence on annotated datasets and broadening chemical language representation understanding. This paper introduces a large encoder-decoder chemical foundation models pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, which is equivalent to 4 billion of molecular tokens. The proposed foundation model supports different complex tasks, including quantum property prediction, and offer flexibility with two main variants (289M and $8\times289M$). Our experiments across multiple benchmark datasets validate the capacity of the proposed model in providing state-of-the-art results for different tasks. We also provide a preliminary assessment of the compositionality of the embedding space as a prerequisite for the reasoning tasks. We demonstrate that the produced latent space is separable compared to the state-of-the-art with few-shot learning capabilities., Comment: 14 pages, 3 figures, 14 tables

Accurate assessment of myocardial tissue stiffness is pivotal for the diagnosis and prognosis of heart diseases. Left ventricular diastolic stiffness ($\beta$) obtained from the end-diastolic pressure-volume relationship (EDPVR) has conventionally been utilized as a representative metric of myocardial stiffness. The EDPVR can be employed to estimate the intrinsic stiffness of myocardial tissues through image-based in-silico inverse optimization. However, whether $\beta$, as an organ-level metric, accurately represents the tissue-level myocardial tissue stiffness in healthy and diseased myocardium remains elusive. We developed a modeling-based approach utilizing a two-parameter material model for the myocardium (denoted by $a_f$ and $b_f$) in image-based in-silico biventricular heart models to generate EDPVRs for different material parameters. Our results indicated a variable relationship between $\beta$ and the material parameters depending on the range of the parameters. Interestingly, $\beta$ showed a very low sensitivity to $a_f$, once averaged across several LV geometries, and even a negative correlation with $a_f$ for small values of $a_f$. These findings call for a critical assessment of the reliability and confoundedness of EDPVR-derived metrics to represent tissue-level myocardial stiffness. Our results also underscore the necessity to explore image-based in-silico frameworks, promising to provide a high-fidelity and potentially non-invasive assessment of myocardial stiffness., Comment: 4 pages, 5 figures, accepted in the IEEE EMBC 2024 conference

Crystal defects, traditionally viewed as detrimental, are now being explored for quantum technology applications. This study focuses on stacking faults in silicon and germanium, forming hexagonal inclusions within the cubic crystal and creating quantum wells that modify electronic properties. By modeling defective structures with varying hexagonal layer counts, we calculated formation energies and electronic band structures. Our results show that hexagonal inclusions in Si and Ge exhibit a direct band gap, changing with inclusion thickness, effectively functioning as quantum wells. We find that Ge inclusions have a direct band gap and form Type-I quantum wells. This research highlights the potential of manipulating extended defects to engineer the optoelectronic properties of Si and Ge, offering new pathways for advanced electronic and photonic device applications.

We present an analytical description of the Alchemical Transfer Method (ATM) for molecular binding using the Potential Distribution Theory (PDT) formalism. ATM models the binding free energy by mapping the bound and unbound states of the complex by translating the ligand coordinates. PDT relates the free energy and the probability densities of the perturbation energy along the alchemical path to the probability density at the initial state, which is the unbound state of the complex in the case of a binding process. Hence, the ATM probability density of the transfer energy at the unbound state is first related by a convolution operation of the probability densities for coupling the ligand to the solvent and coupling it to the solvated receptor--for which analytical descriptions are available--with parameters obtained from maximum likelihood analysis of data from double-decoupling alchemical calculations. PDT is then used to extend this analytical description along the alchemical transfer pathway. We tested the theory on the alchemical binding of five guests to the TEMOA host from the SAMPL8 benchmark set. In each case, the probability densities of the perturbation energy for transfer along the alchemical transfer pathway obtained from numerical calculations match those predicted from the theory and double-decoupling simulations. The work provides a solid theoretical foundation for alchemical transfer, offers physical insights on the form of the probability densities observed in alchemical transfer calculations, and confirms the conceptual and numerical equivalence between the alchemical transfer and double-decoupling processes.

Well-tempered metadynamics simulations are employed to explore the phase diagram of ZIF-4, a porous crystalline metal-organic framework of industrial relevance. Despite the vast amount of experimental efforts, the phase diagram that includes ZIF-4 and its related polymorphs has not yet been fully determined. For example, the crystalline phase called ZIF-4-cp is not experimentally observed when high pressure ramps are applied. Our simulations shed light into the phase diagram topology and allow us to further look into the collective degrees of freedom that drive the phase transitions in the T=150-450 K and P=0-200 MPa region. The porous ZIF-4 phase transforms into ZIF-4-cp through pore closure, while the latter has a phase transition at higher pressures regimes to ZIF-4-cp-II, a transformation which involves subtle changes in swing dihedral angles., Comment: 28 pages, 5 figures, 2 tables, supporting information included with 3 additional figures

We introduce mochi_class, an extension of the Einstein-Boltzmann solver hi_class, designed to unlock the full phenomenological potential of Horndeski gravity. This extension allows for general input functions of time without the need for hard-coded parametrisations or covariant Lagrangians. By replacing the traditional $\alpha$-parametrisation with a set of stable basis functions, mochi_class ensures that the resulting effective theories are inherently free from gradient and ghost instabilities. Additionally, mochi_class features a quasi-static approximation implemented at the level of modified metric potentials, enhancing prediction accuracy, especially for models transitioning between a super- and sub-Compton regime. mochi_class can robustly handle a wide range of models without fine-tuning, and introduces a new approximation scheme that activates modifications to the standard cosmology deep in the matter-dominated era. Furthermore, it incorporates viability conditions on the equation of motion for the scalar field fluctuations, aiding in the identification of numerical instabilities. Through comprehensive validation against other Einstein-Boltzmann solvers, mochi_class demonstrates excellent performance and accuracy, broadening the scope of hi_class by facilitating the study of specific modified gravity models and enabling exploration of previously inaccessible regions of the Horndeski landscape. The code is publicly available at https://github.com/mcataneo/mochi_class_public, Comment: 27 pages, 14 figures. Accepted for publication in The Open Journal of Astrophysics. Minor changes in response to the referee report

Tidal effects related to the traversability across thin shells are examined in spherically symmetric geometries. We focus mainly on shells separating inner from outer regions of gravastars (de Sitter -- i.e., $\Lambda>0$ -- interior and Schwarzschild exterior of mass parameter $M$), but we also examine other related geometries by including the possibility of a negative cosmological constant and, besides, non trivial topologies where the shell separates two outer regions. The analysis is developed for radially traversing objects and for tides in both radial and transverse directions, which present difficulties of somewhat different nature. Transverse tides are unavoidable across shells which satisfy the flare-out condition, while shells in trivial topologies, i.e. geometries with one asymptotic region, are more indulgent with the issue of insurmountable tides. Besides, contradicting other cases analyzed in previous works, we find that large radial tides cannot be avoided when traveling across the shell in the gravastar solution, but in non-trivial topologies they can. We study with special attention the traversability in practice of the transition layer in the thin-shell gravastar solution. In particular, a finite object which traverses radially the shell in a gravastar with $\sqrt{\Lambda}\ll 1/M$ undergoes a compression effect in both the transverse and the radial directions due to the tides associated to the thin layer. The results are interpreted in terms of the total momentum transfer obtained by integrating the travel time of the object., Comment: 14 pages, no figures

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

In this work, we address the problem of eavesdropping on digital video displays by analyzing the electromagnetic waves that unintentionally emanate from the cables and connectors, particularly HDMI. This problem is known as TEMPEST. Compared to the analog case (VGA), the digital case is harder due to a 10-bit encoding that results in a much larger bandwidth and non-linear mapping between the observed signal and the pixel's intensity. As a result, eavesdropping systems designed for the analog case obtain unclear and difficult-to-read images when applied to digital video. The proposed solution is to recast the problem as an inverse problem and train a deep learning module to map the observed electromagnetic signal back to the displayed image. However, this approach still requires a detailed mathematical analysis of the signal, firstly to determine the frequency at which to tune but also to produce training samples without actually needing a real TEMPEST setup. This saves time and avoids the need to obtain these samples, especially if several configurations are being considered. Our focus is on improving the average Character Error Rate in text, and our system improves this rate by over 60 percentage points compared to previous available implementations. The proposed system is based on widely available Software Defined Radio and is fully open-source, seamlessly integrated into the popular GNU Radio framework. We also share the dataset we generated for training, which comprises both simulated and over 1000 real captures. Finally, we discuss some countermeasures to minimize the potential risk of being eavesdropped by systems designed based on similar principles., Comment: Submitted to LADC '24

We present an in-depth, high-resolution spectroscopic analysis of the M dwarf K2-18 that hosts a sub-Neptune exoplanet in its habitable zone. We show our technique to accurately normalize the observed spectrum, which is crucial for a proper spectral fitting. We also introduce a new automatic, line-by-line model-fitting code, AutoSpecFit, that performs an iterative ${\chi}^{2}$ minimization process to measure individual elemental abundances of cool dwarfs. We apply this code to the star K2-18, and measure the abundance of 10 elements - C, O, Na, Mg, Al, K, Ca, Sc, Ti, and Fe. We find these abundances moderately supersolar, except for Fe with a slightly subsolar abundance. The accuracy of the inferred abundances is limited by the systematic errors due to uncertain stellar parameters. We also derive the abundance ratios associated with several planet-building elements such as Al/Mg, Ca/Mg, Fe/Mg, and (a solar-like) C/O=0.568 $\pm$ 0.026, which can be used to constrain the chemical composition and the formation location of the exoplanet. On the other hand, the planet K2-18 b has attracted considerable interest, given the JWST measurements of its atmospheric composition. Early JWST studies reveal an unusual chemistry for the atmosphere of this planet, which is unlikely to be driven by formation in a disk of unusual composition. The comparison between the chemical abundances of K2-18 b from future JWST analyses and those of the host star can provide fundamental insights into the formation of this planetary system., Comment: 29 pages, 14 figures, Accepted to the Astrophysical Journal (ApJ)

To design semiconductor-based optical devices, the optical properties of the used semiconductor materials must be precisely measured over a large band. Transmission spectroscopy stands out as an inexpensive and widely available method for this measurement but requires model assumptions and reconstruction algorithms to convert the measured transmittance spectra into optical properties of the thin films. Amongst the different reconstruction techniques, inverse synthesis methods generally provide high precision but rely on rigid analytical models of a thin film system. In this paper, we demonstrate a novel flexible inverse synthesis method that uses angular spectrum wave propagation and does not rely on rigid model assumptions. Amongst other evaluated parameters, our algorithm is capable of evaluating the geometrical properties of thin film surfaces, which reduces the variance caused by inverse synthesis optimization routines and significantly improves measurement precision. The proposed method could potentially allow for the characterization of "uncommon" thin film samples that do not fit the current model assumptions, as well as the characterization of samples with higher complexity, e.g., multi-layer systems., Comment: 10 pages, 7 figures, 5 tables

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of this data present substantial challenges in data modeling and analysis, which have been tamed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of Large Language Models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and generation of human behavior through the lens of wearable sensor data. This survey explores current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensors data, the capabilities and limitations of LLMs to model them and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensors data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensors data and LLMs, offering insights into the current state and future prospects of this emerging field.

We present a numerical experiment that demonstrates the possibility to capture topological phase transitions via an x-ray absorption spectroscopy scheme. We consider a Chern insulator whose topological phase is tuned via a second-order hopping. We perform time-dynamics simulations of the out-of-equilibrium laser-driven electron motion that enables us to model a realistic attosecond spectroscopy scheme. In particular, we use an ultrafast scheme with a circularly polarized IR pump pulse and an attosecond x-ray probe pulse. A laser-induced dichroism-type spectrum shows a clear signature of the topological phase transition. We are able to connect these signatures with the Berry structure of the system. This work extend the applications of attosecond absorption spectroscopy to systems presenting a non-trivial topological phase.

A set of $2^n$ candidates is presented to a commission. At every round, each member of this commission votes by pairwise comparison, and one-half of the candidates is deleted from the tournament, the remaining ones proceeding to the next round until the $n$-th round (the final one) in which the final winner is declared. The candidates are arranged on a board in a given order, which is maintained among the remaining candidates at all rounds. A study of the size of the commission is carried out in order to obtain the desired result of any candidate being a possible winner. For $2^n$ candidates with $n \geq 3$, we identify a voting profile with $4n -3$ voters such that any candidate could win just by choosing a proper initial order of the candidates. Moreover, in the setting of a random number of voters, we obtain the same results, with high probability, when the expected number of voters is large., Comment: 14 pages

This paper documents our release of a large-scale data collection of TikTok posts related to the upcoming 2024 U.S. Presidential Election. Our current data comprises 1.8 million videos published between November 1, 2023, and May 26, 2024. Its exploratory analysis identifies the most common keywords, hashtags, and bigrams in both Spanish and English posts, focusing on the election and the two main Presidential candidates, President Joe Biden and Donald Trump. We utilized the TikTok Research API, incorporating various election-related keywords and hashtags, to capture the full scope of relevant content. To address the limitations of the TikTok Research API, we also employed third-party scrapers to expand our dataset. The dataset is publicly available at https://github.com/gabbypinto/US2024PresElectionTikToks, Comment: The 2024 Election Integrity Initiative

Tree ensemble methods represent a popular machine learning model, known for their effectiveness in supervised classification and regression tasks. Their performance derives from aggregating predictions of multiple decision trees, which are renowned for their interpretability properties. However, tree ensemble methods do not reliably exhibit interpretable output. Our work aims to extract an optimized list of rules from a trained tree ensemble, providing the user with a condensed, interpretable model that retains most of the predictive power of the full model. Our approach consists of solving a clean and neat set partitioning problem formulated through Integer Programming. The proposed method works with either tabular or time series data, for both classification and regression tasks, and does not require parameter tuning under the most common setting. Through rigorous computational experiments, we offer statistically significant evidence that our method is competitive with other rule extraction methods and effectively handles time series., Comment: - Fixed several typos - Related work have been expanded - Discussion of computational results has been improved for clearness

The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbols (closed-world assumption). This assumption leads to generate hallucinated outputs rather than admitting their lack of knowledge. Hallucinations can lead to wrong or potentially offensive responses to users. Hence, a mechanism to prevent this behavior is crucial to build trusted NSP-based Question Answering agents. To that end, we propose the Hallucination Simulation Framework (HSF), a general setting for stimulating and analyzing NSP model hallucinations. The framework can be applied to any NSP task with a closed-ontology. Using the proposed framework and KQA Pro as the benchmark dataset, we assess state-of-the-art techniques for hallucination detection. We then present a novel hallucination detection strategy that exploits the computational graph of the NSP model to detect the NSP hallucinations in the presence of ontology gaps, out-of-domain utterances, and to recognize NSP errors, improving the F1-Score respectively by ~21, ~24% and ~1%. This is the first work in closed-ontology NSP that addresses the problem of recognizing ontology gaps. We release our code and checkpoints at https://github.com/amazon-science/handling-ontology-gaps-in-semantic-parsing.

Using an optimal quadratic estimator, we measure the large-scale cross-correlation between maps of the stochastic gravitational-wave intensity, constructed from the first three LIGO-Virgo observing runs, and a suite of tomographic samples of galaxies covering the redshift range $z\lesssim 2$. We do not detect any statistically significant cross-correlation, but the tomographic nature of the data allows us to place constraints on the (bias-weighted) production rate density of gravitational waves by astrophysical sources as a function of cosmic time. Our constraints range from $\langle b\dot{\Omega}_{\rm GW}\rangle<3.0\times10^{-9}\,{\rm Gyr}^{-1}$ at $z\sim0.06$ to $\langle b\dot{\Omega}_{\rm GW}\rangle<2.7\times10^{-7}\,{\rm Gyr}^{-1}$ at $z\sim1.5$ (95\% C.L.), assuming a frequency spectrum of the form $f^{2/3}$ (corresponding to an astrophysical background of binary mergers), and a reference frequency $f_{\rm ref}=25\,{\rm Hz}$. Although these constraints are $\sim2$ orders of magnitude higher than the expected signal, we show that a detection may be possible with future experiments., Comment: 14 pages, 11 figures

We study the ultrafast dynamics initiated by a coherent superposition of core-excited states of nitrous oxide molecule. Using high-level \textit{ab-initio} methods, we show that the decoherence caused by the electronic decay and the nuclear dynamics is substantially slower than the induced ultrafast quantum beatings, allowing the system to undergo several oscillations before it dephases. We propose a proof-of-concept experiment using the harmonic up-conversion scheme available at X-ray free-electron laser facilities to trace the evolution of the created core-excited-state coherence through a time-resolved X-ray photoelectron spectroscopy.

Modern astrophysics relies on intricate instrument setups to meet the demands of sensitivity, sky coverage, and multi-channel observations. An example is the CONCERTO project, employing advanced technology like kinetic inductance detectors and a Martin-Puplett interferometer. This instrument, installed at the APEX telescope atop the Chajnantor plateau, began commissioning observations in April 2021. Following a successful commissioning phase that concluded in June 2021, CONCERTO was offered to the scientific community for observations, with a final observing run in December 2022. CONCERTO boasts an 18.5 arcmin field of view and a spectral resolution down to 1.45 GHz in the 130-310 GHz electromagnetic band. We developed a comprehensive instrument model of CONCERTO inspired by Fourier transform spectrometry principles to optimize performance and address systematic errors. This model integrates instrument noises, subsystem characteristics, and celestial signals, leveraging both physical data and simulations. Our methodology involves delineating simulation components, executing on-sky simulations, and comparing results with real observations. The resulting instrument model is pivotal, enabling a precise error correction and enhancing the reliability of astrophysical insights obtained from observational data. In this work, we focus on the description of three white-noise noise components included in the instrument model that characterize the white-noise level: the photon, the generation-recombination, and the amplifier noises., Comment: 8 pages, 1 figure, Proceeding of the SPIE conference Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII, SPIE Astronomical Telescopes + Instrumentation 2024

Semantic communication (SC) is recognized as a promising approach for enabling reliable communication with minimal data transfer while maintaining seamless connectivity for a group of wireless users. Unlocking the advantages of SC for multi-user cases requires revisiting how communication and computing resources are allocated. This reassessment should consider the reasoning abilities of end-users, enabling receiving nodes to fill in missing information or anticipate future events more effectively. Yet, state-of-the-art SC systems primarily focus on resource allocation through compression based on semantic relevance, while overlooking the underlying data generation mechanisms and the tradeoff between communications and computing. Thus, they cannot help prevent a disruption in connectivity. In contrast, in this paper, a novel framework for computing and communication resource allocation is proposed that seeks to demonstrate how SC systems with reasoning capabilities at the end nodes can improve reliability in an end-to-end multi-user wireless system with intermittent communication links. Towards this end, a novel reasoning-aware SC system is proposed for enabling users to utilize their local computing resources to reason the representations when the communication links are unavailable. To optimize communication and computing resource allocation in this system, a noncooperative game is formulated among multiple users whose objective is to maximize the effective semantic information (computed as a product of reliability and semantic information) while controlling the number of semantically relevant links that are disrupted. Simulation results show that the proposed reasoning-aware SC system results in at least a $16.6\%$ enhancement in throughput and a significant improvement in reliability compared to classical communications systems that do not incorporate reasoning., Comment: 7 pages, 5 figures, in submission to IEEE GLOBECOM

Photonic addressing of superconducting circuits has been proposed to overcome wiring complexity and heat load challenges, but superconducting-photonic links suffer from an efficiency-noise trade-off that limits scalability. This trade-off arises because increasing power conversion efficiency requires reducing optical power, which makes the converted signal susceptible to shot noise. We analyze this trade-off and find the infidelity of qubit gates driven by photonic signals scales inversely with the number of photons used, and therefore the power efficiency of the converter. While methods like nonlinear detection or squeezed light could mitigate this effect, we consider generating higher frequency electrical signals, such as millimeter-waves (100 GHz), using laser light. At these higher frequencies, circuits have higher operating temperatures and cooling power budgets. We demonstrate an optically-driven cryogenic millimeter-wave source with a power efficiency of $10^{-4}$ that can generate ${1}~\mathrm{\mu W}$ of RF power at 80 GHz with 1500 thermal photons of added noise at 4 K. Using this source, we perform frequency-domain spectroscopy of superconducting NbTiN resonators at 80-90 GHz. Our results show a promising approach to alleviate the efficiency-noise constraints on optically-driven superconducting circuits while leveraging the benefits of photonic signal delivery. Further optimization of power efficiency and noise at high frequencies could make photonic control of superconducting qubits viable at temperatures exceeding 1 kelvin., Comment: 14 pages, 8 figures

In the shadow of the digital revolution, the insidious issue of human trafficking has found new breeding grounds within the realms of social media and online job boards. Previous research efforts have predominantly centered on identifying victims via the analysis of escort advertisements. However, our work shifts the focus towards enabling a proactive approach: pinpointing potential traffickers before they lure their preys through false job opportunities. In this study, we collect and analyze a vast dataset comprising over a quarter million job postings collected from eight relevant regions across the United States, spanning nearly two decades (2006-2024). The job boards we considered are specifically catered towards Chinese-speaking immigrants in the US. We classify the job posts into distinct groups based on the self-reported information of the posting user. Our investigation into the types of advertised opportunities, the modes of preferred contact, and the frequency of postings uncovers the patterns characterizing suspicious ads. Additionally, we highlight how external events such as health emergencies and conflicts appear to strongly correlate with increased volume of suspicious job posts: traffickers are more likely to prey upon vulnerable populations in times of crises. This research underscores the imperative for a deeper dive into how online job boards and communication platforms could be unwitting facilitators of human trafficking. More importantly, it calls for the urgent formulation of targeted strategies to dismantle these digital conduits of exploitation.

Understanding susceptibility to online influence is crucial for mitigating the spread of misinformation and protecting vulnerable audiences. This paper investigates susceptibility to influence within social networks, focusing on the differential effects of influence-driven versus spontaneous behaviors on user content adoption. Our analysis reveals that influence-driven adoption exhibits high homophily, indicating that individuals prone to influence often connect with similarly susceptible peers, thereby reinforcing peer influence dynamics, whereas spontaneous adoption shows significant but lower homophily. Additionally, we extend the Generalized Friendship Paradox to influence-driven behaviors, demonstrating that users' friends are generally more susceptible to influence than the users themselves, de facto establishing the notion of Susceptibility Paradox in online social influence. This pattern does not hold for spontaneous behaviors, where friends exhibit fewer spontaneous adoptions. We find that susceptibility to influence can be predicted using friends' susceptibility alone, while predicting spontaneous adoption requires additional features, such as user metadata. These findings highlight the complex interplay between user engagement and characteristics in spontaneous content adoption. Our results provide new insights into social influence mechanisms and offer implications for designing more effective moderation strategies to protect vulnerable audiences.

The Yang-Mills-Chern-Simons theory in three-dimensional Minkowski space-time is studied in a gauge-fixing scheme which interpolates between the covariant gauge and light-cone gauge, the interpolating gauge-fixing. The ultraviolet finiteness of the theory is proved via the Becchi-Rouet-Stora (BRS) algebraic renormalization procedure, which allows us to demonstrate the vanishing of all $\beta$-functions and all anomalous dimensions to all orders in perturbation theory., Comment: 6 pages