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Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema of mental disorders, that is, how it internally associates and interprets symptoms. In this work, we leveraged contemporary measurement theory to decode how GPT-4 interrelates depressive symptoms to inform both clinical utility and theoretical understanding. We found GPT-4's assessment of depression: (a) had high overall convergent validity (r = .71 with self-report on 955 samples, and r = .81 with experts judgments on 209 samples); (b) had moderately high internal consistency (symptom inter-correlates r = .23 to .78 ) that largely aligned with literature and self-report; except that GPT-4 (c) underemphasized suicidality's -- and overemphasized psychomotor's -- relationship with other symptoms, and (d) had symptom inference patterns that suggest nuanced hypotheses (e.g. sleep and fatigue are influenced by most other symptoms while feelings of worthlessness/guilt is mostly influenced by depressed mood)., Comment: 21 pages, 3 tables, 6 figures, 1 supplementary table, 83 references

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language models (pLM) training on hundreds of graphical processing units (GPUs). We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs. Its modular design allows the integration of individual components, such as data loaders, into existing workflows and is open to community contributions. We detail technical features of the BioNeMo Framework through use cases such as pLM pre-training and fine-tuning. On 256 NVIDIA A100s, BioNeMo Framework trains a three billion parameter BERT-based pLM on over one trillion tokens in 4.2 days. The BioNeMo Framework is open-source and free for everyone to use.

AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice settings, show that some of the generated advice is beneficial. It helps novices compete with experienced players and in some instances even surpass them. The mere presence of advice can be advantageous, even if players do not follow it., Comment: 9 pages, 3 figures

Over the past few years, a growing number of data platforms have emerged, including data commons, data repositories, and databases containing biomedical, environmental, social determinants of health and other data relevant to improving health outcomes. With the growing number of data platforms, interoperating multiple data platforms to form data meshes, data fabrics and other types of data ecosystems reduces data silos, expands data use, and increases the potential for new discoveries. In this paper, we introduce ten principles, which we call pillars, for data meshes. The goals of the principles are 1) to make it easier, faster, and more uniform to set up a data mesh from multiple data platforms; and, 2) to make it easier, faster, and more uniform, for a data platform to join one or more data meshes. The hope is that the greater availability of data through data meshes will accelerate research and that the greater uniformity of meshes will lower the cost of developing meshes and connecting a data platform to them., Comment: 10 pages, 1 figure

For the family of complex rational functions of the form $R_{n,c,a}(z) = z^n + \dfrac{a}{z^n}+c$, known as ``Generalized McMullen maps'', for $a\neq 0$ and $n \geq 3$ fixed, we study the boundedness locus in some one-dimensional slices of the $(a,c)$-parameter space, by fixing a parameter or imposing a relation. First, if we fix $c$ with $|c|\geq 6$ while allowing $a$ to vary, assuming a modest lower bound on $n$ in terms of $|c|$, we establish the location in the $a$-plane of $n$ ``baby" Mandelbrot sets, that is, homeomorphic copies of the original Mandelbrot set. We use polynomial-like maps, introduced by Douady and Hubbard and applied for the subfamily $R_{n,a,0}$ by Devaney. Second, for slices in which $c=ta$, we again observe what look like baby Mandelbrot sets within these slices, and begin the study of this subfamily by establishing a neighborhood containing the boundedness locus., Comment: 38 pages, 13 figures with 32 subfigures

The recent direct detection of neutrinos at the LHC has opened a new window on high-energy particle physics and highlighted the potential of forward physics for groundbreaking discoveries. In the last year, the physics case for forward physics has continued to grow, and there has been extensive work on defining the Forward Physics Facility and its experiments to realize this physics potential in a timely and cost-effective manner. Following a 2-page Executive Summary, we present the status of the FPF, beginning with the FPF's unique potential to shed light on dark matter, new particles, neutrino physics, QCD, and astroparticle physics. We summarize the current designs for the Facility and its experiments, FASER2, FASER$\nu$2, FORMOSA, and FLArE, and conclude by discussing international partnerships and organization, and the FPF's schedule, budget, and technical coordination., Comment: 32 pages

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, computer-assisted discretization of the continuous-time dynamics, yielding a provably convergent discrete-time algorithm. Our methodology recovers many classical (distributed) optimization algorithms and enables users to quickly design and explore a wide range of new algorithms with convergence guarantees.

We derive an analytical expression for the effective indices of modes of circular step-index fibers valid near their cutoff wavelengths. The approximation, being a first-order Taylor series of a smooth function, is also valid for the real part of the effective index beyond cutoff where the modes become lossy. The approximation is used to derive certain previously unknown mode properties. For example, it is shown that for non-dispersive materials the EH-mode group index at cutoff, surprisingly, does not depend on wavelength, core radius, or even radial mode order.

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that minimizes the model's use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information minimized, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two state-of-the-art T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD). Approximately 4 years before MCI diagnosis, dMRI-based brain age yields better performance than T1w MRI-based brain ages in predicting transition from CN to MCI.

Aerodynamic breakup of vaporizing drops is commonly seen in many spray applications. While it is well known that vaporization can modulate interfacial instabilities, the impact of vaporization on drop aerobreakup is poorly understood. Detailed interface-resolved simulations were performed to systematically study the effect of vaporization, characterized by the Stefan number, on the drop breakup and acceleration for different Weber numbers and density ratios. It is observed that the resulting asymmetric vaporization rates and strengths of Stefan flow on the windward and leeward sides of the drop hinder bag development and prevent drop breakup. The critical Weber number thus generally increases with the Stefan number. The modulation of the boundary layer also contributes to a significant increase of drag coefficient. Numerical experiments were performed to affirm that the drop volume reduction plays a negligible role and the Stefan flow is the dominant reason for the breakup suppression and drag enhancement observed.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

This work explores how human judgement about salient regions of an image can be introduced into deep convolutional neural network (DCNN) training. Traditionally, training of DCNNs is purely data-driven. This often results in learning features of the data that are only coincidentally correlated with class labels. Human saliency can guide network training using our proposed new component of the loss function that ConveYs Brain Oversight to Raise Generalization (CYBORG) and penalizes the model for using non-salient regions. This mechanism produces DCNNs achieving higher accuracy and generalization compared to using the same training data without human salience. Experimental results demonstrate that CYBORG applies across multiple network architectures and problem domains (detection of synthetic faces, iris presentation attacks and anomalies in chest X-rays), while requiring significantly less data than training without human saliency guidance. Visualizations show that CYBORG-trained models' saliency is more consistent across independent training runs than traditionally-trained models, and also in better agreement with humans. To lower the cost of collecting human annotations, we also explore using deep learning to provide automated annotations. CYBORG training of CNNs addresses important issues such as reducing the appetite for large training sets, increasing interpretability, and reducing fragility by generalizing better to new types of data.

Understanding specifically where a model focuses on within an image is critical for human interpretability of the decision-making process. Deep learning-based solutions are prone to learning coincidental correlations in training datasets, causing over-fitting and reducing the explainability. Recent advances have shown that guiding models to human-defined regions of saliency within individual images significantly increases performance and interpretability. Human-guided models also exhibit greater generalization capabilities, as coincidental dataset features are avoided. Results show that models trained with saliency incorporation display an increase in interpretability of up to 30% over models trained without saliency information. The collection of this saliency information, however, can be costly, laborious and in some cases infeasible. To address this limitation, we propose a combination strategy of saliency incorporation and active learning to reduce the human annotation data required by 80% while maintaining the interpretability and performance increase from human saliency. Extensive experimentation outlines the effectiveness of the proposed approach across five public datasets and six active learning criteria.

Question answering (QA)-producing correct answers for input questions-is popular, but we test a reverse question answering (RQA) task: given an input answer, generate a question with that answer. Past work tests QA and RQA separately, but we test them jointly, comparing their difficulty, aiding benchmark design, and assessing reasoning consistency. 16 LLMs run QA and RQA with trivia questions/answers, showing: 1) Versus QA, LLMs are much less accurate in RQA for numerical answers, but slightly more accurate in RQA for textual answers; 2) LLMs often answer their own invalid questions from RQA accurately in QA, so RQA errors are not from knowledge gaps alone; 3) RQA errors correlate with question difficulty and inversely correlate with answer frequencies in the Dolma corpus; and 4) LLMs struggle to give valid multi-hop questions. By finding question and answer types yielding RQA errors, we suggest improvements for LLM RQA reasoning., Comment: In-progress preprint

The first FASER search for a light, long-lived particle decaying into a pair of photons is reported. The search uses LHC proton-proton collision data at $\sqrt{s}=13.6~\text{TeV}$ collected in 2022 and 2023, corresponding to an integrated luminosity of $57.7\text{fb}^{-1}$. A model with axion-like particles (ALPs) dominantly coupled to weak gauge bosons is the primary target. Signal events are characterised by high-energy deposits in the electromagnetic calorimeter and no signal in the veto scintillators. One event is observed, compared to a background expectation of $0.44 \pm 0.39$ events, which is entirely dominated by neutrino interactions. World-leading constraints on ALPs are obtained for masses up to $300~\text{MeV}$ and couplings to the Standard Model W gauge boson, $g_{aWW}$, around $10^{-4}$ GeV$^{-1}$, testing a previously unexplored region of parameter space. Other new particle models that lead to the same experimental signature, including ALPs coupled to gluons or photons, U(1)$_B$ gauge bosons, up-philic scalars, and a Type-I two-Higgs doublet model, are also considered for interpretation, and new constraints on previously viable parameter space are presented in this paper., Comment: 37 pages, 22 figures

Recent advancements of large language models (LLMs) have led to claims of AI surpassing humans in natural language processing (NLP) tasks such as textual understanding and reasoning. This work investigates these assertions by introducing CAIMIRA, a novel framework rooted in item response theory (IRT) that enables quantitative assessment and comparison of problem-solving abilities of question-answering (QA) agents: humans and AI systems. Through analysis of over 300,000 responses from ~70 AI systems and 155 humans across thousands of quiz questions, CAIMIRA uncovers distinct proficiency patterns in knowledge domains and reasoning skills. Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning, while state-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval and fact-based reasoning, particularly when information gaps are well-defined and addressable through pattern matching or data retrieval. These findings highlight the need for future QA tasks to focus on questions that challenge not only higher-order reasoning and scientific thinking, but also demand nuanced linguistic interpretation and cross-contextual knowledge application, helping advance AI developments that better emulate or complement human cognitive abilities in real-world problem-solving., Comment: To appear at EMNLP 2024 (Main)

We propose a bit-flip descent method for optimizing binary spreading codes with large family sizes and long lengths, addressing the challenges of large-scale code design in GNSS and emerging PNT applications. The method iteratively flips code bits to improve the codes' auto- and cross-correlation properties. In our proposed method, bits are selected by sampling a small set of candidate bits and choosing the one that offers the best improvement in performance. The method leverages the fact that incremental impact of a bit flip on the auto- and cross-correlation may be efficiently computed without recalculating the entire function. We apply this method to two code design problems modeled after the GPS L1 C/A and Galileo E1 codes, demonstrating rapid convergence to low-correlation codes. The proposed approach offers a powerful tool for developing spreading codes that meet the demanding requirements of modern and future satellite navigation systems.

The ability to efficiently characterize the spatial correlations of entangled states of light is critical for applications of many quantum technologies such as quantum imaging. Here, we demonstrate highly efficient theoretical and experimental characterization of the spatial Schmidt modes and the Schmidt spectrum of bright multiphoton entangled states of light produced from high-gain parametric down-conversion. In contrast to previous studies, we exploit the approximate quasihomogeneity and isotropy of the signal field and dramatically reduce the numerical computations involved in the experimental and theoretical characterization procedures. In our particular case where our experimental data sets consist of 5000 single-shot images of 256*256 pixels each, our method reduced the overall computation time by 2 orders of magnitude. This speed-up would be even more dramatic for larger input sizes. Consequently, we are able to rapidly characterize the Schmidt modes and Schmidt spectrum for a range of pump amplitudes and study their variation with increasing gain. Our results clearly reveal the broadening of the Schmidt modes and narrowing of the Schmidt spectrum for increasing gain with good agreement between theory and experiment., Comment: 8 pages, 4 figures

A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

Automating the creation of scientific diagrams from academic papers can significantly streamline the development of tutorials, presentations, and posters, thereby saving time and accelerating the process. Current text-to-image models struggle with generating accurate and visually appealing diagrams from long-context inputs. We propose SciDoc2Diagram, a task that extracts relevant information from scientific papers and generates diagrams, along with a benchmarking dataset, SciDoc2DiagramBench. We develop a multi-step pipeline SciDoc2Diagrammer that generates diagrams based on user intentions using intermediate code generation. We observed that initial diagram drafts were often incomplete or unfaithful to the source, leading us to develop SciDoc2Diagrammer-Multi-Aspect-Feedback (MAF), a refinement strategy that significantly enhances factual correctness and visual appeal and outperforms existing models on both automatic and human judgement., Comment: Code and data available at https://github.com/Ishani-Mondal/SciDoc2DiagramGeneration

We explored the efficacy of lab-grown diamonds as potential target materials for the direct detection of sub-GeV dark matter~(DM) using metallic magnetic calorimeters~(MMCs). Diamond, with its excellent phononic properties and the low atomic mass of the constituent carbon, can play a crucial role in detecting low-mass dark matter particles. The relatively long electron-hole pair lifetime inside the crystal may provide discrimination power between the DM-induced nuclear recoil events and the background-induced electron recoil events. Utilizing the the fast response times of the MMCs and their unique geometric versatility, we deployed a novel methodology for quantifying phonon dynamics inside diamond crystals. We demonstrated that lab-grown diamond crystals fabricated via the chemical vapor deposition~(CVD) technique can satisfy the stringent quality requirements for sub-GeV dark matter searches. The high-quality polycrystalline CVD diamond showed a superior athermal phonon collection efficiency compared to that of the reference sapphire crystal, and achieved energy resolution 62.7~eV at the 8.05~keV copper fluorescence line. With this energy resolution, we explored the low-energy range below 100~eV and confirmed the existence of so-called low-energy excess~(LEE) reported by multiple cryogenic experiments.

Efficiently estimating system dynamics from data is essential for minimizing data collection costs and improving model performance. This work addresses the challenge of designing future control inputs to maximize information gain, thereby improving the efficiency of the system identification process. We propose an approach that integrates informative input design into the Dynamic Mode Decomposition with control (DMDc) framework, which is well-suited for high-dimensional systems. By formulating an approximate convex optimization problem that minimizes the trace of the estimation error covariance matrix, we are able to efficiently reduce uncertainty in the model parameters while respecting constraints on the system states and control inputs. This method outperforms traditional techniques like Pseudo-Random Binary Sequences (PRBS) and orthogonal multisines, which do not adapt to the current system model and often gather redundant information. We validate our approach using aircraft and fluid dynamics simulations to demonstrate the practical applicability and effectiveness of our method. Our results show that strategically planning control inputs based on the current model enhances the accuracy of system identification while requiring less data. Furthermore, we provide our implementation and simulation interfaces as an open-source software package, facilitating further research development and use by industry practitioners.

We examine a special case of the multilevel factor model, with covariance given by multilevel low rank (MLR) matrix~\cite{parshakova2023factor}. We develop a novel, fast implementation of the expectation-maximization (EM) algorithm, tailored for multilevel factor models, to maximize the likelihood of the observed data. This method accommodates any hierarchical structure and maintains linear time and storage complexities per iteration. This is achieved through a new efficient technique for computing the inverse of the positive definite MLR matrix. We show that the inverse of an invertible PSD MLR matrix is also an MLR matrix with the same sparsity in factors, and we use the recursive Sherman-Morrison-Woodbury matrix identity to obtain the factors of the inverse. Additionally, we present an algorithm that computes the Cholesky factorization of an expanded matrix with linear time and space complexities, yielding the covariance matrix as its Schur complement. This paper is accompanied by an open-source package that implements the proposed methods.

We report analysis of photometry and spectroscopy of a deep Algol-like minimum of the pre-main-sequence star WW Vul in July and August 2016. This revealed substantial reddening due to absorption by circumstellar material. After dereddening, our spectra of WW Vul were consistent with spectral type A3V throughout the event. H{\alpha} is normally in emission in WW Vul. During the minimum, H{\alpha} emission dropped by ~30% and FWHM of the H{\alpha} line reduced by ~15%., Comment: 6 pages, 8 figures, accepted for publication in Journal of the AAVSO

We explore the important task of applying a phase $exp(i f(x))$ to a computational basis state $\left| x \right>$. The closely related task of rotating a target qubit by an angle depending on $f(x)$ is also studied. Such operations are key in many quantum subroutines, and often the function $f$ can be well-approximated by a piecewise linear composition. Examples range from the application of diagonal Hamiltonian terms (such as the Coulomb interaction) in grid-based many-body simulation, to derivative pricing algorithms. Here we exploit a parallelisation of the piecewise approach so that all constituent elementary rotations are performed simultaneously, that is, we achieve a total rotation depth of one. Moreover, we explore the use of recursive catalyst 'towers' to implement these elementary rotations efficiently. Depending on the choice of implementation strategy, we find a depth as low as $O(log n + log S)$ for a register of $n$ qubits and a piecewise approximation of $S$ sections. In the limit of multiple repetitions of the oracle, we find that catalyst tower approaches have an $O(S \cdot n)$ T-count, whereas linear interpolation with QROM has an $O(n^{log_2(3)})$ T-count., Comment: 14 pages, references added, table I updated, code for table I added

Sub-picosecond timing synchronization can enable future optical timekeeping networks, including coherent phased array radar imaging at GHz levels, intercontinental clock comparisons for the redefinition of the second, chronometric leveling, and synchronization of remote assets, including future satellite-based optical time standards. With optical clocks now operating on mobile platforms, free-space synchronization networks with compatible performance and the ability to operate under platform motion are essential to expand the reach of precision timing. Recently, femtosecond (fs)-level optical time-transfer techniques have been developed that can operate over hundreds of kilometers despite atmospheric turbulence, signal fade, and dropouts. Here we report a two-tone optical time transfer scheme with comparable performance that reduces hardware requirements and can support both fiber and free-space networks. Using this technique, sub-fs synchronization was demonstrated over a $\sim$100 m free-space link for several hours. In addition, the link was used to syntonize two iodine optical clocks and then compare them over four days. The set-up employs an integrated photonics transceiver and telecom-band lasers that are compatible with full photonic integration.

Coherent detection can provide enhanced receiver sensitivity and spectral efficiency in free-space optical (FSO) communications. However, turbulence can cause modal power coupling effects on a Gaussian data beam and significantly degrade the mixing efficiency between the data beam and a Gaussian local oscillator (LO) in the coherent detector. Optical phase conjugation (OPC) in a photorefractive crystal can "automatically" mitigate turbulence by: (a) recording a back-propagated turbulence-distorted probe beam, and (b) creating a phase-conjugate beam that has the inverse phase distortion of the medium as the transmitted data beam. However, previously reported crystal-based OPC approaches for FSO links have demonstrated either: (i) a relatively fast response time of 35 ms but at a relatively low data rate (e.g., <1 Mbit/s), or (ii) a relatively high data rate of 2-Gbit/s but at a slow response time (e.g., >60 s). Here, we report an OPC approach for the automatic mitigation of dynamic turbulence that enables both a high data rate (8 Gbit/s) data beam and a rapid (<5 ms) response time. For a similar data rate, this represents a 10,000-fold faster response time than previous reports, thereby enabling mitigation for dynamic effects. In our approach, the transmitted pre-distorted phase-conjugate data beam is generated by four-wave mixing in a GaAs crystal of three input beams: a turbulence-distorted probe beam, a Gaussian reference beam regenerated from the probe beam, and a Gaussian data beam carrying a high-speed data channel. We experimentally demonstrate our approach in an 8-Gbit/s quadrature-phase-shift-keying coherent FSO link through emulated dynamic turbulence. Our results show ~10-dB improvement in the mixing efficiency of the LO with the data beam under dynamic turbulence with a bandwidth of up to ~260 Hz (Greenwood frequency).

Hot and warm Jupiters might have undergone the same formation and evolution path, but the two populations exhibit different distributions of orbital parameters, challenging our understanding on their actual origin. The present work, which is the results of our warm Jupiters survey carried out with the CHIRON spectrograph within the KESPRINT collaboration, aims to address this challenge by studying two planets that could help bridge the gap between the two populations. We report the confirmation and mass determination of a hot Jupiter (orbital period shorter than 10 days), TOI-2420\,b, and a warm Jupiter, TOI-2485\,b. We performed a joint analysis using a wide variety of spectral and photometric data in order to characterize these planetary systems. We found that TOI-2420\,b has an orbital period of P$_{\rm b}$=5.8 days, a mass of M$_{\rm b}$=0.9 M$_{\rm J}$ and a radius of R$_{\rm b}$=1.3 R$_{\rm J}$, with a planetary density of 0.477 \gc; while TOI-2485\,b has an orbital period of P$_{\rm b}$=11.2 days, a mass of M$_{\rm b}$=2.4 M$_{\rm J}$ and a radius of R$_{\rm b}$=1.1 R$_{\rm J}$ with density 2.36 \gc. With current parameters, the migration history for TOI-2420\,b and TOI-2485\,b is unclear: the high-eccentricity migration scenarios cannot be ruled out, and TOI-2485\,b's characteristics may rather support this scenario.

Type Ia supernovae (SNe Ia) are thermonuclear exploding stars that can be used to put constraints on the nature of our universe. One challenge with population analyses of SNe Ia is Malmquist bias, where we preferentially observe the brighter SNe due to limitations of our telescopes. If untreated, this bias can propagate through to our posteriors on cosmological parameters. In this paper, we develop a novel technique of using a normalising flow to learn the non-analytical likelihood of observing a SN Ia for a given survey from simulations, that is independent of any cosmological model. The learnt likelihood is then used in a hierarchical Bayesian model with Hamiltonian Monte Carlo sampling to put constraints on different sets of cosmological parameters conditioned on the observed data. We verify this technique on toy model simulations finding excellent agreement with analytically-derived posteriors to within $1 \sigma$., Comment: Accepted for the ICML AI for Science Workshop (2024). 13 pages, 5 figures, 2 tables

Effective policy and intervention strategies to combat human trafficking for child sexual exploitation material (CSEM) production require accurate prevalence estimates. Traditional Network Scale Up Method (NSUM) models often necessitate standalone surveys for each geographic region, escalating costs and complexity. This study introduces a partially pooled NSUM model, using a hierarchical Bayesian framework that efficiently aggregates and utilizes data across multiple regions without increasing sample sizes. We developed this model for a novel national survey dataset from the Philippines and we demonstrate its ability to produce detailed municipal-level prevalence estimates of trafficking for CSEM production. Our results not only underscore the model's precision in estimating hidden populations but also highlight its potential for broader application in other areas of social science and public health research, offering significant implications for resource allocation and intervention planning.

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

Fast, reprogrammable control over the transverse structure of light beams plays an essential role in applications such as structured illumination microscopy, optical trapping, and quantum information processing. Existing technologies, such as liquid crystal on silicon spatial light modulators (LCoS-SLMs) and digital micromirror devices (DMDs), suffer from limited refresh rates, low damage thresholds, and high insertion loss. Acousto-optic modulators (AOMs) can resolve the above issues, as they typically handle higher laser power and offer lower insertion loss. By effectively mapping the temporal radio-frequency (RF) waveforms onto the spatial diffraction patterns of the optical field, individual AOMs have been shown to generate one-dimensional (1D) spatial modes at a pixel refresh rate of nearly 20 MHz. We extend this concept to enable fast modulation in a two-dimensional (2D) space using a double-AOM scheme. We demonstrate the generation of 2D Hermite-Gaussian (HG_nm) modes with an average fidelity of 81%, while the highest-order mode generated, HG_53, retains a fidelity of 56%.

We report on spectroscopic and photometric observations of the S-type Mira V667 Cas and the carbon star Mira OR Cep recorded during one pulsation cycle of each star in 2022. Spectra are calibrated in absolute flux using concurrent photometry. We present measurements of V and Ic magnitudes and V-Ic colour index, classify spectra in the revised MK system and investigate how absolute flux in the H-alpha to H-delta emission lines varies with pulsation phase for each star., Comment: 7 pages, 8 figures

Influence propagation in social networks is a central problem in modern social network analysis, with important societal applications in politics and advertising. A large body of work has focused on cascading models, viral marketing, and finite-horizon diffusion. There is, however, a need for more developed, mathematically principled \emph{adversarial models}, in which multiple, opposed actors strategically select nodes whose influence will maximally sway the crowd to their point of view. In the present work, we develop and analyze such a model based on harmonic functions and linear diffusion. We prove that our general problem is NP-hard and that the objective function is monotone and submodular; consequently, we can greedily approximate the solution within a constant factor. Introducing and analyzing a convex relaxation, we show that the problem can be approximately solved using smooth optimization methods. We illustrate the effectiveness of our approach on a variety of example networks., Comment: 23 pages, 5 figures, comments welcome!!

This paper introduces a robust Pareto design approach for selecting Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs), particularly for power amplifier (PA) and low-noise amplifier (LNA) designs in 5G applications. We consider five key design variables and two settings (PAs and LNAs) where we have multiple objectives. We assess designs based on three critical objectives, evaluating each by its worst-case performance across a range of Gate-Source Voltages ($V_{\text{GS}}$). We conduct simulations across a range of $V_{\text{GS}}$ values to ensure a thorough and robust analysis. For PAs, the optimization goals are to maximize the worst-case modulated average output power ($P_{\text{out,avg}}$) and power-added efficiency ($PAE_{\text{avg}}$) while minimizing the worst-case average junction temperature ($T_{\text{j,avg}}$) under a modulated 64-QAM signal stimulus. In contrast, for LNAs, the focus is on maximizing the worst-case maximum oscillation frequency ($f_{\text{max}}$) and Gain, and minimizing the worst-case minimum noise figure ($NF_{\text{min}}$). We utilize a derivative-free optimization method to effectively identify robust Pareto optimal device designs. This approach enhances our comprehension of the trade-off space, facilitating more informed decision-making. Furthermore, this method is general across different applications. Although it does not guarantee a globally optimal design, we demonstrate its effectiveness in GaN device sizing. The primary advantage of this method is that it enables the attainment of near-optimal or even optimal designs with just a fraction of the simulations required for an exhaustive full-grid search.

In this paper, we address the problem of compact model parameter extraction to simultaneously extract tens of parameters via derivative-free optimization. Traditionally, parameter extraction is performed manually by dividing the complete set of parameters into smaller subsets, each targeting different operational regions of the device, a process that can take several days or weeks. Our approach streamlines this process by employing derivative-free optimization to identify a good parameter set that best fits the compact model without performing an exhaustive number of simulations. We further enhance the optimization process to address three critical issues in device modeling by carefully choosing a loss function that focuses on relative errors rather than absolute errors to ensure consistent performance across different orders of magnitude, prioritizes accuracy in key operational regions above a specific threshold, and reduces sensitivity to outliers. Furthermore, we utilize the concept of train-test split to assess the model fit and avoid overfitting. We demonstrate the effectiveness of our approach by successfully modeling a diamond Schottky diode with the SPICE diode model and a GaN-on-SiC HEMT with the ASM-HEMT model. For the latter, which involves extracting 35 parameters for the ASM-HEMT DC model, we identified the best set of parameters in under 6,000 trials. Additional examples using both devices are provided to demonstrate robustness to outliers, showing that an excellent fit is achieved even with over 25% of the data purposely corrupted. These examples demonstrate the practicality of our approach, highlighting the benefits of derivative-free optimization in device modeling.

Adversarial datasets should ensure AI robustness that matches human performance. However, as models evolve, datasets can become obsolete. Thus, adversarial datasets should be periodically updated based on their degradation in adversarialness. Given the lack of a standardized metric for measuring adversarialness, we propose AdvScore, a human-grounded evaluation metric. AdvScore assesses a dataset's true adversarialness by capturing models' and humans' varying abilities, while also identifying poor examples. AdvScore then motivates a new dataset creation pipeline for realistic and high-quality adversarial samples, enabling us to collect an adversarial question answering (QA) dataset, AdvQA. We apply AdvScore using 9,347 human responses and ten language model predictions to track the models' improvement over five years (from 2020 to 2024). AdvScore assesses whether adversarial datasets remain suitable for model evaluation, measures model improvements, and provides guidance for better alignment with human capabilities., Comment: arXiv admin note: text overlap with arXiv:2401.11185

Keyword mnemonics are memorable explanations that link new terms to simpler keywords. Prior work generates mnemonics for students, but they do not train models using mnemonics students prefer and aid learning. We build SMART, a mnemonic generator trained on feedback from real students learning new terms. To train SMART, we first fine-tune LLaMA-2 on a curated set of user-written mnemonics. We then use LLM alignment to enhance SMART: we deploy mnemonics generated by SMART in a flashcard app to find preferences on mnemonics students favor. We gather 2684 preferences from 45 students across two types: expressed (inferred from ratings) and observed (inferred from student learning), yielding three key findings. First, expressed and observed preferences disagree; what students think is helpful does not always capture what is truly helpful. Second, Bayesian models can synthesize complementary data from multiple preference types into a single effectiveness signal. SMART is tuned via Direct Preference Optimization on this signal, which resolves ties and missing labels in the typical method of pairwise comparisons, augmenting data for LLM output quality gains. Third, mnemonic experts assess SMART as matching GPT-4 at much lower deployment costs, showing the utility of capturing diverse student feedback to align LLMs in education., Comment: EMNLP 2024

We introduce a post-processing technique for classical shadow measurement data that enhances the precision of ground state estimation through high-dimensional subspace expansion; the dimensionality is only limited by the amount of classical post-processing resources rather than by quantum resources. Crucial steps of our approach are the efficient identification of useful observables from shadow data, followed by our regularised subspace expansion that is designed to be numerically stable even when using noisy data. We analytically investigate noise propagation within our method, and upper bound the statistical fluctuations due to the limited number of snapshots in classical shadows. In numerical simulations, our method can achieve a reduction in the energy estimation errors in many cases, sometimes by more than an order of magnitude. We also demonstrate that our performance improvements are robust against both coherent errors (bad initial state) and gate noise in the state-preparation circuits. Furthermore, performance is guaranteed to be at least as good - and in many cases better - than direct energy estimation without using additional quantum resources and the approach is thus a very natural alternative for estimating ground state energies directly from classical shadow data., Comment: 13 pages, 6 figures

Large vision-language models (LVLMs) are prone to hallucinations, where certain contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects. While some benchmarks have been developed to investigate LVLM hallucinations, they often rely on hand-crafted corner cases whose failure patterns may not generalize well. Additionally, fine-tuning on these examples could undermine their validity. To address this, we aim to scale up the number of cases through an automated approach, reducing human bias in crafting such corner cases. This motivates the development of AutoHallusion, the first automated benchmark generation approach that employs several key strategies to create a diverse range of hallucination examples. Our generated visual-question pairs pose significant challenges to LVLMs, requiring them to overcome contextual biases and distractions to arrive at correct answers. AutoHallusion enables us to create new benchmarks at the minimum cost and thus overcomes the fragility of hand-crafted benchmarks. It also reveals common failure patterns and reasons, providing key insights to detect, avoid, or control hallucinations. Comprehensive evaluations of top-tier LVLMs, e.g., GPT-4V(ision), Gemini Pro Vision, Claude 3, and LLaVA-1.5, show a 97.7% and 98.7% success rate of hallucination induction on synthetic and real-world datasets of AutoHallusion, paving the way for a long battle against hallucinations. The codebase and data can be accessed at https://github.com/wuxiyang1996/AutoHallusion.

Decay energy spectrometry (DES) is a novel radiometric technique for high-precision analysis of nuclear materials. DES employs the unique thermal detection physics of cryogenic microcalorimeters with ultra-high energy resolution and 100$\%$ detection efficiency to accomplish high precision decay energy measurements. Low-activity nuclear samples of 1 Bq or less, and without chemical separation, are used to provide elemental and isotopic compositions in a single measurement. Isotopic ratio precisions of 1 ppm - 1,000 ppm (isotope dependent), which is close to that of the mass spectrometry, have been demonstrated in 12-hour DES measurements of ~5 Bq samples of certified reference materials of uranium (U) and plutonium (Pu). DES has very different systematic biases and uncertainties, as well as different sensitivities to nuclides, compared to mass-spectrometry techniques. Therefore, the accuracy and confidence of nuclear material assays can be improved by combining this new technique with existing mass-spectrometry techniques. Commercial-level DES techniques and equipment are being developed for the implementation of DES at the Nuclear Material Laboratory (NML) of International Atomic Energy Agency (IAEA) to provide complementary measurements to the existing technologies. The paper describes details of DES measurement methods, as well as DES precision and accuracy to U and Pu standard sources to discuss its capability in analysis of nuclear safeguards samples., Comment: This was submitted to 2022 IAEA symposium on nuclear safeguards (https://www.iaea.org/events/sg-2022), and posted at https://media.superevent.com/documents/20221027/668fdac0ee8d895ec6bcf293b1c42e6a/id-145.pdf