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Mixup is a data augmentation strategy that employs convex combinations of training instances and their respective labels to augment the robustness and calibration of deep neural networks. Despite its widespread adoption, the nuanced mechanisms that underpin its success are not entirely understood. The observed phenomenon of Neural Collapse, where the last-layer activations and classifier of deep networks converge to a simplex equiangular tight frame (ETF), provides a compelling motivation to explore whether mixup induces alternative geometric configurations and whether those could explain its success. In this study, we delve into the last-layer activations of training data for deep networks subjected to mixup, aiming to uncover insights into its operational efficacy. Our investigation, spanning various architectures and dataset pairs, reveals that mixup's last-layer activations predominantly converge to a distinctive configuration different than one might expect. In this configuration, activations from mixed-up examples of identical classes align with the classifier, while those from different classes delineate channels along the decision boundary. Moreover, activations in earlier layers exhibit patterns, as if trained with manifold mixup. These findings are unexpected, as mixed-up features are not simple convex combinations of feature class means (as one might get, for example, by training mixup with the mean squared error loss). By analyzing this distinctive geometric configuration, we elucidate the mechanisms by which mixup enhances model calibration. To further validate our empirical observations, we conduct a theoretical analysis under the assumption of an unconstrained features model, utilizing the mixup loss. Through this, we characterize and derive the optimal last-layer features under the assumption that the classifier forms a simplex ETF., Comment: Published as a conference paper at the International Conference on Learning Representations (ICLR 2024)

We analyze and compare the satellite halo populations at $z\sim2$ in the high-resolution cosmological zoom-in simulations of a $10^{12}\,{\rm M}_{\odot}$ target halo ($z=0$ mass) carried out on eight widely-used astrophysical simulation codes ({\sc Art-I}, {\sc Enzo}, {\sc Ramses}, {\sc Changa}, {\sc Gadget-3}, {\sc Gear}, {\sc Arepo-t}, and {\sc Gizmo}) for the {\it AGORA} High-resolution Galaxy Simulations Comparison Project. We use slightly different redshift epochs near $z=2$ for each code (hereafter ``$z\sim2$') at which the eight simulations are in the same stage in the target halo's merger history. After identifying the matched pairs of halos between the {\it CosmoRun} simulations and the DMO simulations, we discover that each {\it CosmoRun} halo tends to be less massive than its DMO counterpart. When we consider only the halos containing stellar particles at $z\sim2$, the number of satellite {\it galaxies} is significantly fewer than that of dark matter halos in all participating {\it AGORA} simulations, and is comparable to the number of present-day satellites near the Milky Way or M31. The so-called ``missing satellite problem' is fully resolved across all participating codes simply by implementing the common baryonic physics adopted in {\it AGORA} and the stellar feedback prescription commonly used in each code, with sufficient numerical resolution ($\lesssim100$ proper pc at $z=2$). We also compare other properties such as the stellar mass$-$halo mass relation and the mass$-$metallicity relation. Our work highlights the value of comparison studies such as {\it AGORA}, where outstanding problems in galaxy formation theory are studied simultaneously on multiple numerical platforms., Comment: Accepted for publication to the ApJ, 19 pages, 10 figures. Visit the AGORA Collaboration website (www.agorasimulations.org) for more information. For summary video, please see https://www.youtube.com/watch?v=HShRJYvPe1k

We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results of these simulations in terms of the structure, composition, and phase dynamics of the CGM. We show properties such as metal distribution, ionization levels, and kinematics are effective tracers of the effects of the different code feedback and implementation methods, and as such they can be highly divergent between simulations. This is merely a fiducial set of models, against which we will in the future compare multiple feedback recipes for each code. Nevertheless, we find that the large parameter space these simulations establish can help disentangle the different variables that affect observable quantities in the CGM, e.g., showing that abundances for ions with higher ionization energy are more strongly determined by the simulation's metallicity, while abundances for ions with lower ionization energy are more strongly determined by the gas density and temperature., Comment: 32 pages, 17 figures. Published in ApJ

The cold ($\sim 10^{4}\,{\rm K}$) component of the circumgalactic medium (CGM) accounts for a significant fraction of all galactic baryons. However, using current galaxy-scale simulations to determine the origin and evolution of cold CGM gas poses a significant challenge, since it is computationally infeasible to directly simulate a galactic halo alongside the sub-pc scales that are crucial for understanding the interactions between cold CGM gas and the surrounding ''hot'' medium. In this work, we introduce a new approach: the Cold Gas Subgrid Model (CGSM), which models unresolved cold gas as a second fluid in addition to the standard ''normal'' gas fluid. The CGSM tracks the total mass density and bulk momentum of unresolved cold gas, deriving the properties of its unresolved cloudlets from the resolved gas phase. The interactions between the subgrid cold fluid and the resolved fluid are modeled by prescriptions from high-resolution simulations of ''cloud crushing'' and thermal instability. Through a series of idealized tests, we demonstrate the CGSM's ability to overcome the resolution limitations of traditional hydrodynamics simulations, successfully capturing the correct cold gas mass, its spatial distribution, and the timescales for cloud destruction and growth. We discuss the implications of using this model in cosmological simulations to more accurately represent the microphysics that govern the galactic baryon cycle., Comment: Submitted to MNRAS

The Hunting Outbursting Young Stars (HOYS) project performs long-term, optical, multi-filter, high cadence monitoring of 25 nearby young clusters and star forming regions. Utilising Gaia DR3 data we have identified about 17000 potential young stellar members in 45 coherent astrometric groups in these fields. Twenty one of them are clear young groups or clusters of stars within one kiloparsec and they contain 9143 Gaia selected potential members. The cluster distances, proper motions and membership numbers are determined. We analyse long term (about 7yr) V, R, and I-band light curves from HOYS for 1687 of the potential cluster members. One quarter of the stars are variable in all three optical filters, and two thirds of these have light curves that are symmetric around the mean. Light curves affected by obscuration from circumstellar materials are more common than those affected by accretion bursts, by a factor of 2-4. The variability fraction in the clusters ranges from 10 to almost 100 percent, and correlates positively with the fraction of stars with detectable inner disks, indicating that a lot of variability is driven by the disk. About one in six variables shows detectable periodicity, mostly caused by magnetic spots. Two thirds of the periodic variables with disk excess emission are slow rotators, and amongst the stars without disk excess two thirds are fast rotators - in agreement with rotation being slowed down by the presence of a disk., Comment: accepted for publication in MNRAS, 1 table, 9 figures

The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. Optimal QAOA parameter concentration effects for special MaxCut problem instances have been observed, but a rigorous study of the subject is still lacking. Due to clustering of optimal QAOA parameters for MaxCut, successful parameter transferability between different MaxCut instances can be explained and predicted based on local properties of the graphs, including the type of subgraphs (lightcones) from which graphs are composed as well as the overall degree of nodes in the graph (parity). In this work, we apply five different graph embedding techniques to determine good donor candidates for parameter transferability, including parameter transferability between different classes of MaxCut instances. Using this technique, we effectively reduce the number of iterations required for parameter optimization, obtaining an approximate solution to the target problem with an order of magnitude speedup. This procedure also effectively removes the problem of encountering barren plateaus during the variational optimization of parameters. Additionally, our findings demonstrate that the transferred parameters maintain effectiveness when subjected to noise, supporting their use in real-world quantum applications. This work presents a framework for identifying classes of combinatorial optimization instances for which optimal donor candidates can be predicted such that QAOA can be substantially accelerated under both ideal and noisy conditions., Comment: 21 pages, 10 figures, 1 table

We use high-resolution cosmological simulations to compare the effect of bursty star formation histories on dwarf galaxy structure for two different subgrid supernovae (SNe) feedback models in dwarf galaxies with stellar masses from $5000 <$ M$_*$/M$_\odot$ $< 10^{9}$. Our simulations are run using two distinct supernova feedback models: superbubble and blastwave. We show that both models are capable of producing galaxies that are cored and reproduce observed scaling relations for metallicity, luminosity, mass, and size. We show that continuous bursty star formation and the resulting stellar feedback are able to sustain dark matter cores in the higher dwarf galaxy mass regime, while the majority of ultra-faint and classical dwarfs retain cuspy central dark matter density profiles. We find that both subgrid SN models are able to create bursty star formation histories. We find that effective core formation peaks at M$_*$/M$_\odot$ $\simeq 5 \times 10^{-3}$ for both feedback models. Galaxies simulated with superbubble feedback peak at lower mean burstiness values relative to blastwave feedback, indicating that core formation in the superbubble sample may be less motivated by the burstiness of star formation., Comment: 18 pages, 7 figures

Charging a Li ion battery requires Li ion transport between the cathode and the anode. This Li ion transport is dependent upon (among other factors) the electrostatic environment the ion encounters within the Solid Electrolyte Interphase (SEI), which separates the anode from the surrounding electrolyte. Previous first principles work has illuminated the reaction barriers through likely atomistic SEI environments, but has had difficulty accurately reflecting the larger electrostatic potential landscape that an ion encounters moving through the SEI. In this work, we apply the recently developed Quantum Continuum Approximation (QCA) technique to provide an equilibrium electronic potentiostat for first-principles interface calculations. Using QCA, we calculate the potential barrier for Li ion transport through LiF, Li$_2$O, and Li$_2$CO$_3$ SEIs along with LiF-LiF, and LiF-Li$_2$O grain boundaries, all paired with Li metal anodes. We demonstrate that the SEI potential barrier is dependent on the anode electrochemical potentials in each system. Finally, we use these techniques to estimate the change in the diffusion barrier for a Li ion moving in a LiF SEI as a function of anode potential. We find that properly accounting for interface and electronic voltage effects significantly lowers reaction barriers compared to previous literature results., Comment: 30 pages, 9 figures

Hot Jupiters were many of the first exoplanets discovered in the 1990s, but in the decades since their discovery, the mysteries surrounding their origins remain. Here, we present nine new hot Jupiters (TOI-1855 b, TOI-2107 b, TOI-2368 b, TOI-3321 b, TOI-3894 b, TOI-3919 b, TOI-4153 b, TOI-5232 b, and TOI-5301 b) discovered by NASA's TESS mission and confirmed using ground-based imaging and spectroscopy. These discoveries are the first in a series of papers named the Migration and Evolution of giant ExoPlanets (MEEP) survey and are part of an ongoing effort to build a complete sample of hot Jupiters orbiting FGK stars, with a limiting Gaia $G$-band magnitude of 12.5. This effort aims to use homogeneous detection and analysis techniques to generate a set of precisely measured stellar and planetary properties that is ripe for statistical analysis. The nine planets presented in this work occupy a range of masses (0.55 Jupiter masses (M$_{\rm{J}}$) $<$ M$_{\rm{P}}$ $<$ 3.88 M$_{\rm{J}}$) and sizes (0.967 Jupiter radii (R$_{\rm{J}}$) $<$ R$_{\rm{P}}$ $<$ 1.438 R$_{\rm{J}}$) and orbit stars that range in temperature from 5360 K $<$ Teff $<$ 6860 K with Gaia $G$-band magnitudes ranging from 11.1 to 12.7. Two of the planets in our sample have detectable orbital eccentricity: TOI-3919 b ($e = 0.259^{+0.033}_{-0.036}$) and TOI-5301 b ($e = 0.33^{+0.11}_{-0.10}$). These eccentric planets join a growing sample of eccentric hot Jupiters that are consistent with high-eccentricity tidal migration, one of the three most prominent theories explaining hot Jupiter formation and evolution., Comment: 35 pages, 7 tables, and 14 figures. Submitted to AAS Journals on 2023 Dec 28

Young terrestrial worlds are critical test beds to constrain prevailing theories of planetary formation and evolution. We present the discovery of HD 63433d - a nearby (22pc), Earth-sized planet transiting a young sunlike star (TOI-1726, HD 63433). HD 63433d is the third planet detected in this multiplanet system. The kinematic, rotational, and abundance properties of the host star indicate that it belongs to the young (414 $\pm$ 23 Myr) Ursa Major moving group, whose membership we update using new data from Gaia DR3 and TESS. Our transit analysis of the TESS light curves indicates that HD 63433 d has a radius of 1.1 $R_\oplus$ and closely orbits its host star with a period of 4.2 days. To date, HD 63433 d is the smallest confirmed exoplanet with an age less than 500 Myr, and the nearest young Earth-sized planet. Furthermore, the apparent brightness of the stellar host (V $\approx$ 6.9 mag) makes this transiting multiplanet system favorable to further investigations, including spectroscopic follow-up to probe atmospheric loss in a young Earth-sized world., Comment: 24 pages, 7 figures, 6 tables. Accepted for publication in AJ. The first two authors contributed equally to the manuscript

We use the VERITAS imaging air Cherenkov Telescope (IACT) array to obtain the first measured angular diameter of $\beta$ UMa at visual wavelengths using stellar intensity interferometry (SII) and independently constrain the limb-darkened angular diameter. The age of the Ursa Major moving group has been assessed from the ages of its members, including nuclear member Merak ($\beta$ UMa), an A1-type subgiant, by comparing effective temperature and luminosity constraints to model stellar evolution tracks. Previous interferometric limb-darkened angular-diameter measurements of $\beta$ UMa in the near-infrared (CHARA Array, $1.149 \pm 0.014$ mas) and mid-infrared (Keck Nuller, $1.08 \pm 0.07$ mas), together with the measured parallax and bolometric flux, have constrained the effective temperature. This paper presents current VERITAS-SII observation and analysis procedures to derive squared visibilities from correlation functions. We fit the resulting squared visibilities to find a limb-darkened angular diameter of $1.07 \pm 0.04 {\rm (stat)} \pm 0.05$ (sys) mas, using synthetic visibilities from a stellar atmosphere model that provides a good match to the spectrum of $\beta$ UMa in the optical wave band. The VERITAS-SII limb-darkened angular diameter yields an effective temperature of $9700\pm200\pm 200$ K, consistent with ultraviolet spectrophotometry, and an age of $390\pm 29 \pm 32 $ Myr, using MESA Isochrones and Stellar Tracks (MIST). This age is consistent with $408 \pm 6$ Myr from the CHARA Array angular diameter.

Defending from cyberattacks requires practitioners to operate on high-level adversary behavior. Cyberthreat intelligence (CTI) reports on past cyberattack incidents describe the chain of malicious actions with respect to time. To avoid repeating cyberattack incidents, practitioners must proactively identify and defend against recurring chain of actions - which we refer to as temporal attack patterns. Automatically mining the patterns among actions provides structured and actionable information on the adversary behavior of past cyberattacks. The goal of this paper is to aid security practitioners in prioritizing and proactive defense against cyberattacks by mining temporal attack patterns from cyberthreat intelligence reports. To this end, we propose ChronoCTI, an automated pipeline for mining temporal attack patterns from cyberthreat intelligence (CTI) reports of past cyberattacks. To construct ChronoCTI, we build the ground truth dataset of temporal attack patterns and apply state-of-the-art large language models, natural language processing, and machine learning techniques. We apply ChronoCTI on a set of 713 CTI reports, where we identify 124 temporal attack patterns - which we categorize into nine pattern categories. We identify that the most prevalent pattern category is to trick victim users into executing malicious code to initiate the attack, followed by bypassing the anti-malware system in the victim network. Based on the observed patterns, we advocate organizations to train users about cybersecurity best practices, introduce immutable operating systems with limited functionalities, and enforce multi-user authentications. Moreover, we advocate practitioners to leverage the automated mining capability of ChronoCTI and design countermeasures against the recurring attack patterns., Comment: A modified version of this pre-print is submitted to IEEE Transactions on Software Engineering, and is under review

This note and agenda serve as a cause for thought for scholars interested in researching Decentralized Autonomous Organizations (DAOs), addressing both the opportunities and challenges posed by this phenomenon. It covers key aspects of data retrieval, data selection criteria, issues in data reliability and validity such as governance token pricing complexities, discrepancy in treasuries, Mainnet and Testnet data, understanding the variety of DAO types and proposal categories, airdrops affecting governance, and the Sybil problem. The agenda aims to equip scholars with the essential knowledge required to conduct nuanced and rigorous academic studies on DAOs by illuminating these various aspects and proposing directions for future research.

This paper concerns a boundary integral formulation for the two-dimensional massive Dirac equation. The mass term is assumed to jump across a one-dimensional interface, which models a transition between two insulating materials. This jump induces surface waves that propagate outward along the interface but decay exponentially in the transverse direction. After providing a derivation of our integral equation, we prove that it has a unique solution for almost all choices of parameters using holomorphic perturbation theory. We then extend these results to a Dirac equation with two interfaces. Finally, we implement a fast numerical method for solving our boundary integral equations and present several numerical examples of solutions and scattering effects.

We present a novel four-channel OPM sensor for magnetoencephalography that utilizes a two-color pump/probe scheme on a single optical axis. We characterize its performance across 18 built sensor modules. The new sensor implements several improvements over our previously developed sensor including lower vapor-cell operating temperature, improved probe-light detection optics, and reduced optical power requirements. The sensor also has new electromagnetic field coils on the sensor head which are designed using stream-function-based current optimization. We detail the coil design methodology and present experimental characterization of the coil performance. The magnetic sensitivity of the sensor is on average 12.3 fT/rt-Hz across the 18 modules while the average gradiometrically inferred sensitivity is about 6.0 fT/rt-Hz. The sensor 3-dB bandwidth is 100 Hz on average. The on-sensor coil performance is in good agreement with the simulations.

We experimentally implement circuits of one and two mode operations on two motional modes of a single trapped ion. This is achieved by implementing the required displacement, squeezing, two-mode squeezing, and beamsplitter operations using oscillating electric potentials applied to the trap electrodes. The resulting electric fields drive the modes resonantly or parametrically without the need for optical forces. As a demonstration, we implement SU(2) and SU(1,1) interferometers with phase sensitivities near the Cram\'er-Rao bound. We report a maximum sensitivity of a SU(2) interferometer within $0.67(5)\,$dB of the standard quantum limit (SQL) as well as a single and two-mode SU(1,1) sensitivity of $5.9(2)\,$dB and $4.5(2)\,$dB below the SQL respectively., Comment: 9 pages, 7 figures

Calculating dynamical spin correlations is essential for matching model magnetic exchange Hamiltonians to momentum-resolved spectroscopic measurements. A major numerical bottleneck is the diagonalization of the dynamical matrix, especially in systems with large magnetic unit cells, such as those with incommensurate magnetic structures or quenched disorder. In this paper, we demonstrate an efficient scheme based on the kernel polynomial method for calculating dynamical correlations of relevance to inelastic neutron scattering experiments. This method reduces the scaling of numerical cost from cubic to linear in the magnetic unit cell size., Comment: Submission to SciPost. 27 pages, 6 figures. Typos corrected, added additional minor comments to derivations

We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the selected set of $n$ arms at each time step $t\in [T]$ is observed. SGB adopts an optimized stochastic-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms. Unlike existing methods that explore the entire set of unselected base arms during each selection step, our SGB algorithm samples only an optimized proportion of unselected arms and selects actions from this subset. We prove that our algorithm achieves a $(1-1/e)$-regret bound of $\mathcal{O}(n^{\frac{1}{3}} k^{\frac{2}{3}} T^{\frac{2}{3}} \log(T)^{\frac{2}{3}})$ for monotone stochastic submodular rewards, which outperforms the state-of-the-art in terms of the cardinality constraint $k$. Furthermore, we empirically evaluate the performance of our algorithm in the context of online constrained social influence maximization. Our results demonstrate that our proposed approach consistently outperforms the other algorithms, increasing the performance gap as $k$ grows.

Compilation of papers presented by the VERITAS Collaboration at the 38th International Cosmic Ray Conference (ICRC), held July 26 through August 3, 2023 in Nagoya, Japan., Comment: html page. ICRC 2023, Nagoya, Japan

Heterogeneous mechanical dyskinesis during acute myocardial ischaemia is thought to contribute to arrhythmogenic alterations to cardiac electrophysiology. Various forms of mechano-electric coupling (MEC) mechanisms have been suggested to contribute to these changes, with two primary mechanisms being: (1) myofilament-dependent calcium release events, and (2) the activation of stretch-activated currents (SACs). In this computational investigation, we assessed the collective impact of these processes on mechanically-induced alternans that create an arrhythmogenic substrate during acute ischaemia. To appraise the potential involvement of MEC in ischaemia-induced arrhythmias, we developed a coupled model of ventricular myocyte electrophysiology and contraction including SACs and stretch-dependent calcium buffering and release. The model, reflecting observed electrophysiological changes during ischaemia, was exposed to a series of stretch protocols that replicated both physiological and pathological mechanical conditions. Pathologically realistic myofiber stretch variations revealed calcium sensitivity changes dependent on myofilament, leading to alterations in cytosolic calcium concentrations. Under calcium overload conditions, these changes resulted in electrical alternans. The study implies that strain impacts cellular electrophysiology through myofilament calcium release and SAC opening in ventricular mechano-electrical models, parameterised to available data. This supports experimental evidence suggesting that both calcium-driven instability via MEC and SAC-induced effects contribute to electrical alternans in acute ischaemia., Comment: 20 pages; 8 figures; original paper

The ability to modify and extend an operating system is an important feature for improving a system's security, reliability, and performance. The extended Berkeley Packet Filters (eBPF) ecosystem has emerged as the standard mechanism for extending the Linux kernel and has recently been ported to Windows. eBPF programs inject new logic into the kernel that the system will execute before or after existing logic. While the eBPF ecosystem provides a flexible mechanism for kernel extension, it is difficult for developers to write eBPF programs today. An eBPF developer must have deep knowledge of the internals of the operating system to determine where to place logic and cope with programming limitations on the control flow and data accesses of their eBPF program enforced by the eBPF verifier. This paper presents KEN, an alternative framework that alleviates the difficulty of writing an eBPF program by allowing Kernel Extensions to be written in Natural language. KEN uses recent advances in large language models (LLMs) to synthesize an eBPF program given a user's English language prompt. To ensure that LLM's output is semantically equivalent to the user's prompt, KEN employs a combination of LLM-empowered program comprehension, symbolic execution, and a series of feedback loops. KEN's key novelty is the combination of these techniques. In particular, the system uses symbolic execution in a novel structure that allows it to combine the results of program synthesis and program comprehension and build on the recent success that LLMs have shown for each of these tasks individually. To evaluate KEN, we developed a new corpus of natural language prompts for eBPF programs. We show that KEN produces correct eBPF programs on 80% which is an improvement of a factor of 2.67 compared to an LLM-empowered program synthesis baseline.

We simulate the dynamics of systems with $N$ = 1-20 qubits coupled to a cavity in order to assess their potential for quantum metrology of a parameter in the open systems limit. The qubits and the cavity are both allowed to have losses and the system is studied under various coupling strength regimes. The focus is primarily on the coupling between the qubits using the quantum Fisher information as the measured parameter. Some results on estimating the qubit-cavity detuning parameter are also presented. We investigate the scaling of the uncertainty in the estimate of the qubit-cavity coupling with the number of qubits and for different initial states of the qubits that act as the quantum probe. As initial probe states, we consider Dicke states with varying excitation numbers, the GHZ state, and separable X-polarized states. It is shown that in the strong coupling regime, i.e., when the coupling between the qubits and the cavity is greater than the decay parameters of both the qubits and the cavity, Dicke states with a large excitation number can achieve the Heisenberg limit, with the precision scaling improving as the excitation number increases. A particularly intriguing finding of our study is that in the weak coupling regime, as well as in situations where either the qubit or cavity decay parameters exceed the coupling, the separable $X$-polarized state is the best in terms of scaling and is even able to achieve the Heisenberg limit in these lossy regimes for the range of $N$ considered.

We present the kinematic anaylsis of $246$ stars within $4^\prime$ from the center of Orion Nebula Cluster (ONC), the closest massive star cluster with active star formation across the full mass range, which provides valuable insights in the the formation and evolution of star cluster on an individual-star basis. High-precision radial velocities and surface temperatures are retrieved from spectra acquired by the NIRSPEC instrument used with adaptive optics (NIRSPAO) on the Keck II 10-m telescope. A three-dimensional kinematic map is then constructed by combining with the proper motions previously measured by the Hubble Space Telescope (HST) ACS/WFPC2/WFC3IR and Keck II NIRC2. The measured root-mean-squared velocity dispersion is $2.26\pm0.08~\mathrm{km}\,\mathrm{s}^{-1}$, significantly higher than the virial equilibrium's requirement of $1.73~\mathrm{km}\,\mathrm{s}^{-1}$, suggesting that the ONC core is supervirial, consistent with previous findings. Energy equipartition is not detected in the cluster. Most notably, the velocity of each star relative to its neighbors is found to be negatively correlated with stellar mass. Low-mass stars moving faster than their surrounding stars in a supervirial cluster suggests that the initial masses of forming stars may be related to their initial kinematic states. Additionally, a clockwise rotation preference is detected. A weak sign of inverse mass segregation is also identified among stars excluding the Trapezium stars, though it could be a sample bias. Finally, this study reports the discovery of four new candidate spectroscopic binary systems., Comment: Published by ApJ; 28 pages, 20 figures, 3 tables

We report the discovery of TOI-4641b, a warm Jupiter transiting a rapidly rotating F-type star with a stellar effective temperature of 6560 K. The planet has a radius of 0.73 $R_{Jup}$, a mass smaller than 3.87 $M_{Jup}$ $(3\sigma)$, and a period of 22.09 days. It is orbiting a bright star (V=7.5 mag) on a circular orbit with a radius and mass of 1.73 $R_{\odot}$ and 1.41 $M_{\odot}$. Follow-up ground-based photometry was obtained using the Tierras Observatory. Two transits were also observed with the Tillinghast Reflector Echelle Spectrograph (TRES), revealing the star to have a low projected spin-orbit angle ($\lambda$=$1.41^{+0.76}_{-0.76}$ degrees). Such obliquity measurements for stars with warm Jupiters are relatively few, and may shed light on the formation of warm Jupiters. Among the known planets orbiting hot and rapidly-rotating stars, TOI-4641b is one of the longest-period planets to be thoroughly characterized. Unlike hot Jupiters around hot stars which are more often misaligned, the warm Jupiter TOI-4641b is found in a well-aligned orbit. Future exploration of this parameter space can add one more dimension to the star-planet orbital obliquity distribution that has been well-sampled for hot Jupiters., Comment: Accepted MNRAS

Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architecture is to protect the privacy of each client while still contributing to the training of the global model. However, the main advantage of privacy in federated learning is also the easiest aspect to exploit. Without being able to see the clients' data, it is difficult to determine the quality of the data. By utilizing data poisoning methods, such as backdoor or label-flipping attacks, or by sending manipulated information about their data back to the server, malicious clients are able to corrupt the global model and degrade performance across all clients within a federation. Our novel aggregation method, FedBayes, mitigates the effect of a malicious client by calculating the probabilities of a client's model weights given to the prior model's weights using Bayesian statistics. Our results show that this approach negates the effects of malicious clients and protects the overall federation., Comment: Accepted to IEEE CCWC 2024

Psychometric assessment instruments aid clinicians by providing methods of assessing the future risk of adverse events such as aggression. Existing machine learning approaches have treated this as a classification problem, predicting the probability of an adverse event in a fixed future time period from the scores produced by both psychometric instruments and clinical and demographic covariates. We instead propose modelling a patient's future risk using a time series methodology that learns from longitudinal data and produces a probabilistic binary forecast that indicates the presence of the adverse event in the next time period. Based on the recent success of Deep Neural Nets for globally forecasting across many time series, we introduce a global multivariate Recurrent Neural Network for Binary Outcome Forecasting, that trains from and for a population of patient time series to produce individual probabilistic risk assessments. We use a moving window training scheme on a real world dataset of 83 patients, where the main binary time series represents the presence of aggressive events and covariate time series represent clinical or demographic features and psychometric measures. On this dataset our approach was capable of a significant performance increase against both benchmark psychometric instruments and previously used machine learning methodologies.

We present new spatially resolved astrometry and photometry of the CD-27 11535 system, a member of the $\beta$ Pictoris moving group consisting of two resolved K-type stars on a $\sim$20-year orbit. We fit an orbit to relative astrometry measured from NIRC2, GPI, and archival NaCo images, in addition to literature measurements. However, the total mass inferred from this orbit is significantly discrepant from that inferred from stellar evolutionary models using the luminosity of the two stars. We explore two hypotheses that could explain this discrepant mass sum; a discrepant parallax measurement from Gaia due to variability, and the presence of an additional unresolved companion to one of the two components. We find that the $\sim$20-year orbit could not bias the parallax measurement, but that variability of the components could produce a large amplitude astrometric motion, an effect which cannot be quantified exactly without the individual Gaia measurements. The discrepancy could also be explained by an additional star in the system. We jointly fit the astrometric and photometric measurements of the system to test different binary and triple architectures for the system. Depending on the set of evolutionary models used, we find an improved goodness of fit for a triple system architecture that includes a low-mass ($M=0.177\pm0.055$\,$M_{\odot}$) companion to the primary star. Further studies of this system will be required in order to resolve this discrepancy, either by refining the parallax measurement with a more complex treatment of variability-induced astrometric motion, or by detecting a third companion., Comment: 12 pages + references and appendix, 12 figures, 5 tables

Over the past century, the United States workforce has undergone a massive structural shift. Technological change has moved the economy toward skilled labor and away from unskilled labor--a phenomenon known as skill-biased technical change. This structural shift has increased the relative demand for educated and skilled labor, leading to commensurate increases in the relative wages of skilled workers, and changes in the nature of work itself. The authors project that the United States will have 171 million jobs in 2031, compared to 155 million in 2021. This total is even more impressive when compared to the low of 138 million jobs during the COVID-19 pandemic. This report shows: (1) A breakdown of job projections by 13 major industries; and (2) A breakdown of job projections by nine major occupational clusters and 22 total occupational groups. All of the industry and occupational sections include projections for jobs through 2031 by needed level of educational attainment. This report also accounts for the increasing role of technology in American society, particularly in the world of work. The authors find that the nature of work has changed dramatically to incorporate technology not only as a complement to human labor but also as a substitute for tasks within jobs and sometimes even workers. This report includes a national overview of job projections and their educational requirements across industries, occupational clusters, and detailed occupational groups.