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Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. This study introduces a probabilistic framework to represent the highly variable nature of air-sea fluxes, which is missing in deterministic bulk algorithms. Assuming Gaussian distributions conditioned on the input variables, we use artificial neural networks and eddy-covariance measurement data to estimate the mean and variance by minimizing negative log-likelihood loss. The trained neural networks provide alternative mean flux estimates to existing bulk algorithms, and quantify the uncertainty around the mean estimates. Stochastic parameterization of air-sea turbulent fluxes can be constructed by sampling from the predicted distributions. Tests in a single-column forced upper-ocean model suggest that changes in flux algorithms influence sea surface temperature and mixed layer depth seasonally. The ensemble spread in stochastic runs is most pronounced during spring restratification.

We present the first brown dwarf spectral binary characterized with JWST: WISE J014656.66+423410.0, the coldest blended-light brown dwarf binary straddling the T/Y transition. We obtained a moderate resolution (R$\sim$2700) G395H spectrum of this unresolved binary with JWST/NIRSpec and we fit it to late-T and Y dwarf spectra from JWST/NIRSpec, and model spectra of comparable temperatures, both as individual spectra and pairs mimicking an unresolved binary system. We find that this tightly-separated binary is likely composed of two unequal-brightness sources with a magnitude difference of $0.50\pm0.08$ mag in IRAC [4.5] and a secondary $1.01\pm0.13$ mag redder than the primary in [3.6]-[4.5]. Despite the large color difference between the best fit primary and secondary, their temperature difference is only $92\pm23$\,K, a feature reminiscing of the L/T transition. Carbon disequilibrium chemistry strongly shapes the mid-infrared spectra of these sources, as a complex function of metallicity and surface gravity. While a larger library of JWST/NIRSpec spectra is needed to conclusively examine the peculiarities of blended-light sources, this spectral binary is a crucial pathfinder to both understand the spectral features of planetary-mass atmospheres and detect binarity in unresolved, moderate-resolution spectra of the coldest brown dwarfs., Comment: 21 pages, 7 figures

Artificial Intelligence (AI) weather prediction (AIWP) models often produce ``blurry'' precipitation forecasts that overestimate drizzle and underestimate extremes. This study provides a novel solution to tackle this problem -- integrating terrain-following coordinates with global mass and energy conservation schemes into AIWP models. Forecast experiments are conducted to evaluate the effectiveness of this solution using FuXi, an example AIWP model, adapted to 1.0$^\circ$ grid spacing data. Verification results show large performance gains. The conservation schemes are found to reduce drizzle bias, whereas using terrain-following coordinates improves the estimation of extreme events and precipitation intensity spectra. Furthermore, a case study reveals that terrain-following coordinates capture near-surface winds better over mountains, offering AIWP models more accurate information on understanding the dynamics of precipitation processes. The proposed solution of this study can benefit a wide range of AIWP models and bring insights into how atmospheric domain knowledge can support the development of AIWP models.

Machine learning has shown promise in reducing bias in numerical weather model predictions of wind gusts. Yet, they underperform to predict high gusts even with additional observations due to the right-skewed distribution of gusts. Uncertainty quantification (UQ) addresses this by identifying when predictions are reliable or needs cautious interpretation. Using data from 61 extratropical storms in the Northeastern USA, we introduce evidential neural network (ENN) as a novel approach for UQ in gust predictions, leveraging atmospheric variables from the Weather Research and Forecasting (WRF) model as features and gust observations as targets. Explainable artificial intelligence (XAI) techniques demonstrated that key predictive features also contributed to higher uncertainty. Estimated uncertainty correlated with storm intensity and spatial gust gradients. ENN allowed constructing gust prediction intervals without requiring an ensemble. From an operational perspective, providing gust forecasts with quantified uncertainty enhances stakeholders' confidence in risk assessment and response planning for extreme gust events., Comment: Main body 27 pages with 12 figures

Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities., Comment: For associated source code see https://github.com/adamtupper/ultrasound-augmentation

Artificial Intelligence (AI) weather prediction (AIWP) models are powerful tools for medium-range forecasts but often lack physical consistency, leading to outputs that violate conservation laws. This study introduces a set of novel physics-based schemes designed to enforce the conservation of global dry air mass, moisture budget, and total atmospheric energy in AIWP models. The schemes are highly modular, allowing for seamless integration into a wide range of AI model architectures. Forecast experiments are conducted to demonstrate the benefit of conservation schemes using FuXi, an example AIWP model, modified and adapted for 1.0-degree grid spacing. Verification results show that the conservation schemes can guide the model in producing forecasts that obey conservation laws. The forecast skills of upper-air and surface variables are also improved, with longer forecast lead times receiving larger benefits. Notably, large performance gains are found in the total precipitation forecasts, owing to the reduction of drizzle bias. The proposed conservation schemes establish a foundation for implementing other physics-based schemes in the future. They also provide a new way to integrate atmospheric domain knowledge into the design and refinement of AIWP models.

Adversarial perturbations aim to deceive neural networks into predicting inaccurate results. For visual object trackers, adversarial attacks have been developed to generate perturbations by manipulating the outputs. However, transformer trackers predict a specific bounding box instead of an object candidate list, which limits the applicability of many existing attack scenarios. To address this issue, we present a novel white-box approach to attack visual object trackers with transformer backbones using only one bounding box. From the tracker predicted bounding box, we generate a list of adversarial bounding boxes and compute the adversarial loss for those bounding boxes. Experimental results demonstrate that our simple yet effective attack outperforms existing attacks against several robust transformer trackers, including TransT-M, ROMTrack, and MixFormer, on popular benchmark tracking datasets such as GOT-10k, UAV123, and VOT2022STS., Comment: Accepted in The 3rd New Frontiers in Adversarial Machine Learning (AdvML Frontiers @NeurIPS2024)

AI workloads, particularly those driven by deep learning, are introducing novel usage patterns to high-performance computing (HPC) systems that are not comprehensively captured by standard HPC benchmarks. As one of the largest academic research centers dedicated to deep learning, Mila identified the need to develop a custom benchmarking suite to address the diverse requirements of its community, which consists of over 1,000 researchers. This report introduces Milabench, the resulting benchmarking suite. Its design was informed by an extensive literature review encompassing 867 papers, as well as surveys conducted with Mila researchers. This rigorous process led to the selection of 26 primary benchmarks tailored for procurement evaluations, alongside 16 optional benchmarks for in-depth analysis. We detail the design methodology, the structure of the benchmarking suite, and provide performance evaluations using GPUs from NVIDIA, AMD, and Intel. The Milabench suite is open source and can be accessed at github.com/mila-iqia/milabench.

Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast System (IFS), across several global metrics while requiring fewer computational resources. However, existing AI NWP models face limitations related to training datasets and timestep choices, often resulting in artifacts that reduce model performance. To address these challenges, we introduce the Community Research Earth Digital Intelligence Twin (CREDIT) framework, developed at NSF NCAR. CREDIT provides a flexible, scalable, and user-friendly platform for training and deploying AI-based atmospheric models on high-performance computing systems. It offers an end-to-end pipeline for data preprocessing, model training, and evaluation, democratizing access to advanced AI NWP capabilities. We demonstrate CREDIT's potential through WXFormer, a novel deterministic vision transformer designed to predict atmospheric states autoregressively, addressing common AI NWP issues like compounding error growth with techniques such as spectral normalization, padding, and multi-step training. Additionally, to illustrate CREDIT's flexibility and state-of-the-art model comparisons, we train the FUXI architecture within this framework. Our findings show that both FUXI and WXFormer, trained on six-hourly ERA5 hybrid sigma-pressure levels, generally outperform IFS HRES in 10-day forecasts, offering potential improvements in efficiency and forecast accuracy. CREDIT's modular design enables researchers to explore various models, datasets, and training configurations, fostering innovation within the scientific community.

We report the results of a spectroscopic survey of candidate T subdwarfs identified by the Backyard Worlds: Planet 9 program. Near-infrared spectra of 31 sources with red $J-W2$ colors and large $J$-band reduced proper motions show varying signatures of subsolar metallicity, including strong collision-induced H$_2$ absorption, obscured methane and water features, and weak K I absorption. These metallicity signatures are supported by spectral model fits and 3D velocities, indicating thick disk and halo population membership for several sources. We identify three new metal-poor T subdwarfs ([M/H] $\lesssim$ $-$0.5), CWISE J062316.19+071505.6, WISEA J152443.14$-$262001.8, and CWISE J211250.11-052925.2; and 19 new "mild" subdwarfs with modest metal deficiency ([M/H] $\lesssim$ $-$0.25). We also identify three metal-rich brown dwarfs with thick disk kinematics. We provide kinematic evidence that the extreme L subdwarf 2MASS J053253.46+824646.5 and the mild T subdwarf CWISE J113010.07+313944.7 may be part of the Thamnos population, while the T subdwarf CWISE J155349.96+693355.2 may be part of the Helmi stream. We define a metallicity classification system for T dwarfs that adds mild subdwarfs (d/sdT), subdwarfs (sdT), and extreme subdwarfs (esdT) to the existing dwarf sequence. We also define a metallicity spectral index that correlates with metallicities inferred from spectral model fits and iron abundances from stellar primaries of benchmark T dwarf companions. This expansion of the T dwarf classification system supports investigations of ancient, metal-poor brown dwarfs now being uncovered in deep imaging and spectroscopic surveys., Comment: 82 pages, 19 figures, accepted to ApJS

We present the discovery of 118 new ultracool dwarf candidates, discovered using a new machine learning tool, named \texttt{SMDET}, applied to time series images from the Wide-field Infrared Survey Explorer. We gathered photometric and astrometric data to estimate each candidate's spectral type, distance, and tangential velocity. This sample has a photometrically estimated spectral class distribution of 28 M dwarfs, 64 L dwarfs, and 18 T dwarfs. We also identify a T subdwarf candidate, two extreme T subdwarf candidates, and two candidate young ultracool dwarfs. Five objects did not have enough photometric data for any estimations to be made. To validate our estimated spectral types, spectra were collected for 2 objects, yielding confirmed spectral types of T5 (estimated T5) and T3 (estimated T4). Demonstrating the effectiveness of machine learning tools as a new large-scale discovery technique., Comment: 14 pages, 8 figures, 2 tables, extended table 1, accepted to Astronomical Journal

We present the Ultracool Dwarf Companion Catalogue of 278 multiple systems, 32 of which are newly discovered, each with at least one spectroscopically confirmed Ultracool Dwarf, within a 100 pc volume-limited sample. This catalogue is compiled using the Gaia Catalogue of Nearby Stars for stellar primaries and the Gaia Ultracool Dwarf Sample for low-mass companions and includes 241 doubles, 33 triples, and 4 higher-order systems established from positional, proper motion, and parallax constraints. The catalogue seeks to identify probable benchmark systems within 100 pc to obtain model-independent astrophysical parameters of Ultracool Dwarfs. Chance alignment probabilities are calculated to evaluate the physical nature of each system. Astrometric and photometric data from Gaia Data Release 3 and the Two Micron All Sky Survey are included for all objects. We identify potential unseen companions using a combination of the Renormalised Unit Weight Error, Image Parameter Determination statistics, Non-Single Star solutions, and photometric blending as provided by Gaia, identifying hierarchical Ultracool triple systems. Our catalogue includes 17 White Dwarf - Ultracool Dwarf systems, whose ages are determined using cooling models. We also use the Gaia FLAME results and the BANYAN $\Sigma$ procedures to age 40 and 34 systems respectively, and derive mass estimates from evolutionary models., Comment: 26 pages, 8 figures

The LHS 6343 system consists of a resolved M-dwarf binary with an evolved, negligibly irradiated brown dwarf, LHS 6343 C, orbiting the primary star. Such brown dwarf eclipsing binaries present rare and unique opportunities to calibrate sub-stellar evolutionary and atmosphere models since mass, radius, temperature and luminosity can be directly measured. We update this brown dwarf's mass (62.6+/-2.2 MJup) and radius (0.788+/-0.043 RJup) using empirical stellar relations and a Gaia DR3 distance. We use Hubble Space Telescope/WFC3 observations of an LHS 6343 C secondary eclipse to obtain a NIR emission spectrum, which matches to a spectral type of T1.5+/-1. We combine this spectrum with existing Kepler and Spitzer/IRAC secondary eclipse photometry to perform atmospheric characterization using the ATMO-2020, Sonora-Bobcat and BT-Settl model grids. ATMO-2020 models with strong non-equilibrium chemistry yield the best fit to observations across all modelled bandpasses while predicting physical parameters consistent with Gaia-dependant analogs. BT-Settl predicts values slightly more consistent with such analogs but offers a significantly poorer fit to the WFC3 spectrum. Finally, we obtain a semi-empirical measurement of LHS 6343 C's apparent luminosity by integrating its observed and modelled spectral energy distribution. Applying knowledge of the system's distance yields a bolometric luminosity of log(Lbol/Lsun) = -4.77+/-0.03 and, applying the Stefan-Boltzmann law for the known radius, an effective temperature of 1303+/-29 K. We also use the ATMO-2020 and Sonora-Bobcat evolutionary model grids to infer an age for LHS 6343 C of 2.86 +0.40-0.33 Gyr and 3.11 +0.50-0.38 Gyr respectively., Comment: Accepted for publication in ApJ

An ensemble post-processing method is developed to improve the probabilistic forecasts of extreme precipitation events across the conterminous United States (CONUS). The method combines a 3-D Vision Transformer (ViT) for bias correction with a Latent Diffusion Model (LDM), a generative Artificial Intelligence (AI) method, to post-process 6-hourly precipitation ensemble forecasts and produce an enlarged generative ensemble that contains spatiotemporally consistent precipitation trajectories. These trajectories are expected to improve the characterization of extreme precipitation events and offer skillful multi-day accumulated and 6-hourly precipitation guidance. The method is tested using the Global Ensemble Forecast System (GEFS) precipitation forecasts out to day 6 and is verified against the Climate-Calibrated Precipitation Analysis (CCPA) data. Verification results indicate that the method generated skillful ensemble members with improved Continuous Ranked Probabilistic Skill Scores (CRPSSs) and Brier Skill Scores (BSSs) over the raw operational GEFS and a multivariate statistical post-processing baseline. It showed skillful and reliable probabilities for events at extreme precipitation thresholds. Explainability studies were further conducted, which revealed the decision-making process of the method and confirmed its effectiveness on ensemble member generation. This work introduces a novel, generative-AI-based approach to address the limitation of small numerical ensembles and the need for larger ensembles to identify extreme precipitation events.

Adversarial perturbations can deceive neural networks by adding small, imperceptible noise to the input. Recent object trackers with transformer backbones have shown strong performance on tracking datasets, but their adversarial robustness has not been thoroughly evaluated. While transformer trackers are resilient to black-box attacks, existing white-box adversarial attacks are not universally applicable against these new transformer trackers due to differences in backbone architecture. In this work, we introduce TrackPGD, a novel white-box attack that utilizes predicted object binary masks to target robust transformer trackers. Built upon the powerful segmentation attack SegPGD, our proposed TrackPGD effectively influences the decisions of transformer-based trackers. Our method addresses two primary challenges in adapting a segmentation attack for trackers: limited class numbers and extreme pixel class imbalance. TrackPGD uses the same number of iterations as other attack methods for tracker networks and produces competitive adversarial examples that mislead transformer and non-transformer trackers such as MixFormerM, OSTrackSTS, TransT-SEG, and RTS on datasets including VOT2022STS, DAVIS2016, UAV123, and GOT-10k., Comment: Accepted in The 3rd New Frontiers in Adversarial Machine Learning (AdvML Frontiers @NeurIPS2024)

Despite extensive research on adversarial training strategies to improve robustness, the decisions of even the most robust deep learning models can still be quite sensitive to imperceptible perturbations, creating serious risks when deploying them for high-stakes real-world applications. While detecting such cases may be critical, evaluating a model's vulnerability at a per-instance level using adversarial attacks is computationally too intensive and unsuitable for real-time deployment scenarios. The input space margin is the exact score to detect non-robust samples and is intractable for deep neural networks. This paper introduces the concept of margin consistency -- a property that links the input space margins and the logit margins in robust models -- for efficient detection of vulnerable samples. First, we establish that margin consistency is a necessary and sufficient condition to use a model's logit margin as a score for identifying non-robust samples. Next, through comprehensive empirical analysis of various robustly trained models on CIFAR10 and CIFAR100 datasets, we show that they indicate high margin consistency with a strong correlation between their input space margins and the logit margins. Then, we show that we can effectively and confidently use the logit margin to detect brittle decisions with such models. Finally, we address cases where the model is not sufficiently margin-consistent by learning a pseudo-margin from the feature representation. Our findings highlight the potential of leveraging deep representations to assess adversarial vulnerability in deployment scenarios efficiently., Comment: 10 pages, 6 figures, 2 tables. Version Update: Neurips Camera Ready

The Near-InfraRed Planet Searcher or NIRPS is a precision radial velocity spectrograph developed through collaborative efforts among laboratories in Switzerland, Canada, Brazil, France, Portugal and Spain. NIRPS extends to the 0.98-1.8 $\mu$m domain of the pioneering HARPS instrument at the La Silla 3.6-m telescope in Chile and it has achieved unparalleled precision, measuring stellar radial velocities in the infrared with accuracy better than 1 m/s. NIRPS can be used either stand-alone or simultaneously with HARPS. Commissioned in late 2022 and early 2023, NIRPS embarked on a 5-year Guaranteed Time Observation (GTO) program in April 2023, spanning 720 observing nights. This program focuses on planetary systems around M dwarfs, encompassing both the immediate solar vicinity and transit follow-ups, alongside transit and emission spectroscopy observations. We highlight NIRPS's current performances and the insights gained during its deployment at the telescope. The lessons learned and successes achieved contribute to the ongoing advancement of precision radial velocity measurements and high spectral fidelity, further solidifying NIRPS' role in the forefront of the field of exoplanets., Comment: Proceeding at the SPIE Astronomical Telescopes + Instrumentation conference [Yokohama,Japan; June 2024]

New transformer networks have been integrated into object tracking pipelines and have demonstrated strong performance on the latest benchmarks. This paper focuses on understanding how transformer trackers behave under adversarial attacks and how different attacks perform on tracking datasets as their parameters change. We conducted a series of experiments to evaluate the effectiveness of existing adversarial attacks on object trackers with transformer and non-transformer backbones. We experimented on 7 different trackers, including 3 that are transformer-based, and 4 which leverage other architectures. These trackers are tested against 4 recent attack methods to assess their performance and robustness on VOT2022ST, UAV123 and GOT10k datasets. Our empirical study focuses on evaluating adversarial robustness of object trackers based on bounding box versus binary mask predictions, and attack methods at different levels of perturbations. Interestingly, our study found that altering the perturbation level may not significantly affect the overall object tracking results after the attack. Similarly, the sparsity and imperceptibility of the attack perturbations may remain stable against perturbation level shifts. By applying a specific attack on all transformer trackers, we show that new transformer trackers having a stronger cross-attention modeling achieve a greater adversarial robustness on tracking datasets, such as VOT2022ST and GOT10k. Our results also indicate the necessity for new attack methods to effectively tackle the latest types of transformer trackers. The codes necessary to reproduce this study are available at https://github.com/fatemehN/ReproducibilityStudy., Comment: Published in Transactions on Machine Learning Research (05/2024): https://openreview.net/forum?id=FEEKR0Vl9s

Nearby associations of stars which are coeval are important benchmark laboratories because they provide robust measurements of stellar ages. The study of such coeval groups makes it possible to better understand star formation by studying the initial mass function, the binary fraction or the circumstellar disks of stars, to determine how the initially dense populations of young stars gradually disperse to form the field population, and to shed light on how the properties of stars, exoplanets and substellar objects evolve with distinct snapshots along their lifetime. The advent of large-scale missions such as Gaia is reshaping our understanding or stellar kinematics in the Solar neighborhood and beyond, and offers the opportunity to detect a large number of loose, coeval stellar associations for the first time, which evaded prior detection because of their low density or the faintness of their members. In parallel, advances in detection and characterization of exoplanets and substellar objects are starting to unveil the detailed properties of extrasolar atmospheres, as well as population-level distributions in fundamental exoplanet properties such as radii, masses, and orbital parameters. Accurate ages are still sparsely available to interpret the evolution of both exoplanets and substellar objects, and both fields are now ripe for detailed age investigations because we are starting to uncover ever-closer low-density associations that previously escaped detection, as well as exoplanets and ever lower-mass members of more distant open clusters and star-forming regions. In this paper, we review some recent advances in the identification and characterization of nearby associations, the methods by which stellar ages are measured, and some of the direct applications of the study of young associations such as the emergent field of isolated planetary-mass objects., Comment: Accepted for publication in PASP tutorials. 22 pages, 15 figures

We present the discovery of VHS J183135.58$-$551355.9 (hereafter VHS J1831$-$5513), an L/T transition dwarf identified as a result of its unusually red near-infrared colors ($J-K_{\rm S}=3.633\pm0.277$ mag; $J-W2=6.249\pm0.245$ mag) from the VISTA Hemisphere Survey and CatWISE2020 surveys. We obtain low resolution near-infrared spectroscopy of VHS J1831$-$5513 using Magellan/FIRE to confirm its extremely red nature and assess features sensitive to surface gravity (i.e., youth). Its near-infrared spectrum shows multiple CH$_{\rm 4}$ absorption features, indicating an exceptionally low effective temperature for its spectral type. Based on proper motion measurements from CatWISE2020 and a photometric distance derived from its $K_{\rm S}$-band magnitude, we find that VHS J1831$-$5513 is a likely ($\sim$85$\%$ probability) kinematic member of the $\beta$ Pictoris moving group. Future radial velocity and trigonometric parallax measurements will clarify such membership. Follow-up mid-infrared or higher resolution near-infrared spectroscopy of this object will allow for further investigation as to the cause(s) of its redness, such as youth, clouds, and viewing geometry., Comment: 14 pages, 4 figures; Accepted for publication in The Astronomical Journal

Stellar flares are short-duration ($<$ hours) bursts of radiation associated with surface magnetic reconnection events. Stellar magnetic activity generally decreases as a function of both age and Rossby number, $R_0$, a measure of the relative importance of the convective and rotational dynamos. Young stars ($<300$ Myr) have typically been overlooked in population-level flare studies due to challenges with flare-detection methods. Here, we select a sample of stars that are members of 26 nearby moving groups, clusters, or associations with ages $<$300 Myr that have been observed by the Transiting Exoplanet Survey Satellite at 2-minute cadence. We identified 26,355 flares originating from 3,157 stars and robustly measure the rotation periods of 1,847 stars. We measure and find the flare frequency distribution (FFD) slope, $\alpha$, saturates for all spectral types at $\alpha \sim -0.5$ and is constant over 300 Myr. Additionally, we find that flare rates for stars $t_\textrm{age} = 50 - 250$ Myr are saturated below $R_0 < 0.14$, which is consistent with other indicators of magnetic activity. We find evidence of annual flare rate variability in eleven stars, potentially correlated with long term stellar activity cycles. Additionally, we cross match our entire sample with GALEX and find no correlation between flare rate and Far- and Near-Ultraviolet flux. Finally, we find the flare rates of planet hosting stars are relatively lower than comparable, larger samples of stars, which may have ramifications for the atmospheric evolution of short-period exoplanets., Comment: 23 pages, 10 figures, 4 tables, under review at AAS Journals. Manuscript was compiled using showyourwork! The affiliated GitHub repository can be found here: https://github.com/afeinstein20/young-stellar-flares

The Orion Nebula Cluster (ONC) is the closest site of very young ($\sim$ 1 Myrs) massive star formation. The ONC hosts more than 1600 young and X-ray bright stars with masses ranging from $\sim$ 0.1 to 35 $M_\odot$. The Chandra HETGS Orion Legacy Project observed the ONC with the Chandra high energy transmission grating spectrometer (HETGS) for $2.1\,$Ms. We describe the spectral extraction and cleaning processes necessary to separate overlapping spectra. We obtained 36 high resolution spectra which includes a high brilliance X-ray spectrum of $\theta^1$ Ori C with over 100 highly significant X-ray lines. The lines show Doppler broadening between 300 and $400\;\mathrm{km}\;\mathrm{s}^{-1}$. Higher spectral diffraction orders allow us to resolve line components of high Z He-like triplets in $\theta^1$ Ori C with unprecedented spectral resolution. Long term light curves spanning $\sim$20 years show all stars to be highly variable, including the massive stars. Spectral fitting with thermal coronal emission line models reveals that most sources show column densities of up to a few times $10^{22}\,$cm$^{-2}$ and high coronal temperatures of 10 to 90 MK. We observe a bifurcation of the high temperature component where some stars show a high component of 40 MK, while others show above 60 MK indicating heavy flaring activity. Some lines are resolved with Doppler broadening above our threshold of $\sim200\;\mathrm{km}\;\mathrm{s}^{-1}$, up to $500\;\mathrm{km}\;\mathrm{s}^{-1}$. This data set represents the largest collection of HETGS high resolution X-ray spectra from young pre-MS stars in a single star-forming region to date., Comment: Accepted by ApJ, fixed typo in the xlabel of Fig 12 (left panel)

We present the discovery of 13 new widely separated T dwarf companions to M dwarf primaries, identified using WISE/NEOWISE data by the CatWISE and Backyard Worlds: Planet 9 projects. This sample represents a $\sim$60% increase in the number of known M+T systems, and allows us to probe the most extreme products of binary/planetary system formation, a discovery space made available by the CatWISE2020 catalog and the Backyard Worlds: Planet 9 effort. Highlights among the sample are WISEP J075108.79-763449.6, a previously known T9 thought to be old due to its SED, which we now find is part of a common-proper-motion pair with L 34-26 A, a well studied young M3 V star within 10 pc of the Sun; CWISE J054129.32-745021.5 B and 2MASS J05581644-4501559 B, two T8 dwarfs possibly associated with the very fast-rotating M4 V stars CWISE J054129.32-745021.5 A and 2MASS J05581644-4501559 A; and UCAC3 52-1038 B, which is among the widest late T companions to main sequence stars, with a projected separation of $\sim$7100 au. The new benchmarks presented here are prime $JWST$ targets, and can help us place strong constraints on formation and evolution theory of substellar objects as well as on atmospheric models for these cold exoplanet analogs., Comment: Accepted for publication in ApJ. 35 pages, 6 tables, 21 figures

Beyond our solar system, aurorae have been inferred from radio observations of isolated brown dwarfs (e.g. Hallinan et al. 2006; Kao et al. 2023). Within our solar system, giant planets have auroral emission with signatures across the electromagnetic spectrum including infrared emission of H3+ and methane. Isolated brown dwarfs with auroral signatures in the radio have been searched for corresponding infrared features but have only had null detections (e.g. Gibbs et al. 2022). CWISEP J193518.59-154620.3. (W1935 for short) is an isolated brown dwarf with a temperature of ~482 K. Here we report JWST observations of strong methane emission from W1935 at 3.326 microns. Atmospheric modeling leads us to conclude that a temperature inversion of ~300 K centered at 1-10 millibar replicates the feature. This represents an atmospheric temperature inversion for a Jupiter-like atmosphere without irradiation from a host star. A plausible explanation for the strong inversion is heating by auroral processes, although other internal and/or external dynamical processes cannot be ruled out. The best fit model rules out the contribution of H3+ emission which is prominent in solar system gas giants however this is consistent with rapid destruction of H3+ at the higher pressure where the W1935 emission originates (e.g. Helling et al. 2019)., Comment: Accepted in Nature 9 February 2024

Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses., Comment: 19 pages, 8 figures, Accepted at AAAI 2025

The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, from speech emotion recognition to audio event detection, but the community is sparse compared to other ML areas, e.g., computer vision or natural language processing. A major limitation with audio is the available data; with audio being a time-dependent modality, high-quality data collection is time-consuming and costly, making it challenging for academic groups to apply their often state-of-the-art strategies to a larger, more generalizable dataset. In this short white paper, to encourage researchers with limited access to large-datasets, the organizers first outline several open-source datasets that are available to the community, and for the duration of the workshop are making several propriety datasets available. Namely, three vocal datasets, Hume-Prosody, Hume-VocalBurst, an acted emotional speech dataset Modulate-Sonata, and an in-game streamer dataset Modulate-Stream. We outline the current baselines on these datasets but encourage researchers from across audio to utilize them outside of the initial baseline tasks.

Data augmentation is one of the most effective techniques to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability in medical image analysis, it is frequently underutilized. This appears to be due to a gap in our collective understanding of the efficacy of different augmentation techniques across medical imaging tasks and modalities. One domain where this is especially true is breast ultrasound images. This work addresses this issue by analyzing the effectiveness of different augmentation techniques for the classification of breast lesions in ultrasound images. We assess the generalizability of our findings across several datasets, demonstrate that certain augmentations are far more effective than others, and show that their usage leads to significant performance gains., Comment: For associated code, see https://github.com/adamtupper/medical-image-augmentation

We report the identification of 89 new systems containing ultracool dwarf companions to main sequence stars and white dwarfs, using the citizen science project Backyard Worlds: Planet 9 and cross-reference between Gaia and CatWISE2020. Thirty-two of these companions and thirty-three host stars were followed up with spectroscopic observations, with companion spectral types ranging from M7-T9 and host spectral types ranging from G2-M9. These systems exhibit diverse characteristics, from young to old ages, blue to very red spectral morphologies, potential membership to known young moving groups, and evidence of spectral binarity in 9 companions. Twenty of the host stars in our sample show evidence for higher order multiplicity, with an additional 11 host stars being resolved binaries themselves. We compare this sample's characteristics with those of the known stellar binary and exoplanet populations, and find our sample begins to fill in the gap between directly imaged exoplanets and stellary binaries on mass ratio-binding energy plots. With this study, we increase the population of ultracool dwarf companions to FGK stars by $\sim$42\%, and more than triple the known population of ultracool dwarf companions with separations larger than 1,000 au, providing excellent targets for future atmospheric retrievals., Comment: 61 pages, 11 figures, 11 tables. Accepted for publication in AJ

This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with IEEE CVPR 2024. The 6th ABAW Competition addresses contemporary challenges in understanding human emotions and behaviors, crucial for the development of human-centered technologies. In more detail, the Competition focuses on affect related benchmarking tasks and comprises of five sub-challenges: i) Valence-Arousal Estimation (the target is to estimate two continuous affect dimensions, valence and arousal), ii) Expression Recognition (the target is to recognise between the mutually exclusive classes of the 7 basic expressions and 'other'), iii) Action Unit Detection (the target is to detect 12 action units), iv) Compound Expression Recognition (the target is to recognise between the 7 mutually exclusive compound expression classes), and v) Emotional Mimicry Intensity Estimation (the target is to estimate six continuous emotion dimensions). In the paper, we present these Challenges, describe their respective datasets and challenge protocols (we outline the evaluation metrics) and present the baseline systems as well as their obtained performance. More information for the Competition can be found in: https://affective-behavior-analysis-in-the-wild.github.io/6th.

In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. This problem becomes further complicated when considering evolving dynamics between domains. While various approaches have been proposed to address this issue, a comprehensive understanding of the underlying generalization theory is still lacking. In this study, we contribute novel theoretic results that aligning conditional distribution leads to the reduction of generalization bounds. Our analysis serves as a key motivation for solving the Temporal Domain Generalization (TDG) problem through the application of Koopman Neural Operators, resulting in Temporal Koopman Networks (TKNets). By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains. Through empirical evaluations conducted on synthetic and real-world datasets, we validate the effectiveness of our proposed approach., Comment: 15 pages, 7 figures, Accepted by AAAI 2024. arXiv admin note: text overlap with arXiv:2206.00047

We present observations of the T8 dwarf 2MASS 0415-0935 with JWST's NIRSpec spectrograph using the G395H grating ($\sim$ 2.87 - 5.14 $\mu$m). We perform the first atmospheric retrieval analysis at the maximum spectral resolution of NIRSpec (R$\sim$2700) and combine the spectrum with previous observations to study the 0.9-20 $\mu$m spectral energy distribution. We obtain precise constraints on chemical abundances ($\sim$0.02 dex) for a number of species which complicate our understanding of disequilibrium chemistry, particularly for CO$_{2}$ and PH$_{3}$. Furthermore, we measure a $^{12}$CO/$^{13}$CO ratio of $\sim 97^{+9}_{-8}$, making 2MASS 0415-0935 the coldest ($\sim 760$ K) substellar object outside of our solar system with a measured $^{12}$CO/$^{13}$CO ratio. This work shows promise for similar observations with JWST to provide precise abundances of major chemical species as well as isotopologues, allowing for new tests of our understanding of the formation and atmospheres of substellar objects., Comment: 28 pages, 10 figures. Submitted to Nature Astronomy on Oct. 5th, 2023

Despite the intuition that the same population size is not needed throughout the run of an Evolutionary Algorithm (EA), most EAs use a fixed population size. This paper presents an empirical study on the possible benefits of a Simple Variable Population Sizing (SVPS) scheme on the performance of Genetic Algorithms (GAs). It consists in decreasing the population for a GA run following a predetermined schedule, configured by a speed and a severity parameter. The method uses as initial population size an estimation of the minimum size needed to supply enough building blocks, using a fixed-size selectorecombinative GA converging within some confidence interval toward good solutions for a particular problem. Following this methodology, a scalability analysis is conducted on deceptive, quasi-deceptive, and non-deceptive trap functions in order to assess whether SVPS-GA improves performances compared to a fixed-size GA under different problem instances and difficulty levels. Results show several combinations of speed-severity where SVPS-GA preserves the solution quality while improving performances, by reducing the number of evaluations needed for success.

Determining the precise ages of young (tens to a few hundred Myr) kinematic (``moving") groups is important for placing star, protoplanetary disk, and planet observations on an evolutionary timeline. The nearby $\sim$25 Myr-old $\beta$ Pictoris Moving Group (BPMG) is an important benchmark for studying stars and planetary systems at the end of the primordial disk phase. Gaia DR3 astrometry and photometry, combined with ground-based observations and more sophisticated stellar models, permit a systematic re-evaluation of BPMG membership and age. We combined Gaia astrometry with previously published radial velocities to evaluate moving group membership in a Bayesian framework. To minimize the effect of unresolved stellar multiplicity on age estimates, we identified and excluded multi-star systems using Gaia astrometry, ground-based adaptive optics imaging, and multi-epoch radial velocities, as well as literature identifications. We estimated age using isochrone and lithium-depletion-boundary fitting with models that account for the effect of magnetic activity and spots on young, rapidly rotating stars. We find that age estimates are highly model-dependent; Dartmouth magnetic models with ages of 23$\pm$8 Myr and 33$^{+9}_{-11}$ Myr provide best fits to the lithium depletion boundary and Gaia $M_G$ vs. $B_{P}$-$R_{P}$ color-magnitude diagram, respectively, whereas a Dartmouth standard model with an age of 11$^{+4}_{-3}$ Myr provides a best fit to the 2MASS-Gaia $M_{K_S}$ vs. $B_{P}$-$R_{P}$ color-magnitude diagram., Comment: 18 pages, 7 figures, 3 tables, accepted for publication in MNRAS Main Journal Dec 2023

The development of signal unmixing algorithms is essential for leveraging multimodal datasets acquired through a wide array of scientific imaging technologies, including hyperspectral or time-resolved acquisitions. In experimental physics, enhancing the spatio-temporal resolution or expanding the number of detection channels often leads to diminished sampling rate and signal-to-noise ratio (SNR), significantly affecting the efficacy of signal unmixing algorithms. We propose Latent Unmixing, a new approach which applies band-pass filters to the latent space of a multi-dimensional convolutional neural network to disentangle overlapping signal components. It enables better isolation and quantification of individual signal contributions, especially in the context of undersampled distributions. Using multi-dimensional convolution kernels to process all dimensions simultaneously enhances the network's ability to extract information from adjacent pixels, and time- or spectral-bins. This approach enables more effective separation of components in cases where individual pixels do not provide clear, well-resolved information. We showcase the method's practical use in experimental physics through two test cases that highlight the versatility of our approach: fluorescence lifetime microscopy and mode decomposition in optical fibers. The latent unmixing method extracts valuable information from complex signals that cannot be resolved by standard methods. It opens new possibilities in optics and photonics for multichannel separations at an increased sampling rate., Comment: 34 pages, 14 figures (main paper) + 20 pages, 13 figures (supplementary material)

Safe reinforcement learning deals with mitigating or avoiding unsafe situations by reinforcement learning (RL) agents. Safe RL approaches are based on specific risk representations for particular problems or domains. In order to analyze agent behaviors, compare safe RL approaches, and effectively transfer techniques between application domains, it is necessary to understand the types of risk specific to safe RL problems. We performed a systematic literature mapping with the objective to characterize risk in safe RL. Based on the obtained results, we present definitions, characteristics, and types of risk that hold on multiple application domains. Our literature mapping covers literature from the last 5 years (2017-2022), from a variety of knowledge areas (AI, finance, engineering, medicine) where RL approaches emphasize risk representation and management. Our mapping covers 72 papers filtered systematically from over thousands of papers on the topic. Our proposed notion of risk covers a variety of representations, disciplinary differences, common training exercises, and types of techniques. We encourage researchers to include explicit and detailed accounts of risk in future safe RL research reports, using this mapping as a starting point. With this information, researchers and practitioners could draw stronger conclusions on the effectiveness of techniques on different problems., Comment: 36 pages, 8 figures, Submitted to Artificial Intelligence Reviews

A complete accounting of nearby objects -- from the highest-mass white dwarf progenitors down to low-mass brown dwarfs -- is now possible, thanks to an almost complete set of trigonometric parallax determinations from Gaia, ground-based surveys, and Spitzer follow-up. We create a census of objects within a Sun-centered sphere of 20-pc radius and check published literature to decompose each binary or higher-order system into its separate components. The result is a volume-limited census of $\sim$3,600 individual star formation products useful in measuring the initial mass function across the stellar ($<8 M_\odot$) and substellar ($\gtrsim 5 M_{Jup}$) regimes. Comparing our resulting initial mass function to previous measurements shows good agreement above 0.8$M_\odot$ and a divergence at lower masses. Our 20-pc space densities are best fit with a quadripartite power law, $\xi(M) = dN/dM \propto M^{-\alpha}$ with long-established values of $\alpha = 2.3$ at high masses ($0.55 < M < 8.00 M_\odot$) and $\alpha = 1.3$ at intermediate masses ($0.22 < M < 0.55 M_\odot$), but at lower masses we find $\alpha = 0.25$ for $0.05 < M <0.22 M_\odot$ and $\alpha = 0.6$ for $0.01 < M < 0.05 M_\odot$. This implies that the rate of production as a function of decreasing mass diminishes in the low-mass star/high-mass brown dwarf regime before increasing again in the low-mass brown dwarf regime. Correcting for completeness, we find a star to brown dwarf number ratio of, currently, 4:1, and an average mass per object of 0.41 $M_\odot$., Comment: Accepted for publication in The Astrophysical Journal Supplement Series. 123 pages with four ancillary files

Continual learning aims to learn a series of tasks sequentially without forgetting the knowledge acquired from the previous ones. In this work, we propose the Hessian Aware Low-Rank Perturbation algorithm for continual learning. By modeling the parameter transitions along the sequential tasks with the weight matrix transformation, we propose to apply the low-rank approximation on the task-adaptive parameters in each layer of the neural networks. Specifically, we theoretically demonstrate the quantitative relationship between the Hessian and the proposed low-rank approximation. The approximation ranks are then globally determined according to the marginal increment of the empirical loss estimated by the layer-specific gradient and low-rank approximation error. Furthermore, we control the model capacity by pruning less important parameters to diminish the parameter growth. We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method. Empirical results show that our method performs better on different benchmarks, especially in achieving task order robustness and handling the forgetting issue. The source code is at https://github.com/lijiaqi/HALRP., Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)

Convection initiation (CI) nowcasting remains a challenging problem for both numerical weather prediction models and existing nowcasting algorithms. In this study, object-based probabilistic deep learning models are developed to predict CI based on multichannel infrared GOES-R satellite observations. The data come from patches surrounding potential CI events identified in Multi-Radar Multi-Sensor Doppler weather radar products over the Great Plains region from June and July 2020 and June 2021. An objective radar-based approach is used to identify these events. The deep learning models significantly outperform the classical logistic model at lead times up to 1 hour, especially on the false alarm ratio. Through case studies, the deep learning model exhibits the dependence on the characteristics of clouds and moisture at multiple levels. Model explanation further reveals the model's decision-making process with different baselines. The explanation results highlight the importance of moisture and cloud features at different levels depending on the choice of baseline. Our study demonstrates the advantage of using different baselines in further understanding model behavior and gaining scientific insights.

Y dwarfs, the coolest known spectral class of brown dwarfs, overlap in mass and temperature with giant exoplanets, providing unique laboratories for studying low-temperature atmospheres. However, only a fraction of Y dwarf candidates have been spectroscopically confirmed. We present Keck/NIRES near-infrared spectroscopy of the nearby ($d \approx 6-8$ pc) brown dwarf CWISE J105512.11+544328.3. Although its near-infrared spectrum aligns best with the Y0 standard in the $J$-band, no standard matches well across the full $YJHK$ wavelength range. The CWISE J105512.11+544328.3 NH$_3$-$H$ = 0.427 $\pm$ 0.0012 and CH$_4$-$J$ = 0.0385 $\pm$ 0.0007 absorption indices and absolute Spitzer [4.5] magnitude of 15.18 $\pm$ 0.22 are also indicative of an early Y dwarf rather than a late T dwarf. CWISE J105512.11+544328.3 additionally exhibits the bluest Spitzer [3.6]$-$[4.5] color among all spectroscopically confirmed Y dwarfs. Despite this anomalously blue Spitzer color given its low luminosity, CWISE J105512.11+544328.3 does not show other clear kinematic or spectral indications of low metallicity. Atmospheric model comparisons yield a log(g) $\le$ 4.5 and $T_{\rm eff} \approx 500 \pm 150$ K for this source. We classify CWISE J105512.11+544328.3 as a Y0 (pec) dwarf, adding to the remarkable diversity of the Y-type population. JWST spectroscopy would be crucial to understanding the origin of this Y dwarf's unusual preference for low-gravity models and blue 3-5 $\mu$m color., Comment: accepted for publication in The Astrophysical Journal

An ensemble post-processing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to post-process convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1--24 hr forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier Skill Score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the inter-variable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to post-process CAM output using neural networks that can be applied to severe weather prediction.

Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates and can be decomposed physically, but both physics and machine learning ensembles are computationally expensive. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but do not account for epistemic uncertainty.. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to those obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for all graph prediction tasks is a challenging and unsolved problem. Here, we present the Graph Positional and Structural Encoder (GPSE), the first-ever graph encoder designed to capture rich PSE representations for augmenting any GNN. GPSE learns an efficient common latent representation for multiple PSEs, and is highly transferable: The encoder trained on a particular graph dataset can be used effectively on datasets drawn from markedly different distributions and modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly outperform those that employ explicitly computed PSEs, and at least match their performance in others. Our results pave the way for the development of foundational pre-trained graph encoders for extracting positional and structural information, and highlight their potential as a more powerful and efficient alternative to explicitly computed PSEs and existing self-supervised pre-training approaches. Our framework and pre-trained models are publicly available at https://github.com/G-Taxonomy-Workgroup/GPSE. For convenience, GPSE has also been integrated into the PyG library to facilitate downstream applications., Comment: Accepted at ICML 2024; 34 pages, 6 figures

Transformer-based large-scale language models (LLMs) are able to generate highly realistic text. They are duly able to express, and at least implicitly represent, a wide range of sentiments and color, from the obvious, such as valence and arousal to the subtle, such as determination and admiration. We provide a first exploration of these representations and how they can be used for understanding the inner sentimental workings of single sentences. We train predictors of the quantiles of the distributions of final sentiments of sentences from the hidden representations of an LLM applied to prefixes of increasing lengths. After showing that predictors of distributions of valence, determination, admiration, anxiety and annoyance are well calibrated, we provide examples of using these predictors for analyzing sentences, illustrating, for instance, how even ordinary conjunctions (e.g., "but") can dramatically alter the emotional trajectory of an utterance. We then show how to exploit the distributional predictions to generate sentences with sentiments in the tails of distributions. We discuss the implications of our results for the inner workings of thoughts, for instance for psychiatric dysfunction.