Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Parent Mental Health Problems in an Interdisciplinary Feeding Clinic: Short Communication

Parents of children with pediatric feeding disorder (PFD) often experience high stress and may be at advanced risk for mental health conditions. We conducted a retrospective chart review of 413 parents who completed intake surveys at an interdisciplinary feeding clinic to determine the prevalence and types of mental health conditions among parents; 41.8% of parents reported a mental health diagnosis among the mother and/or father. Of the parents with mental health conditions, the most prevalent conditions reported were anxiety (71.1% for mothers, 50% for fathers) and mood disorders (62.7% for mothers, 40.9% for fathers). For mothers, 21.1% indicated their mental health disorder had a postpartum onset. Parents of children with birth complications were more likely to have a mental health condition (50.9%) than parents of children without a birth complication (38.7%), and parents reporting mental health conditions had children with significantly more co-occurring medical conditions. Since parent stress and mental health can influence parent-child interactions during mealtime, this study suggests the importance of addressing both parent mental health factors and child factors when treating children for PFD. Future studies are needed to develop novel interventions that support the specific mental health needs of parents of children with PFD.
A Late Oligocene-Early Miocene cool climate flora in Tasmania

The Late Oligocene-Early Miocene Monpeelyata deposit occurs at 920 m above sea level on the Tasmanian Central Plateau, and was probably at least 700 m a.s. l. at the time of deposition. The palynoflora is dominated by Nothofagidites emarcidus/heterus (43%) and Phyllocladidites mawsonii (45%), although neither is represented in the macroflora. The total palynoflora is interpreted as representing a moderately diverse riparian rainforest, including and possibly dominated by one or more Nothofagus-conifer associations. The macroflora is dominated by imbricate-leaved conifers and microphyllous angiosperms. It is hypothesised that the macroflora represents the litter from exposed forest edges, where the microclimate was more variable than under the forest canopy. This forest edge habitat at high altitudes may represent the environment in which the alpine/subalpine flora evolved. Contemporaneous floras from mainland Australia are compared with Monpeelyata.
Pre‐ and syn‐eruptive geochemistry of volcanic gases from Soufriere Hills of Montserrat, West Indies

Soufriere Hills fumaroles contained magma‐derived volatiles before and during the eruption initiated in 1995 but also preserved a typical and quite steady hydrothermal composition. Chemical changes due to increased boiling and a greater input of oxidizing magmatic gas occurred only at Galway's Soufriere, the most active fumarolic field. Hydrothermal buffering of the fumaroles has been favoured by their remote location (1–2 km) from the eruptive vents and by a preferential degassing of the uprising magma through intrusive conduits under the crater. High temperature (720°C) gas collected from the extruding lava dome in Feb. 1996 was chemically and isotopically representative of the magmatic gas stream. Its composition allows assessment of average eruptive fluxes of H2O, CO2 and HCl which require the degassing of only 2.5–3 times more magma than erupted.
Spectrum of non-degenerate functions with simplicial Newton polyhedra

We prove a simple formula for the spectrum of non-degenerate functions of 3 variables with simplicial convenient Newton polyhedra (which does not hold in the non-simplicial case) generalizing a picture of Arnold in the 2 variable case. Combining this with Steenbrink's conjecture on a refinement of Yomdin's formula (proved long ago), we can deduce a formula for the spectrum of certain non-isolated surface singularities with simplicial non-degenerate Newton boundaries.
Leapfrogging vortex rings as scaling limit of Euler Equations

We consider an incompressible fluid with axial symmetry without swirl, assuming initial data such that the initial vorticity is very concentrated inside $N$ small disjoint rings of thickness $\varepsilon$, each one of vorticity mass and main radius of order $|\log\varepsilon|$. When $\varepsilon \to 0$, we show that, at least for small but positive times, the motion of the rings converges to a dynamical system firstly introduced in [NoDEA Nonlinear Diff. Eq. Appl. 6 (1999), 473-499]. In the special case of two vortex rings with large enough main radius, the result is improved reaching longer times, in such a way to cover the case of several overtakings between the rings, thus providing a mathematical rigorous derivation of the leapfrogging phenomenon.
Responses of the ocean planktonic ecosystem to finite-amplitude perturbations

The responses of the ocean planktonic ecosystem to finite-amplitude perturbations are investigated using an ocean planktonic ecosystem model. Through changing the higher predation rate on zooplankton, multiple equilibria of the model, namely high-nutrient and low-nutrient states, are obtained under certain parameter values. Based on these states, the perturbations with maximum nonlinear growth are determined using the conditional nonlinear optimal perturbation (CNOP) method. The linear and nonlinear evolutions of the CNOP perturbation are compared. The results show that the nonlinear evolution of the perturbation leads to predator-prey oscillations with larger amplitude than the linear evolution. Besides, after the perturbation amplitude exceeds a critical value, the nonlinear evolution of the perturbation will induce the linearly stable ecosystem state to lose the stability and become nonlinearly unstable. This implies that nonlinear processes have important impacts on the stability of the ecosystem. Specifically, we identify the nonlinear processes related to zooplankton grazing to impact the stability most for the high-nutrient state, while for the low-nutrient state the main nonlinear process affecting the stability is the uptake process. These results help to improve our understanding of the sensitivity of the oceanic ecosystem model to finite-amplitude perturbations and the underlying nonlinear stability properties.
Colloquial Persian POS (CPPOS) Corpus: A Novel Corpus for Colloquial Persian Part of Speech Tagging

Introduction: Part-of-Speech (POS) Tagging, the process of classifying words into their respective parts of speech (e.g., verb or noun), is essential in various natural language processing applications. POS tagging is a crucial preprocessing task for applications like machine translation, question answering, sentiment analysis, etc. However, existing corpora for POS tagging in Persian mainly consist of formal texts, such as daily news and newspapers. As a result, smart POS tools, machine learning models, and deep learning models trained on these corpora may not perform optimally for processing colloquial text in social network analysis. Method: This paper introduces a novel corpus,"Colloquial Persian POS"(CPPOS), specifically designed to support colloquial Persian text. The corpus includes formal and informal text collected from various domains such as political, social, and commercial on Telegram, Twitter, and Instagram more than 520K labeled tokens. After collecting posts from these social platforms for one year, special preprocessing steps were conducted, including normalization, sentence tokenizing, and word tokenizing for social text. The tokens and sentences were then manually annotated and verified by a team of linguistic experts. This study also defines a POS tagging guideline for annotating the data and conducting the annotation process. Results: To evaluate the quality of CPPOS, various deep learning models, such as the RNN family, were trained using the constructed corpus. A comparison with another well-known Persian POS corpus named"Bijankhan"and the Persian Hazm POS tool trained on Bijankhan revealed that our model trained on CPPOS outperforms them. With the new corpus and the BiLSTM deep neural model, we achieved a 14% improvement over the previous dataset.
On the Disconnect Between Theory and Practice of Overparametrized Neural Networks

The infinite-width limit of neural networks (NNs) has garnered significant attention as a theoretical framework for analyzing the behavior of large-scale, overparametrized networks. By approaching infinite width, NNs effectively converge to a linear model with features characterized by the neural tangent kernel (NTK). This establishes a connection between NNs and kernel methods, the latter of which are well understood. Based on this link, theoretical benefits and algorithmic improvements have been hypothesized and empirically demonstrated in synthetic architectures. These advantages include faster optimization, reliable uncertainty quantification and improved continual learning. However, current results quantifying the rate of convergence to the kernel regime suggest that exploiting these benefits requires architectures that are orders of magnitude wider than they are deep. This assumption raises concerns that practically relevant architectures do not exhibit behavior as predicted via the NTK. In this work, we empirically investigate whether the limiting regime either describes the behavior of large-width architectures used in practice or is informative for algorithmic improvements. Our empirical results demonstrate that this is not the case in optimization, uncertainty quantification or continual learning. This observed disconnect between theory and practice calls into question the practical relevance of the infinite-width limit.
Solving stationary nonlinear Fokker-Planck equations via sampling

Solving the stationary nonlinear Fokker-Planck equations is important in applications and examples include the Poisson-Boltzmann equation and the two layer neural networks. Making use of the connection between the interacting particle systems and the nonlinear Fokker-Planck equations, we propose to solve the stationary solution by sampling from the $N$-body Gibbs distribution. This avoids simulation of the $N$-body system for long time and more importantly such a method can avoid the requirement of uniform propagation of chaos from direct simulation of the particle systems. We establish the convergence of the Gibbs measure to the stationary solution when the interaction kernel is bounded (not necessarily continuous) and the temperature is not very small. Numerical experiments are performed for the Poisson-Boltzmann equations and the two-layer neural networks to validate the method and the theory.
Evidence of Weak Circumstellar Medium Interaction in the Type II SN 2023axu

We present high-cadence photometric and spectroscopic observations of SN 2023axu, a classical Type II supernova with an absolute V-band peak magnitude of –17.2 ± 0.1 mag. SN 2023axu was discovered by the Distance Less Than 40 Mpc (DLT40) survey within 1 day of the last nondetection in the nearby galaxy NGC 2283 at 13.7 Mpc. We modeled the early light curve using a recently updated shock cooling model that includes the effects of line blanketing and found the explosion epoch to be MJD 59971.48 ± 0.03 and the probable progenitor to be a red supergiant. The shock cooling model underpredicts the overall UV data, which point to a possible interaction with circumstellar material. This interpretation is further supported by spectral behavior. We see a ledge feature around 4600 Å in the very early spectra (+1.1 and +1.5 days after the explosion), which can be a sign of circumstellar interaction. The signs of circumstellar material are further bolstered by the presence of absorption features blueward of Hα and Hβ at day >40, which is also generally attributed to circumstellar interaction. Our analysis shows the need for high-cadence early photometric and spectroscopic data to decipher the mass-loss history of the progenitor.
GASS: Generalizing Audio Source Separation with Large-scale Data

Universal source separation targets at separating the audio sources of an arbitrary mix, removing the constraint to operate on a specific domain like speech or music. Yet, the potential of universal source separation is limited because most existing works focus on mixes with predominantly sound events, and small training datasets also limit its potential for supervised learning. Here, we study a single general audio source separation (GASS) model trained to separate speech, music, and sound events in a supervised fashion with a large-scale dataset. We assess GASS models on a diverse set of tasks. Our strong in-distribution results show the feasibility of GASS models, and the competitive out-of-distribution performance in sound event and speech separation shows its generalization abilities. Yet, it is challenging for GASS models to generalize for separating out-of-distribution cinematic and music content. We also fine-tune GASS models on each dataset and consistently outperform the ones without pre-training. All fine-tuned models (except the music separation one) obtain state-of-the-art results in their respective benchmarks.
Pairwise Proximal Policy Optimization: Harnessing Relative Feedback for LLM Alignment

Large Language Models (LLMs) can acquire extensive world knowledge through pre-training on large corpora. However, due to exposure to low-quality data, LLMs may exhibit harmful behavior without aligning with human values. The dominant approach for steering LLMs towards beneficial behavior involves Reinforcement Learning with Human Feedback (RLHF), with Proximal Policy Optimization (PPO) serving as the default RL optimizer. Despite its effectiveness, PPO has limitations when optimizing rewards trained from comparison-based loss. Primarily, PPO is not invariant to equivalent reward functions containing identical preference information due to the need to calibrate the reward scale. Additionally, PPO's necessity for token-wise updates introduces complexity in both function approximation and algorithm design compared to trajectory-wise optimization. This paper proposes a new framework, reinforcement learning with relative feedback, and a novel trajectory-wise policy gradient algorithm, Pairwise Proximal Policy Optimization (P3O) that operates directly on comparative rewards. We show theoretically that P3O is invariant to equivalent rewards and avoids the complexity of PPO. Empirical evaluations demonstrate that P3O outperforms PPO in the KL-Reward trade-off and can align with human preferences as well as or better than prior methods. In summary, this work introduces a simpler yet effective approach for aligning LLMs to human preferences through relative feedback.
Spectral Retrieval with JWST Photometric data: a Case Study for HIP 65426 b

Half of the JWST high-contrast imaging objects will only have photometric data as of Cycle 2. However, to better understand their atmospheric chemistry that informs formation origin, spectroscopic data are preferred. Using HIP 65426 b, we investigate to what extent planet properties and atmospheric chemical abundance can be retrieved with only JWST photometric data points (2.5–15.5 μm) in conjunction with ground-based archival low-resolution spectral data (1.0–2.3 μm). We find that the data is consistent with an atmosphere with solar metallicity and C/O ratios at 0.40 and 0.55. We rule out 10× solar metallicity and an atmosphere with C/O = 1.0. We also find strong evidence of silicate clouds but no sign of an enshrouding featureless dust extinction. This work offers guidance and cautionary tales on analyzing data in the absence of medium-to-high-resolution spectral data.
The High-energy Spectrum of the Young Planet Host V1298 Tau

V1298 Tau is a young pre-main-sequence star hosting four known exoplanets that are prime targets for transmission spectroscopy with current-generation instruments. This work pieces together observations from the NICER X-ray telescope, the Space Telescope Imaging Spectrograph and Cosmic Origins Spectrograph instruments aboard Hubble Space Telescope, and empirically informed models to create a panchromatic spectral energy distribution for V1298 Tau spanning 1–105 Å. We describe the methods and assumptions used to assemble the panchromatic spectrum and show that despite this star’s brightness, its high-energy spectrum is near the limit of present X-ray and ultraviolet observatories’ abilities to characterize. We conclude by using the V1298 Tau spectrum as a benchmark for the activity saturation stage of high-energy radiation from solar-mass stars to compare the lifetime cumulative high-energy irradiation of the V1298 Tau planets to other planets orbiting similarly massive stars.
Learning State-Augmented Policies for Information Routing in Communication Networks

This paper examines the problem of information routing in a large-scale communication network, which can be formulated as a constrained statistical learning problem having access to only local information. We delineate a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures, by deploying graph convolutions over the topological links of the communication network. The proposed technique leverages only the local information available at each node and efficiently routes desired information to the destination nodes. We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies. In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms. Numerical simulations depict the improved performance of the proposed method in training a GNN parameterization as compared to baseline algorithms.
Unadjusted Langevin Algorithms for SDEs with Hoelder Drift

Consider the following stochastic differential equation for $(X_t)_{t\ge 0}$ on $\mathbb R^d$ and its Euler-Maruyama (EM) approximation $(Y_{t_n})_{n\in \mathbb Z^+}$: \begin{align*}&d X_t=b( X_t) d t+\sigma(X_t) d B_t, \\&Y_{t_{n+1}}=Y_{t_{n}}+\eta_{n+1} b(Y_{t_{n}})+\sigma(Y_{t_{n}})\left(B_{t_{n+1}}-B_{t_{n}}\right), \end{align*} where $b:\mathbb{R}^d \rightarrow \mathbb{R}^d,\ \ \sigma: \mathbb R^d \rightarrow \mathbb{R}^{d \times d}$ are measurable, $B_t$ is the $d$-dimensional Brownian motion, $t_0:=0,t_{n}:=\sum_{k=1}^{n} \eta_{k}$ for constants $\eta_k>0$ satisfying $\lim_{k \rightarrow \infty} \eta_k=0$ and $\sum_{k=1}^\infty\eta_k =\infty$. Under (partial) dissipation conditions ensuring the ergodicity, we obtain explicit convergence rates of $\mathbb W_p(\mathscr{L}(Y_{t_n}), \mathscr{L}(X_{t_n}))+\mathbb W_p(\mathscr{L}(Y_{t_n}), \mu)\rightarrow 0$ as $n\rightarrow \infty$, where $\mathbb W_p$ is the $L^p$-Wasserstein distance for certain $p\in [0,\infty)$, $\mathscr{L}(\xi)$ is the distribution of random variable $\xi$, and $\mu$ is the unique invariant probability measure of $(X_t)_{t \ge 0}$. Comparing with the existing results where $b$ is at least $C^2$-smooth, our estimates apply to Hoelder continuous drift and can be sharp in several specific situations.
Intensification of the Northeast Pacific oxygen minimum zone during the Bölling‐Alleröd Warm Period

Although climate records from several locations around the world show nearly synchronous and abrupt changes, the nature of the inferred teleconnection is still poorly understood. On the basis of preserved laminations and molybdenum enrichments in open margin sediments we demonstrate that the oxygen content of northeast Pacific waters at 800 m depth during the Bolling-Allerod warm period (15–13 kyr) was greatly reduced. Existing oxygen isotopic records of benthic and planktonic foraminifera suggest that this was probably due to suppressed ventilation at higher latitudes of the North Pacific. Comparison with ventilation records for the North Atlantic indicates an antiphased pattern of convection relative to the North Pacific over the past 22 kyr, perhaps due to variations in water vapor transport across Central America.
Multilingual Natural Language Processing Model for Radiology Reports - The Summary is all you need!

The impression section of a radiology report summarizes important radiology findings and plays a critical role in communicating these findings to physicians. However, the preparation of these summaries is time-consuming and error-prone for radiologists. Recently, numerous models for radiology report summarization have been developed. Nevertheless, there is currently no model that can summarize these reports in multiple languages. Such a model could greatly improve future research and the development of Deep Learning models that incorporate data from patients with different ethnic backgrounds. In this study, the generation of radiology impressions in different languages was automated by fine-tuning a model, publicly available, based on a multilingual text-to-text Transformer to summarize findings available in English, Portuguese, and German radiology reports. In a blind test, two board-certified radiologists indicated that for at least 70% of the system-generated summaries, the quality matched or exceeded the corresponding human-written summaries, suggesting substantial clinical reliability. Furthermore, this study showed that the multilingual model outperformed other models that specialized in summarizing radiology reports in only one language, as well as models that were not specifically designed for summarizing radiology reports, such as ChatGPT.
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning

Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning since it allows multiple clients to collaboratively train a global model without granting others access to their private data. It is, however, known that FL can be vulnerable to membership inference attacks (MIAs), where the training records of the global model can be distinguished from the testing records. Surprisingly, research focusing on the investigation of the source inference problem appears to be lacking. We also observe that identifying a training record's source client can result in privacy breaches extending beyond MIAs. For example, consider an FL application where multiple hospitals jointly train a COVID-19 diagnosis model, membership inference attackers can identify the medical records that have been used for training, and any additional identification of the source hospital can result the patient from the particular hospital more prone to discrimination. Seeking to contribute to the literature gap, we take the first step to investigate source privacy in FL. Specifically, we propose a new inference attack (hereafter referred to as source inference attack -- SIA), designed to facilitate an honest-but-curious server to identify the training record's source client. The proposed SIAs leverage the Bayesian theorem to allow the server to implement the attack in a non-intrusive manner without deviating from the defined FL protocol. We then evaluate SIAs in three different FL frameworks to show that in existing FL frameworks, the clients sharing gradients, model parameters, or predictions on a public dataset will leak such source information to the server. We also conduct extensive experiments on various datasets to investigate the key factors in an SIA. The experimental results validate the efficacy of the proposed SIAs.