Miracle-dz/UniFAR-FTUs huggingface dataset:
A Short Intense Dynamo at the Onset of Crystallization in White Dwarfs

The origin of large magnetic fields (≳106 G) in isolated white dwarfs is not clear. One possible explanation is that crystallization of the star’s core drives compositional convection, which when combined with the star’s rotation, can drive a dynamo. However, whether convection is efficient enough to explain the large intensity of the observed magnetic fields is still under debate. Recent work has shown that convection in cooling white dwarfs spans two regimes: efficient convection at the onset of crystallization, and thermohaline convection during most of the star’s cooling history. Here, we calculate the properties of crystallization-driven convection for cooling models of several white dwarfs of different masses. We combine mixing-length theory with scalings from magnetorotational convection to estimate the typical magnitude of the convective velocity and induced magnetic field for both scenarios. In the thermohaline regime, we find velocities ∼10−6–10−5 cm s−1, with fields restricted to ≲ 100 G. However, when convection is efficient, the flow velocity can reach magnitudes of ∼102–103 cm s−1, with fields of ∼106–108 G, independent of the star’s rotation rate. Thus, dynamos driven at the onset of crystallization could explain the large intensity magnetic fields measured for single white dwarfs.
Robust Analysis of Multi-Task Learning on a Complex Vision System

Multi-task learning (MTL) has been widely studied in the past decade. In particular, dozens of optimization algorithms have been proposed for different settings. While each of them claimed improvement when applied to certain models on certain datasets, there is still lack of deep understanding on the performance in complex real-worlds scenarios. We identify the gaps between research and application and make the following 4 contributions. (1) We comprehensively evaluate a large set of existing MTL optimization algorithms on the MetaGraspNet dataset designed for robotic grasping task, which is complex and has high real-world application values, and conclude the best-performing methods. (2) We empirically compare the method performance when applied on feature-level gradients versus parameter-level gradients over a large set of MTL optimization algorithms, and conclude that this feature-level gradients surrogate is reasonable when there are method-specific theoretical guarantee but not generalizable to all methods. (3) We provide insights on the problem of task interference and show that the existing perspectives of gradient angles and relative gradient norms do not precisely reflect the challenges of MTL, as the rankings of the methods based on these two indicators do not align well with those based on the test-set performance. (4) We provide a novel view of the task interference problem from the perspective of the latent space induced by the feature extractor and provide training monitoring results based on feature disentanglement.
Classical and Quantum Computing of Shear Viscosity for $2+1D$ SU(2) Gauge Theory

We perform a nonperturbative calculation of the shear viscosity for $(2+1)$-dimensional SU(2) gauge theory by using the lattice Hamiltonian formulation. The retarded Green's function of the stress-energy tensor is calculated from real time evolution via exact diagonalization of the lattice Hamiltonian with a local Hilbert space truncation and the shear viscosity is obtained via the Kubo formula. When taking the continuum limit, we account for the renormalization group flow of the coupling but no additional operator renormalization. We find the ratio of the shear viscosity and the entropy density $\frac{\eta}{s}$ is consistent with a well-known holographic result $\frac{1}{4\pi}$ at several temperatures on a $4\times4$ hexagonal lattice with the local electric representation truncated at $j_{\rm max}=\frac{1}{2}$. We also find the ratio of the spectral function and frequency $\frac{\rho^{xy}(\omega)}{\omega}$ exhibits a peak structure when the frequency is small. Both the exact diagonalization method and simple matrix product state classical simulation method beyond $j_{\rm max}=\frac{1}{2}$ on bigger lattices require exponentially growing resources. So we develop a quantum computing method to calculate the retarded Green's function and analyze various systematics of the calculation including $j_{\rm max}$ truncation and finite size effects and Trotter errors. We test our quantum circuit on both the Quantinuum emulator and the IBM simulator for a small lattice and obtain results consistent with the classical computing ones.
Evaluating the Environmental Justice Dimensions of Odor in Denver, Colorado

Background Odors are a documented environmental justice challenge in Denver, Colorado. Complaints are an important modality through which residents express their concerns. Objective We investigated disparities in environmental justice related-variables based on home and workplace census block groups (race/ethnicity, education-levels, renter-occupied housing, median income and median home values, gentrification) based on locations of odor complaints as well as that of potentially malodorous facilities. We report key themes identified in complaints. Methods We obtained odor complaints for 2014-2023, and the locations of facilities required to submit odor management plans as of 2023 from the Denver Department of Public Health and Environment. We downloaded residential census block group-level socioeconomic data from the 2016-2020 American Community Survey; and workplace-based socioeconomic data from the Longitudinal Employer-Household Dynamics dataset for 2020. We categorized neighborhoods as gentrified or not based on a typology produced by the Urban Displacement Project. We assessed exposure to potentially malodorous facilities and complaints within each census block group and investigated exposure disparities by comparing distributions of environmental justice-related variables based on if a facility or a complaint has been made, and census block group-level odor intensity categories. Results Less privileged census block groups were significantly disproportionately burdened with potentially malodorous facilities. Importantly, our study also reveals disparities in the location of facilities, not just in traditional residence/-based environmental justice-related variables, but in workplace/-based factors, as well. We did not observe similar disparities for odor complaints. However, complaints were significantly higher in gentrified neighborhoods.
Sparse Graph Representations for Procedural Instructional Documents

Computation of document similarity is a critical task in various NLP domains that has applications in deduplication, matching, and recommendation. Traditional approaches for document similarity computation include learning representations of documents and employing a similarity or a distance function over the embeddings. However, pairwise similarities and differences are not efficiently captured by individual representations. Graph representations such as Joint Concept Interaction Graph (JCIG) represent a pair of documents as a joint undirected weighted graph. JCIGs facilitate an interpretable representation of document pairs as a graph. However, JCIGs are undirected, and don't consider the sequential flow of sentences in documents. We propose two approaches to model document similarity by representing document pairs as a directed and sparse JCIG that incorporates sequential information. We propose two algorithms inspired by Supergenome Sorting and Hamiltonian Path that replace the undirected edges with directed edges. Our approach also sparsifies the graph to $O(n)$ edges from JCIG's worst case of $O(n^2)$. We show that our sparse directed graph model architecture consisting of a Siamese encoder and GCN achieves comparable results to the baseline on datasets not containing sequential information and beats the baseline by ten points on an instructional documents dataset containing sequential information.
Gradient Coding in Decentralized Learning for Evading Stragglers

In this paper, we consider a decentralized learning problem in the presence of stragglers. Although gradient coding techniques have been developed for distributed learning to evade stragglers, where the devices send encoded gradients with redundant training data, it is difficult to apply those techniques directly to decentralized learning scenarios. To deal with this problem, we propose a new gossip-based decentralized learning method with gradient coding (GOCO). In the proposed method, to avoid the negative impact of stragglers, the parameter vectors are updated locally using encoded gradients based on the framework of stochastic gradient coding and then averaged in a gossip-based manner. We analyze the convergence performance of GOCO for strongly convex loss functions. And we also provide simulation results to demonstrate the superiority of the proposed method in terms of learning performance compared with the baseline methods.
The differential evolution of hot- and cold-dust in galaxies in the last 7.5 GYR

The study of dust is crucial for the study of galaxies since it reradiates in the infrared the ultraviolet and optical radiation originating from stellar activity. A dust grain equilibrates to a temperature that is dependent of the intensity of its local radiation field, and also depends on the grain’s optical properties. The heating of dust grains is done primarily by absorption of starlight, however, light from the accretion disk around a super-massive black-hole in an Active Galactic Nucleus is also an important mechanism for the heating of dust. While understanding dust throughout cosmic time, most studies base their assumptions on far-infrared wavelengths, since that is where most of the energy resides, inferring the nearand mid-infrared from local relations or even neglecting those spectral regimes altogether. However, these spectral regimes may play a role in the regions closer to the heating sources and, therefore, may not evolve the same way as the dust radiating in the far-infrared. This study aims to confirm a result obtained in Messias et al. (2013), where they found a clear difference between the luminosity density evolution at nearinfrared wavelengths and that at far-infrared ones as reported in the literature. The goal is to verify if this result is due to a differential evolution of hotand cold-dust, or an effect of cosmic variance. In order to address this, a direct comparison ought to be done for the same sample, which is what is pursued in this dissertation with the Cosmological Evolution Survey. The first course of action was to characterize the far-infrared properties of the sample depending on spectral type and distance in order to assess their evolution. Luminosity density functions were then obtained, enabling a characterization of
Network Analysis of U.S. Non-Fatal Opioid-Involved Overdose Journeys, 2018-2023

We present a nation-wide network analysis of non-fatal opioid-involved overdose journeys in the United States. Leveraging a unique proprietary dataset of Emergency Medical Services incidents, we construct a journey-to-overdose geospatial network capturing nearly half a million opioid-involved overdose events spanning 2018-2023. We analyze the structure and sociological profile of the nodes, which are counties or their equivalents, characterize the distribution of overdose journey lengths, and investigate changes in the journey network between 2018 and 2023. Our findings include that authority and hub nodes identified by the HITS algorithm tend to be located in urban areas and involved in overdose journeys with particularly long geographical distances.
Reconfigurable Power Converters with Increased Utilization for Unbalanced Power Distribution System Applications

A low-cost reconfiguration stage connected at the output of balanced three-phase, multi-terminal ac/dc/ac converters can increase the feasible set of power injections substantially, increasing converter utilization and therefore achieving a lower system cost. However, the approach has yet to be explored for phase unbalance mitigation in power distribution networks, an important application for future energy systems. This study addresses this by considering power converter reconfiguration's potential for increasing the feasible set of power transfers of four-wire power converters. Reconfigurable topologies are compared against both conventional four-wire designs and an idealised, fully reconfigurable converter. Results show that conventional converters need up to 75.3% greater capacity to yield a capability chart of equivalent size to an idealised reconfigurable converter. The number and capacity of legs impact the capability chart's size, as do constraints on dc-side power injections. The proposed approach shows significant promise for maximizing the utilization of power electronics used to mitigate impacts of phase unbalance.
ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing

Peer review is a cornerstone of science. Research communities conduct peer reviews to assess contributions and to improve the overall quality of science work. Every year, new community members are recruited as peer reviewers for the first time. How could technology help novices adhere to their community's practices and standards for peer reviewing? To better understand peer review practices and challenges, we conducted a formative study with 10 novices and 10 experts. We found that many experts adopt a workflow of annotating, note-taking, and synthesizing notes into well-justified reviews that align with community standards. Novices lack timely guidance on how to read and assess submissions and how to structure paper reviews. To support the peer review process, we developed ReviewFlow -- an AI-driven workflow that scaffolds novices with contextual reflections to critique and annotate submissions, in-situ knowledge support to assess novelty, and notes-to-outline synthesis to help align peer reviews with community expectations. In a within-subjects experiment, 16 inexperienced reviewers wrote reviews in two conditions: using ReviewFlow and using a baseline environment with minimal guidance. With ReviewFlow, participants produced more comprehensive reviews, identifying more pros and cons. While participants appreciated the streamlined process support from ReviewFlow, they also expressed concerns about using AI as part of the scientific review process. We discuss the implications of using AI to scaffold the peer review process on scientific work and beyond.
Euclid preparation. Optical emission-line predictions of intermediate-z galaxy populations in GAEA for the Euclid Deep and Wide Surveys

In anticipation of the Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone from the GAEA semi-analytic model to self-consistently model nebular emission from HII regions, narrow-line regions of active galactic nuclei (AGN), and evolved stellar populations. Our analysis focuses on seven optical emission lines: H$\alpha$, H$\beta$, [SII]$\lambda\lambda 6717, 6731$, [NII]$\lambda 6584$, [OI]$\lambda 6300$, [OIII]$\lambda 5007$, and [OII]$\lambda\lambda 3727, 3729$. We find that Euclid will predominantly observe massive, star-forming, and metal-rich line-emitters. Interstellar dust, modelled using a Calzetti law with mass-dependent scaling, may decrease observable percentages by a further 20-30% with respect to our underlying emission-line populations from GAEA. We predict Euclid to observe around 30-70% of H$\alpha$-, [NII]-, [SII]-, and [OIII]-emitting galaxies at redshift below 1 and under 10% at higher redshift. Observability of H$\beta$-, [OII]-, and [OI]- emission is limited to below 5%. For the Euclid-observable sample, we find that BPT diagrams can effectively distinguish between different galaxy types up to around redshift 1.8, attributed to the bias toward metal-rich systems. Moreover, we show that the relationships of H$\alpha$ and [OIII]+H$\beta$ to the star-formation rate, and the [OIII]-AGN luminosity relation, exhibit minimal changes with increasing redshift. Based on line ratios [NII]/H$\alpha$, [NII]/[OII], and [NII]/[SII], we further propose novel z-invariant tracers for the black hole accretion rate-to-star formation rate ratio. Lastly, we find that commonly used metallicity estimators display gradual shifts in normalisations with increasing redshift, while maintaining the overall shape of local calibrations. This is in tentative agreement with recent JWST data.
Role of spontaneously generated coherence (SGC) in laser cooling of atoms

The well-known sub-Doppler polarization gradient cooling in type-I transition ($F_e=F_g+1$) is caused by red-detuned lasers. On the other hand, in type-II transition ($F_e\le F_g$), sub-Doppler cooling takes place through blue-detuned lasers. This opposite behavior for the two types of transitions is due to SGC. In the absence of SGC, both types of transitions show blue-detuned cooling. In this work, we experimentally and theoretically demonstrate blue-detuned cooling for both types of transitions in $^{\textrm{87}}$Rb. For completeness, we compare the temperatures in various configurations.
RTHDet: Rotate Table Area and Head Detection in images

Traditional models focus on horizontal table detection but struggle in rotating contexts, limiting progress in table recognition. This paper introduces a new task: detecting table regions and localizing head-tail parts in rotation scenarios. We propose corresponding datasets, evaluation metrics, and methods. Our novel method, 'Adaptively Bounded Rotation,' addresses dataset scarcity in detecting rotated tables and their head-tail parts. We produced 'TRR360D,' a dataset incorporating semantic information of table head and tail, based on 'ICDAR2019MTD. ' A new metric, 'R360 AP,' measures precision in detecting rotated regions and localizing head-tail parts. Our baseline, the high-speed and accurate 'RTMDet-S,' is chosen after extensive review and testing. We introduce 'RTHDet,' enhancing the baseline with a 'r360' rotated rectangle angle representation and an 'Angle Loss' branch, improving head-tail localization. By applying transfer learning and adaptive boundary rotation augmentation, RTHDet's AP50 (T<90) improved from 23.7% to 88.7% compared to the baseline. This demonstrates RTHDet's effectiveness in detecting rotating table regions and accurately localizing head and tail parts.RTHDet is integrated into the widely-used open-source MMRotate toolkit: https://github.com/open-mmlab/mmrotate/tree/dev-1.x/projects/RR360.
Diffusion World Model

We introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently. As opposed to traditional one-step dynamics models, DWM offers long-horizon predictions in a single forward pass, eliminating the need for recursive queries. We integrate DWM into model-based value estimation, where the short-term return is simulated by future trajectories sampled from DWM. In the context of offline reinforcement learning, DWM can be viewed as a conservative value regularization through generative modeling. Alternatively, it can be seen as a data source that enables offline Q-learning with synthetic data. Our experiments on the D4RL dataset confirm the robustness of DWM to long-horizon simulation. In terms of absolute performance, DWM significantly surpasses one-step dynamics models with a $44\%$ performance gain, and achieves state-of-the-art performance.
nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

In the field of biomedical image analysis, the quest for architectures capable of effectively capturing long-range dependencies is paramount, especially when dealing with 3D image segmentation, classification, and landmark detection. Traditional Convolutional Neural Networks (CNNs) struggle with locality respective field, and Transformers have a heavy computational load when applied to high-dimensional medical images.In this paper, we introduce nnMamba, a novel architecture that integrates the strengths of CNNs and the advanced long-range modeling capabilities of State Space Sequence Models (SSMs). Specifically, we propose the Mamba-In-Convolution with Channel-Spatial Siamese learning (MICCSS) block to model the long-range relationship of the voxels. For the dense prediction and classification tasks, we also design the channel-scaling and channel-sequential learning methods. Extensive experiments on 6 datasets demonstrate nnMamba's superiority over state-of-the-art methods in a suite of challenging tasks, including 3D image segmentation, classification, and landmark detection. nnMamba emerges as a robust solution, offering both the local representation ability of CNNs and the efficient global context processing of SSMs, setting a new standard for long-range dependency modeling in medical image analysis. Code is available at https://github.com/lhaof/nnMamba
Atmospheric Boundary Layer Flows Their Structure And Measurement

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Consistent Joint Decision-Making with Heterogeneous Learning Models

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming(ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions’ prior probability, confidence (uncertainty), and the models’ expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.
First principles evaluation of phase stability in the In-Sn binary system

The In-Sn binary alloy system exhibits several unusual features that challenge crystallographic and thermodynamic expectations. We combine first principles total energy calculation with simple thermodynamic modeling to address two key points. First, we evaluate energies along the Bain path to interpret the discontinuous transition between the phases $\alpha$-In (Pearson type tI2) and $\beta$--In$_3$Sn (also Pearson type tI2) that are identical in symmetry. Second, we demonstrate that the solid solution phases $\beta$-In$_3$Sn and $\gamma$-InSn$_4$ (Pearson type hP1) exist at high temperatures only, and they exhibit eutectoid decompositions at low temperatures.