Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of coal dust from an active mine on the spectral reflectance of Arctic surface snow in Svalbard, Norway

Light‐absorbing particles (LAPs) in snow such as dust and black carbon influence the radiative forcing at the Earth's surface, which has major implications for global climate models. LAPs also significantly influence the melting of glaciers, sea ice, and seasonal snow. Here we present an in situ study of surface snow near an active coal mine in the Norwegian Arctic. We couple measurements of spectral hemispherical directional reflectance factor (HDRF) with measurements of LAPs characterized in two ways, as refractory black carbon using a Single Particle Soot Photometer and the total light absorption of LAPs measured with the Light Absorption Heating Method. The Snow Ice and Aerosol Radiation model was constrained by LAP measurements. Results were compared to observed spectral albedo measurements. Modeled and observed albedos were similar at the cleaner and more remote sites. However, the modeled spectral albedos do not fully account for the low spectral albedo measured next to the mine. LAP measurements also showed a large variation in particle sizes (tenths to tens of microns) related to transport distance of the particles from the mine. Here we find that LAPs from coal dust reduce the spectral HDRF by up to 84% next to the mine and 55% 0.5 km downwind of the mine. The coupling of extreme LAP observations (1 ng g−1 to 4863 ng g−1) with HDRF measurements from 350 to 2500 nm has facilitated the development of spectral band pairs, which could be used in the future to remotely assess LAPs in Arctic snow.
Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR

Recently, there has been increasing concern about the vulnerability of deep neural network (DNN)-based synthetic aperture radar (SAR) automatic target recognition (ATR) to adversarial attacks, where a DNN could be easily deceived by clean input with imperceptible but aggressive perturbations. This paper studies the synthetic-to-measured (S2M) transfer setting, where an attacker generates adversarial perturbation based solely on synthetic data and transfers it against victim models trained with measured data. Compared with the current measured-to-measured (M2M) transfer setting, our approach does not need direct access to the victim model or the measured SAR data. We also propose the transferability estimation attack (TEA) to uncover the adversarial risks in this more challenging and practical scenario. The TEA makes full use of the limited similarity between the synthetic and measured data pairs for blind estimation and optimization of S2M transferability, leading to feasible surrogate model enhancement without mastering the victim model and data. Comprehensive evaluations based on the publicly available synthetic and measured paired labeled experiment (SAMPLE) dataset demonstrate that the TEA outperforms state-of-the-art methods and can significantly enhance various attack algorithms in computer vision and remote sensing applications. Codes and data are available at https://github.com/scenarri/S2M-TEA.
A Tournament of Transformation Models: B-Spline-based vs. Mesh-based Multi-Objective Deformable Image Registration

The transformation model is an essential component of any deformable image registration approach. It provides a representation of physical deformations between images, thereby defining the range and realism of registrations that can be found. Two types of transformation models have emerged as popular choices: B-spline models and mesh models. Although both models have been investigated in detail, a direct comparison has not yet been made, since the models are optimized using very different optimization methods in practice. B-spline models are predominantly optimized using gradient-descent methods, while mesh models are typically optimized using finite-element method solvers or evolutionary algorithms. Multi-objective optimization methods, which aim to find a diverse set of high-quality trade-off registrations, are increasingly acknowledged to be important in deformable image registration. Since these methods search for a diverse set of registrations, they can provide a more complete picture of the capabilities of different transformation models, making them suitable for a comparison of models. In this work, we conduct the first direct comparison between B-spline and mesh transformation models, by optimizing both models with the same state-of-the-art multi-objective optimization method, the Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA). The combination with B-spline transformation models, moreover, is novel. We experimentally compare both models on two different registration problems that are both based on pelvic CT scans of cervical cancer patients, featuring large deformations. Our results, on three cervical cancer patients, indicate that the choice of transformation model can have a profound impact on the diversity and quality of achieved registration outcomes.
The Inner Disk Rim of HD 163296: Linking Radiative Hydrostatic Models with Infrared Interferometry

Previous studies of the protoplanetary disk HD 163296 revealed that the morphology of its sub-au infrared emission encompasses the terminal sublimation front of dust grains, referred to as the inner rim, but also extends into the (supposedly) dust-free region within it. Here, we present a set of radiative hydrostatic simulations of the inner rim in order to assess how much the rim alone can contribute to the observed interferometric visibilities V, half-light radii R hl, and fractional disk fluxes F in the wavelength range 1.5–13 μm. In our set of models, we regulate the cooling efficiency of the disk via the boundary condition for radiation diffusion and we also modify the shape of the sublimation front. We find that when the cooling efficiency is reduced, the infrared photosphere at the rim becomes hotter, leading to an increase in R hl sufficient to match the observations. However, the near-infrared disk flux is typically too low ( F ≃ 0.25 at 1.5 μm), resulting in H-band visibility curves located above the observed data. We show that the match to the H-band observations up to moderate baselines can be improved when a wall-shaped rather than curved sublimation front is considered. Nevertheless, our model visibilities always exhibit a bounce at long baselines, which is not observed, confirming the need for additional emission interior to the rim. In summary, our study illustrates how the temperature structure and geometry of the inner rim need to change in order to boost the rim’s infrared emission.
IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion

Code completion aims to enhance programming productivity by predicting potential code based on the current programming context. Recently, pretrained language models (LMs) have become prominent in this field. Various approaches have been proposed to fine-tune LMs using supervised fine-tuning (SFT) techniques for code completion. However, the inherent exposure bias of these models can cause errors to accumulate early in the sequence completion, leading to even more errors in subsequent completions. To address this problem, deep reinforcement learning (DRL) is an alternative technique for fine-tuning LMs for code completion, which can improve the generalization capabilities and overall performance. Nevertheless, integrating DRL-based strategies into code completion faces two major challenges: 1) The dynamic nature of the code context requires the completion model to quickly adapt to changes, which poses difficulties for conventional DRL strategies that focus on delayed rewarding of the final code state. 2) It is difficult to evaluate the correctness of partial code, thus the reward redistribution-based strategies cannot be adapted to code completion. To tackle these challenges, we propose IRCoCo, a code completion-specific DRL-based fine-tuning framework. This framework is designed to provide immediate rewards as feedback for detecting dynamic context changes arising from continuous edits during code completion. With the aid of immediate feedback, the fine-tuned LM can gain a more precise understanding of the current context, thereby enabling effective adjustment of the LM and optimizing code completion in a more refined manner. Experimental results demonstrate that fine-tuning pretrained LMs with IRCoCo leads to significant improvements in the code completion task, outperforming both SFT-based and other DRL-based baselines.
Resting-State fingerprints of Acceptance and Reappraisal. The role of Sensorimotor, Executive and Affective networks

Acceptance and Reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks contributions to these abilities, especially for what concerns Acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the Affective network and increased activity inside the Executive and Sensorimotor networks to be predicted of acceptance. We also expect that these networks may be associated at least in part with Reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased Affective, and increased Executive, and Sensorimotor networks, while reappraisal was predicted by an increase in the Sensorimotor network. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal, with the Sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, demonstrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation.
Random walks on Coxeter interchange graphs

A tournament is an orientation of a graph. Vertices are players and edges are games, directed away from the winner. Kannan, Tetali and Vempala and McShine showed that tournaments with given score sequence can be rapidly sampled, via simple random walks on the interchange graphs of Brualdi and Li. These graphs are generated by the cyclically directed triangle, in the sense that traversing an edge corresponds to the reversal of such a triangle in a tournament. We study Coxeter tournaments on Zaslavsky's signed graphs. These tournaments involve collaborative and solitaire games, as well as the usual competitive games. The interchange graphs are richer in complexity, as a variety of other generators are involved. We prove rapid mixing by an intricate application of Bubley and Dyer's method of path coupling, using a delicate re-weighting of the graph metric. Geometric connections with the Coxeter permutahedra introduced by Ardila, Castillo, Eur and Postnikov are discussed.
Empirical tight-binding method for large-supercell simulations of disordered semiconductor alloys

We analyze and present applications of a recently proposed empirical tight-binding scheme for investigating the effects of alloy disorder on various electronic and optical properties of semiconductor alloys, such as the band gap variation, the localization of charge carriers, and the optical transitions. The results for a typical antimony-containing III-V alloy, GaAsSb, show that the new scheme greatly improves the accuracy in reproducing the experimental alloy band gaps compared to other widely used schemes. The atomistic nature of the empirical tight-binding approach paired with a reliable parameterization enables more detailed physical insights into the effects of disorder in alloyed materials.
The higher order chain rule in Sobolev spaces

We establish the Fa\`a di Bruno formula, in the sense of almost everywhere equality, for derivatives of the composed function $f \circ g$, for all function $f : R \rightarrow R$ such that $f$ acts on $W^m_p(R^n)$ by composition, and all $g \in W^m_p(R^n)$, possibly modified on a set of measure 0.
Stress and Alignment Response to Curved Obstacles in Growing Bacterial Monolayers

Monolayers of growing bacteria, confined within channel geometries, exhibit self-organization into a highly aligned laminar state along the axis of the channel. Although this phenomenon has been observed in experiments and simulations under various boundary conditions, the underlying physical mechanism driving this alignment remains unclear. In this study, we conduct simulations of growing bacteria in 2D channel geometries perturbed by fixed obstacles, either circular or arc-shaped, placed at the channel's center. Our findings reveal that even sizable obstacles cause only short-ranged disruptions to the baseline laminar state. These disruptions arise from a competition between local planar anchoring and bulk laminar alignment. At smaller obstacle sizes, bulk alignment fully dominates, while at larger sizes, planar anchoring induces increasing local disruptions. Furthermore, our analysis indicates that the resulting configurations of the bacterial system display a striking resemblance to the arrangement of hard-rod smectic liquid crystals around circular obstacles. This suggests that modeling hard-rod bacterial monolayers as smectic, rather than nematic, liquid crystals may yield successful outcomes. The insights gained from our study contribute to the expanding body of research on bacterial growth in channels. Our work provides new perspectives on the stability of the laminar state and extends our understanding to encompass more intricate confinement schemes.
Nuclear scattering via quantum computing

We propose a hybrid quantum-classical framework to solve the elastic scattering phase shift of two well-bound nuclei in an uncoupled channel. Within this framework, we develop a many-body formalism in which the continuum scattering states of the two colliding nuclei are regulated by a weak external harmonic oscillator potential with varying strength. Based on our formalism, we propose an approach to compute the eigenenergies of the low-lying scattering states of the relative motion of the colliding nuclei as a function of the oscillator strength of the confining potential. Utilizing the modified effective range expansion, we extrapolate the elastic scattering phase shift of the colliding nuclei from these eigenenergies to the limit when the external potential vanishes. In our hybrid approach, we leverage the advantage of quantum computing to solve for these eigenenergies from a set of many-nucleon Hamiltonian eigenvalue problems. These eigenenergies are inputs to classical computers to obtain the phase shift. We demonstrate our framework with two simple problems, where we implement the rodeo algorithm to solve the relevant eigenenergies with the IBM Qiskit quantum simulator. The results of both the spectra and the elastic scattering phase shifts agree well with other theoretical results.
Independence numbers in certain families of highly symmetric graphs

FI-graphs were introduced by the second author and White to capture the idea of a family of nested graphs, each member of which is acted on by a progressively larger symmetric group. That work was built on the newly minted foundations of representation stability theory and FI-modules. Examples of such families include the complete graphs and the Kneser and Johnson graphs, among many others. While it was shown in the originating work how various counting invariants in these families behave very regularly, not much has thus far been proven about the behaviors of the typical extremal graph theoretic invariants such as their independence and clique numbers. In this paper we provide a conjecture on the growth of the independence and clique numbers in these families, and prove this conjecture in one case. We also provide computer code that generates experimental evidence in many other cases. All of this work falls into a growing trend in representation stability theory that displays the regular behaviors of a number of extremal invariants that arise when one looks at FI-algebras and modules.
InstructIR: High-Quality Image Restoration Following Human Instructions

Image restoration is a fundamental problem that involves recovering a high-quality clean image from its degraded observation. All-In-One image restoration models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natural language prompts, our model can recover high-quality images from their degraded counterparts, considering multiple degradation types. Our method, InstructIR, achieves state-of-the-art results on several restoration tasks including image denoising, deraining, deblurring, dehazing, and (low-light) image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement. Our code, datasets and models are available at: https://github.com/mv-lab/InstructIR
Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function

Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational intensity of calculating the electron-phonon spectral function, $\alpha^2F(\omega)$, the essential ingredient of Midgal-Eliashberg theory of superconductivity. To overcome this challenge, we adopt a two-step approach. First, we compute $\alpha^2F(\omega)$ for 818 dynamically stable materials. We then train a deep-learning model to predict $\alpha^2F(\omega)$, using an unconventional training strategy to temper the model's overfitting, enhancing predictions. Specifically, we train a Bootstrapped Ensemble of Tempered Equivariant graph neural NETworks (BETE-NET), obtaining an MAE of 0.21, 45 K, and 43 K for the Eliashberg moments derived from $\alpha^2F(\omega)$: $\lambda$, $\omega_{\log}$, and $\omega_{2}$, respectively, yielding an MAE of 2.5 K for the critical temperature, $T_c$. Further, we incorporate domain knowledge of the site-projected phonon density of states to impose inductive bias into the model's node attributes and enhance predictions. This methodological innovation decreases the MAE to 0.18, 29 K, and 28 K, respectively, yielding an MAE of 2.1 K for $T_c$. We illustrate the practical application of our model in high-throughput screening for high-$T_c$ materials. The model demonstrates an average precision nearly five times higher than random screening, highlighting the potential of ML in accelerating superconductor discovery. BETE-NET accelerates the search for high-$T_c$ superconductors while setting a precedent for applying ML in materials discovery, particularly when data is limited.
Approximate oblique duality for fusion frames

Fusion frames are a convenient tool in applications where we deal with a large amount of data or when a combination of local data is needed. Oblique dual fusion frames are suitable in situations where the analysis for the data and its subsequent synthesis have to be implemented in different subspaces of a Hilbert space. These procedures of analysis and synthesis are in general not exact, and also there are circumstances where the exact dual is not available or it is necessary to improve its properties. To resolve these questions we introduce the concept of approximate oblique dual fusion frame, and in particular of approximate oblique dual fusion frame system. We study their properties. We give the relation to approximate oblique dual frames. We provide methods for obtaining them. We show how to construct other duals from a given one that give reconstructions errors as small as we want.
One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization Training

Weight quantization is an effective technique to compress deep neural networks for their deployment on edge devices with limited resources. Traditional loss-aware quantization methods commonly use the quantized gradient to replace the full-precision gradient. However, we discover that the gradient error will lead to an unexpected zig-zagging-like issue in the gradient descent learning procedures, where the gradient directions rapidly oscillate or zig-zag, and such issue seriously slows down the model convergence. Accordingly, this paper proposes a one-step forward and backtrack way for loss-aware quantization to get more accurate and stable gradient direction to defy this issue. During the gradient descent learning, a one-step forward search is designed to find the trial gradient of the next-step, which is adopted to adjust the gradient of current step towards the direction of fast convergence. After that, we backtrack the current step to update the full-precision and quantized weights through the current-step gradient and the trial gradient. A series of theoretical analysis and experiments on benchmark deep models have demonstrated the effectiveness and competitiveness of the proposed method, and our method especially outperforms others on the convergence performance.
Approach to Hyperuniformity of Steady States of Facilitated Exchange Processes

We consider the fluctuations in the number of particles in a box of size L^d in Z^d, d>=1, in the (infinite volume) translation invariant stationary states of the facilitated exclusion process, also called the conserved lattice gas model. When started in a Bernoulli (product) measure at density rho, these systems approach, as t goes to infinity, a"frozen"state for rho<=rho_c, with rho_c=1/2 for d=1 and rho_c<1/2 for d>=2. At rho=rho_c the limiting state is hyperuniform, that is, the variance of the number of particles in the box grows slower than L^d. We give a general description of how the variances at different scales of L behave as rho increases to rho_c. On the largest scale, L>>L_2, the fluctuations are normal (in fact the same as in the original product measure), while in a region L_1<<L<<L_2, with both L_1 and L_2 going to infinity as rho increases to rho_c, the variance grows faster than normal. For 1<<L<<L_1 the variance is the same as in the hyperuniform system. (All results discussed are rigorous for d=1 and based on simulations for d>=2.)
Time-dependent influence metric for cascade dynamics on networks

An algorithm for efficiently calculating the expected size of single-seed cascade dynamics on networks is proposed and tested. The expected size is a time-dependent quantity and so enables the identification of nodes who are the most influential early or late in the spreading process. The measure is accurate for both critical and subcritical dynamic regimes and so generalises the nonbacktracking centrality that was previously shown to successfully identify the most influential single spreaders in a model of critical epidemics on networks.