Miracle-dz/UniFAR-FTUs huggingface dataset:
Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation

Recently, many attempts have been made to construct a transformer base U-shaped architecture, and new methods have been proposed that outperformed CNN-based rivals. However, serious problems such as blockiness and cropped edges in predicted masks remain because of transformers' patch partitioning operations. In this work, we propose a new U-shaped architecture for medical image segmentation with the help of the newly introduced focal modulation mechanism. The proposed architecture has asymmetric depths for the encoder and decoder. Due to the ability of the focal module to aggregate local and global features, our model could simultaneously benefit the wide receptive field of transformers and local viewing of CNNs. This helps the proposed method balance the local and global feature usage to outperform one of the most powerful transformer-based U-shaped models called Swin-UNet. We achieved a 1.68% higher DICE score and a 0.89 better HD metric on the Synapse dataset. Also, with extremely limited data, we had a 4.25% higher DICE score on the NeoPolyp dataset. Our implementations are available at: https://github.com/givkashi/Focal-UNet
Consideration of memory of spin and parity in the fissioning compound nucleus by applying the Hauser-Feshbach fission fragment decay model to photonuclear reactions

Prompt and $\beta$-delayed fission observables, such as the average number of prompt and delayed neutrons, the independent and cumulative fission product yields, and the prompt $\gamma$-ray energy spectra for the photonuclear reactions on $^{235,238}$U and $^{239}$Pu are calculated with the Hauser-Feshbach Fission Fragment Decay (HF$^3$D) model and compared with available experimental data. In the analysis of neutron-induced fission reactions to the case of photo-induced fission, an excellent reproduction of the delayed neutron yields supports a traditional assumption that the photo-fission might be similar to the neutron-induced fission at the same excitation energies regardless of the spin and parity of the fissioning systems.
PHANGS–JWST First Results: Rapid Evolution of Star Formation in the Central Molecular Gas Ring of NGC 1365

Large-scale bars can fuel galaxy centers with molecular gas, often leading to the development of dense ringlike structures where intense star formation occurs, forming a very different environment compared to galactic disks. We pair ∼0. ″3 (30 pc) resolution new JWST/MIRI imaging with archival ALMA CO(2–1) mapping of the central ∼5 kpc of the nearby barred spiral galaxy NGC 1365 to investigate the physical mechanisms responsible for this extreme star formation. The molecular gas morphology is resolved into two well-known bright bar lanes that surround a smooth dynamically cold gas disk (R gal ∼ 475 pc) reminiscent of non-star-forming disks in early-type galaxies and likely fed by gas inflow triggered by stellar feedback in the lanes. The lanes host a large number of JWST-identified massive young star clusters. We find some evidence for temporal star formation evolution along the ring. The complex kinematics in the gas lanes reveal strong streaming motions and may be consistent with convergence of gas streamlines expected there. Indeed, the extreme line widths are found to be the result of inter-“cloud” motion between gas peaks; ScousePy decomposition reveals multiple components with line widths of 〈σ CO,scouse〉 ≈ 19 km s−1 and surface densities of 〈ΣH2,scouse〉≈800M⊙pc−2 , similar to the properties observed throughout the rest of the central molecular gas structure. Tailored hydrodynamical simulations exhibit many of the observed properties and imply that the observed structures are transient and highly time-variable. From our study of NGC 1365, we conclude that it is predominantly the high gas inflow triggered by the bar that is setting the star formation in its CMZ.
StyleTRF: Stylizing Tensorial Radiance Fields✱

Stylized view generation of scenes captured casually using a camera has received much attention recently. The geometry and appearance of the scene are typically captured as neural point sets or neural radiance fields in the previous work. An image stylization method is used to stylize the captured appearance by training its network jointly or iteratively with the structure capture network. The state-of-the-art SNeRF [29] method trains the NeRF and stylization network in an alternating manner. These methods have high training time and require joint optimization. In this work, we present StyleTRF, a compact, quick-to-optimize strategy for stylized view generation using TensoRF [6]. The appearance part is fine-tuned using sparse stylized priors of a few views rendered using the TensoRF representation for a few iterations. Our method thus effectively decouples style-adaption from view capture and is much faster than the previous methods. We show state-of-the-art results on several scenes used for this purpose.
KNIFE: Knowledge Distillation with Free-Text Rationales

Free-text rationales (FTRs) follow how humans communicate by explaining reasoning processes via natural language. A number of recent works have studied how to improve language model (LM) generalization by us-ing FTRs to teach LMs the correct reasoning processes behind correct task outputs. These prior works aim to learn from FTRs by ap-pending them to the LM input or target output, but this may introduce an input distribution shift or conﬂict with the task objective, respectively. We propose KNIFE, which dis-tills FTR knowledge from an FTR-augmented teacher LM (takes both task input and FTR) to a student LM (takes only task input), which is used for inference. Crucially, the teacher LM’s forward computation has a bottleneck stage in which all of its FTR states are masked out, which pushes knowledge from the FTR states into the task input/output states. Then, FTR knowledge is distilled to the student LM by training its task input/output states to align with the teacher LM’s. On two question answering datasets, we show that KNIFE sig-niﬁcantly outperforms existing FTR learning methods, in both fully-supervised and low-resource settings.
Continual Learning for Instruction Following from Realtime Feedback

We propose and deploy an approach to continually train an instruction-following agent from feedback provided by users during collaborative interactions. During interaction, human users instruct an agent using natural language, and provide realtime binary feedback as they observe the agent following their instructions. We design a contextual bandit learning approach, converting user feedback to immediate reward. We evaluate through thousands of human-agent interactions, demonstrating 15.4% absolute improvement in instruction execution accuracy over time. We also show our approach is robust to several design variations, and that the feedback signal is roughly equivalent to the learning signal of supervised demonstration data.
Nodal-antinodal dichotomy from anisotropic quantum critical continua in holographic models

We demonstrate that the absence of stable quasiparticle excitations on parts of the Fermi surface, similar to the “nodal-antinodal dichotomy” in underdoped cuprate superconductors, can be reproduced in models of strongly correlated electrons defined via a holographic dual. We show analytically that the anisotropy of the quantum critical continuum, which is a feature of these models, may lead to washing out the quasiparticle peak in one direction while leaving it intact in the perpendicular one. The effect relies on the qualitatively different scaling of the self-energy in different directions. Using the explicit example of the anisotropic Q-lattice model, we demonstrate how this effect emerges due to specific features of the near horizon geometry of the black hole in the dual description.
Controlling Styles in Neural Machine Translation with Activation Prompt

Controlling styles in neural machine translation (NMT) has attracted wide attention, as it is crucial for enhancing user experience. Earlier studies on this topic typically concentrate on regulating the level of formality and achieve some progress in this area. However, they still encounter two major challenges. The first is the difficulty in style evaluation. The style comprises various aspects such as lexis, syntax, and others that provide abundant information. Nevertheless, only formality has been thoroughly investigated. The second challenge involves excessive dependence on incremental adjustments, particularly when new styles are necessary. To address both challenges, this paper presents a new benchmark and approach. A multiway stylized machine translation (MSMT) benchmark is introduced, incorporating diverse categories of styles across four linguistic domains. Then, we propose a method named style activation prompt (StyleAP) by retrieving prompts from stylized monolingual corpus, which does not require extra fine-tuning. Experiments show that StyleAP could effectively control the style of translation and achieve remarkable performance.
Gait Recognition Using 3-D Human Body Shape Inference

Gait recognition, which identifies individuals based on their walking patterns, is an important biometric technique since it can be observed from a distance and does not require the subject’s cooperation. Recognizing a person’s gait is difficult because of the appearance variants in human silhouette sequences produced by varying viewing angles, carrying objects, and clothing. Recent research has produced a number of ways for coping with these variants. In this paper, we present the usage of inferring 3-D body shapes distilled from limited images, which are, in principle, invariant to the specified variants. Inference of 3-D shape is a difficult task, especially when only silhouettes are provided in a dataset. We provide a method for learning 3-D body inference from silhouettes by transferring knowledge from 3-D shape prior from RGB photos. We use our method on multiple existing state-of-the-art gait baselines and obtain consistent improvements for gait identification on two public datasets, CASIA-B and OUMVLP, on several variants and settings, including a new setting of novel views not seen during training.
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model

Augmenting pretrained language models with retrievers to select the supporting documents has shown promise in effectively solving common NLP problems, including language modeling and question answering, in an interpretable way. In this paper, we first study the strengths and weaknesses of different retriever-augmented language models (REALM, $k$NN-LM, FiD coupled with DPR, and ATLAS and Flan-T5 coupled with Contriever) in reasoning over the retrieved statements in different tasks. We show how the retrieve-then-read models' limitations in reasoning are rooted both in the retriever module as well as the language model. Our experimental results demonstrate that the similarity metric used by the retrievers is generally insufficient for reasoning tasks. Additionally, we show that the language models in retriever-augmented models do not take the complicated relations between the statements into account, which leads to poor reasoning performance even when using the larger models. Moreover, we analyze the reasoning performance of large language models using multihop retrieval but we only observe minor improvements. Overall, this shows great room for further research in this area.
A CONSISTENT MODEL SPECIFICATION TEST FOR A REGRESSION FUNCTION BASED ON NONPARAMETRIC WAVELET ESTIMATION

In this paper we present a consistent specification test of a parametric regression function against a general nonparametric alternative. The proposed test is based on wavelet estimation and it is shown to have similar rates of convergence to the more commonly used kernel based tests. Monte Carlo simulations show that this test statistic has adequate size and high power and that it compares favorably with its kernel based counterparts in small samples.
Phonons of electronic crystals in two‐dimensional semiconductor moiré patterns

We theoretically studied the phonon properties of the triangular-, stripe- and honeycomb-type electronic crystals recently found in two-dimensional semiconductor moir\'e patterns. By analyzing the phonon dispersions, we found the interaction induced lattice deformation in the zigzag-stripe crystal results in a much higher dynamical stability than in the linear-stripe crystal. Moreover, chiral phonons with finite magnetizations and large Berry curvatures can emerge in triangular and honeycomb crystals under time-reversal or inversion symmetry breaking. The small effective mass of the electrons allows the selective and efficient generation of chiral phonons from the optical activity of zone-center phonons combined with the anharmonicity, facilitating the realization of the phonon Hall effect. These findings point to an exciting new platform for exploring chiral phonons and the related topological phononic devices.
Fragmentation and Novel Prethermal Dynamical Phases in Disordered, Strongly-Interacting Floquet Systems

We explore how disorder and interactions conspire in lattice models with sequentially activated hopping to produce novel k-body (or many-body) localized phases. Specifically, we show that when disorder is added to the set of interacting floquet models considered in [Wampler and Klich arXiv:2209.09180], regions in parameter space near the special points where classical-like dynamics emerge are stabilized prethermally (or via many-body localization in some cases) producing new families of interesting phases. We also find that this disordered system exhibits novel phases in regions of parameter space away from the special, Diophantine points. Furthermore, the regions in parameter space where Hilbert space fragmentation occurs in the clean system (leading to Krylov subspaces exhibiting frozen dynamics, cellular automation, and subspaces exhibiting signs of ergodic behavior) may also be stabilized by the addition of disorder. This leads to the emergence of exotic dynamics within the Krylov subspace.
Rethinking Label Smoothing on Multi-hop Question Answering

“Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiringmultiple reasoning components, including document retrieval, supporting sentence prediction,and answer span extraction. In this work, we present the first application of label smoothing tothe MHQA task, aiming to enhance generalization capabilities in MHQA systems while miti-gating overfitting of answer spans and reasoning paths in the training set. We introduce a novellabel smoothing technique, F1 Smoothing, which incorporates uncertainty into the learning pro-cess and is specifically tailored for Machine Reading Comprehension (MRC) tasks. Moreover,we employ a Linear Decay Label Smoothing Algorithm (LDLA) in conjunction with curricu-lum learning to progressively reduce uncertainty throughout the training process. Experimenton the HotpotQA dataset confirms the effectiveness of our approach in improving generaliza-tion and achieving significant improvements, leading to new state-of-the-art performance on theHotpotQA leaderboard. ”
A Study of Primordial Very Massive Star Evolution

We present new evolutionary models of primordial very massive stars with initial masses ranging from 100 to 1000 M ⊙ that extend from the main sequence to the onset of dynamical instability caused by the creation of electron–positron pairs during core C, Ne, or O burning, depending on the star’s mass and metallicity. Mass loss accounts for radiation-driven winds, as well as pulsation-driven mass loss on the main sequence and during the red supergiant phase. After examining the evolutionary properties, we focus on the final outcome of the models and associated compact remnants. Stars that avoid the pair instability supernova channel should produce black holes with masses ranging from ≈40 to ≈1000 M ⊙. In particular, stars with initial masses of about 100 M ⊙ could leave black holes of ≃85–90 M ⊙, values consistent with the estimated primary black hole mass of the GW190521 merger event. Overall, these results may contribute to explaining future data from next-generation gravitational-wave detectors, such as the Einstein Telescope and Cosmic Explorer, which will have access to an as-yet-unexplored black hole mass range of ≈102–104 M ⊙ in the early universe.
On the stable cohomology of the IA-automorphism groups of free groups

Borel's stability and vanishing theorem gives the stable cohomology of $\mathrm{GL}(n,\mathbb{Z})$ with coefficients in algebraic $\mathrm{GL}(n,\mathbb{Z})$-representations. By combining the Borel theorem with the Hochschild-Serre spectral sequence, we compute the twisted first cohomology of the automorphism group $\mathrm{Aut}(F_n)$ of the free group $F_n$ of rank $n$. We also study the stable rational cohomology of the IA-automorphism group $\mathrm{IA}_n$ of $F_n$. We propose a conjectural algebraic structure of the stable rational cohomology of $\mathrm{IA}_n$, and consider some relations to known results and conjectures. We also consider a conjectural structure of the stable rational cohomology of the Torelli groups of surfaces.
Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Data

Implementing effective control mechanisms to ensure the proper functioning and security of deployed NLP models, from translation to chatbots, is essential. A key ingredient to ensure safe system behaviour is Out-Of-Distribution (OOD) detection, which aims to detect whether an input sample is statistically far from the training distribution. Although OOD detection is a widely covered topic in classification tasks, most methods rely on hidden features output by the encoder. In this work, we focus on leveraging soft-probabilities in a black-box framework, i.e. we can access the soft-predictions but not the internal states of the model. Our contributions include: (i) RAINPROOF a Relative informAItioN Projection OOD detection framework; and (ii) a more operational evaluation setting for OOD detection. Surprisingly, we find that OOD detection is not necessarily aligned with task-specific measures. The OOD detector may filter out samples well processed by the model and keep samples that are not, leading to weaker performance. Our results show that RAINPROOF provides OOD detection methods more aligned with task-specific performance metrics than traditional OOD detectors.
Localizability of the approximation method

We use the approximation method of Razborov to analyze the locality barrier which arose from the investigation of the hardness magnification approach to complexity lower bounds. Adapting a limitation of the approximation method obtained by Razborov, we show that in many cases it is not possible to combine the approximation method with typical (localizable) hardness magnification theorems to derive strong circuit lower bounds. In particular, one cannot use the approximation method to derive an extremely strong constant-depth circuit lower bound and then magnify it to an $NC^1$ lower bound for an explicit function. To prove this we show that lower bounds obtained by the approximation method are in many cases localizable in the sense that they imply lower bounds for circuits which are allowed to use arbitrarily powerful oracles with small fan-in.
Stellar coronal mass ejections

Stellar coronal mass ejections (CMEs) are a growing research field, especially during the past decade. The large number of so far detected exoplanets raises the open question for the CME activity of stars, as CMEs may strongly affect exoplanetary atmospheres. In addition, as CMEs contribute to stellar mass and angular momentum loss and are therefore relevant for stellar evolution, there is a need for a better characterization of this phenomenon. In this article we review the different methodologies used up to now to attempt the detection of stellar CMEs. We discuss the limitations of the different methodologies and conclude with possible future perspectives of this research field.
RCD(0,N)-spaces with small linear diameter growth

In this paper, we study some structure properties on the (revised) fundamental group of RCD(0,N) spaces. Our main result generalizes earlier work of Sormani on Riemannian manifolds with nonnegative Ricci curvature and small linear diameter growth. We prove that the revised fundamental group is finitely generated if assuming small linear diameter growth on RCD(0,N) spaces.
Regularity of the optimal sets for a class of integral shape functionals

We prove the first regularity theorem for the free boundary of solutions to shape optimization problems involving integral functionals, for which the energy of a domain Ω is obtained as the integral of a cost function j(u, x) depending on the solution u of a certain PDE problem on Ω. The main feature of these functionals is that the minimality of a domain Ω cannot be translated into a variational problem for a single (real or vector valued) state function. In this paper we focus on the case of affine cost functions j(u, x) = −g(x)u+Q(x), where u is the solution of the PDE −∆u = f with Dirichlet boundary conditions. We obtain the Lipschitz continuity and the non-degeneracy of the optimal u from the inwards/outwards optimality of Ω and then we use the stability of Ω with respect to variations with smooth vector fields in order to study the blow-up limits of the state function u. By performing a triple consecutive blow-up, we prove the existence of blow-up sequences converging to homogeneous stable solution of the onephase Bernoulli problem and according to the blow-up limits, we decompose ∂Ω into a singular and a regular part. In order to estimate the Hausdorff dimension of the singular set of ∂Ω we give a new formulation of the notion of stability for the one-phase problem, which is preserved under blow-up limits and allows to develop a dimension reduction principle. Finally, by combining a higher order Boundary Harnack principle and a viscosity approach, we prove C∞ regularity of the regular part of the free boundary when the data are smooth.
On turbulence and noise of an axisymmetric shear flow

The noise produced by mean flow-turbulence interaction of a circular subsonic jet is investigated theoretically, and expanded in azimuthal constituents of the turbulent pressure fluctuations. It is found that the low-order azimuthal constituents are the most efficient sound sources. On the basis of pressure correlation measurements, the azimuthal constituents are determined in a low Mach number jet. It is found that, in a range of Strouhal numbers between 0·2 and 1, the first three to four azimuthal constituents clearly dominate over the rest of the turbulent source quantity. A strictly axisymmetric ring vortex model for the coherent structure of the turbulence is, however, shown to be inappropriate.
Spatial diffusion of heavy quarks in background magnetic field

The ratio of shear viscosity to entropy density shows a valley-shaped pattern well-known in the community of heavy-ion physics. Diffusion coefficients of heavy quark and meson shows the similar structure, and both sketches have become quite popular in the community. Present work has attempted a finite magnetic field extension of the diffusion coefficients of heavy quark and meson. Using Einstein's diffusion relation, we calculated heavy quark and heavy meson diffusion by the ratio of conductivity to susceptibility in the kinetic theory framework of relaxation time approximation. The relaxation time of heavy quark and meson are tuned from the knowledge of earlier works on spatial diffusion estimations, and then we have extended the framework for a finite magnetic field, where our outcomes have revealed two aspects - anisotropic and quantum aspects of diffusion with future possibilities of phenomenological signature.