Miracle-dz/UniFAR-FTUs huggingface dataset:
Extension of ELM suppression window using n=4 RMPs in EAST

The q 95 window for Type-I edge localized modes (ELMs) suppression using n = 4 even parity resonant magnetic perturbations (RMPs) has been significantly expanded to the ranges [3.9, 4.1] and [4.2, 4.8] in EAST while maintaining good confinement, which is demonstrated to be reliable and repeatable over the last two years. This window is significantly wider than the previous one achieved using n = 4 odd parity RMPs, which is around q95=3.7±0.1 . Here, n represents the toroidal mode number of the applied RMPs and q 95 is the safety factor at 95% of the normalized poloidal magnetic flux. During ELM suppression, there is only a slight drop in the plasma stored energy and density ( ⩽10% ). The comparison of changes in the pedestal profiles suggests that ELM suppression is achieved when the pedestal gradient is kept lower than a threshold. This wide q 95 window for ELM suppression is consistent with the prediction made by MARS-F modeling prior to the experiment, which located it at one of the resonant q 95 windows for plasma response. The Chirikov parameter taking into account plasma response near the pedestal top, which measures plasma edge stochasticity, significantly increases when q 95 exceeds 4, mainly due to the denser neighboring rational surfaces. The modeling of plasma response reveals a strong coupling between resonant and non-resonant components across the pedestal region, which is a characteristic of the kink-peeling like response observed during RMP-ELM suppression in previous studies on EAST. These promising results demonstrate the reliability of ELM suppression using the n = 4 RMPs in EAST and expand the physical understanding on ELM suppression mechanism.
Orientational transition in nanobridges of nematic liquid crystals in slit pores

ABSTRACT In this work, the morphology of nematic capillary nanobridges in slit pores separated by a vertical distance $$D$$D will be characterised by Monte Carlo simulations for oblate molecules nematogen modelled by the Gay-Berne potential. Previous studies on droplets show that the molecules are arranged homeotropically at the nematic-vapour interface and form spherical droplets with an annular disclination located in their equatorial plane. In the presence of pores with attractive substrates that favour homeotropic anchoring, the formation of nanobridges characterised by anchoring angles that decrease with increasing particle-substrate interaction intensity is observed. When the orientational field in the nanobridge is analysed, the formation of an annular disclination of topological charge+1/2 located in the plane perpendicular to the z-axis that passes through the centre of mass of the nanobridge is observed for small $$D$$D. However, when considering higher values of $$D$$D, a change to a biaxial orientational profile within the nanobridge is observed, where now the annular disclination is arranged in a plane perpendicular to one direction of the $$xy$$xy plane. These results are indicative of the existence of an orientational phase transition for an intermediate $$D$$D value between a uniaxial and a biaxial orientational configuration in the capillary nanobridge. GRAPHICAL ABSTRACT
Matrix weights, singular integrals, Jones factorization and Rubio de Francia extrapolation

In this article we give an overview of the problem of finding sharp constants in matrix weighted norm inequalities for singular integrals, the so-called matrix A2 conjecture. We begin by reviewing the history of the problem in the scalar case, including a sketch of the proof of the scalar A2 conjecture. We then discuss the original, qualitative results for singular integrals with matrix weights and the best known quantitative estimates. We give an overview of new results by the author and Bownik, who developed a theory of harmonic analysis on convex set-valued functions. This led to the proof the Jones factorization theorem and the Rubio de Francia extrapolation theorem for matrix weights, two longstanding problems. Rubio de Francia extrapolation was expected to be a major tool in the proof of the matrix A2 conjecture; however, this conjecture was very recently proved false. We discuss this problem.
Ambiguous Medical Image Segmentation Using Diffusion Models

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code: https://github.com/aimansnigdha/Ambiguous-Medical-Image-Segmentation-using-Diffusion-Models.
Birkhoff's variety theorem for relative algebraic theories

An algebraic theory, sometimes called an equational theory, is a theory defined by finitary operations and equations, such as the theories of groups and of rings. It is well known that algebraic theories are equivalent to finitary monads on $\mathbf{Set}$. In this paper, we generalize this phenomenon to locally finitely presentable categories using partial Horn logic. For each locally finitely presentable category $\mathscr{A}$, we define an"algebraic concept"relative to $\mathscr{A}$, which will be called an $\mathscr{A}$-relative algebraic theory, and show that $\mathscr{A}$-relative algebraic theories are equivalent to finitary monads on $\mathscr{A}$. In establishing such equivalence, a generalized Birkhoff's variety theorem plays an important role.
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis

Diffusion-based models have achieved state-of-the-art performance on text-to-image synthesis tasks. However, one critical limitation of these models is the low fidelity of generated images with respect to the text description, such as missing objects, mismatched attributes, and mislocated objects. One key reason for such inconsistencies is the inaccurate cross-attention to text in both the spatial dimension, which controls at what pixel region an object should appear, and the temporal dimension, which controls how different levels of details are added through the denoising steps. In this paper, we propose a new text-to-image algorithm that adds explicit control over spatial-temporal cross-attention in diffusion models. We first utilize a layout predictor to predict the pixel regions for objects mentioned in the text. We then impose spatial attention control by combining the attention over the entire text description and that over the local description of the particular object in the corresponding pixel region of that object. The temporal attention control is further added by allowing the combination weights to change at each denoising step, and the combination weights are optimized to ensure high fidelity between the image and the text. Experiments show that our method generates images with higher fidelity compared to diffusion-model-based baselines without fine-tuning the diffusion model. Our code is publicly available. 1
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language

Visual and linguistic pre-training aims to learn vision and language representations together, which can be transferred to visual-linguistic downstream tasks. However, there exists semantic confusion between language and vision during the pre-training stage. Moreover, current pre-trained models tend to take lots of computation resources for fine-tuning when transferred to downstream tasks. In this work, we present a simple but effective approach for learning Contrastive and Adaptive representations of Vision and Language, namely CAVL. Specifically, we introduce a pair-wise contrastive loss to learn alignments between the whole sentence and each image in the same batch during the pre-training process. At the fine-tuning stage, we introduce two lightweight adaptation networks to reduce model parameters and increase training speed for saving computation resources. We evaluate our CAVL on six main downstream tasks, including Visual Question Answering (VQA), Visual Commonsense Reasoning (VCR), Natural Language for Visual Reasoning (NLVR), Region-to-Phrase Grounding (RPG), Text-to-Image Retrieval (TIR), and Zero-shot Text-to-Image Retrieval (ZS-TIR). Compared to baselines, we achieve superior performance and reduce the fine-tuning time by a large margin (in particular, 76.17%). Extensive experiments and ablation studies demonstrate the efficiency of contrastive pre-training and adaptive fine-tuning proposed in our CAVL.
StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation

Anticipation problem has been studied considering different aspects such as predicting humans’ locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, proposing a new end-to-end architecture named StillFast. Our approach simultaneously processes a still image and a video detecting and localizing next-active objects, predicting the verb which describes the future interaction and determining when the interaction will start. Experiments on the large-scale egocentric dataset EGO4D [17] show that our method outperformed state-of-the-art approaches on the considered task. Our method is ranked first in the public leaderboard of the EGO4D short term object interaction anticipation challenge 2022 and it is the official baseline for the 2023 one. Please see the project web page for code and additional details: https://iplab.dmi. unict.it/stillfast/.
Systematic study of bremsstrahlung emission in reactions with light nuclei in cluster models

A new model of bremsstrahlung emission in the scattering of light nuclei is constructed with main focus on strict cluster formulation of nuclear processes. Analysis is performed in frameworks of the folding approximation of the formalism with participation of $s$-nuclei. Reactions $p$ +$^4$He, $^2$D + $^4$He, $^3$H + $^4$He, $^3$He + $^4$He are included to analysis. Systematic analysis of properties of emission of bremsstrahlung photons in the wide region of kinetic energy of relative motion of two nuclei from 7 to 1000~MeV is performed. Influence of the oscillator length on the calculated spectra of bremsstrahlung emission is analyzed. On the example of $^3$H + $^4$He, dependence of the bremsstrahlung spectra on parameters of nuclear component of interacting potential is established (at first time for the light nuclei). Experimental bremsstrahlung data for the proton-deuteron scattering and proton-$\alpha$-particle scattering are analyzed on the basis of this model.
BRST characterization of the broken phase observables of a confining complex theory

Some time ago we have introduced a route to provide confinement in the sense that particle excitations would appear from condensates of fields that do not have physical asymptotic states. We envisaged this mechanism in an asymmetric vacuum phase of a complex gauge field theory. More recently, we showed how to define a BRST operator to the broken phase of a generic spontaneous broken field theory. Our intention here is to apply these late concepts to the previous complex field theory in order to give a cohomological characterization of those condensed states as actual physical observables of this theory in its broken phase.
GelSight360: An Omnidirectional Camera-Based Tactile Sensor for Dexterous Robotic Manipulation

Camera-based tactile sensors have shown great promise in enhancing a robot's ability to perform a variety of dexterous manipulation tasks. Advantages of their use can be attributed to the high resolution tactile data and 3D depth map reconstructions they can provide. Unfortunately, many of these tactile sensors use either a flat sensing surface, sense on only one side of the sensor's body, or have a bulky form-factor, making it difficult to integrate the sensors with a variety of robotic grippers. Of the camera-based sensors that do have all-around, curved sensing surfaces, many cannot provide 3D depth maps; those that do often require optical designs specified to a particular sensor geometry. In this work, we introduce GelSight360, a fingertip-like, omnidirectional, camera-based tactile sensor capable of producing depth maps of objects deforming the sensor's surface. In addition, we introduce a novel cross-LED lighting scheme that can be implemented in different all-around sensor geometries and sizes, allowing the sensor to easily be reconfigured and attached to different grippers of varying DOFs. With this work, we enable roboticists to quickly and easily customize high resolution tactile sensors to fit their robotic system's needs.
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement

Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have shown promising results in arbitrary-scale super-resolution of natural images, it is not effective to directly apply them in pathology images, because pathology images have special fine-grained image textures different from natural images. To address this challenge, we propose a dual-branch framework with an efficient self-texture enhancement mechanism for arbitrary-scale super-resolution of pathology images. Extensive experiments on two public datasets show that our method outperforms both existing fixed-scale and arbitrary-scale algorithms. To the best of our knowledge, this is the first work to achieve arbitrary-scale super-resolution in the field of pathology images. Codes will be available.
PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures

Neural Radiance Fields (NeRF) have been widely adopted as practical and versatile representations for 3D scenes, facilitating various downstream tasks. However, different architectures, including plain Multi-Layer Perceptron (MLP), Tensors, low-rank Tensors, Hashtables, and their compositions, have their trade-offs. For instance, Hashtables-based representations allow for faster rendering but lack clear geometric meaning, making spatial-relation-aware editing challenging. To address this limitation and maximize the potential of each architecture, we propose Progressive Volume Distillation with Active Learning (PVD-AL), a systematic distillation method that enables any-to-any conversions between different architectures. PVD-AL decomposes each structure into two parts and progressively performs distillation from shallower to deeper volume representation, leveraging effective information retrieved from the rendering process. Additionally, a Three-Levels of active learning technique provides continuous feedback during the distillation process, resulting in high-performance results. Empirical evidence is presented to validate our method on multiple benchmark datasets. For example, PVD-AL can distill an MLP-based model from a Hashtables-based model at a 10~20X faster speed and 0.8dB~2dB higher PSNR than training the NeRF model from scratch. Moreover, PVD-AL permits the fusion of diverse features among distinct structures, enabling models with multiple editing properties and providing a more efficient model to meet real-time requirements. Project website:http://sk-fun.fun/PVD-AL.
A numerical study of the impact of jet magnetic topology on radio galaxy evolution

The propagation of active galactic nucleus jets depends both on the environment into which they propagate and on their internal structure. To test the impact that different magnetic topologies have on the observable properties of radio galaxies on kpc scales, we conducted a series of magnetohydrodynamic simulations of jets injected with different magnetic field configurations propagating into a gaseous atmosphere modelled on the Perseus cluster. The simulations show that the structure of the field affects the collimation and propagation of the jets on cluster scales and thus the morphology of the radio lobes inflated by the jets, due to both magnetic collimation and the development of dynamical instabilities in jets with different magnetic topologies. In all cases, the simulations show a distinct reversal of the synchrotron spectral age gradient in the radio lobes about a dynamical time after the jets turn off due to large-scale circulation inside the radio lobe, driven primarily by buoyancy, which could provide a way to constrain the age of radio sources in cluster environments without the need for detailed spectral modelling and thus constrain the radio-mode feedback efficiency. We suggest a robust diagnostic to search for such age gradients in multifrequency radio data.
Detection Transformer with Stable Matching

This paper is concerned with the matching stability problem across different decoder layers in DEtection TRansformers (DETR). We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR. To address this problem, we show that the most important design is to use and only use positional metrics (like IOU) to supervise classification scores of positive examples. Under the principle, we propose two simple yet effective modifications by integrating positional metrics to DETR’s classification loss and matching cost, named position-supervised loss and position-modulated cost. We verify our methods on several DETR variants. Our methods show consistent improvements over baselines. By integrating our methods with DINO, we achieve 50.4 and 51.5 AP on the COCO detection benchmark using ResNet-50 backbones under 1× (12 epochs) and 2× (24 epochs) training settings, achieving a new record under the same setting. We achieve 63.8 AP on COCO detection test-dev with a Swin-Large backbone. Our code will be made available at https://github.com/IDEA-Research/Stable-DINO.
Experiences with Distributed Acoustic Sensing using both straight and helically wound fibers in surface-deployed cables - a case history in Groningen, The Netherlands

Distributed Acoustic Sensing (DAS) has been limited in its use for surface-seismic reflection measurements, due to the fibers decreased broadside sensitivity when the fiber is deployed horizontally. Deploying the fiber in a helically wound fashion has the promise of being more sensitive to broadside waves (e.g. P-wave reflections) and less sensitive to surface waves than a straight fiber. We examine such claims and compare the responses of straight fibers (SF) and helically wound fibers (HWF) with different wrapping angles, using standard and engineered fibers. These fibers were buried in a 2 m deep trench in a farmland in the province of Groningen in the Netherlands, where we performed an active-source survey. We observe in our field data that using HWF has a destructive effect on the surface-wave amplitudes. Our data confirmed the effect of the wrapping angle on the polarity of the surface-wave arrival and the dampening effect of the helical winding, both behaving in quite a predictable fashion. Apart from the effect of the wrapping angle, the different design choices, e.g. cable filling and material type, did not show a significant effect on the amplitude of the signals. As for P-wave reflections, we observe that both engineered SF and HWF provide reflection images comparable to those obtained from simultaneously deployed geophones at the surface despite the straight fiber's decreased broadside sensitivity. A polarity reversal and an amplitude difference between the SF and HWF fibers are observed. Finally, we show that the combined use of SF and HWF proved to be useful since SF showed better sensitivity in the shallower part and HWF in the deeper part.
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition

This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability can be directly plugged into a variety of visual recognition tasks including image classification, semantic segmentation, and object detection, to boost recognition performances in a zero-shot manner. Empirical evaluation shows that POMP achieves state-of-the-art performances on 21 datasets, e.g., 67.0% average accuracy on 10 classification datasets (+3.1% compared to CoOp) and 84.4 hIoU on open-vocabulary Pascal VOC segmentation (+6.9 compared to ZSSeg). Our code is available at https://github.com/amazon-science/prompt-pretraining.
When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?

We analyze the mixing time of Metropolized Hamiltonian Monte Carlo (HMC) with the leapfrog integrator to sample from a distribution on $\mathbb{R}^d$ whose log-density is smooth, has Lipschitz Hessian in Frobenius norm and satisfies isoperimetry. We bound the gradient complexity to reach $\epsilon$ error in total variation distance from a warm start by $\tilde O(d^{1/4}\text{polylog}(1/\epsilon))$ and demonstrate the benefit of choosing the number of leapfrog steps to be larger than 1. To surpass previous analysis on Metropolis-adjusted Langevin algorithm (MALA) that has $\tilde{O}(d^{1/2}\text{polylog}(1/\epsilon))$ dimension dependency in Wu et al. (2022), we reveal a key feature in our proof that the joint distribution of the location and velocity variables of the discretization of the continuous HMC dynamics stays approximately invariant. This key feature, when shown via induction over the number of leapfrog steps, enables us to obtain estimates on moments of various quantities that appear in the acceptance rate control of Metropolized HMC. Moreover, to deal with another bottleneck on the HMC proposal distribution overlap control in the literature, we provide a new approach to upper bound the Kullback-Leibler divergence between push-forwards of the Gaussian distribution through HMC dynamics initialized at two different points. Notably, our analysis does not require log-concavity or independence of the marginals, and only relies on an isoperimetric inequality. To illustrate the applicability of our result, several examples of natural functions that fall into our framework are discussed.
Existence and stability of shrinkers for the harmonic map heat flow in higher dimensions

We study singularity formation for the heat flow of harmonic maps from $\R^d$. For each $d \geq 4$, we construct a compact, $d$-dimensional, rotationally symmetric target manifold that allows for the existence of a corotational self-similar shrinking solution (shortly \emph{shrinker}) that represents a stable blowup mechanism for the corresponding Cauchy problem.