Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact and Approximate Power and Sample Size Calculations for Analysis of Covariance in Randomized Clinical Trials With or Without Stratification

ABSTRACT Analysis of covariance (ANCOVA) is commonly used in the analysis of randomized clinical trials to adjust for baseline covariates and improve the precision of the treatment effect estimate. We derive the exact power formulas for testing a homogeneous treatment effect in superiority, noninferiority, and equivalence trials under both unstratified and stratified randomizations, and for testing the overall treatment effect and treatment × stratum interaction in the presence of heterogeneous treatment effects when the covariates excluding the intercept, treatment, and prestratification factors are normally distributed. These formulas also work very well for nonnormal covariates. The sample size methods based on the normal approximation or the asymptotic variance generally underestimate the required size. We adapt the recently developed noniterative and two-step sample size procedures to the above tests. Both methods take into account the nonnormality of the t statistic, and the lower order variance term commonly ignored in the sample size estimation. Numerical examples demonstrate the excellent performance of the proposed methods particularly in small samples. We revisit the topic on the prestratification versus post-stratification by comparing their relative efficiency and power. Supplementary materials for this article are available online.
Electron Drift Resonance in the MHD‐Coupled Comprehensive Inner Magnetosphere‐Ionosphere Model

Relativistic electrons in the outer radiation belt are highly dynamic and respond to interplanetary solar wind structures interacting with the Earth's magnetic field. A known mechanism dictating electron dynamics is the drift‐resonant interaction with ultralow frequency (ULF) waves. The present work simulates the ring current and radiation belt electron populations in the bounce‐averaged, kinetic Comprehensive Inner Magnetosphere‐Ionosphere model coupled with the Block Adaptive Tree Solar Wind Roe‐type Upwind Scheme global magnetospheric magnetohydrodynamic (MHD) code using an idealized ULF wave solar wind density driver. ULF waves generated with 10 min periods (at 1.67 mHz frequencies) in the MHD model are characterized and the corresponding energization of electrons and radial transport of electron phase space density is presented. The drift‐resonant electron energy is determined in the simulation and is consistent with the electron resonance conditions in dipolar magnetic fields. The present results will be an important component of understanding inner magnetospheric dynamics and how these inner magnetospheric populations interact with ULF waves resulting from interplanetary solar wind structures.
Comparing ADCP data collected during a seismic survey off the coast of Newfoundland with analysis data from the CONCEPTS operational ocean model

ABSTRACT Oceanographic data collected during seismic surveys have potential to provide a significant resource for oceanographic research. In this paper, acoustic Doppler current profiler (ADCP) data collected during the Chevron 2011 North East Grand Banks survey are analysed. The survey took place in a 32,000-km2 area in the North East Grand banks, Orphan Basin region in water depths ranging from 100 to 1500 m and notably, the survey area is crossed by the offshore branch of the Labrador current. Data quality is evaluated by comparing velocities before and after ship turns where we find no significant velocity bias. The ADCP data are compared to data products from the CONCEPTS global ice ocean prediction system operational ocean model. ADCP data and model output agree qualitatively with the model reproducing the long-term fluctuations seen in the data. At higher frequencies between 0.3 and 2.0 cycles/day, the model tends to under-represent the current velocities by about 20%, but the spectral peak associated with inertial oscillations is well resolved. Comparisons of drift track predictions based on the ADCP observations and model output show rms displacement differences of 8 km after 24 h and ∼10 km separations for drift tracks with length under 50 km.
Robust Perception through Analysis by Synthesis

The intriguing susceptibility of deep neural networks to minimal input perturbations suggests that the gap between human and machine perception is still large. We here argue that despite much effort, even on MNIST the most successful defenses are still far away from the robustness of human perception. We here reconsider MNIST and establish a novel defense that is inspired by the abundant feedback connections present in the human visual cortex. We suggest that this feedback plays a role in estimating the likelihood of a sensory stimulus with respect to the hidden causes inferred by the cortex and allow the brain to mute distracting patterns. We implement this analysis by synthesis idea in the form of a discriminatively ﬁne-tuned Bayesian classiﬁer using a set of class-conditional variational auto encoders (VAEs). To evaluate model robustness we go to great length to ﬁnd maximally effective adversarial attacks, including decision-based, score-based and gradient-based attacks. The results suggest that this ansatz yields state-of-the-art robustness on MNIST against L 0 , L 2 and L ∞ perturbations and we demonstrate that most adversarial examples are strongly perturbed towards the perceptual boundary between the original and the adversarial class.
Extending the Bayesian Framework from Information to Action

In this review, we examine an extended Bayesian inference method and its relation to biological information processing. We discuss the idea of combining two modes of Bayesian inference. The first is the standard Bayesian inference, which contracts probability space. The second is its inverse, which extends and enriches the probability space of latent and observable variables. Their combination has been observed that, greatly, facilitates discovery. Moreover, this dual search during the updating process elucidates a crucial difference between biological and artificial information processing. The latter is restricted due to nonlinearities, while the former utilizes it. This duality is ubiquitous in biological information process dynamics (`flee-or-fight', `explore-or-exploit' etc.) as is the role of fractality and chaos in its underlying nonequilibrium, nonlinear dynamics. We also propose a new experimental set up that stems from testing these ideas.
Rational Cuntz states peak on the free disk algebra

We apply realization theory of noncommutative (NC) rational multipliers of the Fock space, or free Hardy space of square–summable power series in several noncommuting variables to the convex analysis of states on the Cuntz algebra. We show, in particular, that a large class of Cuntz states that arise as the “NC Clark measures” of isometric NC rational multipliers are peak states for Popescu's free disk algebra in the sense of Clouâtre and Thompson.
Let Me Teach You: Pedagogical Foundations of Feedback for Language Models

Natural Language Feedback (NLF) is an increasingly popular avenue to align Large Language Models (LLMs) to human preferences. Despite the richness and diversity of the information it can convey, NLF is often hand-designed and arbitrary. In a different world, research in pedagogy has long established several effective feedback models. In this opinion piece, we compile ideas from pedagogy to introduce FELT, a feedback framework for LLMs that outlines the various characteristics of the feedback space, and a feedback content taxonomy based on these variables. Our taxonomy offers both a general mapping of the feedback space, as well as pedagogy-established discrete categories, allowing us to empirically demonstrate the impact of different feedback types on revised generations. In addition to streamlining existing NLF designs, FELT also brings out new, unexplored directions for research in NLF. We make our taxonomy available to the community, providing guides and examples for mapping our categorizations to future resources.
Sparsity-aware generalization theory for deep neural networks

Deep artificial neural networks achieve surprising generalization abilities that remain poorly understood. In this paper, we present a new approach to analyzing generalization for deep feed-forward ReLU networks that takes advantage of the degree of sparsity that is achieved in the hidden layer activations. By developing a framework that accounts for this reduced effective model size for each input sample, we are able to show fundamental trade-offs between sparsity and generalization. Importantly, our results make no strong assumptions about the degree of sparsity achieved by the model, and it improves over recent norm-based approaches. We illustrate our results numerically, demonstrating non-vacuous bounds when coupled with data-dependent priors in specific settings, even in over-parametrized models.
AVSegFormer: Audio-Visual Segmentation with Transformer

Audio-visual segmentation (AVS) aims to locate and segment the sounding objects in a given video, which demands audio-driven pixel-level scene understanding. The existing methods cannot fully process the fine-grained correlations between audio and visual cues across various situations dynamically. They also face challenges in adapting to complex scenarios, such as evolving audio, the coexistence of multiple objects, and more. In this paper, we propose AVSegFormer, a novel framework for AVS that leverages the transformer architecture. Specifically, It comprises a dense audio-visual mixer, which can dynamically adjust interested visual features, and a sparse audio-visual decoder, which implicitly separates audio sources and automatically matches optimal visual features. Combining both components provides a more robust bidirectional conditional multi-modal representation, improving the segmentation performance in different scenarios. Extensive experiments demonstrate that AVSegFormer achieves state-of-the-art results on the AVS benchmark. The code is available at https://github.com/vvvb-github/AVSegFormer.
A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification

The segmentation and classification of carotid plaques in ultrasound images play important roles in the treatment of atherosclerosis and assessment for the risk of stroke. Although deep learning methods have been used for carotid plaque segmentation and classification, two-stage methods will increase the complexity of the overall analysis and the existing multi-task methods ignored the relationship between the segmentation and classification. These will lead to suboptimal performance as valuable information might not be fully leveraged across all tasks. Therefore, we propose a multi-task learning framework (RCCM-Net) for ultrasound carotid plaque segmentation and classification, which utilizes a region confidence module (RCM) and a sample category confidence module (CCM) to exploit the correlation between these two tasks. The RCM provides knowledge from the probability of plaque regions to the classification task, while the CCM is designed to learn the categorical sample weight for the segmentation task. A total of 1270 2D ultrasound images of carotid plaques were collected from Zhongnan Hospital (Wuhan, China) for our experiments. The results showed that the proposed method can improve both segmentation and classification performance compared to existing single-task networks (i.e., SegNet, Deeplabv3+, UNet++, EfficientNet, Res2Net, RepVGG, DPN) and multi-task algorithms (i.e., HRNet, MTANet), with an accuracy of 85.82% for classification and a Dice-similarity-coefficient of 84.92% for segmentation. In the ablation study, the results demonstrated that both the designed RCM and CCM were beneficial in improving the network's performance. Therefore, we believe that the proposed method could be useful for carotid plaque analysis in clinical trials and practice.
Fractionality and 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="script">PT</mml:mi></mml:math>
 symmetry in an electrical transmission line

We examine the stability of a 1D electrical transmission line in the simultaneous presence of PT-symmetry and fractionality. The array contains a binary gain/loss distribution $\gamma_{n}$ and a fractional Laplacian characterized by a fractional exponent $\alpha$. For an infinite periodic chain, the spectrum is computed in closed form, and its imaginary sector is examined to determine the stable/unstable regions as a function of the gain/loss strength and fractional exponent. In contrast to the non-fractional case where all eigenvalues are complex for any gain/loss, here we observe that a stable region can exist when gain/loss is small, and the fractional exponent is below a critical value, $0<\alpha<\alpha_{c1}$ . As the fractional exponent is decreased further, the spectrum acquires a gap with two nearly-flat bands. We also examined numerically the case of a finite chain of size N. Contrary to what happens in the infinite chain, here the stable region always lies above a critical value $\alpha_{c2}<\alpha<1$. An increase in gain/loss or $N$ always reduces the width of this stable region until it disappears completely.
Understanding recent deep‐learning techniques for identifying collective variables of molecular dynamics

High‐dimensional metastable molecular dynamics (MD) can often be characterised by a few features of the system, that is, collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning‐based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning‐based approaches for finding CVs, either by computing leading eigenfunctions of transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples.
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views

Occlusion and clutter are two scene states that make it difficult to detect anomalies in surveillance video. Furthermore, anomaly events are rare and, as a consequence, class imbalance and lack of labeled anomaly data are also key features of this task. Therefore, weakly supervised methods are heavily researched for this application. In this paper, we tackle these typical problems of anomaly detection in surveillance video by combining Multiple Instance Learning (MIL) to deal with the lack of labels and Multiple Camera Views (MC) to reduce occlusion and clutter effects. In the resulting MC-MIL algorithm we apply a multiple camera combined loss function to train a regression network with Sultani's MIL ranking function. To evaluate the MC-MIL algorithm first proposed here, the multiple camera PETS-2009 benchmark dataset was re-labeled for the anomaly detection task from multiple camera views. The result shows a significant performance improvement in F1 score compared to the single-camera configuration.
An ML approach to resolution of singularities

The solution set of a system of polynomial equations typically contains ill-behaved, singular points. Resolution is a fundamental process in geometry in which we replace singular points with smooth points, while keeping the rest of the solution set unchanged. Resolutions are not unique: the usual way to describe them involves repeatedly performing a fundamental operation known as"blowing-up", and the complexity of the resolution highly depends on certain choices. The process can be translated into various versions of a 2-player game, the so-called Hironaka game, and a winning strategy for the first player provides a solution to the resolution problem. In this paper we introduce a new approach to the Hironaka game that uses reinforcement learning agents to find optimal resolutions of singularities. In certain domains, the trained model outperforms state-of-the-art selection heuristics in total number of polynomial additions performed, which provides a proof-of-concept that recent developments in machine learning have the potential to improve performance of algorithms in symbolic computation.
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity

This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. Our approach identifies both spurious and invariant latent features necessary for achieving accurate reconstruction by placing distinct conditional priors on latent features. The invariant signals are disentangled from noise by enforcing independence which facilitates the construction of an interpretable model with a causal semantic. By exploiting the interplay between data domains and labels, our method simultaneously identifies invariant features and builds invariant predictors. We apply our method to grand biological challenges, such as data integration in single-cell genomics with the aim of capturing biological variations across datasets with many samples, obtained from different conditions or multiple laboratories. Our approach allows for the incorporation of specific biological mechanisms, including gene programs, disease states, or treatment conditions into the data integration process, bridging the gap between the theoretical assumptions and real biological applications. Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest. Through extensive benchmarking using large-scale human hematopoiesis and human lung cancer data, we validate the superiority of our approach over existing methods and demonstrate that it can empower deeper insights into cellular heterogeneity and the identification of disease cell states.
HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection

RGB-D salient object detection (SOD) aims to detect the prominent regions by jointly modeling RGB and depth information. Most RGB-D SOD methods apply the same type of backbones and fusion modules to identically learn the multimodality and multistage features. However, these features contribute differently to the final saliency results, which raises two issues: 1) how to model discrepant characteristics of RGB images and depth maps; 2) how to fuse these cross-modality features in different stages. In this paper, we propose a high-order discrepant interaction network (HODINet) for RGB-D SOD. Concretely, we first employ transformer-based and CNN-based architectures as backbones to encode RGB and depth features, respectively. Then, the high-order representations are delicately extracted and embedded into spatial and channel attentions for cross-modality feature fusion in different stages. Specifically, we design a high-order spatial fusion (HOSF) module and a high-order channel fusion (HOCF) module to fuse features of the first two and the last two stages, respectively. Besides, a cascaded pyramid reconstruction network is adopted to progressively decode the fused features in a top-down pathway. Extensive experiments are conducted on seven widely used datasets to demonstrate the effectiveness of the proposed approach. We achieve competitive performance against 24 state-of-the-art methods under four evaluation metrics.
The Standard Model Theory of Neutron Beta Decay

We review the status of the Standard Model theory of neutron beta decay. Particular emphasis is put on the recent developments in the electroweak radiative corrections. Given that some existing approaches give slightly different results, we thoroughly review the origin of discrepancies, and provide our recommended value for the radiative correction to the neutron and nuclear decay rates. The use of dispersion relation, lattice Quantum Chromodynamics, and an effective field theory framework allows for high-precision theory calculations at the level of 10−4, turning neutron beta decay into a powerful tool to search for new physics, complementary to high-energy collider experiments. We offer an outlook to the future improvements.
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution

Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinement process comes with the drawback of low inference speed, which strongly limits its applications. To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution). Specifically, our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our study also highlights the effectiveness of using a pre-trained SR model to provide the conditional image of the given low-resolution (LR) image to achieve superior high-resolution results. We demonstrate that our method surpasses previous attempts in qualitative and quantitative results through extensive experiments conducted on benchmark datasets such as Set5, Set14, Urban100, BSD100, and Manga109. Moreover, our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
Strain, Young’s modulus, and structural transition of EuTiO3 thin films probed by micro-mechanical methods

EuTiO3 (ETO) is a well-known complex oxide mainly investigated for its magnetic properties and its incipient ferro-electricity. In this work, we demonstrate the realization of suspended micro-mechanical structures, such as cantilevers and micro-bridges, from 100 nm-thick single-crystal epitaxial ETO films deposited on top of SrTiO3(100) substrates. By combining profile analysis and resonance frequency measurements of these devices, we obtain the Young’s modulus, strain, and strain gradients of the ETO thin films. Moreover, we investigate the ETO anti-ferro-distortive transition by temperature-dependent characterizations, which show a non-monotonic and hysteretic mechanical response. The comparison between experimental and literature data allows us to weight the contribution from thermal expansion and softening to the tuning slope, while a full understanding of the origin of such a wide hysteresis is still missing. We also discuss the influence of oxygen vacancies on the reported mechanical properties by comparing stoichiometric and oxygen-deficient samples.