Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical stability of DeepGOPlus inference

Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well with any data with a spatial relationship, besides images, and have been applied to different fields. However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection. These challenges can jeopardise their performance and reliability. This paper investigates DeepGOPlus, a CNN that predicts protein function. DeepGOPlus has achieved state-of-the-art performance and can successfully take advantage and annotate the abounding protein sequences emerging in proteomics. We determine the numerical stability of the model’s inference stage by quantifying the numerical uncertainty resulting from perturbations of the underlying floating-point data. In addition, we explore the opportunity to use reduced-precision floating point formats for DeepGOPlus inference, to reduce memory consumption and latency. This is achieved by instrumenting DeepGOPlus’ execution using Monte Carlo Arithmetic, a technique that experimentally quantifies floating point operation errors and VPREC, a tool that emulates results with customizable floating point precision formats. Focus is placed on the inference stage as it is the primary deliverable of the DeepGOPlus model, widely applicable across different environments. All in all, our results show that although the DeepGOPlus CNN is very stable numerically, it can only be selectively implemented with lower-precision floating-point formats. We conclude that predictions obtained from the pre-trained DeepGOPlus model are very reliable numerically, and use existing floating-point formats efficiently.
Fluid Mechanics of Heart Valves and Their Replacements

As the pulsatile cardiac blood flow drives the heart valve leaflets to open and close, the flow in the vicinity of the valve resembles a pulsed jet through a nonaxisymmetric orifice with a dynamically changing area. As a result, three-dimensional vortex rings with intricate topology emerge that interact with the complex cardiac anatomy and give rise to shear layers, regions of recirculation, and flow instabilities that could ultimately lead to transition to turbulence. Such complex flow patterns, which are inherently valve- and patient-specific, lead to mechanical forces at scales that can cause blood cell damage and thrombosis, increasing the likelihood of stroke, and can trigger the pathogenesis of various life-threatening valvular heart diseases. We summarize the current understanding of flow phenomena induced by heart valves, discuss their linkage with disease pathways, and emphasize the research advances required to translate in-depth understanding of valvular hemodynamics into effective patient ther.
The speech-to-song illusion.

This paper reports the first formal investigation of a surprising illusion: A particular spoken phrase is made to be heard convincingly as sung rather than spoken, simply by repeating it several times over. Three groups of 18 subjects each listened to the spoken phrase repeated ten times, and they judged after each repetition whether, and how strongly, they heard the phrase as spoken or as sung. For all groups, the first and last repetitions were identical. For the first group, the intervening repetitions were also identical. For the second group, the intervening repetitions were transposed upward and downward slightly (by 2/3 semitone and 1 1/3 semitone), so that the pitches differed but the pitch relationships were preserved. For the third group, the intervening repetitions consisted of the untransposed syllables presented in jumbled orders. Comparing the first with the last repetition, a clear and highly significant transition from perceived speech to perceived song occurred when the intervening repeti.
Slip-enhanced Rayleigh–Plateau instability of a liquid film on a fibre

Abstract Boundary conditions at a liquid–solid interface are crucial to dynamics of a liquid film coated on a fibre. Here, a theoretical framework based on axisymmetric Stokes equations is developed to explore the influence of liquid–solid slip on the Rayleigh–Plateau instability of a cylindrical film on a fibre. The new model not only shows that the slip-enhanced growth rate of perturbations is overestimated by the classical lubrication model, but also indicates a slip-dependent dominant wavelength, instead of a constant value obtained by the lubrication method, which leads to larger drops formed on a more slippery fibre. The theoretical findings are validated by direct numerical simulations of Navier–Stokes equations via a volume-of-fluid method. Additionally, the slip-dependent dominant wavelengths predicted by our model agree with the experimental results provided by Haefner et al. (Nat. Commun., vol. 6, issue 1, 2015, 7409).
Structure-Guided Image Completion with Image-level and Object-level Semantic Discriminators

Structure-guided image completion aims to inpaint a local region of an image according to an input guidance map from users. While such a task enables many practical applications for interactive editing, existing methods often struggle to hallucinate realistic object instances in complex natural scenes. Such a limitation is partially due to the lack of semantic-level constraints inside the hole region as well as the lack of a mechanism to enforce realistic object generation. In this work, we propose a learning paradigm that consists of semantic discriminators and object-level discriminators for improving the generation of complex semantics and objects. Specifically, the semantic discriminators leverage pretrained visual features to improve the realism of the generated visual concepts. Moreover, the object-level discriminators take aligned instances as inputs to enforce the realism of individual objects. Our proposed scheme significantly improves the generation quality and achieves state-of-the-art results on various tasks, including segmentation-guided completion, edge-guided manipulation and panoptically-guided manipulation on Places2 datasets. Furthermore, our trained model is flexible and can support multiple editing use cases, such as object insertion, replacement, removal and standard inpainting. In particular, our trained model combined with a novel automatic image completion pipeline achieves state-of-the-art results on the standard inpainting task.
Interrogating solar flare loop models with IRIS observations 2: Plasma properties, energy transport, and future directions

During solar flares a tremendous amount of magnetic energy is released and transported through the Sun’s atmosphere and out into the heliosphere. Despite over a century of study, many unresolved questions surrounding solar flares are still present. Among those are how does the solar plasma respond to flare energy deposition, and what are the important physical processes that transport that energy from the release site in the corona through the transition region and chromosphere? Attacking these questions requires the concert of advanced numerical simulations and high spatial-, temporal-, and spectral-resolution observations. While flares are 3D phenomenon, simulating the NLTE flaring chromosphere in 3D and performing parameter studies of 3D models is largely outwith our current computational capabilities. We instead rely on state-of-the-art 1D field-aligned simulations to study the physical processes that govern flares. Over the last decade, data from the Interface Region Imaging Spectrograph (IRIS) have provided the crucial observations with which we can critically interrogate the predictions of those flare loop models. Here in Paper 2 of a two-part review of IRIS and flare loop models, I discuss how forward modelling flares can help us understand the observations from IRIS, and how IRIS can reveal where our models do well and where we are likely missing important processes, focussing in particular on the plasma properties, energy transport mechanisms, and future directions of flare modelling.
Impurity release from the ICRF antenna screens in JET

During ICRF heating in JET highly-localized impurity influxes from the Faraday screens (FS) of RF antennas are observed. The influxes result from the acceleration of deuterium and impurity ions in the RF-enhanced Bohm-Debye sheaths. The RF-enhanced sheaths form when the equilibrium magnetic field line, linking a large RF magnetic flux, connects two surfaces and closes a loop where the RF voltage is induced. The FS influxes depend on two intimately-linked parameters, the antenna voltage and the density at the FS, as well as on the magnetic field angle, antenna phasing, FS geometry and material.
Considerations for Differentially Private Learning with Large-Scale Public Pretraining

The performance of differentially private machine learning can be boosted significantly by leveraging the transfer learning capabilities of non-private models pretrained on large public datasets. We critically review this approach. We primarily question whether the use of large Web-scraped datasets should be viewed as differential-privacy-preserving. We caution that publicizing these models pretrained on Web data as"private"could lead to harm and erode the public's trust in differential privacy as a meaningful definition of privacy. Beyond the privacy considerations of using public data, we further question the utility of this paradigm. We scrutinize whether existing machine learning benchmarks are appropriate for measuring the ability of pretrained models to generalize to sensitive domains, which may be poorly represented in public Web data. Finally, we notice that pretraining has been especially impactful for the largest available models -- models sufficiently large to prohibit end users running them on their own devices. Thus, deploying such models today could be a net loss for privacy, as it would require (private) data to be outsourced to a more compute-powerful third party. We conclude by discussing potential paths forward for the field of private learning, as public pretraining becomes more popular and powerful.
The density and pressure of helium nano-bubbles encapsulated in silicon

The 1s2→1s2p(1P) excitation in compressed helium atoms in either the bulk material or encapsulated in a bubble is shifted to energies higher than in the free atom. For bulk helium, energy shifts predicted from non-empirical electronic structure computations are in excellent agreement with experimentally determined values. However, there are significant discrepancies both between the results of experiments on different bubbles and between these and the well established descriptions of the bulk. A critique is presented of previous analyses of earlier scanning transmission electron microscope measurements that are untrustworthy because the density dependence of the electron impact excitation cross section was neglected. Experimental electron energy loss data from an earlier study of helium encapsulated in silicon are reanalysed and it is shown that the properties of the helium in these bubbles do not differ significantly from those in the bulk, thereby enabling the densities in the bubbles to be determined. These enable bubble pressures to be deduced from a well established experimentally derived equation of state. It is shown that the errors of up to 80% in the incorrectly determined densities are greatly magnified in the predicted pressures which can be too large by factors of over seven.
Improving Depression estimation from facial videos with face alignment, training optimization and scheduling

Deep learning models have shown promising results in recognizing depressive states using video-based facial expressions. While successful models typically leverage using 3D-CNNs or video distillation techniques, the different use of pretraining, data augmentation, preprocessing, and optimization techniques across experiments makes it difficult to make fair architectural comparisons. We propose instead to enhance two simple models based on ResNet-50 that use only static spatial information by using two specific face alignment methods and improved data augmentation, optimization, and scheduling techniques. Our extensive experiments on benchmark datasets obtain similar results to sophisticated spatio-temporal models for single streams, while the score-level fusion of two different streams outperforms state-of-the-art methods. Our findings suggest that specific modifications in the preprocessing and training process result in noticeable differences in the performance of the models and could hide the actual originally attributed to the use of different neural network architectures.
Frozen Mode Regime in an Optical Waveguide With Distributed Bragg Reflector

We introduce a glide symmetric optical waveguide exhibiting a stationary inflection point (SIP) in the Bloch wavenumber dispersion relation. An SIP is a third order exceptional point of degeneracy (EPD) where three Bloch eigenmodes coalesce to form a so-called frozen mode with vanishing group velocity and diverging amplitude. We show that the incorporation of chirped distributed Bragg reflectors and distributed coupling between waveguides in the periodic structure facilitates the SIP formation and greatly enhances the characteristics of the frozen mode regime. We confirm the existence of an SIP in two ways: by observing the flatness of the dispersion diagram and also by using a coalescence parameter describing the separation of the three eigenvectors collapsing on each other. We find that in the absence of losses, both the quality factor and the group delay at the SIP grow with the cubic power of the cavity length. The frozen mode regime can be very attractive for light amplification and lasing, in optical delay lines, sensors, and modulators.
Face and cycle percolation

We consider face and cycle percolation as models for continuum percolation based on random simplicial complexes in Euclidean space. Face percolation is defined through infinite sequences of $d$-simplices sharing a $(d-1)$-dimensional face. In contrast, cycle percolation demands the existence of infinite $d$-cycles, thereby generalizing the lattice notion of plaquette percolation. We discuss the sharp phase transition for face percolation and derive comparison results between the critical intensities for face and cycle percolation. Finally, we consider an alternate version of simplex percolation, by declaring simplices to be neighbors whenever they are sufficiently close to each other, and prove a strict inequality involving the critical intensity of this alternate version and that of face percolation.
Resonant and non-resonant ionization of helium by XUV ultrashort and intense laser pulses

We study the multiphoton ionization of He by a strong ultrashort laser pulse. We solve the time-dependent Schrodinger equation numerically by means of a spectral method of configuration-interaction type (CI). Our method is based on an expansion on B-spline functions, more precisely each two-electron configuration is built as an antisymmetrized B-spline product for the (two-dimensional) radial part and dipolar spherical harmonics for the (four-dimensional) angular part. We focus on both the atomic structure calculations and the laser-atom dynamics. We show that our approach allows one to properly treat the electron-electron correlations so as to provide an accurate description of many eigenstates simultaneously. The method is applied in two different situations: (a) the multiphoton excitation and ionization of helium with photon energies ranging from 5.4 to 32.6 eV and (b) the two-photon ionization of helium below the He+(2l) threshold and three-photon ionization above the He2+ threshold with photon energies ranging from 25.8 to 34 eV. The results are compared with other CI calculations (where a comparison is possible) and, in case (a), to Hartree-Fock calculations. In case (b) we particularly focus on the dynamics of the production of doubly-excited structure by laser pulses with a duration which is shorter than the autoionization lifetime.
Simplicity Bias Leads to Amplified Performance Disparities

Which parts of a dataset will a given model find difficult? Recent work has shown that SGD-trained models have a bias towards simplicity, leading them to prioritize learning a majority class, or to rely upon harmful spurious correlations. Here, we show that the preference for ‘easy’ runs far deeper: A model may prioritize any class or group of the dataset that it finds simple—at the expense of what it finds complex—as measured by performance difference on the test set. When subsets with different levels of complexity align with demographic groups, we term this difficulty disparity, a phenomenon that occurs even with balanced datasets that lack group/label associations. We show how difficulty disparity is a model-dependent quantity, and is further amplified in commonly-used models as selected by typical average performance scores. We quantify an amplification factor across a range of settings in order to compare disparity of different models on a fixed dataset. Finally, we present two real-world examples of difficulty amplification in action, resulting in worse-than-expected performance disparities between groups even when using a balanced dataset. The existence of such disparities in balanced datasets demonstrates that merely balancing sample sizes of groups is not sufficient to ensure unbiased performance. We hope this work presents a step towards measurable understanding of the role of model bias as it interacts with the structure of data, and call for additional model-dependent mitigation methods to be deployed alongside dataset audits.
A forced thermal ratchet in a memory heat bath

The present work studies a non-Markovian forced thermal ratchet model on an asymmetric periodic potential. The Brownian dynamics is described by a generalized Langevin equation with an Ornstein-Uhlenbeck-type friction memory kernel. We show that for the case of a time-dependent driving force, also in the form of an Ornstein-Uhlenbeck-like process, an exact expression of the probability current can be derived. We also obtain the behavior of the particle's average rate of flow as a function of the external amplitude force and of the bath temperature when the driving force behaves as a square wave modulation. All our results are compared with those obtained in the Markovian case and we find, fairly remarkably, that in some cases a friction memory kernel results in an enhancement of the current
CamoFormer: Masked Separable Attention for Camouflaged Object Detection

How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results. These structures plus a backbone encoder form a new model, dubbed CamoFormer. Extensive experiments show that CamoFormer surpasses all existing state-of-the-art methods on three widely-used camouflaged object detection benchmarks. There are on average around 5% relative improvements over previous methods in terms of S-measure and weighted F-measure.
Edge-Assisted V2X Motion Planning and Power Control Under Channel Uncertainty

Edge-assisted vehicle-to-everything (V2X) motion planning is an emerging paradigm to achieve safe and efficient autonomous driving, since it leverages the global position information shared among multiple vehicles. However, due to the imperfect channel state information (CSI), the position information of vehicles may become outdated and inaccurate. Conventional methods ignoring the communication delays could severely jeopardize driving safety. To fill this gap, this article proposes a robust V2X motion planning policy that adapts between competitive driving under a low communication delay and conservative driving under a high communication delay, and guarantees small communication delays at key waypoints via power control. This is achieved by integrating the vehicle mobility and communication delay models and solving a joint design of motion planning and power control problem via the block coordinate descent framework. Simulation results show that the proposed driving policy achieves the smallest collision ratio compared with other benchmark policies.
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision

Recent developments in in-situ monitoring and process control in Additive Manufacturing (AM), also known as 3D-printing, allows the collection of large amounts of emission data during the build process of the parts being manufactured. This data can be used as input into 3D and 2D representations of the 3D-printed parts. However the analysis and use, as well as the characterization of this data still remains a manual process. The aim of this paper is to propose an adaptive human-in-the-loop approach using Machine Learning techniques that automatically inspect and annotate the emissions data generated during the AM process. More specifically, this paper will look at two scenarios: firstly, using convolutional neural networks (CNNs) to automatically inspect and classify emission data collected by in-situ monitoring and secondly, applying Active Learning techniques to the developed classification model to construct a human-in-the-loop mechanism in order to accelerate the labeling process of the emission data. The CNN-based approach relies on transfer learning and fine-tuning, which makes the approach applicable to other industrial image patterns. The adaptive nature of the approach is enabled by uncertainty sampling strategy to automatic selection of samples to be presented to human experts for annotation.
A Hausdorff-measure boundary element method for acoustic scattering by fractal screens

Sound-soft fractal screens can scatter acoustic waves even when they have zero surface measure. To solve such scattering problems we make what appears to be the first application of the boundary element method (BEM) where each BEM basis function is supported in a fractal set, and the integration involved in the formation of the BEM matrix is with respect to a non-integer order Hausdorff measure rather than the usual (Lebesgue) surface measure. Using recent results on function spaces on fractals, we prove convergence of the Galerkin formulation of this “Hausdorff BEM” for acoustic scattering in $$\mathbb {R}^{n+1}$$ R n + 1 ( $$n=1,2$$ n = 1 , 2 ) when the scatterer, assumed to be a compact subset of $$\mathbb {R}^n\times \{0\}$$ R n × { 0 } , is a d -set for some $$d\in (n-1,n]$$ d ∈ ( n - 1 , n ] , so that, in particular, the scatterer has Hausdorff dimension d . For a class of fractals that are attractors of iterated function systems, we prove convergence rates for the Hausdorff BEM and superconvergence for smooth antilinear functionals, under certain natural regularity assumptions on the solution of the underlying boundary integral equation. We also propose numerical quadrature routines for the implementation of our Hausdorff BEM, along with a fully discrete convergence analysis, via numerical (Hausdorff measure) integration estimates and inverse estimates on fractals, estimating the discrete condition numbers. Finally, we show numerical experiments that support the sharpness of our theoretical results, and our solution regularity assumptions, including results for scattering in $$\mathbb {R}^2$$ R 2 by Cantor sets, and in $$\mathbb {R}^3$$ R 3 by Cantor dusts.