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Success and failure of the spreading law for large drops of dense granular suspensions
No full textThe spreading of large viscous drops of density-matched suspensions of non-Brownian spheres on a smooth solid surface is experimentally investigated at the global drop scale. The focus is on dense suspensions with a solid volume fraction equal to or greater than and for drops larger than the capillary length, i.e. for which the spreading is governed by the balance of gravitational and viscous forces. Our findings indicate that all liquids exhibit a power law behaviour typical of gravity-driven dynamics, albeit with an effective suspension viscosity that is smaller than the bulk value. When the height of the drop is of the order of the particle size, the power law breaks down as the particles freeze while the contact line continues to advance
Swimming efficiency in viscosity gradients
No full textIn this note, we study the effect of viscosity gradients on the energy dissipated by the motion of microswimmers and the associated efficiency of that motion. Using spheroidal squirmer model swimmers in weak linearly varying viscosity fields, we find that efficiency depends on whether they generate propulsion from the back (pushers) or the front (pullers). Pushers are faster and more efficient when moving down gradients but slower and less efficient moving up viscosity gradients, and the opposite is true for pullers. However, both pushers and pullers display negative viscotaxis, therefore pushers dynamically tend to the most efficient orientation while pullers the least. We also evaluate the effect of shape on power expenditure and efficiency when swimming in viscosity gradients and find that in general the change in both due to gradients monotonically decreases with increasing slenderness. This work shows how shape and gait play an important role in determining dynamics and efficiency in inhomogeneous environments, and demonstrating that both efficiency minimizing and maximizing stable dynamical states are possible.12 pages, 1 figur
Probability of Presence Versus
No full textPostulating the identification of with a physical probability density is unsatisfactory conceptually and overly limited practically. For electrons, there is a simple, calculable relativistic correction proportional to . In particular, zeroes of the wave function do not indicate vanishing probability density of presence. We derive a correction of this kind from a Lagrangian, in a form suitable for wide generalization and use in effective field theories. Thus we define a large new class of candidate models for (quasi-)particles and fields, featuring modified {\it kinetic\/} terms. We solve for the stationary states and energy spectrum in some representative problems, finding striking effects including the emergence of negative effective mass at high energy and of localization by energy. \end{abstract}20 pages, 2 figures. Version 2 is a substantial expansion of Version 1, incorporating important additional material and two appendice
Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search
No full textSupervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on GAN latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution image interpretable features. Here, we propose a robust super-resolution method based on regularized latent search~(RLS) that offers an actionable balance between fidelity to the ground-truth and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution image into a computational super-resolution task performed by deep learning followed by a quantification task performed by a handcrafted algorithm and based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the high-resolution details of a specific sample but rather to obtain high-resolution images that preserve explainable and quantifiable differences between conditions.accepted for publication in Biological Imagin
Is Programming by Example solved by LLMs?
No full textProgramming-by-Examples (PBE) aims to generate an algorithm from input-output examples. Such systems are practically and theoretically important: from an end-user perspective, they are deployed to millions of people, and from an AI perspective, PBE corresponds to a very general form of few-shot inductive inference. Given the success of Large Language Models (LLMs) in code-generation tasks, we investigate here the extent to which LLMs can be said to have solved PBE. We experiment on classic domains such as lists and strings, and an uncommon graphics programming domain not well represented in typical pretraining data. We find that pretrained models are not effective at PBE, but that they can be fine-tuned for much higher performance, provided the test problems are in-distribution. We analyze empirically what causes these models to succeed and fail, and take steps toward understanding how to achieve better out-of-distribution generalization. Collectively these results suggest that LLMs make strong progress toward solving the typical suite of PBE tasks, potentially increasing the flexibility and applicability of PBE systems, while also identifying ways in which LLMs still fall short
Magnetising galaxies with cold inflows
No full textHigh-redshift () galaxies accrete circumgalactic gas through cold streams. Recent high-resolution MHD simulations of these streams showed a significant amplification of the intergalactic magnetic field in the shear layer around them. In this work we estimate the magnetisation of high-redshift galaxies that would result purely due to the accretion of already magnetised gas from cold streams. We use the mass inflow rates and saturated magnetic field values from cold stream simulations as input to a simple analytic model that calculates the galactic magnetic field purely from mass accretion. Our model predicts average magnetic field strengths that exceed values at for inflow rates above . For high inflow rates, our model results are consistent with the recent detection of a strong magnetic field in galaxies. Within the assumptions of our simple model, magnetised cold streams emerge as a viable mechanism for seeding a dynamically important galactic magnetic field.9 pages, 6 figures, 3 appendices (3 figures). Accepted for publication in Astronomy & Astrophysic
Modelling of eclipsing binary systems with pulsating components and tertiary companions: BF Vel and RR Lep
No full textThis paper presents a comprehensive analysis of RR Lep and BF Vel, two short-period semi-detached oscillating Algols (oEA stars), which are shown to be triple systems. Spectral types of their primaries were determined and radial velocities calculated from spectra observed with the Australian National University\u27s 2.3 m telescope and Wide Field Spectrograph. Spectra of the Na I D doublet confirmed the presence of tertiary components which were apparent in the broadening function analyses and, with H_a spectra during primary eclipses, indicated chromospherical activity in their secondaries. Ground-based telescopes were used for observations in several pass bands for photometric analyses. These data were complemented by data from the TESS mission to enable the modelling of the light curves, followed by a detailed analysis of pulsations. Eclipse-timing variation (ETV) analyses of both systems were used to determine the most likely mechanisms modulating the orbital period. We found mass values M1 = 2.9 M_sun and M2 = 0.75 M_sun for the components of RR Lep, and M1 = 1.93 M_sun and M2 = 0.97 M_sun for those of BF Vel. By integrating information from photometry, spectroscopy and ETV analysis, we found that tertiary components revolve around both systems. The primary star of RR Lep pulsates in 36 frequencies, of which five were identified as independent modes, with the dominant one being 32.28 d^-1. The pulsating component of BF Vel oscillates in 37 frequencies, with the frequency 46.73 d^-1 revealed as the only independent mode. For both systems, many frequencies were found to be related to the orbital frequency. Their physical properties were compared with other oEA stars in Mass-Radius and H-R diagrams, and the pulsational properties of their delta Sct components were compared with currently known systems of this type within the orbital-pulsation period and logg-pulsation period diagrams.22 pages, 21 figures, 8 tables, 3 appendices, Accepted for publication in A&
DIG-FACE: De-biased Learning for Generalized Facial Expression Category Discovery
No full textWe introduce a novel task, Generalized Facial Expression Category Discovery (G-FACE), that discovers new, unseen facial expressions while recognizing known categories effectively. Even though there are generalized category discovery methods for natural images, they show compromised performance on G-FACE. We identified two biases that affect the learning: implicit bias, coming from an underlying distributional gap between new categories in unlabeled data and known categories in labeled data, and explicit bias, coming from shifted preference on explicit visual facial change characteristics from known expressions to unknown expressions. By addressing the challenges caused by both biases, we propose a Debiased G-FACE method, namely DIG-FACE, that facilitates the debiasing of both implicit and explicit biases. In the implicit debiasing process of DIG-FACE, we devise a novel learning strategy that aims at estimating and minimizing the upper bound of implicit bias. In the explicit debiasing process, we optimize the model\u27s ability to handle nuanced visual facial expression data by introducing a hierarchical category-discrimination refinement strategy: sample-level, triplet-level, and distribution-level optimizations. Extensive experiments demonstrate that our DIG-FACE significantly enhances recognition accuracy for both known and new categories, setting a first-of-its-kind standard for the task
Discovery of Timeline and Crowd Reaction of Software Vulnerability Disclosures
No full textReusing third-party libraries increases productivity and saves time and costs for developers. However, the downside is the presence of vulnerabilities in those libraries, which can lead to catastrophic outcomes. For instance, Apache Log4J was found to be vulnerable to remote code execution attacks. A total of more than 35,000 packages were forced to update their Log4J libraries with the latest version. Although several studies have been conducted to predict software vulnerabilities, the prediction does not cover the vulnerabilities found in third-party libraries. Even if the developers are aware of the forthcoming issue, replicating a function similar to the libraries would be time-consuming and labour-intensive. Nevertheless, it is practically reasonable for software developers to update their third-party libraries (and dependencies) whenever the software vendors have released a vulnerable-free version. In this work, our manual study focuses on the real-world practices (crowd reaction) adopted by software vendors and developer communities when a vulnerability is disclosed. We manually investigated 312 CVEs and identified that the primary trend of vulnerability handling is to provide a fix before publishing an announcement. Otherwise, developers wait an average of 10 days for a fix if it is unavailable upon the announcement. Additionally, the crowd reaction is oblivious to the vulnerability severity. In particular, we identified Oracle as the most vibrant community diligent in releasing fixes. Their software developers also actively participate in the associated vulnerability announcements
Leveraging Computational Pathology AI for Noninvasive Optical Imaging Analysis Without Retraining
No full textNoninvasive optical imaging modalities can probe patient\u27s tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert labelers and the large dataset required (>100,000 images) for model training and tuning are the main hurdles in creating foundation models. In this paper we introduce FoundationShift, a method to apply any AI model from computational pathology without retraining. We show our method is more accurate than state of the art models (SAM, MedSAM, SAM-Med2D, CellProfiler, Hover-Net, PLIP, UNI and ChatGPT), with multiple imaging modalities (OCT and RCM). This is achieved without the need for model retraining or fine-tuning. Applying our method to noninvasive in vivo images could enable physicians to readily incorporate optical imaging modalities into their clinical practice, providing real time tissue analysis and improving patient care