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1CONN Creative Reklam Ajansı
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Brownian motion with geometry-dependent hydrodynamic memory
Micron-sized particles moving through a fluid are subject to viscous resistance and thermal fluctuations. Beyond steady Stokes friction, colloids also exhibit hydrodynamic memory effects arising from conservation laws of the surrounding fluid. Although fluid-flow problems in complex geometries and confinements are often highly involved, numerical or approximate solutions can be obtained; translating these solutions into closed-form equations of motion for individual colloids, however, is rarely possible. Here, we develop a theoretical framework that provides both underdamped and overdamped colloidal dynamics from the solution of an arbitrary flow problem: (i) Using the Lorentz reciprocal theorem, we express the colloidal equation of motion in terms of the geometry's Green's function and the fluid-flow profile. This formulation yields both the corresponding Stokes drag as well as a Basset-like hydrodynamic memory contribution. (ii) As colloid inertia is often negligible, we furthermore derive the corresponding overdamped (colloid-inertia-less) limit, which inherits the Stokes and Basset-like resistances and additionally gives rise to a spurious drift. We propose applications of …
Traces of Abuse: How Generative AI Impacts Image-Based Sexual Abuse (IBSA) Investigations
The introduction of generative AI (GAI) into the workflow of image-based sexual abuse (IBSA) only worsened the ease of creation and distribution, victimizing more people than ever. We outline how the introduction of generative AI (GAI-IBSA) impacts the creation of traces and the type of reasoning they allow. We illustrate the impact by comparing the forensic traces available in four different IBSA scenarios. We discuss the impacts on the (possibility of) investigation, arguing that the advent of generative AI overall benefits abusers by making perpetration easier, and the perpetrator harder to trace.
Reconstructions and Dynamics of $β$-Lithium Thiophosphate Surfaces
Lithium thiophosphate (LPS) is a promising solid electrolyte for next-generation lithium-ion batteries due to its superior energy storage, high ionic conductivity, and low-flammability components. Despite its potential, the high reactivity of LPS with common contaminants such as atmospheric water, preparation solvents, and electrode materials poses significant challenges for commercialization. The lack of understanding regarding the structure, morphology, and chemical behavior of LPS's surface slows down the search for solutions to these issues. Here, we utilize a machine learning interatomic potential to achieve a fundamental, atomistic understanding of the mechanical and chemical properties of the $β$-Li$_3$PS$_4$ surfaces. Employing molecular dynamics simulations, we identify relevant surface complexions formed by surface reconstructions, determine their surface energies and compute the Wulff shape of $β$-LPS. The most stable complexions exhibit properties distinctly different from the bulk, including amorphization, increased density, decreased conductivity and large deformation of the structure building blocks. We demonstrate that these surfaces are not static, but undergo …
Hydrodynamic memory in overdamped colloidal dynamics
Micron-sized colloid particles diffusing through a fluid experience hydrodynamic inertial and memory effects, the latter causing velocity autocorrelation to decay as a power law. At the same time, many theoretical descriptions treat colloidal diffusion in a fluid using overdamped Langevin dynamics for its convenience, eliminating velocity, omitting inertia, but also ignoring the power-law memory. In this Letter, we show that hydrodynamic memory survives in the overdamped (colloid-inertialess) limit. By identifying the dimensionless parameter controlling the crossover between early- and late-time dynamics, we derive in closed form the overdamped Langevin equation in the presence of hydrodynamic memory. This provides a theoretical framework for realistically describing colloidal dynamics in a fluid, and establishes a rigorous basis for the positional memory observed in high-resolution experiments. Our theory predicts that hydrodynamic memory becomes increasingly pronounced for smaller particles and under stronger external forcing, and offers experimental probes for the crossover from conventional exponential relaxation to memory-dominated power-law dynamics.
Machine Learning Based Radiomics for Glial Tumor Classification and Comparison with Volumetric Analysis
Purpose; The purpose of this study is to classify glial tumors into grade II, III and IV categories noninvasively by application of machine learning to multi-modal MRI features in comparison with volumetric analysis. Methods; We retrospectively studied 57 glioma patients with pre and postcontrast T1 weighted, T2 weighted, FLAIR images, and ADC maps acquired on a 3T MRI. The tumors were segmented into enhancing and nonenhancing portions, tumor necrosis, cyst and edema using semiautomated segmentation of ITK-SNAP open source tool. We measured total tumor volume, enhancing-nonenhancing tumor, edema, necrosis volume and the ratios to the total tumor volume. Training of a support vector machine (SVM) classifier and artificial neural network (ANN) was performed with labeled data designed to answer the question of interest. Specificity, sensitivity, and AUC of the predictions were computed by means of ROC analysis. Differences in continuous measures between groups were assessed by using Kruskall Wallis, with post hoc Dunn correction for multiple comparisons. Results; When we compared the volume ratios between groups, there was statistically significant difference between grade IV and …
ProteinGeoBench: Frontier Structural-Biology Agents Are Fluent at Lookup but Lost in Geometry
Frontier agents retrieve structural metadata well and compute geometry from coordinates less reliably. We ask how much of a benchmark score reflects that gap and how much reflects the evaluation configuration around the model. We study ProteinGeoBench, a suite of 131 protein-structure tasks, using 4,249 rollouts from four frontier models and 4,486 further trials across 26 models. Three of the four frontier models fall within 0.031 of one another (0.785 to 0.816) with no difference between them resolved, while the fourth scores 0.707 and all three gaps to it exclude zero. Three of the four do better on metadata lookup than on numeric calculation. The fourth is slightly better on numeric. Numeric tasks are 28 of the 131 but half of the twelve tasks that all four models fail. The ranking also depends on the evaluation budget: with one run per task, 23.9% of bootstrap replicates put a different model first. On a selected 15-task slice, removing network access lowers scores more than blocking eight structural-database domains, but the two contrasts were graded in different passes and cannot be cleanly decomposed. Swapping in each model's own command-line agent changes scores by -0.071 …
The Formation of Population III Binaries from Cosmological Initial Conditions
Previous high resolution cosmological simulations predict the first stars to appear in the early universe to be very massive and to form in isolation. Here we discuss a cosmological simulation in which the central 50 solar mass clump breaks up into two cores, having a mass ratio of two to one, with one fragment collapsing to densities of 10^{-8} g/cc. The second fragment, at a distance of 800 astronomical units, is also optically thick to its own cooling radiation from molecular hydrogen lines, but is still able to cool via collision-induced emission. The two dense peaks will continue to accrete from the surrounding cold gas reservoir over a period of 10^5 years and will likely form a binary star system.
Magnetic Fields in Population III Star Formation
We study the buildup of magnetic fields during the formation of Population III star-forming regions, by conducting cosmological simulations from realistic initial conditions and varying the Jeans resolution. To investigate this in detail, we start simulations from identical initial conditions, mandating 16, 32 and 64 zones per Jeans length, and studied the variation in their magnetic field amplification. We find that, while compression results in some amplification, turbulent velocity fluctuations driven by the collapse can further amplify an initially weak seed field via dynamo action, provided there is sufficient numerical resolution to capture vortical motions (we find this requirement to be 64 zones per Jeans length, slightly larger than, but consistent with previous work run with more idealized collapse scenarios). We explore saturation of amplification of the magnetic field, which could potentially become dynamically important in subsequent, fully-resolved calculations. We have also identified a relatively surprising phenomena that is purely hydrodynamic: the higher-resolved simulations possess substantially different characteristics, including higher infall-velocity, …
Threat Me Right: A Human HARMS Threat Model for Technical Systems
Threat modelling is the process of identifying potential vulnerabilities in a system and prioritising them. Existing threat modelling tools focus primarily on technical systems and are not as well suited to interpersonal threats. In this paper, we discuss traditional threat modelling methods and their shortcomings, and propose a new threat modelling framework (HARMS) to identify non-technical and human factors harms. We also cover a case study of applying HARMS when it comes to IoT devices such as smart speakers with virtual assistants.
Capturing the "Whole Tale" of Computational Research: Reproducibility in Computing Environments
We present an overview of the recently funded "Merging Science and Cyberinfrastructure Pathways: The Whole Tale" project (NSF award #1541450). Our approach has two nested goals: 1) deliver an environment that enables researchers to create a complete narrative of the research process including exposure of the data-to-publication lifecycle, and 2) systematically and persistently link research publications to their associated digital scholarly objects such as the data, code, and workflows. To enable this, Whole Tale will create an environment where researchers can collaborate on data, workspaces, and workflows and then publish them for future adoption or modification. Published data and applications will be consumed either directly by users using the Whole Tale environment or can be integrated into existing or future domain Science Gateways.
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