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We focus on the derivation and analysis of a model for multi-component phase separation occurring on biological membranes, inspired by observations of lipid raft formation. The model integrates local membrane composition with local membrane curvature, describing the membrane's geometry through a perturbation method represented as a graph over an undeformed Helfrich minimising surface, such as a sphere. The resulting energy consists of a small deformation functional coupled to a Cahn-Hilliard functional. By applying Onsager's variational principle, we obtain a multi-component Cahn-Hilliard equation for the vector $\boldsymbol\varphi$ of protein concentrations coupled to an evolution equation for the small deformation $u$ along the normal direction to the reference membrane. Then, in the case of a constant mobility matrix, we consider the Cauchy problem and we prove that it is (globally) well posed in a weak setting. We also demonstrate that any weak solution regularises in finite time and satisfies the so-called "strict separation property". This property allows usto show that any weak solution converges to a single stationary state by a suitable version of the Lojasiewicz-Simon inequality.

Detecting Out-of-Distribution (OOD) sensory data and covariate distribution shift aims to identify new test examples with different high-level image statistics to the captured, normal and In-Distribution (ID) set. Existing OOD detection literature largely focuses on semantic shift with little-to-no consensus over covariate shift. Generative models capture the ID data in an unsupervised manner, enabling them to effectively identify samples that deviate significantly from this learned distribution, irrespective of the downstream task. In this work, we elucidate the ability of generative models to detect and quantify domain-specific covariate shift through extensive analyses that involves a variety of models. To this end, we conjecture that it is sufficient to detect most occurring sensory faults (anomalies and deviations in global signals statistics) by solely modeling high-frequency signal-dependent and independent details. We propose a novel method, CovariateFlow, for OOD detection, specifically tailored to covariate heteroscedastic high-frequency image-components using conditional Normalizing Flows (cNFs). Our results on CIFAR10 vs. CIFAR10-C and ImageNet200 vs. ImageNet200-C demonstrate the effectiveness of the method by accurately detecting OOD covariate shift. This work contributes to enhancing the fidelity of imaging systems and aiding machine learning models in OOD detection in the presence of covariate shift., Comment: ECCV 2024, typos corrected

We derive various novel free boundary problems as limits of a coupled bulk-surface reaction-diffusion system modelling ligand-receptor dynamics on evolving domains. These limiting free boundary problems may be formulated as Stefan-type problems on an evolving hypersurface. Our results are new even in the setting where there is no domain evolution. The models are of particular relevance to a number of applications in cell biology. The analysis utilises $L^\infty$-estimates in the manner of De Giorgi iterations and other technical tools, all in an evolving setting. We also report on numerical simulations.

In this paper, we study difference-in-differences identification and estimation strategies when the parallel trends assumption holds after conditioning on covariates. We consider empirically relevant settings where the covariates can be time-varying, time-invariant, or both. We uncover a number of weaknesses of commonly used two-way fixed effects (TWFE) regressions in this context, even in applications with only two time periods. In addition to some weaknesses due to estimating linear regression models that are similar to cases with cross-sectional data, we also point out a collection of additional issues that we refer to as \textit{hidden linearity bias} that arise because the transformations used to eliminate the unit fixed effect also transform the covariates (e.g., taking first differences can result in the estimating equation only including the change in covariates over time, not their level, and also drop time-invariant covariates altogether). We provide simple diagnostics for assessing how susceptible a TWFE regression is to hidden linearity bias based on reformulating the TWFE regression as a weighting estimator. Finally, we propose simple alternative estimation strategies that can circumvent these issues.

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems.

With basis on (i) the physical principle of local causality and (ii) a certain notion of elements of reality, Einstein, Podolsky, and Rosen (EPR) put forward an argument showing that physical instances may exist in which two non-commuting observables can be joint elements of the physical reality. Here, we introduce a new criterion of joint reality. We demonstrate that, according to this criterion, quantum mechanics generally prevents non-commuting observables from having joint elements of reality. joint elements of reality. In addition, we introduce a measure to quantify the extent to which the criterion is violated and explore the implications of such a measure in connection with incompatibility and correlations. Our findings suggest new manners of interpreting quantum phenomena.

Microbial contamination poses a threat to both the preservation of library and archival collections and the health of staff and users. This study investigated the microbial communities and potential health risks associated with the UNESCO-classified Norwegian Sea Trade Archive (NSTA) collection exhibiting visible microbial colonization and staff health concerns. Dust samples from book surfaces and the storage environment were analysed using culturing methods, qPCR, Next Generation Sequencing, and mycotoxin, cytotoxicity and azole resistance assays. Penicillium sp., Aspergillus sp., and Cladosporium sp. were the most common fungi identified, with some potentially toxic species like Stachybotrys sp., Toxicladosporium sp. and Aspergillus section Fumigati. Fungal resistance to azoles was not detected. Only one mycotoxin, sterigmatocystin, was found in a heavily contaminated book. Dust extracts from books exhibited moderate to high cytotoxicity on human lung cells, suggesting a potential respiratory risk. The collection had higher contamination levels compared to the storage environment, likely due to improved storage conditions. Even though, overall low contamination levels were obtained, which might be underestimated due to the presence of salt (from cod preservation) that could have interfered with the analyses. This study underlines the importance of monitoring microbial communities and implementing proper storage measures to safeguard cultural heritage and staff well-being., Comment: 17 pages, 7 figures, 2 tables

The primary objective is to emphasize the merits of active methodologies and cross-disciplinary curricula in Requirement Engineering. This direction promises a holistic and applied trajectory for Computer Engineering education, supported by the outcomes of our case study, where artifact-centric learning proved effective, with 73% of students achieving the highest grade. Self-assessments further corroborated academic excellence, emphasizing students' engagement in skill enhancement and knowledge acquisition., Comment: 3 pages, 2 figures, accepted for conference (SAC24)

We study sound-soft time-harmonic acoustic scattering by general scatterers, including fractal scatterers, in 2D and 3D space. For an arbitrary compact scatterer $\Gamma$ we reformulate the Dirichlet boundary value problem for the Helmholtz equation as a first kind integral equation (IE) on $\Gamma$ involving the Newton potential. The IE is well-posed, except possibly at a countable set of frequencies, and reduces to existing single-layer boundary IEs when $\Gamma$ is the boundary of a bounded Lipschitz open set, a screen, or a multi-screen. When $\Gamma$ is uniformly of $d$-dimensional Hausdorff dimension in a sense we make precise (a $d$-set), the operator in our equation is an integral operator on $\Gamma$ with respect to $d$-dimensional Hausdorff measure, with kernel the Helmholtz fundamental solution, and we propose a piecewise-constant Galerkin discretization of the IE, which converges in the limit of vanishing mesh width. When $\Gamma$ is the fractal attractor of an iterated function system of contracting similarities we prove convergence rates under assumptions on $\Gamma$ and the IE solution, and describe a fully discrete implementation using recently proposed quadrature rules for singular integrals on fractals. We present numerical results for a range of examples and make our software available as a Julia code.