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This chapter explores anomaly localization in medical images using denoising diffusion models. After providing a brief methodological background of these models, including their application to image reconstruction and their conditioning using guidance mechanisms, we provide an overview of available datasets and evaluation metrics suitable for their application to anomaly localization in medical images. In this context, we discuss supervision schemes ranging from fully supervised segmentation to semi-supervised, weakly supervised, self-supervised, and unsupervised methods, and provide insights into the effectiveness and limitations of these approaches. Furthermore, we highlight open challenges in anomaly localization, including detection bias, domain shift, computational cost, and model interpretability. Our goal is to provide an overview of the current state of the art in the field, outline research gaps, and highlight the potential of diffusion models for robust anomaly localization in medical images.

This case study explores the implementation of personalized learning at an elementary school in Santa Cruz Valley Unified School District 35. The study, conducted by Research for Action and funded by KnowledgeWorks, sheds light on the shift towards personalized, competency-based learning practices within the district. Through qualitative sources and data analysis, the study examines various key aspects such as shared vision and culture, student agency, curriculum, instruction, assessment systems, flexible learning environments, learner support, professional development, and leadership development within the district context. The study outlines how SCV35 reflected many practices associated with personalized, competency-based learning, with a focus on shared vision, student agency, curriculum design, learner support, and development. Key findings revealed positive impacts on students, highlighting increased student agency, ownership of learning, and reflective practices among students and educators. Additionally, the district's efforts to align policies, frameworks, and local partnerships with the principles of personalized learning demonstrate a systematic approach towards sustainable implementation. Furthermore, the report discusses challenges encountered during the implementation, such as adjusting to student-centered instruction, overcoming space limitations, and the need to clarify the practical aspects of personalized, competency-based learning. Despite challenges, success stories were acknowledged, including improved student engagement, individualized support, and teacher collaboration. The study emphasizes the importance of ongoing professional development, teacher collaboration, and student-centered approaches to sustain and enhance the personalized learning model. Recommendations from the study point towards the necessity of continued professional development, strategic collaboration among educators, and addressing the evolving evaluation processes to align with personalized, competency-based instructional models. The report underscores the importance of clarity in implementation practices, acknowledging the varying pace of adoption across the district. By focusing on building teacher capacity, fostering student agency, and maintaining a shared vision, SCV35 aims to further advance personalized, competency-based learning to benefit all stakeholders within the educational community.

The case study explores the adoption and implementation of personalized, competency-based learning at Yuma Union High School District (YUHSD) within the broader context of the Arizona Personalized Learning Network (AZPLN). The study reveals that YUHSD has made notable strides in aligning district-wide systems with personalized learning principles, such as the adoption of standards-based grading and a shared vision for personalized learning. Leadership development, professional learning opportunities, and comprehensive assessment systems have all played a crucial role in supporting the successful implementation of personalized, competency-based learning. The study underscores the positive impact of personalized, competency-based learning on student engagement, agency, and metacognitive skills. Teachers at YUHSD have reported increased student voice and choice through strategies like choice boards, proficiency scales, and flexible learning environments. These practices have led to improved advocacy skills among students and stronger relationships between students and teachers, fostering a culture of transparency and inclusivity. Additionally, the model has facilitated student individualization and differentiated support, enhancing the overall learning experience for students across various subject areas. While the implementation of personalized, competency-based learning has been largely successful, the study also highlights several challenges faced by YUHSD. These challenges include initial resistance due to competing priorities like the AVID program, as well as obstacles in scaling the model to more teachers and classrooms. Faculty members have expressed concerns about the additional time and support needed for effective implementation, especially in addressing the diverse needs of special education students and English language learners. Faculty training and ongoing collaboration are identified as critical areas for improvement to ensure the sustained success of the personalized learning approach. The case study emphasizes the importance of addressing the challenges faced by YUHSD through targeted support for faculty, especially in providing additional training and time for implementation. Efforts should focus on increasing buy-in from core subject area teachers, enhancing support for special education students and English language learners, and promoting a deeper understanding of personalized, competency-based learning principles across the district. Aligning the approach with existing initiatives like AVID and ensuring ongoing technical assistance for faculty are key strategies for successful implementation and continued growth in personalized learning practices. Moreover, the study acknowledges the need for continued collaboration, data-driven decision-making, and stakeholder engagement to sustain and enhance the success of personalized, competency-based learning at YUHSD. By leveraging the insights from this case study, Yuma Union High School District can further advance its efforts to create student-centered learning experiences that prepare all students for success in college, career, and beyond.

This paper presents an innovative method for predicting shape errors in 5-axis machining using graph neural networks. The graph structure is defined with nodes representing workpiece surface points and edges denoting the neighboring relationships. The dataset encompasses data from a material removal simulation, process data, and post-machining quality information. Experimental results show that the presented approach can generalize the shape error prediction for the investigated workpiece geometry. Moreover, by modelling spatial and temporal connections within the workpiece, the approach handles a low number of labels compared to non-graphical methods such as Support Vector Machines.

A direct analogue of V\'{e}lu's formulas is suggested for a genus two curve. Based on the structure of the differential field of Kleinian $\wp$-functions associated with the curve, formulas for cyclic $n$-isogenies are derived. Parameters of the image curve are computed explicitly., Comment: 14 pages

Multiphase flows, characterized by the presence of particles, bubbles, or droplets dispersed within a fluid, are ubiquitous in natural and industrial processes. Studying densely dispersed flows in 4D (3D + time) at very small scales without introducing perturbations is challenging, but crucial to understand their macroscopic behavior. The penetration power of X-rays and the flux provided by advanced X-ray sources, such as synchrotron-radiation facilities, offer an opportunity to address this need. However, current X-ray methods at these facilities require the rotation of the sample to obtain 4D information, thus disturbing the flow. Here, we demonstrate the potential of using X-ray Multi-Projection Imaging (XMPI), a novel technique to temporally resolve any dense particle suspension flows in 4D, while eliminating the need of sample rotation. By acquiring images of a microparticle-seeded flow from multiple viewing directions simultaneously, we can determine their instantaneous three-dimensional positions, both when flowing in a simple liquid and a highly dense and opaque complex fluid (e.g. blood). Along with the recent progress in AI-supported 4D reconstruction from sparse projections, this approach creates new opportunities for high-speed rotation-free 4D microtomography, opening a new spatiotemporal frontier. With XMPI, it is now feasible to track the movement of individual microparticles within dense suspensions, extending even to the chaotic realms of turbulent flows.

We explore, for the first time, {\textit{neutral-current}} events at long-baseline experiments to constrain vector and axial-vector neutrino non-standard interactions (NSI) with quarks. We leverage the flavor dependence of NSIs to perform an oscillation analysis in the neutral-current channel. We first introduce a framework to parametrize the effect of NSI on the cross section. Then, as an example, we analyze NOvA neutral-current data which provides significantly improved constraints on the axial-vector NSI parameters $\varepsilon_{\mu\mu}^A,~\varepsilon_{\tau\tau}^A$ and $\varepsilon_{e\mu}^A$. This is highly complementary to constraints from SNO data, which, differently from long-baseline neutral current data, is not sensitive to isospin conserving NSIs $\varepsilon^A_u=\varepsilon^A_d$. Additionally, we disfavor large values of the diagonal vectorial NSI $\varepsilon_{\mu\mu}^V$ and \varepsilon_{\tau\tau}^V$ which originate from the LMA-Dark solution. We also highlight the complementarity between NSI searches at oscillation experiments using charged current and neutral current channels.

Spatial summary statistics based on point process theory are widely used to quantify the spatial organization of cell populations in single-cell spatial proteomics data. Among these, Ripley's $K$ is a popular metric for assessing whether cells are spatially clustered or are randomly dispersed. However, the key assumption of spatial homogeneity is frequently violated in spatial proteomics data, leading to overestimates of cell clustering and colocalization. To address this, we propose a novel $K$-based method, termed \textit{KAMP} (\textbf{K} adjustment by \textbf{A}nalytical \textbf{M}oments of the \textbf{P}ermutation distribution), for quantifying the spatial organization of cells in spatial proteomics samples. \textit{KAMP} leverages background cells in each sample along with a new closed-form representation of the first and second moments of the permutation distribution of Ripley's $K$ to estimate an empirical null model. Our method is robust to inhomogeneity, computationally efficient even in large datasets, and provides approximate $p$-values for testing spatial clustering and colocalization. Methodological developments are motivated by a spatial proteomics study of 103 women with ovarian cancer, where our analysis using \textit{KAMP} shows a positive association between immune cell clustering and overall patient survival. Notably, we also find evidence that using $K$ without correcting for sample inhomogeneity may bias hazard ratio estimates in downstream analyses. \textit{KAMP} completes this analysis in just 5 minutes, compared to 538 minutes for the only competing method that adequately addresses inhomogeneity.

ChatGPT3 is a chat engine that fulfils the promises of an AI-based chat engine: users can ask a question (prompt) and it answers in a reasonable manner. The coding-related skills of ChatGPT are especially impressive: informal testing shows that it is difficult to find simple questions that ChatGPT3 does not know how to answer properly. Some students are certainly already using it to answer programming assignments. This article studies whether it is safe for students to use ChatGPT3 to answer coding assignments (safe means that they will not be caught for plagiarism if they use it). The main result is that it is generally not safe for students to use ChatGPT3. We evaluated the safety of code generated with ChatGPT3, by performing a search with a Codequiry, a plagiarism detection tool, and searching plagiarized code in Google (only considering the first page of results). In 38% of the cases, Codequiry finds a piece of code that is partially copied by the answer of ChatGPT3. In 96% of the cases, the Google search finds a piece of code very similar to the generated code. Overall, it is not safe for students to use ChatGPT3 in 96% of the cases., Comment: 8 pages

Let $\mathcal{T}$ be an algebraic triangulated category and $\mathcal{C}$ an extension-closed subcategory with $\operatorname{Hom}(\mathcal{C}, \Sigma^{<0} \mathcal{C})=0$. Then $\mathcal{C}$ has an exact structure induced from exact triangles in $\mathcal{T}$. Keller and Vossieck say that there exists a triangle functor $\operatorname{D}^b(\mathcal{C}) \to \mathcal{T}$ extending the inclusion $\mathcal{C} \subseteq \mathcal{T}$. We provide the missing details for a complete proof., Comment: 9 pages

This study investigates the possibility of using a femtosecond pulse laser to remove iron ore slurry used to stabilise blast-hole structures by mining industries, intending to preserve the wall's stability and the chemical and compositional properties of the underlying rock. In situ minerals are often coated in other material deposits, such as dust or slurry in blast holes. To analyse the rock materials beneath, its surface must be exposed by removal of the surface layer. The ablation depth per pulse and ablation efficiency of the slurry were determined using femtosecond laser pulses. Then, the ablation of rocks of economic interest in Australia, including banded iron, limonite, goethite, shale, and hematite, was studied to establish their ablation thresholds and rates. Any damage induced by the laser was investigated by optical microscopy, optical profilometry, colourimetry, VIS/NIR spectroscopy and Fourier Transform Infrared spectroscopy (FTIR)., Comment: 21 pages, 11 figures

Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation in online spaces by exploring the hypothesis that individuals may refrain from expressing minority opinions publicly due to being exposed to toxic behavior. Analyzing conversations under YouTube videos from six prominent US news outlets around the 2020 US presidential elections, we observe patterns of self-censorship signaling the influence of peer toxicity on users' behavior. Using hidden Markov models, we identify a latent state consistent with toxicity-driven silence. Such state is characterized by reduced user activity and a higher likelihood of posting toxic content, indicating an environment where extreme and antisocial behaviors thrive. Our findings offer insights into the intricacies of online political deliberation and emphasize the importance of considering self-censorship dynamics to properly characterize ideological polarization in digital spheres.

We develop a Bayesian Power generalized Weibull shape parameter (PgWSP) test as statistical method for signal detection of possible drug-adverse event associations using electronic health records for pharmacovigilance. The Bayesian approach allows the incorporation of prior knowledge about the likely time of occurrence along time-to-event data. The test is based on the shape parameters of the Power generalized Weibull (PgW) distribution. When both shape parameters are equal to one, the PgW distribution reduces to an exponential distribution, i.e. a constant hazard function. This is interpreted as no temporal association between drug and adverse event. The Bayesian PgWSP test involves comparing a region of practical equivalence (ROPE) around one reflecting the null hypothesis with estimated credibility intervals reflecting the posterior means of the shape parameters. The decision to raise a signal is based on the outcomes of the ROPE test and the selected combination rule for these outcomes. The development of the test requires a simulation study for tuning of the ROPE and credibility intervals to optimize specifcity and sensitivity of the test. Samples are generated under various conditions, including differences in sample size, prevalence of adverse drug reactions (ADRs), and the proportion of adverse events. We explore prior assumptions reflecting the belief in the presence or absence of ADRs at different points in the observation period. Various types of ROPE, credibility intervals, and combination rules are assessed and optimal tuning parameters are identifed based on the area under the curve. The tuned Bayesian PgWSP test is illustrated in a case study in which the time-dependent correlation between the intake of bisphosphonates and four adverse events is investigated.

In this paper the fields of multiply periodic, or Kleinian $\wp$-functions are exposed. Such a field arises on the Jacobian variety of an algebraic curve, and provides natural algebraic models of the Jacobian and Kummer varieties, possesses the addition law, and accommodates dynamical equations with solutions. All this will be explained in detail for plane algebraic curves in their canonical forms. Example of hyperelliptic and non-hyperelliptic curves are presented., Comment: 35 pages

The mechanical, optical, and chemical properties of a wide variety of soft materials are enabled and constrained by their bulk structure. How this structure emerges at small system sizes during self-assembly has been the subject of decades of research, with the aim of designing and controlling material functionality. Despite these efforts, it is still not fundamentally understood how nontrivial interparticle interactions in a finite $N$-body system influence resultant structure, and how that structure depends on $N$. In this study, we investigate the emergence of non-close packings using multi-well isotropic pair potentials to simulate finite cluster formation of four distinct two-dimensional crystal structures. These pair potentials encode multiple preferred length scales into the system, allowing us to understand how anisotropic structural motifs -- as opposed to close-packing -- emerge as cluster size $N$ increases. We find a tendency toward close-packing at small system sizes irrespective of the bulk structure; however, the system size at which bulk structure emerges is influenced by the coordination number of the bulk and the shape of the pair potential. Anisotropic structure emerges through the formation of bonds at a secondary bonding length at larger system sizes, and it is also dependent upon the shape of the pair potential. Our findings demonstrate that tuning particle-particle interactions can enable the engineering of nano- or mesoscale soft matter clusters, in applications as diverse as drug delivery and hierarchical materials design.

Magnetic relaxation in coordination compounds is largely dominated by the interaction of the spin with phonons. Large zero-field splitting and exchange coupling values have been empirically found to strongly suppress spin relaxation and have been used as the main guideline for designing new molecular compounds. Although a comprehensive understanding of spin-phonon relaxation has been achieved for mononuclear complexes, only a qualitative picture is available for polynuclear compounds. Here we fill this critical knowledge gap by providing a full first-principle description of spin-phonon relaxation in an air-stable Co(II) dimer with both large single-ion anisotropy and exchange coupling. Simulations reproduce the experimental relaxation data with excellent accuracy and provide a microscopic understanding of Orbach and Raman relaxation pathways and their dependency on exchange coupling, zero-field splitting, and molecular vibrations. Theory and numerical simulations show that increasing cluster nuclearity to just four cobalt units would lead to a complete suppression of Raman relaxation. These results hold a general validity for single-molecule magnets, providing a deeper understanding of their relaxation and revised strategies for their improvement.

Axions and other putative feebly interacting particles (FIPs) with a mass of tens to several hundreds of keVs can be produced in stellar cores with a Lorentz boost factor $E_a/m_a\lesssim 10$. Thus, starburst galaxies such as M82 are efficient factories of slow axions. Their decay $a\rightarrow\gamma\gamma$ would produce a large flux of X-ray photons, peaking around $100$ keV and spread around the galaxy by an angle that can be relatively large. We use observations of the Nuclear Spectroscopic Telescope Array (NuSTAR) mission to show that the absence of these features can constrain $30-500$ keV axion masses into uncharted regions for axion-photon coupling of $g_{a\gamma}\sim 10^{-10}-10^{-12}\,\rm GeV^{-1}$. Our argument can be applied to other heavy FIPs and astrophysical sources that are hot enough to produce them, yet cold enough to avoid large boost factors which slow down the decay., Comment: 4 pages, 3 figures, plus Supplemental Material

This letter presents a spatial filter based on saturated absorption in gas as a replacement for the solid pinhole in a lens-pinhole-lens filtering system. We show that an ultraviolet laser pulse focused through ozone will have its spatial profile cleaned if its peak fluence rises above the ozone saturation fluence. Specifically, we demonstrate that a 5 ns 266 nm beam with 4.2 mJ of initial energy can be effectively cleaned by focusing through a 1.4% ozone-oxygen mixture, with about 76% of the main beam energy transmitted and 89% of the side lobe energy absorbed. This process can be adapted to other gases and laser wavelengths, providing alignment-insensitive and damage-resistant pinholes for high-repetition-rate high-energy lasers.

Monitoring wastewater concentrations of SARS-CoV-2 yields a low-cost, noninvasive method for tracking disease prevalence and provides early warning signs of upcoming outbreaks in the serviced communities. There is tremendous clinical and public health interest in understanding the exact dynamics between wastewater viral loads and infection rates in the population. As both data sources may contain substantial noise and missingness, in addition to spatial and temporal dependencies, properly modeling this relationship must address these numerous complexities simultaneously while providing interpretable and clear insights. We propose a novel Bayesian functional concurrent regression model that accounts for both spatial and temporal correlations while estimating the dynamic effects between wastewater concentrations and positivity rates over time. We explicitly model the time lag between the two series and provide full posterior inference on the possible delay between spikes in wastewater concentrations and subsequent outbreaks. We estimate a time lag likely between 5 to 11 days between spikes in wastewater levels and reported clinical positivity rates. Additionally, we find a dynamic relationship between wastewater concentration levels and the strength of its association with positivity rates that fluctuates between outbreaks and non-outbreaks.

The Pauli exclusion principle is fundamental to understanding electronic quantum systems. It namely constrains the expected occupancies $n_i$ of orbitals $\varphi_i$ according to $0 \leq n_i \leq 2$. In this work, we first refine the underlying one-body $N$-representability problem by taking into account simultaneously spin symmetries and a potential degree of mixedness $\boldsymbol w$ of the $N$-electron quantum state. We then derive a comprehensive solution to this problem by using basic tools from representation theory, convex analysis and discrete geometry. Specifically, we show that the set of admissible orbital one-body reduced density matrices is fully characterized by linear spectral constraints on the natural orbital occupation numbers, defining a convex polytope $\Sigma_{N,S}(\boldsymbol w) \subset [0,2]^d$. These constraints are independent of $M$ and the number $d$ of orbitals, while their dependence on $N, S$ is linear, and we can thus calculate them for arbitrary system sizes and spin quantum numbers. Our results provide a crucial missing cornerstone for ensemble density (matrix) functional theory.

Multi-objective unconstrained combinatorial optimization problems (MUCO) are in general hard to solve, i.e., the corresponding decision problem is NP-hard and the outcome set is intractable. In this paper we explore special cases of MUCO problems that are actually easy, i.e., solvable in polynomial time. More precisely, we show that MUCO problems with up to two ordinal objective functions plus one real-valued objective function are tractable, and that their complete nondominated set can be computed in polynomial time. For MUCO problems with one ordinal and a second ordinal or real-valued objective function we present an even more efficient algorithm that applies a greedy strategy multiple times.

In invasive breast cancer, cell clusters of varying sizes and shapes are embedded in the fibrous extracellular matrix (ECM). Although the prevailing view attributes this structure to increasing disorder resulting from loss of function and dedifferentiation, our findings reveal that it arises through a process of active self-organization driven by cancer cell motility. Simulations and histological analyses of tumours from over 2,000 breast cancer patients reveal that motile, aligned cancer cells within clusters move as active nematic aggregates through the surrounding highly aligned ECM fibres, which form a confining, passive nematic phase. Cellular motion leads to cluster splitting and coalescence. The degree of cluster activity, combined with heterogeneity in cell motility, is reflected in specific scaling behaviours for cluster shape, size distribution, and the distance between cluster boundaries and nematic defects in ECM alignment. Increased activity estimates correlate with tumour progression and are associated with a poorer prognosis for patients.

Negative index metamaterials (NIMs) can be achieved with uniaxial hyperbolic metamaterials (HMMs) featuring $\epsilon_{parallel}>0$ and $\epsilon_{perpendicular}<0$. This type of approach has been traditionally realized using stacked doped/undoped semiconductor layers. Only recently surface phonon polaritons (SPhPs) have emerged as a promising low-loss alternative to surface plasmon polaritons (SPPs). Despite this advantage, the SPhP-based approach has been underexplored due to the challenges associated with ensuring high crystal quality in the heterostructure when using alloys with different phonon frequencies. In this work, we design a phononic-driven NIM using a ZnO/(Zn,Mg)O heterostructure, demonstrating control over its hyperbolic behavior through the precise selection of the Mg content and the relative layer thicknesses. Our study shows that increasing the Mg content in the ternary layers enhances the type I behavior, and that the optimal layer thickness varies depending on the Mg content. After analyzing the conditions for achieving type I hyperbolic dispersion, we experimentally demonstrate this concept with a sample featuring equal layer thicknesses and a 32% Mg concentration. We characterize the structure by means of polarized reflectance spectroscopy and use attenuated total reflectance spectroscopy to report the presence of a SPhP mode located within the type I hyperbolic region. By employing the transfer matrix method, we demonstrate that this mode exhibits negative frequency dispersion, a hallmark of type I hyperbolic modes, and isofrequency curve calculations further confirm this behavior. Controlling the design of a phononic hyperbolic type I metamaterial lays the groundwork for exploring its potential applications in attaining low-loss, sub-diffraction-limited optical modes using SPhP excitations., Comment: 13 pages, 6 figures

While being very successful in solving many downstream tasks, the application of deep neural networks is limited in real-life scenarios because of their susceptibility to domain shifts such as common corruptions, and adversarial attacks. The existence of adversarial examples and data corruption significantly reduces the performance of deep classification models. Researchers have made strides in developing robust neural architectures to bolster decisions of deep classifiers. However, most of these works rely on effective adversarial training methods, and predominantly focus on overall model robustness, disregarding class-wise differences in robustness, which are critical. Exploiting weakly robust classes is a potential avenue for attackers to fool the image recognition models. Therefore, this study investigates class-to-class biases across adversarially trained robust classification models to understand their latent space structures and analyze their strong and weak class-wise properties. We further assess the robustness of classes against common corruptions and adversarial attacks, recognizing that class vulnerability extends beyond the number of correct classifications for a specific class. We find that the number of false positives of classes as specific target classes significantly impacts their vulnerability to attacks. Through our analysis on the Class False Positive Score, we assess a fair evaluation of how susceptible each class is to misclassification.

We study the dynamics of a first-order phase transition in a strongly coupled gauge theory at non-zero temperature and chemical potential, computing nucleation rates and wall speeds from first principles. The gauge theory is the four-dimensional superconformal SU(N)xSU(N) Klebanov-Witten theory, which at low temperatures displays an instability to forming scalar condensates that higgses the theory. The computation is made possible by utilizing the gravity dual, type IIB string theory on asymptotically AdS_5xT^{1,1} spacetimes. The instability is detected through the nucleation and subsequent localization of D-branes in the bulk, which in the probe limit is amenable to calculations. The nucleation rates suggest a preference for greater asymmetry between the two gauge groups as the temperature is lowered beyond its critical value. The wall speed stays below the speed of sound of the conformal plasma across all parameter values and exhibits nonlinear dependence on the energy difference between the two phases., Comment: 20 pages, 7 figures

We study a generalization of the manifold-valued Rudin-Osher-Fatemi (ROF) model, which involves an initial datum $f$ mapping from a curved compact surface with smooth boundary to a complete, connected and smooth $n$-dimensional Riemannian manifold. We prove the existence and uniqueness of minimizers under curvature restrictions on the target and topological ones on the range of $f$. We obtain a series of regularity results on the associated PDE system of a relaxed functional with Neumann boundary condition. We apply these results to the ROF model to obtain Lipschitz regularity of minimizers without further requirements on the convexity of the boundary. Additionally, we provide variants of the regularity statement of independent interest: for 1-dimensional domains (related to signal denoising), local Lipschitz regularity (meaningful for image processing) and Lipschitz regularity for a version of the Mosolov problem coming from fluid mechanics.

We studied the structural and magnetic properties of the solid solution Fe$_{1-x}$Ni$_{x}$B through theoretical and experimental approaches. Powder X-ray diffraction, X-ray Pair Distribution Function analysis, and energy dispersive X-ray spectroscopy reveal that the Fe$_{1-x}$Ni$_{x}$B solid solution crystallizes in the FeB structure type up to x = 0.6-0.7 and exhibits anisotropic unit cell volume contraction with increasing Ni concentration. Magnetic measurements showed a transition from ferromagnetism to paramagnetism around x = 0.7. For x = 0.5, the low (< 0.3 $\mu_{B}$) magnetic moments suggest itinerant magnetism despite the relatively high Curie temperature (up to 225 K). Theoretical calculations indicated different types of magnetic orderings depending on the Fe/Ni atomic order, with the antiferromagnetic state being stable for ordered FeNiB$_{2}$, whereas the ground state is ferromagnetic for the disordered alloy. Calculations also predicted the coexistence of low- and high-spin states in Fe atoms around the composition with x=0.5, in line with the experimental evidence from $^{57}$Fe M\"ossbauer spectroscopy. The two magnetically distinct Fe sites for x = 0.3, 0.4, and 0.5 observed by $^{57}$Fe M\"ossbauer spectroscopy can also be interpreted as two magnetically different regions or clusters that could affect the critical behavior near a quantum magnetic transition based on a potential ferromagnetic quantum critical point identified computationally and experimentally near x=0.64. This work highlights the complex interplay between structure and magnetism in Fe$_{1-x}$Ni$_{x}$B alloys, suggesting areas for future research on quantum critical behavior.

The notion of a simplicial set originated in algebraic topology, and has also been utilized extensively in category theory, but until relatively recently was not used outside of those fields. However, with the increasing prominence of higher categorical methods in a wide range of applications, it is important for researchers in a range of fields to have a good working knowledge of them. This paper is intended as an introduction to simplicial sets, both as an overview of their development from other concepts, and as a user's guide for someone wanting to read modern literature that makes use of them., Comment: 23 pages. Expository paper

The definition of the homotopy limit of a diagram of left Quillen functors of model categories has been useful in a number of applications. In this paper we review its definition and summarize some of these applications. We conclude with a discussion of why we could work with right Quillen functors instead, but cannot work with a combination of the two., Comment: 18 pages. Expository paper

We give an introduction to the theory of 2-Segal sets, and two of the main applications of them: Hall algebras and a discrete version of Waldhausen's $S_\bullet$-construction. We present several combinatorial examples and how these constructions can be applied to them., Comment: 18 pages. Expanded version of my talk at the ECOGyT conference in August 2024. Comments welcome

The CHaracterising ExOPlanet Satellite (CHEOPS) is a partnership between the European Space Agency and Switzerland with important contributions by 10 additional ESA member States. It is the first S-class mission in the ESA Science Programme. CHEOPS has been flying on a Sun-synchronous low Earth orbit since December 2019, collecting millions of short-exposure images in the visible domain to study exoplanet properties. A small yet increasing fraction of CHEOPS images show linear trails caused by resident space objects crossing the instrument field of view. To characterize the population of satellites and orbital debris observed by CHEOPS, all and every science images acquired over the past 3 years have been scanned with a Hough transform algorithm to identify the characteristic linear features that these objects cause on the images. Thousands of trails have been detected. This statistically significant sample shows interesting trends and features such as an increased occurrence rate over the past years as well as the fingerprint of the Starlink constellation. The cross-matching of individual trails with catalogued objects is underway as we aim to measure their distance at the time of observation and deduce the apparent magnitude of the detected objects. As space agencies and private companies are developing new space-based surveillance and tracking activities to catalogue and characterize the distribution of small debris, the CHEOPS experience is timely and relevant. With the first CHEOPS mission extension currently running until the end of 2026, and a possible second extension until the end of 2029, the longer time coverage will make our dataset even more valuable to the community, especially for characterizing objects with recurrent crossings., Comment: 9 pages, 8 figures, Special Issue of the Journal of Space Safety Engineering

This paper contributes to the "BraTS 2024 Brain MR Image Synthesis Challenge" and presents a conditional Wavelet Diffusion Model (cWDM) for directly solving a paired image-to-image translation task on high-resolution volumes. While deep learning-based brain tumor segmentation models have demonstrated clear clinical utility, they typically require MR scans from various modalities (T1, T1ce, T2, FLAIR) as input. However, due to time constraints or imaging artifacts, some of these modalities may be missing, hindering the application of well-performing segmentation algorithms in clinical routine. To address this issue, we propose a method that synthesizes one missing modality image conditioned on three available images, enabling the application of downstream segmentation models. We treat this paired image-to-image translation task as a conditional generation problem and solve it by combining a Wavelet Diffusion Model for high-resolution 3D image synthesis with a simple conditioning strategy. This approach allows us to directly apply our model to full-resolution volumes, avoiding artifacts caused by slice- or patch-wise data processing. While this work focuses on a specific application, the presented method can be applied to all kinds of paired image-to-image translation problems, such as CT $\leftrightarrow$ MR and MR $\leftrightarrow$ PET translation, or mask-conditioned anatomically guided image generation., Comment: BraTS 2024 (Global Synthesis) submission. Code: https://github.com/pfriedri/cwdm

A pulsar's scintillation bandwidth is inversely proportional to the scattering delay, making accurate measurements of scintillation bandwidth critical to characterize unmitigated delays in efforts to measure low-frequency gravitational waves with pulsar timing arrays. In this pilot work, we searched for a subset of known pulsars within $\sim$97% of the data taken with the PUPPI instrument for the AO327 survey with the Arecibo telescope, attempting to measure the scintillation bandwidths in the dataset by fitting to the 2D autocorrelation function of their dynamic spectra. We successfully measured 38 bandwidths from 23 pulsars (six without prior literature values), finding that: almost all of the measurements are larger than the predictions from NE2001 and YMW16 (two popular galactic models); NE2001 is more consistent with our measurements than YMW16; Gaussian fits to the bandwidth are more consistent with both electron density models than Lorentzian ones; and for the 17 pulsars with prior literature values, the measurements between various sources often vary by factors of a few. The success of Gaussian fits may be due to the use of Gaussian fits to train models in previous work. The variance of literature values over time could relate to the scaling factor used to compare measurements, but also seems consistent with time-varying interstellar medium parameters. This work can be extended to the rest of AO327 to further investigate these trends, highlighting the continuing importance of large archival datasets for projects beyond their initial conception., Comment: 21 pages, 8 figures, 2 tables

Liver transplant can be a life-saving procedure for patients with end-stage liver disease. With the introduction of modern immunosuppressive therapies, short-term survival has significantly improved. However, long-term survival has not substantially improved in decades. Consequently, causes of death are now more likely to be due to the toxicities and side-effects of long-term immunosuppression rather than rejection. In order to study the balance of immunosuppression and rejection, we developed the first mechanistic mathematical model of liver transplant and immune system dynamics. We determined key cells and interactions in the model using literature information; we then used sensitivity analysis to determine key pathways driving the health status of the transplanted liver. We found that dynamics related to cytotoxic T cells and IL-2, in addition to the liver itself, are key determinants of liver graft injury. This has significant implications for the use of tests to monitor patients, and therapeutic strategies to prevent or treat liver transplantation rejection. Future work to collect appropriate data and parametrize the model would be valuable in improving our understanding of the dynamics of this system. We also note that our model could be tailored to model transplant of other organs., Comment: 34 pages, 5 figures

Introduction: The use of chatbots is becoming increasingly important across various aspects of daily life. However, the privacy concerns associated with these communications have not yet been thoroughly addressed. The aim of this study was to investigate user awareness of privacy risks in chatbot interactions, the privacy-preserving behaviours users practice, and how these behaviours relate to their awareness of privacy threats, even when no immediate threat is perceived. Methods: We developed a novel "privacy-safe" setup to analyse user behaviour under the guarantees of anonymization and non-sharing. We employed a mixed-methods approach, starting with the quantification of broader trends by coding responses, followed by conducting a qualitative content analysis to gain deeper insights. Results: Overall, there was a substantial lack of understanding among users about how chatbot providers handle data (27% of the participants) and the basics of privacy risks (76% of the participants). Older users, in particular, expressed fears that chatbot providers might sell their data. Moreover, even users with privacy knowledge do not consistently exhibit privacy-preserving behaviours when assured of transparent data processing by chatbots. Notably, under-protective behaviours were observed among more expert users. Discussion: These findings highlight the need for a strategic approach to enhance user education on privacy concepts to ensure informed decision when interacting with chatbot technology. This includes the development of tools to help users monitor and control the information they share with chatbots, Comment: 10 pages, 25 references, 2 figures and 3 tables

An \emph{outer-RAC drawing} of a graph is a straight-line drawing where all vertices are incident to the outer cell and all edge crossings occur at a right angle. If additionally, all crossing edges are either horizontal or vertical, we call the drawing \emph{outer-apRAC} (\emph{ap} for \emph{axis-parallel)}. A graph is outer-(ap)RAC if it admits an outer-(ap)RAC drawing. We investigate the class of outer-(ap)RAC graphs. We show that the outer-RAC graphs are a proper subset of~the planar graphs with at most $2.5n-4$ edges where $n$ is the number of vertices. This density bound is tight, even for outer-apRAC graphs. Moreover, we provide an SPQR-tree based linear-time algorithm which computes an outer-RAC drawing for every given series-parallel graph of maximum degree four. As a complementing result, we present planar graphs of maximum degree four and series-parallel graphs of maximum degree five that are not outer-RAC. Finally, for series-parallel graphs of maximum degree three we show how to compute an outer-apRAC drawing in linear time.

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended $\texttt{sbi}$, a PyTorch-based package that implements Bayesian SBI algorithms based on neural networks. The $\texttt{sbi}$ toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the $\texttt{sbi}$ toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data.

Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and intra-observer variability. In 2024, Anonymous Company organized an internal challenge to develop models for automatic bone quantification. We prepared and annotated a high-quality dataset of 3D $\mu$CT bone scans from $83$ mice. The challenge attracted over $80$ AI scientists from around the globe who formed $23$ teams. The participants were tasked with developing a solution to identify the plane where the bone growth happens, which is essential for fully automatic segmentation of trabecular bone. As a result, six computer vision solutions were developed that can accurately identify the location of the growth plate plane. The solutions achieved the mean absolute error of $1.91\pm0.87$ planes from the ground truth on the test set, an accuracy level acceptable for practical use by a radiologist. The annotated 3D scans dataset along with the six solutions and source code, is being made public, providing researchers with opportunities to develop and benchmark their own approaches. The code, trained models, and the data will be shared., Comment: Under Review

The catalog of ringed galaxies was compiled through visual classification of synthetic images from the TNG50 simulation. Galaxies were selected based on specific criteria: a redshift range of $0.01 < z < 0.1$, stellar mass $M_\star >10^9 M_\odot$, stellar half-mass radius $r_{50} > 1$ kpc, and specific star formation rate (sSFR), $\rm{log(sSFR/yr}^{-1}) > -13$. Our classification allowed for differentiation between inner rings, outer rings, combinations of rings, and partial rings (pseudo-rings), including barred and non-barred ringed galaxies. We constructed a control sample of non-ringed galaxies with similar redshift, stellar mass, and environmental density distributions. We identified 807 ringed galaxies. Approximately 59% possess an inner ring, 22% a partial ring, 12% an outer ring, and 7% have i+o rings. Our statistical analysis reveals that 64% (507 galaxies) exhibit bars. Ringed galaxies exhibit lower efficiency for star formation, reduced gas fractions, redder colors, and higher metallicities compared to non-ringed disk objects. They also show greater variability in metallicity for a given stellar mass. From the analysis of radial profiles, galaxies with outer rings exhibit a $r_{50}$ similar to or slightly larger than their control group, while those with inner or partial rings tend to have smaller sizes. A deeper exploration of radial density profiles revealed a pronounced central mass deficit preceding the ring structures, with inner and outer rings located at $r_{50}$ and $1.5 , r_{50}$, respectively. Galaxies with both i+o rings have inner rings that are more compact and massive. Additionally, galaxies with partial rings exhibit deeper mass profiles than their controls, particularly in central areas. These findings improve our understanding of galactic evolution and the complex interplay between mass distribution and morphology., Comment: To be published in Astronomy and Astrophysics

Water plays a fundamental role in the structure and function of proteins and other biomolecules. The thermodynamic profile of water molecules surrounding a protein are critical for ligand binding and recognition. Therefore, identifying the location and thermodynamic behavior of relevant water molecules is important for generating and optimizing lead compounds for affinity and selectivity to a given target. Computational methods have been developed to identify these hydration sites, but are largely limited to simplified models that fail to capture multi-body interactions, or dynamics-based methods that rely on extensive sampling. Here we present a method for fast and accurate localization and thermodynamic profiling of hydration sites for protein structures. The method is based on a geometric deep neural network trained on a large, novel dataset of explicit water molecular dynamics simulations. We confirm the accuracy and robustness of our model on experimental data and demonstrate it's utility on several case studies.

Histopathology, the microscopic study of diseased tissue, is increasingly digitized, enabling improved visualization and streamlined workflows. An important task in histopathology is the segmentation of cells and glands, essential for determining shape and frequencies that can serve as indicators of disease. Deep learning tools are widely used in histopathology. However, variability in tissue appearance and cell morphology presents challenges for achieving reliable segmentation, often requiring manual correction to improve accuracy. This work introduces CellPilot, a framework that bridges the gap between automatic and interactive segmentation by providing initial automatic segmentation as well as guided interactive refinement. Our model was trained on over 675,000 masks of nine diverse cell and gland segmentation datasets, spanning 16 organs. CellPilot demonstrates superior performance compared to other interactive tools on three held-out histopathological datasets while enabling automatic segmentation. We make the model and a graphical user interface designed to assist practitioners in creating large-scale annotated datasets available as open-source, fostering the development of more robust and generalized diagnostic models.

Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this prior knowledge. Methods addressing this concern are termed safe semi-supervised clustering (S3C) algorithms. This paper introduces the KNN graph-based safety-aware semi-supervised fuzzy c-means algorithm (K-GBS3FCM), which dynamically assesses neighborhood relationships between labeled and unlabeled data using the K-Nearest Neighbors (KNN) algorithm. This approach aims to optimize the use of labeled data while minimizing the adverse effects of incorrect labels. Additionally, it is proposed a mechanism that adjusts the influence of labeled data on unlabeled ones through regularization parameters and the average safety degree. Experimental results on multiple benchmark datasets demonstrate that the graph-based approach effectively leverages prior knowledge to enhance clustering accuracy. The proposed method was significantly superior in 64% of the 56 test configurations, obtaining higher levels of clustering accuracy when compared to other semi-supervised and traditional unsupervised methods. This research highlights the potential of integrating graph-based approaches, such as KNN, with established techniques to develop advanced clustering algorithms, offering significant applications in fields that rely on both labeled and unlabeled data for more effective clustering., Comment: 10 pages

Recent work has proven that training large language models with self-supervised tasks and fine-tuning these models to complete new tasks in a transfer learning setting is a powerful idea, enabling the creation of models with many parameters, even with little labeled data; however, the number of domains that have harnessed these advancements has been limited. In this work, we formulate a set of geometric tasks suitable for the large-scale study of ordered three-dimensional structures, without requiring any human intervention in data labeling. We build deep rotation- and permutation-equivariant neural networks based on geometric algebra and use them to solve these tasks on both idealized and simulated three-dimensional structures. Quantifying order in complex-structured assemblies remains a long-standing challenge in materials physics; these models can elucidate the behavior of real self-assembling systems in a variety of ways, from distilling insights from learned tasks without further modification to solving new tasks with smaller amounts of labeled data via transfer learning., Comment: Version as submitted to the Learning on Graphs Conference 2022, with small clarifying edits

X-ray dark-field imaging is well-suited to visualizing the health of the lungs because the alveoli create a strong dark-field signal. However, time-resolved and tomographic (i.e., 4D) dark-field imaging is challenging, since most x-ray dark-field techniques require multiple sample exposures, captured while scanning the position of crystals or gratings. Here, we present the first in vivo 4D x-ray dark-field lung imaging in mice. This was achieved by synchronizing the data acquisition process of a single-exposure grid-based imaging approach with the breath cycle. The short data acquisition time per dark-field projection made this approach feasible for 4D x-ray dark-field imaging by minimizing the motion-blurring effect, the total time required and the radiation dose imposed on the sample. Images were captured from a control mouse and from mouse models of muco-obstructive disease and lung cancer, where a change in the size of the alveoli was expected. This work demonstrates that the 4D dark-field signal provides complementary information that is inaccessible from conventional attenuation-based CT images, in particular, how the size of the alveoli from different parts of the lungs changes throughout a breath cycle, with examples shown across the different models. By quantifying the dark-field signal and relating it to other physical properties of the alveoli, this technique could be used to perform functional lung imaging that allows the assessment of both global and regional lung conditions where the size or expansion of the alveoli is affected., Comment: 11 pages, 5 figures, supplementary information available

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research., Comment: 98 pages

We demonstrate a blackbody radiation thermometer based on optically excited rubidium atoms in a vapor cell. The temperature measurement is fast, with statistical uncertainty as low as 0.1% in one second. We resolve temperature with a precision of 0.04% in the range 308 K to 344 K when averaging for several seconds. Additionally, we describe an extension to this measurement scheme where the device operates as a self-calibrated, or primary, thermometer. We make progress toward realizing a primary thermometer by demonstrating a temperature-dependent self-consistent calibration scheme, with temperature accuracy of order 1% limited by the uncertainty in atomic transition dipole matrix elements.

Photonic quantum information processing in metropolitan quantum networks lays the foundation for cloud quantum computing [1, 2], secure communication [3, 4], and the realization of a global quantum internet [5, 6]. This paradigm shift requires on-demand and high-rate generation of flying qubits and their quantum state teleportation over long distances [7]. Despite the last decade has witnessed an impressive progress in the performances of deterministic photon sources [8-11], the exploitation of distinct quantum emitters to implement all-photonic quantum teleportation among distant parties has remained elusive. Here, we overcome this challenge by using dissimilar quantum dots whose electronic and optical properties are engineered by light-matter interaction [12], multi-axial strain [13] and magnetic fields [14] so as to make them suitable for the teleportation of polarization qubits. This is demonstrated in a hybrid quantum network harnessing both fiber connections and 270 m free-space optical link connecting two buildings of the University campus in the center of Rome. The protocol exploits GPS-assisted synchronization, ultra-fast single photon detectors as well as stabilization systems that compensate for atmospheric turbulence. The achieved teleportation state fidelity reaches up to 82+-1%, above the classical limit by more than 10 standard deviations. Our field demonstration of all-photonic quantum teleportation opens a new route to implement solid-state based quantum relays and builds the foundation for practical quantum networks.

To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data processing and acquisition. The implementation of ML in real-time at colliders requires very low latencies that are unachievable with a software-based approach, requiring optimization and synthesis of ML algorithms for deployment on hardware. An analysis of neural network inference efficiency is presented, focusing on the application of collider trigger algorithms in field programmable gate arrays (FPGAs). Trade-offs are evaluated between two frameworks, the SLAC Neural Network Library (SNL) and hls4ml, in terms of resources and latency for different model sizes. Results highlight the strengths and limitations of each approach, offering valuable insights for optimizing real-time neural network deployments at colliders. This work aims to guide researchers and engineers in selecting the most suitable hardware and software configurations for real-time, resource-constrained environments., Comment: 12 pages, 5 figures