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In vitro fertilization (IVF) is a widely utilized assisted reproductive technology, yet predicting its success remains challenging due to the multifaceted interplay of clinical, demographic, and procedural factors. This study develops a robust artificial intelligence (AI) pipeline aimed at predicting live birth outcomes in IVF treatments. The pipeline uses anonymized data from 2010 to 2018, obtained from the Human Fertilization and Embryology Authority (HFEA). We evaluated the prediction performance of live birth success as a binary outcome (success/failure) by integrating different feature selection methods, such as principal component analysis (PCA) and particle swarm optimization (PSO), with different traditional machine learning-based classifiers including random forest (RF) and decision tree, as well as deep learning-based classifiers including custom transformer-based model and a tab transformer model with an attention mechanism. Our research demonstrated that the best performance was achieved by combining PSO for feature selection with the TabTransformer-based deep learning model, yielding an accuracy of 99.50% and an AUC of 99.96%, highlighting its significant performance to predict live births. This study establishes a highly accurate AI pipeline for predicting live birth outcomes in IVF, demonstrating its potential to enhance personalized fertility treatments.

Artificial intelligence (AI) has emerged as a powerful tool to enhance decision-making and optimize treatment protocols in in vitro fertilization (IVF). In particular, AI shows significant promise in supporting decision-making during the ovarian stimulation phase of the IVF process. This review evaluates studies focused on the applications of AI combined with medical imaging in ovarian stimulation, examining methodologies, outcomes, and current limitations. Our analysis of 13 studies on this topic reveals that, reveal that while AI algorithms demonstrated notable potential in predicting optimal hormonal dosages, trigger timing, and oocyte retrieval outcomes, the medical imaging data utilized predominantly came from two-dimensional (2D) ultrasound which mainly involved basic quantifications, such as follicle size and number, with limited use of direct feature extraction or advanced image analysis techniques. This points to an underexplored opportunity where advanced image analysis approaches, such as deep learning, and more diverse imaging modalities, like three-dimensional (3D) ultrasound, could unlock deeper insights. Additionally, the lack of explainable AI (XAI) in most studies raises concerns about the transparency and traceability of AI-driven decisions - key factors for clinical adoption and trust. Furthermore, many studies relied on single-center designs and small datasets, which limit the generalizability of their findings. This review highlights the need for integrating advanced imaging analysis techniques with explainable AI methodologies, as well as the importance of leveraging multicenter collaborations and larger datasets. Addressing these gaps has the potential to enhance ovarian stimulation management, paving the way for efficient, personalized, and data-driven treatment pathways that improve IVF outcomes., Comment: 29 pages, 2 figures, 3 tables

To design type systems that use subtyping, we have to make tradeoffs. Deep subtyping is more expressive than shallow subtyping, because deep subtyping compares the entire structure of types. However, shallow subtyping is easier to reason about. By eta-expanding source programs, we can get the effect of deep subtyping with less of its complexity. An early paper on filter models (Barendregt et al. 1983) examined two similar intersection type systems. The first included a subsumption rule that used a rich subtyping relation, including multiple rules for the top type and a distributivity rule. Their second type system dropped the subsumption rule, but added a rule that allowed a term to be eta-expanded before typing it. This rule in their second type system compensated for the lack of subsumption: where their first type system used subtyping to manipulate intersections deep inside types, their second type system used introduction and elimination rules directly on the subterms created by eta-expansion. Viewed as a computation, their proof of completeness for the second (shallow) system performs eta-expansion. Thus, we can regard their proof as inventing the application of eta-expansion to avoid deep subtyping. This paper serves as a tutorial on using eta-expansion to obviate deep subtyping, puts the invention of the technique by Barendregt et al. (1983) into context, gives a complete proof of the relevant lemma, and discusses how the technique can be used in type system design., Comment: 16 pages, 8 figures

Even though the Dissipative Particle Dynamics (DPD) has shown its worth in a variety of research areas, it has been rarely used for polymer dynamics, particularly in dilute and semi-dilute conditions and under imposed flow fields. For such applications, the most popular technique has been Brownian dynamics (BD), even though the formulation of the same may be complicated for flow in complex geometries, which is straightforward for DPD. This is partly due to the flexibility of BD simulations to mimic any dynamic regime for polymer solutions by independently tuning hydrodynamic interactions (HI) and excluded volume (EV). In this study, we reveal that DPD also offers a similar flexibility and the regimes with respect to dominant EV and HI can be selected as conveniently as BD. This flexibility is achieved by tuning the repulsive interaction parameter of polymer beads and the spring length (which determines the chain resolution). Our results show that the former sets the chain size (and thus, EV) while the latter can be used to set the HI, nearly independently of each other. Thus, any rheological regime of certain level of EV and HI can be attained by appropriately tuning only these two parameters, providing a flexibility of similar levels as BD simulations. We further indicate the suitability of DPD by comparing rheological predictions with equivalent models in BD. For this, we imposed startup uniaxial extensional flows and steady shear flows on the system. Our results indicate the consistency of DPD with BD simulations, which is known to agree well with experiments.

Systematic reviews (SRs) are essential for evidence-based guidelines but are often limited by the time-consuming nature of literature screening. We propose and evaluate an in-house system based on Large Language Models (LLMs) for automating both title/abstract and full-text screening, addressing a critical gap in the literature. Using a completed SR on Vitamin D and falls (14,439 articles), the LLM-based system employed prompt engineering for title/abstract screening and Retrieval-Augmented Generation (RAG) for full-text screening. The system achieved an article exclusion rate (AER) of 99.5%, specificity of 99.6%, a false negative rate (FNR) of 0%, and a negative predictive value (NPV) of 100%. After screening, only 78 articles required manual review, including all 20 identified by traditional methods, reducing manual screening time by 95.5%. For comparison, Rayyan, a commercial tool for title/abstract screening, achieved an AER of 72.1% and FNR of 5% when including articles Rayyan considered as undecided or likely to include. Lowering Rayyan's inclusion thresholds improved FNR to 0% but increased screening time. By addressing both screening phases, the LLM-based system significantly outperformed Rayyan and traditional methods, reducing total screening time to 25.5 hours while maintaining high accuracy. These findings highlight the transformative potential of LLMs in SR workflows by offering a scalable, efficient, and accurate solution, particularly for the full-text screening phase, which has lacked automation tools.

Large language models (LLMs) are powerful but resource intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimentation, ideal for resource-constrained environments. It supports unigrams to 4-grams and incorporates features like context sensitive weighting, Laplace smoothing, and dynamic corpus management to e-hance prediction accuracy, even for unseen word sequences. Experiments demonstrate HITgram's efficiency, achieving 50,000 tokens/second and generating 2-grams from a 320MB corpus in 62 seconds. HITgram scales efficiently, constructing 4-grams from a 1GB file in under 298 seconds on an 8 GB RAM system. Planned enhancements include multilingual support, advanced smoothing, parallel processing, and model saving, further broadening its utility.

In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends., Comment: 12 pages, 3 figures, Accepted and to be published in Proceedings of 2025 International Conference on Applied Algorithms (ICAA), Kolkata, India, Dec 8-10, 2025

Systems with pronounced spin anisotropy play a pivotal role in advancing magnetization switching and spin-wave generation mechanisms, which are fundamental for spintronic technologies. Quasi-van der Waals ferromagnets, particularly Cr$_{1+\delta}$Te$_2$ compounds, represent seminal materials in this field, renowned for their delicate balance between frustrated layered geometries and magnetism. Despite extensive investigation, the precise nature of their magnetic ground state, typically described as a canted ferromagnet, remains contested, as does the mechanism governing spin reorientation under external magnetic fields and varying temperatures. In this work, we leverage a multimodal approach, integrating complementary techniques, to reveal that Cr$_{1+\delta}$Te$_2$ ($\delta = 0.25 - 0.50$) hosts a previously overlooked magnetic phase, which we term orthogonal-ferromagnetism. This single phase consists of alternating atomically sharp single layers of in-plane and out-of-plane ferromagnetic blocks, coupled via exchange interactions and as such, it differs significantly from crossed magnetism, which can be achieved exclusively by stacking multiple heterostructural elements together. Contrary to earlier reports suggesting a gradual spin reorientation in CrTe$_2$-based systems, we present definitive evidence of abrupt spin-flop-like transitions. This discovery, likely due to the improved crystallinity and lower defect density in our samples, repositions Cr$_{1+\delta}$Te$_2$ compounds as promising candidates for spintronic and orbitronic applications, opening new pathways for device engineering.

While billiard systems of various shapes have been used as paradigmatic model systems in the fields of nonlinear dynamics and quantum chaos, few studies have investigated anisotropic billiards. Motivated by the tremendous advances in using and controlling electronic and optical mesoscopic systems with bilayer graphene representing an easily accessible anisotropic material for electrons when trigonal warping is present, we investigate billiards of various anisotropies and geometries using a trajectory tracing approach founded in the concept of ray-wave correspondence. We find that the presence of anisotropy can render the billiards' dynamics dramatically from its isotropic counterpart. It may induce chaotic and mixed dynamics in otherwise integrable systems, and may stabilize originally unstable trajectories. We characterize the dynamics of anisotropic billiards in real and phase space using Lyapunov exponents and the Poincar\'e surface of section as phase space representation.

The simultaneous application of high magnetic fields and high pressures for controlling magnetic ground states is important for testing our understanding of many-body quantum theory. However, the implementation for neutron scattering experiments presents a technical challenge. To overcome this challenge we present an optimized pressure-cell design with a novel bullet shape, which is compatible with horizontal-field magnets, in particular the high-field magnet operating at the Helmholtz-Zentrum Berlin. The cell enabled neutron diffraction and spectroscopy measurements with the combination of three extreme conditions: high pressures, high magnetic fields, and dilution temperatures, simultaneously reaching 0.7 GPa, 25.9 T, and 200 mK. Our results demonstrate the utility of informed material choices and the efficiency of finite-element analysis for future pressure-cell designs to be used in combination with magnetic fields and dilution temperatures for neutron scattering purposes.

Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instructions and examples. However, LLMs can fail to generate predictions that satisfy group fairness, that is, produce equitable outcomes across groups. Critically, conventional debiasing approaches for natural language tasks do not directly translate to mitigating group unfairness in tabular settings. In this work, we systematically investigate four empirical approaches to improve group fairness of LLM predictions on tabular datasets, including fair prompt optimization, soft prompt tuning, strategic selection of few-shot examples, and self-refining predictions via chain-of-thought reasoning. Through experiments on four tabular datasets using both open-source and proprietary LLMs, we show the effectiveness of these methods in enhancing demographic parity while maintaining high overall performance. Our analysis provides actionable insights for practitioners in selecting the most suitable approach based on their specific requirements and constraints.

In their seminal 1992 paper, Ba\~{n}ados, Teitelboim and Zanelli (BTZ) proposed a simple charged generalization of what is now known as the spinning BTZ black hole, the proposal being that a rotating metric can be supported by a `static vector' potential. While with such an ansatz the Einstein equations are satisfied, and the corresponding energy-momentum tensor is divergence-less, the Maxwell equations do not (due to the special degenerate form of the corresponding field strength) hold. More recently, Deshpande and Lunin have proposed a generalized `Einstein--Maxwell' system which yields analytic rotating black holes in all odd dimensions. In this paper, we show that the original charged BTZ solution can be re-interpreted as a solution of the Deshpande--Lunin theory. Moreover, as we shall explicitly illustrate on an example of regularized conformal electrodynamics, similar construction also works for any non-linear electrodynamics in 3-dimensions., Comment: 8 pages

In this paper, we characterize arbitrary polynomial vector fields on $S^n$. We establish a necessary and sufficient condition for a degree one vector field on the odd-dimensional sphere $S^{2n-1}$ to be Hamiltonian. Additionally, we classify polynomial vector fields on $S^n$ up to degree two that possess an invariant great $(n-1)$-sphere. We present a class of completely integrable vector fields on $S^n$. We found a sharp bound for the number of invariant meridian hyperplanes for a polynomial vector field on $S^2$. Furthermore, we compute the sharp bound for the number of invariant parallel hyperplanes for any polynomial vector field on $S^n$. Finally, we study homogeneous polynomial vector fields on $S^n$, providing a characterization of their invariant $(n-1)$-spheres., Comment: 21 pages

This paper proposes robust estimators of the variogram, a statistical tool that is commonly used in geostatistics to capture the spatial dependence structure of data. The new estimators are based on the highly robust minimum covariance determinant estimator and estimate the directional variogram for several lags jointly. Simulations and breakdown considerations confirm the good robustness properties of the new estimators. While Genton's estimator based on the robust estimation of the variance of pairwise sums and differences performs well in case of isolated outliers, the new estimators based on robust estimation of multivariate variance and covariance matrices perform superior to the established alternatives in the presence of outlier blocks in the data. The methods are illustrated by an application to satellite data, where outlier blocks may occur because of e.g. clouds., Comment: 23 pages, 11 figures

Gallenene is a promising low-dimensional material with a structure down to the thickness of a single atom, similar to graphene. However, van der Waals stacking of two-dimensional (2D) gallenene under confinement remain poorly understood. In this study, we present evidence of the formation of parallel-stacked hexagonal gallenene (a100) structures in liquid gallium. The present study demonstrates the AB stacking of 2D gallenene a100 crystals in liquid gallium sandwiched between two graphene layers, as observed through transmission electron microscopy. A nonlinear optical response of the confined hexagonal gallenene was investigated through second harmonic generation (SHG) microscopy. The SHG signal exhibits periodic peak intensity shifts upon angular rotation up to 90 degrees and intensity dampening at elevated temperatures. These findings offer insights on device applications of 2D gallenene., Comment: 14 pages and 5 figures

In this article, we examine the H\"older regularity of solutions to equations involving a mixed local-nonlocal nonlinear nonhomogeneous operator $\fp + \fqs$ with singular data, under the minimal assumption that $p> sq$. The regularity result is twofold: we establish interior gradient H\"older regularity for locally bounded data and boundary regularity for singular data. We prove both boundary H\"older and boundary gradient H\"older regularity depending on the degree of singularity. Additionally, we establish a strong comparison principle for this class of problems, which holds independent significance. As the applications of these qualitative results, we further study sublinear and subcritical perturbations of singular nonlinearity.

We consider high-dimensional estimation problems where the number of parameters diverges with the sample size. General conditions are established for consistency, uniqueness, and asymptotic normality in both unpenalized and penalized estimation settings. The conditions are weak and accommodate a broad class of estimation problems, including ones with non-convex and group structured penalties. The wide applicability of the results is illustrated through diverse examples, including generalized linear models, multi-sample inference, and stepwise estimation procedures., Comment: 47 pages, 1 figure

Measures of textual similarity and divergence are increasingly used to study cultural change. But which measures align, in practice, with social evidence about change? We apply three different representations of text (topic models, document embeddings, and word-level perplexity) to three different corpora (literary studies, economics, and fiction). In every case, works by highly-cited authors and younger authors are textually ahead of the curve. We don't find clear evidence that one representation of text is to be preferred over the others. But alignment with social evidence is strongest when texts are represented through the top quartile of passages, suggesting that a text's impact may depend more on its most forward-looking moments than on sustaining a high level of innovation throughout., Comment: Accepted CHR 2024

We study a generalized Kardar-Parisi-Zhang (KPZ) equation [D. Jana et al, Phys. Rev. E 109, L032104 (2024)], that sets the paradigm for universality in roughening of growing nonequilibrium surfaces without any conservation laws, but with competing local and nonlocal nonlinear effects. We show that such a generalized KPZ equation in two dimensions can describe a strong coupling rough or a crumpled surface, in addition to a weak coupling phase. The conformation fluctuations of such a rough surface are given by nonuniversal exponents, with orientational long-ranged order and positional short-ranged order, whereas the crumpled phase has positional and orientational short range order. Experimental and theoretical implications of these results are discussed.

Semiconducting single-wall carbon nanotubes (SWCNTs) are a promising material platform for near-infrared in-vivo imaging, optical sensing, and single-photon emission at telecommunication wavelengths. The functionalization of SWCNTs with luminescent defects can lead to significantly enhanced photoluminescence (PL) properties due to efficient trapping of highly mobile excitons and red-shifted emission from these trap states. Among the most studied luminescent defect types are oxygen and aryl defects that have largely similar optical properties. So far, no direct comparison between SWCNTs functionalized with oxygen and aryl defects under identical conditions has been performed. Here, we employ a combination of spectroscopic techniques to quantify the number of defects, their distribution along the nanotubes and thus their exciton trapping efficiencies. The different slopes of Raman D/G+ ratios versus calculated defect densities from PL quantum yield measurements indicate substantial dissimilarities between oxygen and aryl defects. Supported by statistical analysis of single-nanotube PL spectra at cryogenic temperatures it reveals clustering of oxygen defects. The clustering of 2-3 oxygen defects, which act as a single exciton trap, occurs irrespective of the functionalization method and thus enables the use of simple equations to determine the density of oxygen defects and oxygen defect clusters in SWCNTs based on standard Raman spectroscopy. The presented analytical approach is a versatile and sensitive tool to study defect distribution and clustering in SWCNTs and can be applied to any new functionalization method.

Ashkin-Teller model is a two-layer lattice model where spins in each layer interact ferromagnetically with strength $J$, and the spin-dipoles (product of spins) interact with neighbors with strength $\lambda.$ The model exhibits simultaneous magnetic and electric transitions along a self-dual line on the $\lambda$-$J$ plane with continuously varying critical exponents. In this article, we investigate the percolation of geometric clusters of spins and spin-dipoles denoted respectively as magnetic and electric clusters. We find that the largest cluster in both cases becomes macroscopic in size and spans the lattice when interaction exceeds a critical threshold given by the same self-dual line where magnetic and electric transitions occur. The fractal dimension of the critical spanning clusters is related to order parameter exponent $\beta_{m,e}$ as $D_{m,e}=d-\frac{5}{12}\frac{\beta_{m,e}}\nu,$ where $d=2$ is the spatial dimension and $\nu$ is the correlation length exponent. This relation determines all other percolation exponents and their variation wrt $\lambda.$ We show that for magnetic Percolation, the Binder cumulant, as a function of $\xi_2/L$ with $\xi_2$ being the second-moment correlation length, remains invariant all along the critical line and matches with that of the spin-percolation in the usual Ising model. The function also remains invariant for the electric percolation, forming a new superuniversality class of percolation transition., Comment: 13 pages + 6 pages (supplemental material)

Null-trajectories, deflection angle, and gravitational lensing are studied in the background spacetime of the generalised Ellis-Bronnikov wormholes and it's five dimensional version embedded in a warped background. We show that these wormholes can be distinguished through the study of gravitational lensing. In particular, the difference in deflection angles, radius of the Einstein rings, and the image positions are quantified by the wormhole parameters -- the steepness constant ($m\geq 2$), the throat radius, and `$\delta$' that characterizes the warping in extra dimension. We demonstrate our results analytically with $m=2$ cases and graphically otherwise., Comment: 25 pages, 2 figures

This study explored a concept for using Augmented Reality (AR) glasses to customize augmented table setup and lighting in a restaurant. The aim was to provide insights into AR usage in restaurants and contribute to existing research by introducing an extendable and versatile concept for scholars and restaurateurs. A controlled laboratory study, using a within-subjects design, was conducted to investigate the effects of a customizable augmented table setup and lighting on user experience (UX), perceived waiting time, psychological ownership, and social acceptability. A simulated restaurant environment was created using a 360-degree image in Virtual Reality (VR). The study implemented default and customizable table setup and lighting. Results from a paired samples t-test showed a statistically significant effect of table setup and lighting on the pragmatic quality of UX, hedonic quality of UX, overall UX, valence, dominance, psychological ownership, and affect. Furthermore, table setup had a significant effect on arousal and perceived waiting time. Moreover, table setup significantly affected AR interaction, isolation, and safety acceptability, while lighting only affected AR interaction acceptability. Findings suggest that these investigated variables are worth considering for AR applications in a restaurant, especially when offering customizable augmented table setup and lighting., Comment: This paper was accepted as short paper at IEEE ISM 2024, and will be presented on the 13th of December 2024

A rotating version of a known static, spherically symmetric, zero Ricci scalar Lorentzian wormhole is constructed. It turns out that for this given non-rotating geometry, the standard Newman-Janis algorithm does not produce a rotating wormhole and, therefore, the method pioneered by Azreg-A\"inou has to be used. The rotating spacetime thus obtained is shown to be regular with wormhole features, though it is no longer a $R=0$ spacetime. The required matter is found to violate the energy conditions, as expected. A few other characteristic properties of this new rotating spacetime are mentioned. Finally, we calculate the shadow for this geometry and discuss its features {\em vis-a-vis} the Kerr geometry and available EHT observations., Comment: minor changes, references added

Changing-look active galactic nuclei (CLAGNs) show the appearance and disappearance of broad emission lines in their UV/optical spectra on timescales of months to decades. Here, we investigate how CL transitions depend on several AGN parameters such as accretion rate, obscuration properties and black hole mass. We study a sample of 20 nearby optically-identified CLAGNs from the BAT AGN Spectroscopic Survey (BASS), using quasi-simultaneous optical and X-ray observations taken in the last $\sim 40$ years. We find that for all CLAGNs, the transition is accompanied by a change in Eddington ratio. The CL transitions are not associated with changes in the obscuration properties of the AGN. CLAGNs are found to have a median Eddington ratio lower than the AGNs in the BASS sample in which CL transitions were not detected. The median of the transition Eddington ratio (Eddington ratio at which AGN changes its state) is found to be $\sim 0.01$ for type 1 $\leftrightarrow$ 1.8/1.9/2 transition, which is consistent with the hard $\leftrightarrow$ soft state transition in black hole X-ray binaries. Most CL events are constrained to occur within 3-4 years, which is considerably shorter than the expected viscous timescale in AGN accretion disk. The transitions of the optical CLAGNs studied here are likely associated to state changes in the accretion flow, possibly driven by disk-instability., Comment: Accepted for publication in Astronomy & Astrophysics

Deciding on appropriate mechanical ventilator management strategies significantly impacts the health outcomes for patients with respiratory diseases. Acute Respiratory Distress Syndrome (ARDS) is one such disease that requires careful ventilator operation to be effectively treated. In this work, we frame the management of ventilators for patients with ARDS as a sequential decision making problem using the Markov decision process framework. We implement and compare controllers based on clinical guidelines contained in the ARDSnet protocol, optimal control theory, and learned latent dynamics represented as neural networks. The Pulse Physiology Engine's respiratory dynamics simulator is used to establish a repeatable benchmark, gather simulated data, and quantitatively compare these controllers. We score performance in terms of measured improvement in established ARDS health markers (pertaining to improved respiratory rate, oxygenation, and vital signs). Our results demonstrate that techniques leveraging neural networks and optimal control can automatically discover effective ventilation management strategies without access to explicit ventilator management procedures or guidelines (such as those defined in the ARDSnet protocol)., Comment: 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), 7 pages, 3 figures

We report the detection of water vapor associated with main-belt comet 358P/PANSTARRS on UT 2024 January 8-9 using the NIRSPEC instrument aboard JWST. We derive a water production rate of Q(H2O)=(5.0+/-0.2)x10^25 molecules/s, marking only the second direct detection of sublimation products of any kind from a main-belt comet, after 238P/Read. Similar to 238P, we find a remarkable absence of hypervolatile species, finding Q(CO2)<7.6x10^22 molecules/s, corresponding to Q(CO2)/Q(H2O)<0.2%. Upper limits on CH3OH and CO emission are also estimated. Photometry from ground-based observations show that the dust coma brightened and faded slowly over ~250 days in 2023-2024, consistent with photometric behavior observed in 2012-2013, but also indicate a ~2.5x decline in the dust production rate between these two periods. Dynamical dust modeling shows that the coma's morphology as imaged by JWST's NIRCAM instrument on 2023 November 22 can be reproduced by asymmetric dust emission from a nucleus with a mid-range obliquity (~80 deg) with a steady-state mass loss rate of ~0.8 kg/s. Finally, we find similar Afrho-to-gas ratios of log10(Afrho/Q(H2O))=-24.8+/-0.2 for 358P and log10(Afrho/QH2O)=-24.4+/-0.2 for 238P, suggesting that Afrho could serve as an effective proxy for estimating water production rates in other active main-belt comets. The confirmation of water vapor outgassing in both main-belt comets observed by JWST to date reinforces the use of recurrent activity near perihelion as an indicator of sublimation-driven activity in active asteroids., Comment: 32 pages, 13 figures. Accepted for publication in The Planetary Science Journal

Fetal brain tissue segmentation in magnetic resonance imaging (MRI) is a crucial tool that supports the understanding of neurodevelopment, yet it faces challenges due to the heterogeneity of data coming from different scanners and settings, and due to data scarcity. Recent approaches based on domain randomization, like SynthSeg, have shown a great potential for single source domain generalization, by simulating images with randomized contrast and image resolution from the label maps. In this work, we investigate how to maximize the out-of-domain (OOD) generalization potential of SynthSeg-based methods in fetal brain MRI. Specifically, when studying data generation, we demonstrate that the simple Gaussian mixture models used in SynthSeg enable more robust OOD generalization than physics-informed generation methods. We also investigate how intensity clustering can help create more faithful synthetic images, and observe that it is key to achieving a non-trivial OOD generalization capability when few label classes are available. Finally, by combining for the first time SynthSeg with modern fine-tuning approaches based on weight averaging, we show that fine-tuning a model pre-trained on synthetic data on a few real image-segmentation pairs in a new domain can lead to improvements in the target domain, but also in other domains. We summarize our findings as five key recommendations that we believe can guide practitioners who would like to develop SynthSeg-based approaches in other organs or modalities.

Designing an explainable model becomes crucial now for Natural Language Processing(NLP) since most of the state-of-the-art machine learning models provide a limited explanation for the prediction. In the spectrum of an explainable model, Tsetlin Machine(TM) is promising because of its capability of providing word-level explanation using proposition logic. However, concern rises over the elaborated combination of literals (propositional logic) in the clause that makes the model difficult for humans to comprehend, despite having a transparent learning process. In this paper, we design a post-hoc pruning of clauses that eliminate the randomly placed literals in the clause thereby making the model more efficiently interpretable than the vanilla TM. Experiments on the publicly available YELP-HAT Dataset demonstrate that the proposed pruned TM's attention map aligns more with the human attention map than the vanilla TM's attention map. In addition, the pairwise similarity measure also surpasses the attention map-based neural network models. In terms of accuracy, the proposed pruning method does not degrade the accuracy significantly but rather enhances the performance up to 4% to 9% in some test data., Comment: 8 pages, 3 figures

Using optical technology for current injection and electromagnetic emission simplifies the comparison between materials. Here, we inject current into monolayer graphene and bulk gallium arsenide (GaAs) using two-color quantum interference and detect the emitted electric field by electro-optic sampling. We find the amplitude of emitted terahertz (THz) radiation scales in the same way for both materials even though they differ in dimension, band gap, atomic composition, symmetry and lattice structure. In addition, we observe the same mapping of the current direction to the light characteristics. With no electrodes for injection or detection, our approach will allow electron scattering timescales to be directly measured. We envisage that it will enable exploration of new materials suitable for generating terahertz magnetic fields., Comment: 4 figures

Control invariant sets play an important role in safety-critical control and find broad application in numerous fields such as obstacle avoidance for mobile robots. However, finding valid control invariant sets of dynamical systems under input limitations is notoriously difficult. We present an approach to safely expand an initial set while always guaranteeing that the set is control invariant. Specifically, we define an expansion law for the boundary of a set and check for control invariance using Linear Programs (LPs). To verify control invariance on a continuous domain, we leverage recently proposed Lipschitz constants of LPs to transform the problem of continuous verification into a finite number of LPs. Using concepts from differentiable optimization, we derive the safe expansion law of the control invariant set and show how it can be interpreted as a second invariance problem in the space of possible boundaries. Finally, we show how the obtained set can be used to obtain a minimally invasive safety filter in a Control Barrier Function (CBF) framework. Our work is supported by theoretical results as well as numerical examples.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Famous results due to von Neumann, Sz.-Nagy and Arveson assert that the following four statements are equivalent; a Hilbert space operator $T$ is a contraction; the closed unit disk $\overline{\mathbb D}$ is a spectral set for $T$; $T$ can be dilated to a Hilbert space isometry; $\overline{\mathbb D}$ is a complete spectral set for $T$. In this article, we show by counter examples that no two of them are equivalent for Banach space operators. If $\mathcal F_r$ is the family of all Banach space operators having norm less than or equal to $r$ and if $D_R$ denotes the open disk in the complex plane with centre at the origin and radius $R$, then we prove by an application of Bohr's theorem that $\overline{D}_R$ is the minimal spectral set for $\mathcal F_r$ if and only if $r=\frac{R}{3}$. Also, we prove the equivalence of the following two facts: the Bohr radius of $D_R$ is $\frac{R}{3}$ and $\sup \{ r>0\,:\, \overline{D}_R \text{ is a spectral set for } \mathcal F_r \}=\frac{R}{3}$. We found several new characterizations for a Hilbert space in terms of spectral set and complete spectral set for different operators., Comment: 15 pages

A crucial question in active patient care is determining if a treatment is having the desired effect, especially when changes are subtle over short periods. We propose using inter-patient data to train models that can learn to detect these fine-grained changes within a single patient. Specifically, can a model trained on multi-patient scans predict subtle changes in an individual patient's scans? Recent years have seen increasing use of deep learning (DL) in predicting diseases using biomedical imaging, such as predicting COVID-19 severity using lung ultrasound (LUS) data. While extensive literature exists on successful applications of DL systems when well-annotated large-scale datasets are available, it is quite difficult to collect a large corpus of personalized datasets for an individual. In this work, we investigate the ability of recent computer vision models to learn fine-grained differences while being trained on data showing larger differences. We evaluate on an in-house LUS dataset and a public ADNI brain MRI dataset. We find that models pre-trained on clips from multiple patients can better predict fine-grained differences in scans from a single patient by employing contrastive learning., Comment: Under review at ISBI 2025 conference

In this paper, we shall compare two metrics in terms of orderly dependence, a notion developed in exponential vector space in the article 'Basis and Dimension of Exponential Vector Space' by Jayeeta Saha and Sandip Jana in Transactions of A. Razmadze Mathematical Institute Vol. 175 (2021), issue 1, 101-115. Exponential vector space, in short 'evs', is a partially ordered space associated with a commutative semigroup structure and a compatible scalar multiplication. In the present paper we shall show that the collection $\mathcal{D(\mathbf X)}$ of all metrics on a non-empty set $\mathbf X$, together with the constant function zero $O$, forms a topological exponential vector space. We shall discuss the orderly dependence of two metrics through our findings of a basis of $\mathcal{D(\mathbf X)}\smallsetminus\{O\}$ in different scenario. We shall characterise orderly independence of two elements of a topological evs in terms of the comparing function, another mechanism developed in topological exponential vector space, which can measure the degree of comparability of two elements of an evs. Finally, we shall discuss existence of orderly independent norms on a linear space. For an infinite dimensional linear space we shall construct a large number of orderly independent norms depending on the dimension of the linear space. Orderly independent norms are precisely those which are totally non-equivalent, in the sense that they produce incomparable topologies., Comment: arXiv admin note: text overlap with arXiv:2305.12154

Active labour market policies are widely used by the Swiss government, enrolling more than half of unemployed individuals. This paper analyses whether the Swiss programmes increase future employment and earnings of the unemployed by using causal machine learning methods and leveraging an administrative dataset that captures the population of unemployed and their labour market histories. The findings indicate a small positive average effect on employment and earnings three years after starting a specific Temporary Wage Subsidy programme. In contrast, we find negative effects for Basic Courses, such as job application training, on both outcomes three years after starting the programme. We find no significant effect for Employment Programmes which are conducted outside the regular labour market and Training Courses, such as language and computer courses. The programmes are most effective for individuals with lower education levels and with a migration background from non-EU countries. Last, shallow policy trees provide practical guidance on how the allocation of individuals to programmes could be optimised.

The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive guidelines for the development and oversight of Artificial Intelligence (AI) and Machine Learning (ML) systems, with significant implications for Graph Neural Networks (GNNs). This paper addresses the unique challenges posed by the AI Act for GNNs, which operate on complex graph-structured data. The legislation's requirements for data management, data governance, robustness, human oversight, and privacy necessitate tailored strategies for GNNs. Our study explores the impact of these requirements on GNN training and proposes methods to ensure compliance. We provide an in-depth analysis of bias, robustness, explainability, and privacy in the context of GNNs, highlighting the need for fair sampling strategies and effective interpretability techniques. Our contributions fill the research gap by offering specific guidance for GNNs under the new legislative framework and identifying open questions and future research directions.