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Giovanni Battista Benedetti (1530--1590) derived two constructions of ovals given their minor and major axes. These were published in 1585 and seem to be the first solution to this problem. Therefore, the generally accepted view that ``the geometrical construction for drawing an oval for any given proportion was not known in the sixteenth century'' (Ana L\'opez-Mozo, 2011; Nexus Network Journal, p. 571) has to be revised. We provide a transcription of Benedetti's Latin text as well as a translation into English and analyze his mathematical reasoning in detail. This is the first main focus of the present note. The second focus is the impact of Benedetti's contributions to ovals. In particular, it is observed that they were rarely mentioned in the mathematical and architectural literature.

Understanding swimming in soft yielding media is challenging due to their complex deformation response to the swimmer's motion. We experimentally show that a scallop-inspired swimmer with reciprocally flapping wings generates locomotion in granular matter. This disagrees with the scallop theorem prohibiting reciprocal swimming in a liquid when its inertia is negligible. We use X-ray tomography and laser profilometry to show that the propulsion is created by the combined effects of jamming and convection of particles near the wings, which break the symmetry in packing density, surface deformation, and kinematics of the granular medium between an opening and a closing stroke.

The inference of stellar parameters (such as radius and mass) through asteroseismic forward modelling depends on the number, accuracy, and precision of seismic and atmospheric constraints. ESA's Gaia space mission is providing precise parallaxes which yield an additional constraint to be included in the model grid search. Using a handful of main-sequence benchmark stars, we perform a uniform characterisation of these stars. We assess the accuracy and precision of stellar parameters inferred from grid-based searches when a Gaia-based luminosity is combined with different stellar constraints. We also examine the precision needed for an interferometric radius (model-independent radius) to have a significant contribution towards the determination of stellar mass in the optimisation process. Our findings show that more precise stellar masses are inferred for some stars when seismic and spectroscopic constraints are complemented with a Gaia-based luminosity, with a scatter varying from 1.9 per cent to 0.8 per cent. However, the inferred stellar radii are underestimated when compared to the interferometric radii and yield a scatter of $\sim$1.9 per cent. In addition, we demonstrate that a precisely measured interferometric radius ($\lesssim$ 1 per cent) when applied in the optimisation process yields a mass with a precision $\lesssim$ 1.5 per cent. Finally, we find that when only $l=0$ mode oscillation frequencies are available, robust masses and radii are still attainable. However, this requires precise and numerous $l=0$ mode oscillations frequencies ($>$ 8) to be coupled with atmospheric constraints., Comment: 14 pages, 10 figures, Accepted for publication in Monthly Notices of the Royal Astronomical Society Journal

In an effective-field-theory approach, the rate for $\mu\to e$ conversion in nuclei depends on a set of effective operators mediating the lepton-flavor-violating interaction at a high scale, renormalization group corrections that describe the evolution to lower scales, and hadronic and nuclear matrix elements that turn quark-level interactions into hadronic ones and then embed the latter into nuclear responses. In particular, combining information from $\mu\to e\gamma$, $\mu\to 3e$, and $\mu\to e $ conversion in nuclei, it becomes possible to disentangle different underlying sources of lepton flavor violation. However, to assess the discriminatory power it is critical that uncertainties at each step of the analysis be controlled and fully quantified. In this regard, nuclear response functions related to the coupling to neutrons are notoriously problematic, since they are not directly constrained by experiment. We address these shortcomings by combining ab initio calculations with a recently improved determination of charge distributions from electron scattering by exploiting strong correlations among charge, point-proton, and point-neutron radii and densities. We present overlap integrals for $^{27}$Al, $^{48}$Ca, and $^{48}$Ti including full covariance matrices, allowing, for the first time, for a comprehensive consideration of nuclear structure uncertainties in the interpretation of $\mu\to e$ experiments., Comment: 12 pages, 10 figures

It is beyond dispute that the potential benefits of artificial intelligence (AI) in military intelligence are considerable. Nevertheless, it remains uncertain precisely how AI can enhance the analysis of military data. The aim of this study is to address this issue. To this end, the AI demonstrator deepCOM was developed in collaboration with the start-up Aleph Alpha. The AI functions include text search, automatic text summarization and Named Entity Recognition (NER). These are evaluated for their added value in military analysis. It is demonstrated that under time pressure, the utilization of AI functions results in assessments clearly superior to that of the control group. Nevertheless, despite the demonstrably superior analysis outcome in the experimental group, no increase in confidence in the accuracy of their own analyses was observed. Finally, the paper identifies the limitations of employing AI in military intelligence, particularly in the context of analyzing ambiguous and contradictory information., Comment: 28 pages, 8 figures, 4 tables

The FAIR principles are globally accepted guidelines for improved data management practices with the potential to align data spaces on a global scale. In practice, this is only marginally achieved through the different ways in which organizations interpret and implement these principles. The concept of FAIR Digital Objects provides a way to realize a domain-independent abstraction layer that could solve this problem, but its specifications are currently diverse, contradictory, and restricted to semantic models. In this work, we introduce a rigorously formalized data model with a set of assertions using formal expressions to provide a common baseline for the implementation of FAIR Digital Objects. The model defines how these objects enable machine-actionable decisions based on the principles of abstraction, encapsulation, and entity relationship to fulfill FAIR criteria for the digital resources they represent. We provide implementation examples in the context of two use cases and explain how our model can facilitate the (re)use of data across domains. We also compare how our model assertions are met by FAIR Digital Objects as they have been described in other projects. Finally, we discuss our results' adoption criteria, limitations, and perspectives in the big data context. Overall, our work represents an important milestone for various communities working towards globally aligned data spaces through FAIRification., Comment: Accepted at the 2024 IEEE International Conference on Big Data (IEEE BigData 2024)

Objective: Proton therapy is a precision-focused cancer treatment where accurate proton beam range monitoring is critical to ensure effective dose delivery. This can be achieved by prompt gamma detection with a Compton camera like the SiFi-CC. This study aims to show the feasibility of optimising the geometry of SiFi-CC Compton camera for verification of dose distribution via prompt gamma detection using a genetic algorithm (GA). Approach: The SiFi-CC key geometric parameters for optimisation with the GA are the source-to-scatterer and scatterer-to-absorber distances, and the module thicknesses. The optimisation process was conducted with a software framework based on the Geant4 toolkit, which included detailed and realistic modelling of gamma interactions, detector response, and further steps such as event selection and image reconstruction. The performance of each individual configuration was evaluated using a fitness function incorporating factors related to gamma detection efficiency and image resolution. Results: The GA-optimised SiFi-CC configuration demonstrated the capability to detect a 5 mm proton beam range shift with a 2 mm resolution using 5e8 protons. The best-performing geometry, with 16 fibre layers in the scatterer, 36 layers in the absorber, source-to-scatterer distance 150 mm and scatterer-to-absorber distance 120 mm, has an imaging sensitivity of 5.58(1)e-5. Significance: This study demonstrates that the SiFi-CC setup, optimised through a GA, can reliably detect clinically relevant proton beam range shifts, improving real-time range verification accuracy in proton therapy. The presented implementation of a GA is a systematic and feasible way of searching for a SiFi-CC geometry that shows the best performance., Comment: 10 figures, 3 tables

This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. We hypothesize that an artificial agent, trained via reinforcement learning (RL) in a virtual environment, can develop preliminary forms of these models as a byproduct of its primary task. The agent's main objective is to learn to play a video game and explore the environment. To evaluate the emergence of world and self models, we employ probes-feedforward classifiers that use the activations of the trained agent's neural networks to predict the spatial positions of the agent itself. Our results demonstrate that the agent can form rudimentary world and self models, suggesting a pathway toward developing machine consciousness. This research provides foundational insights into the capabilities of artificial agents in mirroring aspects of human consciousness, with implications for future advancements in artificial intelligence.

Reservoir computing - information processing based on untrained recurrent neural networks with random connections - is expected to depend on the nonlinear properties of the neurons and the resulting oscillatory, chaotic, or fixpoint dynamics of the network. However, the required degree of nonlinearity and the range of suitable dynamical regimes for a given task are not fully understood. To clarify these questions, we study the accuracy of a reservoir computer in artificial classification tasks of varying complexity, while tuning the neuron's degree of nonlinearity and the reservoir's dynamical regime. We find that, even for activation functions with extremely reduced nonlinearity, weak recurrent interactions and small input signals, the reservoir is able to compute useful representations, detectable only in higher order principal components, that render complex classificiation tasks linearly separable for the readout layer. When increasing the recurrent coupling, the reservoir develops spontaneous dynamical behavior. Nevertheless, the input-related computations can 'ride on top' of oscillatory or fixpoint attractors without much loss of accuracy, whereas chaotic dynamics reduces task performance more drastically. By tuning the system through the full range of dynamical phases, we find that the accuracy peaks both at the oscillatory/chaotic and at the chaotic/fixpoint phase boundaries, thus supporting the 'edge of chaos' hypothesis. Our results, in particular the robust weakly nonlinear operating regime, may offer new perspectives both for technical and biological neural networks with random connectivity.

We introduce a continuous domain for function spaces over topological spaces which are not core-compact. Notable examples of such topological spaces include the real line with the upper limit topology, which is used in solution of initial value problems with temporal discretization, and various infinite dimensional Banach spaces which are ubiquitous in functional analysis and solution of partial differential equations. If a topological space $\mathbb{X}$ is not core-compact and $\mathbb{D}$ is a non-singleton bounded-complete domain, the function space $[\mathbb{X} \to \mathbb{D}]$ is not a continuous domain. To construct a continuous domain, we consider a spectral compactification $\mathbb{Y}$ of $\mathbb{X}$ and relate $[\mathbb{X} \to \mathbb{D}]$ with the continuous domain $[\mathbb{Y} \to \mathbb{D}]$ via a Galois connection. This allows us to perform computations in the native structure $[\mathbb{X} \to \mathbb{D}]$ while computable analysis is performed in the continuous domain $[\mathbb{Y} \to \mathbb{D}]$, with the left and right adjoints used for moving between the two function spaces., Comment: 19 pages, 1 figure, Mathematical Foundations of Programming Semantics (MFPS) 2024

We present the results of Phase I of an ongoing review of Monte Carlo tools relevant for low-energy hadronic cross sections. This includes a detailed comparison of Monte Carlo codes for electron-positron scattering into a muon pair, pion pair, and electron pair, for scan and radiative-return experiments. After discussing the various approaches that are used and effects that are included, we show differential cross sections obtained with AfkQed, BabaYaga@NLO, KKMC, MCGPJ, McMule, Phokhara, and Sherpa, for scenarios that are inspired by experiments providing input for the dispersive evaluation of the hadronic vacuum polarisation., Comment: RadioMonteCarLow 2 Working Group report Phase I, 67 pages, 34 figures; references added and other minor modifications

Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. However, achieving accurate reconstructions remains challenging, particularly in highly accelerated and undersampled acquisitions, which are crucial for reducing scan times. While deep learning techniques have advanced image reconstruction, the recent introduction of diffusion models offers new possibilities for imaging tasks, though their application in the medical field is still emerging. Notably, diffusion models have not yet been explored for the MRF problem. In this work, we propose for the first time a conditional diffusion probabilistic model for MRF image reconstruction. Qualitative and quantitative comparisons on in-vivo brain scan data demonstrate that the proposed approach can outperform established deep learning and compressed sensing algorithms for MRF reconstruction. Extensive ablation studies also explore strategies to improve computational efficiency of our approach., Comment: 12 pages, 5 figures, 2 algorithms

The gradients used to train neural networks are typically computed using backpropagation. While an efficient way to obtain exact gradients, backpropagation is computationally expensive, hinders parallelization, and is biologically implausible. Forward gradients are an approach to approximate the gradients from directional derivatives along random tangents computed by forward-mode automatic differentiation. So far, research has focused on using a single tangent per step. This paper provides an in-depth analysis of multi-tangent forward gradients and introduces an improved approach to combining the forward gradients from multiple tangents based on orthogonal projections. We demonstrate that increasing the number of tangents improves both approximation quality and optimization performance across various tasks.

We systematically investigate whether classical hydrodynamic field theories can predict the long-time dynamics of many-particle quantum systems. As an example, we investigate numerically and analytically the time evolution of a chain of spins (or qubits) subject to a stroboscopic dynamics. The time evolution is implemented by a sequence of local and nearest-neighbor gates which conserve the total magnetization. The long-time dynamics of such a system is believed to be describable by a hydrodynamics field theory, which, importantly, includes the effect of noise. Based on a field theoretical analysis and symmetry arguments, we map each operator in the spin model to corresponding fields in hydrodynamics. This allows us to predict which expectation values decay exponentially, and which of them decay with a hydrodynamics long-time tail, $t^{-\alpha}$, with $\alpha=\frac{1}{2}, 1, \frac{3}{2}, \text{or } \frac{9}{4}$ for different operators. We illustrate these findings by studying the time evolution of all 255 Hermitian operators which can be defined on four neighboring sites. The numerical results are fully consistent with the emergence of hydrodynamics at long times., Comment: 13 pages, 5 figures

Maps of dynamics are effective representations of motion patterns learned from prior observations, with recent research demonstrating their ability to enhance performance in various downstream tasks such as human-aware robot navigation, long-term human motion prediction, and robot localization. Current advancements have primarily concentrated on methods for learning maps of human flow in environments where the flow is static, i.e., not assumed to change over time. In this paper we propose a method to update the CLiFF-map, one type of map of dynamics, for achieving efficient life-long robot operation. As new observations are collected, our goal is to update a CLiFF-map to effectively and accurately integrate new observations, while retaining relevant historic motion patterns. The proposed online update method maintains a probabilistic representation in each observed location, updating parameters by continuously tracking sufficient statistics. In experiments using both synthetic and real-world datasets, we show that our method is able to maintain accurate representations of human motion dynamics, contributing to high performance flow-compliant planning downstream tasks, while being orders of magnitude faster than the comparable baselines.

Agent-based models have proven to be useful tools in supporting decision-making processes in different application domains. The advent of modern computers and supercomputers has enabled these bottom-up approaches to realistically model human mobility and contact behavior. The COVID-19 pandemic showcased the urgent need for detailed and informative models that can answer research questions on transmission dynamics. We present a sophisticated agent-based model to simulate the spread of respiratory diseases. The model is highly modularized and can be used on various scales, from a small collection of buildings up to cities or countries. Although not being the focus of this paper, the model has undergone performance engineering on a single core and provides an efficient intra- and inter-simulation parallelization for time-critical decision-making processes. In order to allow answering research questions on individual level resolution, nonpharmaceutical intervention strategies such as face masks or venue closures can be implemented for particular locations or agents. In particular, we allow for sophisticated testing and isolation strategies to study the effects of minimal-invasive infectious disease mitigation. With realistic human mobility patterns for the region of Brunswick, Germany, we study the effects of different interventions between March 1st and May 30, 2021 in the SARS-CoV-2 pandemic. Our analyses suggest that symptom-independent testing has limited impact on the mitigation of disease dynamics if the dark figure in symptomatic cases is high. Furthermore, we found that quarantine length is more important than quarantine efficiency but that, with sufficient symptomatic control, also short quarantines can have a substantial effect., Comment: 35 pages, 13 figures, to be submitted to Elsevier

We show that, in finite dimensions, around any $m$-partite product state $\rho_{\rm prod}=\rho_1\otimes...\otimes\rho_m$, there exists an ellipsoid of separable states centered around $\rho_{\rm prod}$. The volume of this separable ellipsoid is typically exponentially larger than that of the separable ball proposed in previous works, due to the large hierarchy of eigenvalues occurring in typical states. We further refine this ellipsoidal criterion to a trace formula, generalize it to characterize the separable region around all separable states, and further study biseparability. Our criterion will help numerical procedures to rigorously detect separability. We apply the procedure for separability detection on three-qubit X state in a dephasing environment, and the 1d transverse field Ising model at finite temperature to illustrate the power of our procedure for understanding entanglement in physical systems., Comment: 6 pages, 1 figure

We utilize a method using frequency combs to construct waves that feature superoscillations - local regions of the wave that exhibit a change in phase that the bandlimits of the wave should not otherwise allow. This method has been shown to create superoscillating regions that mimic any analytic function - even ones well outside the bandlimits - to an arbitrary degree of accuracy. We experimentally demonstrate that these waves are extremely robust against noise, allowing for accurate reconstruction of a superoscillating target function thoroughly buried in noise. We additionally show that such a construction can be easily used to range-resolve a signal well below the commonly accepted fundamental limit.

Traffic Sign Recognition (TSR) detection is a crucial component of autonomous vehicles. While You Only Look Once (YOLO) is a popular real-time object detection algorithm, factors like training data quality and adverse weather conditions (e.g., heavy rain) can lead to detection failures. These failures can be particularly dangerous when visual similarities between objects exist, such as mistaking a 30 km/h sign for a higher speed limit sign. This paper proposes a method that combines video analysis and reasoning, prompting with a human-in-the-loop guide large vision model to improve YOLOs accuracy in detecting road speed limit signs, especially in semi-real-world conditions. It is hypothesized that the guided prompting and reasoning abilities of Video-LLava can enhance YOLOs traffic sign detection capabilities. This hypothesis is supported by an evaluation based on human-annotated accuracy metrics within a dataset of recorded videos from the CARLA car simulator. The results demonstrate that a collaborative approach combining YOLO with Video-LLava and reasoning can effectively address challenging situations such as heavy rain and overcast conditions that hinder YOLOs detection capabilities., Comment: 10 pages, 6 figures

500-nm-thick $\mathrm{In}_{x}\mathrm{Ga}_{1-x}\mathrm{N}$ layers with $x=$ 0.05-0.14 are grown using plasma-assisted molecular beam epitaxy, and their properties are assessed by a comprehensive analysis involving x-ray diffraction, secondary ion mass spectrometry, and cathodoluminescence as well as photoluminescence spectroscopy. We demonstrate low degrees of strain relaxation (10% for $x=0.12$), low threading dislocation densities ($\mathrm{1\times10^{9}\,cm^{-2}}$ for $x=0.12$), uniform composition both in the growth and lateral direction, and a narrow emission band. The unique sum of excellent materials properties make these layers an attractive basis for the top-down fabrication of ternary nanowires.

In this work, three fundamentally different machine learning models are combined to create a new, joint model for forecasting the UEFA EURO 2024. Therefore, a generalized linear model, a random forest model, and a extreme gradient boosting model are used to predict the number of goals a team scores in a match. The three models are trained on the match results of the UEFA EUROs 2004-2020, with additional covariates characterizing the teams for each tournament as well as three enhanced variables derived from different ranking methods for football teams. The first enhanced variable is based on historic match data from national teams, the second is based on the bookmakers' tournament winning odds of all participating teams, and the third is based on historic match data of individual players both for club and international matches, resulting in player ratings. Then, based on current covariate information of the participating teams, the final trained model is used to predict the UEFA EURO 2024. For this purpose, the tournament is simulated 100.000 times, based on the estimated expected number of goals for all possible matches, from which probabilities across the different tournament stages are derived. Our combined model identifies France as the clear favourite with a winning probability of 19.2%, followed by England (16.7%) and host Germany (13.7%).

Protecting qubits from environmental noise while maintaining strong coupling for fast high-fidelity control is a central challenge for quantum information processing. Here, we demonstrate a novel control scheme for superconducting fluxonium qubits that eliminates qubit decay through the control channel by reducing the environmental density of states at the transition frequency. Adding a low-pass filter on the flux line allows for flux-biasing and at the same time coherently controlling the fluxonium qubit by parametrically driving it at integer fractions of its transition frequency. We compare the filtered to the unfiltered configuration and find a five times longer $T_1$, and ten times improved $T_2$-echo time in the protected case. We demonstrate coherent control with up to 11-photon sub-harmonic drives, highlighting the strong non-linearity of the fluxonium potential. We experimentally determine Rabi frequencies and drive-induced frequency shifts in excellent agreement with numerical and analytical calculations. Furthermore, we show the equivalence of a 3-photon sub-harmonic drive to an on-resonance drive by benchmarking sub-harmonic gate fidelities above 99.94 %. These results open up a scalable path for full qubit control via a single protected channel, strongly suppressing qubit decoherence caused by control lines., Comment: 18 pages, 9 figures

Understanding the interactions between nucleons in dense matter is one of the outstanding challenges of theoretical physics. Effective field theories have emerged as the dominant approach to address this problem at low energies, with many successful applications to the structure of nuclei and the properties of dense nucleonic matter. However, how far into the interior of neutron stars these interactions can describe dense matter is an open question. Here, we develop a framework that enables the inference of three-nucleon couplings in dense matter directly from astrophysical neutron-star observations. We apply this formalism to the LIGO/Virgo gravitational-wave event GW170817 and the X-ray measurements from NASA's Neutron-Star Interior Composition Explorer and establish direct constraints for the couplings that govern three-nucleon interactions in chiral effective field theory. Furthermore, we demonstrate how next-generation observations of a population of neutron-star mergers can offer stringent constraints on three-nucleon couplings, potentially at a level comparable to those from laboratory data. Our work directly connects the microscopic couplings in quantum field theories to macroscopic observations of neutron stars, providing a way to test the consistency between low-energy couplings inferred from terrestrial and astrophysical data., Comment: 22 pages and 4 figures

In this note, we prove that $K_{2i} (\mathbb{Z}/p^n) \neq 0$ if and only if $p-1$ divides $i$ and $0 \leq i \leq (p-1) p^{n-2}$, refining the even vanishing theorem of Antieau, Nikolaus and the first author in this case. As a corollary of our proof, we determine that the nilpotence order of $v_1$ in $\pi_* K(\mathbb{Z}/p^n)/p$ is equal to $\frac{p^n-1}{p-1}$. Our proof combines the recent crystallinity result for reduced syntomic cohomology of Hahn, Levy and the second author with the explicit complex computing the syntomic cohomology of $\mathcal{O}_K /\varpi^n$ constructed by Antieau, Nikolaus and the first author., Comment: 10 pages

Many statisticians regularly teach large lecture courses on statistics, probability, or mathematics for students from other fields such as business and economics, social sciences and psychology, etc. The corresponding exams often use a multiple-choice or single-choice format and are typically evaluated and graded automatically, either by scanning printed exams or via online learning management systems. Although further examinations of these exams would be of interest, these are frequently not carried out. For example a measurement scale for the difficulty of the questions (or items) and the ability of the students (or subjects) could be established using psychometric item response theory (IRT) models. Moreover, based on such a model it could be assessed whether the exam is really fair for all participants or whether certain items are easier (or more difficult) for certain subgroups of students. Here, several recent methods for assessing measurement invariance and for detecting differential item functioning in the Rasch IRT model are discussed and applied to results from a first-year mathematics exam with single-choice items. Several categorical, ordered, and numeric covariates like gender, prior experience, and prior mathematics knowledge are available to form potential subgroups with differential item functioning. Specifically, all analyses are demonstrated with a hands-on R tutorial using the psycho* family of R packages (psychotools, psychotree, psychomix) which provide a unified approach to estimating, visualizing, testing, mixing, and partitioning a range of psychometric models. The paper is dedicated to the memory of Fritz Leisch (1968-2024) and his contributions to various aspects of this work are highlighted., Comment: 19 pages, 7 figures

Quantitative Acoustic Microscopy (QAM) is an imaging technology utilising high frequency ultrasound to produce quantitative two-dimensional (2D) maps of acoustical and mechanical properties of biological tissue at microscopy scale. Increased frequency QAM allows for finer resolution at the expense of increased acquisition times and data storage cost. Compressive sampling (CS) methods have been employed to produce QAM images from a reduced sample set, with recent state of the art utilising Approximate Message Passing (AMP) methods. In this paper we investigate the use of AMP-Net, a deep unfolded model for AMP, for the CS reconstruction of QAM parametric maps. Results indicate that AMP-Net can offer superior reconstruction performance even in its stock configuration trained on natural imagery (up to 63% in terms of PSNR), while avoiding the emergence of sampling pattern related artefacts.

Managing fluid balance in dialysis patients is crucial, as improper management can lead to severe complications. In this paper, we propose a multimodal approach that integrates visual features from lung ultrasound images with clinical data to enhance the prediction of excess body fluid. Our framework employs independent encoders to extract features for each modality and combines them through a cross-domain attention mechanism to capture complementary information. By framing the prediction as a classification task, the model achieves significantly better performance than regression. The results demonstrate that multimodal models consistently outperform single-modality models, particularly when attention mechanisms prioritize tabular data. Pseudo-sample generation further contributes to mitigating the imbalanced classification problem, achieving the highest accuracy of 88.31%. This study underscores the effectiveness of multimodal learning for fluid overload management in dialysis patients, offering valuable insights for improved clinical outcomes., Comment: In the experiment, for the classification tasks, the network was informed with ground truth during training, significantly improving the performance. This makes the results invalid. Therefore, corrections and more validations are needed to evaluate the performance of the method

We present Sparse R-CNN OBB, a novel framework for the detection of oriented objects in SAR images leveraging sparse learnable proposals. The Sparse R-CNN OBB has streamlined architecture and ease of training as it utilizes a sparse set of 300 proposals instead of training a proposals generator on hundreds of thousands of anchors. To the best of our knowledge, Sparse R-CNN OBB is the first to adopt the concept of sparse learnable proposals for the detection of oriented objects, as well as for the detection of ships in Synthetic Aperture Radar (SAR) images. The detection head of the baseline model, Sparse R-CNN, is re-designed to enable the model to capture object orientation. We also fine-tune the model on RSDD-SAR dataset and provide a performance comparison to state-of-the-art models. Experimental results shows that Sparse R-CNN OBB achieves outstanding performance, surpassing other models on both inshore and offshore scenarios. The code is available at: www.github.com/ka-mirul/Sparse-R-CNN-OBB., Comment: Submitted to 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

We introduce the XBX regression model, a continuous mixture of extended-support beta regressions for modeling bounded responses with or without boundary observations. The core building block of the new model is the extended-support beta distribution, which is a censored version of a four-parameter beta distribution with the same exceedance on the left and right of $(0, 1)$. Hence, XBX regression is a direct extension of beta regression. We prove that both beta regression with dispersion effects and heteroscedastic normal regression with censoring at both $0$ and $1$ -- known as the heteroscedastic two-limit tobit model in the econometrics literature -- are special cases of the extended-support beta regression model, depending on whether a single extra parameter is zero or infinity, respectively. To overcome identifiability issues that may arise in estimating the extra parameter due to the similarity of the beta and normal distribution for certain parameter settings, we assume that the additional parameter has an exponential distribution with an unknown mean. The associated marginal likelihood can be conveniently and accurately approximated using a Gauss-Laguerre quadrature rule, resulting in efficient estimation and inference procedures. The new model is used to analyze investment decisions in a behavioral economics experiment, where the occurrence and extent of loss aversion is of interest. In contrast to standard approaches, XBX regression can simultaneously capture the probability of rational behavior as well as the mean amount of loss aversion. Moreover, the effectiveness of the new model is illustrated through extensive numerical comparisons with alternative models.

The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Restoring a scene distorted by atmospheric turbulence is also a challenging problem. The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions., Comment: 36 Pages, 8 figures

The controlled assembly of twisted 2D structures requires precise determination of the crystal orientation of their component layers. In the established procedure, the second-harmonic generation (SHG) intensity of a noncentrosymmetric layer is recorded while rotating the polarization of both the incident laser field and detected SHG, which can be time-consuming and tedious. Here, we demonstrate that the crystal orientation of transition metal dichalcogenides and hexagonal boron nitride can be directly determined by recording SHG images generated by tightly focused laser beams in Fourier space. Using an azimuthally polarized laser beam, the SHG image distinctly reflects the hexagonal structure of the crystal lattice, revealing its orientation quickly and accurately. This technique could significantly impact the field of twistronics, which studies the effects of the relative angle between the layers of a stacked 2D structure, as well as advances the nanofabrication of 2D materials., Comment: 22 (35) pages, 6 (12) figures (including Supplementary Information)

Parallel-field Hall effect is the appearance of a Hall voltage $V_{\rm H}$ that is transverse to the current $I$ when the magnetic field $B$ is applied parallel to $I$ (i.e. $B \parallel I \perp V_{\rm H}$). Such an effect is symmetry forbidden in most cases and hence is very unusual. Interestingly, the existence of a finite parallel-field Hall effect was reported for the layered topological semimetal ZrTe$_5$ and was proposed to be due to Berry curvature. However, it is forbidden for the known symmetry of ZrTe$_5$ and the possible existence of a misaligned out-of-plane magnetic field was not completely ruled out. Here, we elucidate the existence of the parallel-field Hall effect in ZrTe$_5$ with careful magnetic-field alignment. We interpret this result to originate from symmetry breaking and quantitatively explain the observed parallel-field Hall signal by considering a tilting of the Fermi surface allowed by broken symmetry., Comment: 9 pages total; 6 pages of main text with 3 figures, 3 pages of supplement with 2 figures. The raw data are available at the online depository Zenodo with the identifier 10.5281/zenodo.13350894

In specific motifs of three recurrently connected neurons with probabilistic response, the spontaneous information flux, defined as the mutual information between subsequent states, has been shown to increase by adding ongoing white noise of some optimal strength to each of the neurons \cite{krauss2019recurrence}. However, the precise conditions for and mechanisms of this phenomenon called 'recurrence resonance' (RR) remain largely unexplored. Using Boltzmann machines of different sizes and with various types of weight matrices, we show that RR can generally occur when a system has multiple dynamical attractors, but is trapped in one or a few of them. In probabilistic networks, the phenomenon is bound to a suitable observation time scale, as the system could autonomously access its entire attractor landscape even without the help of external noise, given enough time. Yet, even in large systems, where time scales for observing RR in the full network become too long, the resonance can still be detected in small subsets of neurons. Finally, we show that short noise pulses can be used to transfer recurrent neural networks, both probabilistic and deterministic, between their dynamical attractors. Our results are relevant to the fields of reservoir computing and neuroscience, where controlled noise may turn out a key factor for efficient information processing leading to more robust and adaptable systems.

This study investigates how BERT processes and represents Argument Structure Constructions (ASCs), extending previous LSTM analyses. Using a dataset of 2000 sentences across four ASC types (transitive, ditransitive, caused-motion, resultative), we analyzed BERT's token embeddings across 12 layers. Visualizations with MDS and t-SNE and clustering quantified by Generalized Discrimination Value (GDV) were used. Feedforward classifiers (probes) predicted construction categories from embeddings. CLS token embeddings clustered best in layers 2-4, decreased in intermediate layers, and slightly increased in final layers. DET and SUBJ embeddings showed consistent clustering in intermediate layers, VERB embeddings increased in clustering from layer 1 to 12, and OBJ embeddings peaked in layer 10. Probe accuracies indicated low construction information in layer 1, with over 90 percent accuracy from layer 2 onward, revealing latent construction information beyond GDV clustering. Fisher Discriminant Ratio (FDR) analysis of attention weights showed OBJ tokens were crucial for differentiating ASCs, followed by VERB and DET tokens. SUBJ, CLS, and SEP tokens had insignificant FDR scores. This study highlights BERT's layered processing of linguistic constructions and its differences from LSTMs. Future research will compare these findings with neuroimaging data to understand the neural correlates of ASC processing. This research underscores neural language models' potential to mirror linguistic processing in the human brain, offering insights into the computational and neural mechanisms underlying language understanding., Comment: arXiv admin note: text overlap with arXiv:2408.03062

Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, offering distinct advantages and limitations. In vivo OCT offers rapid, non-invasive imaging but can suffer from clarity issues and motion artifacts, while ex vivo confocal microscopy, providing high-resolution, cellular-detailed color images, is invasive and raises ethical concerns. To bridge the benefits of both modalities, we propose a novel framework based on unsupervised 3D CycleGAN for translating unpaired in vivo OCT to ex vivo confocal microscopy images. This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy. We also introduce a unique dataset, OCT2Confocal, comprising mouse OCT and confocal retinal images, facilitating the development of and establishing a benchmark for cross-modal image translation research. Our model has been evaluated both quantitatively and qualitatively, achieving Fr\'echet Inception Distance (FID) scores of 0.766 and Kernel Inception Distance (KID) scores as low as 0.153, and leading subjective Mean Opinion Scores (MOS). Our model demonstrated superior image fidelity and quality with limited data over existing methods. Our approach effectively synthesizes color information from 3D confocal images, closely approximating target outcomes and suggesting enhanced potential for diagnostic and monitoring applications in ophthalmology., Comment: 30 pages, 11 figures, 5 tables

Understanding how language and linguistic constructions are processed in the brain is a fundamental question in cognitive computational neuroscience. In this study, we explore the representation and processing of Argument Structure Constructions (ASCs) in a recurrent neural language model. We trained a Long Short-Term Memory (LSTM) network on a custom-made dataset consisting of 2000 sentences, generated using GPT-4, representing four distinct ASCs: transitive, ditransitive, caused-motion, and resultative constructions. We analyzed the internal activations of the LSTM model's hidden layers using Multidimensional Scaling (MDS) and t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the sentence representations. The Generalized Discrimination Value (GDV) was calculated to quantify the degree of clustering within these representations. Our results show that sentence representations form distinct clusters corresponding to the four ASCs across all hidden layers, with the most pronounced clustering observed in the last hidden layer before the output layer. This indicates that even a relatively simple, brain-constrained recurrent neural network can effectively differentiate between various construction types. These findings are consistent with previous studies demonstrating the emergence of word class and syntax rule representations in recurrent language models trained on next word prediction tasks. In future work, we aim to validate these results using larger language models and compare them with neuroimaging data obtained during continuous speech perception. This study highlights the potential of recurrent neural language models to mirror linguistic processing in the human brain, providing valuable insights into the computational and neural mechanisms underlying language understanding.