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To investigate the wall-cooling effects on shock wave turbulent/boundary layer interaction (SWTBLI) with limited experimental data, a supersonic wind tunnel wall was cooled using liquid nitrogen as the cryogenic coolant. Under the condition of the mainstream Mach number, in the range of 2.02-2.04, the wall temperature was cooled to 88-92 K, corresponding to a wall-to-recovery temperature ratio of 0.31-0.33. The flow structures with and without wall cooling were observed using the schlieren method. The reflected shock motion or interaction length in the schlieren image suggested that boundary layer separation was suppressed under the cooling condition in relation to that under the non-cooling condition, and the suppressed ratio of the cooling-to-uncooling interaction length was approximately 0.60-0.72. Additionally, while the mainstream state and wall temperature near the separation were constant, a gradual change in the separated flow field was observed under the wall-cooling condition. This was due to the slow wall temperature increase in the upstream wall of the separation region, where the incoming boundary layer developed. Each flow field of SWTBLI in the present experiment, using liquid nitrogen as the cryogenic coolant, was consistent with the classical Chaman's free interaction theory., Comment: 27 pages, 13 figures, accepted for publication in "Physics of Fluids"

This paper proposes an approach in the area of Knowledge-Guided Machine Learning (KGML) via a novel integrated framework comprising CNN (Convolutional Neural Networks) and ViT (Vision Transformers) along with GIS (Geographic Information Systems) to enhance power plant classification in the context of energy management. Knowledge from geoinformatics derived through Spatial Masks (SM) in GIS is infused into an architecture of CNN and ViT, in this proposed KGML approach. It is found to provide much better performance compared to the baseline of CNN and ViT only in the classification of multiple types of power plants from real satellite imagery, hence emphasizing the vital role of the geoinformatics-guided approach. This work makes a contribution to the main theme of KGML that can be beneficial in many AI systems today. It makes broader impacts on AI in Smart Cities, and Environmental Computing.

We consider detecting change points in the correlation structure of streaming large-dimensional data with minimum assumptions posed on the underlying data distribution. Depending on the $\ell_1$ and $\ell_{\infty}$ norms of the squared difference of vectorized pre-change and post-change correlation matrices, detection statistics are constructed for dense and sparse settings, respectively. The proposed detection procedures possess the bless-dimension property, as a novel algorithm for threshold selection is designed based on sign-flip permutation. Theoretical evaluations of the proposed methods are conducted in terms of average run length and expected detection delay. Numerical studies are conducted to examine the finite sample performances of the proposed methods. Our methods are effective because the average detection delays have slopes similar to that of the optimal exact CUSUM test. Moreover, a combined $\ell_1$ and $\ell_{\infty}$ norm approach is proposed and has expected performance for transitions from sparse to dense settings. Our method is applied to forecast El Ni{\~n}o events and achieves state-of-the-art hit rates greater than 0.86, while false alarm rates are 0. This application illustrates the efficiency and effectiveness of our proposed methodology in detecting fundamental changes with minimal delay.

Seismic data preconditioning is essential for subsurface interpretation. It enhances signal quality while attenuating noise, improving the accuracy of geophysical tasks that would otherwise be biased by noise. Although classical poststack seismic data enhancement methods can effectively reduce noise, they rely on predefined statistical distributions, which often fail to capture the complexity of seismic noise. On the other hand, deep learning methods offer an alternative but require large and diverse data sets. Typically, static databases are used for training, introducing domain bias, and limiting adaptability to new noise poststack patterns. This work proposes a novel two-process dynamic training method to overcome these limitations. Our method uses a dynamic database that continuously generates clean and noisy patches during training to guide the learning of a supervised enhancement network. This dynamic-guided learning workflow significantly improves generalization by introducing variability into the training data. In addition, we employ a domain adaptation via a neural style transfer strategy to address the potential challenge of encountering unknown noise domains caused by specific geological configurations. Experimental results demonstrate that our method outperforms state-of-the-art solutions on both synthetic and field data, within and outside the training domain, eliminating reliance on known statistical distributions and enhancing adaptability across diverse data sets of poststack data., Comment: 13 pages, 10 figures. Submitted to Geophysics

Background: Real-time treatment planning in IMRT is challenging due to complex beam interactions. AI has improved automation, but existing models require large, high-quality datasets and lack universal applicability. Deep reinforcement learning (DRL) offers a promising alternative by mimicking human trial-and-error planning. Purpose: Develop a stochastic policy-based DRL agent for automatic treatment planning with efficient training, broad applicability, and robustness against adversarial attacks using Fast Gradient Sign Method (FGSM). Methods: Using the Actor-Critic with Experience Replay (ACER) architecture, the agent tunes treatment planning parameters (TPPs) in inverse planning. Training is based on prostate cancer IMRT cases, using dose-volume histograms (DVHs) as input. The model is trained on a single patient case, validated on two independent cases, and tested on 300+ plans across three datasets. Plan quality is assessed using ProKnow scores, and robustness is tested against adversarial attacks. Results: Despite training on a single case, the model generalizes well. Before ACER-based planning, the mean plan score was 6.20$\pm$1.84; after, 93.09% of cases achieved a perfect score of 9, with a mean of 8.93$\pm$0.27. The agent effectively prioritizes optimal TPP tuning and remains robust against adversarial attacks. Conclusions: The ACER-based DRL agent enables efficient, high-quality treatment planning in prostate cancer IMRT, demonstrating strong generalizability and robustness., Comment: 27 Pages, 8 Figures, 4 Tables

Recent advancements in data-driven weather forecasting models have delivered deterministic models that outperform the leading operational forecast systems based on traditional, physics-based models. However, these data-driven models are typically trained with a mean squared error loss function, which causes smoothing of fine scales through a "double penalty" effect. We develop a simple, parameter-free modification to this loss function that avoids this problem by separating the loss attributable to decorrelation from the loss attributable to spectral amplitude errors. Fine-tuning the GraphCast model with this new loss function results in sharp deterministic weather forecasts, an increase of the model's effective resolution from 1,250km to 160km, improvements to ensemble spread, and improvements to predictions of tropical cyclone strength and surface wind extremes.

Hexagonal boron nitride (hBN) is a promising material for next-generation semiconductor and optoelectronic devices due to its wide bandgap and remarkable optical properties. To apply this material in the semiconductor industry, it is necessary to grow large-area layers on the wafer-scale. For this purpose, chemical vapor deposition methods are highly preferable. However, in the case of epitaxial BN, its fragility and susceptibility to delamination and fold formation during wet processing, such as lithography, present significant challenges to its integration into device fabrication. In this work, we introduce a controlled delamination and redeposition method that effectively prevents the layer from degradation, allowing for multi-step lithographic processes. This approach is applicable to BN layers across a broad thickness range, from tens to hundreds of nanometers, and ensures compatibility with standard photolithographic techniques without compromising the material's intrinsic properties. By addressing key processing challenges, this method paves the way for integrating epitaxial BN into advanced semiconductor and optoelectronic technologies., Comment: 12 pages, 4 figures

In cardiac cells, structural organization is an important indicator of cell maturity and healthy function. Healthy cardiomyocytes exhibit well-aligned morphology with densely packed and organized sarcomeres. Immature or diseased cardiomyocytes typically lack this organized structure. Critically, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer a valuable model for studying human cardiac cells in a controlled environment. However, these cells often exhibit a disorganized structure. In this work, we extend the SarcGraph computational framework -- designed to assess the structural and functional behavior of hiPSC-CMs -- to better accommodate the structural features of immature cells. There are two key enhancements: (1) incorporating a deep learning-based z-disc classifier, and (2) introducing a novel ensemble graph-scoring approach. These modification significantly reduced false positive sarcomere detections in immature cells, and resulted in the detection of longer myofibrils in mature samples. With this enhanced framework, we analyze an open-source dataset published by the Allen Institute for Cell Science, where, for the first time, we are able to extract key structural features from these data using information from each individually detected sarcomere. Not only are we able to use these structural features to predict expert scores, but we are also able to use these structural features to identify bias in expert scoring and offer an alternative unsupervised learning approach based on explainable clustering. These results demonstrate the efficacy of our modified SarcGraph in extracting biologically meaningful features, enabling a deeper understanding of hiPSC-CM structural integrity. By making our code and tools open-source, we aim to empower the broader cardiac research community and foster further development of computational tools for cardiac tissue analysis.

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at hand, subsequent mathematical formulation, and numerical approximation. Data-driven models instead aim to extract relations between input and output data without arguing any causality principle underlining the available data distribution. In recent years, data-driven models have been rapidly developed and popularized. Such a diffusion has been triggered by a huge availability of data (the so-called big data), an increasingly cheap computing power, and the development of powerful machine learning algorithms. SciML leverages the physical awareness of physics-based models and, at the same time, the efficiency of data-driven algorithms. With SciML, we can inject physics and mathematical knowledge into machine learning algorithms. Yet, we can rely on data-driven algorithms' capability to discover complex and non-linear patterns from data and improve the descriptive capacity of physics-based models. After recalling the mathematical foundations of digital modelling and machine learning algorithms, and presenting the most popular machine learning architectures, we discuss the great potential of a broad variety of SciML strategies in solving complex problems governed by partial differential equations. Finally, we illustrate the successful application of SciML to the simulation of the human cardiac function, a field of significant socio-economic importance that poses numerous challenges on both the mathematical and computational fronts. The corresponding mathematical model is a complex system of non-linear ordinary and partial differential equations describing the electromechanics, valve dynamics, blood circulation, perfusion in the coronary tree, and torso potential. Despite the robustness and accuracy of physics-based models, certain aspects, such as unveiling constitutive laws for cardiac cells and myocardial material properties, as well as devising efficient reduced order models to dominate the extraordinary computational complexity, have been successfully tackled by leveraging data-driven models., Comment: 127pages. Accepted for publication in Mathematical Models and Methods in Applied Sciences (M3AS)

The increasing demand for higher data volume and faster transmission in modern wireless telecommunication systems has elevated requirements for 5G high-band RF hardware. Spin-Wave technology offers a promising solution, but its adoption is hindered by significant insertion loss stemming from the low efficiency of magnonic transducers. This work introduces a micromagnetic simulation method for directly computing the spin-wave resistance, the real part of spin-wave impedance, which is crucial for optimizing magnonic transducers. By integrating into finite-difference micromagnetic simulations, this approach extends analytical models to arbitrary transducer geometries. We demonstrate its effectiveness through parameter studies on transducer design and waveguide properties, identifying key strategies to enhance the overall transducer efficiency. Our studies show that by varying single parameters of the transducer geometry or the YIG thickness, the spin-wave efficiency, the parameter describing the efficiency of the transfer of electromagnetic energy to the spin wave, can reach values up to 0.75. The developed numerical model allows further fine-tuning of the transducers to achieve even higher efficiencies., Comment: 14 pages, 6 figures

Scientific optimization problems are usually concerned with balancing multiple competing objectives, which come as preferences over both the outcomes of an experiment (e.g. maximize the reaction yield) and the corresponding input parameters (e.g. minimize the use of an expensive reagent). Typically, practical and economic considerations define a hierarchy over these objectives, which must be reflected in algorithms for sample-efficient experiment planning. Herein, we introduce BoTier, a composite objective that can flexibly represent a hierarchy of preferences over both experiment outcomes and input parameters. We provide systematic benchmarks on synthetic and real-life surfaces, demonstrating the robust applicability of BoTier across a number of use cases. Importantly, BoTier is implemented in an auto-differentiable fashion, enabling seamless integration with the BoTorch library, thereby facilitating adoption by the scientific community.

Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG), including strict privacy constraints, non-i.i.d. local data, and limited domain diversity. We introduce FedAlign, a lightweight, privacy-preserving framework designed to enhance DG in federated settings by simultaneously increasing feature diversity and promoting domain invariance. First, a cross-client feature extension module broadens local domain representations through domain-invariant feature perturbation and selective cross-client feature transfer, allowing each client to safely access a richer domain space. Second, a dual-stage alignment module refines global feature learning by aligning both feature embeddings and predictions across clients, thereby distilling robust, domain-invariant features. By integrating these modules, our method achieves superior generalization to unseen domains while maintaining data privacy and operating with minimal computational and communication overhead., Comment: 9 pages, 4 figures

Aerospace manufacturing demands exceptionally high precision in technical parameters. The remarkable performance of Large Language Models (LLMs), such as GPT-4 and QWen, in Natural Language Processing has sparked industry interest in their application to tasks including process design, material selection, and tool information retrieval. However, LLMs are prone to generating "hallucinations" in specialized domains, producing inaccurate or false information that poses significant risks to the quality of aerospace products and flight safety. This paper introduces a set of evaluation metrics tailored for LLMs in aerospace manufacturing, aiming to assess their accuracy by analyzing their performance in answering questions grounded in professional knowledge. Firstly, key information is extracted through in-depth textual analysis of classic aerospace manufacturing textbooks and guidelines. Subsequently, utilizing LLM generation techniques, we meticulously construct multiple-choice questions with multiple correct answers of varying difficulty. Following this, different LLM models are employed to answer these questions, and their accuracy is recorded. Experimental results demonstrate that the capabilities of LLMs in aerospace professional knowledge are in urgent need of improvement. This study provides a theoretical foundation and practical guidance for the application of LLMs in aerospace manufacturing, addressing a critical gap in the field., Comment: conference paper

Routine and repetitive infrastructure inspections present safety, efficiency, and consistency challenges as they are performed manually, often in challenging or hazardous environments. They can also introduce subjectivity and errors into the process, resulting in undesirable outcomes. Simultaneous localization and mapping (SLAM) presents an opportunity to generate high-quality 3D maps that can be used to extract accurate and objective inspection data. Yet, many SLAM algorithms are limited in their ability to align 3D maps from repeated inspections in GPS-denied settings automatically. This limitation hinders practical long-term asset health assessments by requiring tedious manual alignment for data association across scans from previous inspections. This paper introduces a versatile map alignment algorithm leveraging both visual and lidar data for improved place recognition robustness and presents an infrastructure-focused dataset tailored for consecutive inspections. By detaching map alignment from SLAM, our approach enhances infrastructure inspection pipelines, supports monitoring asset degradation over time, and invigorates SLAM research by permitting exploration beyond existing multi-session SLAM algorithms., Comment: 8 pages, 8 figures, for associated code see https://github.com/jakemclaughlin6/vlma

As opposed to conventional training methods tailored to minimize a given statistical metric or task-agnostic loss (e.g., mean squared error), Decision-Focused Learning (DFL) trains machine learning models for optimal performance in downstream decision-making tools. We argue that DFL can be leveraged to learn the parameters of system dynamics, expressed as constraint of the convex optimization control policy, while the system control signal is being optimized, thus creating an end-to-end learning framework. This is particularly relevant for systems in which behavior changes once the control policy is applied, hence rendering historical data less applicable. The proposed approach can perform system identification - i.e., determine appropriate parameters for the system analytical model - and control simultaneously to ensure that the model's accuracy is focused on areas most relevant to control. Furthermore, because black-box systems are non-differentiable, we design a loss function that requires solely to measure the system response. We propose pre-training on historical data and constraint relaxation to stabilize the DFL and deal with potential infeasibilities in learning. We demonstrate the usefulness of the method on a building Heating, Ventilation, and Air Conditioning day-ahead management system for a realistic 15-zone building located in Denver, US. The results show that the conventional RC building model, with the parameters obtained from historical data using supervised learning, underestimates HVAC electrical power consumption. For our case study, the ex-post cost is on average six times higher than the expected one. Meanwhile, the same RC model with parameters obtained via DFL underestimates the ex-post cost only by 3%., Comment: 12 pages, 9 figures, submitted to ACM e-energy 2025

The machine learning system in the form of Retrieval Augmented Generation (RAG) has developed steadily since about 2021. RAG could be observed as a version of the knowledge transfer. In the studied case, the large computing systems are observed as the application point of RAG, which includes large language model (LLM), as a partner for the developing team. Such an approach has advantages during the development process and further in exploitation time., Comment: 5 pages

Topology optimization (TO) has found applications across a wide range of disciplines but remains underutilized in practice. Key barriers to broader adoption include the absence of versatile commercial software, the need for specialized expertise, and high computational demands. Additionally, challenges such as ensuring manufacturability, optimizing hyper-parameters, and integrating subjective design elements like aesthetics further hinder its widespread use. Emerging technologies like augmented reality (AR) and virtual reality (VR) offer transformative potential for TO. By enabling intuitive, gesture-based human-computer interactions, these immersive tools bridge the gap between human intuition and computational processes. They provide the means to integrate subjective human judgment into optimization workflows in real time, creating a paradigm shift toward interactive and immersive design. Here we introduce the concept of immersive topology optimization (ITO) as a novel design paradigm that leverages AR environments for TO. To demonstrate this concept, we present ARCADE: Augmented Reality Computational Analysis and Design Environment. Developed in Swift for the Apple Vision Pro mixed reality headset, ARCADE enables users to define, manipulate, and solve structural optimization problems within an augmented reality setting. By incorporating real-time human interaction and visualization of the design in its intended target location, ARCADE has the potential to reduce lead times, enhance manufacturability, and improve design integration. Although initially developed for structural optimization, ARCADE's framework could be extended to other disciplines, paving the way for a new era of interactive and immersive computational design., Comment: 9 pages, 5 figures

Accurate rainfall data are crucial for effective climate services, especially in Sub-Saharan Africa, where agriculture heavily depends on rain-fed systems. However, the sparse distribution of rain-gauge networks in the region necessitates reliance on satellite and reanalysis rainfall products (REs) for rainfall estimation. This study evaluated the performance of eight REs -- CHIRPS, TAMSAT, CHIRP, ENACTS, ERA5, AgERA5, PERSIANN-CDR, and PERSIANN-CCS-CDR -- in Zambia and Ghana using a point-to-pixel validation approach. The analysis encompassed spatial consistency, annual rainfall summaries, seasonal patterns, and rainfall intensity detection across 38 ground stations. Results indicated that no single product performed optimally across all contexts, underscoring the need for application-specific recommendations. All products exhibited a high probability of detection (POD) for dry days in Zambia and northern Ghana (with 70% $<$ POD $<$ 100%, and 60% $<$ POD $<$ 85% respectively), suggesting their potential utility for drought-related studies in these areas. Conversely, all products showed limited skill in detecting heavy and violent rains (with POD close to 0%), rendering them unsuitable for extreme rainfall analysis (such as floods) in the current form. Products integrated with station data (ENACTS, CHIRPS, and TAMSAT) outperformed their counterparts under many contexts, highlighting the importance of calibration with local observations. Bias correction is strongly recommended, as varying levels of biases were evident across different rainfall summaries. A critical area for advancement is extreme rainfall detection. Future research should focus on this aspect., Comment: 33 pages, 24 figures

The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT. This review comprehensively covers synthetic CT generation based on CBCT and proton-based studies. Some of the commonly employed architectures explored are convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models. Evaluation metrics including mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) consistently demonstrate the comparability of sCT images with gold-standard planning CTs (pCT), indicating their potential to improve treatment precision and patient outcomes. Challenges such as field-of-view (FOV) disparities and integration into clinical workflows are discussed, along with recommendations for future research and standardization efforts. In general, the findings underscore the promising role of sCT-based approaches in personalized treatment planning and adaptive radiation therapy, with potential implications for improved oncology treatment delivery and patient care., Comment: 21 pages, 14 Figures, Accepted in the IEEE Transactions on Radiation and Plasma Medical Sciences

Quantum contextuality plays a significant role in supporting quantum computation and quantum information theory. The key tools for this are the Kochen--Specker and non-Kochen--Specker contextual sets. Traditionally, their representation has been predominantly operator-based, mainly focusing on specific constructs in dimensions ranging from three to eight. However, nearly all of these constructs can be represented as low-dimensional hypergraphs. This study demonstrates how to generate contextual hypergraphs in any dimension using various methods, particularly those that do not scale in complexity with increasing dimensions. Furthermore, we introduce innovative examples of hypergraphs extending to dimension 32. Our methodology reveals the intricate structural properties of hypergraphs, enabling precise quantifications of contextuality of implemented sets. Additionally, we investigate several promising applications of hypergraphs in quantum communication and quantum computation, paving the way for future breakthroughs in the field., Comment: 37 pages, 14 figures, 4 tables

The study of metamaterials and architected materials has intensified interest in continuum mechanics models that capture size-dependent microstructure interactions. Among these, Consistent Couple-Stress Theory (C-CST) incorporates microscale mechanical interactions by introducing higher-order derivatives in the strain energy. While previous studies have relied on convolutional principles or inverse Laplace transforms to obtain time-dependent solutions, this work demonstrates that implicit time integration applied to a mixed finite element method with a Lagrange multiplier provides stable, direct time-domain solutions for dynamic C-CST modeling. The proposed finite element scheme is tested through the Method of Manufactured Solutions (MMS) for static cases and dynamic simulations of simple mechanical scenarios. Our computational experiments revealed energy dissipation, emphasizing the importance of exploring symplectic integrators in future work to impose energy conservation. Additionally, further research is required to verify temporal stability through time-domain MMS and to investigate complex mechanical scenarios, including those previously restrictive, challenging to simulate, or unfeasible with existing dynamic methods. This work lays the groundwork for studying size-dependent material behavior and provides the foundation for advanced applications in material design and wave propagation., Comment: 28 pages, 13 figures

Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination conditions. Despite SAR's potential for disturbance mapping, processing SAR data to an analysis-ready format requires expertise and significant compute resources, particularly for large-scale global analysis. In October 2023, NASA's Observational Products for End-Users from Remote Sensing Analysis (OPERA) project released the near-global Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) dataset, providing publicly available, analysis-ready SAR imagery. In this work, we utilize this new dataset to systematically analyze land surface disturbances. As labeling SAR data is often prohibitively time-consuming, we train a self-supervised vision transformer - which requires no labels to train - on OPERA RTC-S1 data to estimate a per-pixel distribution from the set of baseline imagery and assess disturbances when there is significant deviation from the modeled distribution. To test our model's capability and generality, we evaluate three different natural disasters - which represent high-intensity, abrupt disturbances - from three different regions of the world. Across events, our approach yields high quality delineations: F1 scores exceeding 0.6 and Areas Under the Precision-Recall Curve exceeding 0.65, consistently outperforming existing SAR disturbance methods. Our findings suggest that a self-supervised vision transformer is well-suited for global disturbance mapping and can be a valuable tool for operational, near-global disturbance monitoring, particularly when labeled data does not exist., Comment: 19 pages, 18 figures, 5 tables. Preprint. Submitted to JSTARS

The spinning electron-electron interaction is described in classical terms by means of two possible classical interactions: The instantaneous Coulomb interaction between the charge centers of both particles and the Poincar\'e invariant interaction developed in a previous work. The numerical integrations are performed with several Mathematica notebooks that are available for the interested readers in the reference Section. One difference of these interactions is that the Poincar\'e invariant interaction does not satisfy the action-reaction principle in the synchronous description and, therefore, there is no conservation of the mechanical linear momentum. It is the total linear momentum of the system what is conserved. In this synchronous description the interaction is not mediated by the retarded fields but is described in terms of the instantaneous positions, velocities and accelerations of the center of charge of both particles. In the Poincar\'e invariant description the net binding force that holds linked two Dirac particles is stronger than in the Coulomb case, thus forming a stable spin 1 system of 2 Dirac particles. This bosonic state of spin 1 does not correspond to a Cooper pair because the separation between the centers of mass of the Dirac particles is below Compton's wavelength, smaller than the correlation distance of the Cooper pair. Since the Poincar\'e invariant interaction is relativistically invariant it can be used for analyzing high energy scattering processes., Comment: 32 pages, 10 figures, 4 Mathematica Notebooks

Random fiber networks form the structural foundation of numerous biological tissues and engineered materials, from living tissue in the human body to everyday materials like fabric and paper. From a mechanics perspective, understanding the structure-function relationships of random fiber networks is particularly interesting because when external force is applied to these networks, only a small subset of fibers will actually carry the majority of the load. Specifically, these load-bearing fibers propagate through the network to form load paths, also called force chains. However, the relationship between fiber network geometric structure, force chains, and the overall mechanical behavior of random fiber network structures remains poorly understood. To this end, we implement a finite element model of random fiber networks with geometrically exact beam elements, and use this model to explore random fiber network mechanical behavior. Our focus is twofold. First, we explore the mechanical behavior of single fiber chains and random fiber networks. Second, we propose and validate an interpretable analytical approach to predicting fiber network mechanics from structural information alone. Key findings include insight into the critical strain-stiffening transition point for single fiber chains and fiber networks, and a connection between force chains and the distance-weighted graph shortest paths that arise by treating fiber networks as spatial graph structures. This work marks an important step towards mapping the structure-function relationships of random fiber networks undergoing large deformations. Additionally, with our code distributed under open-source licenses, we hope that future researchers can directly build on our work to address related problems beyond the scope defined here., Comment: 37 pages, 19 figures

This study investigates the aerodynamic performance of the symmetric NACA 0018 airfoil under harmonic pitching motions at low Reynolds numbers, a regime characterized by the presence of laminar separation bubbles and their impact on aerodynamic forces. The analysis encompasses oscillation frequencies of 1 Hz, 2 Hz, and 13.3 Hz, with amplitudes of 4{\deg} and 8{\deg}, along with steady-state simulations conducted for angles of attack up to 20{\deg} to validate the numerical model. The results reveal that the Transition SST turbulence model provides improved predictions of aerodynamic forces at higher Reynolds numbers but struggles at lower Reynolds numbers, where laminar flow effects dominate. The inclusion of the 13.3 Hz frequency, relevant to Darrieus vertical-axis wind turbines, demonstrates the model's effectiveness in capturing dynamic hysteresis loops and reduced oscillations, contrasting with the \(k-\omega\) SST model. Comparisons with XFOIL further highlight challenges in accurately modeling laminar-to-turbulent transitions and dynamic flow phenomena. These findings offer valuable insights into the aerodynamic behavior of thick airfoils under low Reynolds number conditions and contribute to advancing the understanding of turbulence modeling, particularly in applications involving vertical-axis wind turbines., Comment: 37 pages, 17 figures, currently under review in CEAS Aeronautical Journal

Traditional microlensing event vetting methods require highly trained human experts, and the process is both complex and time-consuming. This reliance on manual inspection often leads to inefficiencies and constrains the ability to scale for widespread exoplanet detection, ultimately hindering discovery rates. To address the limits of traditional microlensing event vetting, we have developed LensNet, a machine learning pipeline specifically designed to distinguish legitimate microlensing events from false positives caused by instrumental artifacts, such as pixel bleed trails and diffraction spikes. Our system operates in conjunction with a preliminary algorithm that detects increasing trends in flux. These flagged instances are then passed to LensNet for further classification, allowing for timely alerts and follow-up observations. Tailored for the multi-observatory setup of the Korea Microlensing Telescope Network (KMTNet) and trained on a rich dataset of manually classified events, LensNet is optimized for early detection and warning of microlensing occurrences, enabling astronomers to organize follow-up observations promptly. The internal model of the pipeline employs a multi-branch Recurrent Neural Network (RNN) architecture that evaluates time-series flux data with contextual information, including sky background, the full width at half maximum of the target star, flux errors, PSF quality flags, and air mass for each observation. We demonstrate a classification accuracy above 87.5%, and anticipate further improvements as we expand our training set and continue to refine the algorithm., Comment: 23 pages, 13 figures, Accepted for publication in the The Astronomical Journal

The ErbB receptor family, including EGFR and HER2, plays a crucial role in cell growth and survival and is associated with the progression of various cancers such as breast and lung cancer. In this study, we developed a deep learning model to predict the binding affinity of ErbB inhibitors using molecular fingerprints derived from SMILES representations. The SMILES representations for each ErbB inhibitor were obtained from the ChEMBL database. We first generated Morgan fingerprints from the SMILES strings and applied AutoDock Vina docking to calculate the binding affinity values. After filtering the dataset based on binding affinity, we trained a deep neural network (DNN) model to predict binding affinity values from the molecular fingerprints. The model achieved significant performance, with a Mean Squared Error (MSE) of 0.2591, Mean Absolute Error (MAE) of 0.3658, and an R-squared value of 0.9389 on the training set. Although performance decreased slightly on the test set (R squared = 0.7731), the model still demonstrated robust generalization capabilities. These results indicate that the deep learning approach is highly effective for predicting the binding affinity of ErbB inhibitors, offering a valuable tool for virtual screening and drug discovery., Comment: 40 pages, 1 graphical abstract, 2 figures, 6 appendices

We present a practical implementation of a secure multiparty computation application enabled by quantum oblivious transfer (QOT) on an entanglement-based physical layer. The QOT protocol uses polarization-encoded entangled states to share oblivious keys between two parties with quantum key distribution (QKD) providing authentication. Our system integrates the post-processing for QKD and QOT, both sharing a single physical layer, ensuring efficient key generation and authentication. Authentication involves hashing messages into a crypto-context, verifying tags, and replenishing keys through a parallel QKD pipeline, which handles both key post-processing and authentication. Oblivious keys are generated over 12.9 km with a channel loss of 8.47 dB. In a back-to-back setup, a QOT rate of $9.3\times10^{-3}$ OTs/second is achieved, corresponding to 1 minute and 48 seconds per OT, primarily limited by the entanglement source. Using pre-distributed keys improved the rate to 0.11 OTs/second, or 9.1 seconds per OT. The considered QOT protocol is statistically correct, computationally secure for an honest receiver, and statistically secure for an honest sender, assuming a computationally hiding, statistically binding commitment, and a verifiable error-correcting scheme. A practical use case is demonstrated for privacy-preserving fingerprint matching against no-fly lists from Interpol and the United Nations. The fingerprint is secret-shared across two sites, ensuring security, while the matching is performed using the MASCOT protocol, supported by QOT. The application required 128 OTs, with the highest security achieved in 20 minutes and 39 seconds. This work demonstrates the feasibility of QOT in secure quantum communication applications., Comment: 20 pages, 8 figures

A century and a half ago, James C. Maxwell conjectured that the number of zeroes of the electric field (equilibria of the potential) generated by a collection of $n$ point charges is at most $(n-1)^2$. In 2007 Gabrielov, Novikov, and Shapiro proved a much larger upper bound and posed two further conjectures. It is quite possible that Maxwell's quadratic upper bound is not tight, so it is prudent to find lower bounds. Our main contributions in this article are: (1) the construction of examples that achieve the highest ratios of the number of equilibria by point charges found to this day; (2) a counterexample to a conjecture of Gabrielov--Novikov--Shapiro, that the number of equilibria cannot exceed those of the distance function defined by the unit point charges.

Two-dimensional (2D) carbon-based materials are promising candidates for developing more efficient green energy conversion and storage technologies. This study presents a new 2D carbon allotrope, DOTT-Carbon, characterized by its distinctive and multi-ring structure featuring 12-, 8-, 4-, and 3-membered rings of carbon atoms. We explore its structural, mechanical, and lithium-ion storage properties by employing density functional theory and machine learning simulations. Phonon calculations confirm its structural stability and ab initio molecular dynamics simulations demonstrate its thermal resilience at elevated temperatures. The material exhibits anisotropic mechanical properties, with Young's modulus values varying between 280-330 GPa. DOTT-Carbon displays a lithium-ion storage capacity of 446.28 mAh/g, complemented by a low diffusion barrier (0.2-0.9 eV) and a high diffusion coefficient ($ > 1.0 \times 10^{-6}$ cm$^{2}$/s), possibly facilitating efficient lithium-ion transport. The stable open circuit voltage of 0.28 V also indicates its suitability as an anode material., Comment: 19 pages

As dual combs play a significant role in numerous high-precision measurements, their efficient generation has been widely researched. Although the single-cavity dual-comb generation can avoid the complex active stabilization methods, achieving and maintaining stable dual-comb mode locking within a single cavity remains a critical challenge. To break through this constraint, a two-part evaluation criterion containing a fitness function and a CNN-Transformer network is employed to achieve mode locking and classify the dual-comb mode-locked state. Simulated time-stretch dispersive Fourier transform (DFT) spectra are used as datasets, which simplifies the optimization process and does not rely on specific experimental data. A developed evolutionary algorithm (EA) for paddle-based motorized polarization controllers (MPCs) is proposed, enabling the intelligent attainment of dual-comb mode-locked states. A real-time library stores fitness and MPC angles, facilitating mode-locked state achievement within 2 seconds. Finally, long term running of dual-comb mode locking is ensured by a random collision algorithm utilizing an evaluation criterion of weak soliton peaks., Comment: 10 pages, 8 figures

We formulate the problem of material identification as a problem of optimal control in which the deformation of the specimen is the state variable and the unknown material law is the control variable. We assume that the material obeys finite elasticity and that the deformation of the specimen is in static equilibrium with prescribed boundary displacements. We further assume that the attendant total energy of the specimen can be measured, e.g., with the aid of the work-energy identity. In particular, no full-field measurements, such as DIC, are required. The cost function measures the maximum discrepancy between the total elastic energy corresponding to a trial material law and the measured total elastic energy over a range of prescribed boundary displacements. The question of material identifiability is thus reduced to the question of existence and uniqueness of controls. We propose a specific functional framework, prove existence of optimal controls and show that the question of material identifiability hinges on the separating properties of the boundary data. The proposed framework naturally suggests and supports approximation by maxout neural networks, i.e., neural networks of piecewise affine or polyaffine functions and a maximum, or join, activation function. We show that maxout neural networks have the requisite density property in the space of energy densities and result in convergent approximations as the number of neurons increases to infinity. Simple examples are also presented that illustrate the minimax structure of the identification problem., Comment: 29 pages, 4 figures

Criminal networks such as human trafficking rings are threats to the rule of law, democracy and public safety in our global society. Network science provides invaluable tools to identify key players and design interventions for Law Enforcement Agencies (LEAs), e.g., to dismantle their organisation. However, poor data quality and the adaptiveness of criminal networks through self-organization make effective disruption extremely challenging. Although there exists a large body of work building and applying network scientific tools to attack criminal networks, these work often implicitly assume that the network measurements are accurate and complete. Moreover, there is thus far no comprehensive understanding of the impacts of data quality on the downstream effectiveness of interventions. This work investigates the relationship between data quality and intervention effectiveness based on classical graph theoretic and machine learning-based approaches. Decentralization emerges as a major factor in network robustness, particularly under conditions of incomplete data, which renders attack strategies largely ineffective. Moreover, the robustness of centralized networks can be boosted using simple heuristics, making targeted attack more infeasible. Consequently, we advocate for a more cautious application of network science in disrupting criminal networks, the continuous development of an interoperable intelligence ecosystem, and the creation of novel network inference techniques to address data quality challenges.

The ST2 center is an optically addressable point defect in diamond that facilitates spin initialization and readout. However, while this study presents the discovery of ST2 centers first observed in a natural diamond and provides a reliable technique for artificially creating them, its chemical structure remains unknown. To assess the potential of ST2, we map out its basic optical characteristics, reveal its electronic level structure, and quantify the intrinsic transition rates. Furthermore, we investigate its response to microwaves, static magnetic fields, and the polarization of excitation laser light, revealing twelve inequivalent orientations of the ST2 center. Simultaneous exposure to microwaves and static magnetic fields also reveals an exceptionally wide acceptance angle for sensing strong magnetic fields, unlike the well-established NV center, which is sensitive only within a narrow cone aligned with its symmetry axis. This finding establishes the ST2 center as a highly promising candidate for nanoscale quantum sensing., Comment: 8 Pages 3 Figures, Supplement: 9 Pages 4 (5) figures

The article is devoted to the study of neural networks with one hidden layer and a modified activation function for solving physical problems. A rectified sigmoid activation function has been proposed to solve physical problems described by the ODE with neural networks. Algorithms for physics-informed data-driven initialization of a neural network and a neuron-by-neuron gradient-free fitting method have been presented for the neural network with this activation function. Numerical experiments demonstrate the superiority of neural networks with a rectified sigmoid function over neural networks with a sigmoid function in the accuracy of solving physical problems (harmonic oscillator, relativistic slingshot, and Lorentz system)., Comment: 9 pages, 1 figure, 2 tables, 4 algthorithms. arXiv admin note: substantial text overlap with arXiv:2412.19235

Identifying tropical cyclones that generate destructive storm tides for risk assessment, such as from large downscaled storm catalogs for climate studies, is often intractable because it entails many expensive Monte Carlo hydrodynamic simulations. Here, we show that surrogate models are promising from accuracy, recall, and precision perspectives, and they "generalize" to novel climate scenarios. We then present an informative online learning approach to rapidly search for extreme storm tide-producing cyclones using only a few hydrodynamic simulations. Starting from a minimal subset of TCs with detailed storm tide hydrodynamic simulations, a surrogate model selects informative data to retrain online and iteratively improves its predictions of damaging TCs. Results on an extensive catalog of downscaled TCs indicate 100% precision in retrieving rare destructive storms using less than 20% of the simulations as training. The informative sampling approach is efficient, scalable to large storm catalogs, and generalizable to climate scenarios., Comment: minor corrections and editing

Magnetic domain walls in antiferromagnets have been proposed as key components for faster conventional information processing, thanks to their enhanced stability and ultrafast propagation. However, how non-conventional computing methods like reservoir computing might take advantage of these properties remains an open question. In this work, we show how complex domain wall patterns can form through the proliferation of multiple domain walls from the energy stored in a single seed domain wall driven to move at a high speed close to the relativistic limit. We demonstrate that the resulting magnetic texture, consisting of up to hundreds of domain walls with an overall conserved topological charge as the initial seed domain wall, can possess chaotic spatiotemporal dynamics depending on the strength of staggered spin-orbit field induced via applied current. These findings allow us to design a multiple-domain-wall reservoir with high short-term memory and nonlinearity with respect to spin-orbit field inputs, that is suitable for ultrafast, energy-efficient, non-conventional reservoir computing., Comment: 30 pages, 5 figures

The research assesses soil salinity in the southwest coastal region of Bangladesh, collecting a total of 162 topsoil samples between March 1 and March 9, 2024, and processing them following the standard operating procedure for soil electrical conductivity (soil/water, 1:5). Electrical conductivity (EC) measurements obtained using a HI-6321 advanced conductivity benchtop meter were analyzed and visualized using bubble density mapping and the Empirical Bayesian Kriging interpolation method. The findings indicate that soil salinity in the study area ranges from 0.05 to 9.09 mS/cm, with the highest levels observed near Debhata and Koyra. A gradient of increasing soil salinity is clearly evident from the northern to southern regions. This dataset provides a critical resource for soil salinity-related research in the region, offering valuable insights to support decision-makers in understanding and mitigating the impacts of soil salinity in Bangladesh's coastal areas.

Differential equations (DEs) are crucial for modeling the evolution of natural or engineered systems. Traditionally, the parameters in DEs are adjusted to fit data from system observations. However, in fields such as politics, economics, and biology, available data are often independently collected at distinct time points from different subjects (i.e., repeated cross-sectional (RCS) data). Conventional optimization techniques struggle to accurately estimate DE parameters when RCS data exhibit various heterogeneities, leading to a significant loss of information. To address this issue, we propose a new estimation method called the emulator-informed deep-generative model (EIDGM), designed to handle RCS data. Specifically, EIDGM integrates a physics-informed neural network-based emulator that immediately generates DE solutions and a Wasserstein generative adversarial network-based parameter generator that can effectively mimic the RCS data. We evaluated EIDGM on exponential growth, logistic population models, and the Lorenz system, demonstrating its superior ability to accurately capture parameter distributions. Additionally, we applied EIDGM to an experimental dataset of Amyloid beta 40 and beta 42, successfully capturing diverse parameter distribution shapes. This shows that EIDGM can be applied to model a wide range of systems and extended to uncover the operating principles of systems based on limited data.

The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of 8x compared to double-precision in extreme compute-intensive workloads, remain largely untapped in most scientific applications. A growing number of efforts have shown that mixed-precision algorithmic innovations can deliver superior performance without sacrificing accuracy. These developments should prompt computational scientists to seriously consider whether their scientific modeling and simulation applications could benefit from the acceleration offered by new hardware and mixed-precision algorithms. In this article, we review the literature on relevant applications, existing mixed-precision algorithms, theories, and the available software infrastructure. We then offer our perspective and recommendations on the potential of mixed-precision algorithms to enhance the performance of scientific simulation applications. Broadly, we find that mixed-precision methods can have a large impact on computational science in terms of time-to-solution and energy consumption. This is true not only for a few arithmetic-dominated applications but also, to a more moderate extent, to the many memory bandwidth-bound applications. In many cases, though, the choice of algorithms and regions of applicability will be domain-specific, and thus require input from domain experts. It is helpful to identify cross-cutting computational motifs and their mixed-precision algorithms in this regard. Finally, there are new algorithms being developed to utilize AI hardware and and AI methods to accelerate first-principles computational science, and these should be closely watched as hardware platforms evolve., Comment: Submitted to IJHPCA

The article discusses the development of various methods and techniques for initializing and training neural networks with a single hidden layer, as well as training a separable physics-informed neural network consisting of neural networks with a single hidden layer to solve physical problems described by ordinary differential equations (ODEs) and partial differential equations (PDEs). A method for strictly deterministic initialization of a neural network with one hidden layer for solving physical problems described by an ODE is proposed. Modifications to existing methods for weighting the loss function are given, as well as new methods developed for training strictly deterministic-initialized neural networks to solve ODEs (detaching, additional weighting based on the second derivative, predicted solution-based weighting, relative residuals). An algorithm for physics-informed data-driven initialization of a neural network with one hidden layer is proposed. A neural network with pronounced generalizing properties is presented, whose generalizing abilities of which can be precisely controlled by adjusting network parameters. A metric for measuring the generalization of such neural network has been introduced. A gradient-free neuron-by-neuron fitting method has been developed for adjusting the parameters of a single-hidden-layer neural network, which does not require the use of an optimizer or solver for its implementation. The proposed methods have been extended to 2D problems using the separable physics-informed neural networks approach. Numerous experiments have been carried out to develop the above methods and approaches. Experiments on physical problems, such as solving various ODEs and PDEs, have demonstrated that these methods for initializing and training neural networks with one or two hidden layers (SPINN) achieve competitive accuracy and, in some cases, state-of-the-art results., Comment: 45 pages, 36 figures, 9 tables

Several numerical simulations of a co-axial particle-laden swirling air flow in a vertical circular pipe were performed. The air flow was modeled using the unsteady Favre-averaged Navier-Stokes equations. A Lagrangian model was used for the particle motion. The gas and particles are coupled through two-way momentum exchange. The results of the simulations using three versions of the k-epsilon turbulence model (standard, re-normalization group (RNG), and realizable) are compared with experimental mean velocity profiles. The standard model achieved the best overall performance. The realizable model was unable to satisfactorily predict the radial velocity; it is also the most computationally-expensive model. The simulations using the RNG model predicted additional recirculation zones. We also compared the particle and parcel approaches in solving the particle motion. In the latter, multiple similar particles are grouped in a single parcel, thereby reducing the amount of computation., Comment: 11 pages, 11 figures, no tables, published conference article, peer-reviewed, open access. International Conference on Modeling, Simulation and Control (ICMSC'09), San Francisco, California, October 2009

The quality factor ($Q$ factor) of nanomechanical resonators is influenced by geometry and stress, a phenomenon called dissipation dilution. Studies have explored maximizing this effect, leading to softly-clamped resonator designs. This paper proposes a topology optimization methodology to design two-dimensional nanomechanical resonators with high $Q$ factors by maximizing dissipation dilution. A formulation based on the ratio of geometrically nonlinear to linear modal stiffnesses of a prestressed finite element model is used, with its corresponding adjoint sensitivity analysis formulation. Systematic design in square domains yields geometries with comparable $Q$ factors to literature. We analyze the trade-offs between resonance frequency and quality factor, and how these are reflected in the geometry of resonators. We further apply the methodology to optimize a resonator on a full hexagonal domain. By using the entire mesh -- i.e., without assuming any symmetries -- we find that the optimizer converges to a two-axis symmetric design comprised of four tethers., Comment: 23 pages, 13 figures, 1 table

This study presents a comprehensive mathematical framework for modeling the flight dynamics of a six-degree-of-freedom fixed-wing aircraft as a rigid body with three control surfaces: rudder, elevators, and ailerons. The framework consists of 35 differential-algebraic equations (DAEs) and requires 30 constants to be specified. It supports both direct and inverse flight dynamics analyses. In direct dynamics, the historical profiles of control inputs (deflection angles and engine thrust) are specified, and the resulting flight trajectory is predicted. In inverse dynamics, the desired flight trajectory and an additional constraint are specified to determine the required control inputs. The framework employs wind axes for linear-momentum equations and body axes for angular-momentum equations, incorporates two flight path angles, and provides formulas for aerodynamic force and moment coefficients. Key advantages include improved computational efficiency, elimination of Euler angle singularities, and independence from symmetry assumptions with regard to the aircraft's moments of inertia. The model also accounts for nonlinear air density variations with altitude, up to 20 km above mean sea level, making it suitable for accurate and efficient flight dynamics simulations., Comment: 22 pages, 9 figures, 3 tables, published journal article, open access, peer-reviewed

Sensors are key components enabling various applications, e.g., home intrusion detection and environmental monitoring. While various software defenses and physical protections are used to prevent sensor manipulation, this paper introduces a new threat vector, PowerRadio, that bypasses existing protections and changes sensor readings from a distance. PowerRadio leverages interconnected ground (GND) wires, a standard practice for electrical safety at home, to inject malicious signals. The injected signal is coupled by the sensor's analog measurement wire and eventually survives the noise filters, inducing incorrect measurement. We present three methods to manipulate sensors by inducing static bias, periodical signals, or pulses. For instance, we show adding stripes into the captured images of a surveillance camera or injecting inaudible voice commands into conference microphones. We study the underlying principles of PowerRadio and identify its root causes: (1) the lack of shielding between ground and data signal wires and (2) the asymmetry of circuit impedance that enables interference to bypass filtering. We validate PowerRadio against a surveillance system, broadcast systems, and various sensors. We believe that PowerRadio represents an emerging threat, exhibiting the advantages of both radiated and conducted EMI, e.g., expanding the effective attack distance of radiated EMI yet eliminating the requirement of line-of-sight or approaching physically. Our insights shall provide guidance for enhancing the sensors' security and power wiring during the design phases., Comment: 18 pages, 21 figures

We explore an unconventional bridge between quantum mechanical density matrices and sound by mapping elements of the density matrix and their phases to auditory signals, thus introducing a framework for Open Quantum Sonification. Employing the eigenstates of the Hamiltonian operator as a basis, each quantum state contributes a frequency proportional to its energy level. The off-diagonal terms, which encode coherence and phase relationships between energy levels, are rendered as binaural signals presented separately to the left and right ears. We illustrate this method within the context of open quantum system dynamics governed by Lindblad equations, presenting first an example of quantum Brownian motion of a particle in a thermal bath, and second, a recoherence process induced by boundary driving that results in spin-helix states. This document serves as a companion to the corresponding audio visual simulations of these models available on the YouTube channel Open Quantum Sonification with the Python Codes on GitHub. The auditory analogy presented here provides an intuitive and experiential means of describing quantum phenomena such as tunnelling, thermalisation, decoherence, and recoherence., Comment: 4 pages, 2 figures

Large language models (LLMs) demonstrate an impressive ability to utilise information within the context of their input sequences to appropriately respond to data unseen by the LLM during its training procedure. This ability is known as in-context learning (ICL). Humans and non-human animals demonstrate similar abilities, however their neural architectures differ substantially from LLMs. Despite this, a critical component within LLMs, the attention mechanism, resembles modern associative memory models, widely used in and influenced by the computational neuroscience community to model biological memory systems. Using this connection, we introduce an associative memory model capable of performing ICL. We use this as inspiration for a novel residual stream architecture which allows information to directly flow between attention heads. We test this architecture during training within a two-layer Transformer and show its ICL abilities manifest more quickly than without this modification. We then apply our architecture in small language models with 8 million parameters, focusing on attention head values, with results also indicating improved ICL performance at this larger and more naturalistic scale., Comment: 18 pages, 6 figures, 3 tables

Differential top quark pair cross sections are measured in the dilepton final state as a function of kinematic variables associated to the dineutrino system. The measurements are performed making use of the Run 2 dataset collected by the CMS experiment at the CERN LHC collider, corresponding to proton-proton collisions recorded at center of mass energy of 13 TeV and an integrated luminosity of 138 fb$^{-1}$. The measured cross sections are found in agreement with theory predictions and Monte Carlo simulations of standard model processes., Comment: Talk at the 17th International Workshop on Top Quark Physics (Top2024), 22-27 September 2024

This paper presents a guidance algorithm for solving the problem of following parametric paths, as well as a curvature-varying speed setpoint for land-based car-type wheeled mobile robots (WMRs). The guidance algorithm relies on Singularity-Free Guiding Vector Fields SF-GVF. This novel GVF approach expands the desired robot path and the Guiding vector field to a higher dimensional space, in which an angular control function can be found to ensure global asymptotic convergence to the desired parametric path while avoiding field singularities. In SF-GVF, paths should follow a parametric definition. This feature makes using Bezier's curves attractive to define the robot's desired patch. The curvature-varying speed setpoint, combined with the guidance algorithm, eases the convergence to the path when physical restrictions exist, such as minimal turning radius or maximal lateral acceleration. We provide theoretical results, simulations, and outdoor experiments using a WMR platform assembled with off-the-shelf components., Comment: 26 pages, 15 figures

Endpoint dilution (TCID50) assays cannot count the number of infectious virions (IVs), and instead are limited to counting the number of Specific INfections caused by the sample (SIN). The latter depends not only on whether virions are infectious, but also on the cells and the experimental conditions under which they interact. These interactions are random and controlled by parameters such as the rates at which IVs lose infectivity, enter cells, or fail to replicate following cell entry. Here, stochastic TCID50 assays are simulated to determine how the random number of infected wells relates to the parameters and the number of IVs in a sample. We introduce a new parameter estimation method based on the likelihood of observing a given TCID50 assay outcome given the model-predicted number of IVs in the sample. We then successively evaluate how parameter estimates are affected by the use of: 1) the new likelihood function vs the typical assumption of Gaussian-distributed measurement errors; 2) IV vs SIN units to express virus in the model; and 3) a stochastic vs an ODE model to simulate the course of a virus infection. Unlike previous methods, the new likelihood correctly handles measurements beyond the detection limits, and results in non-Gaussian distributions. Expressing virus using IV units makes it possible to impose physical constraints (e.g. one IV cannot infect more than one cell), and yields more biologically useful parameters (e.g. mutation emergence likelihood depends on the number of IVs, not SIN, produced). Using a stochastic rather than an ODE model we show that the variability observed between replicate in vitro virus infections is consistent with the level of stochasticity introduced by the TCID50 assay, which can be reduced through better assay design. The framework introduced herein offers several important improvements over current methods and should be widely adopted., Comment: 37 pages, 20 figures