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A Muon (Synchrotron) Ion Collider (MuSIC) can be the successor to the Electron-Ion Collider at Brookhaven National Laboratory, as well as the ideal demonstrator facility for a future multi-TeV Muon Collider. Besides its rich nuclear physics and Standard Model particle physics programs, in this work we show that the MuSIC with a TeV-scale muon beam offers also a unique opportunity to probe New Physics. In particular, the relevant searches have the potential to surpass current experimental limits and explore new regimes of the parameter space for a variety of Beyond the Standard Model scenarios including: lepton-flavor violating leptoquarks, muonphilic vector boson interactions, axion-like particles coupling to photons, and heavy sterile neutrinos. Depending on the particular case, the sensitivity of the searches in the MuSIC may span a wide range of energy scales, namely from sub-GeV particles to the few TeV New Physics mediators. Our analysis demonstrates that the MuSIC can strike a powerful chord in the search for New Physics, thanks to unique combination of features that amplify its capabilities., Comment: 24 pages, 6 figures

We propose that the Higgs potential, a key element in our understanding of Nature, is partially generated by the instantons of new confining dynamics, perhaps from a hidden sector. In this picture, while the Higgs itself is a fundamental field, it controls the strength of the non-perturbative interactions that give rise to its potential. We examine a simple setup in which this instanton contribution is augmented by a quartic term, which is sufficient for a realistic electroweak symmetry breaking mechanism. The minimum of the potential is given by the Lambert $W_0$ function, with these assumptions. We discuss the predictions of this model and how it may be tested through measurements of the Higgs self coupling. Given the connection with non-trivial dynamics, one may also consider the prospects of accessing hidden sector states at colliders; this seems to be typically challenging in our setup. Symmetry restoration in the early Universe in this scenario is briefly examined. We also comment on the possible connection of our general setup with recent work on the physics of field space end points., Comment: 6 pages, 5 figures

The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images.

The emergence of generative artificial intelligence (GAI) and large language models (LLMs) such ChatGPT has enabled the realization of long-harbored desires in software and robotic development. The technology however, has brought with it novel ethical challenges. These challenges are compounded by the application of LLMs in other machine learning systems, such as multi-robot systems. The objectives of the study were to examine novel ethical issues arising from the application of LLMs in multi-robot systems. Unfolding ethical issues in GPT agent behavior (deliberation of ethical concerns) was observed, and GPT output was compared with human experts. The article also advances a model for ethical development of multi-robot systems. A qualitative workshop-based method was employed in three workshops for the collection of ethical concerns: two human expert workshops (N=16 participants) and one GPT-agent-based workshop (N=7 agents; two teams of 6 agents plus one judge). Thematic analysis was used to analyze the qualitative data. The results reveal differences between the human-produced and GPT-based ethical concerns. Human experts placed greater emphasis on new themes related to deviance, data privacy, bias and unethical corporate conduct. GPT agents emphasized concerns present in existing AI ethics guidelines. The study contributes to a growing body of knowledge in context-specific AI ethics and GPT application. It demonstrates the gap between human expert thinking and LLM output, while emphasizing new ethical concerns emerging in novel technology., Comment: 51 pages, 10 figures

Large-scale 3D generative models require substantial computational resources yet often fall short in capturing fine details and complex geometries at high resolutions. We attribute this limitation to the inefficiency of current representations, which lack the compactness required to model the generative models effectively. To address this, we introduce a novel approach called Wavelet Latent Diffusion, or WaLa, that encodes 3D shapes into wavelet-based, compact latent encodings. Specifically, we compress a $256^3$ signed distance field into a $12^3 \times 4$ latent grid, achieving an impressive 2427x compression ratio with minimal loss of detail. This high level of compression allows our method to efficiently train large-scale generative networks without increasing the inference time. Our models, both conditional and unconditional, contain approximately one billion parameters and successfully generate high-quality 3D shapes at $256^3$ resolution. Moreover, WaLa offers rapid inference, producing shapes within two to four seconds depending on the condition, despite the model's scale. We demonstrate state-of-the-art performance across multiple datasets, with significant improvements in generation quality, diversity, and computational efficiency. We open-source our code and, to the best of our knowledge, release the largest pretrained 3D generative models across different modalities.

Current laboratory bounds imply that protons are extremely long-lived. However, this conclusion may not hold for all time and in all of space. We find that the proton lifetime can be $\sim 15$ orders of magnitude shorter in the relatively recent past on Earth, or at the present time elsewhere in the Milky Way. A number of terrestrial and astrophysical constraints are examined and potential signals are outlined. We also sketch possible models that could lead to spatial or temporal variations in the proton lifetime. A positive signal could be compelling evidence for a new long range force of Nature, with important implications for the limitations of fundamental inferences based solely on laboratory measurements., Comment: Revtex4-2, 5 pages, 2 figures

A novel physics-informed operator learning technique based on spectral methods is introduced to model the complex behavior of heterogeneous materials. The Lippmann-Schwinger operator in Fourier space is employed to construct physical constraints with almost no additional overhead, eliminating the need for automatic differentiation. This methodology accelerates the training process by allowing gradient construction on a finite fixed discretization in Fourier space. Later, the spectral physics-informed finite operator learning (SPiFOL) framework is built based on this discretization and trained to map the arbitrary shape of microstructures to their mechanical responses (strain fields) without using any labeled data. The training is done by minimizing equilibrium in Fourier space concerning the macroscopic loading condition, which also guarantees the periodicity. The trained SPiFOL predicts full-field solutions for unseen cases accelerating the conventional fast Fourier transform (FFT) solvers by two orders of magnitude. The accuracy of the SPiFOL is investigated for a wide range of microstructures from dual-phase to multiphase materials and the homogenized stresses computed by SPiFOL show less than 1% error compared to results obtained by conventional FFT solvers. SPiFOL is further enhanced with a Fourier Neural Operator (FNO) to map microstructure to mechanical behavior using only physical equations in Fourier space. Compared to the standard data-driven FNO, SPiFOL shows higher accuracy in predicting stress fields and provides nearly resolution-independent results. Furthermore, the zero-shot-super-resolution feature in heterogeneous domains is investigated. Finally, the effectiveness of SPiFOL as a solver for individual microstructures is demonstrated, highlighting the reduction in solving time when utilizing GPUs.

Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics. To address these concerns, we conduct a large-scale study on several SOTA open and closed models. To overcome the limitations of existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic templates that allow for the generation of a diverse set of questions. GSM-Symbolic enables more controllable evaluations, providing key insights and more reliable metrics for measuring the reasoning capabilities of models.Our findings reveal that LLMs exhibit noticeable variance when responding to different instantiations of the same question. Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the reasoning chain needed for the final answer. Overall, our work offers a more nuanced understanding of LLMs' capabilities and limitations in mathematical reasoning., Comment: preprint

Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative decoding, and early exit strategies leverage the insight that computational demands can vary significantly based on the complexity and nature of the input. However, identifying optimal routing patterns for dynamic execution remains an open challenge, limiting the full potential of these adaptive methods. To address this need, we study adaptive computation in LLMs more systematically. We propose a novel framework that integrates smaller auxiliary modules within each Feed-Forward Network layer of the LLM. This design enables dynamic routing of tokens based on task complexity: tokens can be processed by either the small or big modules at each layer, or even bypass certain layers entirely. This allows us to introduce a novel notion of a token's difficulty, defined by its potential to benefit from additional computational resources. Importantly, by employing oracles to identify optimal patterns of adaptive computations, we gain valuable insights into the internal workings of LLMs and the routing processes in a simplified heterogeneous MoE setup. We show that trained routers operate differently from oracles and often yield suboptimal solutions. Notably, activating a large module in just one layer outperforms models that use large modules across all layers, underscoring the gap between practical implementations of routing in MoE models and theoretical optima for adaptive computation.

The revolutionary advancements in metal additive manufacturing have enabled the production of alloy-based lattice structures with complex geometrical features and high resolutions. This has encouraged the development of nonlinear material models, including plasticity, damage, etc., for such materials. However, the prohibitive computational cost arising from the high number of degrees of freedom for engineering structures composed of lattice structures highlights the necessity of homogenization techniques, such as the two-scale computational homogenization method. In the present work, a two-scale homogenization approach with on-the-fly exchange of information is adopted to study the elastoplastic behavior of truss-based lattice structures. The macroscopic homogenized structure is represented by a two-dimensional continuum, while the underlying microscale lattices are modeled as a network of one-dimensional truss elements. This helps to significantly reduce the associated computational cost by reducing the microscopic degrees of freedom. The microscale trusses are assumed to exhibit an elastoplastic material behavior characterized by a combination of nonlinear exponential isotropic hardening and linear kinematic hardening. Through multiple numerical examples, the performance of the adopted homogenization approach is examined by comparing forces and displacements with direct numerical simulations of discrete structures for three types of stretching-dominated lattice topologies, including triangular, X-braced and X-Plus-braced unit cells. Furthermore, the principle of scale separation, which emphasizes the need for an adequate separation between the macroscopic and microscopic characteristic lengths, is investigated.

The fundamental problem with ultrasound-guided diagnosis is that the acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details. This limitation leads to challenges in ultrasound echocardiography, such as poor visualization of heart valves or foreshortening of ventricles. Clinicians must interpret these images with inherent uncertainty, a nuance absent in machine learning's one-hot labels. We propose Re-Training for Uncertainty (RT4U), a data-centric method to introduce uncertainty to weakly informative inputs in the training set. This simple approach can be incorporated to existing state-of-the-art aortic stenosis classification methods to further improve their accuracy. When combined with conformal prediction techniques, RT4U can yield adaptively sized prediction sets which are guaranteed to contain the ground truth class to a high accuracy. We validate the effectiveness of RT4U on three diverse datasets: a public (TMED-2) and a private AS dataset, along with a CIFAR-10-derived toy dataset. Results show improvement on all the datasets., Comment: This preprint has not undergone any post-submission improvements or corrections. The Version of Record of this contribution is published in: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer (2024) under the same title

Accurate lung nodule detection for computed tomography (CT) scan imagery is challenging in real-world settings due to the sparse occurrence of nodules and similarity to other anatomical structures. In a typical positive case, nodules may appear in as few as 3% of CT slices, complicating detection. To address this, we reframe the problem as an anomaly detection task, targeting rare nodule occurrences in a predominantly normal dataset. We introduce a novel solution leveraging custom data preprocessing and Deformable Detection Transformer (Deformable- DETR). A 7.5mm Maximum Intensity Projection (MIP) is utilized to combine adjacent lung slices into single images, reducing the slice count and decreasing nodule sparsity. This enhances spatial context, allowing for better differentiation between nodules and other structures such as complex vascular structures and bronchioles. Deformable-DETR is employed to detect nodules, with a custom focal loss function to better handle the imbalanced dataset. Our model achieves state-of-the-art performance on the LUNA16 dataset with an F1 score of 94.2% (95.2% recall, 93.3% precision) on a dataset sparsely populated with lung nodules that is reflective of real-world clinical data.

A solitonic model of the early universe is introduced by employing the Double-Sine-Gordon (DSG) potential. The model predicts the appropriate number of e-foldings ($N_e$) required for favored inflation and is an advantage for the model in addressing the flatness, horizon, and magnetic monopole problems. Compatibility of the model with observations, including the Planck $2018$ data \cite{Akrami et al. (2020)} and the Planck $2018$ data+BK$18$+BAO \cite{Ade et al. (2021)} paves the way to estimate the model's free parameters. The results generate acceptable and proper values for the spectral index ($n_s$) and the tensor-to-scalar ratio ($r$) in agreement with the Planck $2018$ data \cite{Akrami et al. (2020)} and the Planck $2018$ data+BK$18$+BAO \cite{Ade et al. (2021)}. Correspondingly, a consistent description of the reheating era is obtained, yielding positive reheating number of e-foldings ($N_{\mathrm{reh}}$) and reheating final temperature ($T_{\mathrm{reh}}$) from $10^{-2}$ GeV to $10^{16}$ GeV. Overall, the model seems viable at the inflationary and reheating eras.

One-third of Mars' surface has shallow-buried H$_2$O, but it is currently too cold for use by life. Proposals to warm Mars using greenhouse gases require a large mass of ingredients that are rare on Mars' surface. However, we show here that artificial aerosols made from materials that are readily available at Mars-for example, conductive nanorods that are ~9 $\mu$m long-could warm Mars >5 $\times$ 10$^3$ times more effectively than the best gases. Such nanoparticles forward-scatter sunlight and efficiently block upwelling thermal infrared. Similar to the natural dust of Mars, they are swept high into Mars' atmosphere, allowing delivery from the near-surface. For a particle lifetime of 10 years, two climate models indicate that sustained release at 30 liters/sec would globally warm Mars by $\gtrsim$30 K and start to melt the ice. Therefore, if nanoparticles can be made at scale on (or delivered to) Mars, then the barrier to warming of Mars appears to not be as high as previously thought.

Auxetic structures, known for their negative Poisson's ratio, exhibit effective elastic properties heavily influenced by their underlying structural geometry and base material properties. While periodic homogenization of auxetic unit cells can be used to investigate these properties, it is computationally expensive and limits design space exploration and inverse analysis. In this paper, surrogate models are developed for the real-time prediction of the effective elastic properties of auxetic unit cells with orthogonal voids of different shapes. The unit cells feature orthogonal voids in four distinct shapes, including rectangular, diamond, oval, and peanut-shaped voids, each characterized by specific void diameters. The generated surrogate models accept geometric parameters and the elastic properties of the base material as inputs to predict the effective elastic constants in real-time. This rapid evaluation enables a practical inverse analysis framework for obtaining the optimal design parameters that yield the desired effective response. The fast Fourier transform (FFT)-based homogenization approach is adopted to efficiently generate data for developing the surrogate models, bypassing concerns about periodic mesh generation and boundary conditions typically associated with the finite element method (FEM). The performance of the generated surrogate models is rigorously examined through a train/test split methodology, a parametric study, and an inverse problem. Finally, a graphical user interface (GUI) is developed, offering real-time prediction of the effective tangent stiffness and performing inverse analysis to determine optimal geometric parameters.

We examine the possibility that dark matter (DM) may be an ultralight scalar that was misaligned via non-minimal coupling to gravity, in the early Universe. For a certain regime of scalar masses, gravitational effects in neutron stars could place interesting bounds on the viable parameter space of the model, even in the absence of non-gravitational interactions between DM and ordinary matter., Comment: Revtex4-2, 8 pages, 4 figures; version accepted for publication in PRD

We explore the feasibility of using TeV-energy muons to probe lepton-flavor-violating (LFV) processes mediated by an axion-like particle (ALP) $a$ with mass $\mathcal{O}(10~\textrm{GeV})$. We focus on $\mu\tau$ LFV interactions and assume that the ALP is coupled to a dark state $\chi$, which can be either less or more massive than $a$. Such a setup is demonstrated to be consistent with $\chi$ being a candidate for dark matter, in the experimentally relevant regime of parameters. We consider the currently operating NA64-$\mu$ experiment and proposed FASER$\nu$2 detector as both the target and the detector for the process $\mu A \to \tau A\, a$, where $A$ is the target nucleus. We also show that a possible future active muon fixed-target experiment operating at a 3 TeV muon collider or in its preparatory phase can provide an impressive reach for the LFV process considered, with future FASER$\nu$2 data providing a pilot study towards that goal. The implications of the muon anomalous magnetic moment $(g-2)_\mu$ measurements for the underlying model, in case of a positive signal, are also examined, and a sample UV completion is outlined., Comment: 16 pages, 7 figures, matches published version

It is conventionally assumed that the physics underlying the Peccei-Quinn (PQ) mechanism for addressing the strong CP problem is at very high energies, orders of magnitude above the weak scale. However, this may not be the case in general and the associated PQ boson $\phi$, besides the signature state, {\it i.e.} the ultralight axion $a$, may emerge well below the weak scale. We consider this possibility and examine some of the conditions for its viability. The example model proposed here may also provide the requisite Standard Model Higgs mass parameter, without invoking new scalars above the GeV scale. The corresponding parameter space can maintain {\it finite naturalness} against quantum corrections. This scenario, depending on choice of parameters, can potentially be constrained by flavor data. We point out that the current mild excess in $B^+\to K^+ \nu \bar \nu$, reported by the Belle II experiment, could be explained in this setup as $B^+\to K^+ \phi$ and $B^+\to K^+ a$, with both $\phi$ and $a$ escaping the detector as missing energy. For a sufficiently heavy PQ boson, in the GeV regime, one can separate these two contributions, due to the difference in $K^+$ momenta. In this case, the axion may also affect lighter meson, {\it e.g.} kaon, decays while $\phi$ would not be a kinematically allowed final state., Comment: 7 pages, 2 figures

Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather image ideas, followed by sketching on paper or using digital tools to develop concepts. Emerging generative AI techniques, such as diffusion models, offer a promising avenue to streamline these processes by swiftly generating design concepts based on text and image inspiration inputs, subsequently using the AI generated design concepts as fresh sources of inspiration for further concept development. However, applying these generative AI techniques directly within a design context has challenges. Firstly, generative AI tools may exhibit a bias towards particular styles, resulting in a lack of diversity of design outputs. Secondly, these tools may struggle to grasp the nuanced meanings of texts or images in a design context. Lastly, the lack of integration with established design processes within design teams can result in fragmented use scenarios. Focusing on these challenges, we conducted workshops, surveys, and data augmentation involving teams of experienced automotive designers to investigate their current practices in generating concepts inspired by texts and images, as well as their preferred interaction modes for generative AI systems to support the concept generation workflow. Finally, we developed a novel generative AI system based on diffusion models to assist conceptual automotive design.

We consider a long-range force, mediated by an ultralight scalar, which can give rise to violation of baryon number. This would lead to very different lifetimes for nucleons in different astrophysical environments. Possible signals of this scenario include a flux of O(10 MeV) solar neutrinos or anomalous heating of old neutron stars; we find the latter to yield the strongest current bounds, which could be improved in the coming years. The ultralight scalar employed here can potentially be a good dark matter candidate., Comment: Based on a talk by the author at the EW Rencontres de Moriond 2024, La Thuile, Italy, March 24-31, 2024

This paper introduces a groundbreaking classification model called the Controllable Ensemble Transformer and CNN (CETC) for the analysis of medical images. The CETC model combines the powerful capabilities of convolutional neural networks (CNNs) and transformers to effectively capture both local and global features present in medical images. The model architecture comprises three main components: a convolutional encoder block (CEB), a transposed-convolutional decoder block (TDB), and a transformer classification block (TCB). The CEB is responsible for capturing multi-local features at different scales and draws upon components from VGGNet, ResNet, and MobileNet as backbones. By leveraging this combination, the CEB is able to effectively detect and encode local features. The TDB, on the other hand, consists of sub-decoders that decode and sum the captured features using ensemble coefficients. This enables the model to efficiently integrate the information from multiple scales. Finally, the TCB utilizes the SwT backbone and a specially designed prediction head to capture global features, ensuring a comprehensive understanding of the entire image. The paper provides detailed information on the experimental setup and implementation, including the use of transfer learning, data preprocessing techniques, and training settings. The CETC model is trained and evaluated using two publicly available COVID-19 datasets. Remarkably, the model outperforms existing state-of-the-art models across various evaluation metrics. The experimental results clearly demonstrate the superiority of the CETC model, emphasizing its potential for accurately and efficiently analyzing medical images.

We revisit the possibility that light axion-like particles (ALPs) with lepton flavor violating couplings could give significant contributions to the electron's anomalous magnetic moment $g_e-2$. Unlike flavor diagonal lepton-ALP couplings, which are exclusively axial, lepton flavor violating couplings can have arbitrary chirality. Focusing on the $e$-$\tau$ ALP coupling, we find that the size of the contribution to $g_e-2$ depends strongly on the chirality of the coupling. A significant part of the parameter space for which such a coupling can explain experimental anomalies in $g_e-2$ can be probed at the Electron-Ion Collider, which is uniquely sensitive to the chirality of the coupling using the polarization of the electron beam., Comment: 5 pages, 3 figures. v2: update to published version

The accurate segmentation of medical images is critical for various healthcare applications. Convolutional neural networks (CNNs), especially Fully Convolutional Networks (FCNs) like U-Net, have shown remarkable success in medical image segmentation tasks. However, they have limitations in capturing global context and long-range relations, especially for objects with significant variations in shape, scale, and texture. While transformers have achieved state-of-the-art results in natural language processing and image recognition, they face challenges in medical image segmentation due to image locality and translational invariance issues. To address these challenges, this paper proposes an innovative U-shaped network called BEFUnet, which enhances the fusion of body and edge information for precise medical image segmentation. The BEFUnet comprises three main modules, including a novel Local Cross-Attention Feature (LCAF) fusion module, a novel Double-Level Fusion (DLF) module, and dual-branch encoder. The dual-branch encoder consists of an edge encoder and a body encoder. The edge encoder employs PDC blocks for effective edge information extraction, while the body encoder uses the Swin Transformer to capture semantic information with global attention. The LCAF module efficiently fuses edge and body features by selectively performing local cross-attention on features that are spatially close between the two modalities. This local approach significantly reduces computational complexity compared to global cross-attention while ensuring accurate feature matching. BEFUnet demonstrates superior performance over existing methods across various evaluation metrics on medical image segmentation datasets.

This paper proposes a new approach to identifying patients with insomnia using a single EEG channel, without the need for sleep stage annotation. Data preprocessing, feature extraction, feature selection, and classification techniques are used to automatically detect insomnia based on features extracted from spectral and temporal domains, including relative power in the delta, sigma, beta and gamma bands, total power, absolute slow wave power, power ratios, mean, zero crossing rate, mobility, and complexity. A Pearson correlation coefficient, t-test, p-value, and two rules are used to select the optimal set of features for accurately classifying insomnia patients and rejecting negatively affecting features. Classification schemes including a general artificial neural network, convolutional neural network, and support vector machine are applied to the optimal feature set to distinguish between insomnia patients and healthy subjects. The performance of the model is validated using 50 insomnia patients and 50 healthy subjects, with the Fp2 channel and 1D-CNN classifier achieving the highest accuracy and Cohen's kappa coefficient at 97.85% and 94.15%, respectively. The developed model has the potential to simplify current sleep monitoring systems and enable in-home ambulatory monitoring., Comment: 30 Pages, 9 figures and 12 tables