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Current image generation paradigms prioritize actualizing user intention - "see what you intend" - but often neglect the sociopolitical dimensions of this process. However, it is increasingly evident that image generation is political, contributing to broader social struggles over visual meaning. This sociopolitical aspect was highlighted by the March 2024 Gemini controversy, where Gemini faced criticism for inappropriately injecting demographic diversity into user prompts. Although the developers sought to redress image generation's sociopolitical dimension by introducing diversity "corrections," their opaque imposition of a standard for "diversity" ultimately proved counterproductive. In this paper, we present an alternative approach: an image generation interface designed to embrace open negotiation along the sociopolitical dimensions of image creation. Grounded in the principles of agonistic pluralism (from the Greek agon, meaning struggle), our interface actively engages users with competing visual interpretations of their prompts. Through a lab study with 29 participants, we evaluate our agonistic interface on its ability to facilitate reflection - engagement with other perspectives and challenging dominant assumptions - a core principle that underpins agonistic contestation. We compare it to three existing paradigms: a standard interface, a Gemini-style interface that produces "diverse" images, and an intention-centric interface suggesting prompt refinements. Our findings demonstrate that the agonistic interface enhances reflection across multiple measures, but also that reflection depends on users perceiving the interface as both appropriate and empowering; introducing diversity without grounding it in relevant political contexts was perceived as inauthentic. Our results suggest that diversity and user intention should not be treated as opposing values to be balanced.

Current image compression models often require separate models for each quality level, making them resource-intensive in terms of both training and storage. To address these limitations, we propose an innovative approach that utilizes latent variables from pre-existing trained models (such as the Stable Diffusion Variational Autoencoder) for perceptual image compression. Our method eliminates the need for distinct models dedicated to different quality levels. We employ overfitted learnable functions to compress the latent representation from the target model at any desired quality level. These overfitted functions operate in the latent space, ensuring low computational complexity, around $25.5$ MAC/pixel for a forward pass on images with dimensions $(1363 \times 2048)$ pixels. This approach efficiently utilizes resources during both training and decoding. Our method achieves comparable perceptual quality to state-of-the-art learned image compression models while being both model-agnostic and resolution-agnostic. This opens up new possibilities for the development of innovative image compression methods.

Conformal prediction is a distribution-free framework for uncertainty quantification that replaces point predictions with sets, offering marginal coverage guarantees (i.e., ensuring that the prediction sets contain the true label with a specified probability, in expectation). In this paper, we uncover a novel connection between conformal prediction and sparse softmax-like transformations, such as sparsemax and $\gamma$-entmax (with $\gamma > 1$), which may assign nonzero probability only to a subset of labels. We introduce new non-conformity scores for classification that make the calibration process correspond to the widely used temperature scaling method. At test time, applying these sparse transformations with the calibrated temperature leads to a support set (i.e., the set of labels with nonzero probability) that automatically inherits the coverage guarantees of conformal prediction. Through experiments on computer vision and text classification benchmarks, we demonstrate that the proposed method achieves competitive results in terms of coverage, efficiency, and adaptiveness compared to standard non-conformity scores based on softmax.

We study subsets of groups and monoids defined by language-theoretic means, generalizing the classical approach to the word problem. We expand on results by Herbst from 1991 to a more general setting, and for a class of languages $\mathbf{C}$ we define the classes of $\mathbf{C}^\forall$-flat and $\mathbf{C}^\exists$-flat groups. We prove several closure results for these classes of groups, prove a connection with the word problem, and characterize $\mathbf{C}^\forall$-flat groups for several classes of languages. In general, we prove that the class of $\mathbf{C}^\forall$-flat groups is a strict subclass of the class of groups with word problem in $\mathbf{C}$, including for the class $\mathrm{REC}$ of recursive languages, for which $\mathbf{C}^\forall$-flatness for a group resp. monoid is proved to be equivalent to the decidability of the subgroup membership problem resp. the submonoid membership problem. We provide a number of examples, including the Tarski monsters of Ol'shanskii, showing the difficulty of characterizing $\mathbf{C}^\exists$-flat groups. As an application of our general methods, we also prove in passing that if $\mathbf{C}$ is a full semi-$\mathrm{AFL}$, then the class of epi-$\mathbf{C}$ groups is closed under taking finite index subgroups. This answers a question recently posed by Al Kohli, Bleak & Elliot., Comment: 24 pages. Comments welcome!

We describe the vision, user interface, governing equations, and numerical methods that underpin new ocean modeling software called ``Oceananigans''. Oceananigans is being developed by the Climate Modeling Alliance as part of a larger project to build a trainable climate model with quantifiable uncertainty. We argue that Oceananigans status as a popular, capable modeling system realizes a vision for accelerating progress in Earth system modeling that balances demands for model accuracy and performance, needed for state-of-the-art science, against accessibility, which is needed to accelerate development. This vision combines three cooperative elements: (i) a relatively simple finite volume algorithm (ii) optimized for high-resolution simulations on GPUs which is (iii) exposed behind an expressive, high-level user interface (using the Julia programming language in our case). We offer evidence for the vision's potential by illustrating the creative potential of our user interface, showcasing Oceananigans physics with example simulations that range from simple classroom problems to a realistic global ocean simulation spanning all scales of oceanic fluid motion, and describing advances in parameterization, numerical methods, and computational efficiency.

In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in both storage and computational requirements. These drawbacks can be avoided by using codecs that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and visualization specifications. While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D representation make it hard to implement flexible solutions that do not compromise the other functionalities of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship between latents obtained with models trained for different RD tradeoffs and/or at different resolutions. Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding standard show that SRQH allows decoding the PC at different qualities and resolutions with a single bitstream while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that would require one bitstream per coding configuration., Comment: Submitted to IEEE and currently under review

The Cuckoo Search Algorithm (CSA), while effective in solving complex optimization problems, faces limitations in random population initialization and reliance on fixed parameters. Random initialization of the population often results in clustered solutions, resulting in uneven exploration of the search space and hindering effective global optimization. Furthermore, the use of fixed values for discovery rate and step size creates a trade-off between solution accuracy and convergence speed. To address these limitations, an Enhanced Cuckoo Search Algorithm (ECSA) is proposed. This algorithm utilizes the Sobol Sequence to generate a more uniformly distributed initial population and incorporates Cosine Annealing with Warm Restarts to dynamically adjust the parameters. The performance of the algorithms was evaluated on 13 benchmark functions (7 unimodal, 6 multimodal). Statistical analyses were conducted to determine the significance and consistency of the results. The ECSA outperforms the CSA in 11 out of 13 benchmark functions with a mean fitness improvement of 30% across all functions, achieving 35% for unimodal functions and 24% for multimodal functions. The enhanced algorithm demonstrated increased convergence efficiency, indicating its superiority to the CSA in solving a variety of optimization problems. The ECSA is subsequently applied to optimize earthquake evacuation space allocation in Intramuros, Manila.

Ensuring the safety and reliability of power grids is critical as global energy demands continue to rise. Traditional inspection methods, such as manual observations or helicopter surveys, are resource-intensive and lack scalability. This paper explores the use of 3D computer vision to automate power grid inspections, utilizing the TS40K dataset -- a high-density, annotated collection of 3D LiDAR point clouds. By concentrating on 3D semantic segmentation, our approach addresses challenges like class imbalance and noisy data to enhance the detection of critical grid components such as power lines and towers. The benchmark results indicate significant performance improvements, with IoU scores reaching 95.53% for the detection of power lines using transformer-based models. Our findings illustrate the potential for integrating ML into grid maintenance workflows, increasing efficiency and enabling proactive risk management strategies.

In the spirit of the Curry-Howard correspondence between proofs and programs, we define and study a syntax and semantics for classical logic equipped with a computationally involutive negation, using a polarised effect calculus. A main challenge in designing a denotational semantics is to accommodate both call-by-value and call-by-name evaluation strategies, which leads to a failure of associativity of composition. Building on the work of the third author, we devise the notion of dialogue duploid, which provides a non-associative and effectful counterpart to the notion of dialogue category introduced by the second author in his 2-categorical account, based on adjunctions, of logical polarities and continuations. We show that the syntax of the polarised calculus can be interpreted in any dialogue duploid, and that it defines in fact a syntactic dialogue duploid. As an application, we establish, by semantic as well as syntactic means, the Hasegawa-Thielecke theorem, which states that the notions of central map and of thunkable map coincide in any dialogue duploid (in particular, for any double negation monad on a symmetric monoidal category)., Comment: 22 pages with appendix

The hemodynamic response (HR) in event-related functional magnetic resonance imaging is typically assumed to be stationary. While there are some approaches in the literature to model nonstationary HRs, few focus on rapid changes. In this work, we propose two procedures to investigate rapid changes in the HR. Both procedures make inference on the existence of rapid changes for multi-subject data. We allow the change point locations to vary between subjects, conditions and brain regions. The first procedure utilizes available information about the change point locations to compare multiple shape parameters of the HR over time. In the second procedure, the change point locations are determined for each subject separately. To account for the estimation of the change point locations, we propose the notion of post selection variance. The power of the proposed procedures is assessed in simulation studies. We apply the procedure for pre-specified change point locations to data from a category learning experiment.

Zonotopal algebras of vector arrangements are combinatorially-defined algebras with connections to approximation theory, introduced by Holtz and Ron and independently by Ardila and Postnikov. We show that the internal zonotopal algebra of a cographical vector arrangement is isomorphic to the cohomology ring of a certain configuration space introduced by Moseley, Proudfoot, and Young. We also study an integral form of this algebra, which in the cographical case is isomorphic to the integral cohomology ring. Our results rely on interpreting the internal zonotopal algebra of a totally unimodular arrangement as an orbit harmonics ring, that is, as the associated graded of the ring of functions on a finite set of lattice points.

Larger models often outperform smaller ones but come with high computational costs. Cascading offers a potential solution. By default, it uses smaller models and defers only some instances to larger, more powerful models. However, designing effective deferral rules remains a challenge. In this paper, we propose a simple yet effective approach for machine translation, using existing quality estimation (QE) metrics as deferral rules. We show that QE-based deferral allows a cascaded system to match the performance of a larger model while invoking it for a small fraction (30% to 50%) of the examples, significantly reducing computational costs. We validate this approach through both automatic and human evaluation., Comment: Preprint

Chain-of-Though (CoT) represents a common strategy for reasoning in Large Language Models (LLMs) by decomposing complex tasks into intermediate inference steps. However, explanations generated via CoT are susceptible to content biases that negatively affect their robustness and faithfulness. To mitigate existing limitations, recent work has proposed using logical formalisms coupled with external symbolic solvers. However, fully symbolic approaches possess the bottleneck of requiring a complete translation from natural language to formal languages, a process that affects efficiency and flexibility. To achieve a trade-off, this paper investigates methods to disentangle content from logical reasoning without a complete formalisation. In particular, we present QuaSAR (for Quasi-Symbolic Abstract Reasoning), a variation of CoT that guides LLMs to operate at a higher level of abstraction via quasi-symbolic explanations. Our framework leverages the capability of LLMs to formalise only relevant variables and predicates, enabling the coexistence of symbolic elements with natural language. We show the impact of QuaSAR for in-context learning and for constructing demonstrations to improve the reasoning capabilities of smaller models. Our experiments show that quasi-symbolic abstractions can improve CoT-based methods by up to 8% accuracy, enhancing robustness and consistency on challenging adversarial variations on both natural language (i.e. MMLU-Redux) and symbolic reasoning tasks (i.e., GSM-Symbolic).

The rapid growth of the Internet of Things (IoT) has revolutionized industries, enabling unprecedented connectivity and functionality. However, this expansion also increases vulnerabilities, exposing IoT networks to increasingly sophisticated cyberattacks. Intrusion Detection Systems (IDS) are crucial for mitigating these threats, and recent advancements in Machine Learning (ML) offer promising avenues for improvement. This research explores a hybrid approach, combining several standalone ML models such as Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), and AdaBoost, in a voting-based hybrid classifier for effective IoT intrusion detection. This ensemble method leverages the strengths of individual algorithms to enhance accuracy and address challenges related to data complexity and scalability. Using the widely-cited IoT-23 dataset, a prominent benchmark in IoT cybersecurity research, we evaluate our hybrid classifiers for both binary and multi-class intrusion detection problems, ensuring a fair comparison with existing literature. Results demonstrate that our proposed hybrid models, designed for robustness and scalability, outperform standalone approaches in IoT environments. This work contributes to the development of advanced, intelligent IDS frameworks capable of addressing evolving cyber threats., Comment: 9 pages, 8 figures, 2 tables, journal submission

We study uncertainty quantification for partial differential equations subject to domain uncertainty. We parameterize the random domain using the model recently considered by Chernov and Le (2024) as well as Harbrecht, Schmidlin, and Schwab (2024) in which the input random field is assumed to belong to a Gevrey smoothness class. This approach has the advantage of being substantially more general than models which assume a particular parametric representation of the input random field such as a Karhunen-Loeve series expansion. We consider both the Poisson equation as well as the heat equation and design randomly shifted lattice quasi-Monte Carlo (QMC) cubature rules for the computation of the expected solution under domain uncertainty. We show that these QMC rules exhibit dimension-independent, essentially linear cubature convergence rates in this framework. In addition, we complete the error analysis by taking into account the approximation errors incurred by dimension truncation of the random input field and finite element discretization. Numerical experiments are presented to confirm the theoretical rates., Comment: 39 pages, 5 figures

This paper introduces ExKLoP, a novel framework designed to evaluate how effectively Large Language Models (LLMs) integrate expert knowledge into logical reasoning systems. This capability is especially valuable in engineering, where expert knowledge-such as manufacturer-recommended operational ranges-can be directly embedded into automated monitoring systems. By mirroring expert verification steps, tasks like range checking and constraint validation help ensure system safety and reliability. Our approach systematically evaluates LLM-generated logical rules, assessing both syntactic fluency and logical correctness in these critical validation tasks. We also explore the models capacity for self-correction via an iterative feedback loop based on code execution outcomes. ExKLoP presents an extensible dataset comprising 130 engineering premises, 950 prompts, and corresponding validation points. It enables comprehensive benchmarking while allowing control over task complexity and scalability of experiments. We leverage the synthetic data creation methodology to conduct extensive empirical evaluation on a diverse set of LLMs including Llama3, Gemma, Mixtral, Mistral, and Qwen. Results reveal that while models generate nearly perfect syntactically correct code, they frequently exhibit logical errors in translating expert knowledge. Furthermore, iterative self-correction yields only marginal improvements (up to 3%). Overall, ExKLoP serves as a robust evaluation platform that streamlines the selection of effective models for self-correcting systems while clearly delineating the types of errors encountered. The complete implementation, along with all relevant data, is available at GitHub.

A global fit for $\alpha_s(m_Z)$ is performed on available $e^+e^-$ data for the heavy jet mass distribution. The state-of-the-art theory prediction includes $\mathcal{O}(\alpha_s^3)$ fixed-order results, N$^3$LL$^\prime$ dijet resummation, N$^2$LL Sudakov shoulder resummation, and a first-principles treatment of power corrections in the dijet region. Theoretical correlations are incorporated through a flat random-scan covariance matrix. The global fit results in $0.1145^{+0.0021}_{-0.0019}$, compatible with similar determinations from thrust and $C$-parameter. Dijet resummation is essential for a robust fit, as it engenders insensitivity to the fit-range lower cutoff; without resummation the fit-range sensitivity is overwhelming. In addition, we find evidence for a negative power correction in the trijet region if and only if Sudakov shoulder resummation is included., Comment: 4 pages + supplemental material, 8 figures, 3 tables

The computational cost of softmax-based attention in transformers limits their applicability to long-context tasks. Adaptive sparsity, of which $\alpha$-entmax attention is an example, offers a flexible data-dependent alternative, but existing implementations are inefficient and do not leverage the sparsity to obtain runtime and memory gains. In this work, we propose AdaSplash, which combines the efficiency of GPU-optimized algorithms with the sparsity benefits of $\alpha$-entmax. We first introduce a hybrid Halley-bisection algorithm, resulting in a 7-fold reduction in the number of iterations needed to compute the $\alpha$-entmax transformation. Then, we implement custom Triton kernels to efficiently handle adaptive sparsity. Experiments with RoBERTa and ModernBERT for text classification and single-vector retrieval, along with GPT-2 for language modeling, show that our method achieves substantial improvements in runtime and memory efficiency compared to existing $\alpha$-entmax implementations. It approaches -- and in some cases surpasses -- the efficiency of highly optimized softmax implementations like FlashAttention-2, enabling long-context training while maintaining strong task performance.

Lorentz invariance is a fundamental symmetry of spacetime and foundational to modern physics. One of its most important consequences is the constancy of the speed of light. This invariance, together with the geometry of spacetime, implies that no particle can move faster than the speed of light. In this article, we present the most stringent neutrino-based test of this prediction, using the highest energy neutrino ever detected to date, KM3-230213A. The arrival of this event, with an energy of $220^{+570}_{-110}\,\text{PeV}$, sets a constraint on $\delta \equiv c_\nu^2-1 < 4\times10^{-22}$.

Thanks to their linguistic capabilities, LLMs offer an opportunity to bridge the gap between informal mathematics and formal languages through autoformalization. However, it is still unclear how well LLMs generalize to sophisticated and naturally occurring mathematical statements. To address this gap, we investigate the task of autoformalizing real-world mathematical definitions -- a critical component of mathematical discourse. Specifically, we introduce two novel resources for autoformalisation, collecting definitions from Wikipedia (Def_Wiki) and arXiv papers (Def_ArXiv). We then systematically evaluate a range of LLMs, analyzing their ability to formalize definitions into Isabelle/HOL. Furthermore, we investigate strategies to enhance LLMs' performance including refinement through external feedback from Proof Assistants, and formal definition grounding, where we guide LLMs through relevant contextual elements from formal mathematical libraries. Our findings reveal that definitions present a greater challenge compared to existing benchmarks, such as miniF2F. In particular, we found that LLMs still struggle with self-correction, and aligning with relevant mathematical libraries. At the same time, structured refinement methods and definition grounding strategies yield notable improvements of up to 16% on self-correction capabilities and 43% on the reduction of undefined errors, highlighting promising directions for enhancing LLM-based autoformalization in real-world scenarios.

Computing shortest paths is one of the most fundamental algorithmic graph problems. It is known since decades that this problem can be solved in near-linear time if all weights are nonnegative. A recent break-through by [Bernstein, Nanongkai, Wulff-Nilsen '22] presented a randomized near-linear time algorithm for this problem. A subsequent improvement in [Bringmann, Cassis, Fischer '23] significantly reduced the number of logarithmic factors and thereby also simplified the algorithm. It is surprising and exciting that both of these algorithms are combinatorial and do not contain any fundamental obstacles for being practical. We launch the, to the best of our knowledge, first extensive investigation towards a practical implementation of [Bringmann, Cassis, Fischer '23]. To this end, we give an accessible overview of the algorithm, discussing what adaptions are necessary to obtain a fast algorithm in practice. We manifest these adaptions in an efficient implementation. We test our implementation on a benchmark data set that is adapted to be more difficult for our implementation in order to allow for a fair comparison. As in [Bringmann, Cassis, Fischer '23] as well as in our implementation there are multiple parameters to tune, we empirically evaluate their effect and thereby determine the best choices. Our implementation is then extensively compared to one of the state-of-the-art algorithms for this problem [Goldberg, Radzik '93]. On the hardest instance type, we are faster by up to almost two orders of magnitude.

Domain wall (DW) dynamics in antiferromagnetic (AFM) systems offer the advantages over their ferromagnetic counterparts of having faster and more energy efficient manipulation due to the absence of net magnetization, leading to reduced magnetic crosstalk and improved performance in spintronic devices. A comprehensive analysis of DW dynamics across regimes such as creep, depinning, and flow is well established in ferromagnetic systems but remains lacking in AFM-coupled systems. In this study, we explore the nature of DW dynamics in synthetic ferrimagnetic multilayers composed of Pt|Co|Tb|Al for different Tb thickness, focusing on the underlying pinning parameters, and on the different regimes of DW dynamics driven by spin-orbit torques (SOTs). We find that due to the AFM coupling between Co and Tb, the magnetic moment of Tb increases with Tb thickness resulting in a reduced saturation magnetization and an enhanced depinning field. The DW disorder interaction is found to vary weakly with the AFM coupling between Co and Tb, while the complete withdrawal of the Tb layer strongly increases the anisotropy and the DW pinning. Furthermore, we propose a novel approach to measure effective SOTs by comparing depinning transitions in current and field-induced DW motion. This research reveals new insights into DW dynamics in coupled AFM systems, highlighting enhancements in mobility through optimized SOTs and pinning landscapes., Comment: 12 pages, 9 figures

We present the first catalog of fast radio burst (FRB) host galaxies from CHIME/FRB Outriggers, selected uniformly in the radio and the optical by localizing 81 new bursts to 2'' x ~60'' accuracy using CHIME and the KKO Outrigger, located 66 km from CHIME. Of the 81 localized bursts, we use the Probabilistic Association of Transients to their Hosts (PATH) algorithm to securely identify 21 new FRB host galaxies, and compile spectroscopic redshifts for 19 systems, 15 of which are newly obtained via spectroscopic observations. The most nearby source is FRB 20231229A, at a distance of 90 Mpc. One burst in our sample is from a previously reported repeating source in a galaxy merger (FRB 20190303A). Three new FRB host galaxies (FRBs 20230203A, 20230703A, and 20231206A) are found towards X-ray and optically selected galaxy clusters, potentially doubling the sample of known galaxy cluster FRBs. A search for radio counterparts reveals that FRB 20231128A is associated with a luminous persistent radio source (PRS) candidate with high significance ($P_{cc} \sim 10^{-2}$). If its compactness is confirmed, it would be the nearest known compact PRS at $z = 0.1079$. Our catalog significantly increases the statistics of the Macquart relation at low redshifts ($z < 0.2$). In the near future, the completed CHIME/FRB Outriggers array will produce hundreds of FRBs localized with very long baseline interferometry (VLBI). This will significantly expand the known sample and pave the way for future telescopes relying on VLBI for FRB localization., Comment: 27 pages, 10 figures

Large language models (LLMs) struggle with compositional generalisation, limiting their ability to systematically combine learned components to interpret novel inputs. While architectural modifications, fine-tuning, and data augmentation improve compositionality, they often have limited adaptability, face scalability constraints, or yield diminishing returns on real data. To address this, we propose CARMA, an intervention that enhances the stability and robustness of compositional reasoning in LLMs while preserving fine-tuned performance. CARMA employs mutual information regularisation and layer-wise stability constraints to mitigate feature fragmentation, ensuring structured representations persist across and within layers. We evaluate CARMA on inverse dictionary modelling and sentiment classification, measuring its impact on semantic consistency, performance stability, and robustness to lexical perturbations. Results show that CARMA reduces the variability introduced by fine-tuning, stabilises token representations, and improves compositional reasoning. While its effectiveness varies across architectures, CARMA's key strength lies in reinforcing learned structures rather than introducing new capabilities, making it a scalable auxiliary method. These findings suggest that integrating CARMA with fine-tuning can improve compositional generalisation while maintaining task-specific performance in LLMs., Comment: 18 pages, 7 figures, 5 tables

The Athena mission entered a redefinition phase in July 2022, driven by the imperative to reduce the mission cost at completion for the European Space Agency below an acceptable target, while maintaining the flagship nature of its science return. This notably called for a complete redesign of the X-ray Integral Field Unit (X-IFU) cryogenic architecture towards a simpler active cooling chain. Passive cooling via successive radiative panels at spacecraft level is now used to provide a 50 K thermal environment to an X-IFU owned cryostat. 4.5 K cooling is achieved via a single remote active cryocooler unit, while a multi-stage Adiabatic Demagnetization Refrigerator ensures heat lift down to the 50 mK required by the detectors. Amidst these changes, the core concept of the readout chain remains robust, employing Transition Edge Sensor microcalorimeters and a SQUID-based Time-Division Multiplexing scheme. Noteworthy is the introduction of a slower pixel. This enables an increase in the multiplexing factor (from 34 to 48) without compromising the instrument energy resolution, hence keeping significant system margins to the new 4 eV resolution requirement. This allows reducing the number of channels by more than a factor two, and thus the resource demands on the system, while keeping a 4' field of view (compared to 5' before). In this article, we will give an overview of this new architecture, before detailing its anticipated performances. Finally, we will present the new X-IFU schedule, with its short term focus on demonstration activities towards a mission adoption in early 2027., Comment: 44 pages, 14 figures, accepted for publication in Experimental Astronomy

Hyperledger Fabric is a platform for permissioned blockchain networks that enables secure and auditable distributed data storage for enterprise applications. There is a growing interest in applications based on this platform, but its use requires the configuration of different blockchain parameters. Various configurations impact the system's non-functional qualities, especially performance and cost. In this article, we propose a Stochastic Petri Net to model the performance of the Hyperledger Fabric platform with different blockchain parameters, computer capacity, and transaction rates. We also present a set of case studies to demonstrate the feasibility of the proposed model. This model serves as a practical guide to help administrators of permissioned blockchain networks find the best performance for their applications. The proposed model allowed us to identify the block size that leads to a high mean response time (ranging from 1 to 25 seconds) caused by a change in the arrival rate.

From a variational perspective, many statistical learning criteria involve seeking a distribution that balances empirical risk and regularization. In this paper, we broaden this perspective by introducing a new general class of variational methods based on Fenchel-Young (FY) losses, treated as divergences that generalize (and encompass) the familiar Kullback-Leibler divergence at the core of classical variational learning. Our proposed formulation -- FY variational learning -- includes as key ingredients new notions of FY free energy, FY evidence, FY evidence lower bound, and FY posterior. We derive alternating minimization and gradient backpropagation algorithms to compute (or lower bound) the FY evidence, which enables learning a wider class of models than previous variational formulations. This leads to generalized FY variants of classical algorithms, such as an FY expectation-maximization (FYEM) algorithm, and latent-variable models, such as an FY variational autoencoder (FYVAE). Our new methods are shown to be empirically competitive, often outperforming their classical counterparts, and most importantly, to have qualitatively novel features. For example, FYEM has an adaptively sparse E-step, while the FYVAE can support models with sparse observations and sparse posteriors., Comment: Under review

Recent research has established a connection between modern Hopfield networks (HNs) and transformer attention heads, with guarantees of exponential storage capacity. However, these models still face challenges scaling storage efficiently. Inspired by psychological theories of continuous neural resource allocation in working memory, we propose an approach that compresses large discrete Hopfield memories into smaller, continuous-time memories. Leveraging continuous attention, our new energy function modifies the update rule of HNs, replacing the traditional softmax-based probability mass function with a probability density, over the continuous memory. This formulation aligns with modern perspectives on human executive function, offering a principled link between attractor dynamics in working memory and resource-efficient memory allocation. Our framework maintains competitive performance with HNs while leveraging a compressed memory, reducing computational costs across synthetic and video datasets.

Let $T$ be a finitely branching rooted tree such that any node has at least two successors. The path space $[T]$ is an ultrametric space where the distance of distinct paths $f,g$ is $1/|T_n|$, with $T_n$ denoting the set of nodes at distance $n$ from the root. Let $S$ be a subtree of $T$ without leaves that is level-wise uniformly branching, in the sense that the number of successors of a node only depends on its distance from the root. We show that the Hausdorff and lower box dimensions coincide for~$[S]$, and the packing and upper box dimensions also coincide. We give geometric proofs, as well as proofs based on the point to set principles. We use the first result to reprove a result of Barnea and Shalev on the dimension of closed subgroups of a profinite group $G$, referring only on the geometric structure of the closed subgroup in the canonical path space given by an inverse system for $G$.

Blockchain, often integrated with distributed systems and security enhancements, has significant potential in various industries. However, environmental concerns and the efficiency of consortia-controlled permissioned networks remain critical issues. We use a Stochastic Petri Net model to analyze transaction flows in Hyperledger Fabric networks, achieving a 95% confidence interval for response times. This model enables administrators to assess the impact of system changes on resource utilization. Sensitivity analysis reveals major factors influencing response times and throughput. Our case studies demonstrate that block size can alter throughput and response times by up to 200%, underscoring the need for performance optimization with resource efficiency.

On February 13th, 2023, the KM3NeT/ARCA telescope detected a neutrino candidate with an estimated energy in the hundreds of PeVs. In this article, the observation of this ultra-high-energy neutrino is discussed in light of null observations above tens of PeV from the IceCube and Pierre Auger observatories. Performing a joint fit of all experiments under the assumption of an isotropic $E^{-2}$ flux, the best-fit single-flavour flux normalisation is $E^2 \Phi^{\rm 1f}_{\nu + \bar \nu} = 7.5 \times 10^{-10}~{\rm GeV cm^{-2} s^{-1} sr^{-1}}$ in the 90% energy range of the KM3NeT event. Furthermore, the ultra-high-energy data are then fit together with the IceCube measurements at lower energies, either with a single power law or with a broken power law, allowing for the presence of a new component in the spectrum. The joint fit including non-observations by other experiments in the ultra-high-energy region shows a slight preference for a break in the PeV regime if the ``High-Energy Starting Events'' sample is included, and no such preference for the other two IceCube samples investigated. A stronger preference for a break appears if only the KM3NeT data is considered in the ultra-high-energy region, though the flux resulting from such a fit would be inconsistent with null observations from IceCube and Pierre Auger. In all cases, the observed tension between KM3NeT and other datasets is of the order of $2.5\sigma-3\sigma$, and increased statistics are required to resolve this apparent tension and better characterise the neutrino landscape at ultra-high energies., Comment: 11 pages, 4 figures

A Latin hypercuboid of order $n$ is a $d$-dimensional matrix of dimensions $n\times n\times\cdots\times n\times k$, with symbols from a set of cardinality $n$ such that each symbol occurs at most once in each axis-parallel line. If $k=n$ the hypercuboid is a Latin hypercube. The Latin hypercuboid is \emph{completable} if it is contained in a Latin hypercube of the same order and dimension. It is \emph{extendible} if it can have one extra layer added. In this note we consider which Latin hypercuboids are completable/extendible. We also consider a generalisation that involves multidimensional arrays of sets that satisfy certain balance properties. The extendibility problem corresponds to choosing representatives from the sets in a way that is analogous to a choice of a Hall system of distinct representatives, but in higher dimensions. The completability problem corresponds to partitioning the sets into such SDRs. We provide a construction for such an array of sets that does not have the property analogous to completability. A related concept was introduced by H\"aggkvist under the name $(m,m,m)$-array. We generalise a construction of $(m,m,m)$-arrays credited to Pebody, but show that it cannot be used to build the arrays that we need.

Hyperledger Fabric stands as a leading framework for permissioned blockchain systems, ensuring data security and auditability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various blockchain parameters becomes vital. These configurations significantly affect the system's performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyperledger Fabric's performance, considering variations in blockchain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding blockchain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size's role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

Blockchain is a paradigm derived from distributed systems, protocols, and security concepts. However, can blockchain applications provide services in industrial environments, especially concerning performance issues? In blockchains, long response times can impair both user and service experience, and intensive resource use may increase the costs of service provision. The proposed paper tries to answer this question by evaluating the performance of one of the most popular permissioned blockchain platforms, the Hyperledger Fabric (HLF). We provide a framework for performance evaluation based on modeling and experimentation. The results indicate that block size and arrival rate can compromise throughput (by -70%), latency (by +1,500%), and environment utilization (by +28%) and that multiple gateways can reduce latency (by -75%), and throughput (by -60%), Comment: 29th IEEE Symposium on Computers and Communications (ISCC)

The KM3NeT observatory detected the most energetic neutrino candidate ever observed, with an energy between 72 PeV and 2.6 EeV at the 90% confidence level. The observed neutrino is likely of cosmic origin. In this article, it is investigated if the neutrino could have been produced within the Milky Way. Considering the low fluxes of the Galactic diffuse emission at these energies, the lack of a nearby potential Galactic particle accelerator in the direction of the event and the difficulty to accelerate particles to such high energies in Galactic systems, we conclude that if the event is indeed cosmic, it is most likely of extragalactic origin.

Water distribution systems (WDSs) are an important part of critical infrastructure becoming increasingly significant in the face of climate change and urban population growth. We propose a robust and scalable surrogate deep learning (DL) model to enable efficient planning, expansion, and rehabilitation of WDSs. Our approach incorporates an improved graph neural network architecture, an adapted physics-informed algorithm, an innovative training scheme, and a physics-preserving data normalization method. Evaluation results on a number of WDSs demonstrate that our model outperforms the current state-of-the-art DL model. Moreover, our method allows us to scale the model to bigger and more realistic WDSs. Furthermore, our approach makes the model more robust to out-of-distribution input features (demands, pipe diameters). Hence, our proposed method constitutes a significant step towards bridging the simulation-to-real gap in the use of artificial intelligence for WDSs.

This paper introduces CodeQUEST, a novel framework leveraging Large Language Models (LLMs) to iteratively evaluate and enhance code quality across multiple dimensions, including readability, maintainability, efficiency, and security. The framework is divided into two main components: an Evaluator that assesses code quality across ten dimensions, providing both quantitative scores and qualitative summaries, and an Optimizer that iteratively improves the code based on the Evaluator's feedback. Our study demonstrates that CodeQUEST can effectively and robustly evaluate code quality, with its assessments aligning closely with established code quality metrics. Through a series of experiments using a curated dataset of Python and JavaScript examples, CodeQUEST demonstrated significant improvements in code quality, achieving a mean relative percentage improvement of 52.6%. The framework's evaluations were validated against a set of proxy metrics comprising of Pylint Score, Radon Maintainability Index, and Bandit output logs, showing a meaningful correlation. This highlights the potential of LLMs in automating code quality evaluation and improvement processes, presenting a significant advancement toward enhancing software development practices. The code implementation of the framework is available at: https://github.com/jpmorganchase/CodeQuest.

Germanium quantum dots in strained $\text{Ge}/\text{Si}_{1-x}\text{Ge}_{x}$ heterostructures exhibit fast and coherent hole qubit control in experiments. In this work, we theoretically and numerically address the effects of random alloy disorder and gate-induced strain on the operation of planar Ge hole spin qubits. Electrical operation of hole quantum dot spin qubits is enabled by the strong Rashba spin-orbit coupling (SOC) originating from the intrinsic SOC in the Ge valence band as well as from the structural inversion asymmetry inherent in the underlying 2D hole gas. We use the atomistic valence force field (VFF) method to compute the strain due to random alloy disorder, and thermal expansion models in COMSOL Multiphysics to obtain the strain from a realistic gate-stack of planar hole quantum dot confinement. Recently, spin-orbit coupling terms $\propto k$ have been shown to be induced by strain inhomogeneity. Our hybrid approach to realistic device modeling suggests that strain inhomogeneity due to both random alloy disorder and gate-induced strain make a strong contribution to the linear-$k$ Dresselhaus spin-orbit coupling, which eventually dominates hole spin EDSR; and there exist specific in-plane orientations of the global magnetic field $\mathbf{B}$ and the microwave drive $\mathbf{\tilde{E}}_{\text{ac}}$ for maximum EDSR Rabi frequency of the hole spin qubit. The current model including strain inhomogeneity accurately predicts the EDSR Rabi frequency to be $\!\sim\!100$ MHz for typical electric and magnetic fields in experiments, which represents at least an order of magnitude improvement in accuracy over phenomenological models assuming uniform uniaxial strain. State-of-the-art atomistic tight binding calculations via nano-electronic modeling (NEMO3D) are in agreement with the $\mathbf{k}{\cdot}\mathbf{p}$ description.

We study the mixing time guarantee for sampling in relative Fisher information via the Proximal Sampler algorithm, which is an approximate proximal discretization of the Langevin dynamics. We show that when the target probability distribution is strongly log-concave, the relative Fisher information converges exponentially fast along the Proximal Sampler; this matches the exponential convergence rate of the relative Fisher information along the continuous-time Langevin dynamics for strongly log-concave target. When combined with a standard implementation of the Proximal Sampler via rejection sampling, this exponential convergence rate provides a high-accuracy iteration complexity guarantee for the Proximal Sampler in relative Fisher information when the target distribution is strongly log-concave and log-smooth. Our proof proceeds by establishing a strong data processing inequality for relative Fisher information along the Gaussian channel under strong log-concavity, and a data processing inequality along the reverse Gaussian channel for a special distribution. The forward and reverse Gaussian channels compose to form the Proximal Sampler, and these data processing inequalities imply the exponential convergence rate of the relative Fisher information along the Proximal Sampler.

Large Language Models have proved surprisingly effective at solving a wide range of tasks from just a handful of examples. However, their lack of reliability and modularity limits their capacity to tackle large problems that require many steps of reasoning. In response, researchers have proposed advanced pipelines that leverage domain-specific knowledge to chain smaller prompts, provide intermediate feedback and improve performance through search. However, the current complexity of writing, tuning, maintaining and improving such pipelines has limited their sophistication. We propose oracular programming, a foundational paradigm for building LLM-enabled applications that lets domain experts express high-level problem-solving strategies as programs with unresolved choice points. These choice points are resolved at runtime by LLMs, which generalize from user-provided examples of correct and incorrect decisions. An oracular program is composed of three orthogonal components: a strategy that consists in a nondeterministic program with choice points that can be reified into a search tree, a policy that specifies how to navigate this tree with the help of LLM oracles, and a set of demonstrations that describe successful and unsuccessful search tree navigation scenarios across diverse problem instances. Each component is expressed in a dedicated programming language and can be independently improved or substituted. We address the key programming language design challenges of modularly composing oracular programs and enforcing consistency between their components as they evolve.

Recent radiation-thermochemical-magnetohydrodynamic simulations resolved formation of quasar accretion disks from cosmological scales down to ~300 gravitational radii $R_{g}$, arguing they were 'hyper-magnetized' (plasma $\beta\ll1$ supported by toroidal magnetic fields) and distinct from traditional $\alpha$-disks. We extend these, refining to $\approx 3\,R_{g}$ around a $10^{7}\,{\rm M_{\odot}}$ BH with multi-channel radiation and thermochemistry, and exploring a factor of 1000 range of accretion rates ($\dot{m}\sim0.01-20$). At smaller scales, we see the disks maintain steady accretion, thermalize and self-ionize, and radiation pressure grows in importance, but large deviations from local thermodynamic equilibrium and single-phase equations of state are always present. Trans-Alfvenic and highly-supersonic turbulence persists in all cases, and leads to efficient vertical mixing, so radiation pressure saturates at levels comparable to fluctuating magnetic and turbulent pressures even for $\dot{m}\gg1$. The disks also become radiatively inefficient in the inner regions at high $\dot{m}$. The midplane magnetic field remains primarily toroidal at large radii, but at super-Eddington $\dot{m}$ we see occasional transitions to a poloidal-field dominated state associated with outflows and flares. Large-scale magnetocentrifugal and continuum radiation-pressure-driven outflows are weak at $\dot{m}<1$, but can be strong at $\dot{m}\gtrsim1$. In all cases there is a scattering photosphere above the disk extending to $\gtrsim 1000\,R_{g}$ at large $\dot{m}$, and the disk is thick and flared owing to magnetic support (with $H/R$ nearly independent of $\dot{m}$), so the outer disk is strongly illuminated by the inner disk and most of the inner disk continuum scatters or is reprocessed at larger scales, giving apparent emission region sizes as large as $\gtrsim 10^{16}\,{\rm cm}$., Comment: Submitted to the Open Journal of Astrophysics. 40 pages, 31 figures, high-level overview sections provided. Comments welcome

We present the status of the Czech Particle Physics Project (CPPP). The CPPP is a learning tool in masterclasses aimed at high school students (aged 15 to 18). The project is structured in modules. The first module is dedicated to the detection of an Axion-Like-Particle (ALP) using the ATLAS Forward Proton (AFP) detector. The second module focuses on the reconstruction of the Higgs boson mass using the Higgs boson golden channel with four leptons in the final state. The third and fourth modules are educational aids and sources for expert information. These web portals are dedicated to Higgs boson research and searches for Supersymmetry. Two databases are created with more than 1000 relevant articles each, using the CERN Document Server API and web scraping methods. The databases are automatically updated when new results on Higgs bosons or searches for Supersymmetric particles become available. Using artificial intelligence and natural language processing, the articles are categorized according to properties of the results. The modules are accessible at https://cern.ch/cppp., Comment: 7 pages, 4 figures, presented at the 42nd International Conference on High Energy Physics (ICHEP2024), 18-24 July 2024, Prague, Czech Republic. arXiv admin note: substantial text overlap with arXiv:2212.10837

We formulate a robust optimal control algorithm to synthesize minimum energy pulses that can transfer a cold atom system into various momentum states. The algorithm uses adaptive linearization of the evolution operator and sequential quadratic programming to iterate the control towards a minimum energy signal that achieves optimal target state fidelity. Robustness to parameter variation is achieved using Legendre polynomial approximation over the domain of variation. The method is applied to optimize the Bragg beamsplitting operation in ultra-cold atom interferometry. Even in the presence of 10-40% variability in the initial momentum dispersion of the atomic cloud and the intensity of the optical pulse, the algorithm reliably converges to a control protocol that robustly achieves unprecedented momentum levels with high fidelity for a single-frequency multi-photon Bragg diffraction scheme (e.g. $|\pm 40\hbar k\rangle$). Advantages of the proposed method are demonstrated by comparison to stochastic optimization using sampled parameter values.

Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science, and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.

Neuromorphic vision sensors, commonly referred to as event cameras, have recently gained relevance for applications requiring high-speed, high dynamic range and low-latency data acquisition. Unlike traditional frame-based cameras that capture 2D images, event cameras generate a massive number of pixel-level events, composed by spatiotemporal and polarity information, with very high temporal resolution, thus demanding highly efficient coding solutions. Existing solutions focus on lossless coding of event data, assuming that no distortion is acceptable for the target use cases, mostly including computer vision tasks. One promising coding approach exploits the similarity between event data and point clouds, thus allowing to use current point cloud coding solutions to code event data, typically adopting a two-point clouds representation, one for each event polarity. This paper proposes a novel lossy Deep Learning-based Joint Event data Coding (DL-JEC) solution adopting a single-point cloud representation, thus enabling to exploit the correlation between the spatiotemporal and polarity event information. DL-JEC can achieve significant compression performance gains when compared with relevant conventional and DL-based state-of-the-art event data coding solutions. Moreover, it is shown that it is possible to use lossy event data coding with its reduced rate regarding lossless coding without compromising the target computer vision task performance, notably for event classification. The use of novel adaptive voxel binarization strategies, adapted to the target task, further enables DL-JEC to reach a superior performance.

Objective: To evaluate the accuracy, computational cost and portability of a new Natural Language Processing (NLP) method for extracting medication information from clinical narratives. Materials and Methods: We propose an original transformer-based architecture for the extraction of entities and their relations pertaining to patients' medication regimen. First, we used this approach to train and evaluate a model on French clinical notes, using a newly annotated corpus from H\^opitaux Universitaires de Strasbourg. Second, the portability of the approach was assessed by conducting an evaluation on clinical documents in English from the 2018 n2c2 shared task. Information extraction accuracy and computational cost were assessed by comparison with an available method using transformers. Results: The proposed architecture achieves on the task of relation extraction itself performance that are competitive with the state-of-the-art on both French and English (F-measures 0.82 and 0.96 vs 0.81 and 0.95), but reduce the computational cost by 10. End-to-end (Named Entity recognition and Relation Extraction) F1 performance is 0.69 and 0.82 for French and English corpus. Discussion: While an existing system developed for English notes was deployed in a French hospital setting with reasonable effort, we found that an alternative architecture offered end-to-end drug information extraction with comparable extraction performance and lower computational impact for both French and English clinical text processing, respectively. Conclusion: The proposed architecture can be used to extract medication information from clinical text with high performance and low computational cost and consequently suits with usually limited hospital IT resources, Comment: Submitted to JAMIA, 17 pages, 3 figures, 2 tables and 5 supplementary tables

The semi-metallic behavior of the perovskite iridate SrIrO3 shifts the end-member of the strongly spin-orbit (SO) coupled Ruddlesden-Popper series Srn+1IrnO3n+1 away from the Mott insulating regime and the half-filled pseudospin Jeff=1/2 ground state well-established in the layered iridates (n = 1 and 2). To investigate the robustness of the SO coupled ground state of SrIrO3, X-ray absorption spectroscopy was carried out at the Ir L2,3 edges under hydrostatic pressure up to 50 GPa at room temperature. The effective SO coupling was deduced from the branching ratio of the Ir L2 and L3 white lines. With increasing pressure, the branching ratio decreases, and the Ir L2,3 peak positions shift to higher energies. The number of 5d holes remains constant indicating that the spectral weight redistribution and peak shifts arise from orbital mixing between t2g and eg related states. The expectation value of the angular part of the SO operator decreases by about 15% at 50 GPa. This reduction, which is very similar to that observed in the layered iridates, is well explained by an increase of the octahedral crystal field due to the shortening of the Ir-O bond-length under compression. Consistent with theoretical predictions, the orbital mixing and decrease as the crystal field increases. However, the effective SO coupling remains robust against pressure and does not indicate a covalency-driven breakdown within the investigated pressure range., Comment: accepeted in Physical Review B

To make predictions or design control, information on local sensitivity of initial conditions and state-space contraction is both central, and often instrumental. However, it is not always simple to reliably determine instability fields or local dissipation rates, due to computational challenges or ignorance of the governing equations. Here, we construct an alternative route towards that goal, by estimating the Jacobian of a discrete-time dynamical system locally from the entries of the transition matrix that approximates the Perron-Frobenius operator for a given state-space partition. Numerical tests on one- and two-dimensional chaotic maps show promising results.

Artificial intelligence (AI) is increasingly being utilized to optimize magnetic resonance imaging (MRI) protocols. Given that image details are critical for diagnostic accuracy, optimizing MRI acquisition protocols is essential for enhancing image quality. While medical physicists are responsible for this optimization, the variability in equipment usage and the wide range of MRI protocols in clinical settings pose significant challenges. This study aims to validate the application of AI in optimizing MRI protocols using dynamic data from clinical practice, specifically DICOM metadata. To achieve this, four MRI spine exam databases were created, with the target attribute being the binary classification of image quality (good or bad). Five AI models were trained to identify trends in acquisition parameters that influence image quality, grounded in MRI theory. These trends were analyzed using SHAP graphs. The models achieved F1 performance ranging from 77% to 93% for datasets containing 292 or more instances, with the observed trends aligning with MRI theory. The models effectively reflected the practical realities of clinical MRI settings, offering a valuable tool for medical physicists in quality control tasks. In conclusion, AI has demonstrated its potential to optimize MRI protocols, supporting medical physicists in improving image quality and enhancing the efficiency of quality control in clinical practice.

The enigmatic phenomenon of dark energy (DE) is regarded as the elusive entity driving the accelerated expansion of our Universe. A plausible candidate for DE is the non-zero Einstein Cosmological Constant $\Lambda_{E}$ manifested as a constant energy density of the vacuum, yet it seemingly defies gravitational effects. In this work, we interpret the non-zero $\Lambda_{E}$ through the lens of scale-invariant cosmology. We revisit the conformal scale factor $\lambda$ and its defining equations within the Scale-Invariant Vacuum (SIV) paradigm. Furthermore, we address the profound problem of the missing mass across galactic and extragalactic scales by deriving an MOND-like relation, $g \sim \sqrt{a_0\,g_N}$, within the SIV context. Remarkably, the values obtained for $\Lambda_{E}$ and the MOND fundamental acceleration, $a_0$, align with observed magnitudes, specifically, $a_0 \approx 10^{-10} \, \mathrm{m} \, \mathrm{s}^{-2}$ and $\Lambda_{E} \approx 1.8 \times 10^{-52} \, \mathrm{m}^{-2}$. Moreover, we propose a novel early dark energy term, $\tilde{T}_{\mu\nu} \sim \kappa H$, within the SIV paradigm, which holds potential relevance for addressing the Hubble tension. Keywords: cosmology; theory; dark energy; dark matter; MOND; Weyl integrable geometry., Comment: 13 pages, no figures