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

Two-Higgs-doublet models come with an augmented parameter space which allows them to possibly solve some of the shortcomings of the Standard Model, and opens the window to a plethora of new phenomena to be discovered. The introduction of scalar-mediated tree-level flavour-changing neutral currents may be tackled with the imposition of extra symmetries on the model or, alternatively, by demanding a strict proportionality between the flavour-changing couplings and fermion mass matrices. The latter is the very idea behind the Aligned-Two-Higgs-Doublet Model (A2HDM). The coefficients that govern such proportionality are, in general, complex and, therefore, possible new sources of CP violation, a calling card of this class of models. We present here the results of new state-of-the-art analyses of the A2HDM where, in particular, we ascertain whether current data allows the A2HDM to accommodate extra scalars lighter than the 125 GeV Higgs boson. To this effect, we make use of theoretical constraints, bounds from Higgs searches at the LHC and LEP, electroweak precision observables, and a set of flavour observables, all globally combined within HEPfit, a software with a Bayesian Markov Chain Monte Carlo approach to statistical inference. Focusing on the light-pseudoscalar scenario, we find a region of parameter space compatible with all the constraints we impose., Comment: 5 pages, 1 figure, 1 table, Proceedings of LHCP2024

Understanding trajectories in multi-agent scenarios requires addressing various tasks, including predicting future movements, imputing missing observations, inferring the status of unseen agents, and classifying different global states. Traditional data-driven approaches often handle these tasks separately with specialized models. We introduce TranSPORTmer, a unified transformer-based framework capable of addressing all these tasks, showcasing its application to the intricate dynamics of multi-agent sports scenarios like soccer and basketball. Using Set Attention Blocks, TranSPORTmer effectively captures temporal dynamics and social interactions in an equivariant manner. The model's tasks are guided by an input mask that conceals missing or yet-to-be-predicted observations. Additionally, we introduce a CLS extra agent to classify states along soccer trajectories, including passes, possessions, uncontrolled states, and out-of-play intervals, contributing to an enhancement in modeling trajectories. Evaluations on soccer and basketball datasets show that TranSPORTmer outperforms state-of-the-art task-specific models in player forecasting, player forecasting-imputation, ball inference, and ball imputation. https://youtu.be/8VtSRm8oGoE, Comment: Accepted to ACCV 2024

Earth Observation (EO) has traditionally involved the transmission of a large volume of raw data to map the Earth surface. This results in congestion to the satellite network and delays in the availability of the results, invalidating the approach for timing-sensitive applications. Instead, the computation resources at the satellites can be used as an edge layer for compressing the data and/or doing inferences. In this paper, we investigate satellite edge computing for vessel detection with a LEO satellite constellation. First, we distribute the computation and inference load among the neighbouring satellites of the one taking the images, based on the VHRShips data set and YOLOv8. This semantic and fragmented information is then routed to a remote ground monitor through the whole constellation. The average and peak Age of Information (AoI) are reformulated to measure the freshness of the aggregated information at the receiver in this image-capture scenario. We then dimension the network (number of orbital planes and satellites per orbital plane) for a given target age and covered area that quantify the level of achievement of the task. The results show that 20 orbital planes with 20 satellites are necessary to keep the peak AoI below 60 seconds with a compression ratio > 23000, i.e., a size reduction of 99.996%, and for a approximately 100% probability of coverage., Comment: This paper was presented at the ComSoc Challenge on 'Future 6G NTN Systems' held at ICCSPA'24, where it was awarded second place

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.

Detections of fast X-ray transients (FXTs) have been accrued over the last few decades. However, their origin has remained mysterious. There is now rapid progress thanks to timely discoveries and localisations with the Einstein Probe mission. Early results indicate that FXTs may frequently, but not always, be associated with gamma-ray bursts (GRBs). Here, we report on the multi-wavelength counterpart of FXT EP240414a, which has no reported gamma-ray counterpart. The transient is located 25.7~kpc in projection from a massive galaxy at $z=0.40$. We perform comprehensive photometric and spectroscopic follow-up. The optical light curve shows at least three distinct emission episodes with timescales of $\sim 1, 4$ and 15 days and peak absolute magnitudes of $M_R \sim -20$, $-21$, and $-19.5$, respectively. The optical spectrum at early times is extremely blue, inconsistent with afterglow emission. It may arise from the interaction of both jet and supernova shock waves with the stellar envelope and a dense circumstellar medium, as has been suggested for some Fast Blue Optical Transients (LFBOTs). At late times, the spectrum evolves to a broad-lined~Type~Ic supernova, similar to those seen in collapsar long-GRBs. This implies that the progenitor of EP240414a is a massive star creating a jet-forming supernova inside a dense envelope, resulting in an X-ray outburst with a luminosity of $\sim 10^{48}$ erg s$^{-1}$, and the complex observed optical/IR light curves. If correct, this argues for a causal link between the progenitors of long-GRBs, FXTs and LFBOTs., Comment: 36 pages, 13 figures, submitted to ApJ

The SaaS paradigm has popularized the usage of pricings, allowing providers to offer customers a wide range of subscription possibilities. This creates a vast configuration space for users, enabling them to choose the features and support guarantees that best suit their needs. Regardless of the reasons why changes in these pricings are made, the frequency of changes within the elements of pricings continues to increase. Therefore, for those responsible for the development and operation of SaaS, it would be ideal to minimize the time required to transfer changes in SaaS pricing to the software and underlying infrastructure, without compromising the quality and reliability.% of the service; %i.e., this development and operation should be Pricing-Driven. This work explores the support offered by the industry for this need. By modeling over 150 pricings from 30 different SaaS over six years, we reveal that the configuration space grows exponentially with the number of add-ons and linearly with the number of plans. We also evaluate 21 different feature toggling solutions, finding that feature toggling, particularly permission toggles, is a promising technique for enabling rapid adaptation to pricing changes. Our results suggest that developing automated solutions with minimal human intervention could effectively reduce the time-to-market for SaaS updates driven by pricing changes, especially with the adoption of a standard for serializing pricings., Comment: 15 pages. Accepted in the 22nd International Conference on Service-Oriented Computing (ICSOC'24)

The Two Higgs Doublet Model (2HDM) is a simple extension of the Standard Model (SM), which provides a rich and very interesting phenomenology. To remove the undesirable flavour-changing neutral currents (FCNCs), an additional $\mathcal Z_2$ symmetry is usually imposed into the 2HDM. However, FCNCs can be avoided in a much more general way by assuming a similar Yukawa structure for the two scalar doublets. But one can circumvent the issue of FCNCs by assuming similar Yukawa structure for the two scalar doublets too. The model with this intriguing feature is termed the Aligned Two Higgs Doublet Model (A2HDM). The phenomenological constraints on the A2HDM are much weaker than the ones on the usual $\mathcal Z_2$ models, opening a broader range of possible scenarios. Moreover, the A2HDM also provides a generic framework to study, as particular cases, the different varieties of $\mathcal Z_2$-symmetric 2HDMs. Here, we illustrate a global fit of the A2HDM using the package HEPfit. We study the possibility of having new scalar particles lighter than the SM Higgs. For this global fit we perform a Bayesian analysis, including stability and perturbativity bounds, flavour and electroweak precision observables, and scalar (and pseudoscalar) searches at LEP and LHC., Comment: Proceeding for ICHEP2024

This paper establishes a rigorous connection between circuit representations and tensor factorizations, two seemingly distinct yet fundamentally related areas. By connecting these fields, we highlight a series of opportunities that can benefit both communities. Our work generalizes popular tensor factorizations within the circuit language, and unifies various circuit learning algorithms under a single, generalized hierarchical factorization framework. Specifically, we introduce a modular "Lego block" approach to build tensorized circuit architectures. This, in turn, allows us to systematically construct and explore various circuit and tensor factorization models while maintaining tractability. This connection not only clarifies similarities and differences in existing models, but also enables the development of a comprehensive pipeline for building and optimizing new circuit/tensor factorization architectures. We show the effectiveness of our framework through extensive empirical evaluations, and highlight new research opportunities for tensor factorizations in probabilistic modeling.

The fronthaul connection is a key component of Centralized RAN (C-RAN) architectures, consistently required to handle high capacity demands. However, this critical feature is at risk when the transport link relies on wireless technology. Fortunately, solutions exist to enhance the reliability of wireless links. In this paper, we recall the theoretical fronthaul model, present a dynamic reconfiguration strategy and perform a conclusive experiment. Specifically, we showcase the setup of a wireless fronthaul testbed and discuss the resulting measurements. For this task, we leveraged the commercial hardware provided by the High-Frequency Campus Lab (HFCL), a private 5G network with millimeter wave (mmWave) radio access interface. Our experiments provide original data on the fronthaul utilization in this real deployment, demonstrating both a good accordance with the theoretical model discussed in [1] and the viability of one stabilizing solution.

This paper presents Dynamic Context Prompting/Programming (DCP/P), a novel framework for interacting with LLMs to generate graph-based content with a dynamic context window history. While there is an existing study utilizing LLMs to generate a visual novel game, the previous study involved a manual process of output extraction and did not provide flexibility in generating a longer, coherent story. We evaluate DCP/P against our baseline, which does not provide context history to an LLM and only relies on the initial story data. Through objective evaluation, we show that simply providing the LLM with a summary leads to a subpar story compared to additionally providing the LLM with the proper context of the story. We also provide an extensive qualitative analysis and discussion. We qualitatively examine the quality of the objectively best-performing generated game from each approach. In addition, we examine biases in word choices and word sentiment of the generated content. We find a consistent observation with previous studies that LLMs are biased towards certain words, even with a different LLM family. Finally, we provide a comprehensive discussion on opportunities for future studies., Comment: 12 pages, 14 figures

The ICH E9(R1) addendum provides guidelines on accounting for intercurrent events in clinical trials using the estimands framework. However, there has been limited attention on the estimands framework for meta-analysis. Using treatment switching, a well-known intercurrent event that occurs frequently in oncology, we conducted a simulation study to explore the bias introduced by pooling together estimates targeting different estimands in a meta-analysis of randomized clinical trials (RCTs) that allowed for treatment switching. We simulated overall survival data of a collection of RCTs that allowed patients in the control group to switch to the intervention treatment after disease progression under fixed-effects and random-effects models. For each RCT, we calculated effect estimates for a treatment policy estimand that ignored treatment switching, and a hypothetical estimand that accounted for treatment switching by censoring switchers at the time of switching. Then, we performed random-effects and fixed-effects meta-analyses to pool together RCT effect estimates while varying the proportions of treatment policy and hypothetical effect estimates. We compared the results of meta-analyses that pooled different types of effect estimates with those that pooled only treatment policy or hypothetical estimates. We found that pooling estimates targeting different estimands results in pooled estimators that reflect neither the treatment policy estimand nor the hypothetical estimand. This finding shows that pooling estimates of varying target estimands can generate misleading results, even under a random-effects model. Adopting the estimands framework for meta-analysis may improve alignment between meta-analytic results and the clinical research question of interest.

Two decades ago the $\chi_{c1}\left(3872\right)$ was discovered in the hadron spectrum with two heavy quarks. The discovery fueled a surge in experimental research, uncovering dozens of so called XYZ exotics states lying outside the conventional quark model, as well as theoretical investigations into new forms of matter, such as quark-gluon hybrids, mesonic molecules, and tetraquarks, with the potential of disclosing new information about the fundamental strong force. Among the XYZs, the $\chi_{c1}\left(3872\right)$ and $T_{cc}^+\left(3875\right)$ stand out for their striking characteristics and unlashed many discussions about their nature. Here, we address this question using the Born--Oppenheimer Effective Field Theory (BOEFT) and show how QCD settles the issue of their composition. Not only we describe well the main features of the $\chi_{c1}\left(3872\right)$ and $T_{cc}^+\left(3875\right)$ but obtain also model independent predictions in the bottomonium sector. This opens the way to systematic applications of BOEFT to all XYZs., Comment: 8 pages, 3 figures

Quantum differential equation solvers aim to prepare solutions as $n$-qubit quantum states over a fine grid of $O(2^n)$ points, surpassing the linear scaling of classical solvers. However, unlike classically stored vectors of solutions, the readout of exact quantum states poses a bottleneck due to the complexity of tomography. Here, we show that the readout problem can be addressed with quantum learning tools where we focus on distilling the relevant features. Treating outputs of quantum differential equation solvers as quantum data, we demonstrate that low-dimensional output can be extracted using a measurement operator adapted to detect relevant features. We apply this quantum scientific machine learning approach to classify solutions for shock wave detection and turbulence modeling in scenarios where data samples come directly from quantum differential equation solvers. We show that the basis chosen for performing analysis greatly impacts classification accuracy. Our work opens up the area of research where quantum machine learning for quantum datasets is inherently required., Comment: 5 pages, 4 figures

Gaining insights from realistic dynamical models of biochemical systems can be challenging given their large number of state variables. Model reduction techniques can mitigate this by decreasing complexity by mapping the model onto a lower-dimensional state space. Exact constrained lumping identifies reductions as linear combinations of the original state variables in systems of nonlinear ordinary differential equations, preserving specific user-defined output variables without error. However, exact reductions can be too stringent in practice, as model parameters are often uncertain or imprecise -- a particularly relevant problem for biochemical systems. We propose approximate constrained lumping. It allows for a relaxation of exactness within a given tolerance parameter $\varepsilon$, while still working in polynomial time. We prove that the accuracy, i.e., the difference between the output variables in the original and reduced model, is in the order of $\varepsilon$. Furthermore, we provide a heuristic algorithm to find the smallest $\varepsilon$ for a given maximum allowable size of the lumped system. Our method is applied to several models from the literature, resulting in coarser aggregations than exact lumping while still capturing the dynamics of the original system accurately.

Telesurgery is an effective way to deliver service from expert surgeons to areas without immediate access to specialized resources. However, many of these areas, such as rural districts or battlefields, might be subject to different problems in communication, especially latency and intermittent periods of communication outage. This challenge motivates the use of a digital twin for the surgical system, where a simulation would mirror the robot hardware and surgical environment in the real world. The surgeon would then be able to interact with the digital twin during communication outage, followed by a recovery strategy on the real robot upon reestablishing communication. This paper builds the digital twin for the da Vinci surgical robot, with a buffering and replay strategy that reduces the mean task completion time by 23% when compared to the baseline, for a peg transfer task subject to intermittent communication outage.

This paper addresses the problem of stabilization of switched affine systems under dwell-time constraint, giving guarantees on the bound of the quadratic cost associated with the proposed state switching control law. Specifically, two switching rules are presented relying on the solution of differential Lyapunov inequalities and Lyapunov-Metzler inequalities, from which the stability conditions are expressed. The first one allows to regulate the state of linear switched systems to zero, whereas the second one is designed for switched affine systems proving practical stability of the origin. In both cases, the determination of a guaranteed cost associated with each control strategy is shown. In the cases of linear and affine systems, the existence of the solution for the Lyapunov-Metzler condition is discussed and guidelines for the selection of a solution ensuring suitable performance of the system evolution are provided. The theoretical results are finally assessed by means of three examples., Comment: 12 pages, 10 figures

The Aldous-Hoover Theorem concerns an infinite matrix of random variables whose distribution is invariant under finite permutations of rows and columns. It states that, up to equality in distribution, each random variable in the matrix can be expressed as a function only depending on four key variables: one common to the entire matrix, one that encodes information about its row, one that encodes information about its column, and a fourth one specific to the matrix entry. We state and prove the theorem within a category-theoretic approach to probability, namely the theory of Markov categories. This makes the proof more transparent and intuitive when compared to measure-theoretic ones. A key role is played by a newly identified categorical property, the Cauchy--Schwarz axiom, which also facilitates a new synthetic de Finetti Theorem. We further provide a variant of our proof using the ordered Markov property and the d-separation criterion, both generalized from Bayesian networks to Markov categories. We expect that this approach will facilitate a systematic development of more complex results in the future, such as categorical approaches to hierarchical exchangeability., Comment: 39 pages

This collection of perspective pieces captures recent advancements and reflections from a dynamic research community dedicated to bridging quantum gravity, hydrodynamics, and emergent cosmology. It explores four key research areas: (a) the interplay between hydrodynamics and cosmology, including analog gravity systems; (b) phase transitions, continuum limits and emergent geometry in quantum gravity; (c) relational perspectives in gravity and quantum gravity; and (d) the emergence of cosmological models rooted in quantum gravity frameworks. Each contribution presents the distinct perspectives of its respective authors. Additionally, the introduction by the editors proposes an integrative view, suggesting how these thematic units could serve as foundational pillars for a novel theoretical cosmology framework termed "hydrodynamics on superspace"., Comment: 37 + 23 pages

The velocities of Ic-BL supernovae can be determined using two techniques (spline fitting and template fitting), sometimes resulting in different velocities for the same event. This work compares and contrasts both methods, identifying sources of error which are not accounted for by most authors and quantifying their impact on the final velocity measurement. Finally, it identifies the cause of velocity discrepancies for events measured using both methods. We quantified the impact of pre-smoothing the spectra prior to use of both methods using two well-sampled cases. To identify the source of velocity discrepancies, two cases were measured and directly compared. Additional sources of error for template fitting arise due to the choice of phase of the template spectrum ($\sim$1000 km/s) and smoothing of the input spectrum ($\sim$500 km/s). The impact of phase shifts is minimised at peak time. The spline fitting method tends to underestimate uncertainties by around 1000 km/s. This method can also be impacted by fine tuning of the smoothing parameters ($\sim$500-1000 km/s). Optimum smoothing parameters for different cases are presented along with suggestions for best practice. Direct comparison of both methods showed that velocity discrepancies are not always present, debunking the claim that the template fitting method always handles blending better than spline fitting. Spline fitting seems to struggle to handle blending only in cases where the Fe II features are superimposed on a red continuum, which biases the minimum of this feature towards the bluest line of the triplet, creating an artificially higher velocity. This situation may be relatively rare among Ic-BLs, based on typical temperature evolution. Both methods can be applied under certain circumstances with similar results. The morphology of the velocity evolution of an SN appears to be the same regardless of the method used., Comment: Submitted to A&A

Statistical process monitoring (SPM) methods are essential tools in quality management to check the stability of industrial processes, i.e., to dynamically classify the process state as in control (IC), under normal operating conditions, or out of control (OC), otherwise. Traditional SPM methods are based on unsupervised approaches, which are popular because in most industrial applications the true OC states of the process are not explicitly known. This hampered the development of supervised methods that could instead take advantage of process data containing labels on the true process state, although they still need improvement in dealing with class imbalance, as OC states are rare in high-quality processes, and the dynamic recognition of unseen classes, e.g., the number of possible OC states. This article presents a novel stream-based active learning strategy for SPM that enhances partially hidden Markov models to deal with data streams. The ultimate goal is to optimize labeling resources constrained by a limited budget and dynamically update the possible OC states. The proposed method performance in classifying the true state of the process is assessed through a simulation and a case study on the SPM of a resistance spot welding process in the automotive industry, which motivated this research.

In this paper we study higher Gaussian (or Wahl) maps for the canonical bundle of certain smooth projective curves. More precisely, we determine the rank of higher Gaussian maps of the canonical bundle for plane curves, for curves contained in certain linear systems in a surface given by a product of two curves and for curves contained in a sufficiently ample line bundle on an Enriques surface.

While previous studies indicated the mobility reversal of an electrically-powered metallo-dielectric Janus particle (JP) with increasing frequency, here we report an intriguing second mobility reversal observed in optoelectronically-driven JPs. In contrast to the commonly used setup with parallel ITO-coated glass substrates to induce a uniform electric field orthogonal to the velocity direction, this setup incorporates a thin photoconductive layer deposited on the bottom ITO-coated glass substrate. We have found that the reversal is associated with the asymmetry of the bottom substrate's photoconductivity, localized underneath the JP, resulting from the self-shading effect of the metallic hemisphere under top optical illumination. Numerous control tests, including optical illumination from the bottom, along with numerical simulations, support this hypothesized mechanism

Optimizing space vehicle routing is crucial for critical applications such as on-orbit servicing, constellation deployment, and space debris de-orbiting. Multi-target Rendezvous presents a significant challenge in this domain. This problem involves determining the optimal sequence in which to visit a set of targets, and the corresponding optimal trajectories: this results in a demanding NP-hard problem. We introduce a framework for the design and refinement of multi-rendezvous trajectories based on heuristic combinatorial optimization and Sequential Convex Programming. Our framework is both highly modular and capable of leveraging candidate solutions obtained with advanced approaches and handcrafted heuristics. We demonstrate this flexibility by integrating an Attention-based routing policy trained with Reinforcement Learning to improve the performance of the combinatorial optimization process. We show that Reinforcement Learning approaches for combinatorial optimization can be effectively applied to spacecraft routing problems. We apply the proposed framework to the UARX Space OSSIE mission: we are able to thoroughly explore the mission design space, finding optimal tours and trajectories for a wide variety of mission scenarios., Comment: 18 pages, 12 figures, 5 tables

Rooted in the explosion of deep learning over the past decade, this thesis spans from AlphaGo to ChatGPT to empirically examine the fundamental concepts needed to realize the vision of an artificial scientist: a machine with the capacity to autonomously generate original research and contribute to the expansion of human knowledge. The investigation begins with {\sc Olivaw}, an AlphaGo Zero-like agent that discovers Othello knowledge from scratch but is unable to communicate it. This realization leads to the development of the Explanatory Learning (EL) framework, a formalization of the problem faced by a scientist when trying to explain a new phenomenon to their peers. The effective EL prescriptions allow us to crack Zendo, a board game simulating the scientific endeavor. This success comes with a fundamental insight: an artificial scientist must develop its own interpretation of the language used to explain its findings. This perspective then leads us to see modern multimodal models as interpreters, and to devise a new way to build interpretable and cost-effective CLIP-like models: by coupling two unimodal models using little multimodal data and no further training. Finally, we discuss what ChatGPT and its siblings are still missing to become artificial scientists, and introduce Odeen, a benchmark about interpreting explanations that sees LLMs going no further than random chance while being instead fully solved by humans., Comment: PhD thesis, 123 pages

This document summarizes the prospective physics plans of the French QCD and Heavy-Ion community, including the experimental programs at the LHC Run 5 and beyond and future colliders at CERN, within the context of the French contribution to the update of the European Strategy in Particle Physics (ESPPU 2025), as discussed in the workshop on European Strategy for Particle Physics Update 2025 organised by the QCD GdR in Oct. 2024., Comment: 6.5 pages without title page and without references, no figures. Keywords: QCD, Heavy Ions, Quark Gluon Plasma, LHC, FCC

The application of Tensor Networks (TN) in quantum computing has shown promise, particularly for data loading. However, the assumption that data is readily available often renders the integration of TN techniques into Quantum Monte Carlo (QMC) inefficient, as complete probability distributions would have to be calculated classically. In this paper the tensor-train cross approximation (TT-cross) algorithm is evaluated as a means to address the probability loading problem. We demonstrate the effectiveness of this method on financial distributions, showcasing the TT-cross approach's scalability and accuracy. Our results indicate that the TT-cross method significantly improves circuit depth scalability compared to traditional methods, offering a more efficient pathway for implementing QMC on near-term quantum hardware. The approach also shows high accuracy and scalability in handling high-dimensional financial data, making it a promising solution for quantum finance applications., Comment: 20 pages, 10 figures

There are more than 60 broad-line Ic (Ic-BL) supernovae (SNe) which are associated with a long Gamma-ray Burst (GRB). A large population of `ordinary' Ic-BLs for which no GRB component is detected also exists. On average, the expansion velocities of GRB-associated Ic-BLs exceed those of ordinary Ic-BLs. This work presents the largest spectroscopic sample of Ic-BL SNe with and without GRBs to date. The goal of this work is to investigate how the expansion velocities evolve in cases where an ultra-relativistic jet has been launched (GRB-SN cases) and compare these to Ic-BL SNe without a GRB detection. We measured the expansion velocities of the Fe II, Si II and Ca II lines observed in the spectra of Ic-BL SNe using a spline fitting method. We fit the expansion velocity evolution with single and broken power-laws. The expansion velocities of the Fe II and Si II features reveal considerable overlap between the two populations. It is not clear that GRB-associated supernovae expand more rapidly. Broken power-law evolution appears to be more common for the Si II feature, which always follows a shallow-steep decay, while the broken power-law Fe II decays are predominantly steep-shallow. The power-law indices for both samples were compared for both Fe II and Si II, and suggest that GRB-SNe decline at a similar rate to non-GRB Ic-BL supernovae. Neither the velocities nor their evolution can be used to distinguish between Ic-BLs with and without GRBs. Expansion velocities consistent with broken power-law evolution may indicate the presence of two velocity components, which may be evidence for a jet in some of these explosions. However, it is not possible to rule in or out the presence of a jet in any Ic-BL supernova purely based on the velocities. These results suggest that GRB-SNe and Ic-BLs are drawn from the same underlying population of events., Comment: 39 pages, 22 figures, 9 tables. Submitted to A&A

This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important advancement in artificial intelligence. We first discuss the architecture and pre-training objectives of MLLMs, highlighting the key components and methodologies that contribute to their multilingual capabilities. We then discuss the construction of multilingual pre-training and alignment datasets, underscoring the importance of data quality and diversity in enhancing MLLM performance. An important focus of this survey is on the evaluation of MLLMs. We present a detailed taxonomy and roadmap covering the assessment of MLLMs' cross-lingual knowledge, reasoning, alignment with human values, safety, interpretability and specialized applications. Specifically, we extensively discuss multilingual evaluation benchmarks and datasets, and explore the use of LLMs themselves as multilingual evaluators. To enhance MLLMs from black to white boxes, we also address the interpretability of multilingual capabilities, cross-lingual transfer and language bias within these models. Finally, we provide a comprehensive review of real-world applications of MLLMs across diverse domains, including biology, medicine, computer science, mathematics and law. We showcase how these models have driven innovation and improvements in these specialized fields while also highlighting the challenges and opportunities in deploying MLLMs within diverse language communities and application scenarios. We listed the paper related in this survey and publicly available at https://github.com/tjunlp-lab/Awesome-Multilingual-LLMs-Papers.

We present a coarse-to-fine neural deformation model to simultaneously recover the camera pose and the 4D reconstruction of an unknown object from multiple RGB sequences in the wild. To that end, our approach does not consider any pre-built 3D template nor 3D training data as well as controlled illumination conditions, and can sort out the problem in a self-supervised manner. Our model exploits canonical and image-variant spaces where both coarse and fine components are considered. We introduce a neural local quadratic model with spatio-temporal consistency to encode fine details that is combined with canonical embeddings in order to establish correspondences across sequences. We thoroughly validate the method on challenging scenarios with complex and real-world deformations, providing both quantitative and qualitative evaluations, an ablation study and a comparison with respect to competing approaches. Our project is available at https://github.com/smontode24/4DPV., Comment: 17th Asian Conference on Computer Vision (ACCV 2024)

Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibility brings the challenge of selecting the appropriate latent multi-rank. To overcome the issue of pre-selecting the latent multi-rank, we introduce a Bayesian adaptive Tucker decomposition model that infers the multi-rank automatically via an infinite increasing shrinkage prior. The model introduces local sparsity in the core tensor, inducing rich and at the same time parsimonious dependency structures. Posterior inference proceeds via an efficient adaptive Gibbs sampler, supporting both continuous and binary data and allowing for straightforward missing data imputation when dealing with incomplete multiway data. We discuss fundamental properties of the proposed modeling framework, providing theoretical justification. Simulation studies and applications to chemometrics and complex ecological data offer compelling evidence of its advantages over existing tensor factorization methods.

Let $f$ be a real function defined on the interval $[0,1]$ which is constant on $(a,b)\subset [0,1]$, and let $B_nf$ be its associated $n$th Bernstein polynomial. We prove that, for any $x\in (a,b)$, $|B_nf(x)-f(x)|$ converges to $0$ as $n\rightarrow \infty $ at an exponential rate of decay. Moreover, we show that this property is no longer true at the boundary of $(a,b)$. Finally, an extension to Bernstein-Kantorovich type operators is also provided, Comment: 7 pages

We study a Dirichlet problem driven by the (degenerate or singular) fractional $p$-Laplacian and involving a $(p-1)$-superlinear reaction at infinity, not necessarily satisfying the Ambrosetti-Rabinowitz condition. Using critical point theory, truncation, and Morse theory, we prove the existence of at least three nontrivial solutions to the problem., Comment: 18 pages

We discuss two optimization problems related to the fractional $p$-Laplacian. First, we prove the existence of at least one minimizer for the principal eigenvalue of the fractional $p$-Laplacian with Dirichlet conditions, with a bounded weight function varying in a rearrangement class. Then, we investigate the maximization of the energy functional for general nonlinear equations driven by the same operator, as the reaction varies in a rearrangement class. In both cases, we provide a pointwise relation between the optimizing datum and the corresponding solution., Comment: 16 pages

We prove that every Lindel\"of scattered subspace of a $\Sigma$-product of first-countable spaces is $\sigma$-compact. In particular, we obtain the result stated in the title. This answers some questions of Tkachuk from [Houston J. Math. 48 (2022), no. 1, 171--181].

Thermal comfort is essential for well-being in urban spaces, especially as cities face increasing heat from urbanization and climate change. Existing thermal comfort models usually overlook temporal dynamics alongside spatial dependencies. We address this problem by introducing a spatio-temporal jump model that clusters data with persistence across both spatial and temporal dimensions. This framework enhances interpretability, minimizes abrupt state changes, and easily handles missing data. We validate our approach through extensive simulations, demonstrating its accuracy in recovering the true underlying partition. When applied to hourly environmental data gathered from a set of weather stations located across the city of Singapore, our proposal identifies meaningful thermal comfort regimes, demonstrating its effectiveness in dynamic urban settings and suitability for real-world monitoring. The comparison of these regimes with feedback on thermal preference indicates the potential of an unsupervised approach to avoid extensive surveys.

We study the inverse problem of determining a time-dependent globally hyperbolic Lorentzian metric from the scattering operator for semilinear wave equations., Comment: 62 pages

The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Face (HF) enable the development, sharing, and deployment of these models, fostering an evolving ecosystem. While previous studies have examined aspects of models hosted on platforms like HF, a comprehensive longitudinal study of how these models change remains underexplored. This study addresses this gap by utilizing both repository mining and longitudinal analysis methods to examine over 200,000 commits and 1,200 releases from over 50,000 models on HF. We replicate and extend an ML change taxonomy for classifying commits and utilize Bayesian networks to uncover patterns in commit and release activities over time. Our findings indicate that commit activities align with established data science methodologies, such as CRISP-DM, emphasizing iterative refinement and continuous improvement. Additionally, release patterns tend to consolidate significant updates, particularly in documentation, distinguishing between granular changes and milestone-based releases. Furthermore, projects with higher popularity prioritize infrastructure enhancements early in their lifecycle, and those with intensive collaboration practices exhibit improved documentation standards. These and other insights enhance the understanding of model changes on community platforms and provide valuable guidance for best practices in model maintenance.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024/019

The dispersive approach to the hadronic light-by-light contribution to the muon $g-2$ involves an integral over three virtual photon momenta appearing in the light-by-light tensor. Building upon previous works, we systematically derive short-distance constraints in the region where two momenta are large compared to the third, the so-called Melnikov-Vainshtein or corner region. We include gluonic corrections for the different scalar functions appearing in a Lorentz decomposition of the underlying tensor, and explicitly check analytic agreement with alternative operator product expansions in overlapping regimes of validity, and observe a very strong pattern of cancellations for the final $g-2$ integrand. The last observation suggests that a very compact expression only containing the axial current form factors can give a good approximation of the corner region of the hadronic light-by-light.

Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While standalone accelerators tailored for specific application scenarios suffer from inflexible control and limited programmability, generic hardware acceleration platforms coupled with RISC-V CPUs can enable high reusability and flexibility, yet typically at the expense of system level efficiency and low utilization. To fill this gap, we propose OpenGeMM, an open-source acceleration platform, jointly demonstrating high efficiency and utilization, as well as ease of configurability and programmability. OpenGeMM encompasses a parameterized Chisel-coded GeMM accelerator, a lightweight RISC-V processor, and a tightly coupled multi-banked scratchpad memory. The GeMM core utilization and system efficiency are boosted through three mechanisms: configuration pre-loading, input pre-fetching with output buffering, and programmable strided memory access. Experimental results show that OpenGeMM can consistently achieve hardware utilization ranging from 81.89% to 99.34% across diverse CNN and Transformer workloads. Compared to the SotA open-source Gemmini accelerator, OpenGeMM demonstrates a 3.58x to 16.40x speedup on normalized throughput across a wide variety ofGeMM workloads, while achieving 4.68 TOPS/W system efficiency.

Previous investigations on the renormalizability properties of Lorentz-violating Yang-Mills (LVYM) theories in the Landau gauge have pointed out the necessity of the inclusion of a mass-like term for the gauge fields. If one aims at generalizing the theory to a more complicated gauge, such a mass-like term can bring severe issues regarding gauge-dependence of correlation functions of observables. We propose a Lorentz-violating Yang-Mills theory supplemented by a gauge-invariant mass term which generalizes the model in the Landau gauge. We discuss the foundational aspects of the model and highlight how it simplifies in the Landau gauge. Following the recent literature in pure Yang-Mills theories, we remark how the proposed massive extension of LVYM theories can be used as an effective model to access infrared properties within perturbation theory. Finally, we present a BRST-invariant formulation of the so-called Refined Gribov-Zwanziger action in the presence of Lorentz-violating terms in linear covariant gauges and the underlying tree-level gauge-field propagator which is enriched by the non-perturbative information carried by the elimination of Gribov copies., Comment: 14 pages, no figures