Search

We present ultraviolet, optical and near-infrared photometric and optical spectroscopic observations of the luminous, fast blue optical transient (LFBOT), CSS161010:045834-081803 (CSS161010). The transient was found in a low-redshift (z=0.033) dwarf galaxy. The light curves of CSS161010 are characterized by an extremely fast evolution and blue colours. The V-band light curve shows that CSS161010 reaches an absolute peak of M$_{V}^{max}=-20.66\pm0.06$ mag in 3.8 days from the start of the outburst. After maximum, CSS161010 follows a power-law decline $\propto t^{-2.8\pm0.1}$ in all optical bands. These photometric properties are comparable to those of well-observed LFBOTs such as AT 2018cow, AT 2020mrf and AT 2020xnd. However, unlike these objects, the spectra of CSS161010 show a remarkable transformation from a blue and featureless continuum to spectra dominated by very broad, entirely blueshifted hydrogen emission lines of velocities of up to 10% of the speed of light. The persistent blueshifted emission and the lack of any emission at the rest wavelength of CSS161010 are unique features not seen in any transient before CSS161010. The combined observational properties of CSS161010 and its M$_{*}\sim10^{8}$ M$_\odot$ dwarf galaxy host favour the tidal disruption of a star by an intermediate-mass black hole as its origin., Comment: 29 pages (including the appendix); 8 figures in the main text, 4 figures and 8 tables in the appendix. Accepted for publication in ApJ

In response to stringent emission reduction targets imposed by the International Maritime Organization (IMO) and the European Green Deal's Fit for 55 legislation package, the maritime industry has shifted its focus towards decarbonization. While significant attention has been placed on vessels exceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping, amounting to approximately 13% of global shipping transportation and 15% within the European Union (EU), have not received adequate consideration. This abstract introduces the Zero Emission Sea Transporter (ZEST) project, designed to address this issue by developing a zero-emissions multi-purpose catamaran for short sea route

Binary systems with comparable masses and a surrounding accretion disk can accrete gas through spiral accretion streams penetrating the central cavity formed by tidal interactions. Using three-dimensional Newtonian magnetohydrodynamics simulations, we investigate the possibility of a magnetically arrested accretion flow through the cavity. Rather than solely continuously feeding the binary through spiral accretion streams, the accretion is regulated by the strong magnetic field inside the cavity. Transport of mass and angular momentum onto the binary then proceeds largely periodically in magnetic flux eruption episodes. The ejected flux tubes carry angular momentum outwards and away from the binary, inject hot plasma into the disk and can launch flares. This likely intermittent scenario could have potential implications for the emission signatures of supermassive black hole binaries, and shed light onto the role magnetic fields play in the binary's orbital evolution., Comment: 13 pages, 6 figures, accepted version to appear in ApJL

The rising integration of variable renewable energy sources (RES), like solar and wind power, introduces considerable uncertainty in grid operations and energy management. Effective forecasting models are essential for grid operators to anticipate the net load - the difference between consumer electrical demand and renewable power generation. This paper proposes a deep learning (DL) model based on long short-term memory (LSTM) networks for net load forecasting in renewable-based microgrids, considering both solar and wind power. The model's architecture is detailed, and its performance is evaluated using a residential microgrid test case based on a typical meteorological year (TMY) dataset. The results demonstrate the effectiveness of the proposed LSTM-based DL model in predicting the net load, showcasing its potential for enhancing energy management in renewable-based microgrids., Comment: Submitted to 2024 IEEE North American Power Symposium

We investigate the spectroscopic characteristics of Intermediate Luminosity Red Transients (ILRTs), a class of elusive objects with peak luminosity between that of classical novae and standard supernovae. We present the extensive optical and near-infrared (NIR) spectroscopic monitoring of four ILRTs, namely NGC 300 2008OT-1, AT 2019abn, AT 2019ahd and AT 2019udc. First we focus on the evolution of the most prominent spectral features observed in the low resolution spectra, then we discuss more in detail the high resolution spectrum collected for NGC 300 2008OT-1 with the Very Large Telescope equipped with UVES. Finally we analyse late time spectra of NGC 300 2008OT-1 and AT 2019ahd through comparisons with both synthetic and observed spectra. Balmer and Ca lines dominate the optical spectra, revealing the presence of slowly moving circumstellar medium (CSM) around the objects. The line luminosity of H$\alpha$, H$\beta$ and Ca II NIR triplet presents a double peaked evolution with time, possibly indicative of interaction between fast ejecta and the slow CSM. The high resolution spectrum of NGC 300 2008OT-1 reveals a complex circumstellar environment, with the transient being surrounded by a slow ($\sim$30 km s$^{-1}$) progenitor wind. At late epochs, optical spectra of NGC 300 2008OT-1 and AT 2019ahd show broad ($\sim$2500 km s$^{-1}$) emission features at $\sim$6170 A and $\sim$7000 A which are unprecedented for ILRTs. We find that these lines originate most likely from the blending of several narrow lines, possibly of iron-peak elements., Comment: 28 pages, 19 figures. Submitted to A&A

We investigate the photometric characteristics of a sample of Intermediate Luminosity Red Transients (ILRTs), a class of elusive objects with peak luminosity between that of classical novae and standard supernovae. We present the multi-wavelength photometric follow-up of four ILRTs, namely NGC 300 2008OT-1, AT 2019abn, AT 2019ahd and AT 2019udc. Through the analysis and modelling of their spectral energy distribution and bolometric light curves we infer the physical parameters associated with these transients. All four objects display a single peaked light curve which ends in a linear decline in magnitudes at late phases. A flux excess with respect to a single black body emission is detected in the infrared domain for three objects in our sample, a few months after maximum. This feature, commonly found in ILRTs, is interpreted as a sign of dust formation. Mid infrared monitoring of NGC 300 2008OT-1 761 days after maximum allows us to infer the presence of $\sim$10$^{-3}$-10$^{-5}$ M$_{\odot}$ of dust, depending on the chemical composition and the grain size adopted. The late time decline of the bolometric light curves of the considered ILRTs is shallower than expected for $^{56}$Ni decay, hence requiring an additional powering mechanism. James Webb Space Telescope observations of AT 2019abn prove that the object has faded below its progenitor luminosity in the mid-infrared domain, five years after its peak. Together with the disappearance of NGC 300 2008OT-1 in Spitzer images seven years after its discovery, this supports the terminal explosion scenario for ILRTs. With a simple semi-analytical model we try to reproduce the observed bolometric light curves in the context of few M$_{\odot}$ of material ejected at few 10$^{3}$ km s$^{-1}$ and enshrouded in an optically thick circumstellar medium., Comment: 21 pages, 15 figures plus 20 additional pages of data in appendix. Submitted to A&A

Next-generation datacenters require highly efficient network load balancing to manage the growing scale of artificial intelligence (AI) training and general datacenter traffic. Existing solutions designed for Ethernet, such as Equal Cost Multi-Path (ECMP) and oblivious packet spraying (OPS), struggle to maintain high network utilizations as datacenter topologies (and network failures as a consequence) continue to grow. To address these limitations, we propose REPS, a lightweight decentralized per-packet adaptive load balancing algorithm designed to optimize network utilization while ensuring rapid recovery from link failures. REPS adapts to network conditions by caching good-performing paths. In case of a network failure, REPS re-routes traffic away from it in less than 100 microseconds. REPS is designed to be deployed with next-generation out-of-order transports, such as Ultra Ethernet, and introduces less than 25 bytes of per-connection state. We extensively evaluate REPS in large-scale simulations and FPGA-based NICs.

Computational reductions are an important and powerful concept in computer science. However, they are difficult for many students to grasp. In this paper, we outline a concept for how the learning of reductions can be supported by educational support systems. We present an implementation of the concept within such a system, concrete web-based and interactive learning material for reductions, and report on our experiences using the material in a large introductory course on theoretical computer science., Comment: Fixed a typo compared to last version; 6 pages + references, including one page of screenshots; accepted at SIGCSE TS 2025

We introduce a novel reservoir engineering approach for stabilizing multi-component Schr\"odinger's cat manifolds. The fundamental principle of the method lies in the destructive interference at crossings of gain and loss Hamiltonian terms in the coupling of an oscillator to a zero-temperature auxiliary system, which are nonlinear with respect to the oscillator's energy. The nature of these gain and loss terms is found to determine the rotational symmetry, energy distributions, and degeneracy of the resulting stabilized manifolds. Considering these systems as bosonic error-correction codes, we analyze their properties with respect to a variety of errors, including both autonomous and passive error correction, where we find that our formalism gives straightforward insights into the nature of the correction. We give example implementations using the anharmonic laser-ion coupling of a trapped ion outside the Lamb-Dicke regime as well as nonlinear superconducting circuits. Beyond the dissipative stabilization of standard cat manifolds and novel rotation symmetric codes, we demonstrate that our formalism allows for the stabilization of bosonic codes linked to cat states through unitary transformations, such as quadrature-squeezed cats. Our work establishes a design approach for creating and utilizing codes using nonlinearity, providing access to novel quantum states and processes across a range of physical systems., Comment: 31 pages, 14 figures. Comments welcome

We present precise bit and degree estimates for the optimal value of the polynomial optimization problem $f^*:=\text{inf}_{x\in \mathscr{X}}~f(x)$, where $\mathscr{X}$ is a semi-algebraic set satisfying some non-degeneracy conditions. Our bounds depend on the degree, the bitsize of $f$, and the polynomials defining $\mathscr{X}$, and are single exponential with respect to the number of variables. They generalize the single exponential bounds from Jeronimo, Perrucci, and Tsigaridas (SIAM Journal on Optimization, 23(1):241--255, 2013) for the minimum of a polynomial function on a compact connected component of a basic closed semi-algebraic set. The tools that we use allow us to obtain specialized bounds and dedicated algorithms for two large families of polynomial optimization problems in which the optimum value might not be attained. The first family forms a dense set of real polynomial functions with a fixed collection of Newton polytopes; we provide the best approximation yet for the bifurcation set, which contains the optimal value, and we deduce an effective method for computations. As for the second family, we consider any unconstrained polynomial optimization problem; we present more precise bounds, together with a better bit complexity estimate of an algorithm to compute the optimal value., Comment: 23 pages, 1 figure, 1 appendix section, comments are welcome!

Neutron stars have solid crusts threaded by strong magnetic fields. Perturbations in the crust can excite non-radial oscillations, which can in turn launch Alfven waves into the magnetosphere. In the case of a compact binary close to merger involving at least one neutron star, this can happen through tidal interactions causing resonant excitations that shatter the neutron star crust. We present the first numerical study that elucidates the dynamics of Alfven waves launched in a compact binary magnetosphere. We seed a magnetic field perturbation on the neutron star crust, which we then evolve in fully general-relativistic force-free electrodynamics using a GPU-based implementation. We show that Alfven waves steepen nonlinearly before reaching the orbital light cylinder, form flares, and dissipate energy in a transient current sheet. Our results predict radio and X-ray precursor emission from this process., Comment: 16 pages, 6 figures, version accepted by ApJL, changed title to match published version

We study tree-level scattering processes of arbitrary string states using the DDF formalism and suitable coherent vertex operators. We obtain new exact compact formulae for heavy-heavy-light-light scattering amplitudes in open or closed bosonic string theories, and derive explicit exact expressions for the absorption cross-sections, and corresponding emission rates, of highly excited string states using the optical theorem and time reversal symmetry. We show that these expressions are independent of the microscopic structure of the excited string states without averaging. For the absorption of massless modes in open string theory, in particular, we find a constant, frequency-independent cross-section. In contrast, the corresponding cross-section for the absorption of massless modes by excited closed strings depends linearly on the frequency, implying a non-trivial grey-body factor. In both cases, at energies below the scale set by the mass of the highly excited strings, we find emission rates with a Boltzmann factor at Hagedorn temperature., Comment: 60 pages, 3 figures

We answer in the affirmative a conjecture of Berarducci, Peterzil and Pillay \cite{BPP10} for solvable groups, which is an o-minimal version of a particular case of Milnor's isomorphism conjecture \cite{jM83}. We prove that every finite central extension of a definably connected solvable definable group in an o-minimal structure is equivalent to a finite central extension which is definable without additional parameters. The proof relies on an o-minimal adaptation of the higher inflation-restriction exact sequence due to Hochschild and Serre. As in \cite{jM83}, we also prove in o-minimal expansions of real closed fields that the conjecture reduces to definably simple groups.

Virtual scenario-based testing methods to validate autonomous driving systems are predominantly centred around collision avoidance, and lack a comprehensive approach to evaluate optimal driving behaviour holistically. Furthermore, current validation approaches do not align with authorisation and monitoring requirements put forth by regulatory bodies. We address these validation gaps by outlining a universal evaluation framework that: incorporates the notion of careful and competent driving, unifies behavioural competencies and evaluation criteria, and is amenable at a scenario-specific and aggregate behaviour level. This framework can be leveraged to evaluate optimal driving in scenario-based testing, and for post-deployment monitoring to ensure continual compliance with regulation and safety standards., Comment: arXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission

Language models can be used to solve long-horizon planning problems in two distinct modes: a fast 'System-1' mode, directly generating plans without any explicit search or backtracking, and a slow 'System-2' mode, planning step-by-step by explicitly searching over possible actions. While System-2 is typically more effective, it is also more computationally expensive, making it infeasible for long plans or large action spaces. Moreover, isolated System-1 or 2 ignores the user's end goals, failing to provide ways to control the model's behavior. To this end, we propose the System-1.x Planner, a controllable planning framework with LLMs that is capable of generating hybrid plans and balancing between the two planning modes based on the difficulty of the problem at hand. System-1.x consists of (i) a controller, (ii) a System-1 Planner, and (iii) a System-2 Planner. Based on a user-specified hybridization factor (x) governing the mixture between System-1 and 2, the controller decomposes a problem into sub-goals, and classifies them as easy or hard to be solved by either System-1 or 2, respectively. We fine-tune all three components on top of a single base LLM, requiring only search traces as supervision. Experiments with two diverse planning tasks -- Maze Navigation and Blocksworld -- show that our System-1.x Planner outperforms a System-1 Planner, a System-2 Planner trained to approximate A* search, and also a symbolic planner (A*). We demonstrate the following key properties of our planner: (1) controllability: increasing the hybridization factor (e.g., System-1.75 vs 1.5) performs more search, improving performance, (2) flexibility: by building a neuro-symbolic variant with a neural System-1 and a symbolic System-2, we can use existing symbolic methods, and (3) generalizability: by being able to learn from different search algorithms, our method is robust to the choice of search algorithm., Comment: 29 pages (10 tables)

We study the relation between the multigraded Castelnuovo-Mumford regularity of a multihomogeneous ideal $I$ and the multidegrees of a Gr\"obner basis of $I$ with respect to the degree reverse lexicographical monomial order in generic coordinates. For the single graded case, forty years ago, Bayer and Stillman unravelled all aspects of this relation, which in turn the use to complexity estimates for the computation with Gr\"obner bases. We build on their work to introduce a bounding region of the multidegrees of minimal generators of multigraded Gr\"obner bases for $I$. We also use this region to certify the presence of some minimal generators close to its boundary. Finally, we show that, up to a certain shift, this region is related to the multigraded Castelnuovo-Mumford regularity of $I$.

This paper introduces a new macroscopic perspective for simulating transportation networks. The idea is to look at the network as connected nodes. Each node sends an information package to its neighbors. Basically, the information package contains a state change that a specific node experienced, and it might affect the traffic network state in the future. Different types of information can be counted in transportation network. Each information type has different characteristics. It propagates through the network and interacts with other IPs and nodes. As a result, the model enables implementing and analyzing dynamic and inconvenient control strategies. This paper focus on flow dynamics and demand routing information under complex environment. The flow dynamics flows the LWR theory along the links. The demand routing follows a node equilibrium model. The node model takes into account the users preferences and choices under dynamic control agents such as dynamic tolling and sends the needed information for other facilities to operate. A DNL model is developed based on the IPM. Several case studies demonstrate the use IPM and its potential to provide a reliable and results for real world complicated transportation applications.

As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agricultural sector. Autonomous robotic systems are one promising pathway to increase productivity by taking over labor-intensive manual tasks like fruit picking. To be effective, such systems need to monitor and interact with plants and fruits precisely, which is challenging due to the cluttered nature of agricultural environments causing, for example, strong occlusions. Thus, being able to estimate the complete 3D shapes of objects in presence of occlusions is crucial for automating operations such as fruit harvesting. In this paper, we propose the first publicly available 3D shape completion dataset for agricultural vision systems. We provide an RGB-D dataset for estimating the 3D shape of fruits. Specifically, our dataset contains RGB-D frames of single sweet peppers in lab conditions but also in a commercial greenhouse. For each fruit, we additionally collected high-precision point clouds that we use as ground truth. For acquiring the ground truth shape, we developed a measuring process that allows us to record data of real sweet pepper plants, both in the lab and in the greenhouse with high precision, and determine the shape of the sensed fruits. We release our dataset, consisting of almost 7,000 RGB-D frames belonging to more than 100 different fruits. We provide segmented RGB-D frames, with camera intrinsics to easily obtain colored point clouds, together with the corresponding high-precision, occlusion-free point clouds obtained with a high-precision laser scanner. We additionally enable evaluation of shape completion approaches on a hidden test set through a public challenge on a benchmark server.

Given a finite group $G$ and a prime $p$, we establish the sieve formula, which is a congruence containing as summands numbers of chains of $p$-subgroups of $G$ of certain orders. This generalises the Theorem of Sylow-Frobenius, using Wielandt's approach. Its name stems from the sieve formula from set theory because of formal similarities.

The Demazure operator associated to a simple reflection satisfies the twisted Leibniz rule. In this paper we introduce a generalization of the twisted Leibniz rule for the Demazure operator associated to any atomic double coset. We prove that this atomic Leibniz rule is equivalent to a polynomial forcing property for singular Soergel bimodules., Comment: 38 pages

Savannahs are vital ecosystems whose sustainability is endangered by the spread of woody plants. This research targets the accurate mapping of fractional woody cover (FWC) at the species level in a South African savannah, using EnMAP hyperspectral data. Field annotations were combined with very high-resolution multispectral drone data to produce land cover maps that included three woody species. The high-resolution labelled maps were then used to generate FWC samples for each woody species class at the 30-m spatial resolution of EnMAP. Four machine learning regression algorithms were tested for FWC mapping on dry season EnMAP imagery. The contribution of multitemporal information was also assessed by incorporating as additional regression features, spectro-temporal metrics from Sentinel-2 data of both the dry and wet seasons. The results demonstrated the suitability of our approach for accurately mapping FWC at the species level. The highest accuracy rates achieved from the combined EnMAP and Sentinel-2 experiments highlighted their synergistic potential for species-level vegetation mapping., Comment: Presented at the 2024 IEEE International Geoscience and Remote Sensing Symposium - IGARSS 2024 (IEEE doi link expected in next months)

Background: The adoption of chatbots into software development tasks has become increasingly popular among practitioners, driven by the advantages of cost reduction and acceleration of the software development process. Chatbots understand users' queries through the Natural Language Understanding component (NLU). To yield reasonable performance, NLUs have to be trained with extensive, high-quality datasets, that express a multitude of ways users may interact with chatbots. However, previous studies show that creating a high-quality training dataset for software engineering chatbots is expensive in terms of both resources and time. Aims: Therefore, in this paper, we present an automated transformer-based approach to augment software engineering chatbot datasets. Method: Our approach combines traditional natural language processing techniques with the BART transformer to augment a dataset by generating queries through synonym replacement and paraphrasing. We evaluate the impact of using the augmentation approach on the Rasa NLU's performance using three software engineering datasets. Results: Overall, the augmentation approach shows promising results in improving the Rasa's performance, augmenting queries with varying sentence structures while preserving their original semantics. Furthermore, it increases Rasa's confidence in its intent classification for the correctly classified intents. Conclusions: We believe that our study helps practitioners improve the performance of their chatbots and guides future research to propose augmentation techniques for SE chatbots.

We propose the pivoting meta algorithm (PM) to enhance optimization algorithms that generate iterates as convex combinations of vertices of a feasible region $C\subseteq \mathbb{R}^n$, including Frank-Wolfe (FW) variants. PM guarantees that the active set (the set of vertices in the convex combination) of the modified algorithm remains as small as $\mathrm{dim}(C)+1$ as stipulated by Carath\'eodory's theorem. PM achieves this by reformulating the active set expansion task into an equivalent linear program, which can be efficiently solved using a single pivot step akin to the primal simplex algorithm; the convergence rate of the original algorithms are maintained. Furthermore, we establish the connection between PM and active set identification, in particular showing under mild assumptions that PM applied to the away-step Frank-Wolfe algorithm or the blended pairwise Frank-Wolfe algorithm bounds the active set size by the dimension of the optimal face plus $1$. We provide numerical experiments to illustrate practicality and efficacy on active set size reduction.

Gathering data and identifying events in various traffic situations remains an essential challenge for the systematic evaluation of a perception system's performance. Analyzing large-scale, typically unstructured, multi-modal, time series data obtained from video, radar, and LiDAR is computationally demanding, particularly when meta-information or annotations are missing. We compare Optical Flow (OF) and Deep Learning (DL) to feed computationally efficient event detection via space-filling curves on video data from a forward-facing, in-vehicle camera. Our first approach leverages unexpected disturbances in the OF field from vehicle surroundings; the second approach is a DL model trained on human visual attention to predict a driver's gaze to spot potential event locations. We feed these results to a space-filling curve to reduce dimensionality and achieve computationally efficient event retrieval. We systematically evaluate our concept by obtaining characteristic patterns for both approaches from a large-scale virtual dataset (SMIRK) and applied our findings to the Zenseact Open Dataset (ZOD), a large multi-modal, real-world dataset, collected over two years in 14 different European countries. Our results yield that the OF approach excels in specificity and reduces false positives, while the DL approach demonstrates superior sensitivity. Both approaches offer comparable processing speed, making them suitable for real-time applications., Comment: 27th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2024)

Proof-of-stake blockchain protocols have emerged as a compelling paradigm for organizing distributed ledger systems. In proof-of-stake (PoS), a subset of stakeholders participate in validating a growing ledger of transactions. For the safety and liveness of the underlying system, it is desirable for the set of validators to include multiple independent entities as well as represent a non-negligible percentage of the total stake issued. In this paper, we study a secondary form of participation in the transaction validation process, which takes the form of stake delegation, whereby an agent delegates their stake to an active validator who acts as a stake pool operator. We study payment schemes that reward agents as a function of their collective actions regarding stake pool operation and delegation. Such payment schemes serve as a mechanism to incentivize participation in the validation process while maintaining decentralization. We observe natural trade-offs between these objectives and the total expenditure required to run the relevant payment schemes. Ultimately, we provide a family of payment schemes which can strike different balances between these competing objectives at equilibrium in a Bayesian game theoretic framework., Comment: 37 pages, 16 figures, accepted for publication at The 17th International Symposium on Algorithmic Game Theory (SAGT)

Active galactic nuclei (AGN) are extremely variable in the X-ray band down to very short timescales. However, the driver behind the X-ray variability is still poorly understood. Previous results suggest that the hot corona responsible for the primary Comptonized emission observed in AGN is expected to play an important role in driving the X-ray variability. In this work, we investigate the connection between the X-ray amplitude variability and the coronal physical parameters; namely, the temperature ($kT$) and optical depth ($\tau$). We present the spectral and timing analysis of 46 {\it NuSTAR} observations corresponding to a sample of 20 AGN. For each source, we derived the coronal temperature and optical depth through X-ray spectroscopy and computed the normalized excess variance for different energy bands on a timescale of $10$ ks. We find a strong inverse correlation between $kT$ and $\tau$, with correlation coefficient of $r<-0.9$ and negligible null probability. No clear dependence was found among the temperature and physical properties, such as the black hole mass or the Eddington ratio. We also see that the observed X-ray variability is not correlated with either the coronal temperature or optical depth under the thermal equilibrium assumption, whereas it is anticorrelated with the black hole mass. These results can be interpreted through a scenario where the observed X-ray variability could primarily be driven by variations in the coronal physical properties on a timescale of less than $10$~ks; whereas we assume thermal equilibrium on such timescales in this work, given the capability of the currently available hard X-ray telescopes. Alternatively, it is also possible that the X-ray variability is mostly driven by the absolute size of the corona, which depends on the supermassive black hole mass, rather than resulting from any of its physical properties., Comment: Accepted for publication in Astronomy & Astrophysics, 21 pages, 9 figures, 4 tables

For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washington, participants were surveyed at least 28 days after testing positive and asked to report current symptom status. This study design yielded current status data: outcome measurements for each respondent consisted only of the time of survey response and a binary indicator of whether symptoms had resolved by that time. Such study design benefits from limited risk of recall bias, but analyzing the resulting data necessitates tailored statistical tools. Here, we review methods for current status data and describe a novel application of modern nonparametric techniques to this setting. The proposed approach is valid under weaker assumptions compared to existing methods, allows use of flexible machine learning tools, and handles potential survey nonresponse. From the university study, we estimate that 19% of participants experienced ongoing symptoms 30 days after testing positive, decreasing to 7% at 90 days. Female sex, history of seasonal allergies, fatigue during acute infection, and higher viral load were associated with slower symptom resolution., Comment: The first two authors contributed equally to this work. Main text: 20 pages, 1 figure, 4 tables. Supplement: 14 pages, 8 figures, 0 tables

Graph Neural Networks (GNNs), especially message-passing-based models, have become prominent in top-k recommendation tasks, outperforming matrix factorization models due to their ability to efficiently aggregate information from a broader context. Although GNNs are evaluated with ranking-based metrics, e.g NDCG@k and Recall@k, they remain largely trained with proxy losses, e.g the BPR loss. In this work we explore the use of ranking loss functions to directly optimize the evaluation metrics, an area not extensively investigated in the GNN community for collaborative filtering. We take advantage of smooth approximations of the rank to facilitate end-to-end training of GNNs and propose a Personalized PageRank-based negative sampling strategy tailored for ranking loss functions. Moreover, we extend the evaluation of GNN models for top-k recommendation tasks with an inductive user-centric protocol, providing a more accurate reflection of real-world applications. Our proposed method significantly outperforms the standard BPR loss and more advanced losses across four datasets and four recent GNN architectures while also exhibiting faster training. Demonstrating the potential of ranking loss functions in improving GNN training for collaborative filtering tasks.

We present a system for Complex Event Recognition (CER) based on automata. While multiple such systems have been described in the literature, they typically suffer from a lack of clear and denotational semantics, a limitation which often leads to confusion with respect to their expressive power. In order to address this issue, our system is based on an automaton model which is a combination of symbolic and register automata. We extend previous work on these types of automata, in order to construct a formalism with clear semantics and a corresponding automaton model whose properties can be formally investigated. We call such automata Symbolic Register Transducers (SRT). We show that SRT are closed under various operators, but are not in general closed under complement and they are not determinizable. However, they are closed under these operations when a window operator, quintessential in Complex Event Recognition, is used. We show how SRT can be used in CER in order to detect patterns upon streams of events, using our framework that provides declarative and compositional semantics, and that allows for a systematic treatment of such automata. For SRT to work in pattern detection, we allow them to mark events from the input stream as belonging to a complex event or not, hence the name "transducers". We also present an implementation of SRT which can perform CER. We compare our SRT-based CER engine against other state-of-the-art CER systems and show that it is both more expressive and more efficient., Comment: arXiv admin note: substantial text overlap with arXiv:2110.04032

Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs), e.g. CLIP, for few-shot image classification. Despite their success, most prompt learning methods trade-off between classification accuracy and robustness, e.g. in domain generalization or out-of-distribution (OOD) detection. In this work, we introduce Global-Local Prompts (GalLoP), a new prompt learning method that learns multiple diverse prompts leveraging both global and local visual features. The training of the local prompts relies on local features with an enhanced vision-text alignment. To focus only on pertinent features, this local alignment is coupled with a sparsity strategy in the selection of the local features. We enforce diversity on the set of prompts using a new ``prompt dropout'' technique and a multiscale strategy on the local prompts. GalLoP outperforms previous prompt learning methods on accuracy on eleven datasets in different few shots settings and with various backbones. Furthermore, GalLoP shows strong robustness performances in both domain generalization and OOD detection, even outperforming dedicated OOD detection methods. Code and instructions to reproduce our results: https://github.com/MarcLafon/gallop.

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants' thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner's evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining the results of this study and a prior heating study of the same house, the estimated year-round energy cost savings were $340-497 or 22-31% (95% confidence intervals); these savings were consistent across both humidity models. However, in the demand response tests, the simplifying assumption of constant humidity led to far more frequent and severe violations of the power constraint. These results suggest that accurate building models are important for nonlinear objectives, such as reducing or constraining peak demand, while for linear objectives such as reducing energy costs or emissions, model accuracy is less important.

The visual appeal of urban environments significantly impacts residents' satisfaction with their living spaces and their overall mood, which in turn, affects their health and well-being. Given the resource-intensive nature of gathering evaluations on urban visual appeal through surveys or inquiries from residents, there is a constant quest for automated solutions to streamline this process and support spatial planning. In this study, we applied an off-the-shelf AI model to automate the analysis of urban visual appeal, using over 1,800 Google Street View images of Helsinki, Finland. By incorporating the GPT-4 model with specified criteria, we assessed these images. Simultaneously, 24 participants were asked to rate the images. Our results demonstrated a strong alignment between GPT-4 and participant ratings, although geographic disparities were noted. Specifically, GPT-4 showed a preference for suburban areas with significant greenery, contrasting with participants who found these areas less appealing. Conversely, in the city centre and densely populated urban regions of Helsinki, GPT-4 assigned lower visual appeal scores than participant ratings. While there was general agreement between AI and human assessments across various locations, GPT-4 struggled to incorporate contextual nuances into its ratings, unlike participants, who considered both context and features of the urban environment. The study suggests that leveraging AI models like GPT-4 allows spatial planners to gather insights into the visual appeal of different areas efficiently, aiding decisions that enhance residents' and travellers' satisfaction and mental health. Although AI models provide valuable insights, human perspectives are essential for a comprehensive understanding of urban visual appeal. This will ensure that planning and design decisions promote healthy living environments effectively., Comment: 42 pages, 4 figures, 6 tables

We put a new spin on Khovanov--Rozansky homology. That is, we equip $\Lambda^n$-colored $\mathfrak{sl}_{2n}$ Khovanov--Rozansky homology with an involution whose $\pm 1$-eigenspaces are link invariants. When $n=1,2,3$ (and assuming technical conjectures for $n \geq 4$), we prove that this refined invariant categorifies the spin-colored $\mathfrak{so}_{2n+1}$ quantum link polynomial. Along the way, we partially develop the theory of quantum $\mathfrak{so}_{2n+1}$ webs and make contact with $\iota$quantum groups., Comment: 137 pages, including 3 appendices; many diagrams; best viewed in color; comments welcome!

The model editing problem concerns how language models should learn new facts about the world over time. While empirical research on model editing has drawn widespread attention, the conceptual foundations of model editing remain shaky -- perhaps unsurprisingly, since model editing is essentially belief revision, a storied problem in philosophy that has eluded succinct solutions for decades. Model editing nonetheless demands a solution, since we need to be able to control the knowledge within language models. With this goal in mind, this paper critiques the standard formulation of the model editing problem and proposes a formal testbed for model editing research. We first describe 12 open problems with model editing, based on challenges with (1) defining the problem, (2) developing benchmarks, and (3) assuming LLMs have editable beliefs in the first place. Many of these challenges are extremely difficult to address, e.g. determining far-reaching consequences of edits, labeling probabilistic entailments between facts, and updating beliefs of agent simulators. Next, we introduce a semi-synthetic dataset for model editing based on Wikidata, where we can evaluate edits against labels given by an idealized Bayesian agent. This enables us to say exactly how belief revision in language models falls short of a desirable epistemic standard. We encourage further research exploring settings where such a gold standard can be compared against. Our code is publicly available at: https://github.com/peterbhase/LLM-belief-revision, Comment: 23 pages, 4 figures

We present a discontinuous Galerkin-finite difference hybrid scheme that allows high-order shock capturing with the discontinuous Galerkin method for general relativistic magnetohydrodynamics in dynamical spacetimes. We present several optimizations and stability improvements to our algorithm that allow the hybrid method to successfully simulate single, rotating, and binary neutron stars. The hybrid method achieves the efficiency of discontinuous Galerkin methods throughout almost the entire spacetime during the inspiral phase, while being able to robustly capture shocks and resolve the stellar surfaces. We also use Cauchy-Characteristic evolution to compute the first gravitational waveforms at future null infinity from binary neutron star mergers. The simulations presented here are the first successful binary neutron star inspiral and merger simulations using discontinuous Galerkin methods., Comment: 31 pages, 8 figures, minor wording updates

We provide a template to derive convergence rates for the following popular versions of the Frank-Wolfe algorithm on polytopes: vanilla Frank-Wolfe, Frank-Wolfe with away steps, Frank-Wolfe with blended pairwise steps, and Frank-Wolfe with in-face directions. Our template shows how the convergence rates follow from two affine-invariant properties of the problem, namely, error bound and extended curvature. These properties depend solely on the polytope and objective function but not on any affine-dependent object like norms. For each one of the above algorithms, we derive rates of convergence ranging from sublinear to linear depending on the degree of the error bound., Comment: 28 pages

Reducing operation and maintenance costs is a key objective for advanced reactors in general and microreactors in particular. To achieve this reduction, developing robust autonomous control algorithms is essential to ensure safe and autonomous reactor operation. Recently, artificial intelligence and machine learning algorithms, specifically reinforcement learning (RL) algorithms, have seen rapid increased application to control problems, such as plasma control in fusion tokamaks and building energy management. In this work, we introduce the use of RL for intelligent control in nuclear microreactors. The RL agent is trained using proximal policy optimization (PPO) and advantage actor-critic (A2C), cutting-edge deep RL techniques, based on a high-fidelity simulation of a microreactor design inspired by the Westinghouse eVinci\textsuperscript{TM} design. We utilized a Serpent model to generate data on drum positions, core criticality, and core power distribution for training a feedforward neural network surrogate model. This surrogate model was then used to guide a PPO and A2C control policies in determining the optimal drum position across various reactor burnup states, ensuring critical core conditions and symmetrical power distribution across all six core portions. The results demonstrate the excellent performance of PPO in identifying optimal drum positions, achieving a hextant power tilt ratio of approximately 1.002 (within the limit of $<$ 1.02) and maintaining criticality within a 10 pcm range. A2C did not provide as competitive of a performance as PPO in terms of performance metrics for all burnup steps considered in the cycle. Additionally, the results highlight the capability of well-trained RL control policies to quickly identify control actions, suggesting a promising approach for enabling real-time autonomous control through digital twins., Comment: 15 pages, 3 figures, and 2 tables

In this work, we present the results of a detailed X-ray spectral analysis of the brightest AGNs detected in the XMM-Newton 1.75 Ms Ultra Narrow Deep Field. We analyzed 23 AGNs that have a luminosity range of $\sim 10^{42} - 10^{46}\, \rm{erg}\, \rm{s}^{-1}$ in the $2 - 10\, \rm{keV}$ energy band, redshifts up to 2.66, and $\sim 10,000$ X-ray photon counts in the $0.3 - 10\, \rm{keV}$ energy band. Our analysis confirms the Iwasawa-Taniguchi effect, an anti-correlation between the X-ray luminosity ($L_x$) and the Fe-k$\alpha$ Equivalent Width ($EW_{Fe}$) possibly associated with the decreasing of the torus covering factor as the AGN luminosity increases. We investigated the relationship among black hole mass ($M_{BH}$), $L_x$, and X-ray variability, quantified by the Normalized Excess Variance ($\sigma^2_{rms}$). Our analysis suggest an anti-correlation in both $M_{BH} - \sigma^2_{rms}$ and $L_x- \sigma^2_{rms}$ relations. The first is described as $\sigma^2_{rms} \propto M^{-0.26 \pm 0.05}_{BH}$, while the second presents a similar trend with $\sigma^2_{rms} \propto L_{x}^{-0.31 \pm 0.04}$. These results support the idea that the luminosity-variability anti-correlation is a byproduct of an intrinsic relationship between the BH mass and the X-ray variability, through the size of the emitting region. Finally, we found a strong correlation among the Eddington ratio ($\lambda_{Edd}$), the hard X-ray photon index ($\Gamma$), and the illumination factor $\log(A)$, which is related to the ratio between the number of Compton scattered photons and the number of seed photons. The $\log(\lambda_{Edd})-\Gamma-\log(A)$ plane could arise naturally from the connection between the accretion flow and the hot corona., Comment: 16 pages, 14 figures, to be published in MNRAS

This paper lays the groundwork for a unifying approach to stochastic dominance testing under survey nonresponse that integrates the partial identification approach to incomplete data and design-based inference for complex survey data. We propose a novel inference procedure for restricted $s$th-order stochastic dominance, tailored to accommodate a broad spectrum of nonresponse assumptions. The method uses pseudo-empirical likelihood to formulate the test statistic and compares it to a critical value from the chi-squared distribution with one degree of freedom. We detail the procedure's asymptotic properties under both null and alternative hypotheses, establishing its uniform validity under the null and consistency against various alternatives. Using the Household, Income and Labour Dynamics in Australia survey, we demonstrate the procedure's utility in a sensitivity analysis of temporal poverty comparisons among Australian households.

There is an abstract notion of connection in any tangent category. In this paper, we show that when applied to the tangent category of affine schemes, this recreates the classical notion of a connection on a module (and similarly, in the tangent category of schemes, this recreates the notion of connection on a quasi-coherent sheaf of modules). By contrast, we also show that in the tangent category of algebras, there are no non-trivial connections., Comment: Minor changes and typos fixes based on reviewer comments

We present results from the occultation of Betelgeuse by asteroid (319) Leona on December 12, 2023, observed using a 64x64 pixel Single-Photon Avalanche Diode (SPAD) array mounted on a 10-inch telescope at the AstroCamp Observatory in Nerpio, Southeast of Spain, just a few kilometers from the center of the occultation shadow path. This study highlights remarkable advancements in applying SPAD technology in astronomy. The SPAD array's asynchronous readout capacity and photon-counting timestamp mode enabled a temporal resolution of 1 microsecond in our light curve observations of Betelgeuse. Our data analysis addressed challenges inherent to SPAD arrays, such as optical cross-talk and afterpulses, which typically cause the photon statistics to deviate from a Poisson distribution. By adopting a generalized negative binomial distribution for photon statistics, we accurately describe the observational data. This method yielded an optical cross-talk estimation of 1.07% in our SPAD array and confirmed a negligible impact of spurious detected events due to afterpulses. The meticulous statistical examination of photon data underscores our SPAD-array's exceptional performance in conducting precise astronomical observations. The observations revealed a major decrease in Betelgeuse's intensity by 77.78% at the occultation's peak, allowing us to measure Betelgeuse's angular diameter at 57.26 mas in the SDSS g-band. This measurement, employing a simplified occultation model and considering the known properties of Leona, demonstrates the potential of SPAD technology for astronomy and sets a new standard for observing ultra-rapid transient celestial events, providing a valuable public dataset for the astronomical community., Comment: 7 pages, 4 figures

Let $\{e^{i\lambda_n t}\}_{n\in\mathbb{Z}}$ be an exponential Schauder Basis for $L^2 (0,1)$, for $\lambda_n\in\mathbb{R}$, and let $\{r_n(t)\}_{n\in\mathbb{Z}}$ be its dual Schauder Basis. Let $A$ be a non-empty subset of the integers containing exactly $M$ elements. We prove that for $\alpha >0$ the weighted system \[ \{t^{\alpha}\cdot r_n(t)\}_{n\in\mathbb{Z}\setminus A} \] is exact in the space $L^2 (0,1)$, that is, it is complete and minimal in $L^2 (0,1)$, if and only if \[ M-\frac{1}{2}\le \alpha< M+\frac{1}{2}. \] We also show that such a system is not a Riesz Basis for $L^2 (0,1)$. In particular, the weighted trigonometric system $\{t^{\alpha}\cdot e^{2\pi i n t}\}_{n\in\mathbb{Z}\setminus A}$ is exact in $L^2 (0,1)$, if and only if $\alpha\in [M-\frac{1}{2}, M+\frac{1}{2})$, but it is not a Schauder Basis for $L^2 (0,1)$., Comment: 8 pages

Therapeutic ultrasound for brain stimulation has increased in the last years. This energy has shown promising results for treating Alzheimers disease, Parkinsons disease, and traumatic brain injury, among other conditions. However, the application of ultrasound in the brain should trespass a natural but highly attenuating and distorting barrier, the cranium. Implantable ceramic materials can be used to replace part of the cranium as an alternate method to enhance ultrasound transmission. In this work, it is presented the acoustic characterization of alumina ceramic disks that can be employed as cranial implants for acoustic windows-to-the-brain. Alumina samples were prepared using current-activated pressure-assisted densification and were acoustically characterized. Acoustic impedance and attenuation of the samples were determined for different porosities. Additionally, measured and modeled acoustic fields are presented and analyzed in terms of the total ultrasound transmitted through the ceramics. Results indicate a resonant behavior in the alumina disks when the thickness corresponds to a half-wavelength of ultrasound; this resonance permits a total of 95.4% of ultrasound transmission; for thicknesses out of the resonant zone, transmission is 53.0%. Alumina proves to be an excellent medium for ultrasound transmission that, in conjunction with its mechanical and optical properties, can be useful for cranium replacement in mixed opto-acoustic applications., Comment: Journal of Physics: Materials

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Vision-language models (VLMs) can respond to queries about images in many languages. However, beyond language, culture affects how we see things. For example, individuals from Western cultures focus more on the central figure in an image while individuals from Eastern cultures attend more to scene context. In this work, we present a novel investigation that demonstrates and localizes VLMs' Western bias in image understanding. We evaluate large VLMs across subjective and objective visual tasks with culturally diverse images and annotations. We find that VLMs perform better on the Western subset than the Eastern subset of each task. Controlled experimentation tracing the source of this bias highlights the importance of a diverse language mix in text-only pre-training for building equitable VLMs, even when inference is performed in English. Moreover, while prompting in the language of a target culture can lead to reductions in bias, it is not a substitute for building AI more representative of the world's languages., Comment: 17 pages, 7 figures. Code/models: https://github.com/amith-ananthram/see-it-from-my-perspective

Metastable nanostructures are kinetically trapped in local energy minima featuring intriguing surface and material properties. To unleash their potential, there is a need for non-equilibrium processes capable of stabilizing a large range of crystal phases outside thermodynamic equilibrium conditions by closely and flexibly controlling atomic reactant composition, spatial temperature distribution and residence time. Here, we demonstrate the capture of metastable pseudo-binary metal oxides at room temperature with scalable combustion-aerosol processes. By a combination of X-ray diffraction, electron microscopy and online flame characterization, we investigate the occurrence of metastable CoCu$_2$O$_3$ with controlled crystal size (4-16 nm) over thermodynamically stable CuO and Co$_3$O$_4$. Immediate practical impact is demonstrated by exceptional sensing and catalytic performance for air pollutant detection (e.g., 15 parts-per-billion benzene). This approach can be extended to various binary, ternary and high entropy oxides with even more components to access novel materials also promising for actuators, energy storage or solar cells.

Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on a large body of research in the two fields. We explore the potential of LLMs as tools for ALife research, for example, as operators for evolutionary computation or the generation of open-ended environments. Reciprocally, principles of ALife, such as self-organization, collective intelligence and evolvability can provide an opportunity for shaping the development and functionalities of LLMs, leading to more adaptive and responsive models. By investigating this dynamic interplay, the paper aims to inspire innovative crossover approaches for both ALife and LLM research. Along the way, we examine the extent to which LLMs appear to increasingly exhibit properties such as emergence or collective intelligence, expanding beyond their original goal of generating text, and potentially redefining our perception of lifelike intelligence in artificial systems.

The generation of structured data in formats such as JSON, YAML and XML is a critical task in Generative AI (GenAI) applications. These formats, while widely used, contain many redundant constructs that lead to inflated token usage. This inefficiency is particularly evident when employing large language models (LLMs) like GPT-4, where generating extensive structured data incurs increased latency and operational costs. We introduce a domain-specific shorthand (DSS) format, underpinned by a context-free grammar (CFG), and demonstrate its usage to reduce the number of tokens required for structured data generation. The method involves creating a shorthand notation that captures essential elements of the output schema with fewer tokens, ensuring it can be unambiguously converted to and from its verbose form. It employs a CFG to facilitate efficient shorthand generation by the LLM, and to create parsers to translate the shorthand back into standard structured formats. The application of our approach to data visualization with LLMs demonstrates a significant (3x to 5x) reduction in generated tokens, leading to significantly lower latency and cost. This paper outlines the development of the DSS and the accompanying CFG, and the implications of this approach for GenAI applications, presenting a scalable solution to the token inefficiency problem in structured data generation.

In classical thermodynamics, heat must spontaneously flow from hot to cold systems. In quantum thermodynamics, the same law applies when considering multipartite product thermal states evolving unitarily. If initial correlations are present, anomalous heat flow can happen, temporarily making cold thermal states colder and hot thermal states hotter. Such effect can happen due to entanglement, but also because of classical randomness, hence lacking a direct connection with nonclassicality. In this work, we introduce scenarios where anomalous heat flow \emph{does} have a direct link to nonclassicality, defined to be the failure of noncontextual models to explain experimental data. We start by extending known noncontextuality inequalities to a setup where sequential transformations are considered. We then show a class of quantum prepare-transform-measure protocols, characterized by time intervals $(0,\tau_c)$ for a given critical time $\tau_c$, where anomalous heat flow happens only if a noncontextuality inequality is violated. We also analyze a recent experiment from Micadei et. al. [Nat. Commun. 10, 2456 (2019)] and find the critical time $\tau_c$ based on their experimental parameters. We conclude by investigating heat flow in the evolution of two qutrit systems, showing that our findings are not an artifact of using two-qubit systems., Comment: 14+10 pages, 2 figures. Comments are welcome!

Ever-increasingly complex business processes are enabled by loosely coupled cloud-native systems. In such fast-paced development environments, data controllers face the challenge of capturing and updating all personal data processing activities due to considerable communication overhead between development teams and data protection staff. To date, established business process management methods generate valuable insights about systems, however, they do not account for all regulatory transparency obligations. For instance, data controllers need to record all information about data categories, legal purpose specifications, third-country transfers, etc. Therefore, we propose to bridge the gap between business processes and application systems by providing three contributions that assist in modeling, discovering, and checking personal data transparency through a process-oriented perspective. We enable transparency modeling for relevant business activities by providing a plug-in extension to BPMN featuring regulatory transparency information. Furthermore, we utilize event logs to record regulatory transparency information in realistic cloud-native systems. On this basis, we leverage process mining techniques to discover and analyze personal data flows in business processes, e.g., through transparency conformance checking. We design and implement prototypes for all contributions, emphasizing the appropriate integration and modeling effort required to create business-process-oriented transparency. Altogether, we connect current business process engineering techniques with regulatory needs as imposed by the GDPR and other legal frameworks., Comment: Preprint, accepted to the BPM Forum 2024