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The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation.

We prove that any tensor product factorization with a commutative factor of a modular group algebra over a prime field comes from a direct product decomposition of the group basis. This extends previous work by Carlson and Kov\'acs for the commutative case and answers a question of them in some cases.

Recent advances in camera design and imaging technology have enabled the capture of high-quality images using smartphones. However, due to the limited dynamic range of digital cameras, the quality of photographs captured in environments with highly imbalanced lighting often results in poor-quality images. To address this issue, most devices capture multi-exposure frames and then use some multi-exposure fusion method to merge those frames into a final fused image. Nevertheless, most traditional and current deep learning approaches are unsuitable for real-time applications on mobile devices due to their heavy computational and memory requirements. We propose a new method for multi-exposure fusion based on an encoder-decoder deep learning architecture with efficient building blocks tailored for mobile devices. This efficient design makes our model capable of processing 4K resolution images in less than 2 seconds on mid-range smartphones. Our method outperforms state-of-the-art techniques regarding full-reference quality measures and computational efficiency (runtime and memory usage), making it ideal for real-time applications on hardware-constrained devices. Our code is available at: https://github.com/LucasKirsten/MobileMEF.

The s-club cluster vertex deletion number of a graph, or sccvd, is the minimum number of vertices whose deletion results in a disjoint union of s-clubs, or graphs whose diameter is bounded above by s. We launch a study of several domination problems on diameter-two graphs, or 2-clubs, and study their parameterized complexity with respect to the 2ccvd number as main parameter. We further propose to explore the class of problems that become solvable in sub-exponential time when the running time is independent of some input parameter. Hardness of problems for this class depends on the Exponential-Time Hypothesis. We give examples of problems that are in the proposed class and problems that are hard for it.

We show, for any $n \geq 2$, that there exists quantum correlations obtained from performing $n$ dichotomic quantum measurements in a bipartite Bell scenario, which cannot be reproduced by $n-1$ measurements in any causal theory. That is, it requires any no-signaling theory an unbounded number of measurements to reproduce the predictions of quantum theory. We prove our results by showing that there exists Bell inequalities that have to be obeyed by any no-signaling theory involving only $n-1$ measurements and show explicitly how these can be violated in quantum theory. Finally, we discuss the relation of our work to previous works ruling out alternatives to quantum theory with some kind of bounded degree of freedom and consider the experimental verifiability of our results., Comment: 6+4 pages, 1 figure. Comments are welcome!

Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess which combinations of lines are the most useful. Therefore, observation campaigns usually try to observe as many lines as possible for as much time as possible. We search for a quantitative statistical criterion to evaluate the constraining power of a (or combination of) tracer(s) with respect to physical conditions in order to improve our understanding of the statistical relationships between ISM tracers and physical conditions and helps observers to motivate their observation proposals. The best tracers are obtained by comparing the mutual information between a physical parameter and different sets of lines. We apply this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula that would be observed at the IRAM 30m telescope. We search for the best lines to constrain the visual extinction $A_v^{tot}$ or the far UV illumination $G_0$. The most informative lines change with the physical regime (e.g., cloud extinction). Short integration time of the CO isotopologue $J=1-0$ lines already yields much information on the total column density most regimes. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on $G_0$ are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface (e.g., [CII] and [CI] lines). This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.

Several paradigms for declarative problem solving start from a specification in a high-level language, which is then transformed to a low-level language, such as SAT or SMT. Often, this transformation includes a "grounding" step to remove first-order quantification. To reduce the time and size of the grounding, it can be useful to simplify formulas along the way, e.g., by already taking into account the interpretation of symbols that are already known. In this paper, we investigate the use of bit vectors to efficiently simplify formulas, thereby taking advantage of the fact that, on modern hardware, logical operations on bit vectors can be executed extremely fast. We conduct an experimental analysis, which shows that bit vectors are indeed fast for certain problems, but also have limitations., Comment: Short version published at LPNMR 2024

Under conventional 5G system design, the authentication and continuous monitoring of user equipment (UE) demands a reliable backhaul connection between the radio access network (RAN) and the core network functions (AMF, AUSF, UDM, etc.). This is not a given, especially in disaster response and military operations. We propose that, in these scenarios, decisions made by core functions can be effectively delegated to the RAN by leveraging the RAN's computing resources and the micro-service programmability of the O-RAN system architecture. This paper presents several concrete designs of core-RAN decision delegation, including caching of core decisions and replicating some of the core decision logic. Each design has revealed interesting performance and security trade-offs that warrant further investigation.

Given a branched cover of manifolds, one can lift homeomorphisms along the cover to obtain a (virtual) homomorphism between mapping class groups. Following a question of Margalit-Winarski, we study the injectivity of this lifting map in the case of $3$-manifolds. We show that in contrast to the case of surfaces, the lifting map is generally not injective for most regular branched covers of $3$-manifolds. This includes the double cover of $S^3$ branched over the unlink, which generalizes the hyperelliptic branched cover of $S^2$. In this case, we find a finite normal generating set for the kernel of the lifting map., Comment: 34 pages, 3 figures. Comments welcome. Version 2: new application for bundles, minor edits

In recent years, deep learning techniques have been used to develop sign language recognition systems, potentially serving as a communication tool for millions of hearing-impaired individuals worldwide. However, there are inherent challenges in creating such systems. Firstly, it is important to consider as many linguistic parameters as possible in gesture execution to avoid ambiguity between words. Moreover, to facilitate the real-world adoption of the created solution, it is essential to ensure that the chosen technology is realistic, avoiding expensive, intrusive, or low-mobility sensors, as well as very complex deep learning architectures that impose high computational requirements. Based on this, our work aims to propose an efficient sign language recognition system that utilizes low-cost sensors and techniques. To this end, an object detection model was trained specifically for detecting the interpreter's face and hands, ensuring focus on the most relevant regions of the image and generating inputs with higher semantic value for the classifier. Additionally, we introduced a novel approach to obtain features representing hand location and movement by leveraging spatial information derived from centroid positions of bounding boxes, thereby enhancing sign discrimination. The results demonstrate the efficiency of our handcrafted features, increasing accuracy by 7.96% on the AUTSL dataset, while adding fewer than 700 thousand parameters and incurring less than 10 milliseconds of additional inference time. These findings highlight the potential of our technique to strike a favorable balance between computational cost and accuracy, making it a promising approach for practical sign language recognition applications., Comment: 28 pages, 12 figures, submitted to Image and Vision Computing Journal

The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field., Comment: 26 pages

Two-dimensional (2D) organic-inorganic hybrid perovskites emerged as a versatile platform for light-emitting and photovol-taic applications due to their unique structural design and chemical flexibility. Their properties depend heavily on both the choice of the inorganic lead halide framework and the surrounding organic layers. Recently, the introduction of chiral cations into 2D perovskites has attracted major interest due to their potential for introducing chirality and tuning the chiro-optical response. Importantly, the optical properties in these materials are dominated by tightly bound excitons that also serve as primary carriers for the energy transport. The mobility of photoinjected excitons is thus important from the perspectives of fundamental material properties and optoelectronic applications, yet remains an open question. Here, we demonstrate exciton propagation in a 2D chiral perovskite methylbenzylammonium lead iodide (MBA2PbI4) using transient photoluminescence microscopy and reveal density-dependent transport over more than 100 nanometers at room temperature with diffusion coeffi-cients as high as 2 cm2/s. We observe two distinct regimes of initially rapid diffusive propagation and subsequent localiza-tion. Moreover, perovskites with enantiomer pure cations are found to exhibit faster exciton diffusion compared to the race-mic mixture, correlated with the impact of the material composition on disorder. Altogether, the observations of efficient exciton diffusion at room temperature highlight the potential of 2D chiral perovskites to merge chiro-optical properties with strong light-matter interaction and efficient energy transport.

In today's digital era, the use of Social Networks (SNs) and Location-Based SNs (LBSNs) has become integral for travelers seeking Points of Interest (POI) and sharing travel experiences. This trend is supported by the fact that a significant majority of American travelers utilize SNs during their trips. However, the abundance of information available on these platforms presents a challenge in identifying the best options. To address this issue, Recommender Systems (RS) are commonly employed to suggest POIs based on user history, with the integration of contextual information enhancing the quality of recommendations. Notably, incorporating user travel purpose, which is often overlooked but holds potential in characterizing travelers' behavior, can lead to more tailored recommendations. In this study, we propose a model to predict whether a trip is leisure or work-related, utilizing state-of-the-art Automatic Text Classification (ATC) models such as BERT, RoBERTa, and BART to enhance the understanding of user travel purposes and improve recommendation accuracy in specific travel scenarios., Comment: 2 pages, 3 tables

Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke applications that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio's flexibility in tackling pressing societal challenges. Curio is available at https://urbantk.org/curio., Comment: Accepted at IEEE VIS 2024. Source code available at https://urbantk.org/curio

Existing sample-based methods, like influence functions and representer points, measure the importance of a training point by approximating the effect of its removal from training. As such, they are skewed towards outliers and points that are very close to the decision boundaries. The explanations provided by these methods are often static and not specific enough for different test points. In this paper, we propose a method to generate an explanation in the form of support spectrums which are based on two main ideas: the support sets and a global-to-local importance measure. The support set is the set of training points, in the predicted class, that ``lie in between'' the test point and training points in the other classes. They indicate how well the test point can be distinguished from the points not in the predicted class. The global-to-local importance measure is obtained by decoupling existing methods into the global and local components which are then used to select the points in the support set. Using this method, we are able to generate explanations that are tailored to specific test points. In the experiments, we show the effectiveness of the method in image classification and text generation tasks.

Autonomous robots consistently encounter unforeseen dangerous situations during exploration missions. The characteristic rimless wheels in the AsguardIV rover allow it to overcome challenging terrains. However, steep slopes or difficult maneuvers can cause the rover to tip over and threaten the completion of a mission. This work focuses on identifying early signs or initial stages for potential tip-over events to predict and detect these critical moments before they fully occur, possibly preventing accidents and enhancing the safety and stability of the rover during its exploration mission. Inertial Measurement Units (IMU) readings are used to develop compact, robust, and efficient Autoencoders that combine the power of sequence processing of Long Short-Term Memory Networks (LSTM). By leveraging LSTM-based Autoencoders, this work contributes predictive capabilities for detecting tip-over risks and developing safety measures for more reliable exploration missions., Comment: 10 pages, 7 figures

We present a mathematical theory of metastable pure states in closed many-body quantum systems with finite-dimensional Hilbert space. Given a Hamiltonian, a pure state is defined to be metastable when all sufficiently local operators either stabilize the state, or raise its average energy. We prove that short-range entangled metastable states are necessarily eigenstates (scars) of a perturbatively close Hamiltonian. Given any metastable eigenstate of a Hamiltonian, in the presence of perturbations, we prove the presence of prethermal behavior: local correlation functions decay at a rate bounded by a time scale nonperturbatively long in the inverse metastability radius, rather than Fermi's Golden Rule. Inspired by this general theory, we prove that the lifetime of the false vacuum in certain $d$-dimensional quantum models grows at least as fast as $\exp(\epsilon^{-d} \log^{-3} \epsilon^{-1} )$, where $\epsilon\rightarrow 0$ is the relative energy density of the false vacuum; up to logarithms, this lower bound matches, for the first time, explicit calculations using quantum field theory. We identify metastable states at finite energy density in the PXP model, along with exponentially many metastable states in "helical" spin chains and the two-dimensional Ising model. Our inherently quantum formalism reveals precise connections between many problems, including prethermalization, robust quantum scars, and quantum nucleation theory, and applies to systems without known semiclassical and/or field theoretic limits., Comment: 26+34 pages; 6+3 figures

Pretrained models have become a commodity and offer strong results on a broad range of tasks. In this work, we focus on classification and seek to learn a unique encoder able to take from several complementary pretrained models. We aim at even stronger generalization across a variety of classification tasks. We propose to learn such an encoder via multi-teacher distillation. We first thoroughly analyse standard distillation when driven by multiple strong teachers with complementary strengths. Guided by this analysis, we gradually propose improvements to the basic distillation setup. Among those, we enrich the architecture of the encoder with a ladder of expendable projectors, which increases the impact of intermediate features during distillation, and we introduce teacher dropping, a regularization mechanism that better balances the teachers' influence. Our final distillation strategy leads to student models of the same capacity as any of the teachers, while retaining or improving upon the performance of the best teacher for each task. Project page and code: https://europe.naverlabs.com/unic, Comment: To be presented at ECCV 2024

This paper demonstrates the efficient extraction of unstable recurrent flows from two-dimensional turbulence by using nonlinear triads to diagnose recurrence in direct numerical simulations. Nearly recurrent episodes are identified from simulations and then converged using a standard Newton- GMRES-hookstep method, however with much greater diversity than previous studies which performed this 'recurrent flow analysis'. Unstable periodic and relative periodic orbits are able to be identified which span larger values of dissipation rate, i.e. corresponding to extreme bursting events. The triad variables are found to provide a more natural way to weight the greater variety of spatial modes active in such orbits than a standard Euclidian norm of complex Fourier amplitudes. Moreover the triad variables build in a reduction of the continuous symmetry of the system which avoids the need to search over translations when obtaining relative periodic orbits. Armed with these orbits we investigate optimal weightings when reconstructing the statistics of turbulence and suggest that, in fact, a simple heuristic weighting based on the solution instability provides a very good prediction, provided enough dynamically relevant orbits are included in the expansion.

Embedding techniques allow the efficient description of correlations within localized fragments of large molecular systems, while accounting for their environment at a lower level of theory. We introduce FragPT2: a novel embedding framework that addresses multiple interacting active fragments. Fragments are assigned separate active spaces, constructed by localizing canonical molecular orbitals. Each fragment is then solved with a multi-reference method, self-consistently embedded in the mean field from other fragments. Finally, inter-fragment correlations are reintroduced through multi-reference perturbation theory. Our framework provides an exhaustive classification of inter-fragment interaction terms, offering a tool to analyze the relative importance of various processes such as dispersion, charge transfer, and spin exchange. We benchmark FragPT2 on challenging test systems, including \ce{N_2} dimers, multiple aromatic dimers, and butadiene. We demonstrate that our method can be succesful even for fragments defined by cutting through a covalent bond.

This paper studies changes in articulatory configurations across genders and periods using an inversion from acoustic to articulatory parameters. From a diachronic corpus based on French media archives spanning 60 years from 1955 to 2015, automatic transcription and forced alignment allowed extracting the central frame of each vowel. More than one million frames were obtained from over a thousand speakers across gender and age categories. Their formants were used from these vocalic frames to fit the parameters of Maeda's articulatory model. Evaluations of the quality of these processes are provided. We focus here on two parameters of Maeda's model linked to total vocal tract length: the relative position of the larynx (higher for females) and the lips protrusion (more protruded for males). Implications for voice quality across genders are discussed. The effect across periods seems gender independent; thus, the assertion that females lowered their pitch with time is not supported., Comment: accepted to InterSpeech 2024, Kos Island, Greece keywords : acoustic to articulatory inversion, diachrony, gender, French, media

In this survey we consider polynomial optimization problems, asking to minimize a polynomial function over a compact semialgebraic set, defined by polynomial inequalities. This models a great variety of (in general, nonlinear nonconvex) optimization problems. Various hierarchies of (lower and upper) bounds have been introduced, having the remarkable property that they converge asymptotically to the global minimum. These bounds exploit algebraic representations of positive polynomials in terms of sums of squares and can be computed using semidefinite optimization. Our focus lies in the performance analysis of these hierarchies of bounds, namely, in how far the bounds are from the global minimum as the degrees of the sums of squares they involve tend to infinity. We present the main state-of-the-art results and offer a gentle introductory overview over the various techniques that have been recently developed to establish them, stemming from the theory of orthogonal polynomials, approximation theory, Fourier analysis, and more., Comment: 21 pages, 1 figure

AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes. We conceptualize these challenges through the lens of vulnerability, the potential for university assessments and learning outcomes to be impacted by student use of generative AI. We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses. Specifically, we compile a novel dataset of textual assessment questions from 50 courses at EPFL and evaluate whether two AI assistants, GPT-3.5 and GPT-4 can adequately answer these questions. We use eight prompting strategies to produce responses and find that GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions. When grouping courses in our dataset by degree program, these systems already pass non-project assessments of large numbers of core courses in various degree programs, posing risks to higher education accreditation that will be amplified as these models improve. Our results call for revising program-level assessment design in higher education in light of advances in generative AI., Comment: 20 pages, 8 figures

There is interest in learning about the causal effect of family planning (FP) on empowerment related outcomes. Experimental data related to this question are available from trials in which FP programs increase access to FP. While program assignment is unconfounded, FP uptake and subsequent empowerment may share common causes. We use principal stratification to estimate the causal effect of an intermediate FP outcome on a primary outcome of interest, among women affected by a FP program. Within strata defined by the potential reaction to the program, FP uptake is unconfounded. To minimize the need for parametric assumptions, we propose to use Bayesian Additive Regression Trees (BART) for modeling stratum membership and outcomes of interest. We refer to the combined approach as Prince BART. We evaluate Prince BART through a simulation study and use it to assess the causal effect of modern contraceptive use on employment in six cities in Nigeria, based on quasi-experimental data from a FP program trial during the first half of the 2010s. We show that findings differ between Prince BART and alternative modeling approaches based on parametric assumptions.

Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can greatly benefit from advances in the field, as state-of-the-art approaches may aid in cases involving, for example, highly dimensional data. To provide the reader with understanding of the terminology, this survey introduces a novel taxonomy where a distinction between online and offline, and training and inference is made. Additionally, it presents the most popular data sets and evaluation metrics used in the literature, as well as a detailed analysis. Furthermore, this survey provides an extensive overview of the state-of-the-art model-based online semi- and unsupervised anomaly detection approaches for multivariate time-series data, categorising them into different model families and other properties. The biggest research challenge revolves around benchmarking, as currently there is no reliable way to compare different approaches against one another. This problem is two-fold: on the one hand, public data sets suffers from at least one fundamental flaw, while on the other hand, there is a lack of intuitive and representative evaluation metrics in the field. Moreover, the way most publications choose a detection threshold disregards real-world conditions, which hinders the application in the real world. To allow for tangible advances in the field, these issues must be addressed in future work.

We give a description of finitely generated prosoluble subgroups of the profinite completion of $3$-manifold groups and virtually compact special groups.

Serverless computing is a growing and maturing field that is the focus of much research, industry interest and adoption. Previous works exploring Functions-as-a-Service providers have focused primarily on the most well known providers AWS Lambda, Google Cloud Functions and Microsoft Azure Functions without exploring other providers in similar detail. In this work, we conduct the first detailed review of ten currently publicly available FaaS platforms exploring everything from their history, to their features and pricing to where they sit within the overall public FaaS landscape, before making a number of observations as to the state of the FaaS., Comment: This paper has been accepted to the 17th IEEE International Conference on Cloud Computing (CLOUD). This paper contains 10 pages and 2 figures

Implementations of Roetteler's shifted bent function algorithm have in recent years been used to test and benchmark both classical simulation algorithms and quantum hardware. These circuits have many favorable properties, including a tunable amount of non-Clifford resources and a deterministic output, and moreover do not belong to any class of quantum circuits which is known to be efficiently simulable. We show that this family of circuits can in fact be simulated in polynomial time via symbolic path integrals. We do so by endowing symbolic sums with a confluent rewriting system and show that this rewriting system suffices to reduce the circuit's path integral to the hidden shift in polynomial-time. We hence resolve an open conjecture about the efficient simulability of this class of circuits.

A leading approach to algorithm design aims to minimize the number of operations in an algorithm's compilation. One intuitively expects that reducing the number of operations may decrease the chance of errors. This paradigm is particularly prevalent in quantum computing, where gates are hard to implement and noise rapidly decreases a quantum computer's potential to outperform classical computers. Here, we find that minimizing the number of operations in a quantum algorithm can be counterproductive, leading to a noise sensitivity that induces errors when running the algorithm in non-ideal conditions. To show this, we develop a framework to characterize the resilience of an algorithm to perturbative noises (including coherent errors, dephasing, and depolarizing noise). Some compilations of an algorithm can be resilient against certain noise sources while being unstable against other noises. We condense these results into a tradeoff relation between an algorithm's number of operations and its noise resilience. We also show how this framework can be leveraged to identify compilations of an algorithm that are better suited to withstand certain noises.

Hydrophobic textured surfaces are studied for their low wettability and their capacity to create a 'slippery' fluid on the surface during lubrication. To this end, the flow between two parallel surfaces is numerically addressed by computing two-dimensional numerical simulations. One of the surfaces moves with a uniform rectilinear motion, while the other is fixed, with a cavity in the middle. The steady-state flow is laminar and monophasic with a low Reynolds number. The reduction of the wall shear stress caused by a vortex in the cavity, with respect to a Couette flow, looks like the creation of an equivalent slip of the fluid on the wall at a macroscopic scale.Three methods are used to calculate the slip length: one is based on the wall shear stress and the other two are based on the speed of the fluid flow. When the slip length is calculated according to these three methods, the obtained results differ. The differences show that the slip often used in the literature is a macroscopic representation of local effects that are not necessarily slippery. The speed profiles and the streamlines are then discussed, in order to propose an explanation for this difference.

We present Purcell-enhanced ($F_\mathrm{P} > 25$) semiconductor InAs quantum dot radiative lifetimes of $T_1 < 30~\mathrm{ps}$, enabled by deterministic hybrid circular Bragg gratings (hCBGs). We investigate the benefits of these short T1 times on the two-photon indistinguishability for quasi-resonant and strictly resonant excitation, and observe visibilities $\geq96\%$ at 12.5 ns time delay of consecutively emitted photons. The strongly Purcell-enhanced decay times enable a high degree of indistinguishability for elevated temperatures of up to 30 K, and moreover, allow for excitation of up to 1.28 GHz repetition rate. Our work highlights the prospects of high Purcell enhanced solid-state quantum emitters for applications in quantum information and technologies operating at GHz clock-rates., Comment: 14 pages, 5 figures. The current manuscript v1 references a Supplementary Information for further experimental details. This S.I. will be added in a v2-version of the manuscript, once the peer-review of the related journal submission is complete

Motorcycle accidents pose significant risks, particularly when riders and passengers do not wear helmets. This study evaluates the efficacy of an advanced vision-language foundation model, OWLv2, in detecting and classifying various helmet-wearing statuses of motorcycle occupants using video data. We extend the dataset provided by the CVPR AI City Challenge and employ a cascaded model approach for detection and classification tasks, integrating OWLv2 and CNN models. The results highlight the potential of zero-shot learning to address challenges arising from incomplete and biased training datasets, demonstrating the usage of such models in detecting motorcycles, helmet usage, and occupant positions under varied conditions. We have achieved an average precision of 0.5324 for helmet detection and provided precision-recall curves detailing the detection and classification performance. Despite limitations such as low-resolution data and poor visibility, our research shows promising advancements in automated vehicle safety and traffic safety enforcement systems.

In these proceedings, we reiterate and extend the discussion of the Next-to-Leading Order (NLO) QCD analysis of the charged pion Parton Distribution Function (PDF) obtained within the Fant\^omas4QCD framework. The goal of the Fant\^omas analysis is to quantify the dependence on the parametrization form in global QCD analyses, with a first application to the pion PDFs. We highlight the anti-correlation between the experimentally allowed gluon and sea distributions in the pion, as made apparent by the sampling over parametrization forms. This result, for the sea and gluon sector, illustrates the importance of accounting for epistemic uncertainties in data-driven QCD analyses. In that regard, we further discuss the meaning of the sampling uncertainty and why it is key in phenomenological studies of hadron structure., Comment: Submitted to the proceedings of 31st International Workshop on Deep-Inelastic Scattering and Related Subjects (DIS2024), PoS format

In this manuscript, we put forth a general scheme for defining initial value problems from Einstein's equations of General Relativity constrained by homogeneous and isotropic expansion. The cosmological models arising as solutions are naturally interpreted as spatially homogeneous and isotropic on ``large scales". In order to show the well-posedness and applicability of such a scheme, we specialize in a class of spacetimes filled with the general homogeneous perfect fluid and inhomogeneous viscoelastic matter. We prove the existence, uniqueness, and relative stability of solutions, and an additional inequality for the energy density. As a consequence of our theorems, a new mechanism of energy transfer appears involving the different components of matter. A class of exact solutions is also obtained to exemplify the general results., Comment: 18 pages; 3 figures

The theory of gauge transformations in linearized gravitation is investigated. After a brief discussion of the fundamentals of the kinetic theory in curved spacetime, the Einstein-Vlasov-Maxwell system of equations in terms of gauge invariant quantities is established without neglecting the equations of motion associated with the dynamics of the non-radiative components of the metric tensor. The established theory is applied to a non-collisional electron-positron plasma, leading to a dispersion relation for gravitational waves in this model system. The problem of Landau damping is addressed and some attention is given to the issue of the energy exchanges between the plasma and the gravitational wave. In a future paper, a more complete set of approximate dispersion relations for waves and oscillations in plasmas will be presented, including the dynamics of non-radiative components of the metric tensor, with special attention to the problems of the Landau damping and of the energy exchanges between matter, the electromagnetic field and the gravitational field.

The bounds on parameters of the eV and higher scale sterile neutrinos from the $0\nu \beta \beta$ decay have been refined and updated. We present a simple and compact analytic expression for the bound in the $\Delta m^2_{41} - \sin^2 2\theta_{14}$ plane, which includes all relevant parameters. Dependencies of the bound on unknown CP-phases and the type of mass spectrum of light neutrinos (mass ordering and level of degeneracy) are studied in detail. We have computed the bounds using the latest and most stringent data from KamLAND-Zen. The projected constraints from future experiments are estimated. The obtained bounds are confronted with positive indications of the presence of sterile neutrinos as well as with the other existing bounds. The $0\nu \beta \beta$ decay results exclude the regions of parameters implied by BEST and Neutrino-4, and the regions indicated by LSND and MiniBooNE are in conflict with $0\nu \beta \beta$ results combined with $\nu_\mu-$ disappearance bounds., Comment: 6 pages + references, 4 figures

To date, there has been no comprehensive study characterizing the effect of diffusion-weighted magnetic resonance imaging voxel resolution on the resulting connectome for high resolution subject data. Similarity in results improved with higher resolution, even after initial down-sampling. To ensure robust tractography and connectomes, resample data to 1 mm isotropic resolution.

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.

Multiple Instance Learning (MIL) models have proven effective for cancer prognosis from Whole Slide Images. However, the original MIL formulation incorrectly assumes the patches of the same image to be independent, leading to a loss of spatial context as information flows through the network. Incorporating contextual knowledge into predictions is particularly important given the inclination for cancerous cells to form clusters and the presence of spatial indicators for tumors. State-of-the-art methods often use attention mechanisms eventually combined with graphs to capture spatial knowledge. In this paper, we take a novel and transversal approach, addressing this issue through the lens of regularization. We propose Context-Aware Regularization for Multiple Instance Learning (CARMIL), a versatile regularization scheme designed to seamlessly integrate spatial knowledge into any MIL model. Additionally, we present a new and generic metric to quantify the Context-Awareness of any MIL model when applied to Whole Slide Images, resolving a previously unexplored gap in the field. The efficacy of our framework is evaluated for two survival analysis tasks on glioblastoma (TCGA GBM) and colon cancer data (TCGA COAD).

In the interplanetary space solar wind plasma, whistler waves are observed in a wide range of heliocentric distance (from ~20 solar radii (RS) to Jupiter's orbit). They are known to interact with solar wind suprathermal electrons (strahl and halo) and to regulate the solar wind heat flux through scattering the strahl electrons. We present the results of applying the technique to determine the whistler wave propagation direction to the spectral data continuously collected by the FIELDS instruments aboard Parker Solar Probe (PSP). The technique was validated based on the results obtained from burst mode magnetic and electric field waveform data collected during Encounter 1. We estimated the effective length of the PSP electric field antennas (EFI) for a variety of solar wind conditions in the whistler wave frequency range and utilized these estimates for determining the whistler wave properties during PSP Encounters 1-11. Our findings show that (1) the enhancement of the whistler wave occurrence rate and wave amplitudes observed between 25-35 RS is predominantly due to the sunward propagating whistler waves population associated with the switchback-related magnetic dips; (2) the anti-sunward or counter-propagating cases are observed at 30-40 RS; (3) between 40-50 RS, sunward and anti-sunward whistlers are observed with comparable occurrence rates; and (4) almost no sunward or counter-propagating whistlers were observed at heliocentric distances above 50 RS., Comment: Accepted for publication in ApJ

In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions (Beltagy et al., 2020a) and group-query attention (Ainslie et al., 2023). We also train the 2B and 9B models with knowledge distillation (Hinton et al., 2015) instead of next token prediction. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger. We release all our models to the community.

The Hubble Tension, a >5 sigma discrepancy between direct and indirect measurements of the Hubble constant (H0), has persisted for a decade and motivated intense scrutiny of the paths used to infer H0. Comparing independently-derived distances for a set of galaxies with different standard candles, such as the tip of the red giant branch (TRGB) and Cepheid variables, can test for systematics in the middle rung of the distance ladder. The I band is the preferred filter for measuring the TRGB due to constancy with color, a result of low sensitivity to population differences in age and metallicity supported by stellar models. We use James Webb Space Telescope (JWST) observations with the maser host NGC 4258 as our geometric anchor to measure I-band (F090W vs F090W-F150W) TRGB distances to 8 hosts of 10 Type Ia supernovae (SNe Ia) within 28 Mpc: NGC 1448, NGC 1559, NGC 2525, NGC 3370, NGC 3447, NGC 5584, NGC 5643, and NGC 5861. We compare these with Hubble Space Telescope (HST) Cepheid-based relative distance moduli for the same galaxies and anchor. We find no evidence of a difference between their weighted means, 0.01 +/- 0.04 (stat) +/- 0.04 (sys) mag. We produce fourteen variants of the TRGB analysis, altering the smoothing level and color range used to measure the tips to explore their impact. For some hosts, this changes the identification of the strongest peak, but this causes little change to the sample mean difference producing a full range of 0.01 to 0.03 mag, all consistent at 1 sigma with no difference. The result matches past comparisons of I-band TRGB and Cepheids when both use HST. SNe and anchor samples observed with JWST are too small to yield a measure of H0 that is competitive with the HST sample of 42 SNe Ia and 4 anchors; however, they already provide a vital systematic crosscheck to HST measurements of the distance ladder., Comment: 15 pages, 5 figures, 4 tables, submitted to ApJ, comments welcome

A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST CEERS galaxy mergers. We train these algorithms on simulated $3

Understanding the quantum nature of the gravitational field is undoubtedly one of the greatest challenges in theoretical physics. Despite significant progress, a complete and consistent theory remains elusive. However, in the weak field approximation -- where curvature effects are small -- we can explore some expected properties of such a theory. Particularly relevant to this study is the quantum nature of gravitational waves, which are represented as small perturbations in flat spacetime. In this framework, a quantum description of these perturbations, as a quantum field, is feasible, leading to the emergence of the graviton. Here we consider a non-relativistic quantum system interacting with such a field. We employ the consistent histories approach to quantum mechanics, which allows us to frame classical questions in a quantum context, to define a fluctuation relation for this system. As a result, thermodynamic entropy must be produced in the system due to its unavoidable interaction with the quantum fluctuations of spacetime., Comment: Comments are welcome

We present a game framework tailored for deduction games, enabling structured analysis from the perspective of Shannon entropy variations. Additionally, we introduce a new forward search algorithm, Information Set Entropy Search (ISES), which effectively solves many single-player deduction games. The ISES algorithm, augmented with sampling techniques, allows agents to make decisions within controlled computational resources and time constraints. Experimental results on eight games within our framework demonstrate the significant superiority of our method over the Single Observer Information Set Monte Carlo Tree Search(SO-ISMCTS) algorithm under limited decision time constraints. The entropy variation of game states in our framework enables explainable decision-making, which can also be used to analyze the appeal of deduction games and provide insights for game designers., Comment: IEEE Conference on Games (IEEE CoG)

We propose a metaheuristic algorithm enhanced with feature-based guidance that is designed to solve the Capacitated Vehicle Routing Problem (CVRP). To formulate the proposed guidance, we developed and explained a supervised Machine Learning (ML) model, that is used to formulate the guidance and control the diversity of the solution during the optimization process. We propose a metaheuristic algorithm combining neighborhood search and a novel mechanism of hybrid split and path relinking to implement the proposed guidance. The proposed guidance has proven to give a statistically significant improvement to the proposed metaheuristic algorithm when solving CVRP. Moreover, the proposed guided metaheuristic is also capable of producing competitive solutions among state-of-the-art metaheuristic algorithms., Comment: https://hal.science/hal-04663574

The National Science Foundation's Daniel K. Inouye Solar Telescope (DKIST) will provide high-resolution, multi-line spectropolarimetric observations that are poised to revolutionize our understanding of the Sun. Given the massive data volume, novel inference techniques are required to unlock its full potential. Here, we provide an overview of our "SPIn4D" project, which aims to develop deep convolutional neural networks (CNNs) for estimating the physical properties of the solar photosphere from DKIST spectropolarimetric observations. We describe the magnetohydrodynamic (MHD) modeling and the Stokes profile synthesis pipeline that produce the simulated output and input data, respectively. These data will be used to train a set of CNNs that can rapidly infer the four-dimensional MHD state vectors by exploiting the spatiotemporally coherent patterns in the Stokes profile time series. Specifically, our radiative MHD model simulates the small-scale dynamo actions that are prevalent in quiet-Sun and plage regions. Six cases with different mean magnetic fields have been conducted; each case covers six solar-hours, totaling 109 TB in data volume. The simulation domain covers at least $25\times25\times8$ Mm with $16\times16\times12$ km spatial resolution, extending from the upper convection zone up to the temperature minimum region. The outputs are stored at a 40 s cadence. We forward model the Stokes profile of two sets of Fe I lines at 630 and 1565 nm, which will be simultaneously observed by DKIST and can better constrain the parameter variations along the line of sight. The MHD model output and the synthetic Stokes profiles are publicly available, with 13.7 TB in the initial release., Comment: Submitted to AAS Journals after revision. Comments welcome

Test smells are coding issues that typically arise from inadequate practices, a lack of knowledge about effective testing, or deadline pressures to complete projects. The presence of test smells can negatively impact the maintainability and reliability of software. While there are tools that use advanced static analysis or machine learning techniques to detect test smells, these tools often require effort to be used. This study aims to evaluate the capability of Large Language Models (LLMs) in automatically detecting test smells. We evaluated ChatGPT-4, Mistral Large, and Gemini Advanced using 30 types of test smells across codebases in seven different programming languages collected from the literature. ChatGPT-4 identified 21 types of test smells. Gemini Advanced identified 17 types, while Mistral Large detected 15 types of test smells. Conclusion: The LLMs demonstrated potential as a valuable tool in identifying test smells., Comment: 7 pages, Accepted at Insightful Ideas and Emerging Results (IIER) Track of the Brazilian Symposium on Software Engineering (SBES 2024)

Compilation is an important process in developing configurable systems, such as Linux. However, identifying compilation errors in configurable systems is not straightforward because traditional compilers are not variability-aware. Previous approaches that detect some of these compilation errors often rely on advanced techniques that require significant effort from programmers. This study evaluates the efficacy of Large Language Models (LLMs), specifically ChatGPT4, Le Chat Mistral and Gemini Advanced 1.5, in identifying compilation errors in configurable systems. Initially, we evaluate 50 small products in C++, Java, and C languages, followed by 30 small configurable systems in C, covering 17 different types of compilation errors. ChatGPT4 successfully identified most compilation errors in individual products and in configurable systems, while Le Chat Mistral and Gemini Advanced 1.5 detected some of them. LLMs have shown potential in assisting developers in identifying compilation errors in configurable systems., Comment: Accepted at NIER track of the Brazilian Symposium on Software Engineering (SBES 2024), 7 Pages

The ComPair balloon instrument is a prototype of the All-sky Medium Energy Gamma-ray Observatory (AMEGO) mission concept. AMEGO aims to bridge the spectral gap in sensitivity that currently exists from $\sim$100 keV to $\sim$100 MeV by being sensitive to both Compton and pair-production events. This is made possible through the use of four subsystems working together to reconstruct events: a double-sided silicon strip detector (DSSD) Tracker, a virtual Frisch grid cadmium zinc telluride (CZT) Low Energy Calorimeter, a ceasium iodide (CsI) High Energy Calorimeter, and an anti-coincidence detector (ACD) to reject charged particle backgrounds. Composed of 10 layers of DSSDs, ComPair's Tracker is designed to measure the position of photons that Compton scatter in the silicon, as well as reconstruct the tracks of electrons and positrons from pair-production as they propagate through the detector. By using these positions, as well as the absorbed energies in the Tracker and 2 Calorimeters, the energy and direction of the incident photon can be determined. This proceeding will present the development, testing, and calibration of the ComPair DSSD Tracker and early results from its balloon flight in August 2023., Comment: SPIE Astronomical Telescopes + Instrumentation Conference