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Jamitons are self-sustained traveling wave solutions that arise in certain second-order macroscopic models of vehicular traffic. A necessary condition for a jamiton to appear is that the local traffic density breaks the so-called sub-characteristic condition. This condition states that the characteristic velocity of the corresponding first-order Lighthill-Whitham-Richards (LWR) model formed with the same desired speed function is enclosed by the characteristic speeds of the corresponding second-order model. The phenomenon of collision of jamitons in second-order models of traffic flow is studied analytically and numerically for the particular case of the second-order Aw-Rascle-Zhang (ARZ) traffic model [A.\ Aw, M.\ Rascle, {\it SIAM J.\ Appl.\ Math.} {\bf 60} (2000) 916--938; H.\ M.\ Zhang, {\it Transp.\ Res.\ B} {\bf 36} (2002) 275--290]. A compatibility condition is first defined to select jamitons that can collide each other. The collision of jamitons produces a new jamiton with a velocity different from the initial ones. It is observed that the exit velocities smooth out the velocity of the test jamiton and the initial velocities of the jamitons that collide. Other properties such as the amplitude of the exit jamitons, lengths, and maximum density are also explored.In the cases of the amplitude and maximum exit density it turns out that over a wide range of sonic densities, the exit values exceed or equal the input values. On the other hand, the resulting jamiton has a greater length than the incoming ones. Finally, the behavior for various driver reaction times is explored. It is obtained that some properties do not depend on that time, such as the amplitude, exit velocity, or maximum density, while the exit length does depend on driver reaction time.

We present updated results on the wave function normalization constants and the first moments of the light cone distribution amplitudes for the lowest-lying baryon octet. The analysis is carried out on a large number of $n_f=2+1$ lattice gauge ensembles, including ensembles at physical pion (and kaon) masses. These are spread across five different lattice spacings, enabling a controlled continuum limit. The main differences with respect to our earlier work are the use of two-loop conversion factors to an $\overline{\mathrm{MS}}$-like scheme and of an updated set of low-energy constants in the parametrization of the quark mass dependence. As a byproduct, for the first time, the anomalous dimensions for local three-quark operators with one derivative are obtained., Comment: 8 pages, 4 figures

The second PyHEP.dev workshop, part of the "Python in HEP Developers" series organized by the HEP Software Foundation (HSF), took place in Aachen, Germany, from August 26 to 30, 2024. This gathering brought together nearly 30 Python package developers, maintainers, and power users to engage in informal discussions about current trends in Python, with a primary focus on analysis tools and techniques in High Energy Physics (HEP). The workshop agenda encompassed a range of topics, such as defining the scope of HEP data analysis, exploring the Analysis Grand Challenge project, evaluating statistical models and serialization methods, assessing workflow management systems, examining histogramming practices, and investigating distributed processing tools like RDataFrame, Coffea, and Dask. Additionally, the workshop dedicated time to brainstorming the organization of future PyHEP.dev events, upholding the tradition of alternating between Europe and the United States as host locations. This document, prepared by the session conveners in the weeks following the workshop, serves as a summary of the key discussions, salient points, and conclusions that emerged., Comment: 16 pages, 0 figures; workshop summary

Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics.

Twisted optical fibers are a promising platform for manipulating circularly polarized light and orbital angular momentum beams for applications such as nonlinear frequency conversion, optical communication, or chiral sensing. However, integration into chip-scale technology is challenging because twisted fibers are incompatible with planar photonics and the achieved twist rates are limited. Here, we address these challenges by introducing the concept of 3D-nanoprinted on-chip twisted hollow-core light cages. We show theoretically and experimentally that geometrical twisting of light cages forces the fundamental core mode of a given handedness to couple with selected higher-order core modes, resulting in strong circular dichroism (CD). These chiral resonances result from the angular momentum harmonics of the fundamental mode, allowing us to predict their spectral locations and the occurrence of circular birefringence. Twisted light cages enable very high twist rates and CD, exceeding those of twisted hollow-core fibers by more than two orders of magnitude (twist period: 90 $\mu$m, CD: 0.8 dB/mm). Moreover, the unique cage design provides lateral access to the central core region, enabling future applications in chiral spectroscopy. Therefore, the presented concept opens a path for translating twisted fiber research to on-chip technology, resulting in a new platform for integrated chiral photonics., Comment: Main text (46 pages, 7 figures) and Supplemental Material (28 pages, 19 figures)

We investigate the ability of a simultaneous fitting of comet 67P/Churyumov-Gerasimenko's non-gravitational forces, torques and total water-outgassing rate, as observed by Rosetta, to constrain complex thermophysical models of cometary material. We extend the previous work of fitting geographically defined surface outgassing models to the Rosetta observations by testing the effects of a more detailed geomorphological mapping, the resolution of the shape-model used, self-heating by neighbouring facets on the shape-model, thermal inertia in the outgassing solution, and variation in the momentum coupling between the gas and the nucleus. We also directly compare the non-gravitational acceleration curves available in the literature. We correct an error in the calculation of pole-orientation in the previous paper. We find that, under the assumptions of the model: non-gravitational forces and torques are driven by water sublimation from the nucleus, thermal inertia and self-heating have only minor effects, spatially uniform activity cannot explain 67P's non-gravitational dynamics, spatially uniform momentum transfer cannot explain 67P's non-gravitational dynamics, and different terrain types have different instantaneous responses to insolation. Consolidated terrain facing south on 67P/Churyumov-Gerasimenko has a high outgassing flux, steep response to insolation, and large gas momentum transfer coefficient. Meanwhile, that facing north behaves differently, producing low-to-no water outgassing, and with a lower momentum transfer efficiency. Dusty terrain also has a lower outgassing rate and momentum transfer efficiency, and either depletes its volatile component or is buried in fall-back as the comet approaches the Sun. Momentum transfer appears correlated with insolation, likely due to an increased enhancement in the gas temperature as the dust it flows through is heated., Comment: 11 pages, 8 figures, accepted for publication in A&A

Large Language Models (LLMs) have revolutionised natural language processing, exhibiting impressive human-like capabilities. In particular, LLMs are capable of "lying", knowingly outputting false statements. Hence, it is of interest and importance to develop methods to detect when LLMs lie. Indeed, several authors trained classifiers to detect LLM lies based on their internal model activations. However, other researchers showed that these classifiers may fail to generalise, for example to negated statements. In this work, we aim to develop a robust method to detect when an LLM is lying. To this end, we make the following key contributions: (i) We demonstrate the existence of a two-dimensional subspace, along which the activation vectors of true and false statements can be separated. Notably, this finding is universal and holds for various LLMs, including Gemma-7B, LLaMA2-13B, Mistral-7B and LLaMA3-8B. Our analysis explains the generalisation failures observed in previous studies and sets the stage for more robust lie detection; (ii) Building upon (i), we construct an accurate LLM lie detector. Empirically, our proposed classifier achieves state-of-the-art performance, attaining 94% accuracy in both distinguishing true from false factual statements and detecting lies generated in real-world scenarios., Comment: NeurIPS 2024 poster

In this paper, we present the Fast Optimizer Benchmark (FOB), a tool designed for evaluating deep learning optimizers during their development. The benchmark supports tasks from multiple domains such as computer vision, natural language processing, and graph learning. The focus is on convenient usage, featuring human-readable YAML configurations, SLURM integration, and plotting utilities. FOB can be used together with existing hyperparameter optimization (HPO) tools as it handles training and resuming of runs. The modular design enables integration into custom pipelines, using it simply as a collection of tasks. We showcase an optimizer comparison as a usage example of our tool. FOB can be found on GitHub: https://github.com/automl/FOB., Comment: 5 pages + 12 appendix pages, submitted to AutoML Conf 2024 Workshop Track

Counterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic process for those involved. In this work, we aim to answer this question, by conducting in-depth interviews with 10 extensively experienced counterspeakers and a large scale public survey with 342 everyday social media users. In participant responses, we identified four main types of barriers and AI needs related to resources, training, impact, and personal harms. However, our results also revealed overarching concerns of authenticity, agency, and functionality in using AI tools for counterspeech. To conclude, we discuss considerations for designing AI assistants that lower counterspeaking barriers without jeopardizing its meaning and purpose., Comment: To appear in CHI 2024. 22 pages, 3 figures, 7 tables

A novel scheme, based on third-order Weighted Essentially Non-Oscillatory (WENO) reconstructions, is presented. It attains unconditionally optimal accuracy when the data is smooth enough, even in presence of critical points, and second-order accuracy if a discontinuity crosses the data. The key to attribute these properties to this scheme is the inclusion of an additional node in the data stencil, which is only used in the computation of the weights measuring the smoothness. The accuracy properties of this scheme are proven in detail and several numerical experiments are presented, which show that this scheme is more efficient in terms of the error reduction versus CPU time than its traditional third-order counterparts as well as several higher-order WENO schemes that are found in the literature.

A modified Weighted Essentially Non-Oscillatory (WENO) reconstruction technique preventing accuracy loss near critical points (regardless of their order) of the underlying data is presented. This approach only uses local data from the reconstruction stencil and does not rely on any sort of scaling parameters. The key novel ingredient is a weight design based on a new smoothness indicator, which defines the first WENO reconstruction procedure that never loses accuracy on smooth data, regardless of the presence of critical points of any order, and is therefore addressed as optimal WENO (OWENO) method. The corresponding weights are non-dimensional and scale-independent. The weight designs are supported by theoretical results concerning the accuracy of the smoothness indicators. The method is validated by numerical tests related to algebraic equations, scalar conservation laws, and systems of conservation laws.

Common smoothness indicators used in Weighted Essentially Non\--Os\-cil\-la\-to\-ry (WENO) reconstructions [Jiang, G.S., Shu, C.W.: Efficient implementation of {Weighted} {ENO} schemes, J.\ Comput.\ Phys. \textbf{126}, 202--228 (1996)] have quadratic cost with respect to the order. A set of novel smoothness indicators with linear cost of computation with respect to the order is presented. These smoothness indicators can be used in the context of schemes of the type introduced by Yamaleev and Carpenter [Yamaleev, N.K., Carpenter, M.H.: A systematic methodology to for constructing high-order energy stable WENO schemes. J. Comput. Phys. \textbf{228}(11), 4248-4272 (2009)]. The accuracy properties of the resulting non-linear weights are the same as those arising from using the traditional Jiang-Shu smoothness indicators in Yamaleev-Carpenter-type reconstructions. The increase of the efficiency and ease of implementation are shown.

A novel central weighted essentially non-oscillatory (central WENO; CWENO)-type scheme for the construction of high-resolution approximations to discontinuous solutions to hyperbolic systems of conservation laws is presented. This procedure is based on the construction of a global average weight using the whole set of Jiang-Shu smoothness indicators associated to every candidate stencil. By this device one does not to have to rely on ideal weights, which, under certain stencil arrangements and interpolating point locations, do not define a convex combination of the lower-degree interpolating polynomials of the corresponding sub-stencils. Moreover, this procedure also prevents some cases of accuracy loss near smooth extrema that are experienced by classical WENO and CWENO schemes. These properties result in a more flexible scheme that overcomes these issues, at the cost of only a few additional computations with respect to classical WENO schemes and with a smaller cost than classical CWENO schemes. Numerical examples illustrate that the proposed CWENO schemes outperform both the traditional WENO and the original CWENO schemes.

An efficient approximate version of implicit Taylor methods for initial-value problems of systems of ordinary differential equations (ODEs) is introduced. The approach, based on an approximate formulation of Taylor methods, produces a method that requires less evaluations of the function that defines the ODE and its derivatives than the usual version. On the other hand, an efficient numerical solution of the equation that arises from the discretization by means of Newton's method is introduced for an implicit scheme of any order. Numerical experiments illustrate that the resulting algorithm is simpler to implement and has better performance than its exact counterpart.

Off-axis twisted waveguides possess unique optical properties such as circular and orbital angular momentum (OAM) birefringence, setting them apart from their straight counterparts. Analyzing mode formation in such helical waveguides relies on the use of specific coordinate frames that follow the twist of the structure, making the waveguide invariant along one of the new coordinates. In this study, the differences between modes forming in high-contrast off-axis twisted waveguides defined in the three most important coordinate systems - the Frenet-Serret, the helicoidal, or the Overfelt frame - are investigated through numerical simulations. We explore modal characteristics up to high twist rates (pitch: 50 $\mu$m) and clarify a transformation allowing to map the modal fields and the effective index back to the laboratory frame. In case the waveguide is single-mode, the fundamental modes of the three types of waveguides show significant differences in terms of birefringence, propagation loss, and polarization. Conversely, the modal characteristics of the investigated waveguides are comparable in the multimode domain. Furthermore, our study examines the impact of twisting on spatial mode properties with the results suggesting a potential influence of the photonic spin Hall and orbital Hall effects. Additionally, modes of single-mode helical waveguides were found to exhibit superchiral fields on their surfaces. Implementation approaches such as 3D-nanoprinting or fiber-preform twisting open the doors to potential applications of such highly twisted waveguides, including chip-integrated devices for broadband spin- and OAM-preserving optical signal transport, as well as applications in chiral spectroscopy or nonlinear frequency conversion., Comment: Main text (15 pages, 7 figures) and Supplemental Material (29 pages, 18 figures)

We study the response of a quantitative trait to exponential directional selection in a finite haploid population at the genetic and the phenotypic level. We assume an infinite sites model, in which the number of new mutations per generation in the population follows a Poisson distribution (with mean $\Theta$) and each mutation occurs at a new, previously monomorphic site. Mutation effects are beneficial and drawn from a distribution. Sites are unlinked and contribute additively to the trait. Assuming that selection is stronger than random genetic drift, we model the initial phase of the dynamics by a supercritical Galton-Watson process. This enables us to obtain time-dependent results. We show that the copy-number distribution of the mutant in generation n, conditioned on non-extinction until n, is described accurately by the deterministic increase from an initial distribution with mean 1. This distribution is related to the absolutely continuous part $W^+$ of the random variable, typically denoted $W$, that characterizes the stochasticity accumulating during the mutant's sweep. On this basis, we derive explicitly the (approximate) time dependence of the mutant frequency distribution, of the expected mean and variance of the trait and of the expected number of segregating sites. Unexpectedly, we obtain highly accurate approximations for all times, even for the quasi-stationary phase where we refine classical results. In addition, we find that $\Theta$ is the main determinant of the pattern of adaptation at the genetic level, i.e., whether the initial allele-frequency dynamics are best described by sweep-like patterns at few loci or small allele-frequency shifts at many. The selection strength determines primarily the rate of adaptation. The accuracy of our results is tested by comprehensive simulations in a Wright-Fisher framework.

Robotic manipulation is currently undergoing a profound paradigm shift due to the increasing needs for flexible manufacturing systems, and at the same time, because of the advances in enabling technologies such as sensing, learning, optimization, and hardware. This demands for robots that can observe and reason about their workspace, and that are skillfull enough to complete various assembly processes in weakly-structured settings. Moreover, it remains a great challenge to enable operators for teaching robots on-site, while managing the inherent complexity of perception, control, motion planning and reaction to unexpected situations. Motivated by real-world industrial applications, this paper demonstrates the potential of such a paradigm shift in robotics on the industrial case of an e-Bike motor assembly. The paper presents a concept for teaching and programming adaptive robots on-site and demonstrates their potential for the named applications. The framework includes: (i) a method to teach perception systems onsite in a self-supervised manner, (ii) a general representation of object-centric motion skills and force-sensitive assembly skills, both learned from demonstration, (iii) a sequencing approach that exploits a human-designed plan to perform complex tasks, and (iv) a system solution for adapting and optimizing skills online. The aforementioned components are interfaced through a four-layer software architecture that makes our framework a tangible industrial technology. To demonstrate the generality of the proposed framework, we provide, in addition to the motivating e-Bike motor assembly, a further case study on dense box packing for logistics automation.

Sequencing batch reactors (SBRs) are devices widely used in wastewater treatment, chemical engineering, and other areas. They allow for the sedimentation and compression of solid particles of biomass simultaneously with biochemical reactions with nutrients dissolved in the liquid. The kinetics of these reactions may be given by one of the established activated sludge models (ASMx). An SBR is operated in various stages and is equipped with a movable extraction and fill device and a discharge opening. A one-dimensional model of this unit can be formulated as a moving-boundary problem for a degenerating system of convection-diffusion reaction equations whose unknowns are the concentrations of the components forming the solid and liquid phases, respectively. This model is transformed to a fixed computational domain and is discretized by an explicit monotone scheme along with an alternative semi-implicit variant. The semi-implicit variant is based on solving, during each time step, a system of nonlinear equations for the total solids concentration followed by the solution of linear systems for the solid component percentages and liquid component concentrations. It is demonstrated that the semi-implicit scheme is well posed and that both variants produce approximations that satisfy an invariant region principle: solids concentrations are nonnegative and less or equal to a set maximal one, percentages are nonnegative and sum up to one, and substrate concentrations are nonnegative. These properties are achieved undera Courant-Friedrichs-Lewy (CFL) condition that is less restrictive for the semi-implicit than the explicit variant. Numerical examples with realistic parameters illustrate that as a consequence, the semi-implicit variant is more efficient than the explicit one.