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Collaborative learning environments that support students' problem solving have been shown to promote better decision-making, greater academic achievement, and more reasonable argumentation about controversial issues. In this research, we developed a technology-based critical discussion platform to support middle school students' argumentation, with a focus on evidence-based reasoning and perspective taking. A feasibility study was conducted to examine the patterns of group interaction and individual students' contributions to the critical discussion and their perceptions of the critical discussion activity. We found that more students used text-based communications than audio, but students who used audio collaborated with each other more frequently. In addition, student engagement in argumentative discourse varied greatly across groups as well as individuals. At the end of the discussion, most groups provided a solution that integrated both sides of the controversial issue. Survey and interview results suggest an overall positive experience with this technology-supported critical discussion activity. Using the insights from our research, we develop a conceptual dialogue analysis framework that identifies relevant skills under the argumentation and collaboration dimensions. In this report, we discuss our design considerations, feasibility study results, and implications of engaging students in computer-supported collaborative argumentation.

Recently, it has been shown that the transition rates of the illness-death model (IDM) for chronic conditions are related to the percentages of people in the states by a three-dimensional system of differential equations [Bri24]. The aim of this article is to introduce a method to estimate the age-specific incidence rate together with the mortality rate ratio from aggregated current status (ACS) data. By ACS data we mean counts of (non-necessarily different) people in the three states of the IDM at different points in time. ACS data stem from epidemiological studies where only current disease status and vital status data need to be collected without following-up people (as, for example, in cohort studies). As an application, we use the theory in a simulation study about diabetes in Germany with 600 study subjects at eleven repeated cross-sections each of which with 50% participation quote. Special focus is given to stochastic dependency of the sampled participants. We find a good agreement between the estimates and the input parameters used for the simulation., Comment: 10 pages, 3 figures, 3 tables

Ni/Al reactive multilayers are promising materials for applications requiring controlled local energy release and superior mechanical performance. This study systematically investigates the impact of compositional variations, ranging from 30 to 70 at.% Ni, and bilayer thicknesses (30 nm and 50 nm) on the mechanical properties and reaction dynamics of Ni/Al multilayers. Multilayers with varying Ni-to-Al ratios were fabricated and subjected to instrumented nanoindentation testing to evaluate hardness and elastic modulus. Combustion experiments, conducted on dogbone-shaped multilayers deposited onto silicon wafers with thermal barrier coatings, characterized the reaction front's speed, temperature, and the resulting phases. The findings revealed that composition variations within this range enable precise tuning of reaction speed and temperature without significant changes in mechanical properties, while deviations in modulus and hardness at higher nickel concentrations suggest microstructural influences. Notably, phase formation in Al-rich samples deviated from equilibrium predictions, highlighting the role of kinetic factors, such as diffusion and rapid quenching, in driving non-adiabatic processes during phase evolution. Molecular dynamics simulations provided complementary atomistic insights into mechanical responses and reaction kinetics, bridging experimental observations with theoretical predictions. This integrated approach advances the understanding of Ni/Al multilayers, offering a framework for optimizing their composition and structural design to achieve tailored performance for application-specific requirements., Comment: 13 pages, 7 figures

The quantum spin Hall insulator bismuthene, a two-third monolayer of bismuth on SiC(0001), is distinguished by helical metallic edge states that are protected by a groundbreaking 800 meV topological gap, making it ideal for room temperature applications. This massive gap inversion arises from a unique synergy between flat honeycomb structure, strong spin orbit coupling, and an orbital filtering effect that is mediated by the substrate. However, the rapid oxidation of bismuthene in air has severely hindered the development of applications, so far confining experiments to ultra-high vacuum conditions. Here, we successfully overcome this barrier, intercalating bismuthene between SiC and a protective sheet of graphene. As we demonstrate through scanning tunneling microscopy and photoemission spectroscopy, graphene intercalation preserves the structural and topological integrity of bismuthene, while effectively shielding it from oxidation in air. We identify hydrogen as the critical component that was missing in previous bismuth intercalation attempts. Our findings facilitate ex-situ experiments and pave the way for the development of bismuthene based devices, signaling a significant step forward in the development of next-generation technologies.

We experimentally realize a quantum clock by using a charge sensor to count charges tunneling through a double quantum dot (DQD). Individual tunneling events are used as the clock's ticks. We quantify the clock's precision while measuring the power dissipated by the DQD and, separately, the charge sensor in both direct-current and radio-frequency readout modes. This allows us to probe the thermodynamic cost of creating ticks microscopically and recording them macroscopically, which we refer to as the quantum-to-classical transition. Our experiment is the first to explore the interplay between the entropy produced by a microscopic clockwork and its macroscopic measurement apparatus. We show that the latter contribution not only dwarfs the former but also unlocks greatly increased precision, because the measurement record can be exploited to optimally estimate time even when the DQD is at equilibrium. Our results suggest that the entropy produced by the amplification and measurement of a clock's ticks, which has often been ignored in the literature, is the most important and fundamental thermodynamic cost of timekeeping at the quantum scale., Comment: 9+16 pages, 10 figures

In this technical report we introduce string diagrams for graded symmetric monoidal categories. Our approach includes a definition of graded monoidal theory and the corresponding freely generated syntactic category. Also, we show how an axiomatic presentation for the graded theory may be modularly obtained from one for the grading theory and one for the base category. The Para construction on monoidal actegories is a motivating example for our framework. As a case study, we show how to axiomatize a variant of the graded category ImP, recently introduced by Liell-Cock and Staton to model imprecise probability.

Patent figure classification facilitates faceted search in patent retrieval systems, enabling efficient prior art search. Existing approaches have explored patent figure classification for only a single aspect and for aspects with a limited number of concepts. In recent years, large vision-language models (LVLMs) have shown tremendous performance across numerous computer vision downstream tasks, however, they remain unexplored for patent figure classification. Our work explores the efficacy of LVLMs in patent figure visual question answering (VQA) and classification, focusing on zero-shot and few-shot learning scenarios. For this purpose, we introduce new datasets, PatFigVQA and PatFigCLS, for fine-tuning and evaluation regarding multiple aspects of patent figures~(i.e., type, projection, patent class, and objects). For a computational-effective handling of a large number of classes using LVLM, we propose a novel tournament-style classification strategy that leverages a series of multiple-choice questions. Experimental results and comparisons of multiple classification approaches based on LVLMs and Convolutional Neural Networks (CNNs) in few-shot settings show the feasibility of the proposed approaches.

The web has become a crucial source of information, but it is also used to spread disinformation, often conveyed through multiple modalities like images and text. The identification of inconsistent cross-modal information, in particular entities such as persons, locations, and events, is critical to detect disinformation. Previous works either identify out-of-context disinformation by assessing the consistency of images to the whole document, neglecting relations of individual entities, or focus on generic entities that are not relevant to news. So far, only few approaches have addressed the task of validating entity consistency between images and text in news. However, the potential of large vision-language models (LVLMs) has not been explored yet. In this paper, we propose an LVLM-based framework for verifying Cross-modal Entity Consistency~(LVLM4CEC), to assess whether persons, locations and events in news articles are consistent across both modalities. We suggest effective prompting strategies for LVLMs for entity verification that leverage reference images crawled from web. Moreover, we extend three existing datasets for the task of entity verification in news providing manual ground-truth data. Our results show the potential of LVLMs for automating cross-modal entity verification, showing improved accuracy in identifying persons and events when using evidence images. Moreover, our method outperforms a baseline for location and event verification in documents. The datasets and source code are available on GitHub at https://github.com/TIBHannover/LVLM4CEC., Comment: Accepted for publication in: European Conference on Information Retrieval (ECIR) 2025

We show that the average trajectories of relativistic quantum particles in Schwarzschild spacetime, obtained via quantum mechanical weak measurements of momentum and energy, are equivalent to the predicted flow lines of probability current in curved spacetime quantum theory. We subsequently demonstrate that these trajectories correspond exactly to classical null geodesics in a hybrid Schwarzschild-Alcubierre spacetime. This threefold equivalence demonstrates how quantum theory in curved spacetime can be formulated via operationally-defined measurements, and that such a theory may be interpreted deterministically, in the spirit of hidden-variable models such as Bohmian mechanics, through the novel connection to an underlying "guiding metric.", Comment: 5+9 pages, 1 figure

Large Language Models (LLMs) like GPT-4o can help automate text classification tasks at low cost and scale. However, there are major concerns about the validity and reliability of LLM outputs. By contrast, human coding is generally more reliable but expensive to procure at scale. In this study, we propose a hybrid solution to leverage the strengths of both. We combine human-coded data and synthetic LLM-produced data to fine-tune a classical machine learning classifier, distilling both into a smaller BERT model. We evaluate our method on a human-coded test set as a validity measure for LLM output quality. In three experiments, we systematically vary LLM-generated samples' size, variety, and consistency, informed by best practices in LLM tuning. Our findings indicate that augmenting datasets with synthetic samples improves classifier performance, with optimal results achieved at an 80% synthetic to 20% human-coded data ratio. Lower temperature settings of 0.3, corresponding to less variability in LLM generations, produced more stable improvements but also limited model learning from augmented samples. In contrast, higher temperature settings (0.7 and above) introduced greater variability in performance estimates and, at times, lower performance. Hence, LLMs may produce more uniform output that classifiers overfit to earlier or produce more diverse output that runs the risk of deteriorating model performance through information irrelevant to the prediction task. Filtering out inconsistent synthetic samples did not enhance performance. We conclude that integrating human and LLM-generated data to improve text classification models in assessment offers a scalable solution that leverages both the accuracy of human coding and the variety of LLM outputs., Comment: Manuscript accepted to the Second Workshop on Generative AI for Learning Analytics (GenAI-LA) at LAK25

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green's function approaches, quantum and ab initio simulations.

We present an algorithmic method for the calculation of the degrees of the iterates of birational mappings, based on Halburd's method for obtaining the degrees from the singularity structure of the mapping. The method uses only integer arithmetic with additions and, in some cases, multiplications by small integers. It is therefore extremely fast. Several examples of integrable and non-integrable mappings are presented. In the latter case the dynamical degree we obtain from our method is always in agreement with that calculated by previously known methods., Comment: 18 pages, 4 figures

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Information search has become essential for learning and knowledge acquisition, offering broad access to information and learning resources. The visual complexity of web pages is known to influence search behavior, with previous work suggesting that searchers make evaluative judgments within the first second on a page. However, there is a significant gap in our understanding of how visual complexity impacts searches specifically conducted with a learning intent. This gap is particularly relevant for the development of optimized information retrieval (IR) systems that effectively support educational objectives. To address this research need, we model visual complexity and aesthetics via a diverse set of features, investigating their relationship with search behavior during learning-oriented web sessions. Our study utilizes a publicly available dataset from a lab study where participants learned about thunderstorm formation. Our findings reveal that while content relevance is the most significant predictor for knowledge gain, sessions with less visually complex pages are associated with higher learning success. This observation applies to features associated with the layout of web pages rather than to simpler features (e.g., number of images). The reported results shed light on the impact of visual complexity on learning-oriented searches, informing the design of more effective IR systems for educational contexts. To foster reproducibility, we release our source code (https://github.com/TIBHannover/sal_visual_complexity).

In this paper we study finite-size effects in the Blume-Capel model through the analysis of the zeros of the partition function. We consider a complete graph and make use of the behaviour of the partition function zeros to elucidate the crossover from effective to asymptotic properties. While in the thermodynamic limit the exact solution yields the asymptotic mean-field behaviour, for finite system sizes an effective critical behaviour is observed. We show that even for large systems, the criticality is not asymptotic. We also present insights into how partition function zeros in different complex fields (temperature, magnetic field, crystal field) give different precision and provide us with different parts of the larger picture. This includes the differences between criticality and tricriticality as seen through the lens of Fisher, Lee-Yang, and Crystal Field zeros., Comment: Submitted to Low Temperature Physics

A 200-member ensemble developed at the Center for Western Weather and Water Extremes based on the Weather Research and Forecast atmospheric model tailored for the prediction of atmospheric rivers and associated heavy-to-extreme precipitation events over the Western US (West-WRF) is presented. The ensemble (WW200En) is generated with initial and boundary conditions from the US National Center for Environmental Prediction's Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts' Ensemble Prediction System (EPS), 100 unique combinations of microphysics, planetary boundary layer, and cumulus schemes, as well as perturbations applied to each of the 200 members based on the stochastic kinetic-energy backscatter scheme. Each member is run with 9-km horizontal increments and 60 vertical levels for a 10-month period spanning two winters. The performance of WW200En is compared to GEFS and EPS for probabilistic forecasts of 24-h precipitation, integrated water vapor transport (IVT), and for several thresholds including high percentiles of the observed climatological distribution. The WW200En precipitation forecast skill is better than GEFS at nearly all thresholds and lead times, and comparable or better than the EPS. For larger rainfall thresholds WW200En typically exhibits the best forecast skill. Additionally, WW200En has a better spread-skill relationship than the global systems, and an improved overall reliability and resolution of the probabilistic prediction. The results for IVT are qualitatively similar to those for precipitation forecasts. A sensitivity analysis of the physics parameterizations and the number of ensemble members provides insights into possible future developments of WW200En., Comment: This work has been submitted to Monthly Weather Review

When the ejecta of supernovae interact with the progenitor star's circumstellar environment, a strong shock is driven back into the ejecta, causing the material to become bright optically and in X-rays. Most notably, as the shock traverses the H-rich envelope, it begins to interact with metal rich material. Thus, continued monitoring of bright and nearby supernovae provides valuable clues about both the progenitor structure and its pre-supernova evolution. Here we present late-time, multi-epoch optical and Chandra} X-ray spectra of the core-collapse supernova SN 1996cr. Magellan IMACS optical spectra taken in July 2017 and August 2021 show a very different spectrum from that seen in 2006 with broad, double-peaked optical emission lines of oxygen, argon, and sulfur with expansion velocities of $\pm 4500$ km s$^{-1}$. Red-shifted emission components are considerably fainter compared to the blue-shifted components, presumably due to internal extinction from dust in the supernova ejecta. Broad $\pm 2400$ km s$^{-1}$ H$\alpha$ is also seen which we infer is shocked progenitor pre-SN mass-loss, H-rich material. Chandra data indicate a slow but steady decline in overall X-ray luminosity, suggesting that the forward shock has broken through any circumstellar shell or torus which is inferred from prior deep Chandra ACIS-S/HETG observations. The X-ray properties are consistent with what is expected from a shock breaking out into a lower density environment. Though originally identified as a SN IIn, based upon late time optical emission line spectra, we argue that the SN 1996cr progenitor was partially or highly stripped, suggesting a SN IIb/Ib., Comment: 20 pages, 11 figures

The measurement of fetal thalamus diameter (FTD) and fetal head circumference (FHC) are crucial in identifying abnormal fetal thalamus development as it may lead to certain neuropsychiatric disorders in later life. However, manual measurements from 2D-US images are laborious, prone to high inter-observer variability, and complicated by the high signal-to-noise ratio nature of the images. Deep learning-based landmark detection approaches have shown promise in measuring biometrics from US images, but the current state-of-the-art (SOTA) algorithm, BiometryNet, is inadequate for FTD and FHC measurement due to its inability to account for the fuzzy edges of these structures and the complex shape of the FTD structure. To address these inadequacies, we propose a novel Swoosh Activation Function (SAF) designed to enhance the regularization of heatmaps produced by landmark detection algorithms. Our SAF serves as a regularization term to enforce an optimum mean squared error (MSE) level between predicted heatmaps, reducing the dispersiveness of hotspots in predicted heatmaps. Our experimental results demonstrate that SAF significantly improves the measurement performances of FTD and FHC with higher intraclass correlation coefficient scores in FTD and lower mean difference scores in FHC measurement than those of the current SOTA algorithm BiometryNet. Moreover, our proposed SAF is highly generalizable and architecture-agnostic. The SAF's coefficients can be configured for different tasks, making it highly customizable. Our study demonstrates that the SAF activation function is a novel method that can improve measurement accuracy in fetal biometry landmark detection. This improvement has the potential to contribute to better fetal monitoring and improved neonatal outcomes.

Sustainable software development involves creating software in a manner that meets present goals without undermining our ability to meet future goals. In a software engineering context, sustainability has at least four dimensions: ecological, economic, social, and technical. No interventions for improving social sustainability in software engineering have been tested in rigorous lab-based experiments, and little evidence-based guidance is available. The purpose of this study is to evaluate the effectiveness of two interventions-stakeholder maps and persona models-for improving social sustainability through software feature prioritization. We conducted a randomized controlled factorial experiment with 79 undergraduate computer science students. Participants were randomly assigned to one of four groups and asked to prioritize a backlog of prosocial, neutral, and antisocial user stories for a shopping mall's digital screen display and facial recognition software. Participants received either persona models, a stakeholder map, both, or neither. We compared the differences in prioritization levels assigned to prosocial and antisocial user stories using Cumulative Link Mixed Model regression. Participants who received persona models gave significantly lower priorities to antisocial user stories but no significant difference was evident for prosocial user stories. The effects of the stakeholder map were not significant. The interaction effects were not significant. Providing aspiring software professionals with well-crafted persona models causes them to de-prioritize antisocial software features. The impact of persona modelling on sustainable software development therefore warrants further study with more experience professionals. Moreover, the novel methodological strategy of assessing social sustainability behavior through backlog prioritization appears feasible in lab-based settings., Comment: 13 pages, 4 figures, 5 tables; accepted for presentation at ICSE 2025

We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside the three CALSPEC standards, from 912 \r{A} to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters ($\log g$, $T_{\text{eff}}$, $A_V$, $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic HST/WFC3 UVIS and IR fluxes, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred SEDs from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory's Legacy Survey of Space and Time, and the Nancy Grace Roman Space Telescope., Comment: 32 pages, 24 figures, 5 tables, submitted to MNRAS

In this work, we combine conventional linear response time-domain THz spectroscopy with non-linear THz-pump THz-probe techniques to study metallic strained thin films of $\mathrm{Ca}_2\mathrm{RuO}_4$, which undergo a transition into a ferromagnetic state at 10 K. Such measurements allowing us to independently measure momentum and energy relaxation rates. We find that while the momentum relaxation rate decreases significantly at the ferromagnetic transition, the energy relaxation rate remains unaffected by the emergence of magnetic order. This shows that the dominant changes to scattering across the transition correspond to scatterings that relax momentum without relaxing energy. It is consistent with a scenario where energy is not carried off by coupling to collective magnetic degrees of freedom. Instead, the principal channel for energy relaxation remains the conventional one e.g. coupling to acoustic phonons. This observation validates the approximation used in the conventional understanding of resistive anomalies of ferromagnets across the Curie temperature, which due to critical slowing down, spin fluctuations can be treated as effectively static and scattering off of them elastic. This scenario can likely be extended to resistive anomalies at other phase transitions to charge- and spin-density wave states in kagome metals or pnictide system, Comment: 6 pages, 4 figures

We propose a fully Bayesian approach to wideband, or broadband, direction-of-arrival (DoA) estimation and signal detection. Unlike previous works in wideband DoA estimation and detection, where the signals were modeled in the time-frequency domain, we directly model the time-domain representation and treat the non-causal part of the source signal as latent variables. Furthermore, our Bayesian model allows for closed-form marginalization of the latent source signals by leveraging conjugacy. To further speed up computation, we exploit the sparse ``stripe matrix structure'' of the considered system, which stems from the circulant matrix representation of linear time-invariant (LTI) systems. This drastically reduces the time complexity of computing the likelihood from $\mathcal{O}(N^3 k^3)$ to $\mathcal{O}(N k^3)$, where $N$ is the number of samples received by the array and $k$ is the number of sources. These computational improvements allow for efficient posterior inference through reversible jump Markov chain Monte Carlo (RJMCMC). We use the non-reversible extension of RJMCMC (NRJMCMC), which often achieves lower autocorrelation and faster convergence than the conventional reversible variant. Detection, estimation, and reconstruction of the latent source signals can then all be performed in a fully Bayesian manner through the samples drawn using NRJMCMC. We evaluate the detection performance of the procedure by comparing against generalized likelihood ratio testing (GLRT) and information criteria.

The preparation of quantum states, especially cooling, is a fundamental technology for nanoscale devices. The past decade has seen important results related to both the limits of state transformation and the limits to their efficiency -- the quantum versions of the third and second law of thermodynamics. The limiting cases always involve an infinite resource cost, typically machine complexity or time. Realistic state preparation takes into account both a finite size of the machine and constraints on the operations we can perform. In this work, we determine in full generality the optimal operation for a predominant quantum paradigm: state transformation under a single unitary operation upon a finite system, in the case where the observables corresponding to the target (such as ground state probability) and cost (such as dissipation) commute. We then extend this result to the case of having a third, commuting, globally conserved quantity (such as total energy). The results are demonstrated with the paradigmatic example of ground state cooling, for both arbitrary and energy-preserving unitary operations., Comment: 16 pages + 12 pages appendix

More than 300 pulsars have been discovered in Galactic globular clusters; however, none have been found in open clusters. Here we present results from 20-hour pulsar searching observations in seven open clusters with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our first discovery is a 1.9-second pulsar (J1922+37) found in the direction of the old open cluster NGC 6791. The measured dispersion measure (DM) implies a distance of 4.79 kpc and 8.92 kpc based on the NE2001 and YMW16 electron density models, respectively. Given the large uncertainty of DM distance estimates, it is likely that PSR J1922+37 is indeed a member of NGC 6791, for which the distance is $4.19\pm0.02$ kpc based on Gaia Data Release 3. If confirmed, PSR J1922+37 will be the first pulsar found in Galactic open clusters. We outline future observations that can confirm this pulsar-open cluster association and discuss the general prospects of finding pulsars in open clusters., Comment: 10 pages, 4 tables, 2 figures. Comments are welcome

Charge transport in organic semiconductors is limited by dynamical disorder. Design rules for new high-mobility materials have therefore focused on limiting its two foundations: structural fluctuations and the transfer integral gradient. However, it has remained unclear how these goals should be translated into molecular structures. Here we show that a specific shape of the frontier orbital, with a lack of nodes along the long molecular axis, reduces the transfer integral gradient and therefore the dynamical disorder. We investigated single crystals of the prototypical molecular semiconductors pentacene and picene by angle-resolved photoemission spectroscopy and dynamical disorder calculations. We found that picene exhibits a remarkably low dynamical disorder. By separating in- and out-of-plane components of dynamical disorder, we identify the reason as a reduced out-of-plane disorder from a small transfer integral derivative. Our results demonstrate that molecules with an armchair $\pi$-electron topology and same-phase frontier orbitals like picene are promising molecular building blocks for the next generation of organic semiconductors.

The hot plasma in galaxy clusters, the intracluster medium (ICM), is expected to be shaped by subsonic turbulent motions, which are key for heating, cooling, and transport mechanisms. The turbulent motions contribute to the non-thermal pressure which, if not accounted for, consequently imparts a hydrostatic mass bias. Accessing information about turbulent motions is thus of major astrophysical and cosmological interest. Characteristics of turbulent motions can be indirectly accessed through surface brightness fluctuations. This study expands on our pilot investigations of surface brightness fluctuations in the SZ and X-ray by examining, for the first time, a large sample of 60 clusters using both SPT-SZ and XMM-Newton data and span the redshift range $0.2 < z < 1.5$, thus constraining the respective pressure and density fluctuations within 0.6~$R_{500}$. We deem density fluctuations to be of sufficient quality for 32 clusters, finding mild correlations between the peak of the amplitude spectra of density fluctuations and various dynamical parameters. We infer turbulent velocities from density fluctuations with an average Mach number $\mathcal{M}_{\text{3D}} = 0.52 \pm 0.14$, in agreement with numerical simulations. For clusters with inferred turbulent Mach numbers from both pressure, $\mathcal{M}_{\text{P}}$ and density fluctuations, $\mathcal{M}_{\rho}$, we find broad agreement between $\mathcal{M}_{\text{P}}$ and $\mathcal{M}_{\rho}$. Our results suggest a bimodal Mach number distribution, with the majority of clusters being turbulence-dominated (subsonic) while the remainder are shock-dominated (supersonic)., Comment: 18 pages, 10 figures, 4 tables. Submitted to ApJ; comments welcome

Quantized anomalous Hall effects (QAHEs) occur in remarkable electronic states which possess not only quantized Hall signals but in some cases regions of dissipationless electron transport. The initial demonstrations of a QAHE in a magnetically-doped topological insulator (TI) required temperatures below 100 mK, and since then a major focus of the field has been to increase the temperature scale. Here, we report quantized Hall signals up to 10 K (in what is known as the parity anomaly state) in TI/magnet bilayers made by mechanical assembly, rather than by conventional deposition techniques. This is a factor of 100 higher temperature than any previous realization of a QAHE in a proximity-coupled TI/magnet heterostructure made by deposition, and approximately twice the previous record for any QAHE system.

Waveguide tapers are critical components for leveraging the benefits of both single-mode and wide waveguides. Adiabatic tapers are typically hundreds of microns in length, dramatically limiting density and scalability. We reenvision the taper design process in an inverse-design paradigm, introducing the novel L-taper. We present a novel approach to inverse-designed tapers where the input and output waveguides are rotated 90 degrees with respect to each other. The resultant design has an order-of-magnitude smaller footprint, and the design process is compatible with a variety of fabrication processes. We demonstrate an L-taper designed on 220 nm silicon-on-insulator that converts a 0.5 micron waveguide to a 12 micron waveguide with -0.38 dB transmission and 40 nm 1-dB bandwidth. The footprint is 16 micron by 6 micron, representing a 12x smaller footprint than a linear taper with the same transmission., Comment: 4 pages, 3 figures

Event cameras capture sparse, asynchronous brightness changes which offer high temporal resolution, high dynamic range, low power consumption, and sparse data output. These advantages make them ideal for Space Situational Awareness, particularly in detecting resident space objects moving within a telescope's field of view. However, the output from event cameras often includes substantial background activity noise, which is known to be more prevalent in low-light conditions. This noise can overwhelm the sparse events generated by satellite signals, making detection and tracking more challenging. Existing noise-filtering algorithms struggle in these scenarios because they are typically designed for denser scenes, where losing some signal is acceptable. This limitation hinders the application of event cameras in complex, real-world environments where signals are extremely sparse. In this paper, we propose new event-driven noise-filtering algorithms specifically designed for very sparse scenes. We categorise the algorithms into logical-based and learning-based approaches and benchmark their performance against 11 state-of-the-art noise-filtering algorithms, evaluating how effectively they remove noise and hot pixels while preserving the signal. Their performance was quantified by measuring signal retention and noise removal accuracy, with results reported using ROC curves across the parameter space. Additionally, we introduce a new high-resolution satellite dataset with ground truth from a real-world platform under various noise conditions, which we have made publicly available. Code, dataset, and trained weights are available at \url{https://github.com/samiarja/dvs_sparse_filter}., Comment: 17 pages, 8 figures, 1 table

We have investigated terahertz (THz) nonlinear responses in a multi-gap superconductor, MgB$_2$, using THz two-dimensional coherent spectroscopy (THz 2DCS). With broad-band THz drives, we identified a well-defined nonlinear response near the lower superconducting gap energy $2\Delta_{\pi}$ only at the lowest temperatures. Using narrow-band THz driving pulses, we observed first (FH) and third harmonic responses, and the FH intensity shows a monotonic increase with decreasing temperature when properly normalized by the driving field strength. This is distinct from the single-gap superconductor NbN, where the FH signal exhibited a resonant enhancement at temperatures near the superconducting transition temperature $T_{\text{c}}$ when the superconducting gap energy was resonant with the driving photon energy and which had been interpreted to originate from the superconducting amplitude mode. Our results in MgB$_2$ are consistent with a well-defined amplitude mode only at the lowest temperatures and indicate strong damping as temperature increases. This likely indicates the importance of interband coupling in MgB$_2$ and its influence on the nature of the amplitude mode and its damping.

New results on the behavior of the double-mode RR Lyrae V338 Boo are presented. The Transiting Exoplanet Survey Satellite (TESS) observed this star again in 2022, and an observing campaign of the American Association of Variable Star Observers (AAVSO) was completed after the TESS observations as a follow-up. We find that the first overtone pulsation mode in this star completely disappears during the TESS observing window. This mode reappears at the end of the TESS observations, and the AAVSO observing campaign shows that in the months that followed, the first overtone mode was not only present, but was the dominant mode of pulsation. This star, and potentially others like it, could hold the key to finally solving the mystery of the Blazhko effect in RR Lyrae., Comment: 11 pages, 7 figures; published in ApJ

Fainter standard stars are essential for the calibration of larger telescopes. This work adds to the CALSPEC (calibration spectra) database 19 faint white dwarfs (WDs) with all-sky coverage and V magnitudes between 16.5 and 18.7. Included for these stars is new UV (ultraviolet) HST (Hubble Space Telescope) STIS (Space Telescope Imaging Spectrometer) spectrophotometry between 1150 and 3000~\AA\ with a resolution of $\sim$500. Pure hydrogen WD models are fit to these UV spectra and to six-band HST/WFC3 (Wide Field Camera 3) photometry at 0.28 to 1.6~\micron\ to construct predicted model SEDs (spectral energy distributions) covering wavelengths from 900~\AA\ to the JWST (James Webb Space Telescope) limit of 30~\micron\ using well-established CALSPEC procedures for producing flux standards with the goal of 1\% accuracy.

The choice between the Schroedinger and Heisenberg pictures can significantly impact the computational resources needed to solve a problem, even though they are equivalent formulations of quantum mechanics. Here we present a method for analysing Bosonic quantum circuits based on the Heisenberg picture that allows, under certain conditions, a useful factoring of the evolution into signal and noise contributions, in a similar way as can be done with classical communication systems. We provide examples which suggest this approach may be particular useful in analysing quantum computing systems based on the Gottesman-Kitaev-Preskill (GKP) qubits.

Gas metallicity, ionization parameter, and gas pressure can affect the observed ratios of specific strong emission lines within galaxies. While the theoretical strong lines diagnostics for gas metallicity, ionization parameters, and gas pressure in star-forming regions are well-established, theoretical diagnostics for active galactic nuclei (AGNs) narrow line regions are still lacking. In Zhu et al. (2023), we presented a new AGN model that provides the best predictions for observations spanning the UV, optical, and infrared wavelengths. This paper presents a suite of theoretical diagnostics for the gas metallicity, ionization parameter, gas pressure, and the peak energy in AGN ionizing radiation field $E_{peak}$ for AGN narrow-line regions spanning the UV and optical wavelengths. We investigate the model dependency on the ionization parameter, gas pressure, $E_{peak}$, and the nitrogen scaling relation and make recommendations on metallicity diagnostics that are most robust against these parameters. We test our new AGN metallicity diagnostics using optical galaxy spectra from Sloan Digital Sky Survey DR16. These tests show that the metallicities measured from different diagnostics in this paper are consistent within $\sim0.3$ dex. We compare consistent HII and AGN diagnostics and demonstrate that HII and AGN diagnostics should not be used interchangeably. With a wide wavelength coverage, we anticipate that these AGN diagnostics will enable new metallicity studies of galaxies dominated by AGN., Comment: 33 pages, 22 figures, 7 tables, Accepted for publication in ApJ

In this work, we introduce a framework that enables highly maneuverable locomotion using non-periodic contacts. This task is challenging for traditional optimization and planning methods to handle due to difficulties in specifying contact mode sequences in real-time. To address this, we use a bi-level contact-implicit planner and hybrid model predictive controller to draft and execute a motion plan. We investigate how this method allows us to plan arm contact events on the shmoobot, a smaller ballbot, which uses an inverse mouse-ball drive to achieve dynamic balancing with a low number of actuators. Through multiple experiments we show how the arms allow for acceleration, deceleration and dynamic obstacle avoidance that are not achievable with the mouse-ball drive alone. This demonstrates how a holistic approach to locomotion can increase the control authority of unique robot morpohologies without additional hardware by leveraging robot arms that are typically used only for manipulation. Project website: https://cmushmoobot.github.io/Wallbounce

We present a systematic review of peer-reviewed research into ways to mitigate manipulative information about climate change on social media. Such information may include disinformation, harmful influence campaigns, or the unintentional spread of misleading information. We find that commonly recommended approaches to addressing manipulation about climate change include corrective information sharing and education campaigns targeting media literacy. However, most relevant research fails to test the approaches and interventions it proposes. We locate research gaps that include the lack of attention to large commercial and political entities involved in generating and disseminating manipulation, video- and image-focused platforms, and computational methods to collect and analyze data. Evidence drawn from many studies demonstrates an emerging consensus about policies required to promote reliable knowledge about climate change and resist manipulation.

We present $[OIII]/H_{\rm \beta}$ emision line flux ratio predictions for galaxies at $z \sim 7-9$ using the MAPPINGS V v5.2.0 photoionization modelling code combined with an analytic galaxy formation model. Properties such as pressure and ionization parameter that determine emission line properties are thought to evolve towards high redshift. In order to determine the range of expected interstellar conditions we extend previous modelling of the Star Formation Rate Density (SFRD) function to calculate the metallicity and ionization parameter, and incorporate the potential impact of turbulence on the density of the ISM. To validate our emission line predictions we calculate the [OIII] line luminosity and its dependence on UV luminosity, as well as the flux ratio $[OIII]/H_{\rm \beta}$ and its variation with the line luminosity, finding that both reproduce recent JWST observations from the FRESCO survey. We also use our model to predict the number counts of emission line galaxies across a range of redshift as well as the dependence of $[OIII]/H_{\rm \beta}$ on ionization parameter and metallicity. Finally, we show that the dependence of flux ratio on luminosity may provide a diagnostic of turbulent motion in galactic discs., Comment: 12 pages, 17 figures, Submitted to MNRAS

Plasmons can drive chemical reactions by directly exciting intramolecular transitions. However, strong coupling of plasmons to single molecules remains a challenge as ultra-small mode volumes are required. In the presented work, we propose Cu-Pd plasmonic network metamaterials as a scalable platform for plasmon-assisted catalysis. Due to the absence of translational symmetry, these networks provide a unique plasmonic environment featuring a large local density of optical states and an unparalleled density of hotspots that effectively localizes light in mode volumes $V<8\cdot10^{-24}$ m$^3$. Catalytic performance tests during CO$_2$ conversion reveal production rates of up to 4.3$\cdot$10$^2$ mmol g$^{-1}$h$^{-1}$ and altered reaction selectivity under light illumination. Importantly, we show that the selectivity of the catalytic process can be tuned by modifying the network's chemical composition, offering a versatile approach to optimize reaction pathways.

We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate windspeeds and directions at altitudes of 3 meters up to 24 meters, the first time winds at such altitudes have been probed on Mars. We compared our estimates to concurrent wind data at 1.5 m height from the meteorology package MEDA onboard the Mars 2020 Perseverance rover and to predictions from meteorological models. Wind directions inferred from the Ingenuity data agreed to within uncertainties with the directions measured by MEDA, when the latter were available, but deviated from model-predicted directions by as much as 180 deg in some cases. Also, the inferred windspeeds are often much higher than expected. For example, meteorological predictions tailored to the time and location of Ingenuity's 59th flight suggest Ingenuity should not have seen windspeeds above about 15 m/s, but we inferred speeds reaching nearly 25 m/s. By contrast, the 61st flight was at a similar time and season and showed weaker winds then the 59th flight, suggesting winds shaped by transient phenomena. For flights during which we have MEDA data to compare to, inferred windspeeds imply friction velocities exceeding 1 m/s and roughness lengths of more than 10 cm based on a boundary layer model that incorporates convective instability, which seem implausibly large. These results suggest Ingenuity was probing winds sensitive to aerodynamic conditions hundreds of meters upwind instead of the conditions very near Mars 2020, but they may also reflect a need for updated boundary layer wind models. An improved model for Ingenuity's aerodynamic response that includes the effects of transient winds may also modify our results. In any case, the work here provides a foundation for exploration of planetary boundary layers using drones and suggests important future avenues for research and development., Comment: Accepted by PSJ

We show that the set of atoms of the limiting empirical marginal distribution in the random $2$-SAT model is $\mathbb Q \cap (0,1)$, for all clause-to-variable densities up to the satisfiability threshold. While for densities up to $1/2$, the measure is purely discrete, we additionally establish the existence of a nontrivial continuous part for any density in $(1/2, 1)$. Our proof is based on the construction of a random variable with the correct distribution as the the root marginal of a multi-type Galton-Watson tree, along with a subsequent analysis of the resulting almost sure recursion., Comment: 13 pages, 1 figure

Nanothermodynamics provides the theoretical foundation for understanding stable distributions of statistically independent subsystems inside larger systems. In this review it is emphasized that adapting ideas from nanothermodynamics to simplistic models improves agreement with the measured properties of many materials. Examples include non-classical critical scaling near ferromagnetic transitions, thermal and dynamic behavior near liquid-glass transitions, and the 1/f-like noise in metal films and qubits. A key feature in several models is to allow separate time steps for distinct conservation laws: one type of step conserves energy and the other conserves momentum (e.g. dipole alignment). This "orthogonal dynamics" explains how the relaxation of a single parameter can exhibit multiple responses such as primary, secondary, and microscopic peaks in the dielectric loss of supercooled liquids, and the crossover in thermal fluctuations from Johnson-Nyquist (white) noise at high frequencies to 1/f-like noise at low frequencies. Nanothermodynamics also provides new insight into three basic questions. First, it gives a novel solution to Gibbs' paradox for the entropy of the semi-classical ideal gas. Second, it yields the stable equilibrium of Ising's original model for finite-sized chains of interacting binary degrees of freedom ("spins"). Third, it confronts Loschmidt's paradox for the arrow of time, showing that an intrinsically irreversible step is required for maximum entropy and the second law of thermodynamics, not only in the thermodynamic limit but also in systems as small as N=2 particles, Comment: 32 pages, 12 figures

We introduce a toluene-filled fiber platform for two-photon absorption measurements. By confining both the light and molecular sample inside the 5 $\mu$m hollow core of the fiber, we increase the distance over which the nonlinear light-matter interaction occurs. With only a 7.3 nL excitation volume, we measure classical two-photon absorption (C2PA) at an average laser power as low as 1.75 nW, which is a 45-fold improvement over a conventional free-space technique. We use this platform to attempt to measure entangled two-photon absorption (E2PA), a process with a limited regime where the quantum advantage is large. This regime arises due to a crossover from linear to quadratic scaling with photon flux as photon flux is increased. Recently, several teams of researchers have reported that E2PA cross-sections are much smaller than previously claimed. As a result, the linear scaling dominates at photon fluxes so low that it is extremely difficult or impossible to measure using conventional free-space techniques. In this report, we implement the first E2PA measurement using a waveguide. We see no evidence of E2PA, and we set an upper bound on the cross-section consistent with these recent reports.

This paper introduces a framework for interactive navigation through adaptive non-prehensile mobile manipulation. A key challenge in this process is handling objects with unknown dynamics, which are difficult to infer from visual observation. To address this, we propose an adaptive dynamics model for common movable indoor objects via learned SE(2) dynamics representations. This model is integrated into Model Predictive Path Integral (MPPI) control to guide the robot's interactions. Additionally, the learned dynamics help inform decision-making when navigating around objects that cannot be manipulated.Our approach is validated in both simulation and real-world scenarios, demonstrating its ability to accurately represent object dynamics and effectively manipulate various objects. We further highlight its success in the Navigation Among Movable Objects (NAMO) task by deploying the proposed framework on a dynamically balancing mobile robot, Shmoobot. Project website: https://cmushmoobot.github.io/AdaptivePushing/., Comment: 7 pages, 8 figures

String diagrammatic calculi have become increasingly popular in fields such as quantum theory, circuit theory, probabilistic programming, and machine learning, where they enable resource-sensitive and compositional algebraic analysis. Traditionally, the equations of diagrammatic calculi only axiomatise exact semantic equality. However, reasoning in these domains often involves approximations rather than strict equivalences. In this work, we develop a quantitative framework for diagrammatic calculi, where one may axiomatise notions of distance between string diagrams. Unlike similar approaches, such as the quantitative theories introduced by Mardare et al., this requires us to work in a monoidal rather than a cartesian setting. We define a suitable notion of monoidal theory, the syntactic category it freely generates, and its models, where the concept of distance is established via enrichment over a quantale. To illustrate the framework, we provide examples from probabilistic and linear systems analysis.