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In this study, Brownian Dynamics simulations are implemented to investigate the motion of a bottom-heavy Janus particle near a wall under varying shear flow conditions and at small P\'eclet (Pe) numbers. The stochastic motion of the Janus particle impacted by surface forces is described using a set of coupled Langevin equations that takes into account the Janus particle orientation. Interactions arising from surface potentials are found to depend on the separation distance between the Janus particle and the wall, the properties of the surfaces involved, and the thickness of the Janus particle cap. When shear flow is introduced in the system, the dynamical behavior of the Janus particle is also governed by the strain rate. Furthermore, the effect of friction on the dynamical behavior of the Janus particle under shear flow is investigated and reveals that the rotational motion of the Janus particle slows down slightly when the particle is close to the surface. In summary, we demonstrate the ability to utilize Brownian Dynamics simulations to capture the rich dynamical behavior of a bottom-heavy Janus particle near a wall and under a range of shear flow conditions, cap thicknesses, and surface charges.

As prominent real-time safety-critical reactive control techniques, Control Barrier Function Quadratic Programs (CBF-QPs) work for control affine systems in general but result in local minima in the generated trajectories and consequently cannot ensure convergence to the goals. Contrarily, Modulation of Dynamical Systems (Mod-DSs), including normal, reference, and on-manifold Mod-DS, achieve obstacle avoidance with few and even no local minima but have trouble optimally minimizing the difference between the constrained and the unconstrained controller outputs, and its applications are limited to fully-actuated systems. We dive into the theoretical foundations of CBF-QP and Mod-DS, proving that despite their distinct origins, normal Mod-DS is a special case of CBF-QP, and reference Mod-DS's solutions are mathematically connected to that of the CBF-QP through one equation. Building on top of the unveiled theoretical connections between CBF-QP and Mod-DS, reference Mod-based CBF-QP and on-manifold Mod-based CBF-QP controllers are proposed to combine the strength of CBF-QP and Mod-DS approaches and realize local-minimum-free reactive obstacle avoidance for control affine systems in general. We validate our methods in both simulated hospital environments and real-world experiments using Ridgeback for fully-actuated systems and Fetch robots for underactuated systems. Mod-based CBF-QPs outperform CBF-QPs as well as the optimally constrained-enforcing Mod-DS approaches we proposed in all experiments.

Microcalorimeter instruments aboard future X-ray observatories will require an anti-coincidence (anti-co) detector to veto charged particle events and reduce the non-X-ray background. We have developed a large-format, TES-based prototype anti-coincidence detector that is particularly suitable for use with spatially-extended (~ 10 cm^2}) TES microcalorimeter arrays, as would be used for a future large field-of-view X-ray missions. This prototype was developed in the context of the Line Emission Mapper (LEM) probe concept, which required a ~ 14 cm^2 anti-co detector with > 95% live time and a low-energy threshold below 20 keV. Our anti-co design employs parallel networks of quasiparticle-trap-assisted electrothermal feedback TESs (QETs) to detect the athermal phonon signal produced in the detector substrate by incident charged particles. We developed multiple prototype anti-co designs featuring 12 channels and up to 6300 QETs. Here we focus on a design utilizing tungsten TESs and present characterization results. Broad energy range measurements have been performed (4.1 keV -- 5.5 MeV). Based on noise and responsivity measurements, the implied low-energy threshold is < 1 keV and a live time fraction of > 96% can be achieved up to 5.5 MeV. We also find evidence of mm-scale-or-better spatial resolution and discuss the potential utility of this for future missions. Finally, we discuss the early development of a soild-state physics model of the anti-co towards understanding phonon propagation and quasiparticle production in the detector., Comment: 26 pages, 16 figures

In this work we revisit vast amount of existing data on physical properties of Ti-Zr-Nb-(Cu,Ni,Co) glassy alloys over a broad range of concentrations (from the high entropy range to that of conventional Cu-, Ni- or Co-rich alloys). By using our new approach based on total content of late transition metal(s), we derive a number of physical parameters of a hypothetical amorphous TiZrNb alloy: lattice parameter $a = (3.42 \pm 0.02)$\r{A}, Sommerfeld coefficient $\gamma = 6.2\,$mJ/mol$\,$K$^2$, density of states at $N(E_F) = 2.6\,(\text{at eV})^{-1}$, magnetic susceptibility $(2.00 \pm 0.05)\,$mJ/T$^2\,$mol, superconducting transition temperature $T_c = (8 \pm 1)\,$K, upper critical field $\mu_0 H_{c2}(0) = (20 \pm 5)\,$T, and coherence length $\xi(0) = (40 \pm 3)\,$\r{A}. We show that our extrapolated results for amorphous TiZrNb alloy would be similar to that of crystalline TiZrNb, except for superconducting properties (most notably the upper critical field $H_{c2}(0)$), which might be attributed to the strong topological disorder of the amorphous phase. Also, we offer an explanation of the discrepancy between the variations of $T_c$ with the average number of valency electrons in neighboring alloys of 4d transition metals and some high entropy alloys. Overall, we find that our novel method of systematic analysis of results is rather general, as it can provide reliable estimates of the properties of any alloy which has not been prepared as yet., Comment: 27 pages, 7 figures

Biologically inspired neural networks offer alternative avenues to model data distributions. FlyVec is a recent example that draws inspiration from the fruit fly's olfactory circuit to tackle the task of learning word embeddings. Surprisingly, this model performs competitively even against deep learning approaches specifically designed to encode text, and it does so with the highest degree of computational efficiency. We pose the question of whether this performance can be improved further. For this, we introduce Comply. By incorporating positional information through complex weights, we enable a single-layer neural network to learn sequence representations. Our experiments show that Comply not only supersedes FlyVec but also performs on par with significantly larger state-of-the-art models. We achieve this without additional parameters. Comply yields sparse contextual representations of sentences that can be interpreted explicitly from the neuron weights., Comment: Accepted at NICE2025

In this article, we numerically study the dynamics of a two-dimensional quasi-fluxon bubble in an oscillatory regime stabilized by a localized annular force under a rapidly oscillating microwave field. The bubble exhibits two distinctly dynamical regimes. At first, the oscillation of the bubble wall scales up linearly with the microwave field frequency until it reaches a cutoff, after which it detaches from the external field, returning to its natural oscillation frequency. The amplitude of the quasi-fluxon oscillations is inversely proportional to the square of the microwave field frequency. Following a simplified model based on the Kapitza approach, we proved that this dynamical behavior is characteristic of systems with a harmonic potential subjected to a rapidly oscillating field. Possible applications of microwave detection are discussed., Comment: 18 pages, 7 figures

This study critically evaluates gender assignment methods within academic contexts, employing a comparative analysis of diverse techniques, including a SVM classifier, gender-guesser, genderize.io, and a Cultural Consensus Theory based classifier. Emphasizing the significance of transparency, data sources, and methodological considerations, the research introduces nomquamgender, a cultural consensus-based method, and applies it to Teseo, a Spanish dissertation database. The results reveal a substantial reduction in the number of individuals with unknown gender compared to traditional methods relying on INE data. The nuanced differences in gender distribution underscore the importance of methodological choices in gender studies, urging for transparent, comprehensive, and freely accessible methods to enhance the accuracy and reliability of gender assignment in academic research. After reevaluating the problem of gender imbalances in the doctoral system we can conclude that it's still evident although the trend is clearly set for its reduction. Finaly, specific problems related to some disciplines, including STEM fields and seniority roles are found to be worth of attention in the near future.

The first direct measurement of gravitational waves by the LIGO and Virgo collaborations has opened up new avenues to explore our Universe. This white paper outlines the challenges and gains expected in gravitational-wave searches at frequencies above the LIGO/Virgo band. The scarcity of possible astrophysical sources in most of this frequency range provides a unique opportunity to discover physics beyond the Standard Model operating both in the early and late Universe, and we highlight some of the most promising of these sources. We review several detector concepts that have been proposed to take up this challenge, and compare their expected sensitivity with the signal strength predicted in various models. This report is the summary of a series of workshops on the topic of high-frequency gravitational wave detection, held in 2019 (ICTP, Trieste, Italy), 2021 (online) and 2023 (CERN, Geneva, Switzerland)., Comment: 138 pages, 13 figures. Update and extension of Living Rev$.$Rel. 24 (2021) 1, 4 on high frequency gravitational waves (arxiv: 2011.12414). Comments welcome

Graphene spintronics offers a promising route to achieve low power 2D electronics for next generation classical and quantum computation. As device length scales are reduced to the limit of the electron mean free path, the transport mechanism crosses over to the ballistic regime. However, ballistic transport has yet to be shown in a graphene spintronic device, a necessary step towards realising ballistic spintronics. Here, we report ballistic injection of spin polarised carriers via one-dimensional contacts between magnetic nanowires and a high mobility graphene channel. The nanowire-graphene interface defines an effective constriction that confines charge carriers over a length scale smaller than that of their mean free path. This is evidenced by the observation of quantised conductance through the contacts with no applied magnetic field and a transition into the quantum Hall regime with increasing field strength. These effects occur in the absence of any constriction in the graphene itself and occur across several devices with transmission probability in the range T = 0.08 - 0.30., Comment: 4 figures

Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine learning in archaeology have only focused only on specific subfields of archaeology. Our review examined an exhaustive corpus of 135 articles published between 1997 and 2022. We observed a significant increase in the number of publications from 2019 onwards. Automatic structure detection and artefact classification were the most represented tasks in the articles reviewed, followed by taphonomy, and archaeological predictive modelling. From the review, clustering and unsupervised methods were underrepresented compared to supervised models. Artificial neural networks and ensemble learning account for two thirds of the total number of models used. However, if machine learning models are gaining in popularity they remain subject to misunderstanding. We observed, in some cases, poorly defined requirements and caveats of the machine learning methods used. Furthermore, the goals and the needs of machine learning applications for archaeological purposes are in some cases unclear or poorly expressed. To address this, we proposed a workflow guide for archaeologists to develop coherent and consistent methodologies adapted to their research questions, project scale and data. As in many other areas, machine learning is rapidly becoming an important tool in archaeological research and practice, useful for the analyses of large and multivariate data, although not without limitations. This review highlights the importance of well-defined and well-reported structured methodologies and collaborative practices to maximise the potential of applications of machine learning methods in archaeology.

In this article, we construct a broad family of spacetimes with spherically symmetric thin shells in unimodular gravity. We analyze the dynamical stability of the configurations under perturbations preserving the symmetry. In particular, we consider the case of thin shells with charge surrounding vacuum, obtaining stable configurations for suitable values of the parameters. We compare our results with those corresponding to general relativity., Comment: 16 pages, 3 figures

In (super)gravity theories, T-duality relates solutions with an exact isometry which can have wildly different asymptotic behaviors: a well-known example is the duality between BTZ black holes and (non-extremal) three-dimensional black strings. Using this dual pair, we show how the knowledge of a phase space which includes one set of solutions (here, BTZ black holes embedded in the Brown-Henneaux phase space) allows to obtain a phase space for the dual set via an asymptotic notion of T-duality. The resulting asymptotic symmetry algebras can be very different. For our particular example, we find a large algebra of symmetries for the black string phase space which includes as subalgebras $\mathfrak{bms}_2$, $\mathfrak{bms}_3$, and a twisted warped conformal algebra. On the way, we show that a chiral half of the Brown-Henneaux boundary conditions are dual to the Comp\`ere-Song-Strominger ones., Comment: 19 pages + 3 appendices, ancillary Mathematica file with some computations of charges

The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by around 2 million people. We have decided to build $\pi$-YALLI, a corpus that will enable to carry out research on Nahuatl in order to develop Language Models (LM), whether dynamic or not, which will make it possible to in turn enable the development of Natural Language Processing (NLP) tools such as: a) a grapheme unifier, b) a word segmenter, c) a POS grammatical analyser, d) a content-based Automatic Text Summarization; and possibly, e) a translator translator (probabilistic or learning-based)., Comment: 9 pages, in French language, 2 figures

Controlling electron dynamics at optical clock rates is a fundamental challenge in lightwave-driven nanoelectronics. Here, we demonstrate ultrafast charge-state manipulation of individual selenium vacancies in monolayer and bilayer tungsten diselenide (WSe$_2$) using picosecond terahertz (THz) source pulses, focused onto the picocavity of a scanning tunneling microscope (STM). Using THz pump--THz probe time-domain sampling of the defect charge population, we capture atomic-scale snapshots of the transient Coulomb blockade, a signature of charge transport via quantized defect states. We identify back tunneling of localized charges to the tip electrode as a key challenge for lightwave-driven STM when probing electronic states with charge-state lifetimes exceeding the pulse duration. However, we show that back tunneling can be mitigated by the Franck-Condon blockade, which limits accessible vibronic transitions and promotes unidirectional charge transport. Our rate equation model accurately reproduces the time-dependent tunneling process across the different coupling regimes. This work builds on recent progress in imaging coherent lattice and quasiparticle dynamics with lightwave-driven STM and opens new avenues for exploring ultrafast charge dynamics in low-dimensional materials, advancing the development of lightwave-driven nanoscale electronics.

We present a novel approach for enhancing human-robot collaboration using physical interactions for real-time error correction of large language model (LLM) powered robots. Unlike other methods that rely on verbal or text commands, the robot leverages an LLM to proactively executes 6 DoF linear Dynamical System (DS) commands using a description of the scene in natural language. During motion, a human can provide physical corrections, used to re-estimate the desired intention, also parameterized by linear DS. This corrected DS can be converted to natural language and used as part of the prompt to improve future LLM interactions. We provide proof-of-concept result in a hybrid real+sim experiment, showcasing physical interaction as a new possibility for LLM powered human-robot interface., Comment: 7 pages, 3 figures; Generative Modeling meets HRI - RSS'24 Workshop

The development and deployment of chatbot technology, while spanning decades and employing different techniques, require innovative frameworks to understand and interrogate their functionality and implications. A mere technocentric account of the evolution of chatbot technology does not fully illuminate how conversational systems are embedded in societal dynamics. This study presents a structured examination of chatbots across three societal dimensions, highlighting their roles as objects of scientific research, commercial instruments, and agents of intimate interaction. Through furnishing a dimensional framework for the evolution of conversational systems, from laboratories to marketplaces to private lives, this article contributes to the wider scholarly inquiry of chatbot technology and its impact in lived human experiences and dynamics.

The paper introduces a novel approach for estimating soil moisture in vegetated surfaces, specifically focusing on sugarcane crops throughout various growth stages in agriculture applications. While existing models typically address bare soil scenarios, this model utilizes data from P-, L-, and C-band Synthetic Aperture Radar (SAR) to estimate soil moisture. The semi-empirical Dubois model forms the basis of the proposed model, which has been adapted to accommodate multiband operation and crop height variations. Synthetic datasets are generated using the adjusted model to train two neural networks incorporated into the overall model. Additionally, a linear expression for estimating crop height is integrated into the model. The model is validated in an Experimental Site at the School of Agricultural Engineering, UNICAMP, and an independent area at the Sugarcane Technology Center in Piracicaba, Brazil. The model utilizes a multiband drone-borne SAR system with a 3-meter image resolution and radiometric accuracy of 0.5 dB. The results indicate that the model can estimate soil moisture with root-mean-square errors of 0.05 cm3.cm-3 (5 vol. %) across crop heights ranging from zero to 2.5 meters., Comment: 9 pages, 12 figures, 2 tables, 17 equations

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry., Comment: 8 pages, 22 figures, 4 tables

Accurate noise characterization is essential for reliable quantum computation. Effective Pauli noise models have emerged as powerful tools, offering detailed description of the error processes with a manageable number of parameters, which guarantees the scalability of the characterization procedure. However, a fundamental limitation in the learnability of Pauli fidelities impedes full high-accuracy characterization of both general and effective Pauli noise, thereby restricting e.g., the performance of noise-aware error mitigation techniques. We introduce Multi-Layer Cycle Benchmarking (MLCB), an enhanced characterization protocol that improves the learnability associated with effective Pauli noise models by jointly analyzing multiple layers of Clifford gates. We show a simple experimental implementation and demonstrate that, in realistic scenarios, MLCB can reduce unlearnable noise degrees of freedom by up to $75\%$, improving the accuracy of sparse Pauli-Lindblad noise models and boosting the performance of error mitigation techniques like probabilistic error cancellation. Our results highlight MLCB as a scalable, practical tool for precise noise characterization and improved quantum computation., Comment: 22 pages, 9 figures

Coronary Artery Disease (CAD) and Coronary Microvascular Disease (CMD) can lead to insufficient blood flow to the myocardium, affecting millions of people globally. Coronary angiography, one of the most commonly used imaging modalities, offers valuable information that assists in diagnosing these diseases. However, these benefits are not fully understood or utilized in current clinical practice. In this study, a 3D-0D coupled multi-physics computational fluid dynamics (CFD) model was developed and calibrated to simulate and better understand the process of contrast injection and washout during clinical angiography. A contrast intensity profile (CIP) was introduced to capture the dynamics of coronary angiography data. Additionally, a sensitivity study was conducted to assess the influence of various coronary artery model parameters on CIP. The results demonstrate that the calibrated 3D-0D coupled multi-physics models are physiologically meaningful and produce accurate hemodynamic results. The sensitivity study further reveals that resistance has a greater impact on CIP than capacitance, with higher resistance amplifying this effect., Comment: 21 pages, 12 figures

Shadows are often under-considered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one versatile framework that enables detection, removal, and controllable synthesis of shadows in natural images in an object-centered fashion. MetaShadow combines the strengths of two cooperative components: Shadow Analyzer, for object-centered shadow detection and removal, and Shadow Synthesizer, for reference-based controllable shadow synthesis. Notably, we optimize the learning of the intermediate features from Shadow Analyzer to guide Shadow Synthesizer to generate more realistic shadows that blend seamlessly with the scene. Extensive evaluations on multiple shadow benchmark datasets show significant improvements of MetaShadow over the existing state-of-the-art methods on object-centered shadow detection, removal, and synthesis. MetaShadow excels in image-editing tasks such as object removal, relocation, and insertion, pushing the boundaries of object-centered image editing.

This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision support system for the evaluation of training courses in accordance with their capacity to enhance the employability of the students. The methodology is delineated in four stages. Firstly, a `working life curve' was defined to provide a quantitative description of an individual's working life. Secondly, an analysis based on K-medoids clustering defined a control group for each individual for comparison. Thirdly, the performance of a course according to each of the four predefined criteria was calculated using a t-test to determine the mean performance value of those who took the course. Ultimately, the unweighted TOPSIS method was used to evaluate the efficacy of the various training courses in relation to the four criteria. This approach effectively addresses the challenge of using extensive datasets within a system while facilitating the application of a multi-criteria unweighted TOPSIS method. The results of the multi-criteria TOPSIS method indicated that training courses related to the professional fields of administration and management, hostel and tourism and community and sociocultural services have positive impact on employability and improving the working conditions of citizens. However, courses that demonstrate the greatest effectiveness in ranking are the least demanded by citizens. The results will help policymakers evaluate the effectiveness of each training course offered by the regional government., Comment: 24 pages, 12 figures

Shock tubes are instrumental in studying high-temperature kinetics and simulating high-speed flows. They swiftly elevate the thermodynamic conditions of test gases, making them ideal for examining rapid chemical reactions and generating high-enthalpy flows for aerodynamic research. However, non-ideal effects, stemming from factors like diaphragm opening processes and viscous effects, can significantly influence thermodynamic conditions behind the shock wave. This study investigates the impact of various diaphragm opening patterns on the shock parameters near the driven section end-wall. Experiments were conducted using helium and argon as driver and driven gases, respectively, at pressures ranging from 1.32 to 2.09 bar and temperatures from 1073 to 2126 K behind the reflected shock. High-speed imaging captured different diaphragm rupture profiles, classified into four distinct types based on their dynamics. Results indicate that the initial stages of diaphragm opening, including the rate and profile of opening, play crucial roles in resulting incident shock Mach number and test time. A sigmoid function was employed to fit the diaphragm opening profiles, allowing for accurate categorization and analysis. New correlations were developed to predict the incident shock attenuation rate and post-shock pressure rise, incorporating parameters such as diaphragm opening time, rupture profile constants, and normalized experimental Mach number. The results emphasize the importance of considering diaphragm rupture dynamics in shock tube experiments to achieve accurate predictions of shock parameters., Comment: 13 pages, 8 figures

The use of multicore optical fibers is now recognized as one of the most promising methods to implement the space-division multiplexing techniques required to overcome the impending capacity limit of conventional single-mode optical fibers. Nonetheless, new devices for networking operations compatible with these fibers will be required in order to implement the next-generation high-capacity optical networks. In this work, we develop a new architecture to build a high-speed core-selective switch, critical for efficiently distributing signals over the network. The device relies on multicore interference, and can change among outputs in less than 0.7 us, while achieving less than -18 dB of average inter-core crosstalk, making it compatible with a wide range of network switching tasks. The functionality of the device was demonstrated by routing a 1GBs optical signal and by successfully switching signals over a field-installed multicore fiber network. Our results demonstrate for the first time the operation of a multicore optical fiber switch functioning under real-world conditions, with switching speeds that are three orders of magnitude faster than current commercial devices. This new optical switch design is also fully compatible with standard multiplexing techniques and, thus, represents an important achievement towards the integration of high-capacity multicore telecommunication networks., Comment: 8 pages, 5 figures

We study in detail the fully inhomogeneous non-linear dynamics of axion inflation, identifying three regimes: weak-, mild-, and strong-backreaction, depending on the duration of inflation. We use lattice techniques that explicitly preserve gauge invariance and shift symmetry, and which we validate against other computational methods of the linear dynamics and of the homogeneous backreaction regime. Notably, we demonstrate that the latter fails to accurately describe the truly local dynamics of strong backreaction. We investigate the convergence of simulations of local backreaction, determining the requirements to achieve an accurate description of the dynamics, and providing useful parametrizations of the delay of the end of inflation. Additionally, we identify key features emerging from a proper local treatment of strong backreaction: the dominance of magnetic energy against the electric counterpart, the excitation of the longitudinal mode, and the generation of a scale-dependent chiral (im)balance. Our results underscore the necessity to accurately capture the local nature of the non-linear dynamics of the system, in order to correctly assess phenomenological predictions, such as e.g. the production of gravitational waves and primordial black holes., Comment: 39 pages, 25 figures

This work analytically compares the growth of Backward Stimulated Raman Scattering (B-SRS) induced by temporal laser pulses on the order of a few tens of laser cycles or a couple of picoseconds for nominally 10 um wavelength IR pulse with Gaussian versus the constant intensity profile assumed in the original theory, both delivering an equivalent energy. By evaluating the growth factors of these two pulse shapes, we demonstrate that the temporal structure of the laser pulse significantly influences the maximum growth rate of the instability. Specifically, while the growth rate for a square pulse increases linearly with time, the Gaussian pulse resultant growth rate tracks with the temporal intensity profile of the pulse. We utilize the newly calculated total maximum growth that a temporally varying pulse delivers above the B-SRS threshold and then determine the normalized intensity for a pulse that has same duration. and contains the same amount of total energy. We show that these energy equivalent square pulses yield the same total B-SRS growth as the Gaussian pulses., Comment: 7 pages, 3 figures, 1 table

The orbital period of a cataclysmic variable stands as a crucial parameter for investigating the structure and physics of these binary systems, as well as understanding their evolution. We use photometric Gaia data for dwarf novae (DNe) in the quiescent state which are available for a number of years to determine new orbital periods and improve/modify previously suggested values. Two approaches are implemented for selecting high-inclination targets, either eclipsing or with ellipsoidal variations. We determine new orbital periods for 75 DNe and improve ephemerides for 27 more (three of which change significantly), contributing 9.4% of the known DNe periods of 0.05-2.0 days, and doubling the number of known periods exceeding 0.44 days. Their phase-folded light curves are presented and arranged by orbital period, illustrating the transition from short-period systems, dominated by radiation from the accretion disc and the hot spot, to longer-period DNe, where the Roche-lobe-filling secondary star is the primary visual flux source. This transition -- which occurs around the well-known period gap (around 2-3 hours) -- is expected, as DNe with larger orbital periods typically harbour more massive donors, which contribute to the visible flux. However, this transition is not abrupt. Within the same range of periods, we observe systems dominated by ellipsoidal variations, where the companion star is clearly visible, as well as others dominated by the disc and hot spot. The presence of some DNe with ellipsoidal variations near the lower edge of the period gap is striking, as the companions in these systems are expected to be cool low-mass M-dwarfs not visible in the light curve. This could indicate that we are observing systems where the donor star was originally much more massive and underwent significant nuclear evolution before mass-transfer began, as has been suggested previously for QZ Ser., Comment: Accepted for publication in A&A, 15 pages, 3 figures

We analyse the evolution of the reduced density matrix of inflationary perturbations, coupled to a heavy entropic field via the leading-order term within the Effective Field Theory of Inflation, for two nearly de Sitter backgrounds. We perform a full quantum treatment of the open system and derive a Fokker-Planck equation to describe decoherence and the entanglement structure of the adiabatic perturbations. We find that exotic phenomena, such as recoherence and transient negative growth of entanglement entropy, appearing for the attractor solution, are absent for the non-attractor background. We comment on the relationship of these to the non-Markovian nature of the system. Finally, we generalise to the case where a few e-folds of ultra-slow roll evolution are sandwiched between phases of slow-roll inflation to find its (memory) effects on the curvature perturbation., Comment: 42 pages, 14 figures

In this article, we give a characterization of log Calabi--Yau pairs of complexity zero and arbitrary index. As an application, we show that a log Calabi--Yau pair of birational complexity zero admits a crepant birational model which is a generalized Bott tower., Comment: 36 pages, comments welcome

Let $B\subset \mathbb{P}^3$ be an slc quartic surface. The existence of an embedding $\mathbb{G}_m^3\hookrightarrow \mathbb{P}^3\setminus B$ implies that $B$ has coregularity zero. In this article, we initiate the classification of coregularity zero slc quartic surfaces $B\subset \mathbb{P}^3$ for which $\mathbb{P}^3\setminus B$ contains an algebraic torus $\mathbb{G}_m^3$. Equivalently, the classification of cluster type pairs $(\mathbb{P}^3,B)$. Along the way, we give criteria for a log Calabi--Yau pair $(X,B)$ over a toric variety $T$ to be of cluster type., Comment: 24 pages

We propose an object-centric recovery policy framework to address the challenges of out-of-distribution (OOD) scenarios in visuomotor policy learning. Previous behavior cloning (BC) methods rely heavily on a large amount of labeled data coverage, failing in unfamiliar spatial states. Without relying on extra data collection, our approach learns a recovery policy constructed by an inverse policy inferred from object keypoint manifold gradient in the original training data. The recovery policy serves as a simple add-on to any base visuomotor BC policy, agnostic to a specific method, guiding the system back towards the training distribution to ensure task success even in OOD situations. We demonstrate the effectiveness of our object-centric framework in both simulation and real robot experiments, achieving an improvement of 77.7% over the base policy in OOD. Project Website: https://sites.google.com/view/ocr-penn, Comment: Accepted for Spotlight (5 out of 21 papers) at CoRL 2024 Workshop on Lifelong Learning for Home Robots

Context. Green valley (GV) galaxies are objects defined on a color-magnitude, or color-mass, diagram which are associated with a transition from a star-forming to a quiescent state (quenching), or vice versa (rejuvenation). Aims. We study the sub-mm emission of galaxies in the GV and link it with their physical evolutionary properties. Methods. We exploit a semi-analytic model (SAM) for galaxy evolution which includes a detailed treatment of dust production and evolution in galactic contexts. We model the observational properties of simulated galaxies by post-processing the SAM catalogs with the spectral synthesis and radiative transfer code GRASIL. Results. Our model produces a clear bimodality (and thus a GV) in the color-mass diagram, although some tensions arise when compared to observations. After having introduced a new criterion for identifying the GV in any dataset, we find that most blue and GV galaxies in our model are emitters at $250\,{\mu \rm m}$ (with fluxes $\geq 25\,{\rm mJy}$), in keeping with GAMA results. In the color-$S_{250\, \mu \rm m}$ diagram, GV galaxies exhibit intermediate colors yet maintain significant sub-mm fluxes. This occurs because, while specific star formation rates drop sharply during quenching, the dust content remains relatively high during the GV transition, powering sub-mm emission. Rejuvenating galaxies in the GV, which were previously red, have experienced a star formation burst that shifts their color to green, but their $S_{250\, \mu \rm m}$ fluxes remain low due to still low dust mass. Conclusions. Our galaxy evolution model highlights the delay between star formation and dust evolution, showing that sub-mm emission is not always a safe indicator of star formation activity, with quenching (rejuvenating) GV galaxies featuring relatively large (low) sub-mm emission., Comment: 12 pages, 13 figures. Submitted to A&A

Teacher shortages have intensified across several OECD countries, making this an urgent priority for education systems. Between 2015 and 2022, the share of students whose principals reported shortages rose from 29% to 46.7% on average across the OECD. Simultaneously, rapid technological advances, such as artificial intelligence (AI), and broader socio-economic shifts, increase the need to support teachers in delivering quality education in evolving contexts. Addressing both the number of teachers and ensuring they possess the skills to meet new educational needs is essential for student success. This report presents a policy roadmap to help education systems balance the supply and demand for quality teaching in these changing times. Drawing on responses from 33 education systems to the Education Policy Outlook National Survey for Comparative Analysis, as well as additional evidence, the report outlines current challenges and key factors in their interplay. It also explores policy responses to attract, retain, and develop teachers, enhancing their teaching practices and professional learning. In doing so, the report aims to support countries in advancing the goals set by the 2022 OECD Declaration on Building Equitable Societies Through Education. This report is part of the Education Policy Outlook series -- the OECD's analytical observatory of education policy.

The University of the Philippines Open University has been at the forefront of providing Massive Open Online Courses to address knowledge and skill gaps, aiming to make education accessible and contributing to societal goals. Recognising challenges in student engagement and completion rates within Massive Open Online Courses, the authors conducted a study by incorporating gamification into one of the University of the Philippines Open University's Massive Open Online Courses to assess its impact on these aspects. Gamification involves integrating game elements to motivate and engage users. This study explored the incorporation of Moodle elements such as badges, leaderboards, and progress bars. Using Moodle analytics, the study also tracked student engagement, views, and posts throughout the course, offering valuable insights into the influence of gamification on user behaviour. Furthermore, the study delved into participant feedback gathered through post-evaluation surveys, providing a comprehensive understanding of their experiences with the gamified course design. With a 28.86% completion rate and positive participant reception, the study concluded that gamification can enhance learner motivation, participation, and overall satisfaction. This research contributes to the ongoing discourse on innovative educational methods, positioning gamification as a promising avenue for creating interactive and impactful online learning experiences in the Philippines and beyond.