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Millimeter-wave observations of pulsars, while crucial for understanding their emission mechanisms, remain scarce. We demonstrate that high-precision cosmic microwave background (CMB) experiments like the Atacama Cosmology Telescope (ACT), though designed for cosmology, offer a unique capability for such time-domain science due to their high cadence and broad sky coverage in millimeter bands. While previous ACT searches have focused on transients lasting minutes or longer, we develop and validate analysis methods to search for periodic, millisecond-scale transients, a capability not typically associated with CMB experiments. We describe a phase-resolved mapmaking approach, which leverages the known periodicity of the signal to enhance sensitivity and offers advantages in diagnosing systematic errors. We also introduce a template-based fit to the raw data timestreams that independently validate our results. Applying these methods to estimate the millimeter flux of the Crab Pulsar (PSR B0531+21), we derive 95% confidence upper limits of 4.6 mJy, 4.4 mJy, and 20.7 mJy on the pulsar's period-averaged flux density at 96 GHz, 148 GHz, and 225 GHz, respectively. These constraints fill a gap in our knowledge of the Crab Pulsar's spectral energy distribution, suggesting that it does not significantly flatten or invert at millimeter wavelengths. This work demonstrates the potential for future searches of short-timescale astrophysical phenomena with the next-generation CMB experiments like the Simons Observatory.

Large Language Models (LLMs) have shown impressive performance on various benchmarks, yet their ability to engage in deliberate reasoning remains questionable. We present NYT-Connections, a collection of 358 simple word classification puzzles derived from the New York Times Connections game. This benchmark is designed to penalize quick, intuitive "System 1" thinking, isolating fundamental reasoning skills. We evaluated six recent LLMs, a simple machine learning heuristic, and humans across three configurations: single-attempt, multiple attempts without hints, and multiple attempts with contextual hints. Our findings reveal a significant performance gap: even top-performing LLMs like GPT-4 fall short of human performance by nearly 30%. Notably, advanced prompting techniques such as Chain-of-Thought and Self-Consistency show diminishing returns as task difficulty increases. NYT-Connections uniquely combines linguistic isolation, resistance to intuitive shortcuts, and regular updates to mitigate data leakage, offering a novel tool for assessing LLM reasoning capabilities.

Graham-Lovász-Pollak \cite{GL,GP} obtained the celebrated formula $$\det({\sf D}(T_{n+1}))=(-1)^nn2^{n-1},$$ for the determinant of the distance matrix ${\sf D}(T_{n+1})$ for any tree $T_{n+1}$ with $n+1$ vertices. Later, Hou and Woo \cite{HW} extended this formula to the Smith normal form (SNF) obtaining that $\SNF({\sf D}(T_{n+1}))={\sf I}_2\oplus 2{\sf I}_{n-2}\oplus [2n]$, for any tree $T_{n+1}$ with $n+1$ vertices. A $k$-{\it tree} is either a complete graph on $k$ vertices or a graph obtained from a smaller $k$-tree by adjoining a new vertex together with $k$ edges connecting it to a $k$-clique. If $\tau$ and $\tau'$ are $d$-cliques in a $k$-tree $T$, a $d$-{\it walk} between $\tau$ and $\tau'$ is a finite sequence $\tau_1\sigma_1\tau_2\sigma_2\cdots\tau_l$, where $\tau_1=\tau$, $\tau_l=\tau'$, and the $d$-cliques $\tau_i$ and $\tau_{i+1}$ are incident to the same $(d+1)$-clique $\sigma_i$. For $d\in\{1,\dots,k\}$, the $d$-{\it distance} from the $d$-cliques $\tau$ and $\tau'$ is the number of $(d+1)$-cliques in a minimum $d$-walk from $\tau$ and $\tau'$, and is denoted by $\dist^d(\tau,\tau')$. Let $c_d$ denote the number of $d$-cliques in the $k$-tree $T$. Then the $d$-distance matrix ${\sf D}^d(T)$ of the $k$-tree $T$ is the $c_d\times c_d$ matrix, indexed by the $d$-cliques of $T$, such that the $(i,j)$-entry is $0$ if $i=j$, and $\dist^d(\tau_i,\tau_j)$ otherwise. Here, we show that, for $k$ and $n$ fixed, the SNF of the $k$-distance matrix is the same for any $k$-tree with $n$ vertices. Specifically, for any $k$-tree $T_{n}$ with $n$ vertices such that $n\geq k+2$, the Smith normal form of ${\sf D}^{k}(T_{n})$ is $${\sf I}_{(k-1)(n-k)+2}\oplus (k+1){\sf I}_{n-k-2}\oplus [k(k+1)(n-k)],$$ which extends Graham-Lovász-Pollak and Hou-Woo results.

The dSphs around the Milky Way are commonly considered as systems that are supported by velocity dispersion against self-gravitation. They have been long accounted among the best targets to search for indirect DM signatures in the GeV-to-TeV gamma-rays due to absence of astrophysical gamma-ray foreground or background emission. We present forecasts on the sensitivity of the future CTAO for the search for annihilating or decaying DM in such targets. We perform an original selection of candidates out of the current catalog of known objects, including both classical and ultra-faint targets. For each of them, we calculate the expected amount of DM using the most updated and complete available samples of photometric and spectroscopic data of member stars, adopting a common framework of data treatment for both classes of objects. In this way, we are able to generate novel astrophysical factor profiles for general indirect DM searches that we compare with the current literature. Out of a starting sample of 64 dSphs, we highlight the 8 most promising targets - DraI, CBe, UMaII, UMi and Wil1 in the Northern hemisphere; RetII, Scl and SgrII in the Southern hemisphere - for which different DM density models (either cored or cuspy) lead to similar expectations, at variance with what happens for other DM targets - thus resulting in more robust predictions. We find that CTAO will provide the strongest limits above ~10 TeV, down to values of velocity-averaged annihilation cross section of ~5$\times 10^{-25}$ cm$^3$ s$^{-1}$ and up to decay lifetimes of ~10$^{26}$ s for combined limits on the best targets. We argue that the largest source of inaccuracy is due to the still imprecise determination of the DM content, especially for ultra-faint dSphs. We propose possible strategies of observation for CTAO, either optimized on a deep focus on the best known candidates, or on the diversification of targets.

We demonstrate the existence of a frequency band exhibiting acoustic transparency in 2D and 3D dense granular suspensions, enabling the transmission of a low-frequency ballistic wave excited by a high-frequency broadband ultrasound pulse. This phenomenon is attributed to spatial correlations in the structural disorder of the medium. To support this interpretation, we use an existing model that incorporates such correlations via the structure factor. Its predictions are shown to agree well with those of the Generalized Coherent Potential Approximation (GCPA) model, which is known to apply at high volume fractions, including the close packing limit, but does not explicitly account for disorder correlation. Within the transparency band, attenuation is found to be dominated by absorption rather than scattering. Measurements of the frequency dependence of the absorption coefficient reveal significant deviations from conventional models, challenging the current understanding of acoustic absorption in dense granular media.

The onset of generalized synchronization of chaos in directionally-coupled systems corresponds to the formation of a continuous mapping which enables one to persistently define the state of the response system from the trajectory of the drive system. The recently developed theory of generalized synchronization of chaos deals only with the case where this synchronization mapping is a single-valued function. In this paper, we explore generalized synchronization in a regime where the synchronization mapping can become a multi-valued function. Specifically, we study the properties of the multi-valued mapping which occurs between the drive and response systems when the systems are synchronized with a frequency ratio other than one-to-one, and address the issues of the existence and continuity of such mappings. The basic theoretical framework underlying the considered synchronization regimes is then developed.

We present exponential and super factorial lower bounds on the number of Hamiltonian cycles passing through any edge of the basis graphs of a graphic, generalized Catalan and uniform matroids. All lower bounds were obtained by a common general strategy based on counting appropriated cycles of length four in the corresponding matroid basis graph.

The results of a search for $\pi^0$ decays to a photon and an invisible massive dark photon at the NA62 experiment at the CERN SPS are reported. From a total of $4.12\times10^8$ tagged $\pi^0$ mesons, no signal is observed. Assuming a kinetic-mixing interaction, limits are set on the dark photon coupling to the ordinary photon as a function of the dark photon mass, improving on previous searches in the mass range 60--110 MeV/$c^2$. The present results are interpreted in terms of an upper limit of the branching ratio of the electro-weak decay $\pi^0 \to \gamma \nu \overline{\nu}$, improving the current limit by more than three orders of magnitude.

Graphene's electronic structure can be fundamentally altered when a substrate- or adatom-induced Kekulé superlattice couples the valley and isospin degrees of freedom. Here, we show that the band structure of Kekulé-textured graphene can be re-engineered through layer stacking. We predict a family of Kekulé graphene bilayers that exhibit band structures with up to six valleys, and room-temperature Dirac quasiparticles whose masses can be tuned electrostatically. Fermi velocities half as large as in pristine graphene put this system in the strongly coupled regime, where correlated ground states can be expected.

A search for the flavor-changing neutral-current decay $B^{+}\to K^{+}\nu\bar{\nu}$ is performed at the Belle II experiment at the SuperKEKB asymmetric energy electron-positron collider. The results are based on a data sample corresponding to an integrated luminosity of $63\,\mbox{fb}^{-1}$ collected at the $\Upsilon{(4S)}$ resonance and a sample of $9\,\mbox{fb}^{-1}$ collected at an energy $60\mathrm{\,Me\kern -0.1em V}$ below the resonance. A novel measurement method is employed, which exploits topological properties of the $B^{+}\to K^{+}\nu\bar{\nu}$ decay that differ from both generic bottom-meson decays and light-quark pair production. This inclusive tagging approach offers a higher signal efficiency compared to previous searches. No significant signal is observed. An upper limit on the branching fraction of $B^{+}\to K^{+}\nu\bar{\nu}$ of $4.1 \times 10^{-5}$ is set at the 90% confidence level.

The NA62 experiment at the CERN SPS reports the first detection of a tagged neutrino candidate based on the data collected in 2022. The candidate consists of a $K^+ \rightarrow \mu^+ \nu_\mu$ decay where the charged particles are reconstructed and the neutrino is detected through a charged-current interaction in a liquid krypton calorimeter.

Gravitational lensing offers a powerful probe into the properties of dark matter and is crucial to infer cosmological parameters. The Legacy Survey of Space and Time (LSST) is predicted to find O(10^5) gravitational lenses over the next decade, demanding automated classifiers. In this work, we introduce GraViT, a PyTorch pipeline for gravitational lens detection that leverages extensive pretraining of state-of-the-art Vision Transformer (ViT) models and MLP-Mixer. We assess the impact of transfer learning on classification performance by examining data quality (source and sample size), model architecture (selection and fine-tuning), training strategies (augmentation, normalization, and optimization), and ensemble predictions. This study reproduces the experiments in a previous systematic comparison of neural networks and provides insights into the detectability of strong gravitational lenses on that common test sample. We fine-tune ten architectures using datasets from HOLISMOKES VI and SuGOHI X, and benchmark them against convolutional baselines, discussing complexity and inference-time analysis.

Impact crater experiments in granular media traditionally involve loosely packed sand targets. However, this study investigates granular impact craters on both loosely and more tightly packed sand targets. We report granular vs. granular experiments that consistently adhere to power-law scaling laws for diameter as a function of impacting energy, similar to those reported by other groups for their experiments utilizing both solid and granular projectiles. In contrast, we observe significant deviations in the depth vs. energy power-law predicted by previous models. To address this discrepancy, we introduce a physical model of uniaxial compression that explains how depth saturates in granular collisions. Furthermore, we present an energy balance alongside this model that aligns well with our crater volume measurements and describes the energy transfer mechanisms acting during crater formation. Central peak formation also plays an essential role in better transferring vertical momentum to horizontal degrees of freedom, resulting in shallow craters on compacted sandbox targets. Our results reveal a depth-to-diameter aspect ratio of approximately $\sim 1/5$, allowing us to interpret the shallowness of planetary craters in light of the uniaxial compression mechanism proposed in this work.

We further investigate novel features of the $T-$vacuum state, originally defined in the context of quantum field theory in a (1+1) dimensional radiation dominated universe [Modak, JHEP 12, 031 (2020)]. Here we extend the previous work to a realistic (3+1) dimensional set up and show that $T-$vacuum gives rise to an anisotropic particle creation phenomena in the radiation dominated early universe. Unlike the Hawking or Unruh effect, where the particle content is thermal and asymptotically defined, here it is non-thermal and instantaneous. This novel example of particle creation is interesting because these particles are detected in the frame of physical/cosmological observers, who envision $T-$vacuum as a particle excited state. Such results comes with a potential to be eventually compared with the observed anisotropies from the early universe and may provide new insights on cosmological particle creation.

This study introduces the syntropy function ($S_N$) and expectancy function ($E_N$), derived from the novel function $\phi$, to provide a refined perspective on complexity, extending beyond conventional entropy analysis. $S_N$ is designed to detect localized coherent events, whereas $E_N$ encapsulates expected system behaviors, offering a comprehensive framework for understanding system dynamics. The manuscript explores essential theorems and properties, underscoring their theoretical and practical implications. Future research will further elucidate their roles, particularly in biological signals and dynamic systems, suggesting a deep interplay between order and chaos.

In this paper we address the relation between the star exponentials emerging within the Deformation Quantization formalism and Feynman's path integrals associated with propagators in quantum dynamics. In order to obtain such a relation, we start by visualizing the quantum propagator as an integral transform of the star exponential by means of the symbol corresponding to the time evolution operator and, thus, we introduce Feynman's path integral representation of the propagator as a sum over all the classical histories. The star exponential thus constructed has the advantage that it does not depend on the convergence of formal series, as commonly understood within the context of Deformation Quantization. We include some basic examples to illustrate our findings, recovering standard results reported in the literature. Further, for an arbitrary finite dimensional system, we use the star exponential introduced here in order to find a particular representation of the star product which resembles the one encountered in the context of the quantum field theory for a Poisson sigma model.

Motile liquid crystal (LC) colloids show peculiar behavior due to the high sensitivity to external stimuli driven by the LC elastic and surface effects. However, few studies focus on harnessing the LC phase transitions to propel colloidal inclusions by the nematic-isotropic (NI) interface. We engineer a quasi-2D active system consisting of solid micron-sized light-absorbent platelets immersed in a thermotropic nematic LC. The platelets self-propel in the presence of light while self-inducing a localized NI phase transition. The sample's temperature, light intensity, and confinement determine three different regimes: a 2D large regime where the platelet-isotropic phase bubble is static and the NI interface remains stable; a compact motile-2D regime where the NI interface lies closer to the platelet's contour; and a motile-3D-confinement regime characterized by the emergence of multipolar configurations of the LC. We perform continuum-theory simulations that predict stationary platelet-LC states when confined in 3D. Our study in an intrinsically far-from-equilibrium landscape is crucial for designing simple synthetic systems that contribute to our understanding of harnessing liquid crystals' phase transitions to propel colloidal inclusions and trigger tunable topological reconfigurations leading to photonic responses.

In recent years, the security concerns about the vulnerability of Deep Convolutional Neural Networks (DCNN) to Adversarial Attacks (AA) in the form of small modifications to the input image almost invisible to human vision make their predictions untrustworthy. Therefore, it is necessary to provide robustness to adversarial examples in addition to an accurate score when developing a new classifier. In this work, we perform a comparative study of the effects of AA on the complex problem of art media categorization, which involves a sophisticated analysis of features to classify a fine collection of artworks. We tested a prevailing bag of visual words approach from computer vision, four state-of-the-art DCNN models (AlexNet, VGG, ResNet, ResNet101), and the Brain Programming (BP) algorithm. In this study, we analyze the algorithms' performance using accuracy. Besides, we use the accuracy ratio between adversarial examples and clean images to measure robustness. Moreover, we propose a statistical analysis of each classifier's predictions' confidence to corroborate the results. We confirm that BP predictions' change was below 2\% using adversarial examples computed with the fast gradient sign method. Also, considering the multiple pixel attack, BP obtained four out of seven classes without changes and the rest with a maximum error of 4\% in the predictions. Finally, BP also gets four categories using adversarial patches without changes and for the remaining three classes with a variation of 1\%. Additionally, the statistical analysis showed that the predictions' confidence of BP were not significantly different for each pair of clean and perturbed images in every experiment. These results prove BP's robustness against adversarial examples compared to DCNN and handcrafted features methods, whose performance on the art media classification was compromised with the proposed perturbations.

It is general wisdom that likely charged colloidal particles repel each other when suspended in liquids. This is in perfect agreement with mean field theories being developed more than 60 years ago. Accordingly, it was a big surprise when several groups independently reported long-ranged attractive components in the pair potential U(r) of equally charged colloids. This so-called like-charge attraction (LCA) was only observed in thin sample cells while the pair-interaction in unconfined suspensions has been experimentally confirmed to be entirely repulsive. Despite considerable experimental and theoretical efforts, LCA remains one of the most challenging mysteries in colloidal science. We experimentally reinvestigate the pair-potential U(r) of charged colloidal particles with digital video microscopy and demonstrate that optical distortions in the particles images lead to slightly erroneous particle positions. If not properly taken into account, this artefact pretends a minimum in U(r) which was in the past misleadingly interpreted as LCA. After correcting optical distortions we obtain entirely repulsive pair interactions which show good agreement with linearized mean field theories.

In this article, we analyze the Pontryagin model adopting different geometric-covariant approaches. In particular, we focus on the manner in which boundary conditions must be imposed on the background manifold in order to reproduce an unambiguous theory on the boundary. At a Lagrangian level, we describe the symmetries of the theory and construct the Lagrangian covariant momentum map which allows for an extension of Noether's theorems. Through the multisymplectic analysis we obtain the covariant momentum map associated with the action of the gauge group on the covariant multimomenta phase-space. By performing a space plus time decomposition by means of a foliation of the appropriate bundles, we are able to recover not only the $t$-instantaneous Lagrangian and Hamiltonian of the theory, but also the generator of the gauge transformations. In the polysymplectic framework we perform a Poisson-Hamilton analysis with the help of the De Donder-Weyl Hamiltonian and the Poisson-Gerstenhaber bracket. Remarkably, as long as we consider a background manifold with boundary, in all of these geometric formulations, we are able to recover the so-called differentiability conditions as a straightforward consequence of Noether's theorem.