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We propose a DRL-assisted approach for service provisioning in multi-band elastic optical networks. Our simulation environment uses an accurate QoT estimator based on the GN/EGN model. Results show that the proposed approach reduces request blocking by 50% compared with heuristics from the literature., Comment: This paper has been accepted by 50th European Conference on Optical Communications (ECOC 2O24)

Models of soil organic carbon (SOC) frequently overlook the effects of spatial dimensions and microbiological activities. In this paper, we focus on two reaction-diffusion chemotaxis models for SOC dynamics, both supporting chemotaxis-driven instability and exhibiting a variety of spatial patterns as stripes, spots and hexagons when the microbial chemotactic sensitivity is above a critical threshold. We use symplectic techniques to numerically approximate chemotaxis-driven spatial patterns and explore the effectiveness of the piecewice dynamic mode decomposition (pDMD) to reconstruct them. Our findings show that pDMD is effective at precisely recreating chemotaxis-driven spatial patterns, therefore broadening the range of application of the method to classes of solutions different than Turing patterns. By validating its efficacy across a wider range of models, this research lays the groundwork for applying pDMD to experimental spatiotemporal data, advancing predictions crucial for soil microbial ecology and agricultural sustainability.

We present circuit-loaded metasurfaces that behave differently in a passive manner even at the same frequency in accordance with the incoming waveform, specifically, its pulse width. Importantly, the time-varying waveform-selective metasurfaces reported thus far were mostly able to change their electric properties but not their magnetic properties; this severely limited the design range of their corresponding wave impedances and refractive indices and thus hindered the development of potential applications in antennas, sensors, imagers, signal processing, and wireless communications. In this study, passive time-varying waveform-selective metasurfaces were found to attain magnetic property control by introducing an additional circuit-loaded layer that generated an artificial magnetic dipole moment; this magnetic moment only occurred during the designed pulse duration in the time domain. Our proposed concept and structures were validated numerically and experimentally; thus, our results could be used to address electromagnetic and related issues sharing the same frequency component via the variation of the pulse width as an additional degree of freedom., Comment: 8 pages, 7 figures

Irradiations at Ultra High Dose Rate (UHDR) regimes, exceeding 40 Gy/s in single fractions lasting less than 200 ms, have shown an equivalent antitumor effect compared to conventional radio-therapy with reduced harm to normal tissues. This work details the hardware and software modi-fications implemented to deliver 10 MeV UHDR electron beams with a Linear Accelerator Elekta SL 18 MV and the beam characteristics obtained. GafChromic EBT XD films and an Advanced Markus chamber were used for the dosimetry characterization, while a silicon sensor assessed the machine's beam pulses stability and repeatability. Dose per pulse, average dose rate and instantaneous dose rate in the pulse were evaluated for four experimental settings, varying the source-to-surface dis-tance and the beam collimation, i.e. with and without the use of a cylindrical applicator. Results showed dose per pulse from 0.6 Gy to a few tens of Gy and average dose rate up to 300 Gy/s. The obtained results demonstrate the possibility to perform in-vitro radiobiology experiments and test of new technologies for beam monitoring and dosimetry at the upgraded LINAC, thus contributing to the electron UHDR research field.

Semiconductor nanocrystals (quantum dots) are light emitters with high quantum yield that are relatively easy to manufacture. There is therefore much interest in their possible application for the development of high-performance scintillators for use in high-energy physics. However, few previous studies have focused on the response of these materials to high-energy particles. To evaluate the potential for the use of nanocomposite scintillators in calorimetry, we are performing side-by-side tests of fine-sampling shashlyk calorimeter prototypes with both conventional and nanocomposite scintillators using electron and minimum-ionizing particle beams, allowing direct comparison of the performance obtained., Comment: 3 pages, 2 figures, 1 table

The Hitchin component of the character variety of representations of a surface group $\pi_1(S)$ into $\mathrm{PSL}_d(\mathbb R)$ for some $d\geq 3$ can be equipped with a pressure metric whose restriction to the Fuchsian locus equals the Weil-Petersson metric up to a constant factor. We show that if the genus of $S$ is at least $3$, then the Fuchsian locus contains quasi-convex subsets of infinite diameter for the Weil--Petersson metric whose diameter for the path metric of the pressure metric is finite. This is established through showing that biinfinite paths of bending deformations have controlled bounded length. To this end we give a geometric interpretation of Fock--Goncharov positivity and show that bending deformations of Fuchsian representations stabilize a uniform Finsler quasi-convex disk in the symmetric space $\mathrm{PSL}_d(\mathbb R)/\mathrm{PSO}(d)$., Comment: 119 pages, 6 figures, comments welcome!

We present new $P -\phi_{31}-{\rm [Fe/H]}$ and $P -\phi_{31}- A_2 - {\rm [Fe/H]}$ relations for fundamental-mode (RRab) and first-overtone mode (RRc) RR Lyrae stars (RRLs), respectively. The relations were calibrated based on pulsation periods and Fourier parameters of the RRL light curves in the Gaia $G$-band published in the Gaia Data Release 3 (DR3), and accurate spectroscopically measured metallicities available in the literature. We apply the feature selection algorithm to identify the most relevant parameters for the determination of metallicity. To fit the relations, we used the Bayesian approach, which allowed us to carefully take into account uncertainties in various parameters and the intrinsic scatter of the relations. The root mean squared errors of the predicted metallicity values in the training samples are 0.28 dex and 0.21 dex for RRab and RRc stars, respectively, comparable with the typical uncertainty of low/intermediate resolution spectroscopic metallicity measurements. We applied the new relations to measure individual metallicities and distances to $\sim$ 134,000 RRLs from the Gaia DR3 catalogue, as well as mean metallicities and distances to 38 Milky Way globular clusters. We also estimate the mean metallicity and distance to the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC): ${\rm [Fe/H]_{LMC} = -1.63\pm0.36}$ and $\mu_{\rm LMC}=18.55\pm0.18$~mag, ${\rm [Fe/H]_{SMC}=-1.86\pm0.36}$~dex and $\mu_{\rm SMC}=19.01\pm 0.17$~mag, respectively, in excellent agreement with previous measurements., Comment: 21 pages, 20 figure, submitted to MNRAS on May 13

Understanding how light modifies long-range order is key to improve our ability to control material functionality on an ultrafast timescale. Transient spatial heterogeneity has been proposed in many materials, but isolating the dynamics of different regions experimentally has been challenging. Here we address this issue and measure the dynamics of orbital order melting in the layered manganite, La0.5Sr1.5MnO4, and isolate the surface dynamics from the bulk for the first time. Bulk measurements show orbital order is rapidly suppressed, but the correlation length surprisingly increases. However, the surface dynamics, show a stronger suppression and a significant decrease in correlation length. By isolating the surface changes, we find that light preferentially melts a less ordered surface and the loss of long-range order is likely driven by the formation of local and disordered polarons. Melting the disordered surface effectively increases the average correlation of the bulk probed volume, resolving the contradictory response. These results show that surface scattering methods are necessary to understand both surface and bulk dynamics in heterogeneous materials., Comment: 22 pages, 8 figures

The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbols (closed-world assumption). This assumption leads to generate hallucinated outputs rather than admitting their lack of knowledge. Hallucinations can lead to wrong or potentially offensive responses to users. Hence, a mechanism to prevent this behavior is crucial to build trusted NSP-based Question Answering agents. To that end, we propose the Hallucination Simulation Framework (HSF), a general setting for stimulating and analyzing NSP model hallucinations. The framework can be applied to any NSP task with a closed-ontology. Using the proposed framework and KQA Pro as the benchmark dataset, we assess state-of-the-art techniques for hallucination detection. We then present a novel hallucination detection strategy that exploits the computational graph of the NSP model to detect the NSP hallucinations in the presence of ontology gaps, out-of-domain utterances, and to recognize NSP errors, improving the F1-Score respectively by ~21, ~24% and ~1%. This is the first work in closed-ontology NSP that addresses the problem of recognizing ontology gaps. We release our code and checkpoints at https://github.com/amazon-science/handling-ontology-gaps-in-semantic-parsing.

Oxygen vacancies (VO's) are of paramount importance in influencing the properties and applications of ceria (CeO2). Yet, comprehending the distribution and nature of the VO's poses a significant challenge due to the vast number of electronic configurations and intricate many-body interactions among VO's and polarons (Ce3+'s). In this study, we employed a combination of LASSO regression in machine learning, in conjunction with a cluster expansion model and first-principles calculations to decouple the interactions among the Ce3+'s and VO's, thereby circumventing the limitations associated with sampling electronic configurations. By separating these interactions, we identified specific electronic configurations characterized by the most favorable VO-Ce3+ attractions and the least Ce3+-Ce3+/VO-VO repulsions, which are crucial in determining the stability of vacancy structures. Through more than 10^8 Metropolis Monte Carlo samplings of Vo's and Ce3+ in the near-surface of CeO2(111), we explored potential configurations within an 8x8 supercell. Our findings revealed that oxygen vacancies tend to aggregate and are most abundant in the third oxygen layer, primarily due to extensive geometric relaxation-an aspect previously overlooked. This behavior is notably dependent on the concentration of Vo. This work introduces a novel theoretical framework for unraveling the complex vacancy structures in metal oxides, with potential applications in redox and catalytic chemistry., Comment: 22 pages, 1 scheme, 5 figures

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated., Comment: Prepared for submission to JINST

Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though the limited size of existing datasets and the cost of collecting more annotations are prohibitive. With both engineering and scientific questions in mind, we introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph annotations, currently the largest and most diverse of its kind: AMR graphs for 1,685 information-seeking utterances mapped to 50+ typologically diverse languages. We describe how we built our resource and its unique features before reporting on experiments using large language models for multilingual AMR and SPARQL parsing as well as applying AMRs for hallucination detection in the context of knowledge base question answering, with results shedding light on persistent issues using LLMs for structured parsing.

With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD) - an approach that allows disentangling biologically relevant features from potential technical biases. MFD incorporates target metadata into model training, by conditioning weights of the model output layer on different experimental factors. It then separates the factors into disjoint groups and enforces independence of the corresponding feature subspaces with an adversarially learned penalty. We show that the metadata-driven disentanglement approach allows for better model introspection, by connecting latent features to experimental factors, without compromising, or even improving performance in downstream tasks, such as enhancer prediction, or genetic variant discovery. The code for our implemementation is available at https://github.com/HealthML/MFD

Recent studies have shown that the electromagnetic shower induced by a high-energy electron, positron or photon incident along the axis of an oriented crystal develops in a space more compact than the ordinary. On the other hand, the properties of the hadronic interactions are not affected by the lattice structure. This means that, inside an oriented crystal, the natural difference between the hadronic and the electromagnetic shower profile is strongly accentuated. Thus, a calorimeter composed of oriented crystals could be intrinsically capable of identifying more accurately the nature of the incident particles, with respect to a detector composed only of non-aligned crystals. Since no oriented calorimeter has ever been developed, this possibility remains largely unexplored and can be investigated only by mean of numerical simulations. In this work, we report the first quantitative evaluation of the particle identification capability of such a calorimeter, focusing on the case of neutron-gamma discrimination. We demonstrate through Geant4 simulations that the use of oriented crystals significantly improves the performance of a Random Forest classifier trained on the detector data. This work is a proof that oriented calorimeters could be a viable option for all the environments where particle identification must be performed with a very high accuracy, such as future high-intensity particle physics experiments and satellite-based gamma-ray telescopes.

Large language models (LLMs) tend to inadequately integrate input context during text generation, relying excessively on encoded prior knowledge in model parameters, potentially resulting in generated text with factual inconsistencies or contextually unfaithful content. LLMs utilize two primary knowledge sources: 1) prior (parametric) knowledge from pretraining, and 2) contextual (non-parametric) knowledge from input prompts. The study addresses the open question of how LLMs effectively balance these knowledge sources during the generation process, specifically in the context of open-domain question answering. To address this issue, we introduce a novel approach integrating contrastive decoding with adversarial irrelevant passages as negative samples to enhance robust context grounding during generation. Notably, our method operates at inference time without requiring further training. We conduct comprehensive experiments to demonstrate its applicability and effectiveness, providing empirical evidence showcasing its superiority over existing methodologies. Our code is publicly available at: https://github.com/amazon-science/ContextualUnderstanding-ContrastiveDecoding., Comment: Accepted to NAACL 2024

This study proposes a methodology using OpenAlex (OA) for tracking Open Access publications in the case of Argentina, a country where a self-archiving mandate has been in effect since 2013 ( Law 26.899, 2013). A sample of 167,240 papers by researchers from the National Council for Scientific and Technical Research (CONICET) was created and analyzed using statistical techniques. We estimate that OA is able to capture between 85-93% of authors for all disciplines, with the exception of Social Sciences and Humanities, where it only reaches an estimated 47%. The availability of papers in Open Access was calculated to be 41% for the period 1953-2021 and 46% when considering exclusively the post-law period (2014-2021). In both periods, gold Open Access made up the most common route. When comparing equal periods post and pre-law, we observed that the upward trend of gold Open Access was pre-existing to the legislation and the availability of closed articles in repositories increased by 5% to what is estimated based on existing trends. However, while the green route has had a positive evolution, it has been the publication in gold journals that has boosted access to Argentine production more rapidly. We concluded that the OA-based methodology, piloted here for the first time, is viable for tracking Open Access in Argentina since it yields percentages similar to other national and international studies. En este estudio se propone una metodolog\'ia utilizando OpenAlex (OA) para monitorear el acceso abierto (AA) a las publicaciones cient\'ificas para el caso de Argentina, pa\'is donde rige el mandato de autoarchivo -Ley 26.899 (2013)-. Se conform\'o una muestra con 167.240 art\'iculos de investigadores del Consejo Nacional de Investigaciones Cient\'ificas y T\'ecnicas (CONICET) que se analizaron con t\'ecnicas estad\'isticas. Se estim\'o que OA puede representar entre 85-93% de los autores para todas las disciplinas, excepto Ciencias Sociales y Humanidades, donde solo alcanza al 47%. Se calcul\'o que 41% de los art\'iculos publicados entre 1953-2021 incluidos en la fuente est\'an en AA, porcentaje que sube a 46% al considerar exclusivamente el periodo post ley (2014-2021). En ambos periodos es la v\'ia dorada la que representa mayor proporci\'on. Al comparar periodos iguales post y pre ley, se observ\'o que la tendencia en alza de la v\'ia dorada era preexistente a la legislaci\'on y la disponibilidad de art\'iculos cerrados en repositorios aument\'o un 5% a lo que se estima en base a tendencias existentes. Se concluye que si bien la v\'ia verde ha tenido una evoluci\'on positiva, ha sido la publicaci\'on en revistas doradas lo que ha impulsado m\'as r\'apidamente el acceso a la producci\'on argentina. Asimismo, que la metodolog\'ia basada en OA, piloteada aqu\'i por primera vez, es viable para monitorear el AA en Argentina ya que arroja porcentajes similares a otros estudios nacionales e internacionales., Comment: in Spanish (en Espa\~nol)

We observed a substantial increase of the scintillation light output of lead tungstate (PbWO$_4$) at a small incidence angle with respect to two main lattice axes. This reflects the acceleration of electromagnetic shower development that occurs in the crystalline Strong Field. We measured the scintillation light generated by $120$-$\mathrm{GeV}$ electrons and $10$-$100$-$\mathrm{GeV}$ $\gamma$ rays on thick samples. This result deepens the knowledge of the shower development mechanisms in crystal scintillators and could pave the way to the development of innovative accelerator- and space-borne calorimeters.

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations. To promote responsible open-source LLM development, Aurora-M and its variants are released at https://huggingface.co/collections/aurora-m/aurora-m-models-65fdfdff62471e09812f5407 ., Comment: Preprint

In this study, we investigate the feasibility of designing reconfigurable transmitting metasurfaces through the use of Drude-like scatterers with purely electric response. Theoretical and numerical analyses are provided to demonstrate that the response of spherical Drude-like scatterers can be tailored to achieve complete transmission, satisfying a generalized Kerker's condition at half of their plasma frequency. This phenomenon, which arises from the co-excitation of the electric dipole and the electric quadrupole within the scatterer, also exhibits moderate broadband performance. Subsequently, we present the application of these particles as meta-atoms in the design of reconfigurable multipolar Huygens metasurfaces, outlining the technical prerequisites for achieving effective beam-steering capabilities. Finally, we explore a plausible implementation of these low-loss Drude-like scatterers at microwave frequencies using plasma discharges. Our findings propose an alternative avenue for Huygens metasurface designs, distinct from established approaches relying on dipolar meta-atoms or on core-shell geometries. Unlike these conventional methods, our approach fosters seamless integration of reconfigurability strategies in beam-steering devices.

A Generalized Hyperelliptic Variety (GHV) is the quotient of an abelian variety by a free action of a finite group which does not contain any translation. These varieties are natural generalizations of bi-elliptic surfaces. In this paper we prove the Kawamata-Morrison Cone Conjecture for these manifolds using the analogous results established by Prendergast-Smith for abelian varieties., Comment: arXiv admin note: text overlap with arXiv:1008.4509 by other authors

The problems of heating and acceleration of solar wind particles are of significant and enduring interest in astrophysics. The interactions between waves and particles are crucial in determining the distributions of proton and alpha particles, resulting in non-Maxwellian characteristics including temperature anisotropies and particle beams. These processes can be better understood as long as the beam can be separated from the core for the two major components of the solar wind. We utilized an alternative numerical approach that leverages the clustering technique employed in Machine Learning to differentiate the primary populations within the velocity distribution, rather than employing the conventional bi-Maxwellian fitting method. Separation of the core and beam revealed new features for protons and alphas. We estimated that the total temperature of the two beams was slightly higher than that of their respective cores, and the temperature anisotropy for the cores and beams was larger than 1. We concluded that the temperature ratio between alphas and protons largely over 4 is due to the presence of a massive alpha beam, which is approximately 50\% of the alpha core. We provided evidence that the alpha core and beam populations are sensitive to Alfv\'enic fluctuations and the surfing effect found in the literature can be recovered only when considering the core and beam as a single population. Several similarities between proton and alpha beams would suggest a common and local generation mechanism not shared with the alpha core, which may not have necessarily been accelerated and heated locally., Comment: paper accepted by APJ on May 4th 2024, 24 pages, 21 figures

The use of computer simulations to study the properties of aqueous systems is, today more than ever, an active area of research. In this context, during the last decade there has been a tremendous growth in the use of data-driven approaches to develop more accurate potentials for water as well as to characterize its complexity in chemical and biological contexts. We highlight the progress, giving a historical context, on the path to the development of many-body and reactive potentials to model aqueous chemistry, including the role of machine learning strategies. We focus specifically on conceptual and methodological challenges along the way in performing simulations that seek to tackle problems in modeling the chemistry of aqueous solutions. In conclusion, we summarize our perspectives on the use and integration of advanced data-science techniques to provide chemical insights in physical chemistry and how this will influence computer simulations of aqueous systems in the future., Comment: 37 Pages. 7 Figures. Submitted to Chemical Physics Reviews

In Natural Language Processing (NLP) classification tasks such as topic categorisation and sentiment analysis, model generalizability is generally measured with standard metrics such as Accuracy, F-Measure, or AUC-ROC. The diversity of metrics, and the arbitrariness of their application suggest that there is no agreement within NLP on a single best metric to use. This lack suggests there has not been sufficient examination of the underlying heuristics which each metric encodes. To address this we compare several standard classification metrics with more 'exotic' metrics and demonstrate that a random-guess normalised Informedness metric is a parsimonious baseline for task performance. To show how important the choice of metric is, we perform extensive experiments on a wide range of NLP tasks including a synthetic scenario, natural language understanding, question answering and machine translation. Across these tasks we use a superset of metrics to rank models and find that Informedness best captures the ideal model characteristics. Finally, we release a Python implementation of Informedness following the SciKitLearn classifier format., Comment: Appeared in AACL 2023

MoTe2 is a paradigmatic van der Waals layered semimetal with two energetically close electronic phases, the topologically trivial 1Tprime and the low-temperature Td type-II Weyl semimetal phase. The ability to manipulate this phase transition, perhaps towards occurring near room temperature, would open new avenues for harnessing the full potential of Weyl semimetals for high-efficiency electronic and spintronic applications. Here, we show that potassium dosing on 1Tprime-MoTe2 induces a Lifshitz transition by a combination of angle-resolved photoemission spectroscopy, scanning tunneling microscopy, x-ray spectroscopy and density functional theory. While the electronic structure shifts rigidly for small concentrations of K, MoTe2 undergoes significant band structure renormalization for larger concentrations. Our results demonstrate that the origin of this electronic structure change stems from alkali metal intercalation. We show that these profound changes are caused by effectively decoupling the 2D sheets, bringing K-intercalated 1Tprime-MoTe2 to the quasi-2D limit, but do not cause a topological phase transition.

Flexible canopy flows are often encountered in natural scenarios, e.g., when crops sway in the wind or when submerged kelp forests are agitated by marine currents. Here, we provide a detailed characterisation of the turbulent flow developed above and between the flexible filaments of a fully submerged dense canopy and we describe their dynamical response to the turbulent forcing. We investigate a wide range of flexibilities, encompassing the case in which the filaments are completely rigid and standing upright as well as that where they are fully compliant to the flow and deflected in the streamwise direction. We are thus able to isolate the effect of the canopy flexibility on the drag and on the inner-outer flow interactions, as well as the two flapping regimes of the filaments already identified for a single fiber. Furthermore, we offer a detailed description of the Reynolds stresses throughout the wall-normal direction resorting to the Lumley triangle formalism, and we show the multi-layer nature of turbulence inside and above the canopy. The relevance of our investigation is thus twofold: the fundamental physical understanding developed here paves the way towards the investigation of more complex and realistic scenarios, while we also provide a thorough characterisation of the turbulent state that can prove useful in the development of accurate turbulence models for RANS and LES., Comment: 33 pages, 23 figures

One-dimensional (1D) van der Waals materials have emerged as an intriguing playground to explore novel electronic and optical effects. We report on inorganic one-dimensional SbPS4 nanotubes bundles obtained via mechanical exfoliation from bulk crystals. The ability to mechanically exfoliate SbPS4 nanobundles offers the possibility of applying modern 2D material fabrication techniques to create mixed-dimensional van der Waals heterostructures. We find that SbPS4 can readily be exfoliated to yield long (> 10 {\mu}m) nanobundles with thicknesses that range from of 1.3 - 200 nm. We investigated the optical response of semiconducting SbPS4 nanobundles and discovered that upon excitation with blue light, they emit bright and ultra-broadband red light with a quantum yield similar to that of hBN-encapsulated MoSe2. We discovered that the ultra-broadband red light emission is a result of a large ~1 eV exciton binding energy and a ~200 meV exciton self-trapping energy, unprecedented in previous material studies. Due to the bright and ultra-broadband light emission, we believe that this class of inorganic 1D van der Waals semiconductors has numerous potential applications including on-chip tunable nanolasers, and applications that require ultra-violet to visible light conversion such as lighting and sensing. Overall, our findings open avenues for harnessing the unique characteristics of these nanomaterials, advancing both fundamental research and practical optoelectronic applications.

Transport properties of Weyl semimetals are intimately connected to the underlying band structure. The signature of Weyl semimetals are linear dispersing bands that touch, forming Weyl points that result in high charge carrier mobilities. The layered transition metal dichalcogenide MoTe2, undergoes a temperature dependent phase transition that directly converts the trivial 1Tprime phase to the nontrivial Td phase, providing an opportunity to understand how the formation of a Weyl point manifests in the ultrafast carrier dynamics. In this study, we use resonant X-ray photoemission to monitor the element specific evolution of excited carriers 1Tprime-MoTe2 and in the vicinity of the Weyl point in Td-MoTe2. We find that the delocalization time of 1Tprime-MoTe2 is a factor 1.5 times faster than in Td-MoTe2. We argue that this is a result of the change in the density of states and screening length, to a higher carrier scattering rate in Td-MoTe2. Our study tracks the fate of carriers in MoTe2 on sub-fs time-scales and with atomic site specificity.

Measuring the spectral phase of a pulse is key for performing wavelength resolved ultrafast measurements in the few femtosecond regime. However, accurate measurements in real experimental conditions can be challenging. We show that the reflectivity change induced by coherent phonons in a quantum material can be used to infer the spectral phase of an optical probe pulse with few-femtosecond accuracy., Comment: 5 figures

Coherent multistatic radio imaging represents a pivotal opportunity for forthcoming wireless networks, which involves distributed nodes cooperating to achieve accurate sensing resolution and robustness. This paper delves into cooperative coherent imaging for vehicular radar networks. Herein, multiple radar-equipped vehicles cooperate to improve collective sensing capabilities and address the fundamental issue of distinguishing weak targets in close proximity to strong ones, a critical challenge for vulnerable road users protection. We prove the significant benefits of cooperative coherent imaging in the considered automotive scenario in terms of both probability of correct detection, evaluated considering several system parameters, as well as resolution capabilities, showcased by a dedicated experimental campaign wherein the collaboration between two vehicles enables the detection of the legs of a pedestrian close to a parked car. Moreover, as \textit{coherent} processing of several sensors' data requires very tight accuracy on clock synchronization and sensor's positioning -- referred to as \textit{phase synchronization} -- (such that to predict sensor-target distances up to a fraction of the carrier wavelength), we present a general three-step cooperative multistatic phase synchronization procedure, detailing the required information exchange among vehicles in the specific automotive radar context and assessing its feasibility and performance by hybrid Cram\'er-Rao bound., Comment: 13 pages