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This report describes the current version of the Why System, a script-based Socratic tutor which uses tutoring strategies formulated as production rules. The current system is capable of carrying on a dialogue about the factors influencing rainfall by presenting different cases to the student, asking for predictions, probing for relevant factors, entrapping the student when he has not identified all necessary factors, and presenting counterexamples. This system is incomplete because it lacks a goal structure to guide the tutorial sessions. The report outlines a more complete theory of the goal structure of Socratic tutors based on analysis of human tutorial dialogues. The main goals are to refine the student's causal model and predictive abilities. Sub-goals include the diagnosis of deficiencies in the student's knowledge and the correction of these deficiencies. (Author)

Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost and accuracy. This paper introduces RouteNet-Gauss, a novel integration of a testbed network with a Machine Learning (ML) model to address these challenges. By using the testbed as a hardware accelerator, RouteNet-Gauss generates training datasets rapidly and simulates network scenarios with high fidelity to real-world conditions. Experimental results show that RouteNet-Gauss significantly reduces prediction errors by up to 95% and achieves a 488x speedup in inference time compared to state-of-the-art DES-based methods. RouteNet-Gauss's modular architecture is dynamically constructed based on the specific characteristics of the network scenario, such as topology and routing. This enables it to understand and generalize to different network configurations beyond those seen during training, including networks up to 10x larger. Additionally, it supports Temporal Aggregated Performance Estimation (TAPE), providing configurable temporal granularity and maintaining high accuracy in flow performance metrics. This approach shows promise in improving both simulation efficiency and accuracy, offering a valuable tool for network operators., Comment: 13 pages, 11 figures

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

Precision Medicine (PM) transforms the traditional "one-drug-fits-all" paradigm by customising treatments based on individual characteristics, and is an emerging topic for HCI research on digital health. A key element of PM, the Polygenic Risk Score (PRS), uses genetic data to predict an individual's disease risk. Despite its potential, PRS faces barriers to adoption, such as data inclusivity, psychological impact, and public trust. We conducted a mixed-methods study to explore how people perceive PRS, formed of surveys (n=254) and interviews (n=11) with UK-based participants. The interviews were supplemented by interactive storyboards with the ContraVision technique to provoke deeper reflection and discussion. We identified ten key barriers and five themes to PRS adoption and proposed design implications for a responsible PRS framework. To address the complexities of PRS and enhance broader PM practices, we introduce the term Human-Precision Medicine Interaction (HPMI), which integrates, adapts, and extends HCI approaches to better meet these challenges., Comment: This paper is conditionally accepted for publication at the SIGCHI Conference on Human Factors in Computing Systems (CHI 2025)

The performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference. Speculative decoding has been proposed as a technique to accelerate autoregressive generation, leveraging a fast draft model to propose candidate tokens, which are then verified in parallel based on their likelihood under the target model. While this approach guarantees to reproduce the target output, it incurs a substantial penalty: many high-quality draft tokens are rejected, even when they represent objectively valid continuations. Indeed, we show that even powerful draft models such as GPT-4o, as well as human text cannot achieve high acceptance rates under the standard verification scheme. This severely limits the speedup potential of current speculative decoding methods, as an early rejection becomes overwhelmingly likely when solely relying on alignment of draft and target. We thus ask the following question: Can we adapt verification to recognize correct, but non-aligned replies? To this end, we draw inspiration from the LLM-as-a-judge framework, which demonstrated that LLMs are able to rate answers in a versatile way. We carefully design a dataset to elicit the same capability in the target model by training a compact module on top of the embeddings to produce ``judgements" of the current continuation. We showcase our strategy on the Llama-3.1 family, where our 8b/405B-Judge achieves a speedup of 9x over Llama-405B, while maintaining its quality on a large range of benchmarks. These benefits remain present even in optimized inference frameworks, where our method reaches up to 141 tokens/s for 8B/70B-Judge and 129 tokens/s for 8B/405B on 2 and 8 H100s respectively.

Chemical language models for molecular design have the potential to find solutions to multi-parameter optimization problems in drug discovery via reinforcement learning (RL). A key requirement to achieve this is the capacity to "search" chemical space to identify all molecules of interest. Here, we propose a challenging new benchmark to discover dissimilar molecules that possess similar bioactivity, a common scenario in drug discovery, but a hard problem to optimize. We show that a population of RL agents can solve the benchmark, while a single agent cannot. We also find that cooperative strategies are not significantly better than independent agents. Moreover, the performance on the benchmark scales log-linearly with the number of independent agents, showing a test-time training scaling law for chemical language models.

Knowledge Graphs (KGs) store human knowledge in the form of entities (nodes) and relations, and are used extensively in various applications. KG embeddings are an effective approach to addressing tasks like knowledge discovery, link prediction, and reasoning. This is often done by allocating and learning embedding tables for all or a subset of the entities. As this scales linearly with the number of entities, learning embedding models in real-world KGs with millions of nodes can be computationally intractable. To address this scalability problem, our model, PathE, only allocates embedding tables for relations (which are typically orders of magnitude fewer than the entities) and requires less than 25% of the parameters of previous parameter efficient methods. Rather than storing entity embeddings, we learn to compute them by leveraging multiple entity-relation paths to contextualise individual entities within triples. Evaluated on four benchmarks, PathE achieves state-of-the-art performance in relation prediction, and remains competitive in link prediction on path-rich KGs while training on consumer-grade hardware. We perform ablation experiments to test our design choices and analyse the sensitivity of the model to key hyper-parameters. PathE is efficient and cost-effective for relationally diverse and well-connected KGs commonly found in real-world applications.

In diffraction-based crystal structure analysis, thermal ellipsoids, quantified via Anisotropic Displacement Parameters (ADPs), are critical yet challenging to determine. ADPs capture atomic vibrations, reflecting thermal and structural properties, but traditional computation is often expensive. This paper introduces CartNet, a novel graph neural network (GNN) for efficiently predicting crystal properties by encoding atomic geometry into Cartesian coordinates alongside the crystal temperature. CartNet integrates a neighbour equalization technique to emphasize covalent and contact interactions, and a Cholesky-based head to ensure valid ADP predictions. We also propose a rotational SO(3) data augmentation strategy during training to handle unseen orientations. An ADP dataset with over 200,000 experimental crystal structures from the Cambridge Structural Database (CSD) was curated to validate the approach. CartNet significantly reduces computational costs and outperforms existing methods in ADP prediction by 10.87%, while delivering a 34.77% improvement over theoretical approaches. We further evaluated CartNet on other datasets covering formation energy, band gap, total energy, energy above the convex hull, bulk moduli, and shear moduli, achieving 7.71% better results on the Jarvis Dataset and 13.16% on the Materials Project Dataset. These gains establish CartNet as a state-of-the-art solution for diverse crystal property predictions. Project website and online demo: https://www.ee.ub.edu/cartnet

One-loop $S^{d+1}$ path integrals were shown to factorize into two parts: a bulk thermal ideal gas partition function in a $dS_{d+1}$ static patch and an edge partition function associated with degrees of freedom living on $S^{d-1}$. Here, we analyze the $\mathfrak{so}(d)$ structure of the edge partition functions for massive and massless totally symmetric tensors of arbitrary rank in any $d\geq 3$. For linearized Einstein gravity on $S^{d+1}$, we find that the edge partition function receives contributions from shift-symmetric vector and scalar fields on $S^{d-1}$ that nonlinearly realize the isometry group $SO(d+2)$ of $S^{d+1}$, suggesting a possible interpretation in terms of an embedded $S^{d-1}$ brane., Comment: 52 pages+appendices, 2 figures

In this paper, we provide a technical vision for key enabling elements for the architecture of the UK National Data Library (NDL) with a strong focus on building it as an AI-ready data infrastructure through standardised vocabularies, automated analysis tools, and interoperability services. We follow the ODI Multilayer Interoperability Framework (MIF) for data stewardship, covering central socio-technical aspects for the NDL including user-centric approaches to service design and governance., Comment: UK National Data Library: Technical White Paper Challenge

We present a novel theoretical approach for computing and analyzing two-dimensional spectroscopy of bosonic collective excitations in disordered many-body systems. Specifically, we employ the Keldysh formalism to derive the nonlinear response and obtain two-dimensional spectroscopy maps with particular emphasis on the rephasing sector, which allows to disentangle different sources of broadening. Our many-body approach successfully distinguishes elastic and inelastic scattering mechanisms contributing to the excitation linewidth. Additionally, using a non-perturbative conserving approach, we demonstrate that the echo peak exhibits a universal asymmetric shape in the sole presence of static disorder, a feature that remains robust against quantum fluctuations. This is in stark contrast to the standard theory based on isolated two-level systems, which fails to account for the dispersive nature of excitations and the interactions between different momentum components., Comment: 14 + 4 pages, 9 + 2 figures

Chemical language models, combined with reinforcement learning, have shown significant promise to efficiently traverse large chemical spaces in drug design. However, the performance of various RL algorithms and their best practices for practical drug design are still unclear. Here, starting from the principles of the REINFORCE algorithm, we investigate the effect of different components from RL theory including experience replay, hill-climbing, baselines to reduce variance, and alternative reward shaping. Additionally we demonstrate how RL hyperparameters can be fine-tuned for effectiveness, efficiency, or chemical regularization as demonstrated using the MolOpt benchmark.

A periodically loaded waveguide composed of periodic discrete nonlinear gain and radiating elements supports a stable oscillation regime related to the presence of an exceptional point of degeneracy (EPD). After reaching saturation, the EPD in the system establishes the oscillation frequency. We demonstrate a synchronization regime at a stable oscillation frequency, resulting in uniform saturated gain across the array and uniform radiating power. Unlike conventional one-dimensional cavity resonances, the oscillation frequency is independent of the array length. Our investigations further show that when small-signal gain is non-uniformly distributed across the array, the saturated gain results in having a uniform distribution at a gain value that generates an EPD. Experimental validation using the measured board confirmed that the system saturates at an EPD, with a measured spectrum exhibiting very low phase noise. This low noise allows for operation at a clean oscillation frequency. Additionally, the measured uniform power across the array corresponds to the simulation results. The proposed scheme can pave the way for a new generation of high-power radiating arrays with distributed active elements.

Quantum computers have advanced rapidly in qubit number and gate fidelity. However, they would still lack practicalness without utilizing fault-tolerant quantum error correction code (QECC) implementations to suppress noise. Manually or experimentally verifying the fault-tolerance of complex QECCs is impractical due to the vast error combinations. This paper formalizes fault-tolerance within the language of quantum programs. By incorporating the techniques of quantum symbolic execution, we provide an automatic verification tool for quantum fault-tolerance. We evaluate and demonstrate the effectiveness of our tool on a universal set of logical operations across different QECCs., Comment: 26 pages, 4 figures

We present a systematic investigation of the optical response to circularly polarized illumination in twisted stacked plasmonic nanostructures. The system consissts in two identical, parallel gold triskelia centrally aligned and rotated at a central angle relative to each other. Sample fabrication was acomplished through a double electron beam lithograpy process. This stack holds two plasmonic modes of multipolar character in the near-infrared range, showing a strong dependence of their excitation intensities on the handedness of the circularly polarized incident light. This translates in a large circular dicrhoism which can be modulated by adjusting the twist angle of the stack. Fourier-transform infrared spectroscopy and numerical simulations were employed to characterize the spectral features of the modes. Remarkable, in contrast to previous results in other stacked nanostructures, the system's response exhibits a behavious analogous to that of two interacting dipoles only at small angles. As the angle approaches 15 degrees, where the maximum dichroism is observed, more complex modes of the stack emerge. These modes evolve towards two in-phase multipolar excitations of the two triskelia as the angle increases uo to 60 degrees. Finally, simulations for a triangular array of such stacked elements show a sharp mode arising from the hybridization of a surface lattice resonance with the low-energy mode of the stack.

Dense prediction tasks have enjoyed a growing complexity of encoder architectures, decoders, however, have remained largely the same. They rely on individual blocks decoding intermediate feature maps sequentially. We introduce banks, shared structures that are used by each decoding block to provide additional context in the decoding process. These structures, through applying them via resampling and feature fusion, improve performance on depth estimation for state-of-the-art transformer-based architectures on natural and synthetic images whilst training on large-scale datasets.

Single-view novel view synthesis (NVS) is a notorious problem due to its ill-posed nature, and often requires large, computationally expensive approaches to produce tangible results. In this paper, we propose CheapNVS: a fully end-to-end approach for narrow baseline single-view NVS based on a novel, efficient multiple encoder/decoder design trained in a multi-stage fashion. CheapNVS first approximates the laborious 3D image warping with lightweight learnable modules that are conditioned on the camera pose embeddings of the target view, and then performs inpainting on the occluded regions in parallel to achieve significant performance gains. Once trained on a subset of Open Images dataset, CheapNVS outperforms the state-of-the-art despite being 10 times faster and consuming 6% less memory. Furthermore, CheapNVS runs comfortably in real-time on mobile devices, reaching over 30 FPS on a Samsung Tab 9+., Comment: Accepted to ICASSP 2025

Gaussian splatting (GS) for 3D reconstruction has become quite popular due to their fast training, inference speeds and high quality reconstruction. However, GS-based reconstructions generally consist of millions of Gaussians, which makes them hard to use on computationally constrained devices such as smartphones. In this paper, we first propose a principled analysis of advances in efficient GS methods. Then, we propose Trick-GS, which is a careful combination of several strategies including (1) progressive training with resolution, noise and Gaussian scales, (2) learning to prune and mask primitives and SH bands by their significance, and (3) accelerated GS training framework. Trick-GS takes a large step towards resource-constrained GS, where faster run-time, smaller and faster-convergence of models is of paramount concern. Our results on three datasets show that Trick-GS achieves up to 2x faster training, 40x smaller disk size and 2x faster rendering speed compared to vanilla GS, while having comparable accuracy., Comment: Accepted at ICASSP'25

Brown dwarfs lack nuclear fusion and cool with time; the coldest known have an effective temperature below 500 K, and are known as Y dwarfs. We present a James Webb Space Telescope (JWST) photometric dataset of Y dwarfs: twenty-three were imaged in wide-field mode, 20 using NIRCam with the F150W and F480M filters, and 3 using NIRISS with the F480M filter. We present an F480M vs. F150W $-$ F480M color-magnitude diagram for our sample, and other brown dwarfs with F150W and F480M colors synthesized from JWST spectra by Beiler et al. (2024). For one target, WISEA J083011.95$+$283716.0, its detection in the near-infrared confirms it as one of the reddest Y dwarfs known, with F150W $-$ F480M $= 9.62$ mag. We provide its updated parallax and proper motion. One of the Beiler et al. Y dwarfs, CWISEP J104756.81+545741.6, is unusually blue, consistent with strong CO absorption seen in its spectrum which the F480M filter is particularly sensitive to. The strong CO and the kinematics of the object suggest it may be very low-mass and young. We update the resolved photometry for the close binary system WISE J033605.05$-$014350.4 AB, and find that the secondary is almost as cold as WISE 085510.83$-$071442.5, with $T_{\rm eff} \lesssim 300$ K, however the F150W $-$ F480M color is significantly bluer, possibly suggesting the presence of water clouds. Astrometry is measured at the JWST epoch for the sample which is consistent with parallax and proper motion values reported by Kirkpatrick et al. (2021) and Marocco et al. (in prep)., Comment: Accepted by AJ Jan 23 2025

Highly-irradiated gas giant exoplanets are predicted to show circulation patterns dominated by day-to-night heat transport and a spatial distribution of clouds that is driven by advection and local heating. Hot-Jupiters have been extensively studied from broadband phase-curve observations at infrared and optical wavelengths, but spectroscopic observations in the reflected light are rare and the regime of smaller and higher-metallicity ultra-hot planets, such as hot-Neptunes, remains largely unexplored to date. Here we present the phase-resolved reflected-light and thermal-emission spectroscopy of the ultra-hot Neptune LTT 9779b, obtained through observing its full phase-curve from 0.6 to 2.8 $\mu$m with JWST NIRISS/SOSS. We detect an asymmetric dayside in reflected light (3.1$\sigma$ significance) with highly-reflective white clouds on the western dayside (A = 0.79$\pm$0.15) and a much lower-albedo eastern dayside (A = 0.41$\pm$0.10), resulting in an overall dayside albedo of A = 0.50$\pm$0.07. The thermal phase curve is symmetric about the substellar point, with a dayside effective temperature of T$_\mathrm{eff,day}$ = 2,260$^{+40}_{-50}$ K and a cold nightside (T$_\mathrm{eff,night}$ <1,330 K at 3-$\sigma$ confidence), indicative of short radiative timescales. We propose an atmospheric circulation and cloud distribution regime in which heat is transported eastward from the dayside towards the cold nightside by an equatorial jet, leading to a colder western dayside where temperatures are sufficiently low for the condensation of silicate clouds., Comment: Accepted for publication

This paper provides a dual domain derivation of the error exponent of maximum mutual information (MMI) decoding with constant composition codes, showing it coincides with that of maximum likelihood decoding for discrete memoryless channels. The analysis is further extended to joint source-channel coding, demonstrating that the generalized MMI decoder achieves the same random coding error exponent as the maximum a posteriori decoder., Comment: This paper has been submitted to ISIT 2025 for review

The almost identical charge-to-mass ratios of fully ionized helium-4 and carbon-12 ions enable simultaneous acceleration in hadron therapy synchrotrons. At the same energy per mass, helium ions exhibit a stopping range approximately three times greater than carbon ions. They can therefore be exploited for online range verification downstream of the patient during carbon ion beam irradiation. One possibility for creating this mixed beam is accelerating the two ion species sequentially through the LINAC and subsequently "mixing" them at injection energy in the synchrotron with a double injection scheme. This work reports the first successful generation, acceleration, and extraction of a mixed helium and carbon ion beam using this double multi-turn injection scheme, which was achieved at the MedAustron therapy accelerator in Austria. A description of the double multi-turn injection scheme, particle tracking simulations, and details on the implementation at the MedAustron accelerator facility are presented and discussed. Finally, measurements of the mixed beam at delivery in the irradiation room using a radiochromic film and a low-gain avalanche diode (LGAD) detector are presented., Comment: 14 pages, 12 figures

Advances in self-supervised learning (SSL) for machine vision have improved representation robustness and model performance, giving rise to pre-trained backbones like \emph{ResNet} and \emph{ViT} models tuned with SSL methods such as \emph{SimCLR}. Due to the computational and data demands of pre-training, the utilization of such backbones becomes a strenuous necessity. However, employing these backbones may inherit vulnerabilities to adversarial attacks. While adversarial robustness has been studied under \emph{white-box} and \emph{black-box} settings, the robustness of models tuned on pre-trained backbones remains largely unexplored. Additionally, the role of tuning meta-information in mitigating exploitation risks is unclear. This work systematically evaluates the adversarial robustness of such models across $20,000$ combinations of tuning meta-information, including fine-tuning techniques, backbone families, datasets, and attack types. We propose using proxy models to transfer attacks, simulating varying levels of target knowledge by fine-tuning these proxies with diverse configurations. Our findings reveal that proxy-based attacks approach the effectiveness of \emph{white-box} methods, even with minimal tuning knowledge. We also introduce a naive "backbone attack," leveraging only the backbone to generate adversarial samples, which outperforms \emph{black-box} attacks and rivals \emph{white-box} methods, highlighting critical risks in model-sharing practices. Finally, our ablations reveal how increasing tuning meta-information impacts attack transferability, measuring each meta-information combination.

We present the first interferometric imaging of molecular line emission from the Ring Nebula, NGC~6720, in the form of Submillimeter Array (SMA) observations of CO $J=2\rightarrow 1$ emission. The SMA $^{12}$CO(2--1) mapping data, with $\sim$3$''$ spatial resolution and 2 km s$^{-1}$ velocity resolution, provide an unprecedentedly detailed, 3D view of the Ring's clumpy molecular envelope. The morphology of the velocity-integrated SMA $^{12}$CO(2--1) image closely resembles those of near-IR H$_2$ and PAH emission in JWST/NIRCam imaging of NGC~6720, with the molecular gas forming a geometrically thin layer surrounding the ionized gas imaged by HST and JWST. A simple, geometrical model of the $^{12}$CO(2--1) data shows that the intrinsic structure of NGC~6720's molecular envelope closely resembles a truncated, triaxial ellipsoid that is viewed close to pole-on, and that the dynamical age of the molecular envelope is $\sim$6000 yr. The SMA $^{12}$CO(2--1) data furthermore reveal that filamentary features seen projected in the Ring's interior in JWST imaging are in fact fast-moving polar knots or bullets with radial velocities of $\pm$45--50 km s$^{-1}$ relative to systemic, and that the hot progenitor star remnant is positioned at the precise geometric center of the clumpy, ellipsoidal molecular shell. We assert that the Ring's molecular envelope was formed via a relatively sudden, AGB-terminating mass ejection event $\sim$6000 yr ago, and that this ellipsoidal envelope was then punctured by fast, collimated polar outflows resulting from interactions between the progenitor and one or more companion stars. Such an evolutionary scenario may describe most molecule-rich, ``Ring-like'' planetary nebulae., Comment: 17 pages, 10 figures; to appear in The Astrophysical Journal (accepted 20 Jan. 2025)

During the past decade, state-of-the-art planet-finder instruments like SPHERE@VLT, coupling coronagraphic devices and extreme adaptive optics systems, unveiled, thanks to large surveys, around 20 planetary mass companions at semi-major axis greater than 10 astronomical units. Direct imaging being the only detection technique to be able to probe this outer region of planetary systems, the SPHERE infrared survey for exoplanets (SHINE) was designed and conducted from 2015 to 2021 to study the demographics of such young gas giant planets around 400 young nearby solar-type stars. In this paper, we present the observing strategy, the data quality, and the point sources analysis of the full SHINE statistical sample as well as snapSHINE. Both surveys used the SPHERE@VLT instrument with the IRDIS dual band imager in conjunction with the integral field spectrograph IFS and the angular differential imaging observing technique. All SHINE data (650 datasets), corresponding to 400 stars, including the targets of the F150 survey, are processed in a uniform manner with an advanced post-processing algorithm called PACO ASDI. An emphasis is put on the classification and identification of the most promising candidate companions. Compared to the previous early analysis SHINE F150, the use of advanced post-processing techniques significantly improved by one or 2 magnitudes (x3-x6) the contrast detection limits, which will allow us to put even tighter constraints on the radial distribution of young gas giants. This increased sensitivity directly places SHINE as the largest and deepest direct imaging survey ever conducted. We detected and classified more than 3500 physical sources. One additional substellar companion has been confirmed during the second phase of the survey (HIP 74865 B), and several new promising candidate companions are awaiting second epoch confirmations., Comment: Accepted for publication in A&A. 19 pages, 19 figures. Abstract shortened to comply with ArxiV standards. Data ingestion at the CDS is ongoing

Nonreciprocal optics enables asymmetric transmission of light when its sources and detectors are exchanged. A canonical example -- optical isolator -- enables light propagation in only one direction, similar to how electrical diodes enable unidirectional flow of electric current. Nonreciprocal optics today, unlike nonreciprocal electronics, remains bulky. Recently, nonlinear metasurfaces opened up a pathway to strong optical nonreciprocity at the nanoscale. However, demonstrations to date were based on optically slow nonlinearities involving thermal effects or phase transition materials. In this work, we demonstrate a nonreciprocal metasurface with an ultra-fast optical response based on indium tin oxide in its epsilon-near-zero regime. It operates in the spectral range of 1200-1300 nm with incident power densities of 40-70 GW/cm$^2$. Furthermore, the nonreciprocity of the metasurface extends to both amplitude and phase of the forward/backward transmission opening a pathway to nonreciprocal wavefront control at the nanoscale., Comment: 8 pages 3 figures

In the era of precision measurements of neutrino oscillation parameters, it is necessary for experiments to disentangle discrepancies that may indicate physics beyond the Standard Model in the neutrino sector. KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study the oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on the initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photosensors. A high-purity neutrino sample was extracted during 2020 and 2021, corresponding to an exposure of 433 kton-years. This sample is analysed following a binned log-likelihood approach to search for invisible neutrino decay, in a three-flavour neutrino oscillation scenario, where the third neutrino mass state $\nu_3$ decays into an invisible state, e.g. a sterile neutrino. The resulting best fit of the invisible neutrino decay parameter is $\alpha_3 = 0.92^{+1.08}_{-0.57}\times 10^{-4}~\mathrm{eV^2}$, corresponding to a scenario with $\theta_{23}$ in the second octant and normal neutrino mass ordering. The results are consistent with the Standard Model, within a $2.1\,\sigma$ interval., Comment: 20 pages, 8 figures

The proton-proton collisions at the Large Hadron Collider (LHC) produce an intense, high-energy beam of neutrinos of all flavors, collimated in the forward direction. Recently two dedicated neutrino experiments, FASER and SND@LHC, have started operating to take advantage of the TeV energy LHC neutrino beam, with first results released in 2023 and further results released in 2024. The first detection of neutrinos produced at a particle collider opens up a new avenue of research, allowing to study the highest energy neutrinos produced in a controlled laboratory environment, with an associated broad and rich physics program. Neutrino measurements at the LHC will provide important contributions to QCD, neutrino and BSM physics, with impactful implications for astro-particle physics. This review article summarizes the physics motivation, status and plans of, present and future neutrino experiments at the LHC., Comment: the article has been submitted to the Annual Review of Nuclear and Particle Science

Incoherent stochastic processes added to unitary dynamics are typically deemed detrimental since they are expected to diminish quantum features such as superposition and entanglement. Instead of exhibiting energy-conserving persistent coherent motion, the dynamics of such open systems feature, in most cases, a steady state, which is approached in the long-time limit from all initial conditions. This can, in fact, be advantageous as it offers a mechanism for the creation of robust quantum correlations on demand without the need for fine-tuning. Here, we show this for a system consisting of two coherently coupled bosonic modes, which is a paradigmatic scenario for the realization of quantum resources such as squeezed entangled states. Rather counterintuitively, the mere addition of incoherent hopping, which results in a statistical coupling between the bosonic modes, leads to steady states with robust quantum entanglement and enables the emergence of persistent coherent non-stationary behavior.

In 2021, Navarro and Tiep proposed a conjecture on character fields of finite quasi-simple groups. We develop some theory on sums of roots of unity and use this theory to prove the conjecture for some infinite families of finite quasi-simple groups with known character table. We then use the classification of the irreducible complex characters of the finite general linear groups developed by Green to obtain some partial results about the conjecture for the finite general and special linear groups in arbitrary dimension., Comment: 19 pages

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include {\gamma}-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy {\gamma}-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy., Comment: 67 pages, 24 figures, accepted for publication in SCIENCE CHINA Physics, Mechanics & Astronomy

We use an infinite $k$-ary tree with a self-loop at the root as our underlying graph. We consider a chip-firing process starting with $N$ chips at the root. We describe the stable configurations. We calculate the number of fires for each vertex and the total number of fires. We study a sequence of the number of root fires for a given $k$ as a function of $N$ and study its properties. We do the same for the total number of fires., Comment: 22 pages, 1 figure

Titanium and its alloys are technologically important materials that display a rich phase behaviour. In order to enable large-scale, realistic modelling of Ti and its alloys on the atomistic scale, Machine Learning Interatomic Potentials (MLIPs) are crucial, but rely on databases of atomic configurations. We report databases of such configurations that represent the {\alpha}, \b{eta}, {\omega} and liquid phases of Ti and the Ti-6Al-4V alloy, where we provide total energy, force and stress values evaluated by Density Functional Theory (DFT) using the PBE exchange-correlation functional. We have also leveraged and extended a data reduction strategy, via non-diagonal supercells, for the vibrational properties of Ti and sampling of atomic species within bulk crystalline data for Ti-6Al-4V. These configurations may be used to fit MLIP models that can accurately model the phase behaviour of Ti and Ti-6Al-4V across a broad range of thermodynamic conditions. To validate models, we assembled a set of benchmark protocols, which can be used to rapidly develop and evaluate MLIP models. We demonstrated the utility of our databases and validation tools by fitting models based on the Gaussian Approximation Potential (GAP) and Atomic Cluster Expansion (ACE) frameworks.

Since the first realization of borophene on Ag(111), two-dimensional (2D) boron nanomaterials have attracted significant interest due to their polymorphic diversity and potential for hosting solid-state quantum phenomena. Here, we use atomic-resolution scanning tunneling microscopy (STM) and field-emission resonance (FER) spectroscopy to elucidate the structure and properties of atomically thin boron phases grown on Cu(111). Specifically, FER spectroscopy reveals unique charge transfer and electronic states compared to the distinct borophene phases observed on silver, suggesting that the deposition of boron on copper can result in strong covalent bonding characteristic of a 2D copper boride. This conclusion is reinforced by detailed STM characterization of line defects that are consistent with density functional theory (DFT) calculations for atomically thin Cu8B14. This evidence for 2D copper boride is likely to motivate future synthetic efforts aimed at expanding the relatively unexplored family of atomically thin metal boride materials., Comment: 19 pages including supplementary information, 4 main figures

Distortion products are tones produced through nonlinear effects of a system simultaneously detecting two or more frequencies. These combination tones are ubiquitous to vertebrate auditory systems and are generally regarded as byproducts of nonlinear signal amplification. It has previously been shown that several species of infectious-disease-carrying mosquitoes utilize these distortion products for detecting and locating potential mates. It has also been shown that their auditory systems contain multiple oscillatory components within the sensory structure, which respond at different frequency ranges. Using a generic theoretical model for acoustic detection, we show the signal-detection advantages that are implied by these two detection schemes: distortion product detection and cascading a signal through multiple layers of oscillator elements. Lastly, we show that the combination of these two schemes yields immense benefits for signal detection. These benefits could be essential for male mosquitoes to be able to identify and pursue a particular female within a noisy swarm environment.

Single-enzyme catalysis offers a promising approach for unravelling the dynamic behaviour of individual enzymes as they undergo a reaction, revealing the complex heterogeneity that is lost in the averaged ensembles. Here we demonstrate real-time, label-free monitoring of the electrical transduction of single-protein enzymatic activity for two redox enzymes, cytochrome P450cam and glutathione reductase, trapped in an electrochemically controlled nanoscale tunnelling junction immersed in the aqueous enzymatic mixture. The conductance switching signal observed in individual transients of the electrical current flowing through the single-protein junction shows that the tunnelling conductance is modulated by the enzymatic reaction; subtle changes of the enzyme redox state occurring during the chemical catalysis process result in fluctuations of the enzyme junction conductivity, which are captured as a switching signal. At the applied electrochemical reducing potential for electrocatalysis, the transient oxidation of the trapped enzyme in every catalytic cycle opens an additional redox-mediated electron tunnelling channel in the single protein junction that results in a temporary current jump, contributing to the observed conductance switching features. The latter is experimentally assessed via electrochemically controlled conductance measurements of the single-protein junction. The statistical analysis of the switching events captured over long time periods results in average frequencies that correlate well with the reported catalytic turnover values of both enzymes obtained in standard bulk assays. The single-enzyme experiments reveal the acute heterogenous behaviour of enzymatic catalysis and the quantification of single enzyme turnover frequencies.

Simulation methods have always been instrumental in finance, and data-driven methods with minimal model specification, commonly referred to as generative models, have attracted increasing attention, especially after the success of deep learning in a broad range of fields. However, the adoption of these models in financial applications has not kept pace with the growing interest, probably due to the unique complexities and challenges of financial markets. This paper aims to contribute to a deeper understanding of the limitations of generative models, particularly in portfolio and risk management. To this end, we begin by presenting theoretical results on the importance of initial sample size, and point out the potential pitfalls of generating far more data than originally available. We then highlight the inseparable nature of model development and the desired use case by touching on a paradox: generic generative models inherently care less about what is important for constructing portfolios (in particular the long-short ones). Based on these findings, we propose a pipeline for the generation of multivariate returns that meets conventional evaluation standards on a large universe of US equities while being compliant with stylized facts observed in asset returns and turning around the pitfalls we previously identified. Moreover, we insist on the need for more delicate evaluation methods, and suggest, through an example of mean-reversion strategies, a method designed to identify poor models for a given application based on regurgitative training, i.e. retraining the model using the data it has itself generated, which is commonly referred to in statistics as identifiability.

Over the last years, the search of new regular black bounce solutions has drawn a lot of attentional over the international community working in gravitation. Indeed, in the era of gravitational waves detections out of binary mergers and of the imaging of the plasma around supermassive black holes, the study of everywhere regular solutions has become a common trend given the unique opportunity posed by multi-messenger astronomy to test deviations from the Kerr family of solutions. Among them, in this paper we consider the black bounce paradigm introduced by Simpson and Visser in [JCAP \textbf{02}, 042 (2019)], and provide a general procedure for reconstructing static spherically symmetric black bounce-type solutions that might interpolate between regular black holes and wormholes. We show that even after imposing some smoothness and flatness conditions on the metric components, additional analysis is required to obtain a well-defined black bounce solution. Then, the corresponding matter Lagrangian is reconstructed by using non-linear electrodynamics and the energy conditions are studied., Comment: 10 pages, 4 figures

Green hydrogen is thought to be a game changer for reaching sustainability targets. However, the transition to a green hydrogen economy faces a critical challenge known as the `chicken-and-egg dilemma', wherein establishing a hydrogen supply network relies on demand, while demand only grows with reliable supply. In addition, as the hydrogen market is in the early stage, predicting demand distributions is challenging due to lack of data availability. This paper addresses these complex issues through a risk-averse framework with the introduction of a distributionally robust hydrogen network expansion planning problem under decision-dependent demand ambiguity. The problem optimizes location and production capacity decisions of the suppliers considering the moments of the stochastic hydrogen demand as a function of these investment decisions. To obtain tractable representations of this problem, we derive two different reformulations that consider continuous and discrete hydrogen demand support sets under different forms of decision dependencies. To efficiently solve the reformulations, we develop a tailored algorithm based on the column-and-constraint generation approach, and enhance the computational performance through solving the master problems to a relative optimality gap, decomposing the subproblems, and integrating pre-generated columns and constraints. To validate the effectiveness of our approach, we investigate a real case study leveraging data from the "Hydrogen Energy Applications in Valley Environments for Northern Netherlands (HEAVENN)" project. The results reveal that considering the chicken-and-egg dilemma under uncertain hydrogen market conditions leads to earlier and more diverse investments, providing critical insights for policymakers based on the degree of decision dependency.

The tensor-vector contraction (TVC) is the most memory-bound operation of its class and a core component of the higher order power method (HOPM). This paper brings distributed-memory parallelization to a native TVC algorithm for dense tensors that overall remains oblivious to contraction mode, tensor splitting and tensor order. Similarly, we propose a novel distributed HOPM, namely dHOPM3, that can save up to one order of magnitude of streamed memory and is about twice as costly in terms of data movement as a distributed TVC operation (dTVC) when using task-based parallelization. The numerical experiments carried out in this work on three different architectures featuring multi-core and accelerated systems confirm that the performance of dTVC and dHOPM3 remains relatively close to the peak system memory bandwidth (50%-80%, depending on the architecture) and on par with STREAM reference values. On strong scalability scenarios, our native multi-core implementations of these two algorithms can achieve similar and sometimes even greater performance figures than those based upon state-of-the-art CUDA batched kernels. Finally, we demonstrate that both computation and communication can benefit from mixed precision arithmetic also in cases where the hardware does not support low precision data types natively., Comment: 15 pages, 7 figures, preprint (accepted for publication at Journal of Future Generation Computer Systems)