Search

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

We prove that if the shape of the metric unit ball in a homogeneous group enjoys a precise symmetry property, then the associated distance yields the standard form of the area formula. The result applies to some classes of smooth and nonsmooth submanifolds. We finally prove the equality between spherical measure and centered Hausdorff measure, under two different geometric conditions on the shape of the metric unit ball., Comment: 19 pages

Graphs are often used to model relationships between entities. The identification and visualization of clusters in graphs enable insight discovery in many application areas, such as life sciences and social sciences. Force-directed graph layouts promote the visual saliency of clusters, as they bring adjacent nodes closer together, and push non-adjacent nodes apart. At the same time, matrices can effectively show clusters when a suitable row/column ordering is applied, but are less appealing to untrained users not providing an intuitive node-link metaphor. It is thus worth exploring layouts combining the strengths of the node-link metaphor and node ordering. In this work, we study the impact of node ordering on the visual saliency of clusters in orderable node-link diagrams, namely radial diagrams, arc diagrams and symmetric arc diagrams. Through a crowdsourced controlled experiment, we show that users can count clusters consistently more accurately, and to a large extent faster, with orderable node-link diagrams than with three state-of-the art force-directed layout algorithms, i.e., `Linlog', `Backbone' and `sfdp'. The measured advantage is greater in case of low cluster separability and/or low compactness. A free copy of this paper and all supplemental materials are available at https://osf.io/kc3dg/.

Modularity maximization is a widely used community detection technique for deterministic networks. However, little research has been performed to develop efficient modularity calculation algorithms for probabilistic networks. Particularly, it is challenging to efficiently calculate expected modularity when all possible worlds are considered. To address this problem, we propose two algorithms, namely $\mathrm{PWP}^{\mathrm{EMOD}}$ and $\mathrm{APWP}^{\mathrm{EMOD}}$, partitioning the possible worlds based on their modularities to significantly reduce the number of probability calculations. We evaluate the accuracy and time efficiency of our algorithms through comprehensive experiments.

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 oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.18^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 29 pages, 12 figures

Embedding-based neural retrieval (EBR) is an effective search retrieval method in product search for tackling the vocabulary gap between customer search queries and products. The initial launch of our EBR system at Walmart yielded significant gains in relevance and add-to-cart rates [1]. However, despite EBR generally retrieving more relevant products for reranking, we have observed numerous instances of relevance degradation. Enhancing retrieval performance is crucial, as it directly influences product reranking and affects the customer shopping experience. Factors contributing to these degradations include false positives/negatives in the training data and the inability to handle query misspellings. To address these issues, we present several approaches to further strengthen the capabilities of our EBR model in terms of retrieval relevance. We introduce a Relevance Reward Model (RRM) based on human relevance feedback. We utilize RRM to remove noise from the training data and distill it into our EBR model through a multi-objective loss. In addition, we present the techniques to increase the performance of our EBR model, such as typo-aware training, and semi-positive generation. The effectiveness of our EBR is demonstrated through offline relevance evaluation, online AB tests, and successful deployments to live production. [1] Alessandro Magnani, Feng Liu, Suthee Chaidaroon, Sachin Yadav, Praveen Reddy Suram, Ajit Puthenputhussery, Sijie Chen, Min Xie, Anirudh Kashi, Tony Lee, et al. 2022. Semantic retrieval at walmart. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3495-3503., Comment: 8 pages, 3 figures, CIKM 2024

In embedding-based retrieval, Approximate Nearest Neighbor (ANN) search enables efficient retrieval of similar items from large-scale datasets. While maximizing recall of relevant items is usually the goal of retrieval systems, a low precision may lead to a poor search experience. Unlike lexical retrieval, which inherently limits the size of the retrieved set through keyword matching, dense retrieval via ANN search has no natural cutoff. Moreover, the cosine similarity scores of embedding vectors are often optimized via contrastive or ranking losses, which make them difficult to interpret. Consequently, relying on top-K or cosine-similarity cutoff is often insufficient to filter out irrelevant results effectively. This issue is prominent in product search, where the number of relevant products is often small. This paper introduces a novel relevance filtering component (called "Cosine Adapter") for embedding-based retrieval to address this challenge. Our approach maps raw cosine similarity scores to interpretable scores using a query-dependent mapping function. We then apply a global threshold on the mapped scores to filter out irrelevant results. We are able to significantly increase the precision of the retrieved set, at the expense of a small loss of recall. The effectiveness of our approach is demonstrated through experiments on both public MS MARCO dataset and internal Walmart product search data. Furthermore, online A/B testing on the Walmart site validates the practical value of our approach in real-world e-commerce settings., Comment: 8 pages, 3 figures, CIKM 2024

Permafrost thaw is a major concern raised by the ongoing climate change. An understudied phenomenon possibly affecting the pace of permafrost thaw is the onset of convective motions within the active layer caused by the density anomaly of water. Here, we explore the effects of groundwater convection on permafrost thawing using a model that accounts for ice - water phase transitions, coupled with the dynamics of the temperature field transported by the Darcy's flow across a porous matrix. Numerical simulations of this model show that ice thawing in the presence of convection is much faster than in the diffusive case and deepens at a constant velocity proportional to the soil permeability. A scaling argument is able to predict correctly the asymptotic velocity. Since in the convective regime the heat transport is mediated by the coherent motion of thermal plumes across the thawed layer, we find that the depth of the thawing interface becomes highly heterogeneous.

Quantum phase transitions (QPTs) are explored to improve quantum sensing and weak signal detection. Changes in the ground state at a QPT enhance indicators of parameter estimation, such as the quantum Fischer information. Here, we show that in systems that lack a QPT, quantum sensitivity can still be enhanced due to excited-state quantum phase transitions (ESQPTs). Our analysis is done for a Kerr parametric oscillator with two ESQPTs associated with the onset of a hyperbolic point and a local maximum in the classical limit. These points change the system's phase space structure, which results in the amplification of the quantum Fisher information and the squeezing of the uncertainty in position at specific values of the control parameter. Our study showcases the relationship between non-conventional quantum critical phenomena and quantum sensing with potential experimental applications in exciton-polariton condensates and superconducting circuits., Comment: 6 pages, 3 figures

The widespread adoption of cloud computing, edge, and IoT has increased the attack surface for cyber threats. This is due to the large-scale deployment of often unsecured, heterogeneous devices with varying hardware and software configurations. The diversity of these devices attracts a wide array of potential attack methods, making it challenging for individual organizations to have comprehensive knowledge of all possible threats. In this context, powerful anomaly detection models can be developed by combining data from different parties using Federated Learning. FL enables the collaborative development of ML-based IDSs without requiring the parties to disclose sensitive training data, such as network traffic or sensor readings. However, deploying the resulting models can be challenging, as they may require more computational resources than those available on target devices with limited capacity or already allocated for other operations. Training device-specific models is not feasible for an organization because a significant portion of the training data is private to other participants in the FL process. To address these challenges, this paper introduces INTELLECT, a novel solution that integrates feature selection, model pruning, and fine-tuning techniques into a cohesive pipeline for the dynamic adaptation of pre-trained ML models and configurations for IDSs. Through empirical evaluation, we analyze the benefits of INTELLECT's approach in tailoring ML models to the specific resource constraints of an organization's devices and measure variations in traffic classification accuracy resulting from feature selection, pruning, and fine-tuning operations. Additionally, we demonstrate the advantages of incorporating knowledge distillation techniques while fine-tuning, enabling the ML model to consistently adapt to local network patterns while preserving historical knowledge., Comment: 14 pages, 10 figures, 4 tables, 3 algorithms, submitted to Elsevier Computers and Security

We study the quantum thermal transport of elastic excitations through a two-dimensional elastic waveguide between two thermal reservoirs. We solve the classical Kirchhoff-Love equation for rectangular wires and explore the dispersion relation for both the symmetric and antisymmetric solutions. Then, we study the phonon transport of these modes within the second quantization framework by analyzing the mean quadratic displacement, from which the energy density current, the temperature field, and conductance are determined. We identify the relevant modes contributing to thermal transport and explore the average temperature difference to reach the high-temperature limit. We expect our results to pave the way for understanding phonon-mediated thermal transport in two-dimensional mesoscopic quantum devices., Comment: Submitted to Journal of Applied Physics

Phase and amplitude modes are emergent phenomena that manifest across diverse physical systems, from condensed matter and particle physics to quantum optics. Also called polariton modes, we study their behavior in an anisotropic Dicke model that includes collective matter interactions. We study the low-lying spectrum in the thermodynamic limit via the Holstein-Primakoff transformation and contrast the results with the semiclassical energy surface obtained via coherent states. We also explore the geometric phase for both boson and spin contours in the parameter space as a function of the phases in the system. We unveil novel phenomena due to the unique critical features provided by the interplay between the anisotropy and matter interactions. We expect our results to serve for the observation of phase and amplitude modes in current quantum information platforms., Comment: 17 pages, 4 figures

Modeling dynamical systems, e.g. in climate and engineering sciences, often necessitates solving partial differential equations. Neural operators are deep neural networks designed to learn nontrivial solution operators of such differential equations from data. As for all statistical models, the predictions of these models are imperfect and exhibit errors. Such errors are particularly difficult to spot in the complex nonlinear behaviour of dynamical systems. We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes. Our approach can be interpreted as a probabilistic analogue of the concept of currying from functional programming and provides a practical yet theoretically sound way to apply the linearized Laplace approximation to neural operators. In a case study on Fourier neural operators, we show that, even for a discretized input, our method yields a Gaussian closure--a structured Gaussian process posterior capturing the uncertainty in the output function of the neural operator, which can be evaluated at an arbitrary set of points. The method adds minimal prediction overhead, can be applied post-hoc without retraining the neural operator, and scales to large models and datasets. We showcase the efficacy of our approach through applications to different types of partial differential equations.

High relevance of retrieved and re-ranked items to the search query is the cornerstone of successful product search, yet measuring relevance of items to queries is one of the most challenging tasks in product information retrieval, and quality of product search is highly influenced by the precision and scale of available relevance-labelled data. In this paper, we present an array of techniques for leveraging Large Language Models (LLMs) for automating the relevance judgment of query-item pairs (QIPs) at scale. Using a unique dataset of multi-million QIPs, annotated by human evaluators, we test and optimize hyper parameters for finetuning billion-parameter LLMs with and without Low Rank Adaption (LoRA), as well as various modes of item attribute concatenation and prompting in LLM finetuning, and consider trade offs in item attribute inclusion for quality of relevance predictions. We demonstrate considerable improvement over baselines of prior generations of LLMs, as well as off-the-shelf models, towards relevance annotations on par with the human relevance evaluators. Our findings have immediate implications for the growing field of relevance judgment automation in product search., Comment: 10 pages, 1 figure, 11 tables - SIGIR 2024, LLM4Eval

This review article describes major advances associated with the Dicke model, starting in the 1950s when it was introduced to explain the transition from a normal to a superradiant phase. Since then, this spin-boson interacting model has raised significant theoretical and experimental interest in various contexts. The present review focuses on the isolated version of the model and covers properties and phenomena that are better understood when seen from both the classical and quantum perspectives, in particular, the onset of chaos, localization, and scarring., Comment: 90 pages, 40 figures, suggestions for missing references or comments are welcome to (hirsch[at]nucleares.unam.mx)

We generalize arc coordinates for maximal representations from a hyperbolic surface with boundary into $\text{PSp}(4,\mathbb{R})$, focusing on the case where the surface is a pair of pants. We introduce geometric parameters within the space of right-angled hexagons in the Siegel space $\mathcal{X}$. These parameters enable the visualization of a right-angled hexagon as a polygonal chain inside the hyperbolic plane $\mathbb{H}^{2}$. We explore the geometric properties of reflections in $\mathcal{X}$ and introduce the notion of maximal representation of the reflection group $W_{3}=\mathbb{Z}/2\mathbb{Z}*\mathbb{Z}/2\mathbb{Z}*\mathbb{Z}/2\mathbb{Z}$. We parametrize maximal representations from $W_{3}$ into $\text{PSp}^{\pm}(4,\mathbb{R})$, this induces a natural parametrization of a subset of maximal and Shilov hyperbolic representations into $\text{PSp}(4,\mathbb{R})$.

We propose the use of natural minerals as detectors to study the past flux of cosmic rays. This novel application of the \textit{paleo-detector} technique requires a specific approach as it needs samples that have been exposed to secondary cosmic rays for a well defined period of time. We suggest here the use of the evaporites formed during the desiccation of the Mediterranean sea ${\sim}6$ Myr ago. These minerals have been created and exposed to the air or under a shallow water basin for ${\sim}500$ kyr before being quickly submerged again by a km-scale overburden of water. We show that, by looking at the damages left in the minerals by muons in cosmic ray showers, we could detect differences in the primary cosmic ray flux during that period, as the ones expected from nearby supernova explosions, below the percent-level. We show also that little to no background from radioactive contamination and other astroparticles is expected for this kind of analysis., Comment: 6 pages, 2 figures. To be submitted to PRX

Gamma-ray bursts are promising candidate sources of high-energy astrophysical neutrinos. The recent GRB 221009A event, identified as the brightest gamma-ray burst ever detected, provides a unique opportunity to investigate hadronic emissions involving neutrinos. The KM3NeT undersea neutrino detectors participated in the worldwide follow-up effort triggered by the event, searching for neutrino events. In this letter, we summarize subsequent searches, in a wide energy range from MeV up to a few PeVs. No neutrino events are found in any of the searches performed. Upper limits on the neutrino emission associated with GRB 221009A are computed., Comment: 11 pages, 2 PDF figures. Submitted to JCAP

We introduce infinite dimensional Hilbertian H-type groups equipped with weak, graded, left invariant Riemannian metrics. For these Lie groups, we show that the vanishing of the geodesic distance and the local unboundedness of the sectional curvature coexist. The result validates a deep phenomenon conjectured in an influential 2005 paper by Michor and Mumford, namely, the vanishing of the geodesic distance is linked to the local unboundedness of the sectional curvature. We prove that degenerate geodesic distances appear for a large class of weak, left invariant Riemannian metrics. Their vanishing is rather surprisingly related to the infinite dimensional sub-Riemannian structure of Hilbertian H-type groups. The same class of weak Riemannian metrics yields the nonexistence of the Levi-Civita covariant derivative., Comment: 18 pages

Due to the hybridization of charged excitons with cavity photons, trion-polaritons (TP) in microcavity semiconductors are a promising avenue for realizing strong polariton interactions and many-body polariton phases. We develop a quantum field theoretical formalism to study the formation of trion-polaritons in a microcavity semiconductor doped with itinerant electrons within the ultra-strong light-matter coupling regime. We predict a new class of many-body states resulting from the intriguing interplay of polarons and virtual photons in an uncharted territory of light-matter interactions and Feshbach physics., Comment: 5+3 pages, 4 + 2 figures

The KM3NeT/ARCA neutrino detector is currently under construction at 3500 m depth offshore Capo Passero, Sicily, in the Mediterranean Sea. The main science objectives are the detection of high-energy cosmic neutrinos and the discovery of their sources. Simulations were conducted for the full KM3NeT/ARCA detector, instrumenting a volume of 1 km$^3$, to estimate the sensitivity and discovery potential to point-like neutrino sources and an all-sky diffuse neutrino flux. This paper covers the reconstruction of track- and shower-like signatures, as well as the criteria employed for neutrino event selection. By leveraging both the track and shower observation channels, the KM3NeT/ARCA detector demonstrates the capability to detect the diffuse astrophysical neutrino flux within half a year of operation, achieving a 5$\sigma$ statistical significance. With an angular resolution below 0.1$^\circ$ for tracks and under 2$^\circ$ for showers, the sensitivity to point-like neutrino sources surpasses existing observed limits across the entire sky., Comment: 20 pages, 30 figures Submitted to EPJ-C

Collective effects on the light scattered by disordered media such as Anderson localization and coherent backscattering critically depend on the reciprocity between interfering optical paths. In this work, we explore the breaking of reciprocity for the light scattered by a disordered cold atom setup, taking advantage of the non-commutation of optical elements that manipulate the polarization of the interfering paths. This breaking of symmetry manifests itself in the reduction of the fringes contrast as the light scattered by the cloud interferes with that from its mirror image. We provide a geometrical interpretation in terms of the Pancharatnam-Berry phase, which we directly access from the fringes displacement. Our work paves the way toward the manipulation of path reciprocity and interference for light scattered by disordered media., Comment: 5 pages, 5 figures + 4 pages supplemental material

This article develops a fundamental insight into the behavior of neuronal membranes, focusing on their responses to stimuli measured with power spectra in the frequency domain. It explores the use of linear and nonlinear (quadratic sinusoidal analysis) approaches to characterize neuronal function. It further delves into the random theory of internal noise of biological neurons and the use of stochastic Markov models to investigate these fluctuations. The text also discusses the origin of conductance noise and compares different power spectra for interpreting this noise. Importantly, it introduces a novel sequential chemical state model, named p2, which is more general than the Hodgkin-Huxley formulation, so that the probability for an ion channel to be open does not imply exponentiation. In particular, it is demonstrated that the p2 (without exponentiation) and n4 (with exponentiation) models can produce similar neuronal responses. A striking relationship is also shown between fluctuation and quadratic power spectra, suggesting that voltage-dependent random mechanisms can have a significant impact on deterministic nonlinear responses, themselves known to have a crucial role in the generation of action potentials in biological neural networks.

A popular way to cluster a temporal network is to transform it into a sequence of networks, also called slices, where each slice corresponds to a time interval and contains the vertices and edges existing in that interval. A reason to perform this transformation is that after a network has been sliced, existing algorithms designed to find clusters in multilayer networks can be used. However, to use this approach, we need to know how many slices to generate. This chapter discusses how to select the number of slices when generalized modularity is used to identify the clusters.

This is the first year of the TREC Product search track. The focus this year was the creation of a reusable collection and evaluation of the impact of the use of metadata and multi-modal data on retrieval accuracy. This year we leverage the new product search corpus, which includes contextual metadata. Our analysis shows that in the product search domain, traditional retrieval systems are highly effective and commonly outperform general-purpose pretrained embedding models. Our analysis also evaluates the impact of using simplified and metadata-enhanced collections, finding no clear trend in the impact of the expanded collection. We also see some surprising outcomes; despite their widespread adoption and competitive performance on other tasks, we find single-stage dense retrieval runs can commonly be noncompetitive or generate low-quality results both in the zero-shot and fine-tuned domain., Comment: 14 pages, 4 figures, 11 tables - TREC 2023

We establish an area formula for computing the spherical measure of an intrinsic graph of any codimension in an arbitrary homogeneous group. Our approach only assumes that the map generating the intrinsic graph is continuously intrinsically differentiable. The important novelty lies in the notion of Jacobian, which is built by the auxiliary Euclidean distance. The introduction of this Jacobian allows the spherical factor to appear in the area formula and enables explicit computations., Comment: 46 pages