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We introduce a novel experimental approach to probe many-body quantum systems by developing a protocol to measure generalized temporal entropies. We demonstrate that the recently proposed generalized temporal entropies [Phys. Rev. Research 6, 033021] are equivalent to observing the out-of-equilibrium dynamics of a replicated system induced by a double quench protocol using local operators as probes. This equivalence, confirmed through state-of-the-art tensor network simulations for one-dimensional systems, validates the feasibility of measuring generalized temporal entropies experimentally. Our results reveal that the dynamics governed by the transverse field Ising model integrable Hamiltonian differ qualitatively from those driven by the same model with an additional parallel field, breaking integrability. They thus suggest that generalized temporal entropies can serve as a tool for identifying different dynamical classes. This work represents the first practical application of generalized temporal entropy characterization in one-dimensional many-body quantum systems and offers a new pathway for experimentally detecting integrability. We conclude by outlining the experimental requirements for implementing this protocol with state of the art quantum simulators., Comment: 10 pages, 5 figure, all comments welcome

Runtime verification of temporal properties over timed sequences of observations is crucial in various applications within cyber-physical systems ranging from autonomous vehicles over smart grids to medical devices. In this paper, we are addressing the challenge of effectively predicting the failure or success of properties in a continuous real-time setting. Our approach allows predictions to exploit assumptions on the system being monitored and supports predictions of non-observable system behaviour (e.g. internal faults). More concretely, in our approach properties are expressed in Metric Interval Temporal Logic (MITL), assumptions on the monitored system are specified in terms of Timed Automata, and observations are to be provided in terms of sequences of timed constraints. We present an assumption-based runtime verification algorithm and its implementation on top of the real-time verification tool UPPAAL. We show experimentally that assumptions can be effective in anticipating the satisfaction/violation of timed properties and in handling monitoring properties that predicate over unobservable events.

As a socio-technical activity, software development involves the close interconnection of people and technology. The integration of Large Language Models (LLMs) into this process exemplifies the socio-technical nature of software development. Although LLMs influence the development process, software development remains fundamentally human-centric, necessitating an investigation of the human factors in this adoption. Thus, with this study we explore the factors influencing the adoption of LLMs in software development, focusing on the role of professionals' cultural values. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT2) and Hofstede's cultural dimensions, we hypothesized that cultural values moderate the relationships within the UTAUT2 framework. Using Partial Least Squares-Structural Equation Modelling and data from 188 software engineers, we found that habit and performance expectancy are the primary drivers of LLM adoption, while cultural values do not significantly moderate this process. These findings suggest that, by highlighting how LLMs can boost performance and efficiency, organizations can encourage their use, no matter the cultural differences. Practical steps include offering training programs to demonstrate LLM benefits, creating a supportive environment for regular use, and continuously tracking and sharing performance improvements from using LLMs.

Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for AI training has heightened concerns about energy consumption and carbon emissions. Existing energy estimation tools often assume exclusive use of computing nodes, a premise that becomes problematic with the advent of supercomputers integrating microservices, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing. This work investigates the impact of executed instructions on overall power consumption, providing insights into the comprehensive behaviour of HPC systems. We introduce two novel mathematical models to estimate a process's energy consumption based on the total node energy, process usage, and a normalised vector of the probability distribution of instruction types for CPU and GPU processes. Our approach enables energy accounting for specific processes without the need for isolation. Our models demonstrate high accuracy, predicting CPU power consumption with a mere 1.9% error. For GPU predictions, the models achieve a central relative error of 9.7%, showing a clear tendency to fit the test data accurately. These results pave the way for new tools to measure and account for energy consumption in shared supercomputing environments., Comment: Accepted in Latin American High-Performance Computing Conference (CARLA 2024)

Electron optics deals with the wave-nature of charge carriers to induce, investigate and exploit coherent phenomena in solid state devices, in analogy with optics and photonics. Typically, these goals are achieved in complex electronic devices taking advantage of the macroscopically coherent charge transport in two dimensional electron gases and superconductors. Here, we demonstrate a simple counterintuitive architecture employing intentionally-created lattice defects to induce collective coherent effects in the charge transport of graphene. More specifically, multiple Fabry-P\'erot cavities are produced by irradiating graphene via low-energy electron-beam to form periodically alternated defective and pristine nano-stripes. The enhanced hole-doping in the defective stripes creates potential barriers behaving as partially reflecting mirrors and resonantly confining the carrier-waves within the pristine areas. The interference effects are both theoretically and experimentally investigated and manifest as sheet resistance oscillations up to 30 K for both polarities of charge carriers, contrarily to traditional electrostatically-created Fabry-P\'erot interferometers. Our findings propose defective graphene as an original platform for the realization of innovative coherent electronic devices with applications in nano and quantum technologies., Comment: 29 pages, 4 figures main text, 8 figures supplementary

We count points on the character varieties associated with punctured surfaces and regular semisimple generic conjugacy classes in reductive groups. We find that the number of points are palindromic polynomials. This suggests a $P=W$ conjecture for these varieties. We also count points on the corresponding additive character varieties and find that the number of points are also polynomials, which we conjecture have non-negative coefficients. These polynomials can be considered as the reductive analogues of the Kac polynomials of comet-shaped quivers.

Leveraging the power of a graph neural network (GNN) with message passing, we present a Monte Carlo Tree Search (MCTS) method to solve stochastic orienteering problems with chance constraints. While adhering to an assigned travel budget the algorithm seeks to maximize collected reward while incurring stochastic travel costs. In this context, the acceptable probability of exceeding the assigned budget is expressed as a chance constraint. Our MCTS solution is an online and anytime algorithm alternating planning and execution that determines the next vertex to visit by continuously monitoring the remaining travel budget. The novelty of our work is that the rollout phase in the MCTS framework is implemented using a message passing GNN, predicting both the utility and failure probability of each available action. This allows to enormously expedite the search process. Our experimental evaluation shows that with the proposed method and architecture we manage to efficiently solve complex problem instances while incurring in moderate losses in terms of collected reward. Moreover, we demonstrate how the approach is capable of generalizing beyond the characteristics of the training dataset. The paper's website, open-source code, and supplementary documentation can be found at ucmercedrobotics.github.io/gnn-sop., Comment: 8 pages, 6 figures

We present a geometric design rule for size-controlled clustering of self-propelled particles. Active particles that tend to rotate under an external force have an intrinsic signed-parameter with units of curvature, which we term curvity, derivable from first principles. Robot experiments and numerical simulations show that the properties of the individual robot alone -- radius and curvity -- control pair-cohesion in a binary system as well as the stability of flocking and clustering in a swarm. Our results have applications in meta-materials and embodied decentralized control., Comment: 7 pages, 4 figures

Wave-optics phenomena in gravitational lensing occur when the signal's wavelength is commensurate to the gravitational radius of the lens. Although potentially detectable in lensed gravitational waves, fast radio bursts and pulsars, accurate numerical predictions are challenging to compute. Here we present novel methods for wave-optics lensing that allow the treatment of general lenses. In addition to a general algorithm, specialized methods optimize symmetric lenses (arbitrary number of images) and generic lenses in the single-image regime. We also develop approximations for simple lenses (point-like and singular isothermal sphere) that drastically outperform known solutions without compromising accuracy. These algorithms are implemented in Gravitational Lensing of Waves (GLoW): an accurate, flexible, and fast code. GLoW efficiently computes the frequency-dependent amplification factor for generic lens models and arbitrary impact parameters in O(1 ms) to O(10 ms) depending on the lens configuration and complexity. GLoW is readily applicable to model lensing diffraction on gravitational-wave signals, offering new means to investigate the distribution of dark-matter and large-scale structure with signals from ground and space detectors., Comment: 21 pages, 10 figures. GLoW is publicly available at https://github.com/miguelzuma/GLoW_public . All the scripts needed to reproduce the figures in this paper can also be found in the repository

The Master Stability Function is a robust and useful tool for determining the conditions of synchronization stability in a network of coupled systems. While a comprehensive classification exists in the case in which the nodes are chaotic dynamical systems, its application to periodic systems has been less explored. By studying several well-known periodic systems, we establish a comprehensive framework to understand and classify their properties of synchronizability. This allows us to define five distinct classes of synchronization stability, including some that are unique to periodic systems. Specifically, in periodic systems, the Master Stability Function vanishes at the origin, and it can therefore display behavioral classes that are not achievable in chaotic systems, where it starts, instead, at a strictly positive value. Moreover, our results challenge the widely-held belief that periodic systems are easily put in a stable synchronous state, showing, instead, the common occurrence of a lower threshold for synchronization stability., Comment: 8 pages, 6 figures, plus supplementary material with 5 pages and 6 figures

We present a multi-wavelength analysis of MCG+11-11-032, a nearby AGN with the unique classification of both a binary and a dual AGN candidate. With new Chandra observations we aim to resolve any dual AGN system via imaging data, and search for signs of a binary AGN via analysis of the X-ray spectrum. Analyzing the Chandra spectrum, we find no evidence of previously suggested double-peaked Fe K$\alpha$ lines; the spectrum is instead best fit by an absorbed powerlaw with a single Fe K$\alpha$ line, as well as an additional line centered at $\approx$7.5 keV. The Chandra observation reveals faint, soft, and extended X-ray emission, possibly linked to low-level nuclear outflows. Further analysis shows evidence for a compact, hard source -- MCG+11-11-032 X2 -- located 3.27'' from the primary AGN. Modeling MCG+11-11-032 X2 as a compact source, we find that it is relatively luminous ($L_{\text{2$-$10 keV}} = 1.52_{-0.48}^{+0.96}\times 10^{41}$ erg s$^{-1}$), and the location is coincident with an compact and off-nuclear source resolved in Hubble Space Telescope infrared (F105W) and ultraviolet (F621M, F547M) bands. Pairing our X-ray results with a 144 MHz radio detection at the host galaxy location, we observe X-ray and radio properties similar to those of ESO 243-49 HLX-1, suggesting that MCG+11-11-032 X2 may be a hyper-luminous X-ray source. This detection with Chandra highlights the importance of a high-resolution X-ray imager, and how previous binary AGN candidates detected with large-aperture instruments benefit from high-resolution follow-up. Future spatially resolved optical spectra, and deeper X-ray observations, can better constrain the origin of MCG+11-11-032 X2., Comment: 16 pages, 7 figures, submitted to ApJ

Since Parker Solar Probe's (Parker's) first perihelion pass at the Sun, large amplitude Alfv\'en waves grouped in patches have been observed near the Sun throughout the mission. Several formation processes for these magnetic switchback patches have been suggested with no definitive consensus. To provide insight to their formation, we examine the heavy ion properties of several adjacent magnetic switchback patches around Parker's 11th perihelion pass capitalizing on a spacecraft lineup with Solar Orbiter where each samples the same solar wind streams over a large range of longitudes. Heavy ion properties (Fe/O, C$^{6+}$/C$^{5+}$, O$^{7+}$/O$^{6+}$) related to the wind's coronal origin, measured with Solar Orbiter can be linked to switchback patch structures identified near the Sun with Parker. We find that switchback patches do not contain distinctive ion and elemental compositional signatures different than the surrounding non-switchback solar wind. Both the patches and ambient wind exhibit a range of fast and slow wind qualities, indicating coronal sources with open and closed field lines in close proximity. These observations and modeling indicate switchback patches form in coronal hole boundary wind and with a range of source region magnetic and thermal properties. Furthermore, the heavy ion signatures suggest interchange reconnection and/or shear driven processes may play a role in their creation., Comment: Accepted for publication in ApJ on September 4th, 2024

In this paper, we provide an efficient algorithm to construct almost optimal $(k,n,d)$-superimposed codes with runlength constraints. A $(k,n,d)$-superimposed code of length $t$ is a $t \times n$ binary matrix such that any two 1's in each column are separated by a run of at least $d$ 0's, and such that for any column $\mathbf{c}$ and any other $k-1$ columns, there exists a row where $\mathbf{c}$ has $1$ and all the remaining $k-1$ columns have $0$. These combinatorial structures were introduced by Agarwal et al. [1], in the context of Non-Adaptive Group Testing algorithms with runlength constraints. By using Moser and Tardos' constructive version of the Lov\'asz Local Lemma, we provide an efficient randomized Las Vegas algorithm of complexity $\Theta(t n^2)$ for the construction of $(k,n,d)$-superimposed codes of length $t=O(dk\log n +k^2\log n)$. We also show that the length of our codes is shorter, for $n$ sufficiently large, than that of the codes whose existence was proved in [1]., Comment: Accepted for publication in Discrete Applied Mathematics

Efficient control schemes that enable fast, high-fidelity operations are essential for any practical quantum computation. However, current optimization protocols are intractable due to stringent requirements imposed by the microscopic systems encoding the qubit, including dense energy level spectra and cross talk, and generally require a trade-off between speed and fidelity of the operation. Here, we address these challenges by developing a general framework for optimal control based on the quantum metric tensor. This framework allows for fast and high-fidelity adiabatic pulses, even for a dense energy spectrum, based solely on the Hamiltonian of the system instead of the full time evolution propagator and independent of the size of the underlying Hilbert space. Furthermore, the framework suppresses diabatic transitions and state-dependent crosstalk effects without the need for additional control fields. As an example, we study the adiabatic charge transfer in a double quantum dot to find optimal control pulses with improved performance. We show that for the geometric protocol, the transfer fidelites are lower bounded $F>99\%$ for ultrafast 20 ns pulses, regardless of the size of the anti-crossing., Comment: 16 pages, 12 figures

The search for useful applications of noisy intermediate-scale quantum (NISQ) devices in quantum simulation has been hindered by their intrinsic noise and the high costs associated with achieving high accuracy. A promising approach to finding utility despite these challenges involves using quantum devices to generate training data for classical machine learning (ML) models. In this study, we explore the use of noisy data generated by quantum algorithms in training an ML model to learn a density functional for the Fermi-Hubbard model. We benchmark various ML models against exact solutions, demonstrating that a neural-network ML model can successfully generalize from small datasets subject to noise typical of NISQ algorithms. The learning procedure can effectively filter out unbiased sampling noise, resulting in a trained model that outperforms any individual training data point. Conversely, when trained on data with expressibility and optimization error typical of the variational quantum eigensolver, the model replicates the biases present in the training data. The trained models can be applied to solving new problem instances in a Kohn-Sham-like density optimization scheme, benefiting from automatic differentiability and achieving reasonably accurate solutions on most problem instances. Our findings suggest a promising pathway for leveraging NISQ devices in practical quantum simulations, highlighting both the potential benefits and the challenges that need to be addressed for successful integration of quantum computing and ML techniques., Comment: 13 pages, 6 figures

Deep learning models have been shown to be a powerful solution for Time Series Classification (TSC). State-of-the-art architectures, while producing promising results on the UCR and the UEA archives , present a high number of trainable parameters. This can lead to long training with high CO2 emission, power consumption and possible increase in the number of FLoating-point Operation Per Second (FLOPS). In this paper, we present a new architecture for TSC, the Light Inception with boosTing tEchnique (LITE) with only 2.34% of the number of parameters of the state-of-the-art InceptionTime model, while preserving performance. This architecture, with only 9, 814 trainable parameters due to the usage of DepthWise Separable Convolutions (DWSC), is boosted by three techniques: multiplexing, custom filters, and dilated convolution. The LITE architecture, trained on the UCR, is 2.78 times faster than InceptionTime and consumes 2.79 times less CO2 and power. To evaluate the performance of the proposed architecture on multivariate time series data, we adapt LITE to handle multivariate time series, we call this version LITEMV. To bring theory into application, we also conducted experiments using LITEMV on multivariate time series representing human rehabilitation movements, showing that LITEMV not only is the most efficient model but also the best performing for this application on the Kimore dataset, a skeleton based human rehabilitation exercises dataset. Moreover, to address the interpretability of LITEMV, we present a study using Class Activation Maps to understand the classification decision taken by the model during evaluation.

The Moyal-Higher-Spin (MHS) formalism, involving fields dependent on spacetime and auxiliary coordinates, is an approach to studying higher spin (HS)-like models. To determine the particle content of the MHS model of the Yang-Mills type, we calculate the quartic Casimir operator for on-shell MHS fields, finding it generally non-vanishing, indicative of infinite/continuous-spin degrees of freedom. We propose an on-shell basis for these infinite/continuous-spin states. Additionally, we analyse the content of a massive MHS model., Comment: 32 pages

Detecting monotonic changes in heart rate (HR) is crucial for early identification of cardiac conditions and health management. This is particularly important for dementia patients, where HR trends can signal stress or agitation. Developing wearable technologies that can perform always-on monitoring of HRs is essential to effectively detect slow changes over extended periods of time. However, designing compact electronic circuits that can monitor and process bio-signals continuously, and that can operate in a low-power regime to ensure long-lasting performance, is still an open challenge. Neuromorphic technology offers an energy-efficient solution for real-time health monitoring. We propose a neuromorphic implementation of a Neural State Machine (NSM) network to encode different health states and switch between them based on the input stimuli. Our focus is on detecting monotonic state switches in electrocardiogram data to identify progressive HR increases. This innovative approach promises significant advancements in continuous health monitoring and management.

We show how the 2-Higgs Doublet Model (2HDM) Type-I can explain some excesses recently seen at the Large Hadron Collider (LHC) in $\gamma\gamma$ and $\tau^+\tau^-$ final states in turn matching Large Electron Positron (LEP) data in $b\bar b$ signatures, all anomalies residing over the 90-100 GeV or so region. The explanation to such anomalous data is found in the aforementioned scenario when in inverted mass hierarchy, in two configurations: i) when the lightest CP-even Higgs state is alone capable of reproducing the excesses; ii) when a combination of such a state and the CP-odd Higgs boson is able to do so. To test further this scenario, we present some Benchmark Points (BPs) of it amenable to phenomenological investigation.

We investigate the problem of enforcing a desired centrality measure in complex networks, while still keeping the original pattern of the network. Specifically, by representing the network as a graph with suitable nodes and weighted edges, we focus on computing the smallest perturbation on the weights required to obtain a prescribed PageRank or Katz centrality index for the nodes. Our approach relies on optimization procedures that scale with the number of modified edges, enabling the exploration of different scenarios and altering network structure and dynamics.

High-quality real-time view synthesis methods are based on volume rendering, splatting, or surface rendering. While surface-based methods generally are the fastest, they cannot faithfully model fuzzy geometry like hair. In turn, alpha-blending techniques excel at representing fuzzy materials but require an unbounded number of samples per ray (P1). Further overheads are induced by empty space skipping in volume rendering (P2) and sorting input primitives in splatting (P3). These problems are exacerbated on low-performance graphics hardware, e.g. on mobile devices. We present a novel representation for real-time view synthesis where the (P1) number of sampling locations is small and bounded, (P2) sampling locations are efficiently found via rasterization, and (P3) rendering is sorting-free. We achieve this by representing objects as semi-transparent multi-layer meshes, rendered in fixed layer order from outermost to innermost. We model mesh layers as SDF shells with optimal spacing learned during training. After baking, we fit UV textures to the corresponding meshes. We show that our method can represent challenging fuzzy objects while achieving higher frame rates than volume-based and splatting-based methods on low-end and mobile devices.

We present a new Monte Carlo Tree Search (MCTS) algorithm to solve the stochastic orienteering problem with chance constraints, i.e., a version of the problem where travel costs are random, and one is assigned a bound on the tolerable probability of exceeding the budget. The algorithm we present is online and anytime, i.e., it alternates planning and execution, and the quality of the solution it produces increases as the allowed computational time increases. Differently from most former MCTS algorithms, for each action available in a state the algorithm maintains estimates of both its value and the probability that its execution will eventually result in a violation of the chance constraint. Then, at action selection time, our proposed solution prunes away trajectories that are estimated to violate the failure probability. Extensive simulation results show that this approach can quickly produce high-quality solutions and is competitive with the optimal but time-consuming solution., Comment: Paper to appear on the IEEE Transactions on Automation Science and Engineering

We present a comprehensive X-ray analysis and spectral energy distribution (SED) fitting of WISEA J171419.96+602724.6, an extremely luminous type 2 quasar at $z$ = 2.99. The source was suggested as a candidate Compton-thick (column density N$_{\rm H}>$1.5 $\times$ 10$^{24}$ cm$^{-2}$) quasar by a short XMM-Newton observation in 2011. We recently observed the source with deep NuSTAR and XMM-Newton exposures in 2021 and found that the source has a lower obscuration of N$_{\rm H}\sim$5 $\times$ 10$^{22}$ cm$^{-2}$ with an about four times lower flux. The two epochs of observations suggested that the source was significantly variable in X-ray obscuration, flux, and intrinsic luminosity at 2-3~$\sigma$ in less than 2.5 years (in the source rest frame). We performed SED fitting of this source using CIGALE thanks to its great availability of multiwavelength data (from hard X-rays to radio). The source is very luminous with a bolometric luminosity of $L_{\rm BOL}\sim$ 2.5 $\times$ 10$^{47}$ erg s$^{-1}$. Its host galaxy has a huge star formation rate (SFR) of $\sim$1280 Solar mass yr$^{-1}$ and a huge stellar mass of $\sim$1.1 $\times$ 10$^{12}$ Solar mass. The correlation between the SFR and stellar mass of this source is consistent with what was measured in the high-$z$ quasars. It is also consistent with what was measured in the main-sequence star-forming galaxies, suggesting that the presence of the active nucleus in our target does not enhance or suppress the SFR of its host galaxy. The source is an Infrared hyper-luminous, obscured galaxy with significant amount of hot dust in its torus and shares many similar properties with hot, dust obscured galaxies., Comment: 19 pages, 10 figure, 5 tables, accepted to ApJ

A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists -- a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor towards this direction, where differentiable neural functions are parametrized to fit ab initio energies, and furthermore forces through automatic differentiation. We argue that, as of now, the utility of the MLFF models is no longer bottlenecked by accuracy but primarily by their speed (as well as stability and generalizability), as many recent variants, on limited chemical spaces, have long surpassed the chemical accuracy of $1$ kcal/mol -- the empirical threshold beyond which realistic chemical predictions are possible -- though still magnitudes slower than MM. Hoping to kindle explorations and designs of faster, albeit perhaps slightly less accurate MLFFs, in this review, we focus our attention on the design space (the speed-accuracy tradeoff) between MM and ML force fields. After a brief review of the building blocks of force fields of either kind, we discuss the desired properties and challenges now faced by the force field development community, survey the efforts to make MM force fields more accurate and ML force fields faster, envision what the next generation of MLFF might look like.

Softwarized and programmable Radio Access Networks (RANs) come with virtualized and disaggregated components, increasing the supply chain robustness and the flexibility and dynamism of the network deployments. This is a key tenet of Open RAN, with open interfaces across disaggregated components specified by the O-RAN ALLIANCE. It is mandatory, however, to validate that all components are compliant with the specifications and can successfully interoperate, without performance gaps with traditional, monolithic appliances. Open Testing & Integration Centers (OTICs) are entities that can verify such interoperability and adherence to the standard through rigorous testing. However, how to design, instrument, and deploy an OTIC which can offer testing for multiple tenants, heterogeneous devices, and is ready to support automated testing is still an open challenge. In this paper, we introduce a blueprint for a programmable OTIC testing infrastructure, based on the design and deployment of the Open6G OTIC at Northeastern University, Boston, and provide insights on technical challenges and solutions for O-RAN testing at scale., Comment: Presented at IEEE VTC Fall RitiRAN Workshop, 5 pages, 3 figures, 3 tables

Cosmic microwave background (CMB) temperature and polarization observations indicate that in the best-fit $\Lambda$ Cold Dark Matter model of the Universe, the local geometry is consistent with at most a small amount of positive or negative curvature, i.e., $\vert\Omega_K\vert\ll1$. However, whether the geometry is flat ($E^3$), positively curved ($S^3$) or negatively curved ($H^3$), there are many possible topologies. Among the topologies of $S^3$ geometry, the lens spaces $L(p,q)$, where $p$ and $q$ ($p>1$ and $0

Out-of-Distribution (OOD) detection in computer vision is a crucial research area, with related benchmarks playing a vital role in assessing the generalizability of models and their applicability in real-world scenarios. However, existing OOD benchmarks in the literature suffer from two main limitations: (1) they often overlook semantic shift as a potential challenge, and (2) their scale is limited compared to the large datasets used to train modern models. To address these gaps, we introduce SOOD-ImageNet, a novel dataset comprising around 1.6M images across 56 classes, designed for common computer vision tasks such as image classification and semantic segmentation under OOD conditions, with a particular focus on the issue of semantic shift. We ensured the necessary scalability and quality by developing an innovative data engine that leverages the capabilities of modern vision-language models, complemented by accurate human checks. Through extensive training and evaluation of various models on SOOD-ImageNet, we showcase its potential to significantly advance OOD research in computer vision. The project page is available at https://github.com/bach05/SOODImageNet.git., Comment: Accepeted as long paper at "The 3rd Workshop for Out-of-Distribution Generalization in Computer Vision Foundation Models", ECCV 2024

We investigate the ultrafast electron dynamics of a model of a wide-bandgap material with inner, valence, and conduction bands excited by an intense few-femtosecond pump and monitored by a delayed attosecond extreme-ultraviolet probe pulse. Complementary computational methods are utilized and compared, based on the semiconductor Bloch equations (SBEs) and time-dependent density functional theory (TDDFT). TDDFT is employed to study a finite-size system, while the SBEs are utilized to investigate the corresponding solid with periodic boundary conditions imposed, with the crystal-momentum-dependent energy bands and interband couplings calculated in the parallel-transport structure gauge. The resulting strong-field electron dynamics are employed to predict experimentally accessible attosecond transient absorption spectroscopy (ATAS) signals as a function of the probe-pulse frequency and pump-probe interpulse delay. Both simulation protocols similarly capture the time-delay-dependent spectral features in the ATAS signals. The very good agreement between our TDDFT and SBE-based results allows us to interpret the ab-initio TDDFT simulations in terms of SBEs' interband couplings, validating our SBE-based model and corroborating its conclusions., Comment: 21 pages, 8 figures

We consider the problem of distinguishing two vectors (visualized as images or barcodes) and learning if they are related to one another. For this, we develop a geometric quantum machine learning (GQML) approach with embedded symmetries that allows for the classification of similar and dissimilar pairs based on global correlations, and enables generalization from just a few samples. Unlike GQML algorithms developed to date, we propose to focus on symmetry-aware measurement adaptation that outperforms unitary parametrizations. We compare GQML for similarity testing against classical deep neural networks and convolutional neural networks with Siamese architectures. We show that quantum networks largely outperform their classical counterparts. We explain this difference in performance by analyzing correlated distributions used for composing our dataset. We relate the similarity testing with problems that showcase a proven maximal separation between the BQP complexity class and the polynomial hierarchy. While the ability to achieve advantage largely depends on how data are loaded, we discuss how similar problems can benefit from quantum machine learning., Comment: 5 pages, 5 figures

Colloidal Solids (COLIS) is a state-of-the-art light scattering setup developed for experiments onboard the International Space Station (ISS). COLIS allows for probing the structure and dynamics of soft matter systems on a wide range of length scales, from a few nm to tens of microns, and on time scales from 100 ns to tens of hours. In addition to conventional static and dynamic light scattering, COLIS includes depolarized dynamic light scattering, a small-angle camera, photon correlation imaging, and optical manipulation of thermosensitive samples through an auxiliary near-infrared laser beam, thereby providing a unique platform for probing soft matter systems. We demonstrate COLIS through ground tests on standard Brownian suspensions, and on protein, colloidal glasses, and gel systems similar to those to be used in future ISS experiments.

This paper discusses the necessary considerations and adjustments that allow a recently proposed avatar system architecture to be used with different robotic avatar morphologies (both wheeled and legged robots with various types of hands and kinematic structures) for the purpose of enabling remote (intercontinental) telepresence under communication bandwidth restrictions. The case studies reported involve robots using both position and torque control modes, independently of their software middleware.

Nonlinear model predictive locomotion controllers based on the reduced centroidal dynamics are nowadays ubiquitous in legged robots. These schemes, even if they assume an inherent simplification of the robot's dynamics, were shown to endow robots with a step-adjustment capability in reaction to small pushes, and, moreover, in the case of uncertain parameters - as unknown payloads - they were shown to be able to provide some practical, albeit limited, robustness. In this work, we provide rigorous certificates of their closed loop stability via a reformulation of the centroidal MPC controller. This is achieved thanks to a systematic procedure inspired by the machinery of adaptive control, together with ideas coming from Control Lyapunov functions. Our reformulation, in addition, provides robustness for a class of unmeasured constant disturbances. To demonstrate the generality of our approach, we validated our formulation on a new generation of humanoid robots - the 56.7 kg ergoCub, as well as on a commercially available 21 kg quadruped robot, Aliengo.

After leaving the Sun's corona, the solar wind continues to accelerate and cools, but more slowly than expected for a freely expanding adiabatic gas. We use in situ measurements from the Parker Solar Probe and Solar Orbiter spacecrafts to investigate a stream of solar wind as it traverses the inner heliosphere. The observations show heating and acceleration of the the plasma between the outer edge of the corona and near the orbit of Venus, in connection to the presence of large amplitude Alfv\'en waves. Alfv\'en waves are perturbations in the interplanetary magnetic field that transport energy. Our calculations show the damping and mechanical work performed by the Alfv\'en waves is sufficient to power the heating and acceleration of the fast solar wind in the inner heliosphere., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on August 30 2024, DOI: 10.1126/science.adk6953

This paper examines the performance of two Large Language Models (LLMs), GPT3.5 and Llama2 and one Small Language Model (SLM) Gemma, across three different classification tasks within the climate change (CC) and environmental domain. Employing BERT-based models as a baseline, we compare their efficacy against these transformer-based models. Additionally, we assess the models' self-evaluation capabilities by analyzing the calibration of verbalized confidence scores in these text classification tasks. Our findings reveal that while BERT-based models generally outperform both the LLMs and SLM, the performance of the large generative models is still noteworthy. Furthermore, our calibration analysis reveals that although Gemma is well-calibrated in initial tasks, it thereafter produces inconsistent results; Llama is reasonably calibrated, and GPT consistently exhibits strong calibration. Through this research, we aim to contribute to the ongoing discussion on the utility and effectiveness of generative LMs in addressing some of the planet's most urgent issues, highlighting their strengths and limitations in the context of ecology and CC., Comment: 11 pages, to be published in NLDB 2024

Context: Software Quality Assurance (SQA) is a fundamental part of software engineering to ensure stakeholders that software products work as expected after release in operation. Machine Learning (ML) has proven to be able to boost SQA activities and contribute to the development of quality software systems. In this context, Causal Reasoning is gaining increasing interest as a methodology to go beyond a purely data-driven approach by exploiting the use of causality for more effective SQA strategies. Objective: Provide a broad and detailed overview of the use of causal reasoning for SQA activities, in order to support researchers to access this research field, identifying room for application, main challenges and research opportunities. Methods: A systematic review of the scientific literature on causal reasoning for SQA. The study has found, classified, and analyzed 86 articles, according to established guidelines for software engineering secondary studies. Results: Results highlight the primary areas within SQA where causal reasoning has been applied, the predominant methodologies used, and the level of maturity of the proposed solutions. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain, but other tasks like testing are rapidly gaining popularity. Both causal inference and causal discovery are exploited, with the Pearl's graphical formulation of causality being preferred, likely due to its intuitiveness. Tools to favour their application are appearing at a fast pace - most of them after 2021. Conclusions: The findings show that causal reasoning is a valuable means for SQA tasks with respect to multiple quality attributes, especially during V&V, evolution and maintenance to ensure reliability, while it is not yet fully exploited for phases like ..., Comment: Accepted to Information and Software Technology journal

$\require{mediawiki-texvc}$Cosmic Reionisation commenced when ultraviolet (UV) radiation produced in the first galaxies began illuminating the cold, neutral gas that filled the primordial Universe. Recent James Webb Space Telescope (JWST) observations have shown that surprisingly UV-bright galaxies were in place beyond redshift $z = 14$, when the Universe was less than 300 Myr old. Smooth turnovers of their UV continua have been interpreted as damping-wing absorption of Lyman-$\alpha$ (Ly$\alpha$), the principal hydrogen transition. However, spectral signatures encoding crucial properties of these sources, such as their emergent radiation field, largely remain elusive. Here we report spectroscopy from the JWST Advanced Deep Extragalactic Survey (JADES) of a galaxy at redshift $z = 13.0$ that reveal a singular, bright emission line unambiguously identified as Ly$\alpha$, in addition to a smooth turnover. We observe an equivalent width of $\text{EW}_\mathrm{Ly\alpha} > 40 \, \AA$ (rest frame), previously only seen at $z < 9$ where the intervening intergalactic medium (IGM) becomes increasingly ionised. Together with a very blue UV continuum, the Ly$\alpha$ line indicates the galaxy is a prolific producer of ionising photons, a significant fraction of which may escape. This suggests it resides in an early reionised region preventing complete extinction of Ly$\alpha$, thus shedding new light on the nature of the earliest galaxies and the onset of Reionisation only 330 Myr after the Big Bang., Comment: 19 pages, 9 figures, 3 tables. Comments welcome

Solving large-scale continuous-time algebraic Riccati equations is a significant challenge in various control theory applications. This work demonstrates that when the matrix coefficients of the equation are quasiseparable, the solution also exhibits numerical quasiseparability. This property enables us to develop two efficient Riccati solvers. The first solver is applicable to the general quasiseparable case, while the second is tailored to the particular case of banded coefficients. Numerical experiments confirm the effectiveness of the proposed algorithms on both synthetic examples and case studies from the control of partial differential equations and agent-based models.

Cavity quantum electrodynamics (QED) studies the interaction between resonator-confined radiation and natural atoms or other formally equivalent quantum excitations, under conditions where the quantum nature of photons is relevant. Phenomena studied in cavity QED can also be explored using superconducting artificial atoms and microwave photons in superconducting resonators. These circuit QED systems offer the possibility to reach the ultrastrong coupling regime with individual artificial atoms, unlike their natural counterparts. In this regime, the light-matter coupling strength reaches a considerable fraction of the bare resonance frequencies in the system. Here, we provide a careful analysis of both incoherent and coherent spectra in circuit QED systems consisting of a flux qubit interacting with an LC resonator. Despite these systems can be effectively described by the quantum Rabi model, as the corresponding cavity QED ones, we find distinctive features, depending on how the system is coupled to the output port, which become evident in the ultrastrong coupling regime., Comment: 14 pages, 12 figures

A charge-density-wave (CDW) is characterized by a dynamical order parameter consisting of a time-dependent amplitude and phase, which manifest as optically-active collective modes of the CDW phase. Studying the behaviour of such collective modes in the time-domain, and their coupling with electronic and lattice order, provides important insight into the underlying mechanisms behind CDW formation. In this work, we report on femtosecond broadband transient reflectivity experiments on bulk 1$T$-VSe$_2$ using near-infrared excitation. At low temperature, we observe coherent oscillations associated with the CDW amplitude mode and phonons of the distorted lattice. Across the expected transition temperature at 110 K, we confirm signatures of a rearrangement of the electronic structure evident in the quasiparticle dynamics. However, we find that the amplitude mode instead softens to zero frequency at 80 K, possibly indicating an additional phase transition at this temperature. In addition, we demonstrate photoinduced CDW melting, associated with a collapse of the electronic and lattice order, which occurs at moderate excitation densities, consistent with a dominant electron-phonon CDW mechanism., Comment: 13 pages, 6 figures, supporting information

In a binary fluid mixture, the concentration gradient of a heavier molecular solute leads to a diffusive flux of solvent and solute to achieve thermodynamic equilibrium. If the solute concentration decreases with height, the system is always in a condition of stable mechanical equilibrium against gravity. We show experimentally that this mechanical equilibrium becomes unstable in case colloidal particles are dispersed uniformly within the mixture, and that the resulting colloidal suspension undergoes a transient convective instability with the onset of convection patterns. By means of a numerical analysis, we clarify the microscopic mechanism from which the observed destabilisation process originates. The solute concentration gradient drives an upward diffusiophoretic migration of colloids, in turn causing the development of a mechanically unstable layer within the sample, where the density of the suspension increases with height. Convective motions arise to minimize this localized rise in gravitational potential energy.

Robust 3D human pose estimation is crucial to ensure safe and effective human-robot collaboration. Accurate human perception,however, is particularly challenging in these scenarios due to strong occlusions and limited camera viewpoints. Current 3D human pose estimation approaches are rather vulnerable in such conditions. In this work we present a novel approach for robust 3D human pose estimation in the context of human-robot collaboration. Instead of relying on noisy 2D features triangulation, we perform multi-view fusion on 3D skeletons provided by absolute monocular methods. Accurate 3D pose estimation is then obtained via reprojection error optimization, introducing limbs length symmetry constraints. We evaluate our approach on the public dataset Human3.6M and on a novel version Human3.6M-Occluded, derived adding synthetic occlusions on the camera views with the purpose of testing pose estimation algorithms under severe occlusions. We further validate our method on real human-robot collaboration workcells, in which we strongly surpass current 3D human pose estimation methods. Our approach outperforms state-of-the-art multi-view human pose estimation techniques and demonstrates superior capabilities in handling challenging scenarios with strong occlusions, representing a reliable and effective solution for real human-robot collaboration setups., Comment: ECCV workshops 2024

Large Language Models (LLMs) and Large Multimodal Models (LMMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems. However, most existing approaches either rely on text-only prompting or employ basic multimodal strategies that do not fully exploit the complementary information available from both textual and visual modalities. This paper introduces a novel framework, Cross-Reflection Prompting, termed X-Reflect, designed to address these limitations by prompting LMMs to explicitly identify and reconcile supportive and conflicting information between text and images. By capturing nuanced insights from both modalities, this approach generates more comprehensive and contextually richer item representations. Extensive experiments conducted on two widely used benchmarks demonstrate that our method outperforms existing prompting baselines in downstream recommendation accuracy. Additionally, we evaluate the generalizability of our framework across different LMM backbones and the robustness of the prompting strategies, offering insights for optimization. This work underscores the importance of integrating multimodal information and presents a novel solution for improving item understanding in multimodal recommendation systems.

Address Space Layout Randomization (ASLR) is a crucial defense mechanism employed by modern operating systems to mitigate exploitation by randomizing processes' memory layouts. However, the stark reality is that real-world implementations of ASLR are imperfect and subject to weaknesses that attackers can exploit. This work evaluates the effectiveness of ASLR on major desktop platforms, including Linux, MacOS, and Windows, by examining the variability in the placement of memory objects across various processes, threads, and system restarts. In particular, we collect samples of memory object locations, conduct statistical analyses to measure the randomness of these placements and examine the memory layout to find any patterns among objects that could decrease this randomness. The results show that while some systems, like Linux distributions, provide robust randomization, others, like Windows and MacOS, often fail to adequately randomize key areas like executable code and libraries. Moreover, we find a significant entropy reduction in the entropy of libraries after the Linux 5.18 version and identify correlation paths that an attacker could leverage to reduce exploitation complexity significantly. Ultimately, we rank the identified weaknesses based on severity and validate our entropy estimates with a proof-of-concept attack. In brief, this paper provides the first comprehensive evaluation of ASLR effectiveness across different operating systems and highlights opportunities for Operating System (OS) vendors to strengthen ASLR implementations., Comment: Extended version of the accepted paper at ACM CCS 2024

Immersive technologies such as Metaverse, AR, and VR are at a crossroads, with many actors pondering their adoption and potential sectors interested in integration. The cultural and tourism industries are particularly impacted, facing significant pressure to make decisions that could shape their future landscapes. Stakeholders' perceptions play a crucial role in this process, influencing the speed and extent of technology adoption. As immersive technologies promise to revolutionize experiences, stakeholders in these fields weigh the benefits and challenges of embracing such innovations. The current choices will likely determine the trajectory of cultural preservation and tourism enhancement, potentially transforming how we engage with history, art, and travel. Starting from a decomposition of stakeholders' perceptions into principal components using Q-methodology, this article employs an evolutionary game model to attempt to map possible scenarios and highlight potential decision-making trajectories. The proposed approach highlights how evolutionary dynamics lead to identifying a dominant long-term strategy that emerges from the complex system of coexistence among various stakeholders.

Density deconvolution addresses the estimation of the unknown (probability) density function $f$ of a random signal from data that are observed with an independent additive random noise. This is a classical problem in statistics, for which frequentist and Bayesian nonparametric approaches are available to deal with static or batch data. In this paper, we consider the problem of density deconvolution in a streaming or online setting where noisy data arrive progressively, with no predetermined sample size, and we develop a sequential nonparametric approach to estimate $f$. By relying on a quasi-Bayesian sequential approach, often referred to as Newton's algorithm, we obtain estimates of $f$ that are of easy evaluation, computationally efficient, and with a computational cost that remains constant as the amount of data increases, which is critical in the streaming setting. Large sample asymptotic properties of the proposed estimates are studied, yielding provable guarantees with respect to the estimation of $f$ at a point (local) and on an interval (uniform). In particular, we establish local and uniform central limit theorems, providing corresponding asymptotic credible intervals and bands. We validate empirically our methods on synthetic and real data, by considering the common setting of Laplace and Gaussian noise distributions, and make a comparison with respect to the kernel-based approach and a Bayesian nonparametric approach with a Dirichlet process mixture prior., Comment: 62 pages

In machine learning, contamination refers to situations where testing data leak into the training set. The issue is particularly relevant for the evaluation of the performance of Large Language Models (LLMs), which are generally trained on gargantuan, and generally opaque, corpora of text scraped from the world wide web. Developing tools to detect contamination is therefore crucial to be able to fairly and properly track the evolution of the performance of LLMs. Most recent works in the field are not tailored to quantify contamination on short sequences of text like we find in psychology questionnaires. In the present paper we introduce LogProber, a novel, efficient, algorithm that we show able to detect contamination using token probability in given sentences. In the second part we investigate the limitations of the method and discuss how different training methods can contaminate models without leaving traces in the token probabilities.

A celebrated result of Bertrand states that the only central force potentials on the plane with the property that all bounded orbits are periodic are the Kepler potential and the potential of the harmonic oscillator. In this paper, we complement Bertrand's theorem showing the existence of an infinite dimensional space of central force potentials on the plane which are Zoll at a given energy level, meaning that all non-collision orbits with given energy are closed and of the same length. We also determine all natural systems on the (not necessarily flat) plane which are invariant under rotations and Zoll at a given energy and prove several existence and rigidity results for systems which are Zoll at multiple energies., Comment: 17 pages, comments welcome

DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ~100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half serving as target in a dual-phase time projection chamber (TPC). The UAr cryogenics system must maintain stable thermodynamic conditions throughout the experiment's lifetime of >10 years. Continuous removal of impurities and radon from the UAr is essential for maximising signal yield and mitigating background. We are developing an efficient and powerful cryogenics system with a gas purification loop with a target circulation rate of 1000 slpm. Central to its design is a condenser operated with liquid nitrogen which is paired with a gas heat exchanger cascade, delivering a combined cooling power of >8 kW. Here we present the design choices in view of the DS-20k requirements, in particular the condenser's working principle and the cooling control, and we show test results obtained with a dedicated benchmarking platform at CERN and LNGS. We find that the thermal efficiency of the recirculation loop, defined in terms of nitrogen consumption per argon flow rate, is 95 % and the pressure in the test cryostat can be maintained within $\pm$(0.1-0.2) mbar. We further detail a 5-day cool-down procedure of the test cryostat, maintaining a cooling rate typically within -2 K/h, as required for the DS-20k inner detector. Additionally, we assess the circuit's flow resistance, and the heat transfer capabilities of two heat exchanger geometries for argon phase change, used to provide gas for recirculation. We conclude by discussing how our findings influence the finalisation of the system design, including necessary modifications to meet requirements and ongoing testing activities., Comment: 45 pages, 24 figures

Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the meta-evaluation of Machine Translation (MT) metrics, ranking them according to their correlation with human judgments. Their results guide researchers toward enhancing the next generation of metrics and MT systems. With the recent introduction of neural metrics, the field has witnessed notable advancements. Nevertheless, the inherent opacity of these metrics has posed substantial challenges to the meta-evaluation process. This work highlights two issues with the meta-evaluation framework currently employed in WMT, and assesses their impact on the metrics rankings. To do this, we introduce the concept of sentinel metrics, which are designed explicitly to scrutinize the meta-evaluation process's accuracy, robustness, and fairness. By employing sentinel metrics, we aim to validate our findings, and shed light on and monitor the potential biases or inconsistencies in the rankings. We discover that the present meta-evaluation framework favors two categories of metrics: i) those explicitly trained to mimic human quality assessments, and ii) continuous metrics. Finally, we raise concerns regarding the evaluation capabilities of state-of-the-art metrics, emphasizing that they might be basing their assessments on spurious correlations found in their training data., Comment: Presented at ACL 2024 Main Conference. 29 pages

Speech deepfake detection has recently gained significant attention within the multimedia forensics community. Related issues have also been explored, such as the identification of partially fake signals, i.e., tracks that include both real and fake speech segments. However, generating high-quality spliced audio is not as straightforward as it may appear. Spliced signals are typically created through basic signal concatenation. This process could introduce noticeable artifacts that can make the generated data easier to detect. We analyze spliced audio tracks resulting from signal concatenation, investigate their artifacts and assess whether such artifacts introduce any bias in existing datasets. Our findings reveal that by analyzing splicing artifacts, we can achieve a detection EER of 6.16% and 7.36% on PartialSpoof and HAD datasets, respectively, without needing to train any detector. These results underscore the complexities of generating reliable spliced audio data and lead to discussions that can help improve future research in this area., Comment: Accepted at ASVspoof 5 Workshop (Interspeech2024 Satellite)