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The application of Tensor Networks (TN) in quantum computing has shown promise, particularly for data loading. However, the assumption that data is readily available often renders the integration of TN techniques into Quantum Monte Carlo (QMC) inefficient, as complete probability distributions would have to be calculated classically. In this paper the tensor-train cross approximation (TT-cross) algorithm is evaluated as a means to address the probability loading problem. We demonstrate the effectiveness of this method on financial distributions, showcasing the TT-cross approach's scalability and accuracy. Our results indicate that the TT-cross method significantly improves circuit depth scalability compared to traditional methods, offering a more efficient pathway for implementing QMC on near-term quantum hardware. The approach also shows high accuracy and scalability in handling high-dimensional financial data, making it a promising solution for quantum finance applications., Comment: 20 pages, 10 figures

We compute the evolution of linear perturbations on top of a background solution of a general nonlinear electromagnetic theory. This evolution can be described in terms of two effective metrics, and we analyse under what conditions they are conformally related, so that they can be regarded as analogue models of non-trivial gravitational fields in the eikonal approximation. This is the case of Born-Infeld theory. For the background created by a static point electric charge in the Born-Infeld theory, the effective metric describes a wormhole geometry for light rays. Depending on the impact parameter, incoming light rays are either scattered to infinity or approach the wormhole slowing down their pace until they hit the charge at vanishing speed. The same effective wormhole geometry is obtained for a magnetic monopole and a dyon and we relate it to the duality invariance of Born-Infeld electromagnetism. Finally, we analyse the scalar Dirac-Born-Infeld theory and show that the effective wormhole geometry is not generated by a particle with scalar charge., Comment: 21 pages, 5 figures

Random Forest (RF) and Gradient Boosting Regression Trees (GBRT) regression models along with three cheminformatics data sets (RDkit, Mordred, Morgan) have been used to predict the power conversion efficiency (PCE) of organic solar cells (OSCs). The data consists of cheinformatics descriptors of the electron donor used in 433 OSCs for which the experimental PCE (target variable) is reported in the literature. The donor is either a polymer or a small organic molecule, and the acceptor the fullerene derivatives PCBM or PC71BM. Unlike previous methods, our ML approach considers the type of donor and the acceptor by adding four extra donor's features using the one-hot encoder tool. It is demonstrated that this additional information improves the prediction performance up to 34%. We have also exploited this feature to theoretically forecast the PCE of new OSCs by evaluating the ML model for a different acceptor. It is predicted that more than 50% of the OCSs obtained by exchanging the acceptor would have higher experimental PCE. The prediction accuracy of a given ML approach is analyzed for different PCE intervals. RF using RDkit descriptors resulted in the best ML approach with a Pearson's correlation coefficient for the training and testing sets equal to 0.96 and 0.62, respectively., Comment: 12 pages, 4 Figures, 1 Table

The increasing quality of cosmological data has revealed some tensions that could be signalling the necessity of incorporating new physics into our cosmological model. One particularly intriguing possibility is the existence of elastic interactions between dark matter and dark energy. Not only do these interactions provide a natural mechanism to relief cosmological tensions, but there is also compelling observational evidence for them, to the extent that a detection could even be claimed. We review the potential of these scenarios in relation to the cosmological tensions and discuss distinctive signatures that can be probed with future data, thus providing a smoking gun for these interactions., Comment: 11 pages, 6 Figures

Robots hold great promise for performing repetitive or hazardous tasks, but achieving human-like dexterity, especially in contact-rich and dynamic environments, remains challenging. Rigid robots, which rely on position or velocity control, often struggle with maintaining stable contact and applying consistent force in force-intensive tasks. Learning from Demonstration has emerged as a solution, but tasks requiring intricate maneuvers, such as powder grinding, present unique difficulties. This paper introduces Diffusion Policies For Compliant Manipulation (DIPCOM), a novel diffusion-based framework designed for compliant control tasks. By leveraging generative diffusion models, we develop a policy that predicts Cartesian end-effector poses and adjusts arm stiffness to maintain the necessary force. Our approach enhances force control through multimodal distribution modeling, improves the integration of diffusion policies in compliance control, and extends our previous work by demonstrating its effectiveness in real-world tasks. We present a detailed comparison between our framework and existing methods, highlighting the advantages and best practices for deploying diffusion-based compliance control.

Developing industry-wide standards and making sure producers of mission-critical systems comply with them is crucial to foster consumer acceptance. Producers of such systems can rely on assurance cases to demonstrate to regulatory authorities how they have complied with such standards to help prevent system failure, which could result in fatalities and environmental damage. In this paper, we introduce SmartGSN, an innovative online tool that relies on Large Language Models to (semi-)automate the management of assurance cases complying with GSN -a very popular notation to graphically represent assurance cases. The evaluation of SmartGSN demonstrates its strong capability to detect assurance case patterns within the assurance cases manually created for five systems spanning several application domains. SmartGSN is accessible online at [https://smartgsn.vercel.app], and a demonstration video can be viewed at [https://youtu.be/qLrTHf-SZbM].

Network verification (NWV), broadly defined as the verification of properties of distributed protocols used in network systems, cannot be efficiently solved on classical hardware via brute force. Prior work has developed a variety of methods that scale by observing a structure in the search space and then evaluating classes within the search space instead of individual instances. However, even these classification mechanisms have their limitations. In this paper, we consider a radically different approach: applying quantum computing to more efficiently solve NWV problems. We provide an overview of how to map variants of NWV problems into unstructured search problems that can be solved via quantum computing with quadratic speedup, making the approach feasible in theory to problems that are double in size (of the input). Emerging quantum systems cannot yet tackle problems of practical interest, but rapid advances in hardware and algorithm development make now a great time to start thinking about their application. With this in mind, we explore the limits of scale of the problem for which quantum computing can solve NWV problems as unstructured search.

Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented. One hypothesis suggests that these capabilities are primarily executed by small subnetworks within the LLM, known as circuits. But how can we evaluate this hypothesis? In this paper, we formalize a set of criteria that a circuit is hypothesized to meet and develop a suite of hypothesis tests to evaluate how well circuits satisfy them. The criteria focus on the extent to which the LLM's behavior is preserved, the degree of localization of this behavior, and whether the circuit is minimal. We apply these tests to six circuits described in the research literature. We find that synthetic circuits -- circuits that are hard-coded in the model -- align with the idealized properties. Circuits discovered in Transformer models satisfy the criteria to varying degrees. To facilitate future empirical studies of circuits, we created the \textit{circuitry} package, a wrapper around the \textit{TransformerLens} library, which abstracts away lower-level manipulations of hooks and activations. The software is available at \url{https://github.com/blei-lab/circuitry}., Comment: Code available here: https://github.com/blei-lab/circuitry

Risk perception plays a key role in shaping the collective behavior of moving animal groups, yet the effects of variation in perceived risk within groups is unknown. Here, we merge two subgroups of fish with different levels of perceived risk, manipulated through habituation to the experimental arena, and quantitatively analyze their movement. Fish with heightened risk perception tend to occupy more central positions within the group, consistent with the selfish herd hypothesis. This behavior appears to be driven by adjusted local interactions with nearby individuals. Compared to more habituated fish, these individuals also exhibit greater coordination, shorter burst-and-coast dynamics, more efficient information transmission, and a stronger tendency to adopt follower roles. To validate the findings, we develop a machine learning tool that successfully classifies the subgroup identity of individual fish. Our study demonstrates how differences in local-scale inter-individual interactions can drive spatial assortment within groups, based on heterogeneity in the perception of risk., Comment: Main paper (12 pages) + Supplementary Information (14 pages)

A theorem of Z. Arad and E. Fisman establishes that if $A$ and $B$ are two conjugacy classes of a finite group $G$ such that either $AB=A\cup B$ or $AB=A^{-1} \cup B$, then $G$ cannot be non-abelian simple. We demonstrate that, in fact, $\langle A\rangle = \langle B\rangle$ is solvable, the elements of $A$ and $B$ are $p$-elements for some prime $p$, and $\langle A\rangle $ is $p$-nilpotent. Moreover, under the second assumption, it turns out that $A=B$ and this is the only possible case. This research is done by appealing to recently developed techniques and results that are based on the Classification of Finite Simple Groups.

Einstein's article on the EPR paradox is the most cited of his works, but not many know that it was not fully representative of the way he thought about the incompleteness of the quantum formalism. Indeed, his main worry was not Heisenberg's uncertainty principle, which he accepted, but the experimental non-separability of spatially separate systems. The same problem was also recognized, years later, by one of us, as part of an axiomatic analysis of the quantum formalism, which revealed an unexpected structural limitation of the quantum formalism in Hilbert space, preventing the description of separate systems. As we will explain, this limitation does not manifest at the level of the states, but of the projectors describing the properties, in the sense that there are not enough properties in the formalism to describe separate systems. The question remains whether separability is a possibility at the fundamental level and if a formalism should integrate it into its mathematical structure, as a possibility. To aid our intuition, we offer a reflection based on a powerful analogy between physical systems and human conceptual entities, as the question of separability also arises for the latter., Comment: 32 pages, no figures

We present the Cosmic Piano, an innovative outreach tool designed to detect muons from air showers produced by cosmic rays colliding with the atmosphere. The Cosmic Piano consists of scintillator-based modules with avalanche photodiodes (APD) readout, which produce a musical note and a flash of light whenever a muon passes through one of them and triggers its electronics. The Cosmic Piano has been showcased at various high-profile events, including the Montreux Jazz Festival and the EuroScience Open Forum, seamlessly fusing scientific phenomena with live music. Building on its success, we have developed a new, comprehensive Masterclass session aimed at engaging high school and university students. This session includes analysing both simulated cosmic ray events and real data from the Cosmic Piano. The Masterclass provides participants with a hands-on experience that mirrors the experimental procedures used in major physics experiments, such as those conducted at CERN with the Large Hadron Collider (LHC) collision data. This initiative offers a unique opportunity for students to engage with an active experiment, bridging the gap between outreach and serious scientific research., Comment: 6 pages, 6 figures

Single-dish observations suggest that the abundances of organic species in star-forming regions of the outer Galaxy, characterised by sub-Solar metallicities, are comparable to those found in the local Galaxy. To understand this counter-intuitive result, and avoid misleading interpretation due to beam dilution effects at such large distances, spatially resolved molecular emission maps are needed to link correctly measured abundances and local physical properties. We observed several organic molecules with the Atacama Large Millimeter Array towards WB89-671, the source with the largest Galactocentric distance (23.4~kpc) of the project "CHEMical complexity in star-forming regions of the OUTer Galaxy" (CHEMOUT), at a resolution of 15000~au. We compared the observed molecular abundances with chemical model predictions. We detected emission of c-C3H2, C4H, CH3OH, H2CO, HCO, H13CO+, HCS+, CS, HN13C, and SO. The emission morphology is complex, extended, and different in each tracer. The most intense emission in H13CO+, H2CO and c-C3H2 arises from two millimeter continuum, infrared-bright cores. The most intense CH3OH and SO emission arises predominantly from the part of the filament with no continuum sources. The narrow linewidths across the filament indicate quiescent gas, despite the two embedded protostars. Derived molecular column densities are comparable with those in local star-forming regions, and suggest anti-correlation between hydrocarbons, ions, HCO, and H2CO on one side, and CH3OH and SO on the other. Static chemical models that best match the observed column densities favour low energetic conditions, expected at large Galactocentric radii, but carbon elemental abundances 3 times higher than that derived extrapolating the [C/H] Galactocentric gradient at 23~kpc. This would indicate a flatter [C/H] trend at large Galactocentric radii, in line with a flat abundance of organics., Comment: accepted for publication in Astronomy & Astrophysics, 17 pages, 14 figures, 6 tables, 3 Appendices

The growing interest in cybersecurity has significantly increased articles designing and implementing various Cyber Deception (CYDEC) mechanisms. This trend reflects the urgent need for new strategies to address cyber threats effectively. Since its emergence, CYDEC has established itself as an innovative defense against attackers, thanks to its proactive and reactive capabilities, finding applications in numerous real-life scenarios. Despite the considerable work devoted to CYDEC, the literature still presents significant gaps. In particular, there has not been (i) a comprehensive analysis of the main components characterizing CYDEC, (ii) a generic classification covering all types of solutions, nor (iii) a survey of the current state of the literature in various contexts. This article aims to fill these gaps through a detailed review of the main features that comprise CYDEC, developing a comprehensive classification taxonomy. In addition, the different frameworks used to generate CYDEC are reviewed, presenting a more comprehensive one. Existing solutions in the literature using CYDEC, both without Artificial Intelligence (AI) and with AI, are studied and compared. Finally, the most salient trends of the current state of the art are discussed, offering a list of pending challenges for future research., Comment: 38 pages

When using unitary gate sequences, the growth in depth of many quantum circuits with output size poses significant obstacles to practical quantum computation. The quantum fan-out operation, which reduces the circuit depth of quantum algorithms such as the quantum Fourier transform and Shor's algorithm, is an example that can be realized in constant depth independent of the output size. Here, we demonstrate a quantum fan-out gate with real-time feedforward on up to four output qubits using a superconducting quantum processor. By performing quantum state tomography on the output states, we benchmark our gate with input states spanning the entire Bloch sphere. We decompose the output-state error into a set of independently characterized error contributions. We extrapolate our constant-depth circuit to offer a scaling advantage compared to the unitary fan-out sequence beyond 25 output qubits with feedforward control, or beyond 17 output qubits if the classical feedforward latency is negligible. Our work highlights the potential of mid-circuit measurements combined with real-time conditional operations to improve the efficiency of complex quantum algorithms.

This study presents the design, simulation, and experimental characterization of a superconducting transmon qubit circuit prototype for potential applications in dark matter detection experiments. We describe a planar circuit design featuring two non-interacting transmon qubits, one with fixed frequency and the other flux tunable. Finite-element simulations were employed to extract key Hamiltonian parameters and optimize component geometries. The qubit was fabricated and then characterized at $20$ mK, allowing for a comparison between simulated and measured qubit parameters. Good agreement was found for transition frequencies and anharmonicities (within 1\% and 10\% respectively) while coupling strengths exhibited larger discrepancies (30\%). We discuss potential causes for measured coherence times falling below expectations ($T_1\sim\,$1-2 \textmu s) and propose strategies for future design improvements. Notably, we demonstrate the application of a hybrid 3D-2D simulation approach for energy participation ratio evaluation, yielding a more accurate estimation of dielectric losses. This work represents an important first step in developing planar Quantum Non-Demolition (QND) single-photon counters for dark matter searches, particularly for axion and dark photon detection schemes.

Within the context of creating new Socially Assistive Robots, emotion recognition has become a key development factor, as it allows the robot to adapt to the user's emotional state in the wild. In this work, we focused on the analysis of two voice recording Spanish datasets: ELRA-S0329 and EmoMatchSpanishDB. Specifically, we centered our work in the paralanguage, e.~g. the vocal characteristics that go along with the message and clarifies the meaning. We proposed the use of the DeepSpectrum method, which consists of extracting a visual representation of the audio tracks and feeding them to a pretrained CNN model. For the classification task, DeepSpectrum is often paired with a Support Vector Classifier --DS-SVC--, or a Fully-Connected deep-learning classifier --DS-FC--. We compared the results of the DS-SVC and DS-FC architectures with the state-of-the-art (SOTA) for ELRA-S0329 and EmoMatchSpanishDB. Moreover, we proposed our own classifier based upon Attention Mechanisms, namely DS-AM. We trained all models against both datasets, and we found that our DS-AM model outperforms the SOTA models for the datasets and the SOTA DeepSpectrum architectures. Finally, we trained our DS-AM model in one dataset and tested it in the other, to simulate real-world conditions on how biased is the model to the dataset.

The increasing number of observations towards different environments in the Milky Way, as well as theoretical and experimental works, are improving our knowledge of the astrochemical processes in the interstellar medium (ISM). In this chapter we report some of the main projects to study the chemical complexity and isotopic ratios across the Galaxy. High-sensitivity spectral surveys covering broad bandwidths towards Galactic Center molecular clouds (e.g. G+0.693-0.027) and star-forming regions (e.g. the hot core G31.41+0.31) are revealing very rich astrochemical reservoirs, which include molecules of prebiotic interest. At the same time, isotopic ratios (e.g. $^{12}$C/$^{13}$C and $^{14}$N/$^{15}$N) can give important information on the Galactic chemical evolution, as well as on chemical local processes due to the physical conditions of the molecular clouds. We also highlight the role of cosmic rays as a key agent affecting the interstellar chemistry described above., Comment: 9 pages, 1 figures - Chapter II of the National Congress of (proto-)Planetary Astrochemistry (CNAP II) Proceedings - Accepted for publication on MemSAIt, in print

This study addresses contact-rich object insertion tasks under unstructured environments using a robot with a soft wrist, enabling safe contact interactions. For the unstructured environments, we assume that there are uncertainties in object grasp and hole pose and that the soft wrist pose cannot be directly measured. Recent methods employ learning approaches and force/torque sensors for contact localization; however, they require data collection in the real world. This study proposes a sim-to-real approach using a privileged training strategy. This method has two steps. 1) The teacher policy is trained to complete the task with sensor inputs and ground truth privileged information such as the peg pose, and then 2) the student encoder is trained with data produced from teacher policy rollouts to estimate the privileged information from sensor history. We performed sim-to-real experiments under grasp and hole pose uncertainties. This resulted in 100\%, 95\%, and 80\% success rates for circular peg insertion with 0, +5, and -5 degree peg misalignments, respectively, and start positions randomly shifted $\pm$ 10 mm from a default position. Also, we tested the proposed method with a square peg that was never seen during training. Additional simulation evaluations revealed that using the privileged strategy improved success rates compared to training with only simulated sensor data. Our results demonstrate the advantage of using sim-to-real privileged training for soft robots, which has the potential to alleviate human engineering efforts for robotic assembly., Comment: This paper has been accepted at IROS 2024

The formation of the massive stars, and in particular, the role that the magnetic fields play in their early evolutionary phase is still far from being completely understood. Here, we present Atacama Large Millimeter/Submillimeter Array (ALMA) 1.2 mm full polarized continuum, and H$^{13}$CO$^+$(3$-$2), CS(5$-$4), and HN$^{13}$C(3$-$2) line observations with a high angular resolution ($\sim$0.4$''$ or 1100 au). In the 1.2 mm continuum emission, we reveal a dusty envelope surrounding the massive protostars, IRAS16547-E and IRAS16547-W, with dimensions of $\sim$10,000 au. This envelope has a bi-conical structure likely carved by the powerful thermal radio jet present in region. The magnetic fields vectors follow very-well the bi-conical envelope. The polarization fraction is $\sim$2.0\% in this region. Some of these vectors seem to converge to IRAS 16547-E, and IRAS 16547-W, the most massive protostars. Moreover, the velocity fields revealed from the spectral lines H$^{13}$CO$^+$(3$-$2), and HN$^{13}$C(3$-$2) show velocity gradients with a good correspondence with the magnetic fields, that maybe are tracing the cavities of molecular outflows or maybe in some parts infall. We derived a magnetic field strength in some filamentary regions that goes from 2 to 6.1\,mG. We also find that the CS(5$-$4) molecular line emission reveals multiple outflow cavities or bow-shocks with different orientations, some of which seem to follow the NW-SE radio thermal jet., Comment: Accepted by the Astrophysical Journal, 13 pages

We use data from the ALMA Evolutionary Study of High Mass Protocluster Formation in the Galaxy (ALMAGAL) survey to study 100 ALMAGAL regions at $\sim$ 1 arsecond resolution located between $\sim$ 2 and 6 kpc distance. Using ALMAGAL $\sim$ 1.3mm line and continuum data we estimate flow rates onto individual cores. We focus specifically on flow rates along filamentary structures associated with these cores. Our primary analysis is centered around position velocity cuts in H$_2$CO (3$_{0,3}$ - 2$_{0,2}$) which allow us to measure the velocity fields, surrounding these cores. Combining this work with column density estimates we derive the flow rates along the extended filamentary structures associated with cores in these regions. We select a sample of 100 ALMAGAL regions covering four evolutionary stages from quiescent to protostellar, Young Stellar Objects (YSOs), and HII regions (25 each). Using dendrogram and line analysis, we identify a final sample of 182 cores in 87 regions. In this paper, we present 728 flow rates for our sample (4 per core), analysed in the context of evolutionary stage, distance from the core, and core mass. On average, for the whole sample, we derive flow rates on the order of $\sim$10$^{-4}$ M$_{sun}$yr$^{-1}$ with estimated uncertainties of $\pm$50%. We see increasing differences in the values among evolutionary stages, most notably between the less evolved (quiescent/protostellar) and more evolved (YSO/HII region) sources. We also see an increasing trend as we move further away from the centre of these cores. We also find a clear relationship between the flow rates and core masses $\sim$M$^{2/3}$ which is in line with the result expected from the tidal-lobe accretion mechanism. Overall, we see increasing trends in the relationships between the flow rate and the three investigated parameters; evolutionary stage, distance from the core, and core mass., Comment: 11 pages, 11 figures, accepted for publication in A&A

Electrochemical cells employing Sodium (Na) and Zinc (Zn) electrodes and a chloride salt electrolyte have been imaged by neutron radiography during cycling. The use of such abundant raw materials confers a very low energy-normalised cost to the Na-Zn system, but its operation requires them to be entirely molten, and therefore to be operated at 600 {\deg}C. To suppress the self-discharge that results from this all-molten configuration, porous ceramic diaphragms are used to partition the electrolyte and thereby impede the movement of the Zn2+ ions responsible towards the Na electrode. Neutron images reveal large gas bubbles trapped beneath these diaphragms, formed during the cell fabrication process due to the large volume change that accompanies melting/solidifying of the electrolyte. Cycling data confirm that these bubbles interfere with cell operation by substantially increasing ohmic resistance. They indicate the need for either a new diaphragm design, or a cell fabrication process that prevents their formation in the first instance., Comment: 8 pages, 3 figures

The behaviour of the generalized Hilbert operator associated with a positive finite Borel measure $\mu$ on $[0,1)$ is investigated when it acts on weighted Banach spaces of holomorphic functions on the unit disc defined by sup-norms and on Korenblum type growth Banach spaces. It is studied when the operator is well defined, bounded and compact. To this aim, we study when it can be represented as an integral operator. We observe important differences with the behaviour of the Ces\`aro-type operator acting on these spaces, getting that boundedness and compactness are equivalent concepts for some standard weights. For the space of bounded holomorphic functions on the disc and for the Wiener algebra, we get also this equivalence, which is characterized in turn by the summability of the moments of the measure $\mu.$ In the latter case, it is also equivalent to nuclearity. Nuclearity of the generalized Hilbert operator acting on related spaces, such as the classical Hardy space, is also analyzed.

We analyze the entanglement generation in a pair of qubits that experience the vacuum fluctuations of a scalar field in the Cosmic String spacetime. The qubits are modeled as Unruh-DeWitt detectors coupled to a massless scalar field. We introduce a Heisenberg $XY$-interaction between the qubits that enhances the generation of quantum correlations. It is supposed that the qubits begin at a general mixed state described by a density operator with no entanglement while the field stays at its vacuum state. In this way, we find the general properties and conditions to create entanglement between the qubits by exploiting the field vacuum fluctuations. We quantify the qubits entanglement using the Negativity measure based on the Peres-Horodecki positive partial transpose criterion. We find that the Cosmic String would increase the entanglement harvesting when both qubits are near the Cosmic String. When the qubits locations are far from the Cosmic String we recover the usual results for Minkowski space. The Heisenberg $XY$-interaction enhance the entanglement harvesting irrespective of the coupling nature (ferromagnetic or anti-ferromagnetic). When the qubits are far apart from each other we find a maximum entanglement harvesting at the resonance points between the Heisenberg coupling constant and the qubits energy gap., Comment: 54 pages, 29 figures

The contribution of the magnetic field to the formation of high-mass stars is poorly understood. We report the high-angular resolution ($\sim0.3^{\prime\prime}$, 870 au) map of the magnetic field projected on the plane of the sky (B$_\mathrm{POS}$) towards the high-mass star forming region G333.46$-$0.16 (G333), obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) at 1.2 mm as part of the Magnetic Fields in Massive Star-forming Regions (MagMaR) survey. The B$_\mathrm{POS}$ morphology found in this region is consistent with a canonical ``hourglass'' which suggest a dynamically important field. This region is fragmented into two protostars separated by $\sim1740$ au. Interestingly, by analysing H$^{13}$CO$^{+}$ ($J=3-2$) line emission, we find no velocity gradient over the extend of the continuum which is consistent with a strong field. We model the B$_\mathrm{POS}$, obtaining a marginally supercritical mass-to-flux ratio of 1.43, suggesting an initially strongly magnetized environment. Based on the Davis-Chandrasekhar-Fermi method, the magnetic field strength towards G333 is estimated to be 5.7 mG. The absence of strong rotation and outflows towards the central region of G333 suggests strong magnetic braking, consistent with a highly magnetized environment. Our study shows that despite being a strong regulator, the magnetic energy fails to prevent the process of fragmentation, as revealed by the formation of the two protostars in the central region.

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Identical systems, or entities, are indistinguishable in quantum mechanics (QM), and the symmetrization postulate rules the possible statistical distributions of a large number of identical quantum entities. However, a thorough analysis on the historical development of QM attributes the origin of quantum statistics, in particular, Bose-Einstein statistics, to a lack of statistical independence of the micro-states of identical quantum entities. We have recently identified Bose-Einstein statistics in the combination of words in large texts, as a consequence of the entanglement created by the meaning carried by words when they combine in human language. Relying on this investigation, we put forward the hypothesis that entanglement, hence the lack of statistical independence, is due to a mechanism of contextual updating, which provides deeper reasons for the appearance of Bose-Einstein statistics in human language. However, this investigation also contributes to a better understanding of the origin of quantum mechanical statistics in physics. Finally, we provide new insights into the intrinsically random behaviour of microscopic entities that is generally assumed within classical statistical mechanics., Comment: 30 pages, 5 figures

We present our contribution to the 7th ABAW challenge at ECCV 2024, by utilizing a Dual-Direction Attention Mixed Feature Network (DDAMFN) for multitask facial expression recognition, we achieve results far beyond the proposed baseline for the Multi-Task ABAW challenge. Our proposal uses the well-known DDAMFN architecture as base to effectively predict valence-arousal, emotion recognition, and facial action units. We demonstrate the architecture ability to handle these tasks simultaneously, providing insights into its architecture and the rationale behind its design. Additionally, we compare our results for a multitask solution with independent single-task performance., Comment: 11 pages

Federated Learning (FL) has emerged as a promising approach to address privacy concerns inherent in Machine Learning (ML) practices. However, conventional FL methods, particularly those following the Centralized FL (CFL) paradigm, utilize a central server for global aggregation, which exhibits limitations such as bottleneck and single point of failure. To address these issues, the Decentralized FL (DFL) paradigm has been proposed, which removes the client-server boundary and enables all participants to engage in model training and aggregation tasks. Nevertheless, as CFL, DFL remains vulnerable to adversarial attacks, notably poisoning attacks that undermine model performance. While existing research on model robustness has predominantly focused on CFL, there is a noteworthy gap in understanding the model robustness of the DFL paradigm. In this paper, a thorough review of poisoning attacks targeting the model robustness in DFL systems, as well as their corresponding countermeasures, are presented. Additionally, a solution called DART is proposed to evaluate the robustness of DFL models, which is implemented and integrated into a DFL platform. Through extensive experiments, this paper compares the behavior of CFL and DFL under diverse poisoning attacks, pinpointing key factors affecting attack spread and effectiveness within the DFL. It also evaluates the performance of different defense mechanisms and investigates whether defense mechanisms designed for CFL are compatible with DFL. The empirical results provide insights into research challenges and suggest ways to improve the robustness of DFL models for future research.

Scalar field theories with a shift symmetry come equipped with the $K$-mouflage (or kinetic screening) mechanism that suppresses the scalar interaction between massive objects below a certain distance, the screening radius. In this work, we study the linear response of the scalar field distribution around a screened (point-like) object subject to a long range external scalar field perturbation for the Dirac-Born-Infeld theory. We find that, for regular boundary conditions at the position of the particle, some multipoles have vanishing response for a lacunar series of the multipole order $\ell$ for any dimension. Some multipoles also exhibit a logarithmic running when the number of spatial dimensions is even. We construct a ladder operator structure, with its associated ladder symmetries, formed by two sets of ladders that are related to the properties of the linear response and the existence of conserved charges. Our results exhibit a remarkable resemblance with the Love numbers properties of black holes in General Relativity, although some intriguing differences subsist., Comment: 32 pages, 3 figures

We show that the operator \begin{equation*} \mathcal{C} f(x,y) := \sup_{v\in \mathbb{R}} \Big|\mathrm{p.v.} \int_{\mathbb{R}} f(x-t, y-t^2) e^{i v t^3} \frac{\mathrm{d} t}{t} \Big| \end{equation*} is bounded on $L^p(\mathbb{R}^2)$ for every $1 < p < \infty$. This gives an affirmative answer to a question of Pierce and Yung., Comment: 39 pages

End-to-end neural speaker diarization systems are able to address the speaker diarization task while effectively handling speech overlap. This work explores the incorporation of speaker information embeddings into the end-to-end systems to enhance the speaker discriminative capabilities, while maintaining their overlap handling strengths. To achieve this, we propose several methods for incorporating these embeddings along the acoustic features. Furthermore, we delve into an analysis of the correct handling of silence frames, the window length for extracting speaker embeddings and the transformer encoder size. The effectiveness of our proposed approach is thoroughly evaluated on the CallHome dataset for the two-speaker diarization task, with results that demonstrate a significant reduction in diarization error rates achieving a relative improvement of a 10.78% compared to the baseline end-to-end model., Comment: Submitted to Odyssey 2024

Submillimeter and millimeter wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities. In this report we summarise the science that is guiding the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST). We demonstrate how tranformational advances in topics including star formation in high redshift galaxies, the diffuse circumgalactic medium, Galactic ecology, cometary compositions and solar flares motivate the need for a 50m, single-dish telescope with a 1-2 degree field of view and a new generation of highly multiplexed continuum and spectral cameras. AtLAST will have the resolution to drastically lower the confusion limit compared to current single-dish facilities, whilst also being able to rapidly map large areas of the sky and detect extended, diffuse structures. Its high sensitivity and large field of view will open up the field of submillimeter transient science by increasing the probability of serendipitous detections. Finally, the science cases listed here motivate the need for a highly flexible operations model capable of short observations of individual targets, large surveys, monitoring programmes, target of opportunity observations and coordinated observations with other observatories. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned submillimeter observatories., Comment: 47 pages, 12 figures. For further details on AtLAST see https://atlast.uio.no

Theories formulated in the arena of teleparallel geometries are generically plagued by ghost-like instabilities or other pathologies that are ultimately caused by the breaking of some symmetries. In this work, we construct a class of ghost-free theories based on a symmetry under Transverse Diffeomorphisms that is naturally realised in symmetric teleparallelism. We explicitly show their equivalence to a family of theories with an extra scalar field plus a global degree of freedom and how Horndeski theories and healthy couplings to matter fields can be readily accommodated., Comment: 11 pages, no figures

Differentially private stochastic gradient descent (DP-SGD) is the standard algorithm for training machine learning models under differential privacy (DP). The most common DP-SGD privacy accountants rely on Poisson subsampling for ensuring the theoretical DP guarantees. Implementing computationally efficient DP-SGD with Poisson subsampling is not trivial, which leads to many implementations ignoring this requirement. We conduct a comprehensive empirical study to quantify the computational cost of training deep learning models under DP given the requirement of Poisson subsampling, by re-implementing efficient methods using Poisson subsampling and benchmarking them. We find that using the naive implementation DP-SGD with Opacus in PyTorch has between 2.6 and 8 times lower throughput of processed training examples per second than SGD. However, efficient gradient clipping implementations with e.g. Ghost Clipping can roughly halve this cost. We propose alternative computationally efficient ways of implementing DP-SGD with JAX that are using Poisson subsampling and achieve only around 1.2 times lower throughput than SGD based on PyTorch. We highlight important implementation considerations with JAX. Finally, we study the scaling behaviour using up to 80 GPUs and find that DP-SGD scales better than SGD. We share our re-implementations using Poisson subsampling at https://github.com/DPBayes/Towards-Efficient-Scalable-Training-DP-DL., Comment: 17 pages, 12 figures

Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by their actuation through position commands, face issues of excessive contact forces due to their inability to adapt to contact with the environment, potentially causing damage. While compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors, they are hampered by the need for fine-tuning task-specific controller parameters. Learning from Demonstrations (LfD) offers an intuitive alternative, allowing robots to learn manipulations through observed actions. In this work, we introduce a novel system to enhance the teaching of dexterous, contact-rich manipulations to rigid robots. Our system is twofold: firstly, it incorporates a teleoperation interface utilizing Virtual Reality (VR) controllers, designed to provide an intuitive and cost-effective method for task demonstration with haptic feedback. Secondly, we present Comp-ACT (Compliance Control via Action Chunking with Transformers), a method that leverages the demonstrations to learn variable compliance control from a few demonstrations. Our methods have been validated across various complex contact-rich manipulation tasks using single-arm and bimanual robot setups in simulated and real-world environments, demonstrating the effectiveness of our system in teaching robots dexterous manipulations with enhanced adaptability and safety. Code available at: https://github.com/omron-sinicx/CompACT, Comment: Accepted to IROS 2024

The knowledge of scintillation quenching of $\alpha$-particles plays a paramount role in understanding $\alpha$-induced backgrounds and improving the sensitivity of liquid argon-based direct detection of dark matter experiments. We performed a relative measurement of scintillation quenching in the MeV energy region using radioactive isotopes ($^{222}$Rn, $^{218}$Po and $^{214}$Po isotopes) present in trace amounts in the DEAP-3600 detector and quantified the uncertainty of extrapolating the quenching factor to the low-energy region., Comment: 9 pages, 6 figures (added 1 figure, revised 3 figures), 2 tables, revised sections 3, 4, 5. Accepted in Eur. Phys. J. C

Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume parametric responses, such as Gaussian or Poisson distributions. When these assumptions fail, such models lead to bad predictions and poorly calibrated uncertainty. In this paper, we propose Treeffuser, an easy-to-use method for probabilistic prediction on tabular data. The idea is to learn a conditional diffusion model where the score function is estimated using gradient-boosted trees. The conditional diffusion model makes Treeffuser flexible and non-parametric, while the gradient-boosted trees make it robust and easy to train on CPUs. Treeffuser learns well-calibrated predictive distributions and can handle a wide range of regression tasks -- including those with multivariate, multimodal, and skewed responses. We study Treeffuser on synthetic and real data and show that it outperforms existing methods, providing better calibrated probabilistic predictions. We further demonstrate its versatility with an application to inventory allocation under uncertainty using sales data from Walmart. We implement Treeffuser in https://github.com/blei-lab/treeffuser., Comment: NeurIPS 2024

This paper presents a method for estimating the hallucination rate for in-context learning (ICL) with generative AI. In ICL, a conditional generative model (CGM) is prompted with a dataset and a prediction question and asked to generate a response. One interpretation of ICL assumes that the CGM computes the posterior predictive of an unknown Bayesian model, which implicitly defines a joint distribution over observable datasets and latent mechanisms. This joint distribution factorizes into two components: the model prior over mechanisms and the model likelihood of datasets given a mechanism. With this perspective, we define a hallucination as a generated response to the prediction question with low model likelihood given the mechanism. We develop a new method that takes an ICL problem and estimates the probability that a CGM will generate a hallucination. Our method only requires generating prediction questions and responses from the CGM and evaluating its response log probability. We empirically evaluate our method using large language models for synthetic regression and natural language ICL tasks.

We present a controller for a power electronic system composed of a fuel cell (FC) connected to a boost converter which feeds a resistive load. The controller aims to regulate the output voltage of the converter regardless of the uncertainty of the load. Leveraging the monotonicity feature of the fuel cell polarization curve we prove that the nonlinear system can be controlled by means of a passivity-based proportional-integral approach. We afterward extend the result to an adaptive version, allowing the controller to deal with parameter uncertainties, such as inductor parasitic resistance, load, and FC polarization curve parameters. This adaptive design is based on an indirect control approach with online parameter identification performed by a ``hybrid'' estimator which combines two techniques: the gradient-descent and immersion-and-invariance algorithms. The overall system is proved to be stable with the output voltage regulated to its reference. Experimental results validate our proposal under two real-life scenarios: pulsating load and output voltage reference changes., Comment: 11 pages, 8 Figs

We introduce a machine learning (ML) supervised model function that is inspired by the variational principle of physics. This ML hypothesis evolutionary method, termed ML-Omega, allows us to go from data to differential equation(s) underlying the physical (chemical, engineering, etc.) phenomena the data are derived from. The fundamental equations of physics can be derived from this ML-Omega evolutionary method when provided the proper training data. By training the ML-Omega model function with only three hydrogen-like atom energies, the method can find Schr\"odinger's exact functional and, from it, Schr\"odinger's fundamental equation. Then, in the field of density functional theory (DFT), when the model function is trained with the energies from the known Thomas-Fermi (TF) formula E = -0.7687Z^7/3, it correctly finds the exact TF functional. Finally, the method is applied to find a local orbital-free (OF) functional expression of the independent electron kinetic energy functional Ts based on the gamma-TF-lambda-vW model. By considering the theoretical energies of only 5 atoms (He, Be, Ne, Mg, Ar) as the training set, the evolutionary ML-Omega method finds an ML-Omega-OF-DFT local Ts functional (gamma-TF-lambda-vW (0.964, 1/4)) that outperforms all the OF- DFT functionals of a representative group. Moreover, our ML-Omega-OF functional overcomes the LDA's and some local GGA-DFT's functionals' difficulty to describe the stretched bond region at the correct spin configuration of diatomic molecules. Although our evolutionary ML-Omega model function can work without an explicit prior-form functional, by using the techniques of symbolic regression, in this work we exploit prior-form functional expressions to make the training process faster in the example problems presented here.

We explore long-lived particle (LLP) searches using non-pointing photons at the LHC as a probe for sterile-to-sterile and active-to-sterile transition magnetic dipole moments of sterile neutrinos. We consider heavy sterile neutrinos with masses ranging from a few~GeV to several hundreds of GeV. We discuss transition magnetic dipole moments using the Standard Model effective field theory and low-energy effective field theory extended by sterile neutrinos ($N_R$SMEFT and $N_R$LEFT) and also provide a simplified UV-complete model example. LLP searches at the LHC using non-pointing photons will probe sterile-to-sterile dipole moments two orders of magnitude below the current best constraints from LEP, while an unprecedented sensitivity to sterile neutrino mass of about 700 GeV is expected for active-to-sterile dipole moments. For the UV model example with one-loop transition magnetic moments, the searches for charged lepton flavour violating processes in synergy with LLP searches at the LHC can probe new physics at several TeV mass scales and provide valuable insights into the lepton flavour structure of new physics couplings., Comment: New version matches the journal published version

Federated learning (FL) is a novel collaborative machine learning framework designed to preserve privacy while enabling the creation of robust models. This paradigm addresses a growing need for data security by allowing multiple participants to contribute to a model without exposing their individual datasets. A pivotal issue within this framework, however, concerns the fair and accurate attribution of contributions from various participants to the creation of the joint global model. Incorrect contribution distribution can erode trust among participants, result in inequitable compensation, and ultimately diminish the willingness of parties to engage or actively contribute to the federation. While several methods for remunerating participants have been proposed, little attention was given to the analysis of the stability of these methods when evaluating contributions, which is critical to ensure the long-term viability and fairness of FL systems. In this paper, we analyse this stability through the calculation of contributions by gradient-based model reconstruction techniques with Shapley values. Our investigation reveals that Shapley values fail to reflect baseline contributions, especially when employing different aggregation techniques. To address this issue, we extend on established aggregation techniques by introducing FedRandom, which is designed to sample contributions in a more equitable and distributed manner. We demonstrate that this approach not only serves as a viable aggregation technique but also significantly improves the accuracy of contribution assessment compared to traditional methods. Our results suggest that FedRandom enhances the overall fairness and stability of the federated learning system, making it a superior choice for federations with limited number of participants.