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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: 33 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: under second revision 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 major drawback of DP-SGD is the drop in utility which prior work has comprehensively studied. However, in practice another major drawback that hinders the large-scale deployment is the significantly higher computational cost. We conduct a comprehensive empirical study to quantify the computational cost of training deep learning models under DP and benchmark methods that aim at reducing the cost. Among these are more efficient implementations of DP-SGD and training with lower precision. Finally, we study the scaling behaviour using up to 80 GPUs., Comment: 15 pages, 12 figures, Accepted to the Workshop on Advancing Neural Network Training at International Conference on Machine Learning (WANT@ICML 2024)

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: Submitted to 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. % , as well as categorical predictors and missing data 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 \href{https://github.com/blei-lab/treeffuser}{https://github.com/blei-lab/treeffuser}.

This work is about 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 asked to make a prediction based on that dataset. The Bayesian interpretation of ICL assumes that the CGM is calculating a posterior predictive distribution over an unknown Bayesian model of a latent parameter and data. With this perspective, we define a \textit{hallucination} as a generated prediction that has low-probability under the true latent parameter. We develop a new method that takes an ICL problem -- that is, a CGM, a dataset, and a prediction question -- and estimates the probability that a CGM will generate a hallucination. Our method only requires generating queries and responses from the model and evaluating its response log probability. We empirically evaluate our method on synthetic regression and natural language ICL tasks using large language models.

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.

Context. Dust polarization observations of the massive protocluster G31.41+0.31 carried out at ~1'' (~3750 au) resolution with the SMA at 870 microm have revealed one of the clearest examples to date of an hourglass-shaped magnetic field morphology in the high-massregime. Additionally, ~0.24'' (~900 au) resolution observations with ALMA at 1.3 mm have confirmed these results. The next step is to investigate whether the magnetic field maintains its hourglass-shaped morphology down to circumstellar scales. Aims. To study the magnetic field morphology toward the four (proto)stars A, B, C, and D contained in G31.41+0.31 and examine whether the self-similarity observed at core scales (1'' and 0.24'' resolution) still holds at circumstellar scales, we carried out ALMA observations of the polarized dust continuum emission at 1.3 mm and 3.1 mm at an angular resolution of ~0.068'' (~250 au), sufficient to resolve the envelope emission of the embedded protostars. Methods. We used ALMA to perform full polarization observations at 233 GHz (Band 6) and 97.5 GHz (Band 3) with a synthesized beam of 0.072'' x 0.064''. We carried out polarization observations at two different wavelengths to confirm that the polarization traces magnetically aligned dust grains and is not due to dust self-scattering. Results. The polarized emission and the direction of the magnetic field obtained at the two wavelengths are basically the same, except for an area between the embedded sources C and B. In such an area, the emission at 1.3 mm could be optically thick and affected by dichroic extinction. ..., Comment: 15 pages, 15 figures, Accepted for publication in A&A

We revisit certain localised variants of the Bennett-Carbery-Tao multilinear restriction theorem, recently proved by Bejenaru. We give a new proof of Bejenaru's theorem, relating the estimates to the theory of Kakeya-Brascamp-Lieb inequalities. Moreover, the new proof allows for a substantial generalisation, exploiting the full power of the Kakeya-Brascamp-Lieb theory.

Cooking robots can enhance the home experience by reducing the burden of daily chores. However, these robots must perform their tasks dexterously and safely in shared human environments, especially when handling dangerous tools such as kitchen knives. This study focuses on enabling a robot to autonomously and safely learn food-cutting tasks. More specifically, our goal is to enable a collaborative robot or industrial robot arm to perform food-slicing tasks by adapting to varying material properties using compliance control. Our approach involves using Reinforcement Learning (RL) to train a robot to compliantly manipulate a knife, by reducing the contact forces exerted by the food items and by the cutting board. However, training the robot in the real world can be inefficient, and dangerous, and result in a lot of food waste. Therefore, we proposed SliceIt!, a framework for safely and efficiently learning robot food-slicing tasks in simulation. Following a real2sim2real approach, our framework consists of collecting a few real food slicing data, calibrating our dual simulation environment (a high-fidelity cutting simulator and a robotic simulator), learning compliant control policies on the calibrated simulation environment, and finally, deploying the policies on the real robot., Comment: Accepted to ICRA 2024

The goal of this work is to scrutinise the surface brightness fluctuation (SBF) calculation methodology. We analysed the SBF derivation procedure, measured the accuracy of the fitted SBF under controlled conditions, retrieved the uncertainty associated with the variability of a system that is inherently stochastic, and studied the SBF reliability under a wide range of conditions. Additionally, we address the possibility of an SBF gradient detection. We also examine the problems related with biased measurements of the SBF and low luminosity sources. All of this information allows us to put forward guidelines to ensure a valid SBF retrieval. To perform all these experiments, we carried out Monte Carlo simulations of mock galaxies as an ideal laboratory. Knowing its underlying properties, we attempted to retrieve SBFs under different conditions. We show how the SBF uncertainty can be obtained and we present a collection of qualitative recommendations for a safe SBF retrieval: it is important to model the instrumental noise, rather than fitting it; the target galaxies must be observed under appropriate observational conditions; in a traditional SBF derivation, one should avoid pixels with fluxes lower than ten times the SBF estimate to prevent biased results. We offer our computational implementation in the form of a simple code designed to estimate the uncertainty of the SBF measurement. This code can be used to predict the quality of future observations or to evaluate the reliability of those already conducted., Comment: 25 pages, 14 figures, 5 tables, accepted by A&A

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup., Comment: Project website: https://droid-dataset.github.io/

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

Cosmological models featuring an elastic interaction in the dark sector at linear order has been shown to provide a promising scenario for alleviating the $\sigma_8$ tension. A natural question for these scenarios is whether there could be a degeneracy between the interaction and massive neutrinos that also contribute to erasing structures at late times. In this work we investigate the presence of such a degeneracy and show that the two effects do not exhibit strong correlations.

Most stars, including the Sun, are born in rich stellar clusters containing massive stars. Therefore, the study of the chemical reservoir of massive star-forming regions is crucial to understand the basic chemical ingredients available at the dawn of planetary systems. We present a detailed study of the molecular inventory of the hot molecular core G31.41+0.31 from the project GUAPOS (G31.41+0.31 Unbiased ALMA sPectral Observational Survey). We analyze 34 species for the first time plus 20 species analyzed in previous GUAPOS works, including oxygen, nitrogen, sulfur, phosphorus, and chlorine species. We compare the abundances derived in G31.41+0.31 with those observed in other chemically-rich sources that represent the initial and last stages of the formation of stars and planets: the hot corino in the Solar-like protostar IRAS 16293-2422 B, and the comets 67P/Churyumov-Gerasimenko and 46P/Wirtanen. The comparative analysis reveals that the chemical feedstock of the two star-forming regions are similar. The abundances of oxygen- and nitrogen-bearing molecules exhibit a good correlation for all pair of sources, including the two comets, suggesting a chemical heritage of these species during the process of star formation, and hence an early phase formation of the molecules. However, sulfur- and phosphorus-bearing species present worse correlations, being more abundant in comets. This suggests that while sulfur- and phosphorus-bearing species are predominantly trapped on the surface of icy grains in the hot close surroundings of protostars, they could be more easily released into gas phase in comets, allowing their cosmic abundances to be almost recovered., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS)

As we learn more about the multi-scale interstellar medium (ISM) of our Galaxy, we develop a greater understanding for the complex relationships between the large-scale diffuse gas and dust in Giant Molecular Clouds (GMCs), how it moves, how it is affected by the nearby massive stars, and which portions of those GMCs eventually collapse into star forming regions. The complex interactions of those gas, dust and stellar populations form what has come to be known as the ecology of our Galaxy. Because we are deeply embedded in the plane of our Galaxy, it takes up a significant fraction of the sky, with complex dust lanes scattered throughout the optically recognisable bands of the Milky Way. These bands become bright at (sub-)millimetre wavelengths, where we can study dust thermal emission and the chemical and kinematic signatures of the gas. To properly study such large-scale environments, requires deep, large area surveys that are not possible with current facilities. Moreover, where stars form, so too do planetary systems, growing from the dust and gas in circumstellar discs, to planets and planetesimal belts. Understanding the evolution of these belts requires deep imaging capable of studying belts around young stellar objects to Kuiper belt analogues around the nearest stars. Here we present a plan for observing the Galactic Plane and circumstellar environments to quantify the physical structure, the magnetic fields, the dynamics, chemistry, star formation, and planetary system evolution of the galaxy in which we live with AtLAST; a concept for a new, 50m single-dish sub-mm telescope with a large field of view which is the only type of facility that will allow us to observe our Galaxy deeply and widely enough to make a leap forward in our understanding of our local ecology., Comment: 27 pages, submitted to Open Research Europe as part of the AtLAST collection: https://open-research-europe.ec.europa.eu/collections/atlast/about

Let $p$ be a prime number and suppose that every maximal subgroup of a finite group is either $p$-nilpotent or has prime index. Such group need not be $p$-solvable, and we study its structure by proving that only one nonabelian simple group of order divisible by $p$, which belongs to the family ${\rm PSL}_n(q)$, can be involved in it. For $p=2$, we specify more, and in fact, such simple group must be isomorphic to ${\rm PSL}_2({r^a})$ for certain values of the prime $r$ and the parameter $a$.

This study tackles the representative yet challenging contact-rich peg-in-hole task of robotic assembly, using a soft wrist that can operate more safely and tolerate lower-frequency control signals than a rigid one. Previous studies often use a fully observable formulation, requiring external setups or estimators for the peg-to-hole pose. In contrast, we use a partially observable formulation and deep reinforcement learning from demonstrations to learn a memory-based agent that acts purely on haptic and proprioceptive signals. Moreover, previous works do not incorporate potential domain symmetry and thus must search for solutions in a bigger space. Instead, we propose to leverage the symmetry for sample efficiency by augmenting the training data and constructing auxiliary losses to force the agent to adhere to the symmetry. Results in simulation with five different symmetric peg shapes show that our proposed agent can be comparable to or even outperform a state-based agent. In particular, the sample efficiency also allows us to learn directly on the real robot within 3 hours., Comment: Accepted at ICRA-2024