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This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

In this work, a novel deep neural network, designed to enhance the efficiency and effectiveness of unsupervised sound anomaly detection, is presented. The proposed model exploits an attention module and separable convolutions to identify salient time-frequency patterns in audio data to discriminate between normal and anomalous sounds with reduced computational complexity. The approach is validated through extensive experiments using the Task 2 dataset of the DCASE 2020 challenge. Results demonstrate superior performance in terms of anomaly detection accuracy while having fewer parameters than state-of-the-art methods. Implementation details, code, and pre-trained models are available in https://github.com/michaelneri/unsupervised-audio-anomaly-detection., Comment: Accepted for publication in IEEE Sensors Letters. 4 pages, 4 figures

The Small and Large Magellanic Clouds are the only galaxies outside our own in which radio pulsars have been discovered to date. The sensitivity of the MeerKAT radio interferometer offers an opportunity to search for a population of more distant extragalactic pulsars. The TRAPUM (TRansients And PUlsars with MeerKAT) collaboration has performed a radio-domain search for pulsars and transients in the dwarf star-forming galaxies Sextans A and B, situated at the edge of the local group 1.4 Mpc away. We conducted three 2-hour multi-beam observations at L-band (856-1712 MHz) with the full array of MeerKAT. No pulsars were found down to a radio pseudo-luminosity upper limit of 7.9$\pm$0.4 Jy kpc$^{2}$ at 1400 MHz, which is 28 times more sensitive than the previous limit from the Murriyang telescope. This luminosity is 30 per cent greater than that of the brightest known radio pulsar and sets a cut-off on the luminosity distributions of the entire Sextans A and B galaxies for unobscured radio pulsars beamed in our direction. A Fast Radio Burst was detected in one of the Sextans A observations at a Dispersion Measure (DM) of 737 pc cm$^{-3}$. We believe this is a background event not associated with the dwarf galaxy due to its large DM and its S/N being strongest in the wide-field incoherent beam of MeerKAT., Comment: 11 pages, 9 figures, 5 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

We prove new stability results for a class of regular Gabor frames $G(h; a,b )$ subject to frequency-dependent timing jitter under various conditions on the window function $h$.

This paper investigates the existence of a separation principle between model identification and control design in the context of model predictive control. First, we elucidate that the separation principle holds asymptotically in the number of data in a Fisherian setting, and universally in a Bayesian setting. Then, by formulating model predictive control within a Gaussian regression framework, we describe how the Bayesian separation principle can be used to derive explicit, uncertainty-aware expressions for the control cost and optimal input sequence, thereby bridging direct and indirect data-driven approaches., Comment: 13 pages, 1 figure

This report describes our proposed solution for the second AI Olympics competition held at IROS 2024. Our solution is based on a recent Model-Based Reinforcement Learning algorithm named MC-PILCO. Besides briefly reviewing the algorithm, we discuss the most critical aspects of the MC-PILCO implementation in the tasks at hand.

In the following report, we describe the solution we propose for the AI Olympics competition held at IROS 2024. Our solution is based on a Model-free Deep Reinforcement Learning approach combined with an evolutionary strategy. We will briefly describe the algorithms that have been used and then provide details of the approach, Comment: Added Sec 9 after testing on real robot

The Large Magellanic Cloud (LMC) presents a unique environment for pulsar population studies due to its distinct star formation characteristics and proximity to the Milky Way. As part of the TRAPUM (TRAnsients and PUlsars with MeerKAT) Large Survey Project, we are using the core array of the MeerKAT radio telescope (MeerKAT) to conduct a targeted search of the LMC for radio pulsars at L-band frequencies, 856-1712$\,$MHz. The excellent sensitivity of MeerKAT, coupled with a 2-hour integration time, makes the survey 3 times more sensitive than previous LMC radio pulsar surveys. We report the results from the initial four survey pointings which has resulted in the discovery of seven new radio pulsars, increasing the LMC radio pulsar population by 30 per cent. The pulse periods of these new pulsars range from 278 to 1690$\,$ms, and the highest dispersion measure is 254.20$\,$pc$\,$cm$^{-3}$. We searched for, but did not find any significant pulsed radio emission in a beam centred on the SN$\,$1987A remnant, establishing an upper limit of 6.3$\,{\mu}$Jy on its minimum flux density at 1400$\,$MHz., Comment: 12 pages, 4 figures, 4 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

We report new radio timing solutions from a three-year observing campaign conducted with the MeerKAT and Murriyang telescopes for nine Small Magellanic Cloud pulsars, increasing the number of characterised rotation-powered extragalactic pulsars by 40 per cent. We can infer from our determined parameters that the pulsars are seemingly all isolated, that six are ordinary pulsars, and that three of the recent MeerKAT discoveries have a young characteristic age of under 100 kyr and have undergone a spin-up glitch. Two of the sources, PSRs J0040$-$7337 and J0048$-$7317, are energetic young pulsars with spin-down luminosities of the order of 10$^{36}$ erg s$^{-1}$. They both experienced a large glitch, with a change in frequency of about 30 $\mu$Hz, and a frequency derivative change of order $-10^{-14}$ Hz s$^{-1}$. These glitches, the inferred glitch rate, and the properties of these pulsars (including potentially high inter-glitch braking indices) suggest these neutron stars might be Vela-like repeating glitchers and should be closely monitored in the future. The position and energetics of PSR J0048$-$7317 confirm it is powering a new Pulsar Wind Nebula (PWN) detected as a radio continuum source; and similarly the association of PSR J0040$-$7337 with the PWN of Supernova Remnant (SNR) DEM S5 (for which we present a new Chandra image) is strengthened. Finally, PSR J0040$-$7335 is also contained within the same SNR but is a chance superposition. It has also been seen to glitch with a change of frequency of $10^{-2}$ $\mu$Hz. This work more than doubles the characterised population of SMC radio pulsars., Comment: 20 pages, 13 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society

The Sculptor Galaxy (NGC 253), located in the Southern Hemisphere, far off the Galactic Plane, has a relatively high star-formation rate of about 7 M$_{\odot}$ yr$^{-1}$ and hosts a young and bright stellar population, including several super star clusters and supernova remnants. It is also the first galaxy, apart from the Milky Way Galaxy to be associated with two giant magnetar flares. As such, it is a potential host of pulsars and/or fast radio bursts in the nearby Universe. The instantaneous sensitivity and multibeam sky coverage offered by MeerKAT therefore make it a favourable target. We searched for pulsars, radio-emitting magnetars, and fast radio bursts in NGC 253 as part of the TRAPUM large survey project with MeerKAT. We did not find any pulsars during a four-hour observation, and derive a flux density limit of 4.4 $\mu$Jy at 1400 MHz, limiting the pseudo-luminosity of the brightest putative pulsar in this galaxy to 54 Jy kpc$^2$. Assuming universality of pulsar populations between galaxies, we estimate that detecting a pulsar as bright as this limit requires NGC 253 to contain a pulsar population of $\gtrsim$20 000. We also did not detect any single pulses and our single pulse search flux density limit is 62 mJy at 1284 MHz. Our search is sensitive enough to have detected any fast radio bursts and radio emission similar to the brighter pulses seen from the magnetar SGR J1935+2154 if they had occurred during our observation., Comment: 6 pages, 3 figures, added citation in section 3, corrected values in section 4, published in MNRAS

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

In the symposium contributions we discuss research in physics education and the consequences of its results for physics teaching. The symposium presents four different aspects of physics teaching and learning, but all of them have research-based problem analysis in common. The problems analysed cover different aspects of the physics teaching-learning process. Innovative aspects such as the effect on learning of the integration of engineering projects in the science teaching process, the influence on the learning process of conceptions about science and attitudes, and aspects related to teaching contents and students' learning difficulties. Its conclusions are not merely intuitive proposals based on teaching experience, but on a careful planning of data collection, analysis of results and empirical basis, Comment: Symposium Organized by GTG Physics Education Research at University-PERU

We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for nonparametric, robust, and data-efficient density estimation. BMTI estimates the logarithm of the density by initially computing log-density differences between neighbouring data points. Subsequently, such differences are integrated, weighted by their associated uncertainties, using a maximum-likelihood formulation. This procedure can be seen as an extension to a multidimensional setting of the thermodynamic integration, a technique developed in statistical physics. The method leverages the manifold hypothesis, estimating quantities within the intrinsic data manifold without defining an explicit coordinate map. It does not rely on any binning or space partitioning, but rather on the construction of a neighbourhood graph based on an adaptive bandwidth selection procedure. BMTI mitigates the limitations commonly associated with traditional nonparametric density estimators, effectively reconstructing smooth profiles even in high-dimensional embedding spaces. The method is tested on a variety of complex synthetic high-dimensional datasets, where it is shown to outperform traditional estimators, and is benchmarked on realistic datasets from the chemical physics literature.

We provide a primal-dual framework for randomized approximation algorithms utilizing semidefinite programming (SDP) relaxations. Our framework pairs a continuum of APX-complete problems including MaxCut, Max2Sat, MaxDicut, and more generally, Max-Boolean Constraint Satisfaction and MaxQ (maximization of a positive semidefinite quadratic form over the hypercube) with new APX-complete problems which are stated as convex optimization problems with exponentially many variables. These new dual counterparts, based on what we call Grothendieck covers, range from fractional cut covering problems (for MaxCut) to tensor sign covering problems (for MaxQ). For each of these problem pairs, our framework transforms the randomized approximation algorithms with the best known approximation factors for the primal problems to randomized approximation algorithms for their dual counterparts with reciprocal approximation factors which are tight with respect to the Unique Games Conjecture. For each APX-complete pair, our algorithms solve a single SDP relaxation and generate feasible solutions for both problems which also provide approximate optimality certificates for each other. Our work utilizes techniques from areas of randomized approximation algorithms, convex optimization, spectral sparsification, as well as Chernoff-type concentration results for random matrices.

We present the first successful trapping of single erbium atoms in an array of optical tweezers. Using a single narrow-line optical transition, we achieve deep cooling for direct tweezer loading, pairwise ejection, and continous imaging without additional recoil suppression techniques. Our tweezer wavelength choice enables us to reach the magic trapping condition by tuning the ellipticity of the trapping light. Additionally, we implement an ultrafast high-fidelity fluorescence imaging scheme using a broad transition, allowing time-resolved study of the tweezer population dynamics from many to single atoms during light-assisted collisions. In particular, we extract a pair-ejection rate that qualitatively agrees with the semiclassical predictions by the Gallagher-Pritchard model. This work represents a promising starting point for the exploration of erbium as a powerful resource for quantum simulation in optical tweezers.

The ability to achieve precise and smooth trajectory tracking is crucial for ensuring the successful execution of various tasks involving robotic manipulators. State-of-the-art techniques require accurate mathematical models of the robot dynamics, and robustness to model uncertainties is achieved by relying on precise bounds on the model mismatch. In this paper, we propose a novel adaptive robust feedback linearization scheme able to compensate for model uncertainties without any a-priori knowledge on them, and we provide a theoretical proof of convergence under mild assumptions. We evaluate the method on a simulated RR robot. First, we consider a nominal model with known model mismatch, which allows us to compare our strategy with state-of-the-art uncertainty-aware methods. Second, we implement the proposed control law in combination with a learned model, for which uncertainty bounds are not available. Results show that our method leads to performance comparable to uncertainty-aware methods while requiring less prior knowledge.

The sensitivity of the MeerKAT radio interferometer is an opportunity to probe deeper into the population of rare and faint extragalactic pulsars. The TRAPUM (TRAnsients and PUlsars with MeerKAT) collaboration has conducted a radio-domain search for accelerated pulsars and transients in the Small Magellanic Cloud (SMC). This partially targeted survey, performed at L-band (856-1712 MHz) with the core array of the MeerKAT telescope in 2-h integrations, is twice as sensitive as the latest SMC radio pulsar survey. We report the discovery of seven new SMC pulsars, doubling this galaxy's radio pulsar population and increasing the total extragalactic population by nearly a quarter. We also carried out a search for accelerated millisecond pulsars in the SMC Globular Cluster NGC 121 using the full array of MeerKAT. This improved the previous upper limit on pulsed radio emission from this cluster by a factor of six. Our discoveries reveal the first radio pulsar-PWN systems in the SMC, with only one such system previously known outside our galaxy (the "Crab pulsar twin" in the Large Magellanic Cloud, PSR J0540$-$6919). We associate the 59 ms pulsar discovery PSR J0040$-$7337, now the fastest spinning radio pulsar in the SMC, with the bow-shock Pulsar Wind Nebula (PWN) of Supernova Remnant DEM S5. We also present a new young pulsar with a 79 ms period, PSR J0048$-$7317, in a PWN recently discovered in a MeerKAT radio continuum image. Using the multi-beam capability of MeerKAT, we localised our pulsar discoveries, and two previous Murriyang discoveries, to a positional uncertainty of a few arcseconds., Comment: 32 pages, 14 figures, 10 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

Multi-agent systems are increasingly widespread in a range of application domains, with optimization and learning underpinning many of the tasks that arise in this context. Different approaches have been proposed to enable the cooperative solution of these optimization and learning problems, including first- and second-order methods, and dual (or Lagrangian) methods, all of which rely on consensus and message-passing. In this article we discuss these algorithms through the lens of non-expansive operator theory, providing a unifying perspective. We highlight the insights that this viewpoint delivers, and discuss how it can spark future original research.

We present the description and initial results of the TRAPUM (TRAnsients And PUlsars with MeerKAT) search for pulsars associated with supernova remnants (SNRs), pulsar wind nebulae and unidentified TeV emission. The list of sources to be targeted includes a large number of well-known candidate pulsar locations but also new candidate SNRs identified using a range of criteria. Using the 64-dish MeerKAT radio telescope, we use an interferometric beamforming technique to tile the potential pulsar locations with coherent beams which we search for radio pulsations, above a signal-to-noise of 9, down to an average flux density upper limit of 30 $\mu$Jy. This limit is target-dependent due to the contribution of the sky and nebula to the system temperature. Coherent beams are arranged to overlap at their 50 per cent power radius, so the sensitivity to pulsars is not degraded by more than this amount, though realistically averages around 65 per cent if every location in the beam is considered. We report the discovery of two new pulsars; PSR J1831$-$0941 is an adolescent pulsar likely to be the plerionic engine of the candidate PWN G20.0+0.0, and PSR J1818$-$1502 appears to be an old and faint pulsar that we serendipitously discovered near the centre of a SNR already hosting a compact central object. The survey holds importance for better understanding of neutron star birth rates and the energetics of young pulsars., Comment: 17 pages, 6 figures, 3 tables. Accepted for publication in Monthly Notices of the Royal Astronomical Society

The advent of Decentralized Physical Infrastructure Networks (DePIN) represents a shift in the digital infrastructure of today's Internet. While Centralized Service Providers (CSP) monopolize cloud computing, DePINs aim to enhance data sovereignty and confidentiality and increase resilience against a single point of failure. Due to the novelty of the emerging field of DePIN, this work focuses on the potential of DePINs to disrupt traditional centralized architectures by taking advantage of the Internet of Things (IoT) devices and crypto-economic design in combination with blockchains. This combination yields Acurast, a more distributed, resilient, and user-centric physical infrastructure deployment. Through comparative analysis with centralized systems, particularly in serverless computing contexts, this work seeks to lay the first steps in scientifically evaluating DePINs and quantitatively comparing them in terms of efficiency and effectiveness in real-world applications. The findings suggest DePINs' potential to (i) reduce trust assumptions and physically decentralized infrastructure, (ii) increase efficiency and performance simultaneously while improving the computation's (iii) confidentiality and verifiability.

Fourier matrices naturally appear in many applications and their stability is closely tied to performance guarantees of algorithms. The starting point of this article is a result that characterizes properties of an exponential system on a union of cubes in $\mathbb{R}^d$ in terms of a general class of Fourier matrices and their extreme singular values. This relationship is flexible in the sense that it holds for any dimension $d$, for many types of exponential systems (Riesz bases, Riesz sequences, or frames) and for Fourier matrices with an arbitrary number of rows and columns. From there, we prove new stability results for Fourier matrices by exploiting this connection and using powerful stability theorems for exponential systems. This paper provides a systematic exploration of this connection and suggests some natural open questions., Comment: 16 pages, 1 figure

Accurate dynamic models are crucial for many robotic applications. Traditional approaches to deriving these models are based on the application of Lagrangian or Newtonian mechanics. Although these methods provide a good insight into the physical behaviour of the system, they rely on the exact knowledge of parameters such as inertia, friction and joint flexibility. In addition, the system is often affected by uncertain and nonlinear effects, such as saturation and dead zones, which can be difficult to model. A popular alternative is the application of Machine Learning (ML) techniques - e.g., Neural Networks (NNs) - in the context of a "black-box" methodology. This paper reports on our experience with this approach for a real-life 6 degrees of freedom (DoF) manipulator. Specifically, we considered several NN architectures: single NN, multiple NNs, and cascade NN. We compared the performance of the system by using different policies for selecting the NN hyperparameters. Our experiments reveal that the best accuracy and performance are obtained by a cascade NN, in which we encode our prior physical knowledge about the dependencies between joints, complemented by an appropriate optimisation of the hyperparameters., Comment: 6 pages, 5 figures, submitted to CASE 2024

Distance estimation from audio plays a crucial role in various applications, such as acoustic scene analysis, sound source localization, and room modeling. Most studies predominantly center on employing a classification approach, where distances are discretized into distinct categories, enabling smoother model training and achieving higher accuracy but imposing restrictions on the precision of the obtained sound source position. Towards this direction, in this paper we propose a novel approach for continuous distance estimation from audio signals using a convolutional recurrent neural network with an attention module. The attention mechanism enables the model to focus on relevant temporal and spectral features, enhancing its ability to capture fine-grained distance-related information. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using audio recordings in controlled environments with three levels of realism (synthetic room impulse response, measured response with convolved speech, and real recordings) on four datasets (our synthetic dataset, QMULTIMIT, VoiceHome-2, and STARSS23). Experimental results show that the model achieves an absolute error of 0.11 meters in a noiseless synthetic scenario. Moreover, the results showed an absolute error of about 1.30 meters in the hybrid scenario. The algorithm's performance in the real scenario, where unpredictable environmental factors and noise are prevalent, yields an absolute error of approximately 0.50 meters. For reproducible research purposes we make model, code, and synthetic datasets available at https://github.com/michaelneri/audio-distance-estimation., Comment: Accepted for publication in IEEE/ACM Transactions on Audio, Speech, and Language Processing

Continuous control Deep Reinforcement Learning (RL) approaches are known to suffer from estimation biases, leading to suboptimal policies. This paper introduces innovative methods in RL, focusing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning. We design a Bias Exploiting (BE) mechanism to dynamically select the most advantageous estimation bias during training of the RL agent. Most State-of-the-art Deep RL algorithms can be equipped with the BE mechanism, without hindering performance or computational complexity. Our extensive experiments across various continuous control tasks demonstrate the effectiveness of our approaches. We show that RL algorithms equipped with this method can match or surpass their counterparts, particularly in environments where estimation biases significantly impact learning. The results underline the importance of bias exploitation in improving policy learning in RL.

We present new radio continuum images and a source catalogue from the MeerKAT survey in the direction of the Small Magellanic Cloud (SMC). The observations, at a central frequency of 1.3 GHz across a bandwidth of 0.8 GHz, encompass a field of view ~7 x 7 degrees and result in images with resolution of 8 arcsec. The median broad-band Stokes I image Root Mean Squared noise value is ~11 microJy/beam. The catalogue produced from these images contains 108,330 point sources and 517 compact extended sources. We also describe a UHF (544-1088 MHz) single pointing observation. We report the detection of a new confirmed Supernova Remnant (SNR) (MCSNR J0100-7211) with an X-ray magnetar at its centre and 10 new SNR candidates. This is in addition to the detection of 21 previously confirmed SNRs and two previously noted SNR candidates. Our new SNR candidates have typical surface brightness an order of magnitude below those previously known, and on the whole they are larger. The high sensitivity of the MeerKAT survey also enabled us to detect the bright end of the SMC Planetary Nebulae (PNe) sample - point-like radio emission is associated with 38 of 102 optically known PNe, of which 19 are new detections. Lastly, we present the detection of three foreground radio stars amidst 11 circularly polarised sources, and a few examples of morphologically interesting background radio galaxies from which the radio ring galaxy ESO 029-G034 may represent a new type of radio object., Comment: 31 pages, 27 figures

Tilted lattice potentials with periodic driving play a crucial role in the study of artificial gauge fields and topological phases with ultracold quantum gases. However, driving-induced heating and the growth of phonon modes restrict their use for probing interacting many-body states. Here, we experimentally investigate phonon modes and interaction-driven instabilities of superfluids in the lowest band of a shaken optical lattice. We identify stable and unstable parameter regions and provide a general resonance condition. In contrast to the high-frequency approximation of a Floquet description, we use the superfluids' micromotion to analyze the growth of phonon modes from slow to fast driving frequencies. Our observations enable the prediction of stable parameter regimes for quantum-simulation experiments aimed at studying driven systems with strong interactions over extended time scales., Comment: 12 pages, 9 figures

Mobile robotic systems are becoming increasingly popular. These systems are used in various indoor applications, raging from warehousing and manufacturing to test benches for assessment of advanced control strategies, such as artificial intelligence (AI)-based control solutions, just to name a few. Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF), and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator to expedite the learning phase and increase robustess to the simulation-to-reality gap. The paper also presents the formulation of a vehicle model along with an effective yet simple procedure for identifying tis paramters. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm. Experimental results confirm the ability of the FEKF to improve the estimate of the mobile robot's position. Furthermore, the effectiveness of the DRL path tracking strateguy is experimentally tested along manoeuvres not considered during training, showing also the ability of the AI-based solution to outpeform model-based control strategies and the demonstrator. The comparison with benchmraking controllers is quantitavely evalueted through a set of key performance indicators., Comment: Under review on IEEE Transactions