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We study the possibility of using the $LiteBIRD$ satellite $B$-mode survey to constrain models of inflation producing specific features in CMB angular power spectra. We explore a particular model example, i.e. spectator axion-SU(2) gauge field inflation. This model can source parity-violating gravitational waves from the amplification of gauge field fluctuations driven by a pseudoscalar "axionlike" field, rolling for a few e-folds during inflation. The sourced gravitational waves can exceed the vacuum contribution at reionization bump scales by about an order of magnitude and can be comparable to the vacuum contribution at recombination bump scales. We argue that a satellite mission with full sky coverage and access to the reionization bump scales is necessary to understand the origin of the primordial gravitational wave signal and distinguish among two production mechanisms: quantum vacuum fluctuations of spacetime and matter sources during inflation. We present the expected constraints on model parameters from $LiteBIRD$ satellite simulations, which complement and expand previous studies in the literature. We find that $LiteBIRD$ will be able to exclude with high significance standard single-field slow-roll models, such as the Starobinsky model, if the true model is the axion-SU(2) model with a feature at CMB scales. We further investigate the possibility of using the parity-violating signature of the model, such as the $TB$ and $EB$ angular power spectra, to disentangle it from the standard single-field slow-roll scenario. We find that most of the discriminating power of $LiteBIRD$ will reside in $BB$ angular power spectra rather than in $TB$ and $EB$ correlations., Comment: 22 pages, 13 figures. Submitted to JCAP

Sequential logic synthesis can provide better Power-Performance-Area (PPA) than combinational logic synthesis since it explores a larger solution space. As the gate cost in advanced technologies keeps rising, sequential logic synthesis provides a powerful alternative that is gaining momentum in the EDA community. In this work, we present a new scalable algorithm for don't-care-based sequential logic synthesis. Our new approach is based on sequential k-step induction and can apply both redundancy removal and resubstitution transformations under Sequential Observability Don't Cares (SODCs). Using SODC-based optimizations with induction is a challenging problem due to dependencies and alignment of don't cares among the base case and the inductive case. We propose a new approach utilizing the full power of SODCs without limiting the solution space. Our algorithm is implemented as part of an industrial tool and achieves 6.9% average area improvement after technology mapping when compared to state-of-the-art sequential synthesis methods. Moreover, all the new sequential optimizations can be verified using state-of-the-art sequential verification tools., Comment: Accepted for the conference Design, Automation and Test in Europe (DATE) 2024

Context: The measurement of the Yarkovsky effect on near-Earth asteroids (NEAs) is common practice in orbit determination today, and the number of detections will increase with the developments of new and more accurate telescopic surveys. However, the process of finding new detections and identifying spurious ones is not yet automated, and it often relies on personal judgment. Aims: We aim to introduce a more automated procedure that can search for NEA candidates to measure the Yarkovsky effect, and that can identify spurious detections. Methods: The expected semi-major axis drift on an NEA caused by the Yarkovsky effect was computed with a Monte Carlo method on a statistical model of the physical parameters of the asteroid that relies on the most recent NEA population models and data. The expected drift was used to select candidates in which the Yarkovsky effect might be detected, according to the current knowledge of their orbit and the length of their observational arc. Then, a nongravitational acceleration along the transverse direction was estimated through orbit determination for each candidate. If the detected acceleration was statistically significant, we performed a statistical test to determine whether it was compatible with the Yarkovsky effect model. Finally, we determined the dependence on an isolated tracklet. Results: Among the known NEAs, our procedure automatically found 348 detections of the Yarkovsky effect that were accepted. The results are overall compatible with the predicted trend with the the inverse of the diameter, and the procedure appears to be efficient in identifying and rejecting spurious detections. This algorithm is now adopted by the ESA NEO Coordination Centre to periodically update the catalogue of NEAs with a measurable Yarkovsky effect, and the results are automatically posted on the web portal., Comment: Accepted for publication on A&A

Significant nonradial, nongravitational accelerations with magnitudes incompatible with radiation-driven effects have been reported in seven small, photometrically inactive near-Earth objects. Two of these objects exhibit large transverse accelerations (i.e., within the orbital plane but orthogonal to the radial direction), and six exhibit significant out-of-plane accelerations. Here, we find that anisotropic outgassing resulting from differential heating on a nucleus with nonzero spin-pole obliquity, averaged over an eccentric orbit, can explain these accelerations for most of the objects. This balanced outgassing model depends on three parameters -- the spin pole orientation (R.A. and Dec.) and an acceleration magnitude. For these "dark comets" (excepting 2003 RM), we obtain parameter values that reproduce the observed nongravitational accelerations. We derive formulae for the component accelerations under certain assumptions for the acceleration scaling over heliocentric distance. Although we lack estimates of these objects' spin axes to confirm our values, this mechanism is nevertheless a plausible explanation for the observed accelerations, and produces accurate perturbations to the heliocentric motions of most of these objects. This model may also be applied to active objects outside of the dark comets group., Comment: 17 pages, 4 figures. Published in Icarus

Using the OSIRIS-REx mission and ground-based tracking data for the asteroid Bennu, we derive new constraints on fifth forces and ultralight dark matter. The bounds we obtain are strongest for mediator masses $m \sim 10^{-18} - 10^{-17}\,{\rm eV}$, where we currently achieve the tightest bounds. Our limits can be translated to a wide class of models leading to Yukawa-type fifth forces, and we demonstrate how they apply to $U(1)_B$ dark photons and baryon-coupled scalars. Our results demonstrate the potential of asteroid tracking in probing well-motivated extensions of the Standard Model and ultralight dark matter satisfying the fuzzy dark matter constraints., Comment: 1 figure, 7 pages plus references

Max-Plus Linear (MPL) systems are an algebraic formalism with practical applications in transportation networks, manufacturing and biological systems. In this paper, we investigate the problem of automatically analyzing the properties of MPL, taking into account both structural properties such as transient and cyclicity, and the open problem of user-defined temporal properties. We propose Time-Difference LTL (TDLTL), a logic that encompasses the delays between the discrete time events governed by an MPL system, and characterize the problem of model checking TDLTL over MPL. We first consider a framework based on the verification of infinite-state transition systems, and propose an approach based on an encoding into model checking. Then, we leverage the specific features of MPL systems to devise a highly optimized, combinational approach based on Satisfiability Modulo Theory (SMT). We experimentally evaluate the features of the proposed approaches on a large set of benchmarks. The results show that the proposed approach substantially outperforms the state of the art competitors in expressiveness and effectiveness, and demonstrate the superiority of the combinational approach over the reduction to model checking., Comment: 28 pages (including appendixes)

We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach is architectural, as we introduce an additional Bayesian network mapping edge features into discrete states to be used by the original model. In doing so, we are also able to build richer graph representations even in the absence of edge features, which is confirmed by the performance improvements on standard graph classification benchmarks. Moreover, we successfully test our proposal in a graph regression scenario where edge features are of fundamental importance, and we show that the learned edge representation provides substantial performance improvements against the original model on three link prediction tasks. By keeping the computational complexity linear in the number of edges, the proposed model is amenable to large-scale graph processing., Comment: Releasing pre-print version to comply with TAILOR project requirements

The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network management procedures. The objective of our study is to provide a thorough analysis of predictive latency within 5G networks by utilizing real-world network data that is accessible to mobile network operators (MNOs). In particular, (i) we present an analytical formulation of the user-plane latency as a Hypoexponential distribution, which is validated by means of a comparative analysis with empirical measurements, and (ii) we conduct experimental results of probabilistic regression, anomaly detection, and predictive forecasting leveraging on emerging domains in Machine Learning (ML), such as Bayesian Learning (BL) and Machine Learning on Graphs (GML). We test our predictive framework using data gathered from scenarios of vehicular mobility, dense-urban traffic, and social gathering events. Our results provide valuable insights into the efficacy of predictive algorithms in practical applications.

Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can leverage longer-range interactions between nodes is hindered by the issues of over-smoothing and over-squashing. In particular, the latter is attributed to the graph topology which guides the message-passing, causing a node representation to become insensitive to information contained at distant nodes. Many graph rewiring methods have been proposed to remedy or mitigate this problem. However, properly evaluating the benefits of these methods is made difficult by the coupling of over-squashing with other issues strictly related to model training, such as vanishing gradients. Therefore, we propose an evaluation setting based on message-passing models that do not require training to compute node and graph representations. We perform a systematic experimental comparison on real-world node and graph classification tasks, showing that rewiring the underlying graph rarely does confer a practical benefit for message-passing.

Node classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node's neighbourhood. While effective on graphs that exhibit a high ratio of intra-class edges, this approach poses challenges in the opposite case, i.e. heterophily, where nodes belonging to the same class are usually further apart. In graphs with a high degree of heterophily, the smoothed representations based on close neighbours computed by convolutional models are no longer effective. So far, architectural variations in message-passing models to reduce excessive smoothing or rewiring the input graph to improve longer-range message passing have been proposed. In this paper, we address the challenges of heterophilic graphs with Graph Echo State Network (GESN) for node classification. GESN is a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. Our experiments show that reservoir models are able to achieve better or comparable accuracy with respect to most fully trained deep models that implement ad hoc variations in the architectural bias or perform rewiring as a preprocessing step on the input graph, with an improvement in terms of efficiency/accuracy trade-off. Furthermore, our analysis shows that GESN is able to effectively encode the structural relationships of a graph node, by showing a correlation between iterations of the recursive embedding function and the distribution of shortest paths in a graph., Comment: 15 pages, 10 figures. arXiv admin note: text overlap with arXiv:2212.06538

Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building., Comment: 6 pages, 5 figures. Submitted to IFAC World Congress 2023

In this paper we give constructions for infinite sequences of finite non-linear locally recoverable codes $\mathcal C\subseteq \prod\limits^N_{i=1}\mathbb F_{q_i}$ over a product of finite fields arising from basis expansions in algebraic number fields. The codes in our sequences have increasing length and size, constant rate, fixed locality, and minimum distance going to infinity.

Let $m$ be a positive integer and $q$ be a prime power. For large finite base fields $\mathbb F_q$, we show that any curve can be used to produce a complete $m$-arc as long as some generic explicit geometric conditions on the curve are verified. To show the effectiveness of our theory, we derive complete $m$-arcs from hyperelliptic curves and from Artin-Schreier curves., Comment: Comments are welcome!

Increasing our knowledge of the orbits and compositions of Near-Earth Objects (NEOs) is important for a better understanding of the evolution of the Solar System and of life. The detection of serendipitous NEO appearances among the millions of archived exposures from large astronomical imaging surveys can provide a contribution which is complementary to NEO surveys. Using the AstroWISE information system, this work aims to assess the detectability rate, the achieved recovery rate and the quality of astrometry when data mining the ESO archive for the OmegaCAM wide-field imager at the VST. We developed an automatic pipeline that searches for the NEO appearances inside the AstroWISE environment. Throughout the recovery process, the pipeline uses several public web-tools to identify possible images that overlap with the position of NEOs, and acquires information on the NEOs predicted position and other properties (e.g., magnitude, rate and direction of motion) at the time of observations. We have recovered 196 appearances of NEOs from a set of 968 appearances predicted to be recoverable. It includes appearances for three NEOs which were on the impact risk list at that point. These appearances were well before their discovery. The subsequent risk assessment using the extracted astrometry removes these NEOs from the risk list. We estimate a detectability rate of 0.05 per NEO at an SNR>3 for NEOs in the OmegaCAM archive. Our automatic recovery rates are 40% and 20% for NEOs on the risk list and the full list, respectively. The achieved astrometric and photometric accuracy is on average 0.12 arcsec and 0.1 mag. These results show the high potential of the archival imaging data of the ground-based wide-field surveys as useful instruments for the search, (p)recovery and characterization of NEOs. Highly automated approaches, as possible using AstroWISE, make this undertaking feasible., Comment: 13 pages, 13 figures, accepted for publication in Astronomy & Astrophysics

The performance of a real fifth generation base station was studied by using a reverberation chamber as a real life propagating environment. Preliminary tests were conducted in order to define 5G base station operation conditions at mm-wave and emulated scenarios where reconfigurable intelligent surface(s) (RISs) will successively be tested. Measurements campaign was carried out under the H2020 European project RISE-6G and a collaboration program between TIM S.p.A., Nokia and Universita` Politecnica delle Marche., Comment: Accepted in EuCAP 2023

We report observations of nine MBCs or candidate MBCs, most of which were obtained when the targets were apparently inactive. We find effective nucleus radii (assuming albedos of p_V=0.05+/-0.02) of r_n=(0.24+/-0.05) km for 238P/Read, r_n=(0.9+/-0.2) km for 313P/Gibbs, r_n=(0.6+/-0.1) km for 324P/La Sagra, r_n=(1.0+/-0.2) km for 426P/PANSTARRS, r_n=(0.5+/-0.1) km for 427P/ATLAS, r_n<(0.3+/-0.1) km for P/2016 J1-A (PANSTARRS), r_n<(0.17+/-0.04) km for P/2016 J1-B (PANSTARRS), r_n<(0.5+/-0.2) km for P/2017 S9 (PANSTARRS), and r_n=(0.4+/-0.1) km for P/2019 A3 (PANSTARRS). We identify evidence of activity in observations of 238P in 2021, and find similar inferred activity onset times and net initial mass loss rates for 238P during perihelion approaches in 2010, 2016, and 2021. P/2016 J1-A and P/2016 J1-B are also found to be active in 2021 and 2022, making them collectively the tenth MBC confirmed to be recurrently active near perihelion and therefore likely to be exhibiting sublimation-driven activity. The nucleus of 313P is found to have colors of g'-r'=0.52+/-0.05 and r'-i'=0.22+/-0.07, consistent with 313P being a Lixiaohua family member. We also report non-detections of P/2015 X6 (PANSTARRS), where we conclude that its current nucleus size is likely below our detection limits (r_n<0.3 km). Lastly, we find that of 17 MBCs or candidate MBCs for which nucleus sizes (or inferred parent body sizes) have been estimated, >80% have r_n<1.0 km, pointing to an apparent physical preference toward small MBCs, where we suggest that YORP spin-up may play a significant role in triggering and/or facilitating MBC activity., Comment: 27 pages, 14 figures. Accepted for publication in The Planetary Science Journal

Infrared measurements of asteroids are crucial for the determination of physical and thermal properties of individual objects, and for the understanding of the small-body populations in the solar system as a whole. But standard radiometric methods can only be applied if the orbit of an object is known, hence its position at the time of the observation. We present MIRI observations of the outer-belt asteroid 10920 and an unknown object, detected in all 9 MIRI bands in close proximity to 10920. We developed a new method "STM-ORBIT" to interpret the multi-band measurements without knowing the object's true location. The method leads to a confirmation of radiometric size-albedo solution for 10920 and puts constraints on the asteroid's location and orbit in agreement with its true orbit. Groundbased lightcurve observations of 10920, combined with Gaia data, indicate a very elongated object (a/b >= 1.5), with a spin-pole at (l, b) = (178{\deg}, 81{\deg}), and a rotation period of 4.861191 h. A thermophysical study leads to a size of 14.5 - 16.5 km, a geometric albedo between 0.05 and 0.10, and a thermal inertia in the range 9 to 35 Jm-2s-0.5K-1. For the newly discovered MIRI object, the STM-ORBIT method revealed a size of 100-230 m. The new asteroid must be on a very low-inclination orbit and it was located in the inner main-belt region during JWST observations. A beaming parameter {\eta} larger than 1.0 would push the size even below 100 meter, a main-belt regime which escaped IR detections so far. These kind of MIRI observations can therefore contribute to formation and evolution studies via classical size-frequency studies which are currently limited to objects larger than about one kilometer in size. We estimate that MIRI frames with pointings close to the ecliptic and only short integration times of a few seconds will always include a few asteroids, most of them will be unknown objects., Comment: 17 pages, 10 figures, 4 tables, accepted for A&A publication on Nov 22, 2022

We report a statistically significant detection of nongravitational acceleration on the sub-kilometer near-Earth asteroid (523599) 2003 RM. Due to its orbit, 2003 RM experiences favorable observing apparitions every 5 years. Thus, since its discovery, 2003 RM has been extensively tracked with ground-based optical facilities in 2003, 2008, 2013, and 2018. We find that the observed plane-of-sky positions cannot be explained with a purely gravity-driven trajectory. Including a transverse nongravitational acceleration allows us to match all observational data, but its magnitude is inconsistent with perturbations typical of asteroids such as the Yarkovsky effect or solar radiation pressure. After ruling out that the orbital deviations are due to a close approach or collision with another asteroid, we hypothesize that this anomalous acceleration is caused by unseen cometary outgassing. A detailed search for evidence of cometary activity with archival and deep observations from Pan-STARRS and the VLT does not reveal any detectable dust production. However, the best-fitting H$_2$O sublimation model allows for brightening due to activity consistent with the scatter of the data. We estimate the production rate required for H$_2$O outgassing to power the acceleration, and find that, assuming a diameter of 300 m, 2003 RM would require Q(H$_2$O)$\sim10^{23}$ molec s$^{-1}$ at perihelion. We investigate the recent dynamical history of 2003 RM and find that the object most likely originated in the mid-to-outer main belt ($\sim86\%$) as opposed to from the Jupiter-family comet region ($\sim11\%$). Further observations, especially in the infrared, could shed light on the nature of this anomalous acceleration., Comment: 14 pages, 9 figures, 2 tables, accepted by the Planetary Science Journal

We report statistically significant detections of non-radial nongravitational accelerations based on astrometric data in the photometrically inactive objects 1998 KY$_{26}$, 2005 VL$_1$, 2016 NJ$_{33}$, 2010 VL$_{65}$, 2016 RH$_{120}$, and 2010 RF$_{12}$. The magnitudes of the nongravitational accelerations are greater than those typically induced by the Yarkovsky effect and there is no radiation-based, non-radial effect that can be so large. Therefore, we hypothesize that the accelerations are driven by outgassing, and calculate implied H$_2$O production rates for each object. We attempt to reconcile outgassing induced acceleration with the lack of visible comae or photometric activity via the absence of surface dust and low levels of gas production. Although these objects are small and some are rapidly rotating, surface cohesive forces are stronger than the rotational forces and rapid rotation alone cannot explain the lack of surface debris. It is possible that surface dust was removed previously, perhaps via outgassing activity that increased the rotation rates to their present day value. We calculate dust production rates of order $\sim10^{-4}$ g s$^{-1}$ in each object assuming that the nuclei are bare, within the upper limits of dust production from a sample stacked image of 1998 KY$_{26}$ of $\dot{M}_{\rm Dust}<0.2$ g s$^{-1}$. This production corresponds to brightness variations of order $\sim0.0025\%$, which are undetectable in extant photometric data. We assess the future observability of each of these targets, and find that the orbit of 1998 KY$_{26}$ -- which is also the target for the extended Hayabusa2 mission -- exhibits favorable viewing geometry before 2025., Comment: 21 pages, 9 figures, 3 tables, accepted for publication in the Planetary Science Journal

One popular technique to solve temporal planning problems consists in decoupling the causal decisions, demanding them to heuristic search, from temporal decisions, demanding them to a simple temporal network (STN) solver. In this architecture, one needs to check the consistency of a series of STNs that are related one another, therefore having methods to incrementally re-use previous computations and that avoid expensive memory duplication is of paramount importance. In this paper, we describe in detail how STNs are used in temporal planning, we identify a clear interface to support this use-case and we present an efficient data-structure implementing this interface that is both time- and memory-efficient. We show that our data structure, called \deltastn, is superior to other state-of-the-art approaches on temporal planning sequences of problems., Comment: V2: Fixed a typo in the algorithm pseudocode

Recent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring mechanisms have been proposed as preprocessing steps. Graph Echo State Networks (GESNs) are a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. In this paper, we show that GESNs can achieve a significantly better accuracy on six heterophilic node classification tasks without altering the graph connectivity, thus suggesting a different route for addressing the over-squashing problem., Comment: Extended Abstract. Presented at the First Learning on Graphs Conference (LoG 2022), Virtual Event, December 9-12, 2022