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We study the linear problems in $z,t$ (time) associated to the Painlev\'e III$_3$ and III$_1$ equations when the Painlev\'e solution $q(t)$ approaches a pole or a zero. In this limit the problem in $z$ for the Painlev\'e III$_3$ reduces to the modified Mathieu equation, while that for the Painlev\'e III$_1$ to the Doubly Confluent Heun Equation. These equations appear as Nekrasov-Shatashvili quantisations/deformations of Seiberg-Witten differentials for $SU(2)$ ${\cal N}=2$ super Yang-Mills gauge theory with number of flavours $N_f=0$ and $N_f=2$, respectively. These results allow us to conjecture that this link holds for any Painlev\'e equation relating each of them to a different matter theory, which is actually the same as in the well-established Painlev\'e gauge correspondence, but {\it with another deformation ($\Omega$-background)}. An explicit expression for the dual gauge period (and then prepotential) is also found. As a by-product, a new solution to the connexion problem is illustrated., Comment: Research work

With the development of quantum technology, hybrid devices that combine superconductors (S) and semiconductors (Sm) have attracted great attention due to the possibility of engineering structures that benefit from the integration of the properties of both materials. However, until now, none of the experiments have reported good control of band alignment at the interface, which determines the strength of S-Sm coupling and the proximitized superconducting gap. Here, we fabricate hybrid devices in a generic way with argon milling to modify the interface while maintaining its high quality. First, after the milling the atomically connected S-Sm interfaces appear, resulting in a large induced gap, as well as the ballistic transport revealed by the multiple Andreev reflections and quantized above-gap conductance plateaus. Second, by comparing transport measurement with Schr\"odinger-Poisson (SP) calculations, we demonstrate that argon milling is capable of varying the band bending strength in the semiconducting wire as the electrons tend to accumulate on the etched surface for longer milling time. Finally, we perform nonlocal measurements on advanced devices to demonstrate the coexistence and tunability of crossed Andreev reflection (CAR) and elastic co-tunneling (ECT) -- key ingredients for building the prototype setup for realization of Kitaev chain and quantum entanglement probing. Such a versatile method, compatible with the standard fabrication process and accompanied by the well-controlled modification of the interface, will definitely boost the creation of more sophisticated hybrid devices for exploring physics in solid-state systems., Comment: 41 pages, 16 figures

Quantum devices based on InSb nanowires (NWs) are a prime candidate system for realizing and exploring topologically-protected quantum states and for electrically-controlled spin-based qubits. The influence of disorder on achieving reliable topological regimes has been studied theoretically, highlighting the importance of optimizing both growth and nanofabrication. In this work we investigate both aspects. We developed InSb nanowires with ultra-thin diameters, as well as a new gating approach, involving few-layer graphene (FLG) and Atomic Layer Deposition (ALD)-grown AlOx. Low-temperature electronic transport measurements of these devices reveal conductance plateaus and Fabry-P\'erot interference, evidencing phase-coherent transport in the regime of few quantum modes. The approaches developed in this work could help mitigate the role of material and fabrication-induced disorder in semiconductor-based quantum devices., Comment: 14 pages, 5 figures

The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity").

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection., Comment: 13 pages, 4 figures, 4 tables

We propose a model to simulate different traffic-flow conditions in terms of quantum graphs hosting an (N+1)-level dot at each site. Our model allows us to keep track of the type and of the destination of each vehicle. The traffic flow inside the system is encoded in a proper set of Lindbladian local dissipators that describe the time evolution of the system density matrix. Taking advantage of the invariance of the Lindblad master equation under inhomogeneous transformations we derive the quantum Hamiltonian for the bulk dynamics in a proper experimental setup., Comment: 12 pages, 3 figures, IQSA22 Conference Proceedings special issue of the International Journal of Theoretical Physics (Springer)

The thesis arises in the context of deep learning applications to particle physics. The dissertation follows two main parallel streams: the development of hardware-accelerated tools for event simulation in high-energy collider physics, and the optimization of deep learning models for reconstruction algorithms at neutrino detectors. Event generation is a central concept in high-energy physics phenomenology studies. The state-of-the-art software dedicated to Monte Carlo simulation is often written for general-purpose computing architectures (CPUs), which allow great flexibility but are not compatible with specialized accelerating devices, GPUs. We present two original tools, PDFFlow and MadFlow, that manage to combine these two aspects in Python. PDFFlow, is a Parton Distribution Functions interpolator, while MadFlow aims at building a complete tool suite to accelerate the whole event generation framework. The reconstruction pipeline at neutrino detectors is comprised of many different algorithms that work in synergy to extract a high-level representation of detector data. All the most important experiments in neutrino physics are developing software to automatically process and extract this information. This work describes the implementation of deep learning techniques to improve neutrino reconstruction efficiency at the ProtoDUNE-SP detector. Two original contributions are presented concerning raw data denoising and a hit-clustering procedure named "slicing". Both denoising and slicing involve the implementation and the training of novel neural network architectures, based on state-of-the-art models in machine learning, such as feed-forward, convolutional and graph neural networks. They represent a proof of concept that these models are indeed capable of providing an important impact on signal reconstruction at neutrino detectors., Comment: PhD thesis work

It is shown that a compound elastic structure, which displays a dynamic instability, may be designed as the union (or 'fusion') of two structures which are stable when separately analyzed. The compound elastic structure has two degrees of freedom and is made up of a rigid rod connected with two springs to a smooth support, which evidences a jump in the curvature at the equilibrium configuration. Instability is proven in a linearized context and is related to the application of a non-conservative load of the follower type, so that the instability disappears under dead loads. In the fully nonlinear range, the instability is also confirmed through numerical simulations. The obtained results may be useful in the design of new mechanical sensors, or devices for energy harvesting, or architected materials. In addition, our findings have conceptual implications on piecewise-linear theories of mechanics such as for instance plasticity or frictional contact., Comment: 39 pages, 19 figures; post-print version; added Journal reference

Using the Lindblad equation approach, we study the nonequilibrium stationary state of a benzene ring connected to two reservoirs in the large bias regime, a prototype of a generic molecular electronic device. We show the emergence of an optimal working point (corresponding to a change in the monotonicity of the stationary current, as a function of the applied bias) and its robustness against chemical potential and bond disorder., Comment: 5 pages, 3 figures, published in Journal of Physics: Conference Series, proceedings paper from SCES 2020 conference

We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The automation process of generating all the required components for MC simulation of a generic physics process and its deployment on hardware accelerator is still a big challenge nowadays. In order to solve this challenge, we design a workflow and code library which provides to the user the possibility to simulate custom processes through the MadGraph5_aMC@NLO framework and a plugin for the generation and exporting of specialized code in a GPU-like format. The exported code includes analytic expressions for matrix elements and phase space. The simulation is performed using the VegasFlow and PDFFlow libraries which deploy automatically the full simulation on systems with different hardware acceleration capabilities, such as multi-threading CPU, single-GPU and multi-GPU setups. The package also provides an asynchronous unweighted events procedure to store simulation results. Crucially, although only Leading Order is automatized, the library provides all ingredients necessary to build full complex Monte Carlo simulators in a modern, extensible and maintainable way. We show simulation results at leading-order for multiple processes on different hardware configurations., Comment: 7 pages, 4 figures, to appear at Off-Shell 2021, code available at https://github.com/N3PDF/madflow. arXiv admin note: text overlap with arXiv:2105.10529

If we start from certain functional relations as definition of a quantum integrable theory, then we can derive from them a linear integral equation. It can be extended, by introducing dynamical variables, to become an equation with the form of Marchenko's. Then, we derive from the latter a classical (differential) Lax pair. We exemplify our method by focusing on the massive version of the ODE/IM (Ordinary Differential Equations/Integrable Models) correspondence from Quantum sine-Gordon (sG) with many moduli/masses to the classical sinh-Gordon (shG) equation, so describing, in a particular case, some super-symmetric gauge theories and the $AdS_3$ strong coupling scattering amplitudes/Wilson loops. Yet, we present it in a way which reveals its generality of application. In fact, we give some hints on how it works for spin chains., Comment: More comprehensive and explicit version (v2 is not to be considered as appeared wrongly preceded by v1)

We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champion and scheduling a set of `live' challengers over time based on sample complexity bounds. It is guaranteed to have sublinear regret after the optimal configuration is added into consideration by an application-dependent oracle based on the champions. Empirically, we show that ChaCha provides good performance across a wide array of datasets when optimizing over featurization and hyperparameter decisions., Comment: 16 pages (including supplementary appendix). Appearing at ICML 2021

In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In order to automate MC simulation for a generic number of processes, we design a program which provides to the user the possibility to simulate custom processes through the MadGraph5_aMC@NLO framework. The pipeline includes a first stage where the analytic expressions for matrix elements and phase space are generated and exported in a GPU-like format. The simulation is then performed using the VegasFlow and PDFFlow libraries which deploy automatically the full simulation on systems with different hardware acceleration capabilities, such as multi-threading CPU, single-GPU and multi-GPU setups. We show some preliminary results for leading-order simulations on different hardware configurations., Comment: 6 pages, 3 figures, to appear in the vCHEP 2021 conference proceedings

We propose a model to implement and simulate different traffic-flow conditions in terms of quantum graphs hosting an ($N$+1)-level dot at each site, which allows us to keep track of the type and of the destination of each vehicle. By implementing proper Lindbladian local dissipators, we derive the master equations that describe the traffic flow in our system. To show the versatility and the reliability of our technique, we employ it to model different types of traffic flow (the symmetric three-way roundabout and the three-road intersection). Eventually, we successfully compare our predictions with results from classical models., Comment: 24 pages, 13 figures

In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. The reconstruction workchain consists of converting digital detector signals into physical high-level quantities. We address the first step in reconstruction, namely raw data denoising, leveraging deep learning algorithms. We design two architectures based on graph neural networks, aiming to enhance the receptive field of basic convolutional neural networks. We benchmark this approach against traditional algorithms implemented by the DUNE collaboration. We test the capabilities of graph neural network hardware accelerator setups to speed up training and inference processes., Comment: 9 pages, 7 figures, 3 tables. Code available at https://github.com/marcorossi5/DUNEdn

We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. The evaluation of a generic set of PDFs for quarks and gluons at a given momentum fraction and energy scale requires the implementation of interpolation algorithms as introduced for the first time by the LHAPDF project. PDFFlow extends and implements these interpolation algorithms using Google's TensorFlow library providing the possibility to perform PDF evaluations taking fully advantage of multi-threading CPU and GPU setups. We benchmark the performance of this library on multiple scenarios relevant for the particle physics community., Comment: 6 pages, 6 figures. Code available at "https://github.com/N3PDF/pdfflow". Refer also to arXiv:2009.06635

Large software systems tune hundreds of 'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A 'one-size-fits-all' approach is often sub-optimal as the best value depends on runtime context. In this paper, we provide an experimental approach to replace constants with learned contextual functions for Skype - a widely used real-time communication (RTC) application. We present Resonance, a system based on contextual bandits (CB). We describe experiences from three real-world experiments: applying it to the audio, video, and transport components in Skype. We surface a unique and practical challenge of performing machine learning (ML) inference in large software systems written using encapsulation principles. Finally, we open-source FeatureBroker, a library to reduce the friction in adopting ML models in such development environments, Comment: Workshop on ML for Systems at NeurIPS 2020, Accepted

Using the Lindblad equation approach, we derive the range of the parameters of an interacting one-dimensional electronic chain connected to two reservoirs in the large bias limit in which an optimal working point (corresponding to a change in the monotonicity of the stationary current as a function of the applied bias) emerges in the nonequilibrium stationary state. In the specific case of the one-dimensional spinless fermionic Hubbard chain, we prove that an optimal working point emerges in the dependence of the stationary current on the coupling between the chain and the reservoirs, both in the interacting and in the noninteracting case. We show that the optimal working point is robust against localized defects of the chain, as well as against a limited amount of quenched disorder. Eventually, we discuss the importance of our results for optimizing the performance of a quantum circuit by tuning its components as close as possible to their optimal working point., Comment: 21 pages, 22 figures

We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. The evaluation of a generic set of PDFs for quarks and gluon at a given momentum fraction and energy scale requires the implementation of interpolation algorithms as introduced for the first time by the LHAPDF project. PDFFlow extends and implements these interpolation algorithms using Google's TensorFlow library providing the capabilities to perform PDF evaluations taking fully advantage of multi-threading CPU and GPU setups. We benchmark the performance of this library on multiple scenarios relevant for the particle physics community., Comment: 8 pages, 7 figures, 2 tables. Code available at https://github.com/N3PDF/pdfflow

We speculate on Dyson series for the $S$-matrix when the interaction depends on derivatives of the fields. We stick on two particular examples: the scalar electrodynamics and the renormalised $\phi ^4$ theory. We eventually give evidence that Lorentz invariance is satisfied and that usual Feynman rules can be applied to the interaction Lagrangian., Comment: 15 pages; improved Introduction and added two references; improved proofs in Section 3 and 4

We formulate a generalized susceptible exposed infectious recovered (SEIR) model on a graph, describing the population dynamics of an open crowded place with an arbitrary topology. As a sample calculation, we discuss three simple cases, both analytically, and numerically, by means of a cellular automata simulation of the individual dynamics in the system. As a result, we provide the infection ratio in the system as a function of controllable parameters, which allows for quantifying how acting on the human behavior may effectively lower the disease spread throughout the system., Comment: 14 pages, 9 figures

We provide the two fundamental sets of functional relations which describe the strong coupling limit of scattering amplitudes in $\mathcal{N} = 4$ SYM dual to Wilson loops in $AdS_3$: the basic $QQ$-system and the derived $TQ$-system. We use the $TQ$ relations and the knowledge of the main properties of the $Q$-function (eigenvalue of some $Q$-operator) to write the Bethe Ansatz equations, {\it viz.} a set of ('complex') non-linear-integral equations, whose solutions give exact values to the strong coupling amplitudes/Wilson loops. Moreover, they have some advantages with respect to the ('real') non-linear-integral equations of Thermodynamic Bethe Ansatz and still reproduce, both analytically and numerically, the findings coming from the latter. In any case, these new functional and integral equations give a larger perspective on the topic., Comment: Latex, 22 pages