Miracle-dz/UniFAR-FTUs huggingface dataset:
A-B transition in superfluid $^3$He and cosmological phase transitions

First order phase transitions in the very early universe are a prediction of many extensions of the Standard Model of particle physics and could provide the departure from equilibrium needed for a dynamical explanation of the baryon asymmetry of the Universe. They could also produce gravitational waves of a frequency observable by future space-based detectors such as the Laser Interferometer Space Antenna (LISA). All calculations of the gravitational wave power spectrum rely on a relativistic version of the classical nucleation theory of Cahn-Hilliard and Langer, due to Coleman and Linde. The high purity and precise control of pressure and temperature achievable in the laboratory made the first-order A to B transition of superfluid $^3$He an ideal for test of classical nucleation theory. As Leggett and others have noted the theory fails dramatically. The lifetime of the metastable A phase is measurable, typically of order minutes to hours, far faster than classical nucleation theory predicts. If the nucleation of B phase from the supercooled A phase is due to a new, rapid intrinsic mechanism that would have implications for first-order cosmological phase transitions as well as predictions for gravitational wave (GW) production in the early universe. Here we discuss studies of the AB phase transition dynamics in $^3$He, both experimental and theoretical, and show how the computational technology for cosmological phase transition can be used to simulate the dynamics of the A-B transition, support the experimental investigations of the A-B transition in the QUEST-DMC collaboration with the goal of identifying and quantifying the mechanism(s) responsible for nucleation of stable phases in ultra-pure metastable quantum phases.
Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications

Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads. As such, NP represents an interesting opportunity for implementing AI tasks on board power-limited satellite communication spacecraft. In this article, we disseminate the findings of a recently completed study which targeted the comparison in terms of performance and power-consumption of different satellite communication use cases implemented on standard AI accelerators and on NPs. In particular, the article describes three prominent use cases, namely payload resource optimization, onboard interference detection and classification, and dynamic receive beamforming; and compare the performance of conventional convolutional neural networks (CNNs) implemented on Xilinx's VCK5000 Versal development card and SNNs on Intel's neuromorphic chip Loihi 2.
Estimates for the full maximal function on graded Lie groups

On $\mathbb{R}^n,$ a classical result due to Bourgain establishes the restricted weak $(\frac{n}{n-1},1)$ inequality for the full maximal function $M_F^{d\sigma}$ associated to the spherical averages. In this work we present an extension to Bourgain's result on graded Lie groups for a family of full maximal operators. We formulate this extension using the group Fourier transform of the measures under consideration and the symbols of (positive Rockland operators which are) positive left-invariant hypoelliptic partial differential operators on graded Lie groups.
Diagnosing epilepsy using entropy measures and embedding parameters of EEG signals

Epilepsy is a neurological disorder that affects normal neural activity. These electrical activities can be recorded as signals containing information about the brain known as Electroencephalography (EEG) signals. Analysis of the EEG signals by individuals for epilepsy diagnosis is subjective and time-consuming. So, an automatic classification system with high detection accuracy is required to overcome possible errors. In this study, the discrete wavelet transform has been applied to EEG signals. Then, entropy measures and embedding parameters have been extracted. These features have been investigated individually to find the most discriminating ones. The significance level of each feature was evaluated by statistical analysis. Consequently, LDA and SVM algorithms have been employed to categorize the EEG signals. The results have indicated that the features of Embedding parameters, PermutationEntropy, FuzzyEntropy, SampleEntropy, NormEntropy, SureEntropy, LogEntropy, and ThresholdEntropy have the potential to discriminate epileptic patients from healthy subjects significantly. Also, SVM classifier has achieved the highest classification accuracy. In this study, we could find effective embedding-based and entropy-based features as appropriate single measures for identifying abnormal activities that can efficiently discriminate the EEG signals of epileptics from healthy individuals. According to the results, they can be used for automatic classification of epileptic EEG signals that are difficult to examine visually.
Cesium Tiles for High-realism Simulation and Comparing SLAM Results in Corresponding Virtual and Real-world Environments

This article discusses the use of a simulated environment to predict algorithm results in the real world. Simulators are crucial in allowing researchers to test algorithms, sensor integration, and navigation systems without deploying expensive hardware. This article examines how the AirSim simulator, Unreal Engine, and Cesium plugin can be used to generate simulated digital twin models of real-world locations. Several technical challenges in completing the analysis are discussed and the technical solutions are detailed in this article. Work investigates how to assess mapping results for a real-life experiment using Cesium Tiles provided by digital twins of the experimental location. This is accompanied by a description of a process for duplicating real-world flights in simulation. The performance of these methods is evaluated by analyzing real-life and experimental image telemetry with the Direct Sparse Odometry (DSO) mapping algorithm. Results indicate that Cesium Tiles environments can provide highly accurate models of ground truth geometry after careful alignment. Further, results from real-life and simulated telemetry analysis indicate that the virtual simulation results accurately predict real-life results. Findings indicate that the algorithm results in real life and in the simulated duplicate exhibited a high degree of similarity. This indicates that the use of Cesium Tiles environments as a virtual digital twin for real-life experiments will provide representative results for such algorithms. The impact of this can be significant, potentially allowing expansive virtual testing of robotic systems at specific deployment locations to develop solutions that are tailored to the environment and potentially outperforming solutions meant to work in completely generic environments.
The"Sommerfeld Puzzle"and Its Extensions

The exact agreement between the Sommerfeld (1916) and Dirac (1928) results for the energy levels of the relativistic hydrogen atom (the so-called"Sommerfeld puzzle") is analyzed and extended. Werner Heisenberg called this coincidence a `miracle' but Erwin Schroedinger described it as a fortuitous computational accident.
Exploring dark matter-gauge boson effective interactions at the current and future colliders

In this work, we investigate the collider constraints on effective interactions between Dark Matter (DM) particles and electroweak gauge bosons in a systematic way. We consider the simplified models in which scalar or Dirac fermion DM candidates only couple to electroweak gauge bosons through high dimensional effective operators. Taking into account the induced DM-quarks and DM-gluons operators from the Renormalization Group Evolution (RGE) running effect, we present comprehensive constraints on effective energy scale $\Lambda$ and Wilson coefficients $C_B(\Lambda),\,C_W(\Lambda)$ from direct detection, indirect detection, and collider searches. In particular, we present the corresponding sensitivity from the Large Hadron Electron Collider (LHeC) and the Future Circular Collider in electron-proton mode (FCC-ep) for the first time, update the mono-$j$ and mono-$\gamma$ search limits at the Large Hadron Collider (LHC), and derive the new limits at the Circular Electron Positron Collider (CEPC). Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series

Irregularly sampled time series are ubiquitous, presenting significant challenges for analysis due to missing values. Despite existing methods address imputation, they predominantly focus on leveraging intra-series information, neglecting the potential benefits that inter-series information could provide, such as reducing uncertainty and memorization effect. To bridge this gap, we propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series. Our framework comprises a prototype memory module for learning inter-series information, a bidirectional gated recurrent unit utilizing prototype information for imputation, and an attentive prototypical refinement module for adjusting imputations. We conducted extensive experiments on three datasets, and the results underscore PRIME's superiority over the state-of-the-art models by up to 26% relative improvement on mean square error.
Dynamical displacements, persistence and semiconjugacies

This survey gives a unified treatment of topics from Abelian and non-Abelian Nielsen Theory integrated with the semiconjugacy theorems of Franks and Handel. The main focus is to develop an analog of the rotation set that is valid when the dynamics are not isotopic to the identity and to connect this theory to the dynamical persistence under homotopy/isotopy intrinsic in the theorems of Franks and Handel. For this dynamical persistence, expansion/hyperbolicity at some scale is essential.
Spectral analysis of periodic $b$-KP equation under transverse perturbation

The $b$-family-Kadomtsev-Petviashvili equation ($b$-KP) is a two dimensional generalization of the $b$-family equation. In this paper, we study the spectral stability of the one-dimensional small-amplitude periodic traveling waves with respect to two-dimensional perturbations which are either co-periodic in the direction of propagation, or nonperiodic (localized or bounded). We perform a detailed spectral analysis of the linearized problem associated to the above mentioned perturbations, and derive various stability and instability criteria which depends in a delicate way on the parameter value of $b$, the transverse dispersion parameter $\sigma$, and the wave number $k$ of the longitudinal waves.
Long-term study of the 2020 magnetar-like outburst of the young pulsar PSRJ1846-0258 in Kes 75

Magnetar-like activity has been observed in a large variety of neutron stars. PSR J1846-0258 is a young 327 ms radio-quiet pulsar with a large rotational power ($\sim 8 \times 10^{36}$ erg s$^{-1}$), and resides at the center of the supernova remnant Kes 75. It is one of the rare examples of a high magnetic field pulsar showing characteristics both of magnetars and radio pulsars, and can thus provide important clues on the differences in the emission mechanisms between these two classes. In 2006, PSR J1846-0258 was detected to undergo an outburst for the first time, accompanied by a large flux increase, millisecond X-ray bursts, significant spectral changes and a large timing glitch. On August 1st 2020, after nineteen years of quiescent stable emission, the source underwent a second magnetar-like outburst, which was followed up with several observations by NICER, XMM-Newton, NuSTAR and Swift. In this work, we report on the long-term timing and X-ray spectral properties of the source following the 2020 outburst, and place upper limits on any radio activity. We demonstrate that the pulsed flux increased by a factor $>6$ during the outburst, followed by non-trivial variability in the spin-down rate. Our timing analysis shows that the spin frequency and its derivative are clearly affected by magnetospheric activity due to the outburst. We find hints for an oscillation in the frequency derivative with a timescale of 50-60 days, recovering later on to stable quiescence.
A polynomial algorithm for the most degree-central shortest path problem

The degree centrality of a node, defined as the number of nodes adjacent to it, is often used as a measure of importance of a node to the structure of a network. This metric can be extended to paths in a network, where the degree centrality of a path is defined as the number of nodes adjacent to it. In this paper, we reconsider the problem of finding the most degree-central shortest path in an unweighted network. We propose a polynomial algorithm with the worst-case running time of $O(|E||V|^2\Delta(G))$, where $|V|$ is the number of vertices in the network, $|E|$ is the number of edges in the network, and $\Delta(G)$ is the maximum degree of the graph. We conduct a numerical study of our algorithm on synthetic and real-world networks and compare our results to the existing literature. In addition, we show that the same problem is NP-hard when a weighted graph is considered.
Optimization of thermo-spin voltage in vertical nanostructures by geometrical means

The thermo-spin conversion provides new concepts for further developing the green energy-harvesting technology because spin can be controlled with minimal energy in nanostructures. Through theoretical analysis of thermo-spin generation, transportation and conversion in ferromagnet/non-ferromagnet/heavy metal (FM/NM/HM) vertical structures, we found that the output transverse thermo-spin voltage is independent of the structure's width, but varies in a linear function with the structure's length. To validate our predictions, we fabricated the thermo-spin devices with a CoFeAl/Cu/Pt structure. Our results indicate that FM/NM/HM structures can be utilized to design flexible thermo-spin conversion devices.
Preserving Power Optimizations Across the High Level Synthesis of Distinct Application-Specific Circuits

We evaluate the use of software interpretation to push High Level Synthesis of application-specific accelerators toward a higher level of abstraction. Our methodology is supported by a formal power consumption model that computes the power consumption of accelerator components, accurately predicting the power consumption on new designs from prior optimization estimations. We demonstrate how our approach simplifies the re-use of power optimizations across distinct designs, by leveraging the higher level of design abstraction, using two accelerators representative of the robotics domain, implemented through the Bambu High Level Synthesis tool. Results support the research hypothesis, achieving predictions accurate within +/- 1%.
On the free metabelian Lie-admissible algebras

In this paper, we consider Lie-admissible algebras, which are free Novikov and free Lie-admissible algebras with an additional metabelian identity. We construct a linear basis for both free metabelian Novikov and free metabelian Lie-admissible algebras. Additionally, we describe a space of symmetric polynomials for both the free metabelian Novikov algebra and the free metabelian Lie-admissible algebra.
Space Networking Kit: A Novel Simulation Platform for Emerging LEO Mega-constellations

This paper presents SNK, a novel simulation platform designed to evaluate the network performance of constellation systems for global Internet services. SNK offers realtime communication visualization and supports the simulation of routing between edge node of network. The platform enables the evaluation of routing and network performance metrics such as latency, stretch, network capacity, and throughput under different network structures and density. The effectiveness of SNK is demonstrated through various simulation cases, including the routing between fixed edge stations or mobile edge stations and analysis of space network structures.
Layerwise Quantum Convolutional Neural Networks Provide a Unified Way for Estimating Fundamental Properties of Quantum Information Theory

The estimation of fundamental properties in quantum information theory, including von Neumann entropy, R\'enyi entropy, Tsallis entropy, quantum relative entropy, trace distance, and fidelity, has received significant attention. While various algorithms exist for individual property estimation, a unified approach is lacking. This paper proposes a unified methodology using Layerwise Quantum Convolutional Neural Networks (LQCNN). Recent studies exploring parameterized quantum circuits for property estimation face challenges such as barren plateaus and complexity issues in large qubit states. In contrast, our work overcomes these challenges, avoiding barren plateaus and providing a practical solution for large qubit states. Our first contribution offers a mathematical proof that the LQCNN structure preserves fundamental properties. Furthermore, our second contribution analyzes the algorithm's complexity, demonstrating its avoidance of barren plateaus through a structured local cost function.
Partial entanglement network and bulk geometry reconstruction in AdS/CFT

In the context of Anti-de Sitter / Conformal Field Theory (AdS/CFT) correspondence, we present a general scheme to reconstruct bulk geometric quantities in terms of a specific measure of the entanglement structure on the boundary CFT, the partial entanglement entropy (PEE). The PEE between any two points $\mathcal{I}(\vec x, \vec y)$ is the fundamental building block of the PEE structure. It can be geometrized into a bulk geodesic connecting the two boundary points $\vec x$ and $\vec y$, which we refer to as the PEE thread. Thus, we ave a network of the PEE threads in the bulk with a density of the threads determined by the boundary PEE structure \cite{Lin:2023rbd}. We demonstrate that, for any static boundary region $A$, the homologous surface $\Sigma_{A}$ that has the minimal flux of the PEE threads passing through it is exactly the Ryu-Takayanagi (RT) surface of $A$, and the minimal flux coincides with the holographic entanglement entropy of $A$. Furthermore, we show that the strength of the PEE flux at any bulk point along any direction is $1/4G$. Based on this observation, we prove that any area element in the bulk can be reconstructed by the PEE threads passing through it, which corresponds to a set of two-point PEEs on the CFT.
The role of the Agulhas in the Benguela Current system: A numerical modeling approach

A modeling approach is used to investigate the influence of the Agulhas on the southern Benguela system. Two climatological ROMS simulations are run that are identical except that in one of them the effect of the Agulhas is removed. Comparing their outputs allows for a clear indication of the role of the Agulhas on both the large-scale and shelf dynamics. About 15 Sv of the mean transport of the Benguela Current is shown to be contributed by Agulhas influx, with the most intense flow being associated with extreme turbulence and its meandering nature is a reflection of the passage of Agulhas Rings. The injection of warm water is particularly evident beyond the shelf-edge, producing a perennially intense cross-shelf density front that is enhanced during upwelling season. This gradient drives a jet that is fastest at the shelf-edge but that extends from the midshelf to at least 100 km beyond it and is associated with dynamic uplift via vortex squashing. Similarly generated is the Good Hope Jet that extends northwestward from the western Agulhas Bank. Turbulence associated with Agulhas leakage increases rapidly beyond the shelf-edge causing the upwelling front in the southern Benguela to be subject to intense mixing, leaving a relatively uniform front there. Locally generated regions of high turbulence exist in the vicinity of the shelf-edge jets as well as further north in association with the large filaments that originate from the perennial L€ uderitz upwelling cell, with or without the influence of the Agulhas.
Effective detection of quantum discord by using Convolutional Neural Networks

Quantum discord is a form of correlation that is defined as the difference between quantum and classical mutual information of two parties. Due to the optimization involved in the definition of classical mutual information of quantum systems, calculating and distinguishing between discordant and non-discordant states is not a trivial task. Additionally, complete tomography of a quantum state is the prerequisite for the calculation of its quantum discord, and it is indeed resource consuming. Here, by using the relation between the kernels of the convolutional layers of an artificial neural network and the expectation value of operators in quantum mechanical measurements, we design a Convolutional Neural Network (CNN) that uses 16 kernels to completely distinguish between the discordant and non-discordant general two-qubit states. We have also designed a Branching Convolutional Neural Network (BCNN) that can effectively detect quantum discord. Our BCNN achieves an accuracy of around 85% or 99%, by utilizing only 5 or 8 kernels, respectively. Our results show that to detect the existence of quantum discord up to the desired accuracy, instead of complete tomography, one can use suitable quantum circuits to directly measure the expectation values of the kernels, and then a fully connected network will solve the detection problem.
Accelerated Gradient Methods with Gradient Restart: Global Linear Convergence

Gradient restarting has been shown to improve the numerical performance of accelerated gradient methods. This paper provides a mathematical analysis to understand these advantages. First, we establish global linear convergence guarantees for the gradient restarted accelerated proximal gradient method when solving strongly convex composite optimization problems. Second, through analysis of the corresponding ordinary differential equation model, we prove the continuous trajectory of gradient restarted Nesterov's accelerated gradient method exhibits global linear convergence for quadratic strongly convex objectives, while the non-restarted version provably lacks this property by [Su, Boyd, and Cand\'es, J. Mach. Learn. Res., 2016, 17(153), 1-43].