Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of the ionic polarization in multiple sequential ionization: General equations and an illustrative example

The modern Free-Electron-Lasers generate a highly intense polarized radiation which initiate a sequence of ionization and decay events. Their probability depends on the polarization of each state as function of time. Its complete accounting is limited by the fact that a state can be formed in various ways. Here we present the equivalent of rate equations for population that completely accounts polarization of radiation and formulated in terms of the statistical tensors. To illustrate our approach we theoretically consider sequential photoionization of krypton by an intense extreme ultraviolet femtosecond pulse for the photon energies below the 3d-shell excitation threshold. The calculations of the ion yields, photoelectron spectra and ionic polarization for various photon fluence are presented and role of polarization is discussed.
A flexible PageRank-based graph embedding framework closely related to spectral eigenvector embeddings

We study a simple embedding technique based on a matrix of personalized PageRank vectors seeded on a random set of nodes. We show that the embedding produced by the leading singular vectors of an element-wise logarithm of this matrix is related to the spectral embedding of Laplacian eigenvectors for degree regular graphs. Moreover, this log-PageRank embedding procedure produces useful results for global graph visualization even when the spectral embedding does not. Most importantly, the general nature of this embedding strategy opens up many emerging applications, where eigenvector and spectral techniques may not be well established, to the PageRank-based relatives. For instance, similar techniques can be used on PageRank vectors from hypergraphs to get “spectral-like” embeddings.
A deep investigation of NiO and MnO through the first principle calculations and Monte Carlo simulations

In this study, we use Hubbard-corrected density functional theory (DFT+U) to derive spin model Hamiltonians consisting of Heisenberg exchange interactions up to the fourth nearest neighbors and bi-quadratic interactions. We map the DFT+U results of several magnetic configurations to the Heisenberg spin model Hamiltonian to estimate Heisenberg exchanges. We demonstrate that the number of magnetic configurations should be at least twice the number of exchange parameters to estimate exchange parameters correctly. To calculate biquadratic interaction, we propose specific non-collinear magnetic configurations that do not change the energy of the Heisenberg spin model. We use classical Monte Carlo (MC) simulations to evaluate DFT+U results. We obtain the temperature dependence of magnetic susceptibility and specific heat to determine the Curie–Weiss and Néel temperatures. The MC simulations reveal that although the biquadratic interaction can not change the Néel temperature, it modifies the order parameter. We indicate that for a fair comparison between classical MC simulations and experiments, we need to consider the quantum effect by applying (S+1)/S correction in classical MC simulations.
Homomorphism Autoencoder - Learning Group Structured Representations from Observed Transitions

How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations containing not just observational but also interventional knowledge, we study this problem using tools from representation learning and group theory. We propose methods enabling an agent acting upon the world to learn internal representations of sensory information that are consistent with actions that modify it. We use an autoencoder equipped with a group representation acting on its latent space, trained using an equivariance-derived loss in order to enforce a suitable homomorphism property on the group representation. In contrast to existing work, our approach does not require prior knowledge of the group and does not restrict the set of actions the agent can perform. We motivate our method theoretically, and show empirically that it can learn a group representation of the actions, thereby capturing the structure of the set of transformations applied to the environment. We further show that this allows agents to predict the effect of sequences of future actions with improved accuracy.
An Insight into the Dynamics of a Dual Active Bridge

This paper aims at analyzing the effect of the zero dynamics of the Dual Active Bridge Isolated Bidirectional dc-dc converter (DAB) on the dynamics of the complete DAB system. It also explains its influence on controller design for the DAB system. In carrying out these analyses, the state space model of the DAB, as well as the first harmonic approximation (FHA) of the model are derived. The ZVS and the stability analysis of the system are undertaken based on the FHA model of the system. The system is shown to be stable for a constant output voltage operation for the entire power range while it is unstable for the constant power load (CPL) operation for load demands close to the system maximum power. It is also shown that the transformer winding currents are part of the zero dynamic states and are always stable regardless of the operating conditions of the system.
Channel Estimation for LEO Satellite Massive MIMO OFDM Communications

In this paper, we investigate the massive multiple-input multiple-output orthogonal frequency division multiplexing channel estimation for low-earth-orbit satellite communication systems. First, we use the angle-delay domain channel to characterize the space-frequency domain channel. Then, we show that the asymptotic minimum mean square error (MMSE) of the channel estimation can be minimized if the array response vectors of the user terminals (UTs) that use the same pilot are orthogonal. Inspired by this, we design an efficient graph-based pilot allocation strategy to enhance the channel estimation performance. In addition, we devise a novel two-stage channel estimation (TSCE) approach, in which the received signals at the satellite are manipulated with per-subcarrier space domain processing followed by per-user frequency domain processing. Moreover, the space domain processing of each UT is shown to be identical for all the subcarriers, and an asymptotically optimal vector for the per-subcarrier space domain linear processing is derived. The frequency domain processing can be efficiently implemented by means of the fast Toeplitz system solver. Simulation results show that the proposed TSCE approach can achieve a near performance to the MMSE estimation with much lower complexity.
Airfoil Optimization using Design-by-Morphing

Design-by-Morphing (DbM) is a novel design methodology that creates a search space for topology optimization. Traditional design techniques often impose geometric constraints and, sometimes, the designer’s biases on the design space, which restricts the novelty of the designs and allows for only small local changes. On contrary, we show in this paper that DbM does not impose such restrictions on the design space, thus allowing for a radical and expansive search space with only a few design parameters. We compare DbM with other methods in the case of design space generation for 2D airfoils and found that DbM can reconstruct the entire UIUC database to >99.5% accuracy. Furthermore, using a bi-objective genetic algorithm, we optimize the airfoil designs created by DbM to maximize both the lift-over-drag ratio, CLDmax, and stall angle tolerance, Δα, which results in a Pareto-front of innovative airfoils that exhibit substantial improvements in both objectives.
Screened scalar fields in hydrogen and muonium

We compute bounds on screened scalar field theories from hydrogen-like systems. New light scalar fields generically have a direct coupling to matter. Such a coupling is strongly constrained by myriad experimental measurements. However, certain theories possess a {\it screening mechanism} that allows the effects of this coupling to weaken dynamically, and to evade many such bounds. We compute the perturbations to the energy levels of hydrogen-like systems due to screened scalar fields. We then use this result in two ways. First, we compute bounds from hydrogen spectroscopy, finding significantly weaker bounds than have been reported before as screening effects were overlooked. Second, we show that muonium is an intrinsically much more sensitive probe of screened scalar fields. For chameleon models, muonium experiments probe a large part of the parameter space that is as yet unexplored by low energy physics and has so far only been tested by high-energy particle physics experiments.
The Interplay of Spectral Efficiency, User Density, and Energy in Random Access Protocols with Retransmissions

The ﬁfth-generation of wireless communication networks is required to support a range of use cases such as enhanced mobile broadband (eMBB), ultra-reliable, low-latency communications (URLLC), massive machine-type communications (mMTCs), with heterogeneous data rate, delay, and power requirements. The 4G LTE air interface uses extra overhead to enable scheduled access, which is not justiﬁed for small payload sizes. We employ a random access communication model with retransmissions for multiple users with small payloads at the low spectral efﬁciency regime. The radio resources are split non-orthogonally in the time and frequency dimensions. Retransmissions are combined via Hybrid Automatic Repeat reQuest (HARQ) methods, namely Chase Combining and Incremental Redundancy with a ﬁnite buffer size constraint C buf . We determine the best scaling for the spectral efﬁciency (SE) versus signal-to-noise ratio (SNR) per bit and for the user density versus SNR per bit, for the sum-optimal regime and when the interference is treated as noise, using a Shannon capacity approximation. Numerical results show that the scaling results are applicable over a range of η , T , C buf , J , at low received SNR values. The proposed analytical framework provides insights for resource allocation in general random access systems and speciﬁc 5G use cases for massive URLLC uplink access.
Multisymplectic Formulation of Deep Learning Using Mean-Field Type Control and Nonlinear Stability of Training Algorithm

As it stands, a robust mathematical framework to analyse and study various topics in deep learning is yet to come to the fore. Nonetheless, viewing deep learning as a dynamical system allows the use of established theories to investigate the behaviour of deep neural networks. In order to study the stability of the training process, in this article, we formulate training of deep neural networks as a hydrodynamics system, which has a multisymplectic structure. For that, the deep neural network is modelled using a stochastic differential equation and, thereby, mean–field type control is used to train it. The necessary conditions for optimality of the mean–field type control reduce to a system of Euler–Poincaré equations, which has the a similar geometric structure to that of compressible fluids. The mean–field type control is solved numerically using a multisymplectic numerical scheme that takes advantage of the underlying geometry. Moreover, the numerical scheme, yields an approximated solution which is also an exact solution of a hydrodynamics system with a multisymplectic structure and it can be analysed using backward error analysis. Further, nonlinear stability yields the condition for selecting the number of hidden layers and the number of nodes per layer, that makes the training stable while approximating the solution of a residual neural network with a number of hidden layers approaching infinity.
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems. However, the issue of contextual bias (i.e., how much consumers prefer one situation over another) has received little attention from the research community. This paper examines the effect of temporal bias, defined as the difference between users’ check-in hours, leisure vs. work hours, on the consumer-side fairness of context-aware recommendation algorithms. We believe that eliminating this type of temporal (and geographical) bias might contribute to a drop in traffic-related air pollution, noting that rush-hour traffic may be more congested. To surface effective POI recommendation, we evaluated the sensitivity of state-of-the-art context-aware models to the temporal bias contained in users’ check-in activities on two POI datasets, namely Gowalla and Yelp. The findings show that the examined context-aware recommendation models prefer one group of users over another based on the time of check-in and that this preference persists even when users have the same amount of interactions.
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data

Imaging data-based prognostic models focus on using an asset’s degradation images to predict its time to failure (TTF). Most image-based prognostic models have two common limitations. First, they require degradation images to be complete (i.e., images are observed continuously and regularly over time). Second, they usually employ an unsupervised dimension reduction method to extract low-dimensional features and then use the features for TTF prediction. Because unsupervised dimension reduction is conducted on the degradation images without the involvement of TTFs, there is no guarantee that the extracted features are effective for failure time prediction. To address these challenges, this article develops a supervised tensor dimension reduction-based prognostic model. The model first proposes a supervised dimension reduction method for tensor data. It uses historical TTFs to guide the detection of a tensor subspace to extract low-dimensional features from high-dimensional incomplete degradation imaging data. Next, the extracted features are used to construct a prognostic model based on (log)-location-scale regression. An optimization algorithm for parameter estimation is proposed, and analytical solutions are discussed. Simulated data and a real-world data set are used to validate the performance of the proposed model. History: Bianca Maria Colosimo served as the senior editor for this article Funding: This work was supported by National Science Foundation [2229245]. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://github.com/czhou9/Code-and-Data-for-IJDS .
New Additive Spanner Lower Bounds by an Unlayered Obstacle Product

For an input graph G, an additive spanner is a sparse subgraph H whose shortest paths match those of G up to small additive error. We prove two new lower bounds in the area of additive spanners:•We construct n-node graphs G for which any spanner on $O(n)$ edges must increase a pairwise distance by $+\Omega(n^{1/7})$. This improves on a recent lower bound of $+\Omega(n^{1/10.5})$ by Lu, Wein, Vassilevska Williams, and Xu [SODA 22]. •A classic result by Coppersmith and Elkin [SODA 05] proves that for any n-node graph G and set of $p=O(n^{1/2})$ demand pairs, one can exactly preserve all pairwise distances among demand pairs using a spanner on $O(n)$ edges. They also provided a lower bound construction, establishing that that this range $p=O(n^{1/2})$ cannot be improved. We strengthen this lower bound by proving that, for any constant k, this range of p is still unimprovable even if the spanner is allowed $+k$ additive error among the demand pairs. This negatively resolves an open question asked by Coppersmith and Elkin [SODA 05] and again by Cygan, Grandoni, and Kavitha [STACS 13] and Abboud and Bodwin [SODA 16]. At a technical level, our lower bounds are obtained by an improvement to the entire obstacle product framework used to compose “inner” and outer” graphs into lower bound instances. In particular, we develop a new strategy for analysis that allows certain non-layered graphs to be used in the product, and we use this freedom to design better inner and outer graphs that lead to our new lower bounds.
Investigating the Validity of Botometer-based Social Bot Studies

The idea that social media platforms like Twitter are inhabited by vast numbers of social bots has become widely accepted in recent years. Social bots are assumed to be automated social media accounts operated by malicious actors with the goal of manipulating public opinion. They are credited with the ability to produce content autonomously and to interact with human users. Social bot activity has been reported in many different political contexts, including the U.S. presidential elections, discussions about migration, climate change, and COVID-19. However, the relevant publications either use crude and questionable heuristics to discriminate between supposed social bots and humans or-in the vast majority of the cases-fully rely on the output of automatic bot detection tools, most commonly Botometer. In this paper, we point out a fundamental theoretical flaw in the widely-used study design for estimating the prevalence of social bots. Furthermore, we empirically investigate the validity of peer-reviewed Botometer-based studies by closely and systematically inspecting hundreds of accounts that had been counted as social bots. We were unable to find a single social bot. Instead, we found mostly accounts undoubtedly operated by human users, the vast majority of them using Twitter in an inconspicuous and unremarkable fashion without the slightest traces of automation. We conclude that studies claiming to investigate the prevalence, properties, or influence of social bots based on Botometer have, in reality, just investigated false positives and artifacts of this approach.
Tikhonov Regularization of Sphere-Valued Signals

—It is common to have to process signals, whose values are points on the 3-D sphere. We consider a Tikhonov- type regularization model to smoothen or interpolate sphere-valued signals deﬁned on arbitrary graphs. We propose a convex relaxation of this nonconvex problem as a semideﬁnite program, which is easy to solve numerically and is efﬁcient in practice.
FastATDC: Fast Anomalous Trajectory Detection and Classification*

Automated detection of anomalous trajectories is an important problem with considerable applications in intelligent transportation systems. Many existing studies have focused on distinguishing anomalous trajectories from normal trajectories, ignoring the large differences between anomalous trajectories. A recent study has made great progress in identifying abnormal trajectory patterns and proposed a two-stage algorithm for anomalous trajectory detection and classification (ATDC). This algorithm has excellent performance but suffers from a few limitations, such as high time complexity and poor interpretation. Here, we present a careful theoretical and empirical analysis of the ATDC algorithm, showing that the calculation of anomaly scores in both stages can be simplified, and that the second stage of the algorithm is much more important than the first stage. Hence, we develop a FastATDC algorithm that introduces a random sampling strategy in both stages. Experimental results show that FastATDC is 10 to 20 times faster than ATDC on real datasets. Moreover, FastATDC outperforms the baseline algorithms and is comparable to the ATDC algorithm.
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery

In urban environments, the complex and uncertain intersection scenarios are challenging for autonomous driving. To ensure safety, it is crucial to develop an adaptive decision making system that can handle the interaction with other vehicles. Manually designed model-based methods are reliable in common scenarios. But in uncertain environments, they are not reliable, so learning-based methods are proposed, especially reinforcement learning (RL) methods. However, current RL methods need retraining when the scenarios change. In other words, current RL methods cannot reuse accumulated knowledge. They forget learned knowledge when new scenarios are given. To solve this problem, we propose a hierarchical framework that can autonomously accumulate and reuse knowledge. The proposed method combines the idea of motion primitives (MPs) with hierarchical reinforcement learning (HRL). It decomposes complex problems into multiple basic subtasks to reduce the difficulty. The proposed method and other baseline methods are tested in a challenging intersection scenario based on the CARLA simulator. The intersection scenario contains three different subtasks that can reflect the complexity and uncertainty of real traffic flow. After offline learning and testing, the proposed method is proved to have the best performance among all methods.
Dive into Big Model Training

The increasing scale of model size and continuous improvement of performance herald the arrival of the Big Model era. In this report, we explore what and how the big model training works by diving into training objectives and training methodologies. Specifically,training objectives describe how to leverage web-scale data to develop extremely capable and incredibly large models based on self-supervised learning, and training methodologies which are based on distributed training describe how to make big model training a reality. We summarize the existing training methodologies into three main categories: training parallelism, memory-saving technologies, and model sparsity design. Training parallelism can be categorized into data, pipeline, and tensor parallelism according to the dimension of parallelism that takes place. Memory-saving technologies are orthogonal and complementary to training parallelism. And model sparsity design further scales up the model size with a constant computational cost. A continuously updated paper list of big model training is provided at https://github.com/qhliu26/BM-Training.
On Confidence Sequences for Bounded Random Processes via Universal Gambling Strategies

This paper considers the problem of constructing a confidence sequence, which is a sequence of confidence intervals that hold uniformly over time, for estimating the mean of bounded real-valued random processes. This paper revisits the gambling-based approach established in the recent literature from a natural \emph{two-horse race} perspective, and demonstrates new properties of the resulting algorithm induced by Cover (1991)'s universal portfolio. The main result of this paper is a new algorithm based on a mixture of lower bounds, which closely approximates the performance of Cover's universal portfolio with constant per-round time complexity. A higher-order generalization of a lower bound on a logarithmic function in (Fan et al., 2015), which is developed as a key technique for the proposed algorithm, may be of independent interest.