Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Linear Reduced Modeling by Generalized Kernel-Based Dynamic Mode Decomposition

Reduced modeling of a computationally demanding dynamical system aims at approximating its trajectories, while optimizing the trade-off between accuracy and computational complexity. In this work, we propose to achieve such an approximation by first embedding the trajectories in a reproducing kernel Hilbert space (RKHS), which exhibits appealing approximation and computational capabilities, and then solving the associated reduced model problem. More specifically, we propose a new efficient algorithm for data-driven reduced modeling of non-linear dynamics based on linear approximations in a RKHS. This algorithm takes advantage of the closed-form solution of a low-rank constraint optimization problem while exploiting advantageously kernel-based computations. Reduced modeling with this algorithm reveals a gain in approximation accuracy, as shown by numerical simulations, and in complexity with respect to existing approaches.
Fast In Situ Observation of Atomic Feshbach Resonances by Photoassociative Ionization.

We propose and experimentally investigate a scheme for observing Feshbach resonances in atomic quantum gases in situ and with a high temporal resolution of several tens of nanoseconds. The method is based on the detection of molecular ions, which are optically generated from atom pairs at small interatomic distances. As a test system we use a standard rubidium gas (^{87}Rb) with well known magnetically tunable Feshbach resonances. The fast speed and the high sensitivity of our detection scheme allows us to observe a complete Feshbach resonance within one millisecond and without destroying the gas.
Electrodynamics Correlates Knock-on and Knock-off: Current is Spatially Uniform in Ion Channels.

Ions in channels have been imagined as hard balls in a macroscopic mechanical model, for a very long time. Hard balls interact by collisions in such models, randomly knocking each other on and off `binding' sites in thermal motion. But ions have large charge, and the hard balls of classical models do not. The electrodynamics of charge guarantee strong correlations between the movements of ions on all time scales, even those of atomic scale thermal motion. Correlations are present whenever Maxwell's equations apply, so they are present in individual trajectories, not just averages. Indeed, in a series system like an idealized narrow channel, the correlation is perfect (within the accuracy of Maxwell's equations) because of conservation of total current (that includes Maxwell's displacement current, the ethereal ${\varepsilon }_0{\partial \boldsymbol{\mathrm{E}}}/{\partial t}$). The ethereal component of current prevents spatial variation of total current in a series system. The stochastic complexity of spatial thermal motion disappears for current }in a series system. Total current does not depend on location in a series system like a narrow ion channel on any time scale. Spatial variables are not needed in the description of total current in a one dimensional channel on any time scale, according to the Maxwell equations. Removing the spatial dependence of total current should dramatically simplify theories and simulations, one might imagine.
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework

Randomized classifiers have been shown to provide a promising approach for achieving certified robustness against adversarial attacks in deep learning. However, most existing methods only leverage Gaussian smoothing noise and only work for $\ell_2$ perturbation. We propose a general framework of adversarial certification with non-Gaussian noise and for more general types of attacks, from a unified functional optimization perspective. Our new framework allows us to identify a key trade-off between accuracy and robustness via designing smoothing distributions, helping to design new families of non-Gaussian smoothing distributions that work more efficiently for different $\ell_p$ settings, including $\ell_1$, $\ell_2$ and $\ell_\infty$ attacks. Our proposed methods achieve better certification results than previous works and provide a new perspective on randomized smoothing certification.
Embeddings using universal words in the free group of rank 2

For a countable group G = presented by its generators A and defining relations R we discuss a simple method to embed G into such a 2-generator group T that the images of generators from A are explicitly given in T, and the defining relations for T can automatically be deduced from the relations R. The obtained method is applied on particular examples of groups, and references of its application for embeddings of recursive groups into finitely presented groups are given.
A general kernelization technique for domination and independence problems in sparse classes

We unify and extend previous kernelization techniques in sparse classes [5, 16] by defining a structure we call water lilies and show how it can be used in bounded expansion classes to construct linear bikernels for $(r, c)$-DOMINATING SET, $(r, c)$-SCATTERED SET, TOTAL $r$-DOMINATION, r-Roman Domination, and a problem we call $(r, [{\lambda}, {\mu}])$-DOMINATION (implying a bikernel for r-PERFECT CODE). At the cost of slightly changing the output graph class these bikernels can be turned into into kernels. Finally, we demonstrate how these constructions can be combined to create 'multikernels', meaning graphs who represent kernels for multiple problems at once. Concretely, we show that $r$-DOMINATING SET, TOTAL $r$-DOMINATION, and $r$-ROMAN DOMINATION admit a multikernel; as well as $r$-DOMINATING SET and $2r$-INDEPENDENT SET for multiple values of $r$ at once.
Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization

Overfitting is one of the most common problems when training deep neural networks on comparatively small datasets. Here, we demonstrate that neural network activation sparsity is a reliable indicator for overfitting which we utilize to propose novel targeted sparsity visualization and regularization strategies. Based on these strategies we are able to understand and counteract overfitting caused by activation sparsity and filter correlation in a targeted layer-by-layer manner. Our results demonstrate that targeted sparsity regularization can efficiently be used to regularize well-known datasets and architectures with a significant increase in image classification performance while outperforming both dropout and batch normalization. Ultimately, our study reveals novel insights into the contradicting concepts of activation sparsity and network capacity by demonstrating that targeted sparsity regularization enables salient and discriminative feature learning while exploiting the full capacity of deep models without suffering from overfitting, even when trained excessively.
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables

Hamiltonian Monte Carlo (HMC) has emerged as a powerful Markov Chain Monte Carlo (MCMC) method to sample from complex continuous distributions. However, a fundamental limitation of HMC is that it can't be applied to distributions with mixed discrete and continuous variables. In this paper, we propose mixed HMC as a general framework to address this limitation. Mixed HMC is a novel family of MCMC algorithms that evolves the discrete and continuous variables in tandem in a way analogous to HMC, allowing more frequent updates of discrete variables while maintaining HMC's ability to suppress random-walk behavior. We establish mixed HMC's theoretical properties, and present an efficient implementation with Laplace momentum that can be incorporated into existing HMC implementations with minimal overhead. The superior performances of mixed HMC over existing methods are demonstrated with numerical experiments on Gaussian mixture models (GMMs), variable selection in Bayesian logistic regression (BLR), and correlated topic models (CTMs).
Symbolic Execution Game Semantics

We present a framework for symbolically executing and model checking higher-order programs with external (open) methods. We focus on the client-library paradigm and in particular we aim to check libraries with respect to any definable client. We combine traditional symbolic execution techniques with operational game semantics to build a symbolic execution semantics that captures arbitrary external behaviour. We prove the symbolic semantics to be sound and complete. This yields a bounded technique by imposing bounds on the depth of recursion and callbacks. We provide an implementation of our technique in the K framework and showcase its performance on a custom benchmark based on higher-order coding errors such as reentrancy bugs.
Network reconstruction from asynchronously updated evolutionary game

The interactions between players of prisoner's dilemma (PD) game are reconstructed with evolutionary game data. All participants play the game with their counterparts and gain corresponding rewards during each round of the game. However, their strategies are updated asynchronously during the evolutionary PD game. Two inference methods of the interactions between players are derived with naive mean-field (nMF) approximation and maximum log-likelihood estimation (MLE) respectively. The two methods are tested numerically also for fully connected asymmetric Sherrington-Kirkpatrick (SK) models, varying the data length, asymmetric degree, payoff and system noise (coupling strength). We find that the reconstruction mean square error (MSE) of MLE method is proportional to the inverse of data length and typically half (benefit from the extra information of update times) of that by nMF. Both methods are robust to the asymmetric degree but works better for large payoff. Compared with MLE, nMF is more sensitive to the couplings strength which prefers weak couplings.
Rapid Quantification of White Matter Disconnection in the Human Brain

With an estimated five million new stroke survivors every year and a rapidly aging population suffering from hyperintensities and diseases of presumed vascular origin that affect white matter and contribute to cognitive decline, it is critical that we understand the impact of white matter damage on brain structure and behavior. Current techniques for assessing the impact of lesions consider only location, type, and extent, while ignoring how the affected region was connected to the rest of the brain. Regional brain function is a product of both local structure and its connectivity. Therefore, obtaining a map of white matter disconnection is a crucial step that could help us predict the behavioral deficits that patients exhibit. In the present work, we introduce a new practical method for computing lesion-based white matter disconnection maps that require only moderate computational resources. We achieve this by creating diffusion tractography models of the brains of healthy adults and assessing the connectivity between small regions. We then interrupt these connectivity models by projecting patients’ lesions into them to compute predicted white matter disconnection. A quantified disconnection map can be computed for an individual patient in approximately 35 seconds using a single core CPU-based computation. In comparison, a similar quantification performed with other tools provided by MRtrix3 takes 5.47 minutes.
E2E Migration Strategies Towards 5G: Long-term Migration Plan and Evolution Roadmap

After freezing the first phase of the fifth generation of wireless networks (5G) standardization, it finally goes live now and the rollout of the commercial launch (most in fixed 5G broadband services) and migration has been started. However, some challenges are arising in the deployment, integration of each technology, and the interoperability in the network of the communication service providers (CSPs). At the same time, the evolution of 5G is not clear and many questions arise such as whether 5G has long-term evolution or when 5G will change to a next-generation one. This paper provides long-term migration options and paths towards 5G considering many key factors such as the cost, local/national data traffic, marketing, and the standardization trends in the radio access network (RAN), the transport network (TN), the core network (CN), and E2E network. Moreover, we outline some 5G evolution road maps emphasizing on the technologies, standards, and service time lines. The proposed migration paths can be the answer to some CSPs concerns about how to do long-term migration to 5G and beyond.
Graph4Code: A Machine Interpretable Knowledge Graph for Code

Knowledge graphs have proven extremely useful in powering diverse applications in semantic search and natural language understanding. Graph4Code is a knowledge graph about program code that can similarly power diverse applications such as program search, code understanding, refactoring, bug detection, and code automation. The graph uses generic techniques to capture the semantics of Python code: the key nodes in the graph are classes, functions and methods in popular Python modules. Edges indicate function usage (e.g., how data flows through function calls, as derived from program analysis of real code), and documentation about functions (e.g., code documentation, usage documentation, or forum discussions such as StackOverflow). We make extensive use of named graphs in RDF to make the knowledge graph extensible by the community. We describe a set of generic extraction techniques that we applied to over 1.3M Python files drawn from GitHub, over 2,300 Python modules, as well as 47M forum posts to generate a graph with over 2 billion triples. We also provide a number of initial use cases of the knowledge graph in code assistance, enforcing best practices, debugging and type inference. The graph and all its artifacts are available to the community for use.
Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks

This paper presents an audiovisual-based emotion recognition hybrid network. While most of the previous work focuses either on using deep models or hand-engineered features extracted from images, we explore multiple deep models built on both images and audio signals. Specifically, in addition to convolutional neural networks (CNN) and recurrent neutral networks (RNN) trained on facial images, the hybrid network also contains one SVM classifier trained on holistic acoustic feature vectors, one long short-term memory network (LSTM) trained on short-term feature sequences extracted from segmented audio clips, and one Inception(v2)-LSTM network trained on image-like maps, which are built based on short-term acoustic feature sequences. Experimental results show that the proposed hybrid network outperforms the baseline method by a large margin.
ODUSSEAS: a machine learning tool to derive effective temperature and metallicity for M dwarf stars

Aims. The derivation of spectroscopic parameters for M dwarf stars is very important in the fields of stellar and exoplanet characterization. The goal of this work is the creation of an automatic computational tool able to quickly and reliably derive the Teff and [Fe/H] of M dwarfs using optical spectra obtained by different spectrographs with different resolutions. Methods. ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes) is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package “scikit-learn” for predicting the stellar parameters. Results. We show that our tool is able to derive parameters accurately and with high precision, having precision errors of ~30 K for Teff and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions of between 48 000 and 115 000 and a signal-to-noise ratio above 20.
Distributed Mean Estimation with Optimal Error Bounds

Motivated by applications to distributed optimization and machine learning, we consider the distributed mean estimation problem, in which n nodes are each assigned a multidimensional input vector, and must cooperate to estimate the mean of the input vectors, while minimizing communication. In this paper, we provide the first tight bounds for this problem, in terms of the trade-off between the amount of communication between nodes and the variance of the node estimates relative to the true value of the mean.
Green Security Game with Community Engagement

While game-theoretic models and algorithms have been developed to combat illegal activities, such as poaching and over-fishing, in green security domains, none of the existing work considers the crucial aspect of community engagement: community members are recruited by law enforcement as informants and can provide valuable tips, e.g., the location of ongoing illegal activities, to assist patrols. We fill this gap and (i) introduce a novel two-stage security game model for community engagement, with a bipartite graph representing the informant-attacker social network and a level-$\kappa$ response model for attackers inspired by cognitive hierarchy; (ii) provide complexity results and exact, approximate, and heuristic algorithms for selecting informants and allocating patrollers against level-$\kappa$ ($\kappa<\infty$) attackers; (iii) provide a novel algorithm to find the optimal defender strategy against level-$\infty$ attackers, which converts the problem of optimizing a parameterized fixed-point to a bi-level optimization problem, where the inner level is just a linear program, and the outer level has only a linear number of variables and a single linear constraint. We also evaluate the algorithms through extensive experiments.
A new decomposition ensemble approach for tourism demand forecasting: Evidence from major source countries in
 Asia‐Pacific
 region

The Asian-pacific region is the major international tourism demand market in the world, and its tourism demand is deeply affected by various factors. Previous studies have shown that different market factors influence the tourism market demand at different timescales. Accordingly, the decomposition ensemble learning approach is proposed to analyze the impact of different market factors on market demand, and the potential advantages of the proposed method on forecasting tourism demand in the Asia-pacific region are further explored. This study carefully explores the multi-scale relationship between tourist destinations and the major source countries, by decomposing the corresponding monthly tourist arrivals with noise-assisted multivariate empirical mode decomposition. With the China and Malaysia as case studies, their respective empirical results show that decomposition ensemble approach significantly better than the benchmarks which include statistical model, machine learning and deep learning model, in terms of the level forecasting accuracy and directional forecasting accuracy.
Accessing Higher-level Representations in Sequential Transformers with Feedback Memory

Transformers are feedforward networks that can process input tokens in parallel. While this parallelization makes them computationally efficient, it restricts the model from fully exploiting the sequential nature of the input - the representation at a given layer can only access representations from lower layers, rather than the higher level representations already built in previous time steps. In this work, we propose the Feedback Transformer architecture that exposes all previous representations to all future representations, meaning the lowest representation of the current timestep is formed from the highest-level abstract representation of the past. We demonstrate on a variety of benchmarks in language modeling, neural machine translation, summarization, and reinforcement learning that the increased representation capacity can improve over Transformer baselines.
Real-Time Optimal Guidance and Control for Interplanetary Transfers Using Deep Networks

We consider the Earth-Venus mass-optimal interplanetary transfer of a low-thrust spacecraft and show how the optimal guidance can be represented by deep networks in a large portion of the state space and to a high degree of accuracy. Imitation (supervised) learning of optimal examples is used as a network training paradigm. The resulting models are suitable for an on-board, real-time, implementation of the optimal guidance and control system of the spacecraft and are called G&CNETs. A new general methodology called Backward Generation of Optimal Examples is introduced and shown to be able to efficiently create all the optimal state action pairs necessary to train G&CNETs without solving optimal control problems. With respect to previous works, we are able to produce datasets containing a few orders of magnitude more optimal trajectories and obtain network performances compatible with real missions requirements. Several schemes able to train representations of either the optimal policy (thrust profile) or the value function (optimal mass) are proposed and tested. We find that both policy learning and value function learning successfully and accurately learn the optimal thrust and that a spacecraft employing the learned thrust is able to reach the target conditions orbit spending only 2 permil more propellant than in the corresponding mathematically optimal transfer. Moreover, the optimal propellant mass can be predicted (in case of value function learning) within an error well within 1%. All G&CNETs produced are tested during simulations of interplanetary transfers with respect to their ability to reach the target conditions optimally starting from nominal and off-nominal conditions.
A construction of $G_2$-manifolds from K3 surfaces with a $\mathbb{Z}^2_2$-action

A product of a K3 surface $S$ and a flat 3-dimensional torus $T^3$ is a manifold with holonomy $SU(2)$. Since $SU(2)$ is a subgroup of $G_2$, $S\times T^3$ carries a torsion-free $G_2$-structure. We assume that $S$ admits an action of $\mathbb{Z}^2_2$ with certain properties. There are several possibilities to extend this action to $S\times T^3$. A recent result of Joyce and Karigiannis allows us to resolve the singularities of $(S\times T^3)/\mathbb{Z}^2_2$ such that we obtain smooth $G_2$-manifolds. We classify the quotients $(S\times T^3)/\mathbb{Z}^2_2$ under certain restrictions and compute the Betti numbers of the corresponding $G_2$-manifolds. Moreover, we study a class of quotients by a non-abelian group. Several of our examples have new values of $(b^2,b^3)$.
Generalized Tonnetz and discrete Abel-Jacobi map

Motivated by classical Euler's $Tonnetz$, we introduce and study the combinatorics and topology of more general simplicial complexes $Tonn^{n,k}(L)$ of "Tonnetz type". Out main result is that for a sufficiently generic choice of parameters the generalized tonnetz $Tonn^{n,k}(L)$ is a triangulation of a $(k-1)$-dimensional torus $T^{k-1}$. In the proof we construct and use the properties of a "discrete Abel-Jacobi map", which takes values in the torus $T^{k-1} \cong \mathbb{R}^{k-1}/\Lambda$ where $\Lambda \cong \mathbb{A}^\ast_{k-1}$ is the permutohedral lattice.