Miracle-dz/UniFAR-FTUs huggingface dataset:
Entropy estimates for uniform attractors of dissipative PDEs with non translation-compact external forces

We study the Kolmogorov’s entropy of uniform attractors for non-autonomous dissipative PDEs. The main attention is payed to the case where the external forces are not translation-compact. We present a new general scheme which allows us to give the upper bounds of this entropy for various classes of external forces through the entropy of proper projections of their hulls to the space of translation-compact functions. This result generalizes well known estimates of Vishik and Chepyzhov for the translation-compact case. The obtained results are applied to three model problems: sub-quintic 3D damped wave equation with Dirichlet boundary conditions, quintic 3D wave equation with periodic boundary conditions and 2D Navier-Stokes system in a bounded domain. The examples of finite-dimensional uniform attractors for some special external forces which are not translation-compact are also given.
Stability of line-node semimetals with strong Coulomb interactions and properties of the symmetry-broken state

We employ diagrammatic Monte Carlo simulations to establish criteria for the stability of line-node semimetals in the presence of Coulomb interactions. Our results indicate a phase transition to a chiral insulating state that occurs at a finite interaction threshold which we determine. We also compute the Landau levels for out-ofplane and in-plane magnetic fields in the symmetric and symmetry-broken phases. We find that the magnetic field couples to the chiral order parameter, implying that this degree of freedom can be manipulated in situ in experiments. Finally, we check the existence of edge states in the symmetry-broken phase. On the system’s boundary, we note that the metallic "drum-head" states that exist in the symmetric phase are gapped out. However, the symmetry-broken phase permits topological defects in the macroscopic order parameter in the form of domain walls, which host metallic "interface states. " These consist of line-like gap-closings that occur on the two-dimensional interfaces.
Mergers of Supermassive and Intermediate-mass Black Holes in Galactic Nuclei from Disruptions of Star Clusters

Gravitational waves (GWs) offer an unprecedented opportunity to survey the sky and detect mergers of compact objects. While intermediate-mass black holes (IMBHs) have not been detected beyond any reasonable doubt with either dynamical or accretion signatures, the GW landscape appears very promising. Mergers of an IMBH with a supermassive black hole (SMBH) will be primary sources for the planned space-based mission LISA and could be observed up to the distant universe. SMBH–IMBH binaries can be formed as a result of the migration and merger of stellar clusters at the center of galaxies, where an SMBH lurks. We build for the first time a semianalytical framework to model this scenario and find that the comoving merger rate of SMBH–IMBH binaries is ∼10−4 Gpc−3 yr−1 in the local universe for a unity IMBH occupation fraction, scales linearly with it, and has a peak at z ≈ 0.5–2. Our model predicts ∼0.1 events yr−1 within redshift z ≈ 3.5 if 10% of the inspiraled star clusters hosted an IMBH, while ∼1 event yr−1 for a unity occupation fraction. More than 90% of these systems will be detectable with LISA with a signal-to-noise ratio larger than 10, promising to potentially find a family of IMBHs.
Online Decision Transformer

Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches similar to large-scale language modeling. However, any practical instantiation of RL also involves an online component, where policies pretrained on passive offline datasets are finetuned via taskspecific interactions with the environment. We propose Online Decision Transformers (ODT), an RL algorithm based on sequence modeling that blends offline pretraining with online finetuning in a unified framework. Our framework uses sequence-level entropy regularizers in conjunction with autoregressive modeling objectives for sample-efficient exploration and finetuning. Empirically, we show that ODT is competitive with the state-of-the-art in absolute performance on the D4RL benchmark but shows much more significant gains during the finetuning procedure.
KNIFE: Kernelized-Neural Differential Entropy Estimation

Mutual Information (MI) has been widely used as a loss regularizer for training neural networks. This has been particularly effective when learn dis-entangled or compressed representations of high dimensional data. However, differential entropy (DE), another fundamental measure of information, has not found widespread use in neural network training. Although DE offers a potentially wider range of applications than MI, off-the-shelf DE estimators are either non differentiable, computationally intractable or fail to adapt to changes in the underlying distribution. These drawbacks prevent them from being used as regularizers in neural networks training. To address shortcomings in previously proposed estimators for DE, here we introduce K NIFE , a fully parameterized, differentiable kernel-based estimator of DE. The ﬂexibility of our approach also allows us to construct K NIFE -based estimators for conditional (on either discrete or continuous variables) DE, as well as MI. We empirically validate our method on high-dimensional synthetic data and further apply it to guide the training of neural networks for real-world tasks. Our experiments on a large variety of tasks, including visual domain adaptation, textual fair classiﬁcation, and textual ﬁne-tuning demonstrate the effectiveness of K NIFE - based estimation. Code can be found at https: //github.com/g-pichler/knife .
Modeling and Simulation of Turning Operation

Turning is one of the metal cutting operation, is most widely used manufacturing technique in the industry and there are lots of studies to investigate this complex process in both academic and industrial world. Metal cutting operations still represent the largest class of manufacturing operations where turning is the most commonly employed material removal process. Advances in CNC/NC machining and high speed machining technologies have boosted the productivity of machining process. One of the state of the art efforts in manufacturing engineering is to use the FEM for simulation of the metal cutting process. Simulation can increase the understanding of the cutting process and reduce the number of experiments. Researchers find these variables by using experimental techniques which makes the investigation very time consuming and expensive. At this point, finite element modeling and simulation becomes main tool. These important cutting variables can be predicted without doing any experiment with finite element method. In this study the metal cutting process is analyzed with FEM model for 3D simulation of turning process with solid single point cutting tool. This tool is modeled with CATIAV5, and exported STL files and imported in DEFORM 3D. (1)
QED and strong isospin corrections in the hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon

L. Parato, f ,∗ Sz. Borsanyi, Z. Fodor, J. N. Guenther, f C. Hoelbling, S. D. Katz, L. Lellouch, f T. Lippert, K. Miura, f ,g,h K. K. Szabo, F. Stokes, B. C. Toth, Cs. Torok and L. Varnhorst f (Budapest-Marseille-Wuppertal collaboration) Department of Physics, University of Wuppertal, D-42119 Wuppertal, Germany Jülich Supercomputing Centre, Forschungszentrum Jülich, D-52428 Jülich, Germany Department of Physics, Pennsylvania State University, University Park, PA 16802, USA Institute for Theoretical Physics, Eötvös University, H-1117 Budapest, Hungary University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA f Aix Marseille Univ, Université de Toulon, CNRS, CPT, IPhU, Marseille, France Helmholtz Institute Mainz, D-55099 Mainz, Germany Kobayashi-Maskawa Institute for the Origin of Particles and the Universe, Nagoya University, Nagoya 464-8602, Japan E-mail: parato@cpt.univ-mrs.fr
Financial Data Mining Using Flexible ICA-GARCH Models

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity ( GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has al - ways been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.
Games of Artificial Intelligence: A Continuous-Time Approach

This paper studies the strategic interaction of algorithms in economic games. We analyze games where learning algorithms play against each other while searching for the best strategy. We ﬁrst establish a ﬂuid approximation technique that enables us to characterize the learning outcomes in continuous time. This tool allows to identify the equilibria of games played by Artiﬁcial Intelligence algorithms and perform comparative statics analysis. Thus, our results bridge a gap between traditional learning theory and applied models, allowing quantitative analysis of traditionally experimental systems. We describe the outcomes of a social dilemma, and we provide analytical guidance for the design of pricing algorithms in a Bertrand game. We uncover a new phenomenon, the coordination bias, which explains how algorithms may fail to learn dominant strategies
Audio-Based Deep Learning Frameworks for Detecting COVID-19

This paper evaluates a wide range of audio-based deep learning frameworks applied to the breathing, cough, and speech sounds for detecting COVID-19. In general, the audio recording inputs are transformed into low-level spectrogram features, then they are fed into pre-trained deep learning models to extract high-level embedding features. Next, the dimension of these high-level embedding features are reduced before fine-tuning using Light Gradient Boosting Machine (LightGBM) as a back-end classification. Our experiments on the Second DiCOVA Challenge achieved the highest Area Under the Curve (AUC), F1 score, sensitivity score, and specificity score of 89.03%, 64.41%, 63.33%, and 95.13%, respectively. Based on these scores, our method outperforms the state-of-the-art systems, and improves the challenge baseline by 4.33%, 6.00% and 8.33% in terms of AUC, F1 score and sensitivity score, respectively.
Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis

Our universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work we develop Translation and Rotation Equivariant Normalizing Flow (TRENF), a generative Normalizing Flow (NF) model which explicitly incorporates these symmetries, defining the data likelihood via a sequence of Fourier space-based convolutions and pixel-wise nonlinear transforms. TRENF gives direct access to the high dimensional data likelihood p(x|y) as a function of the labels y, such as cosmological parameters. In contrast to traditional analyses based on summary statistics, the NF approach has no loss of information since it preserves the full dimensionality of the data. On Gaussian random fields, the TRENF likelihood agrees well with the analytical expression and saturates the Fisher information content in the labels y. On nonlinear cosmological overdensity fields from N-body simulations, TRENF leads to significant improvements in constraining power over the standard power spectrum summary statistic. TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal. Finally, we develop a generalization of this model that can handle effects that break the symmetry of the data, such as the survey mask, which enables likelihood analysis on data without periodic boundaries.
Efficient Natural Gradient Descent Methods for Large-Scale Optimization Problems

We propose efficient numerical schemes for implementing the natural gradient descent (NGD) for a broad range of metric spaces with applications to PDE-based optimization problems. Our technique represents the natural gradient direction as a solution to a standard least-squares problem. Hence, instead of calculating, storing, or inverting the information matrix directly, we apply efficient methods from numerical linear algebra. We treat both scenarios where the Jacobian, i.e., the derivative of the state variable with respect to the parameter, is either explicitly known or implicitly given through constraints. We can thus reliably compute several natural NGDs for a large-scale parameter space. In particular, we are able to compute Wasserstein NGD in thousands of dimensions, which was believed to be out of reach. Finally, our numerical results shed light on the qualitative differences between the standard gradient descent and various NGD methods based on different metric spaces in nonconvex optimization problems.
The Complexity of Matching Games: A Survey

Matching games naturally generalize assignment games, a well-known class of cooperative games. Interest in matching games has grown recently due to some breakthrough results and new applications. This state-of-the-art survey provides an overview of matching games and extensions, such as b-matching games and partitioned matching games; the latter originating from the emerging area of international kidney exchange. In this survey we focus on computational complexity aspects of various game-theoretical solution concepts, such as the core, nucleolus and Shapley value, when the input is restricted to a matching game or one of its variants.
Audio-Visual Fusion Layers for Event Type Aware Video Recognition

Human brain is continuously inundated with the multisensory information and their complex interactions coming from the outside world at any given moment. Such information is automatically analyzed by binding or segregating in our brain. While this task might seem effortless for human brains, it is extremely challenging to build a machine that can perform similar tasks since complex interactions cannot be dealt with single type of integration but requires more sophisticated approaches. In this paper, we propose a new model to address the multisensory integration problem with individual event-specific layers in a multi-task learning scheme. Unlike previous works where single type of fusion is used, we design event-specific layers to deal with different audio-visual relationship tasks, enabling different ways of audio-visual formation. Experimental results show that our event-specific layers can discover unique properties of the audio-visual relationships in the videos. Moreover, although our network is formulated with single labels, it can output additional true multi-labels to represent the given videos. We demonstrate that our proposed framework also exposes the modality bias of the video data category-wise and dataset-wise manner in popular benchmark datasets.
Efficient Kernel UCB for Contextual Bandits

In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems. Specifically, our method relies on incremental Nystrom approximations of the joint kernel embedding of contexts and actions. This allows us to achieve a complexity of O(CTm^2) where m is the number of Nystrom points. To recover the same regret as the standard kernelized UCB algorithm, m needs to be of order of the effective dimension of the problem, which is at most O(\sqrt(T)) and nearly constant in some cases.
An Introduction to Neural Data Compression

Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such as normalizing flows, variational autoencoders, diffusion probabilistic models, and generative adversarial networks. The present article aims to introduce this field of research to a broader machine learning audience by reviewing the necessary background in information theory (e.g., entropy coding, rate-distortion theory) and computer vision (e.g., image quality assessment, perceptual metrics), and providing a curated guide through the essential ideas and methods in the literature thus far.
Hybrid Soft Robots Incorporating Soft and Stiff Elements

Soft robots are inherently compliant and have a solid potential for realizing human-friendly and safe robots. However, despite continued research highlighting the potential of soft robots, they remain primarily confined to laboratory settings. Inspired by spider monkeys' tails, this paper proposes a hybrid soft robot (HSR) design. We detail the design objectives and methodology to improve the controllable stiffness range and achieve independent stiffness and shape control. We extend the curve-parametric approach to obtain a kinematic model of the proposed HSR. We experimentally demonstrate that the proposed HSR has about a 100% stiffness range increase than a previous soft robot design with identical physical dimensions. In addition, we empirically map HSR's bending shape-pressure-stiffness and present an application example - a soft robotic gripper - to demonstrate the decoupled nature of stiffness and shape variations. Experimental results show that the proposed HSR can be successfully used in applications where independent stiffness and shape control is desired.
Improved Upper Bounds for Finding Tarski Fixed Points

We study the query complexity of finding a Tarski fixed point over the k-dimensional grid {1,. ,n}k. Improving on the previous best upper bound of O(log⌈2k/3⌉n)[7], we give a new algorithm with query complexity O(log⌈(k+1)/2⌉n). This is based on a novel decomposition theorem about a weaker variant of the Tarski fixed point problem, where the input consists of a monotone function f:[n]k→[n]k and a monotone sign function b:[n]k→ {-1,0,1} and the goal is to find a point x ∈ [n]k that satisfies either f(x) ≼ x and b(x) ≤ 0 or f(x) ≽ x and b(x) ≥ 0.
Lattice studies of the 
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 gauge theory with two fundamental and three antisymmetric Dirac f

We consider the $Sp(4)$ gauge theory coupled to $N_f=2$ fundamental and $n_f=3$ antisymmetric flavours of Dirac fermions in four dimensions. This theory serves as the microscopic origin for composite Higgs models with $SU(4)/Sp(4)$ coset, supplemented by partial top compositeness. We study numerically its lattice realisation, and couple the fundamental plaquette action to Wilson-Dirac fermions in mixed representations, by adopting a (rational) hybrid Monte Carlo method, to perform non-trivial tests of the properties of the resulting lattice theory. We find evidence of a surface (with boundaries) of first-order bulk phase transitions in the three-dimensional space of bare parameters (one coupling and two masses). Explicit evaluation of the Dirac eigenvalues confirms the expected patterns of global symmetry breaking. After investigating finite volume effects in the weak-coupling phase of the theory, for the largest available lattice we study the mass spectra of the lightest spin-0 and spin-1 flavoured mesons composed of fermions in each representation, and of the lightest half-integer spin composite particle made of fermions in different representations -- the chimera baryon. This work sets the stage for future systematical studies of the non-perturbative dynamics in phenomenologically relevant regions of parameter space.
Ab initio study of magnetic tunnel junctions based on half-metallic and spin-gapless semiconducting Heusler compounds: Reconfigurable diode and inverse TMR effect for magnetic memory and logic applications

Magnetic tunnel junctions (MTJs) have attracted strong research interest within the last decades due to their potential use as non-volatile memory such as MRAM as well as for magnetic logic applications. Half-metallic magnets (HMMs) have been suggested as ideal electrode materials for MTJs to achieve an extremely large tunnel magnetoresistance (TMR) effect. Despite their high TMR ratios, MTJs based on HMMs do not exhibit current rectification, i.e., a diode effect, which was achieved in a novel MTJ concept [ACS Appl. Electron. Mater. 1, 1552–1559 (2019)] based on HMMs and type-II spin-gapless semiconductors (SGSs). The proposed concept has been recently experimentally demonstrated using Heusler compounds. In the present work, we investigate from first-principles MTJs based on type-II SGS and HMM quaternary Heusler compounds FeVTaAl, FeVTiSi, MnVTiAl, and CoVTiSb. Our ab initio quantum transport calculations based on a nonequilibrium Green’s function method have demonstrated that the MTJs under consideration exhibit current rectification with relatively high on/off ratios. We show that, in contrast to conventional semiconductor diodes, the rectification bias voltage window (or breakdown voltage) of the MTJs is limited by the spin gap of the HMM and SGS Heusler compounds, which can be tuned by doping the electrode materials. A unique feature of the present MTJs is that the diode effect can be configured dynamically, i.e., depending on the relative orientation of the magnetization of the electrodes, the MTJ allows the electrical current to pass either in one or the other direction, which leads to an inverse TMR effect. The combination of nonvolatility, reconfigurable diode functionality, tunable rectification voltage window, and high Curie temperature of the electrode materials make the proposed MTJs very promising for room temperature spintronic applications and opens new ways to magnetic memory and logic concepts as well as logic-in-memory computing.