Miracle-dz/UniFAR-FTUs huggingface dataset:
Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods

It is well known that Reinforcement Learning (RL) can be formulated as a convex program with linear constraints. The dual form of this formulation is unconstrained, which we refer to as dual RL, and can leverage preexisting tools from convex optimization to improve the learning performance of RL agents. We show that several state-of-the-art deep RL algorithms (in online, ofﬂine, and imitation settings) can be viewed as dual RL approaches in a uniﬁed framework. This uniﬁcation calls for the methods to be studied on common ground, so as to identify the components that actually contribute to the success of these methods. Our uniﬁcation also reveals that prior off-policy imitation learning methods in the dual space are based on an unrealistic coverage assumption and are restricted to matching a particular f -divergence. We propose a new method using a simple modiﬁcation to the dual framework that allows for imitation learning with arbitrary off-policy data to obtain near-expert performance.
Minimizing Dynamic Regret on Geodesic Metric Spaces

In this paper, we consider the sequential decision problem where the goal is to minimize the general dynamic regret on a complete Riemannian manifold. The task of offline optimization on such a domain, also known as a geodesic metric space, has recently received significant attention. The online setting has received significantly less attention, and it has remained an open question whether the body of results that hold in the Euclidean setting can be transplanted into the land of Riemannian manifolds where new challenges (e.g., curvature) come into play. In this paper, we show how to get optimistic regret bound on manifolds with non-positive curvature whenever improper learning is allowed and propose an array of adaptive no-regret algorithms. To the best of our knowledge, this is the first work that considers general dynamic regret and develops"optimistic"online learning algorithms which can be employed on geodesic metric spaces.
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation

This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on predefined rules to determine molecular bonds based on the 3D conformation, MiDi offers an end-to-end differentiable approach that streamlines the molecule generation process. Our experimental results demonstrate the effectiveness of this approach. On the challenging GEOM-DRUGS dataset, MiDi generates 92% of stable molecules, against 6% for the previous EDM model that uses interatomic distances for bond prediction, and 40% using EDM followed by an algorithm that directly optimize bond orders for validity. Our code is available at github.com/cvignac/MiDi.
Primordial black holes dark matter and secondary gravitational waves from warm Higgs-G inflation

We explore the role of dissipative effects during warm inflation leading to the small-scale enhancement of the power spectrum of curvature perturbations. In this paper, we specifically focus on non-canonical warm inflationary scenarios and study a model of warm Higgs-G inflation, in which the Standard Model Higgs boson drives inflation, with a Galileon-like non-linear kinetic term. We show that in the Galileon-dominated regime, the primordial power spectrum is strongly enhanced, leading to the formation of primordial black holes (PBH) with a wide range of the mass spectrum. Interestingly, PBHs in the asteroid mass window ∼ (1017–1023) g are generated in this model, which can explain the total abundance of dark matter in the Universe. In our analysis, we also calculate the secondary gravitational waves (GW) sourced by these small-scale overdense fluctuations and find that the induced GW spectrum can be detected in future GW detectors, such as LISA, BBO, DECIGO, etc. Our scenario thus provides a novel way of generating PBHs as dark matter and a detectable stochastic GW background from warm inflation. We also show that our scenario is consistent with the swampland and the trans-Planckian censorship conjectures and, thus, remains in the viable landscape of UV complete theories.
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection

Split learning enables efficient and privacy-aware training of a deep neural network by splitting a neural network so that the clients (data holders) compute the first layers and only share the intermediate output with the central compute-heavy server. This paradigm introduces a new attack medium in which the server has full control over what the client models learn, which has already been exploited to infer the private data of clients and to implement backdoors in the client models. Although previous work has shown that clients can successfully detect such training-hijacking attacks, the proposed methods rely on heuristics, require tuning of many hyperparameters, and do not fully utilize the clients' capabilities. In this work, we show that given modest assumptions regarding the clients' compute capabilities, an out-of-the-box outlier detection method can be used to detect existing training-hijacking attacks with almost-zero false positive rates. We conclude through experiments on different tasks that the simplicity of our approach we name SplitOut makes it a more viable and reliable alternative compared to the earlier detection methods.
Discretized quadratic optimal control for continuous-time two-dimensional systems

In this paper, discretized quadratic optimal control for continuous-time two-dimensional (2D) systems is newly proposed. It introduces a new state vector (a new virtual control input) to directly convert the original continuous-time 2D quadratic cost function into a decoupled discretized form. As a result, a new virtual discrete-time 2D model with the new virtual control input is constructed to indirectly find the desired discretized quadratic optimal regulator for the continuous-time 2D system. The recently developed dynamic programming in discrete-time 1D descriptor form is utilized to determine the desired discretized quadratic optimal regulator. This method provides a novel approach for discretized quadratic optimal control of continuous-time 2D systems. An illustrative example is presented to demonstrate the effectiveness of the proposed procedure.
Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation

In this manuscript we consider the problem of generalized linear estimation on Gaussian mixture data with labels given by a single-index model. Our first result is a sharp asymptotic expression for the test and training errors in the high-dimensional regime. Motivated by the recent stream of results on the Gaussian universality of the test and training errors in generalized linear estimation, we ask ourselves the question:"when is a single Gaussian enough to characterize the error? ". Our formula allow us to give sharp answers to this question, both in the positive and negative directions. More precisely, we show that the sufficient conditions for Gaussian universality (or lack of thereof) crucially depend on the alignment between the target weights and the means and covariances of the mixture clusters, which we precisely quantify. In the particular case of least-squares interpolation, we prove a strong universality property of the training error, and show it follows a simple, closed-form expression. Finally, we apply our results to real datasets, clarifying some recent discussion in the literature about Gaussian universality of the errors in this context.
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks

Multiple recent studies show a paradox in graph convolutional networks (GCNs), that is, shallow architectures limit the capability of learning information from high-order neighbors, while deep architectures suffer from over-smoothing or over-squashing. To enjoy the simplicity of shallow architectures and overcome their limits of neighborhood extension, in this work, we introduce Biaffine technique to improve the expressiveness of graph convolutional networks with a shallow architecture. The core design of our method is to learn direct dependency on long-distance neighbors for nodes, with which only one-hop message passing is capable of capturing rich information for node representation. Besides, we propose a multi-view contrastive learning method to exploit the representations learned from long-distance dependencies. Extensive experiments on nine graph benchmark datasets suggest that the shallow biaffine graph convolutional networks (BAGCN) significantly outperforms state-of-the-art GCNs (with deep or shallow architectures) on semi-supervised node classification. We further verify the effectiveness of biaffine design in node representation learning and the performance consistency on different sizes of training data.
Diabetes mellitus differently affects electrical membrane properties of vagal afferent neurons of rats

To study whether diabetes mellitus (DM) would cause electrophysiological alterations in nodose ganglion (NG) neurons, we used patch clamp and intracellular recording for voltage and current clamp configuration, respectively, on cell bodies of NG from rats with DM. Intracellular microelectrodes recording, according to the waveform of the first derivative of the action potential, revealed three neuronal groups (A0, Ainf, and Cinf), which were differently affected. Diabetes only depolarized the resting potential of A0 (from −55 to −44 mV) and Cinf (from −49 to −45 mV) somas. In Ainf neurons, diabetes increased action potential and the after‐hyperpolarization durations (from 1.9 and 18 to 2.3 and 32 ms, respectively) and reduced dV/dtdesc (from −63 to ‐52 V s−1). Diabetes reduced the action potential amplitude while increasing the after‐hyperpolarization amplitude of Cinf neurons (from 83 and −14 mV to 75 and −16 mV, respectively). Using whole cell patch clamp recording, we observed that diabetes produced an increase in peak amplitude of sodium current density (from −68 to −176 pA pF−1) and displacement of steady‐state inactivation to more negative values of transmembrane potential only in a group of neurons from diabetic animals (DB2). In the other group (DB1), diabetes did not change this parameter (−58 pA pF−1). This change in sodium current did not cause an increase in membrane excitability, probably explainable by the alterations in sodium current kinetics, which are also induced by diabetes. Our data demonstrate that diabetes differently affects membrane properties of different nodose neuron subpopulations, which likely have pathophysiological implications for diabetes mellitus.
Inertial effects on the orientation of nearly spherical particles in simple shear flow

We investigate theoretically the first effects of inertia on the orientation dynamics of a torque-free spheroidal particle in simple shear flow when the deviation from sphericity is small. The inertialess motion of any axisymmetric particle in simple shear represents a degenerate limit, the spheroidal geometry being a special case; as originally found by Jeffery (Proc. R. Soc. Lond. A, vol. 102, 1922, p. 161), the orientation vector moves indefinitely along any one of a single-parameter family of closed orbits centred around the vorticity axis, the distribution across orbits being determined by initial conditions. We consider both the inertia of the particle and that of the suspending fluid, characterized by the Stokes ($\hbox{\it St}$) and Reynolds numbers ($\hbox{\it Re}\,{=}\, \rho_f/\rho_p\hbox{\it St}$, $\rho_p$ and $\rho_f$ being the particle and fluid densities), respectively, as mechanisms for breaking the aforementioned degeneracy. The former is defined as $\hbox{\it St} \,{=}\, a^2\dot{\gamma}\rho_p/\mu$, where $\dot{\gamma}$ is the shear rate, $a$ is the radius of the unperturbed sphere and $\mu$ is the fluid viscosity. When the particles are much denser than the suspending fluid, as is the case for aerosols, $\hbox{\it St} \,{\gg}\, \hbox{\it Re}$ (both parameters being much less than unity), inertial forces in the fluid may be neglected. It is then found, in the absence of gravity, that a slightly prolate spheroid drifts toward the shearing plane, while the axis of a slightly oblate spheroid tends toward the vorticity axis, both on a time scale of $O(|\epsilon | \hbox{\it St} \dot{\gamma})^{-1}$, where $\epsilon ({\ll}\,1)$ is the deviation from sphericity. For the case of neutrally buoyant particles ($\hbox{\it St} \,{=}\,\hbox{\it Re}$), inertia of both the particle and fluid come into play. In contrast to the small but finite $\hbox{\it St}$ zero $\hbox{\it Re}$ case, the orientation vector of a neutrally buoyant prolate spheroid now migrates toward the direction of vorticity, while that of an oblate spheroid drifts towards the shearing plane. The time scale of drift towards the asymptotic state is $O(| \epsilon | \hbox{\it Re} \dot{\gamma})^{-1}$ in both cases. Thereafter, we also examine the rotations of prolate and oblate spheroids in the presence of both gravity and shear, the analysis again being restricted to weak inertial effects. A wide range of interesting orientational behaviour arises, and the long-time orientation dynamics of the spheroids are determined as a function of both the density ratio $\rho_p/\rho_f$ and a shear parameter $N$, defined as $N\,{=}\, 2a \rho_f g/(9\mu \dot{\gamma})$.
Battery-Powered RF Pre-Ionization System for the Caltech Magnetohydrodynamically-Driven Jet Experiment: RF Discharge Properties and MHD-Driven Jet Dynamics

This thesis describes investigations of two classes of laboratory plasmas with rather different properties: partially ionized low pressure radiofrequency (RF) discharges, and fully ionized high density magnetohydrodynamically (MHD)-driven jets. An RF pre-ionization system was developed to enable neutral gas breakdown at lower pressures and create hotter, faster jets in the Caltech MHD-Driven Jet Experiment. The RF plasma source used a custom pulsed 3 kW 13.56 MHz RF power amplifier that was powered by AA batteries, allowing it to safely float at 4-6 kV with the cathode of the jet experiment. The argon RF discharge equilibrium and transport properties were analyzed, and novel jet dynamics were observed. Although the RF plasma source was conceived as a wave-heated helicon source, scaling measurements and numerical modeling showed that inductive coupling was the dominant energy input mechanism. A one-dimensional time-dependent fluid model was developed to quantitatively explain the expansion of the pre-ionized plasma into the jet experiment chamber. The plasma transitioned from an ionizing phase with depressed neutral emission to a recombining phase with enhanced emission during the course of the experiment, causing fast camera images to be a poor indicator of the density distribution. Under certain conditions, the total visible and infrared brightness and the downstream ion density both increased after the RF power was turned off. The time-dependent emission patterns were used for an indirect measurement of the neutral gas pressure. The low-mass jets formed with the aid of the pre-ionization system were extremely narrow and collimated near the electrodes, with peak density exceeding that of jets created without pre-ionization. The initial neutral gas distribution prior to plasma breakdown was found to be critical in determining the ultimate jet structure. The visible radius of the dense central jet column was several times narrower than the axial current channel radius, suggesting that the outer portion of the jet must have been force free, with the current parallel to the magnetic field. The studies of non-equilibrium flows and plasma self-organization being carried out at Caltech are relevant to astrophysical jets and fusion energy research.
STRONGLY TORSION-FREE MODULES AND LOCAL COHOMOLOGY OVER COHEN–MACAULAY RINGS

ABSTRACT In this paper we investigate the relationship between strongly torsion-free, maximal Cohen–Macaulay, and strongly cotorsion modules over Cohen–Macaulay local rings via local cohomology. Results are given analogous to those known for Gorenstein flat, totally reflexive, and Gorenstein injective modules over a Gorenstein local ring.
MHD model merging with IMF By : Lobe cells, sunward polar cap convection, and overdraped lobes

Sunward convection in the polar cap has long been given as evidence of convection cells that close within the polar cap. These are called “lobe cells” because they are confined to the open field lines that form the tail lobes. Whether lobe-cell convection is physically possible, however, has been seriously questioned. In support of lobe cells, we demonstrate their existence in an MHD model and illustrate the geometry of their circulating magnetic field lines for merging with a purely azimuthal (+By) interplanetary magnetic field (IMF). We find that the ratio of flux circulating in lobe cells compared with merging cells increases with decreasing Alfven Mach number and that sunward flow in the polar cap occurs both in lobe cells and on newly opened field lines in normal or “merging” cells. Although at any given time the sunward moving fields are configured in an overdraped pattern similar to that associated with northward IMF, the concept of sunward motion driven by electric field mapping or, equivalently, tugging on these overturned field lines does not apply, at least for the azimuthal IMF case. The overdraped fields and the null points to which they converge are immersed in a diffusion region at the magnetopause and support field-aligned potential drops so that in the course of time, the field lines lose their identity by changing partners. Thus the sunward flowing fields in the ionosphere are decoupled from their magnetopause counterparts and move in response to the ionospheric potential, which has been modified from the applied solar wind potential by diffusion effects. These MHD model results both support and elucidate concepts deduced from primitive superposition and phenomenological models.
Statistical physics of shear flow: a non-equilibrium problem

Complex fluids are easily and reproducibly driven into non-equilibrium steady states by the action of shear flow. The statistics of the microstructure of non-equilibrium fluids is important to the material properties of every complex fluid that flows, e.g. axle grease on a rotating bearing; blood circulating in capillaries; molten plastic flowing into a mould; the non-equilibrium onion phase of amphiphiles used for drug delivery; the list is endless. Such states are as diverse and interesting as equilibrium states, but are not governed by the same statistics as equilibrium materials. I review some recently discovered principles governing the probabilities of various types of molecular re-arrangements taking place within a sheared fluid. As well as providing new foundations for the study of non-equilibrium matter, the principles are applied to some simple models of particles interacting under flow, showing that the theory exhibits physically convincing behaviour.
Cloud Droplet Residual Particle Microphysics in Marine Stratocumulus Clouds Observed during the Monterey Area Ship Track Experiment

Abstract The effect of marine boundary layer pollution level (as determined by the aerosol particle number concentration) on the size distribution of aerosol particles that formed cloud droplets in marine stratiform clouds is examined. In situ measurements of cloud droplet residual particles with a counterflow virtual impactor during the Monterey Area Ship Track experiment are presented. The variation in residual particle size distribution and number concentration in clouds that formed in marine boundary layers with different pollution levels are discussed. The measurements show a constant shape of the residual particle size distributions for the variety of pollution levels encountered, even though the aerosol number concentration varied by up to a factor of 6. The measurements indicate that particles smaller than 0.1-μm radius controlled cloud droplet number concentrations in the clouds investigated. From literature values of the maximum supersaturations encountered in stratocumulus clouds, one can estim.
Small-Amplitude Free-Vibration Analysis of Piezoelectric Composite Plates Subject to Large Deflections and Initial Stresses

A coupled theoretical and computational framework is presented for analyzing the small amplitude-free vibrational response of composite laminated plates with piezoelectric actuators and sensors, subject to nonlinear effects due to large rotations and initial stresses. Coupled laminate mechanics incorporating nonlinear governing equations with mixed-field shear-layerwise assumptions for the piezoelectric laminate are implemented. A finite element method is formulated to yield the linearized discrete dynamic equations of a piezocomposite plate on top of its nonlinear electrostatic response, and a novel eight-node coupled nonlinear plate finite element forms the basis of numerical analyses. The natural frequencies in a beam with a piezoceramic actuator and sensor subject to in-plane mechanical loading, high enough to induce buckling and postbuckling are also experimentally characterized, and comparisons to numerical results show excellent correlation. Additional numerical evaluations quantify the active shifting of natural frequencies in adaptive beams and plates subject to high out-of-plane and in-plane electromechanical loading, and the variation of modal frequencies during buckling and postbuckling response. Finally, the possibility to detect and actively manage buckling in adaptive piezocomposite plates is illustrated.
Semi-active Vibration Control Schemes for Suspension Systems Using Magnetorheological Dampers

Three semi-active control methods are investigated for use in a suspension system using a commercial magnetorheological damper. The three control methods are the limited relative displacement method, the modified skyhook method, and the modified Rakheja-Sankar method. The method of averaging has been adopted to provide an analytical platform for analyzing the performance of the different control methods. The analytical results are verified using numerical simulation, and further are used to assess the efficiency of different control methods. An experimental test bed has been developed to examine the three control methods under sinusoidal and random excitations. Both analytical and experimental results confirm that the Rakheja-Sankar control and modified skyhook control methods significantly reduce the root-mean-square response of both the acceleration and relative displacement of the sprung mass, while the limited relative displacement controller can only control the relative displacement of the suspension system.