Miracle-dz/UniFAR-FTUs huggingface dataset:
Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning

Action advising endeavors to leverage supplementary guidance from expert teachers to alleviate the issue of sampling inefficiency in Deep Reinforcement Learning (DRL). Previous agent-specific action advising methods are hindered by imperfections in the agent itself, while agent-agnostic approaches exhibit limited adaptability to the learning agent. In this study, we propose a novel framework called Agent-Aware trAining yet Agent-Agnostic Action Advising (A7) to strike a balance between the two. The underlying concept of A7 revolves around utilizing the similarity of state features as an indicator for soliciting advice. However, unlike prior methodologies, the measurement of state feature similarity is performed by neither the error-prone learning agent nor the agent-agnostic advisor. Instead, we employ a proxy model to extract state features that are both discriminative (adaptive to the agent) and generally applicable (robust to agent noise). Furthermore, we utilize behavior cloning to train a model for reusing advice and introduce an intrinsic reward for the advised samples to incentivize the utilization of expert guidance. Experiments are conducted on the GridWorld, LunarLander, and six prominent scenarios from Atari games. The results demonstrate that A7 significantly accelerates the learning process and surpasses existing methods (both agent-specific and agent-agnostic) by a substantial margin. Our code will be made publicly available.
Fill-ins with scalar curvature lower bounds and applications to positive mass theorems

Given a constant C and a smooth closed $(n-1)$-dimensional Riemannian manifold $(\Sigma, g)$ equipped with a positive function $H$, a natural question to ask is whether this manifold can be realised as the boundary of a smooth $n$-dimensional Riemannian manifold with scalar curvature bounded below by C. That is, does there exist a fill-in of $(\Sigma,g,H)$ with scalar curvature bounded below by C? We use variations of an argument due to Miao and the author [arXiv:1701.04805] to explicitly construct fill-ins with different scalar curvature lower bounds, where we permit the fill-in to contain another boundary component provided it is a minimal surface. Our main focus is to illustrate the applications of such fill-ins to geometric inequalities in the context of general relativity. By filling in a manifold beyond a boundary, one is able to obtain lower bounds on the mass in terms of the boundary geometry through positive mass theorems and Penrose inequalities. We consider fill-ins with both positive and negative scalar curvature lower bounds, which from the perspective of general relativity corresponds to the sign of the cosmological constant, as well as a fill-in suitable for the inclusion of electric charge.
StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models

We propose StyleCap, a method to generate natural language descriptions of speaking styles appearing in speech. Although most of conventional techniques for para-/non-linguistic information recognition focus on the category classification or the intensity estimation of pre-defined labels, they cannot provide the reasoning of the recognition result in an interpretable manner. StyleCap is a first step towards an end-to-end method for generating speaking-style prompts from speech, i.e., automatic speaking-style captioning. StyleCap is trained with paired data of speech and natural language descriptions. We train neural networks that convert a speech representation vector into prefix vectors that are fed into a large language model (LLM)-based text decoder. We explore an appropriate text decoder and speech feature representation suitable for this new task. The experimental results demonstrate that our StyleCap leveraging richer LLMs for the text decoder, speech self-supervised learning (SSL) features, and sentence rephrasing augmentation improves the accuracy and diversity of generated speaking-style captions. Samples of speaking-style captions generated by our StyleCap are publicly available.
A Geometric Tension Dynamics Model of Epithelial Convergent Extension

Epithelial tissue elongation by convergent extension is a key motif of animal morphogenesis. On a coarse scale, cell motion resembles laminar fluid flow; yet in contrast to a fluid, epithelial cells adhere to each other and maintain the tissue layer under actively generated internal tension. To resolve this apparent paradox, we formulate a model in which tissue flow occurs through adiabatic remodelling of the cellular force balance causing local cell rearrangement. We propose that the gradual shifting of the force balance is caused by positive feedback on myosin-generated cytoskeletal tension. Shifting force balance within a tension network causes active T1s oriented by the global anisotropy of tension. Rigidity of cells against shape changes converts the oriented internal rearrangements into net tissue deformation. Strikingly, we find that the total amount of tissue extension depends on the initial magnitude of anisotropy and on cellular packing order. T1s degrade this order so that tissue flow is self-limiting. We explain these findings by showing that coordination of T1s depends on coherence in local tension configurations, quantified by a certain order parameter in tension space. Our model reproduces the salient tissue- and cell-scale features of germ band elongation during Drosophila gastrulation, in particular the slowdown of tissue flow after approximately twofold extension concomitant with a loss of order in tension configurations. This suggests local cell geometry contains morphogenetic information and yields predictions testable in future experiments. Furthermore, our focus on defining biologically controlled active tension dynamics on the manifold of force-balanced states may provide a general approach to the description of morphogenetic flow.
An algebraic criterion for the vanishing of bounded cohomology

We prove the vanishing of bounded cohomology with separable dual coefficients for many groups of interest in geometry, dynamics, and algebra. These include compactly supported structure-preserving diffeomorphism groups of certain manifolds; the group of interval exchange transformations of the half line; piecewise linear and piecewise projective groups of the line, giving strong answers to questions of Calegari and Navas; direct limit linear groups of relevance in algebraic K-theory, thereby answering a question by Kastenholz and Sroka and a question of two of the authors and L\"oh; and certain subgroups of big mapping class groups, such as the stable braid group and the stable mapping class group, proving a conjecture of Bowden. Moreover, we prove that in the recently introduced framework of enumerated groups, the generic group has vanishing bounded cohomology with separable dual coefficients. At the heart of our approach is an elementary algebraic criterion called the commuting cyclic conjugates condition that is readily verifiable for the aforementioned large classes of groups.
A Test for IP-ECN Propagation by a Remote Tunnel Endpoint

This memo defines a brief set of tests to determine the decapsulation behaviour of an unknown remote tunnel endpoint with respect to the Explicit Congestion Notification (ECN) field in the Internet Protocol (IP) header. The tests could be automated to be used by a tunnel ingress to determine whether the egress that it is paired with will propagate ECN correctly.
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings

External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval in non-randomized settings. However, the main challenge of implementing ECA lies in accessing real-world data or historical clinical trials. Indeed, data sharing is often not feasible due to privacy considerations related to data leaving the original collection centers, along with pharmaceutical companies' competitive motives. In this paper, we leverage a privacy-enhancing technology called federated learning (FL) to remove some of the barriers to data sharing. We introduce a federated learning inverse probability of treatment weighted (IPTW) method for time-to-event outcomes called FedECA which eases the implementation of ECA by limiting patients' data exposure. We show with extensive experiments that FedECA outperforms its closest competitor, matching-adjusted indirect comparison (MAIC), in terms of statistical power and ability to balance the treatment and control groups. To encourage the use of such methods, we publicly release our code which relies on Substra, an open-source FL software with proven experience in privacy-sensitive contexts.
Goal-conditioned Offline Planning from Curious Exploration

Curiosity has established itself as a powerful exploration strategy in deep reinforcement learning. Notably, leveraging expected future novelty as intrinsic motivation has been shown to efficiently generate exploratory trajectories, as well as a robust dynamics model. We consider the challenge of extracting goal-conditioned behavior from the products of such unsupervised exploration techniques, without any additional environment interaction. We find that conventional goal-conditioned reinforcement learning approaches for extracting a value function and policy fall short in this difficult offline setting. By analyzing the geometry of optimal goal-conditioned value functions, we relate this issue to a specific class of estimation artifacts in learned values. In order to mitigate their occurrence, we propose to combine model-based planning over learned value landscapes with a graph-based value aggregation scheme. We show how this combination can correct both local and global artifacts, obtaining significant improvements in zero-shot goal-reaching performance across diverse simulated environments.
SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization

Makeup transfer is a process of transferring the makeup style from a reference image to the source images, while preserving the source images' identities. This technique is highly desirable and finds many applications. However, existing methods lack fine-level control of the makeup style, making it challenging to achieve high-quality results when dealing with large spatial misalignments. To address this problem, we propose a novel Spatial Alignment and Region-Adaptive normalization method (SARA) in this paper. Our method generates detailed makeup transfer results that can handle large spatial misalignments and achieve part-specific and shade-controllable makeup transfer. Specifically, SARA comprises three modules: Firstly, a spatial alignment module that preserves the spatial context of makeup and provides a target semantic map for guiding the shape-independent style codes. Secondly, a region-adaptive normalization module that decouples shape and makeup style using per-region encoding and normalization, which facilitates the elimination of spatial misalignments. Lastly, a makeup fusion module blends identity features and makeup style by injecting learned scale and bias parameters. Experimental results show that our SARA method outperforms existing methods and achieves state-of-the-art performance on two public datasets.
Remarks on SHD spaces and more divergence properties

The class of SHD spaces was recently introduced in [12]. The first part of this paper focuses on answering most of the questions presented in that article. For instance, we exhibit an example of a non-SHD Tychonoff space $X$ such that $\mathscr{F}[X]$, the Pixley-Roy hyperspace of $X$, $\beta X$, the Stone-\v{C}ech compactification of $X$, and $C_p(X)$, the ring of continuous functions over $X$ equipped with the topology of pointwise convergence, are SHD. In the second part of this work we will present some variations of the SHD notion, namely, the WSHD property and the OHD property. Furthermore, we will pay special attention to the relationships between $X$ and $\mathscr{F}[X]$ regarding these new concepts.
Spanwise variations in membrane flutter dynamics

We study the large-amplitude flutter of rectangular membranes in 3-D inviscid flows, focusing on cases where the deflections have significant spanwise nonuniformity. We determine when such cases occur and how the dynamics vary over the parameter space of membrane mass and pretension for two sets of boundary conditions and two values of both the Poisson ratio and the membrane aspect ratio. With spanwise symmetric and asymmetric initial perturbations, the motions differ for long times but eventually reach the same steady state in most cases. At large times, spanwise symmetric and asymmetric oscillations are seen, with the latter more common. Oscillations are often in the form of"side-to-side"and other standing wave motions along the span, as well as traveling wave motions, particularly with free side-edges. Motions are generally nonperiodic and more spatially complex with a large membrane mass, and sometimes periodic at small-to-moderate membrane mass. A large Poisson ratio gives somewhat smoother spatial and temporal features in the dynamics at a given pretension. Increasing the aspect ratio makes the membrane"flatter,"i.e. decreases spanwise variations in the deflection. With different chordwise and spanwise pretensions we find motions that are qualitatively similar to cases with isotropic pretensions between the anisotropic values.
Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM?

This paper challenges the existing victim-focused counter-based RowHammer detection mechanisms by experimentally demonstrating a novel multi-sided fault injection attack technique called Threshold Breaker. This mechanism can effectively bypass the most advanced counter-based defense mechanisms by soft-attacking the rows at a farther physical distance from the target rows. While no prior work has demonstrated the effect of such an attack, our work closes this gap by systematically testing 128 real commercial DDR4 DRAM products and reveals that the Threshold Breaker affects various chips from major DRAM manufacturers. As a case study, we compare the performance efficiency between our mechanism and a well-known double-sided attack by performing adversarial weight attacks on a modern Deep Neural Network (DNN). The results demonstrate that the Threshold Breaker can deliberately deplete the intelligence of the targeted DNN system while DRAM is fully protected.
Pointwise Ergodic Theorems for Uniformly Behaved in ${\mathbb N}$ Sequences

We define a uniformly behaved in ${\mathbb N}$ arithmetic sequence ${\bf a}$ and an ${\bf a}$-mean Lyapunov stable dynamical system $f$. We consider the mean partial sum of a continuous function $\phi$ over the ${\bf a}$-orbit of $f$ up to $N$. The main result we prove in the paper is that the mean partial sum converges pointwise if ${\bf a}$ is uniformly behaved in ${\mathbb N}$ and $f$ is minimal and uniquely ergodic and ${\bf a}$-mean Lyapunov stable. In addition, if ${\bf a}$ is also completely additive, we then prove that the mean partial sum of a continuous function $\phi$ over the square-free ${\bf a}$-orbit of $f$ up to $N$ converges pointwise as well. All equicontinuous dynamical systems are ${\bf a}$-mean Lyapunov stable for any sequence ${\bf a}$. When ${\bf a}$ is uniformly distributed in ${\mathbb Z}$, we give two non-trivial examples of ${\bf a}$-mean Lyapunov stable dynamical systems. We give several examples of uniformly behaved in ${\mathbb N}$ sequences, including the counting function of the prime factors in natural numbers, the subsequence of natural numbers indexed by the Thue-Morse (or Rudin-Shapiro) sequence, and the sequence of even (or odd) prime factor natural numbers. We also show that the sequence of square-free natural numbers (or even (or odd) prime factor square-free natural numbers) is rotationally distributed in ${\mathbb N}$ but not uniformly distributed in ${\mathbb Z}$, thus not uniformly behaved in ${\mathbb N}$. We derive other consequences from the main result relevant to number theory and ergodic theory/dynamical systems.
Temporal and Spatial Changes of Urban Efficiency in the 1990s

Based on the data from 23 cities in 1983 and 1984, Charnes (1989) the first to employ DEA to evaluate the urban efficacy in China. Surprisingly, the corresponding Chinese literature has been in dark, even though scholars have highly recognized DEA and frequently used DEA in economic management and analysis. We, therefore, attempt to employ DEA to evaluate all the cities efficiency. Based on the data from 202 cities through 1990 to 2000, we find that the urban efficiency is relatively low, and diminishing from the east to the west, which coincides with the spatial pattern of economic development in China at present. Decomposing the urban efficiency into scale efficiency, congestion efficiency and pure technical efficiency, we further find it is the scale efficiency that determines the temporal and spatial patterns of urban efficiency. Specifically, both the congestion efficiency and the pure technical efficiency are high, and uniformly distributed across provinces. Scale efficiency, however, is low, and the principal factor among the three decompositions results in the low urban efficiency in China. The findings shed some highlights on how to implement the coordinated development of cities in China at present.
RGBGrasp: Image-based Object Grasping by Capturing Multiple Views during Robot Arm Movement with Neural Radiance Fields

Robotic research encounters a significant hurdle when it comes to the intricate task of grasping objects that come in various shapes, materials, and textures. Unlike many prior investigations that heavily leaned on specialized point-cloud cameras or abundant RGB visual data to gather 3D insights for object-grasping missions, this paper introduces a pioneering approach called RGBGrasp. This method depends on a limited set of RGB views to perceive the 3D surroundings containing transparent and specular objects and achieve accurate grasping. Our method utilizes pre-trained depth prediction models to establish geometry constraints, enabling precise 3D structure estimation, even under limited view conditions. Finally, we integrate hash encoding and a proposal sampler strategy to significantly accelerate the 3D reconstruction process. These innovations significantly enhance the adaptability and effectiveness of our algorithm in real-world scenarios. Through comprehensive experimental validations, we demonstrate that RGBGrasp achieves remarkable success across a wide spectrum of object-grasping scenarios, establishing it as a promising solution for real-world robotic manipulation tasks. The demonstrations of our method can be found on: https://sites.google. com/view/rgbgrasp
Survey of thermal ions in the Io plasma torus with the Galileo spacecraft

The densities, temperatures, and bulk flow velocities are reported for a series of six passages of the Galileo spacecraft through Io's torus in Jupiter's magnetosphere. These observations were gained with the plasma analyzers on December 7, 1995, and July 1–2, August 12, September 14, October 11, and November 25, all of the latter in 1999. These plasma analyzers provided high-resolution measurements of the energy/charge (E/Q) spectra of the three-dimensional velocity distributions of the thermal ions that included determinations of the mass/charge (M/Q) of the primary ions with a mass spectrometer. The four dominant ions in the hot torus were two populations of ions with M/Q = 16 and two smaller populations with M/Q = 8 and 32, respectively. The identification of these four ions was based upon a best fit to the E/Q spectra of the measured three-dimensional ion velocity distributions. The two distributions with M/Q =16 were characterized by two different temperatures, in the ranges of 20 to 30 eV and 60 to 80 eV, respectively. On the basis of expectations of higher temperatures with higher masses for the pickup ions the cooler population is identified as O+, and the hotter as S++. The two smaller populations with M/Q = 8 and 32 are identified as O++ and S+, respectively. The temperatures were ∼20 eV for the O++ and 60 to 80 eV for the S+. The densities and temperatures of the ions in the hot torus remained constant during the period July through November 1999. However, the ion densities during the initial passage on December 7, 1995, were greater by factors of ∼3, which support the presence of long-term density variations of the torus plasmas but with relatively small fluctuations in the temperatures. A complete survey of System III longitudes was acquired with the set of six passages. The presence of an “active sector” at longitudes in the approximate range of 180° to 230° as originally found with remote observations of the torus brightnesses is confirmed with these Galileo measurements. In addition, further evidence for the importance of interchange motions for radial plasma transport was also evident in several of the Galileo passages, a dynamical process which was first identified in fields and particles measurements during the first passage through the torus on December 7, 1995. A persistent lag with an average in the range of ∼2 to 4 km/s in the azimuthal flows relative to that for rigid corotation was detected and supports the previously proposed System IV coordinate system that has a slightly slower rotation rate relative to that for System III.
Rumors with Changing Credibility

Randomized rumor spreading processes diffuse information on an undirected graph and have been widely studied. In this work, we present a generic framework for analyzing a broad class of such processes on regular graphs. Our analysis is protocol-agnostic, as it only requires the expected proportion of newly informed vertices in each round to be bounded, and a natural negative correlation property. This framework allows us to analyze various protocols, including PUSH, PULL, and PUSH-PULL, thereby extending prior research. Unlike previous work, our framework accommodates message failures at any time $t\geq 0$ with a probability of $1-q(t)$, where the credibility $q(t)$ is any function of time. This enables us to model real-world scenarios in which the transmissibility of rumors may fluctuate, as seen in the spread of ``fake news'' and viruses. Additionally, our framework is sufficiently broad to cover dynamic graphs.
Identifiable Feature Learning for Spatial Data with Nonlinear ICA

Recently, nonlinear ICA has surfaced as a popular alternative to the many heuristic models used in deep representation learning and disentanglement. An advantage of nonlinear ICA is that a sophisticated identifiability theory has been developed; in particular, it has been proven that the original components can be recovered under sufficiently strong latent dependencies. Despite this general theory, practical nonlinear ICA algorithms have so far been mainly limited to data with one-dimensional latent dependencies, especially time-series data. In this paper, we introduce a new nonlinear ICA framework that employs $t$-process (TP) latent components which apply naturally to data with higher-dimensional dependency structures, such as spatial and spatio-temporal data. In particular, we develop a new learning and inference algorithm that extends variational inference methods to handle the combination of a deep neural network mixing function with the TP prior, and employs the method of inducing points for computational efficacy. On the theoretical side, we show that such TP independent components are identifiable under very general conditions. Further, Gaussian Process (GP) nonlinear ICA is established as a limit of the TP Nonlinear ICA model, and we prove that the identifiability of the latent components at this GP limit is more restricted. Namely, those components are identifiable if and only if they have distinctly different covariance kernels. Our algorithm and identifiability theorems are explored on simulated spatial data and real world spatio-temporal data.