Miracle-dz/UniFAR-FTUs huggingface dataset:
A self-tuning adaptive distributed observer approach to the cooperative output regulation problem for networked multi-agent systems

ABSTRACT In this paper, we first propose a self-tuning distributed observer for a multi-agent system, which is capable of providing the estimation of the leader's signal to various followers without assuming all the followers know the system matrix of the leader. We then further develop a novel adaptive distributed control law to solve the cooperative output regulation problem for linear heterogeneous multi-agent systems. This control law offers two advantages in that it makes less use of the information of the network as well as the information of the leader and external disturbances, and it avoids calculating the observer gain.
Field-Free Switching in Symmetry-Breaking Multilayers: The Critical Role of Interlayer Chiral Exchange

It is crucial to realize field-free, deterministic, current-induced switching in spin-orbit torque magnetic random-access memory (SOT-MRAM) with perpendicular magnetic anisotropy (PMA). A tentative solution has emerged recently, which employs the interlayer chiral exchange coupling or the interlayer Dzyaloshinskii-Moriya interaction (i-DMI) to achieve symmetry breaking. We hereby investigate the interlayer DMI in a Pt/Co multilayer system with orthogonally magnetized layers, using repeatedly stacked [Pt/Co]n structure with PMA, and a thick Co layer with in-plane magnetic anisotropy (IMA). We clarify the origin and the direction of such symmetry breaking with relation to the i-DMI effective field, and show a decreasing trend of the said effective field magnitude to the stacking number (n). By comparing the current-induced field-free switching behavior for both PMA and IMA layers, we confirm the dominating role of i-DMI in such field-free switching, excluding other possible mechanisms such as tilted-anisotropy and unconventional spin currents that may have arisen from the symmetry breaking.
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization

Driven by the latest trend towards self-supervised learning (SSL), the paradigm of"pretraining-then-finetuning"has been extensively explored to enhance the performance of clinical applications with limited annotations. Previous literature on model finetuning has mainly focused on regularization terms and specific policy models, while the misalignment of channels between source and target models has not received sufficient attention. In this work, we revisited the dynamics of batch normalization (BN) layers and observed that the trainable affine parameters of BN serve as sensitive indicators of domain information. Therefore, Affine Collaborative Normalization (AC-Norm) is proposed for finetuning, which dynamically recalibrates the channels in the target model according to the cross-domain channel-wise correlations without adding extra parameters. Based on a single-step backpropagation, AC-Norm can also be utilized to measure the transferability of pretrained models. We evaluated AC-Norm against the vanilla finetuning and state-of-the-art fine-tuning methods on transferring diverse pretrained models to the diabetic retinopathy grade classification, retinal vessel segmentation, CT lung nodule segmentation/classification, CT liver-tumor segmentation and MRI cardiac segmentation tasks. Extensive experiments demonstrate that AC-Norm unanimously outperforms the vanilla finetuning by up to 4% improvement, even under significant domain shifts where the state-of-the-art methods bring no gains. We also prove the capability of AC-Norm in fast transferability estimation. Our code is available at https://github.com/EndoluminalSurgicalVision-IMR/ACNorm.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.
Simulation of EAST vertical displacement events by tokamak simulation code

Vertical instability is a potentially serious hazard for elongated plasma. In this paper, the tokamak simulation code (TSC) is used to simulate vertical displacement events (VDE) on the experimental advanced superconducting tokamak (EAST). Key parameters from simulations, including plasma current, plasma shape and position, flux contours and magnetic measurements match experimental data well. The growth rates simulated by TSC are in good agreement with TokSys results. In addition to modeling the free drift, an EAST fast vertical control model enables TSC to simulate the course of VDE recovery. The trajectories of the plasma current center and control currents on internal coils (IC) fit experimental data well.
PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization

In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Also, a recent study has demonstrated the cross-modal transferability phenomenon of this joint space. From these observations, we propose PromptStyler which simulates various distribution shifts in the joint space by synthesizing diverse styles via prompts without using any images to deal with source-free domain generalization. The proposed method learns to generate a variety of style features (from "a S∗ style of a") via learnable style word vectors for pseudo-words S∗. To ensure that learned styles do not distort content information, we force style-content features (from "a S∗ style of a [class]") to be located nearby their corresponding content features (from "[class]") in the joint vision-language space. After learning style word vectors, we train a linear classifier using synthesized style-content features. PromptStyler achieves the state of the art on PACS, VLCS, OfficeHome and DomainNet, even though it does not require any images for training.
Boosting the production of sterile neutrino dark matter with self-interactions

Sterile neutrinos are well-motivated and simple dark matter (DM) candidates. However, sterile neutrino DM produced through oscillations by the Dodelson-Widrow mechanism is excluded by current $X$-ray observations and bounds from structure formation. One minimal extension, that preserves the attractive features of this scenario, is self-interactions among sterile neutrinos. In this work, we analyze how sterile neutrino self-interactions mediated by a scalar affect the production of keV sterile neutrinos for a wide range of mediator masses. We find four distinct regimes of production characterized by different phenomena, including partial thermalization for low and intermediate masses and resonant production for heavier mediators. We show that significant new regions of parameter space become available which provide a target for future observations.
A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurrence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs. Code is released on https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.
On multidimensional inverse scattering under the Stark effect

We study one of multidimensional inverse scattering problems for quantum systems in a constant electric field, by utilization of the Enss-Weder time-dependent method. The main purpose of this paper is to propose some methods of sharpening key estimates in the analysis, which are much simpler than those in the previous works. Our methods give an appropriate class of short-range potentials which can be determined uniquely by scattering operators, that seems natural in terms of direct scattering problems.
Exploring Strange Entanglement: Experimental and Theoretical Perspectives on Neutral Kaon Systems

This chapter provides an in-depth analysis of the properties and phenomena associated with neutral K-mesons. Kaons are quantum systems illustrating strange behaviours. We begin by examining the significance of strangeness and charge parity violation in understanding these particles. The concept of strangeness oscillations is then introduced, explaining oscillations between $K^0$ and $\bar{K}^0$ states. The regeneration of $K_S$ is investigated, uncovering the underlying mechanisms involved. The discussion moves on to quasi-spin space, exploring its bases and their implications. The entangled states of kaon pairs $(K^0,\bar K^0)$ are considered, with a focus on maximally entangled neutral kaons and non-maximally entangled states. Decoherence effects on entangled kaons are examined, utilizing the density matrix description to capture the dynamics. A dedicated decoherence parameter is introduced to quantify the impact of decoherence. Furthermore, the chapter investigates the loss of entanglement through measures such as Von Neumann entanglement entropy, entanglement of formation, and concurrence. These measures provide insights into quantifying and characterizing entanglement in the context of neutral kaons. Through this comprehensive exploration of properties, phenomena, and entanglement dynamics, this chapter aims to pointing out recent works on neutral kaons, contributing to advancements in particle physics.
A High-Order Perturbation of Envelopes (HOPE) Method for Vector Electromagnetic Scattering by Periodic Inhomogeneous Media

The scattering of electromagnetic waves by three--dimensional periodic structures is important for many problems of crucial scientific and engineering interest. Due to the complexity and three-dimensional nature of these waves, the fast, accurate, and reliable numerical simulations of these are indispensable for engineers and scientists alike. For this, High Order Spectral methods are frequently employed and here we describe an algorithm in this class. Our approach is perturbative in nature where we view the deviation of the permittivity from a constant value as the deformation and we pursue regular perturbation theory. This work extends our previous contribution regarding the Helmholtz equation to the full vector Maxwell equations, by providing a rigorous analyticity theory, both in deformation size and spatial variable (provided that the permittivity is, itself, analytic).
Evidence for a Correlation between Binary Black Hole Mass Ratio and Black Hole Spins

The astrophysical origins of the binary black hole systems seen with gravitational waves are still not well understood. However, features in the distribution of black hole masses, spins, redshifts, and eccentricities provide clues into how these systems form. Much has been learned by investigating these distributions one parameter at a time. However, we can extract additional information by studying the covariance between pairs of parameters. Previous work has shown preliminary support for an anticorrelation between mass ratio q ≡ m 2/m 1 and effective inspiral spin χ eff in the binary black hole population. In this study, we test for the existence of this anticorrelation using updated data from the third gravitational-wave transient catalog and improve our copula-based framework to employ a more robust model for black hole spins. We find evidence for an anticorrelation in (q, χ eff) with 99.7% credibility. This may imply high common-envelope efficiencies, stages of super-Eddington accretion, or a tendency for binary black hole systems to undergo mass-ratio reversal during isolated evolution. Covariance in (q, χ eff) may also be used to investigate the physics of tidal spinup as well as the properties of binary black hole–forming active galactic nuclei.
RSGPT: A Remote Sensing Vision Language Model and Benchmark

The emergence of large-scale large language models, with GPT-4 as a prominent example, has significantly propelled the rapid advancement of artificial general intelligence and sparked the revolution of Artificial Intelligence 2.0. In the realm of remote sensing (RS), there is a growing interest in developing large vision language models (VLMs) specifically tailored for data analysis in this domain. However, current research predominantly revolves around visual recognition tasks, lacking comprehensive, large-scale image-text datasets that are aligned and suitable for training large VLMs, which poses significant challenges to effectively training such models for RS applications. In computer vision, recent research has demonstrated that fine-tuning large vision language models on small-scale, high-quality datasets can yield impressive performance in visual and language understanding. These results are comparable to state-of-the-art VLMs trained from scratch on massive amounts of data, such as GPT-4. Inspired by this captivating idea, in this work, we build a high-quality Remote Sensing Image Captioning dataset (RSICap) that facilitates the development of large VLMs in the RS field. Unlike previous RS datasets that either employ model-generated captions or short descriptions, RSICap comprises 2,585 human-annotated captions with rich and high-quality information. This dataset offers detailed descriptions for each image, encompassing scene descriptions (e.g., residential area, airport, or farmland) as well as object information (e.g., color, shape, quantity, absolute position, etc). To facilitate the evaluation of VLMs in the field of RS, we also provide a benchmark evaluation dataset called RSIEval. This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.
Group-Heterogeneous Changes-in-Changes and Distributional Synthetic Controls

We develop new methods for changes-in-changes and distributional synthetic controls when there exists group level heterogeneity. For changes-in-changes, we allow individuals to belong to a large number of heterogeneous groups. The new method extends the changes-in-changes method in Athey and Imbens (2006) by finding appropriate subgroups within the control groups which share similar group level unobserved characteristics to the treatment groups. For distributional synthetic control, we show that the appropriate synthetic control needs to be constructed using units in potentially different time periods in which they have comparable group level heterogeneity to the treatment group, instead of units that are only in the same time period as in Gunsilius (2023). Implementation and data requirements for these new methods are briefly discussed.
Sparsity aware coding for single photon sensitive vision using Selective Sensing

Optical coding has been widely adopted to improve the imaging techniques. Traditional coding strategies developed under additive Gaussian noise fail to perform optimally in the presence of Poisson noise. It has been observed in previous studies that coding performance varies significantly between these two noise models. In this work, we introduce a novel approach called selective sensing, which leverages training data to learn priors and optimizes the coding strategies for downstream classification tasks. By adapting to the specific characteristics of photon-counting sensors, the proposed method aims to improve coding performance under Poisson noise and enhance overall classification accuracy. Experimental and simulated results demonstrate the effectiveness of selective sensing in comparison to traditional coding strategies, highlighting its potential for practical applications in photon counting scenarios where Poisson noise are prevalent.
A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity

We investigate the fixed-budget best-arm identification (BAI) problem for linear bandits in a potentially non-stationary environment. Given a finite arm set $\mathcal{X}\subset\mathbb{R}^d$, a fixed budget $T$, and an unpredictable sequence of parameters $\left\lbrace\theta_t\right\rbrace_{t=1}^{T}$, an algorithm will aim to correctly identify the best arm $x^* := \arg\max_{x\in\mathcal{X}}x^\top\sum_{t=1}^{T}\theta_t$ with probability as high as possible. Prior work has addressed the stationary setting where $\theta_t = \theta_1$ for all $t$ and demonstrated that the error probability decreases as $\exp(-T /\rho^*)$ for a problem-dependent constant $\rho^*$. But in many real-world $A/B/n$ multivariate testing scenarios that motivate our work, the environment is non-stationary and an algorithm expecting a stationary setting can easily fail. For robust identification, it is well-known that if arms are chosen randomly and non-adaptively from a G-optimal design over $\mathcal{X}$ at each time then the error probability decreases as $\exp(-T\Delta^2_{(1)}/d)$, where $\Delta_{(1)} = \min_{x \neq x^*} (x^* - x)^\top \frac{1}{T}\sum_{t=1}^T \theta_t$. As there exist environments where $\Delta_{(1)}^2/ d \ll 1/ \rho^*$, we are motivated to propose a novel algorithm $\mathsf{P1}$-$\mathsf{RAGE}$ that aims to obtain the best of both worlds: robustness to non-stationarity and fast rates of identification in benign settings. We characterize the error probability of $\mathsf{P1}$-$\mathsf{RAGE}$ and demonstrate empirically that the algorithm indeed never performs worse than G-optimal design but compares favorably to the best algorithms in the stationary setting.
A note on Schur-Weyl dualities for $GL(m)$ and $GL(m|n)$

We use a unified elementary approach to prove the second part of classical, mixed, super, and mixed super Schur Weyl dualities for general linear groups and supergroups over an algebraically closed ground field of any characteristic. These dualities describe the endomorphism algebras of the tensor space and mixed tensor space, respectively, over the group algebra of the symmetric group and the Brauer wall algebra, respectively.
Generation of Near-Equipartition Magnetic Fields in Turbulent Collisionless Plasmas.

The mechanisms that generate "seed" magnetic fields in our Universe and that amplify them throughout cosmic time remain poorly understood. By means of fully kinetic particle-in-cell simulations of turbulent, initially unmagnetized plasmas, we study the genesis of magnetic fields via the Weibel instability and follow their dynamo growth up to near-equipartition levels. In the kinematic stage of the dynamo, we find that the rms magnetic field strength grows exponentially with rate γ_{B}≃0. 4u_{rms}/L, where L/2π is the driving scale and u_{rms} is the rms turbulent velocity. In the saturated stage, the magnetic field energy reaches about half of the turbulent kinetic energy. Here, magnetic field growth is balanced by dissipation via reconnection, as revealed by the appearance of plasmoid chains. At saturation, the integral-scale wave number of the magnetic spectrum approaches k_{int}≃12π/L. Our results show that turbulence-induced by, e.g., the gravitational buildup of galaxies and galaxy clusters-can magnetize collisionless plasmas with large-scale near-equipartition fields.
All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection

Recent advances in deep learning and computer vision have made the synthesis and counterfeiting of multimedia content more accessible than ever, leading to possible threats and dangers from malicious users. In the audio field, we are witnessing the growth of speech deepfake generation techniques, which solicit the development of synthetic speech detection algorithms to counter possible mischievous uses such as frauds or identity thefts. In this paper, we consider three different feature sets proposed in the literature for the synthetic speech detection task and present a model that fuses them, achieving overall better performances with respect to the state-of-the-art solutions. The system was tested on different scenarios and datasets to prove its robustness to anti-forensic attacks and its generalization capabilities.
R-Block: Regularized Block of Dropout for convolutional networks

Dropout as a regularization technique is widely used in fully connected layers while is less effective in convolutional layers. Therefore more structured forms of dropout have been proposed to regularize convolutional networks. The disadvantage of these methods is that the randomness introduced causes inconsistency between training and inference. In this paper, we apply a mutual learning training strategy for convolutional layer regularization, namely R-Block, which forces two outputs of the generated difference maximizing sub models to be consistent with each other. Concretely, R-Block minimizes the losses between the output distributions of two sub models with different drop regions for each sample in the training dataset. We design two approaches to construct such sub models. Our experiments demonstrate that R-Block achieves better performance than other existing structured dropout variants. We also demonstrate that our approaches to construct sub models outperforms others.
Optimal Approximation of Zonoids and Uniform Approximation by Shallow Neural Networks

We study the following two related problems. The first is to determine to what error an arbitrary zonoid in $\mathbb{R}^{d+1}$ can be approximated in the Hausdorff distance by a sum of $n$ line segments. The second is to determine optimal approximation rates in the uniform norm for shallow ReLU$^k$ neural networks on their variation spaces. The first of these problems has been solved for $d\neq 2,3$, but when $d=2,3$ a logarithmic gap between the best upper and lower bounds remains. We close this gap, which completes the solution in all dimensions. For the second problem, our techniques significantly improve upon existing approximation rates when $k\geq 1$, and enable uniform approximation of both the target function and its derivatives.