Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect Detection

Recent advancements in cognitive computing, with the integration of deep learning techniques, have facilitated the development of intelligent cognitive systems (ICS). This is particularly beneficial in the context of rail defect detection, where the ICS would emulate human-like analysis of image data for defect patterns. Despite the success of Convolutional Neural Networks (CNN) in visual defect classification, the scarcity of large datasets for rail defect detection remains a challenge due to infrequent accident events that would result in defective parts and images. Contemporary researchers have addressed this data scarcity challenge by exploring rule-based and generative data augmentation models. Among these, Variational Autoencoder (VAE) models can generate realistic data without extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for rail defects, incorporating weight decay regularization and image reconstruction loss to prevent overfitting. The proposed method is applied to create a synthetic dataset for the Canadian Pacific Railway (CPR) with just 50 real samples across five classes. Remarkably, 500 synthetic samples are generated with a minimal reconstruction loss of 0.021. A Visual Transformer (ViT) model underwent fine-tuning using this synthetic CPR dataset, achieving high accuracy rates (98%-99%) in classifying the five defect classes. This research offers a promising solution to the data scarcity challenge in rail defect detection, showcasing the potential for robust ICS development in this domain.
Assessing replication success via skeptical mixture priors

There is a growing interest in the analysis of replication studies of original findings across many disciplines. When testing a hypothesis for an effect size, two Bayesian approaches stand out for their principled use of the Bayes factor (BF), namely the replication BF and the skeptical BF. In particular, the latter BF is based on the skeptical prior, which represents the opinion of an investigator who is unconvinced by the original findings and wants to challenge them. We embrace the skeptical perspective, and elaborate a novel mixture prior which incorporates skepticism while at the same time controlling for prior-data conflict within the original data. Consistency properties of the resulting skeptical mixture BF are provided together with an extensive analysis of the main features of our proposal. Finally, we apply our methodology to data from the Social Sciences Replication Project. In particular we show that, for some case studies where prior-data conflict is an issue, our method uses a more realistic prior and leads to evidence-classification for replication success which differs from the standard skeptical approach.
TWO-CARDINAL DERIVED TOPOLOGIES, INDESCRIBABILITY AND RAMSEYNESS

We introduce a natural two-cardinal version of Bagaria's sequence of derived topologies on ordinals. We prove that for our sequence of two-cardinal derived topologies, limit points of sets can be characterized in terms of a new iterated form of pairwise simultaneous reflection of certain kinds of stationary sets, the first few instances of which are often equivalent to notions related to strong stationarity, which has been studied previously in the context of strongly normal ideals. The non-discreteness of these two-cardinal derived topologies can be obtained from certain two-cardinal indescribability hypotheses, which follow from local instances of supercompactness. Additionally, we answer several questions posed by the first author, Peter Holy and Philip White on the relationship between Ramseyness and indescribability in both the cardinal context and in the two-cardinal context.
Efficient Two-Phase Offline Deep Reinforcement Learning from Preference Feedback

In this work, we consider the offline preference-based reinforcement learning problem. We focus on the two-phase learning approach that is prevalent in previous reinforcement learning from human preference works. We find a challenge in applying two-phase learning in the offline PBRL setting that the learned utility model can be too hard for the learning agent to optimize during the second learning phase. To overcome the challenge, we propose a two-phasing learning approach under behavior regularization through action clipping. The insight is that the state-actions which are poorly covered by the dataset can only provide limited information and increase the complexity of the problem in the second learning phase. Our method ignores such state-actions during the second learning phase to achieve higher learning efficiency. We empirically verify that our method has high learning efficiency on a variety of datasets in robotic control environments.
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise

Stochastic approximation (SA) is an iterative algorithm to find the fixed point of an operator given noisy samples of this operator. SA appears in many areas such as optimization and Reinforcement Learning (RL). When implemented in practice, the noise that appears in the update of RL algorithms is naturally Markovian. Furthermore, in some settings, such as gradient TD, SA is employed in a two-time-scale manner. The mix of Markovian noise along with the two-time-scale structure results in an algorithm which is complex to analyze theoretically. In this paper, we characterize a tight convergence bound for the iterations of linear two-time-scale SA with Markovian noise. Our results show the convergence behavior of this algorithm given various choices of step sizes. Applying our result to the well-known TDC algorithm, we show the first $O(1/\epsilon)$ sample complexity for the convergence of this algorithm, outperforming all the previous work. Similarly, our results can be applied to establish the convergence behavior of a variety of RL algorithms, such as TD-learning with Polyak averaging, GTD, and GTD2.
SVFAP: Self-supervised Video Facial Affect Perceiver

Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction. Previous studies mainly focus on developing various deep learning architectures and training them in a fully supervised manner. Although significant progress has been achieved by these supervised methods, the longstanding lack of large-scale high-quality labeled data severely hinders their further improvements. Motivated by the recent success of self-supervised learning in computer vision, this paper introduces a self-supervised approach, termed Self-supervised Video Facial Affect Perceiver (SVFAP), to address the dilemma faced by supervised methods. Specifically, SVFAP leverages masked facial video autoencoding to perform self-supervised pre-training on massive unlabeled facial videos. Considering that large spatiotemporal redundancy exists in facial videos, we propose a novel temporal pyramid and spatial bottleneck Transformer as the encoder of SVFAP, which not only enjoys low computational cost but also achieves excellent performance. To verify the effectiveness of our method, we conduct experiments on nine datasets spanning three downstream tasks, including dynamic facial expression recognition, dimensional emotion recognition, and personality recognition. Comprehensive results demonstrate that SVFAP can learn powerful affect-related representations via large-scale self-supervised pre-training and it significantly outperforms previous state-of-the-art methods on all datasets. Codes will be available at https://github.com/sunlicai/SVFAP.
Calculation of Gilbert damping and magnetic moment of inertia using torque-torque correlation model within ab initio Wannier framework

Magnetization dynamics in magnetic materials are well described by the modified semiclassical Landau-Lifshitz-Gilbert (LLG) equation, which includes the magnetic damping $\alpha$ and the magnetic moment of inertia $\mathrm{I}$ tensors as key parameters. Both parameters are material-specific and physically represent the time scales of damping of precession and nutation in magnetization dynamics. $\alpha$ and $\mathrm{I}$ can be calculated quantum mechanically within the framework of the torque-torque correlation model. The quantities required for the calculation are torque matrix elements, the real and imaginary parts of the Green's function and its derivatives. Here, we calculate these parameters for the elemental magnets such as Fe, Co and Ni in an ab initio framework using density functional theory and Wannier functions. We also propose a method to calculate the torque matrix elements within the Wannier framework. We demonstrate the effectiveness of the method by comparing it with the experiments and the previous ab initio and empirical studies and show its potential to improve our understanding of spin dynamics and to facilitate the design of spintronic devices.
Partitioning the hypercube into smaller hypercubes

Denote by Q_d the d-dimensional hypercube. Addressing a recent question we estimate the number of ways the vertex set of Q_d can be partitioned into vertex disjoint smaller cubes. Among other results, we prove that the asymptotic order of this function is not much larger than the number of perfect matchings of Q_d. We also describe several new (and old) questions.
Stability for the 2-D plane Poiseuille flow in finite channel

In this paper, we study the stability for 2-D plane Poiseuille flow $(1-y^2,0)$ in a channel $\mathbb{T}\times (-1,1)$ with Navier-slip boundary condition. We prove that if the initial perturbation for velocity field $u_0$ satisfies that $\|u_0\|_{H^{\frac{7}{2}+}} \leq \epsilon_1 \nu^{2/3}$ for some suitable small $0<\epsilon_1 \ll 1$ independent of viscosity coefficient $\nu$, then the solution to the Navier-Stokes equations is global in time and does not transit from the plane Poiseuille flow. This result improves the result of \cite{DL1} from $3/4$ to $2/3$.
Plug-and-Play regularized 3D seismic inversion with 2D pre-trained denoisers

Post-stack seismic inversion is a widely used technique to retrieve high-resolution acoustic impedance models from migrated seismic data. Its modelling operator assumes that a migrated seismic data can be generated from the convolution of a source wavelet and the time derivative of the acoustic impedance model. Given the band-limited nature of the seismic wavelet, the convolutional model acts as a filtering operator on the acoustic impedance model, thereby making the problem of retrieving acoustic impedances from seismic data ambiguous. In order to compensate for missing frequencies, post-stack seismic inversion is often regularized, meaning that prior information about the structure of the subsurface is included in the inversion process. Recently, the Plug-and-Play methodology has gained wide interest in the inverse problem community as a new form of implicit regularization, often outperforming state-of-the-art regularization. Plug-and-Play can be applied to any proximal algorithm by simply replacing the proximal operator of the regularizer with any denoiser of choice. We propose to use Plug-and-Play regularization with a 2D pre-trained, deep denoiser for 2D post-stack seismic inversion. Additionally, we show that a generalization of Plug-and-Play, called Multi-Agent Consensus Equilibrium, can be adopted to solve 3D post-stack inversion whilst leveraging the same 2D pre-trained denoiser used in the 2D case. More precisely, Multi-Agent Consensus Equilibrium combines the results of applying such 2D denoiser in the inline, crossline, and time directions in an optimal manner. We verify the proposed methods on a portion of the SEAM Phase 1 velocity model and the Sleipner field dataset. 1
Fault-tolerant quantum computation by hybrid qubits with bosonic cat-code and single photons

Hybridizing different degrees of freedom or physical platforms potentially offers various advantages in building scalable quantum architectures. We here introduce a fault-tolerant hybrid quantum computation by taking the advantages of both discrete variable (DV) and continuous variable (CV) systems. Particularly, we define a CV-DV hybrid qubit with bosonic cat-code and single photon, which is implementable in current photonic platforms. By the cat-code encoded in the CV part, the dominant loss errors are readily correctable without multi-qubit encoding, while the logical basis is inherently orthogonal due to the DV part. We design fault-tolerant architectures by concatenating hybrid qubits and an outer DV quantum error correction code such as topological codes, exploring their potential merits in developing scalable quantum computation. We demonstrate by numerical simulations that our scheme is at least an order of magnitude more resource-efficient over all previous proposals in photonic platforms, allowing to achieve a record-high loss threshold among existing CV and hybrid approaches. We discuss its realization not only in all-photonic platforms but also in other hybrid platforms including superconduting and trapped-ion systems, which allows us to find various efficient routes towards fault-tolerant quantum computing.
Persistent gravitational wave observables: Nonlinearities in (non-)geodesic deviation

The usual gravitational wave memory effect can be understood as a change in the separation of two initially comoving observers due to a burst of gravitational waves. Over the past few decades, a wide variety of other,"persistent"observables which measure permanent effects on idealized detectors have been introduced, each probing distinct physical effects. These observables can be defined in (regions of) any spacetime where there exists a notion of radiation, such as perturbation theory off of a fixed background, nonlinear plane wave spacetimes, or asymptotically flat spacetimes. Many of the persistent observables defined in the literature have been considered only in asymptotically flat spacetimes, and the perturbative nature of such calculations has occasionally obscured deeper relationships between these observables that hold more generally. The goal of this paper is to show how these more general results arise, and to do so we focus on two observables related to the separation between two, potentially accelerated observers. The first is the curve deviation, which is a natural generalization of the displacement memory, and also contains what this paper proposes to call drift memory (previously called"subleading displacement memory") and ballistic memory. The second is a relative proper time shift that arises between the two observers, either at second order in their initial separation and relative velocity, or in the presence of relative acceleration. The results of this paper are, where appropriate, entirely non-perturbative in the curvature of spacetime, and so could be used beyond leading order in asymptotically flat spacetimes.
Unicron: Economizing Self-Healing LLM Training at Scale

Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current failure recovery methods in cloud-based settings inadequately address the diverse and complex scenarios that arise, focusing narrowly on erasing downtime for individual tasks without considering the overall cost impact on a cluster. We introduce Unicron, a workload manager designed for efficient self-healing in large-scale language model training. Unicron optimizes the training process by minimizing failure-related costs across multiple concurrent tasks within a cluster. Its key features include in-band error detection for real-time error identification without extra overhead, a dynamic cost-aware plan generation mechanism for optimal reconfiguration, and an efficient transition strategy to reduce downtime during state changes. Deployed on a 128-GPU distributed cluster, Unicron demonstrates up to a 1.9x improvement in training efficiency over state-of-the-art methods, significantly reducing failure recovery costs and enhancing the reliability of large-scale language model training.
$SU(2)_L$ deconstruction and flavour (non)-universality

We study two-site deconstructions of the $SU(2)_L$ gauge group factor of the SM. Models based on this approach can explain the hierarchies of the quark masses and CKM mixing between third and light families if these fields are localised on different sites by the presence of hierarchical new physics scales. The model leads to an accidental global $U(2)_q\times U(3)_u\times U(3)_d$ flavour symmetry which prevents dangerously large effects in flavour observables, making a TeV extension of the SM possible. Given the structure of the PMNS matrix in the neutrino sector, we explore different possibilities for the arrangement of the leptons on the two sites, and consider different models with $U(2)_{\ell}$ or $U(3)_{\ell}$ flavour symmetries. The phenomenology of the models is mostly governed by a massive vector triplet of $SU(2)_L$. We study the interesting interplay between LHC searches and precision observables. In particular, one of the models can give a sizeable lepton flavour universal effect in the Wilson coefficient $C_9$ while naturally suppressing contributions to $C_{10}$, as suggested by current $b\to s\ell^+\ell^-$ data, predicting simultaneously a mild positive shift in the $W$ boson mass.
Kernel Density Estimation for Multiclass Quantification

Several disciplines, like the social sciences, epidemiology, sentiment analysis, or market research, are interested in knowing the distribution of the classes in a population rather than the individual labels of the members thereof. Quantification is the supervised machine learning task concerned with obtaining accurate predictors of class prevalence, and to do so particularly in the presence of label shift. The distribution-matching (DM) approaches represent one of the most important families among the quantification methods that have been proposed in the literature so far. Current DM approaches model the involved populations by means of histograms of posterior probabilities. In this paper, we argue that their application to the multiclass setting is suboptimal since the histograms become class-specific, thus missing the opportunity to model inter-class information that may exist in the data. We propose a new representation mechanism based on multivariate densities that we model via kernel density estimation (KDE). The experiments we have carried out show our method, dubbed KDEy, yields superior quantification performance with respect to previous DM approaches. We also investigate the KDE-based representation within the maximum likelihood framework and show KDEy often shows superior performance with respect to the expectation-maximization method for quantification, arguably the strongest contender in the quantification arena to date.
Analytical Critical Phenomena of Rotating Bardeen AdS Black Holes with Dark Energy

This study is centered on precisely calculating analytical critical points for rotating Bardeen-AdS black holes, examining scenarios with and without external dark field contributions. Importantly, this study represents the inaugural attempt to address the computation of critical points specifically for this category of black holes. Our primary focus is on investigating the impact resulting from variations in the charge of nonlinear electrodynamics on the critical phenomena of rotating Bardeen black holes, incorporating the influence of quintessence field contributions. The analytical investigation is concentrated on the horizon radius, employing two distinct approaches to simplify the complexity and length of the calculations. Furthermore, our examination extends to deciphering the intricate relationship between dark energy and critical phenomena. This includes visually portraying a range of critical behaviors while detailing a recent discovery regarding how the intensity of quintessence affects phase transitions. The shifts in these transitions conform to either a concave or convex function, a characteristic dependent on the sign of quintessence intensity.
SAR-RARP50: Segmentation of surgical instrumentation and Action Recognition on Robot-Assisted Radical Prostatectomy Challenge

Surgical tool segmentation and action recognition are fundamental building blocks in many computer-assisted intervention applications, ranging from surgical skills assessment to decision support systems. Nowadays, learning-based action recognition and segmentation approaches outperform classical methods, relying, however, on large, annotated datasets. Furthermore, action recognition and tool segmentation algorithms are often trained and make predictions in isolation from each other, without exploiting potential cross-task relationships. With the EndoVis 2022 SAR-RARP50 challenge, we release the first multimodal, publicly available, in-vivo, dataset for surgical action recognition and semantic instrumentation segmentation, containing 50 suturing video segments of Robotic Assisted Radical Prostatectomy (RARP). The aim of the challenge is twofold. First, to enable researchers to leverage the scale of the provided dataset and develop robust and highly accurate single-task action recognition and tool segmentation approaches in the surgical domain. Second, to further explore the potential of multitask-based learning approaches and determine their comparative advantage against their single-task counterparts. A total of 12 teams participated in the challenge, contributing 7 action recognition methods, 9 instrument segmentation techniques, and 4 multitask approaches that integrated both action recognition and instrument segmentation. The complete SAR-RARP50 dataset is available at: https://rdr.ucl. ac.uk/projects/SARRARP50_Segmentation_of_surgical_instrumentation_and_Action_Recognition_on_Robot-Assisted_Radical_Prostatectomy_Challenge/191091
Cluster algebras and monotone Lagrangian tori

Motivated by recent developments in the construction of Newton--Okounkov bodies and toric degenerations via cluster algebras in [GHKK18, FO20], we consider a family of Newton--Okounkov polytopes of a complex smooth projective variety $X$ related by a composition of tropicalized cluster mutations. According to the work of [HK15], the toric degeneration associated with each Newton--Okounkov polytope $\Delta$ in the family produces a Lagrangian torus fibration of $X$ over $\Delta$. We investigate circumstances in which each Lagrangian torus fibration possesses a monotone Lagrangian torus fiber. We provide a sufficient condition, based on the data of tropical integer points and exchange matrices, for the family of constructed monotone Lagrangian tori to contain infinitely many monotone Lagrangian tori, no two of which are related by any symplectomorphisms. By employing this criterion and exploiting the correspondence between the tropical integer points and the dual canonical basis elements, we generate infinitely many distinct monotone Lagrangian tori on flag manifolds of arbitrary type except in a few cases.