Miracle-dz/UniFAR-FTUs huggingface dataset:
Isometric Representations in Neural Networks Improve Robustness

Artificial and biological agents cannon learn given completely random and unstructured data. The structure of data is encoded in the metric relationships between data points. In the context of neural networks, neuronal activity within a layer forms a representation reflecting the transformation that the layer implements on its inputs. In order to utilize the structure in the data in a truthful manner, such representations should reflect the input distances and thus be continuous and isometric. Supporting this statement, recent findings in neuroscience propose that generalization and robustness are tied to neural representations being continuously differentiable. In machine learning, most algorithms lack robustness and are generally thought to rely on aspects of the data that differ from those that humans use, as is commonly seen in adversarial attacks. During cross-entropy classification, the metric and structural properties of network representations are usually broken both between and within classes. This side effect from training can lead to instabilities under perturbations near locations where such structure is not preserved. One of the standard solutions to obtain robustness is to add ad hoc regularization terms, but to our knowledge, forcing representations to preserve the metric structure of the input data as a stabilising mechanism has not yet been studied. In this work, we train neural networks to perform classification while simultaneously maintaining within-class metric structure, leading to isometric within-class representations. Such network representations turn out to be beneficial for accurate and robust inference. By stacking layers with this property we create a network architecture that facilitates hierarchical manipulation of internal neural representations. Finally, we verify that isometric regularization improves the robustness to adversarial attacks on MNIST.
An Aggregation of Aggregation Methods in Computational Pathology

Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels. In this paper, we present a review of existing literature on various types of aggregation methods with a view to help guide future research in the area of computational pathology (CPath). We propose a general CPath workflow with three pathways that consider multiple levels and types of data and the nature of computation to analyse WSIs for predictive modelling. We categorize aggregation methods according to the context and representation of the data, features of computational modules and CPath use cases. We compare and contrast different methods based on the principle of multiple instance learning, perhaps the most commonly used aggregation method, covering a wide range of CPath literature. To provide a fair comparison, we consider a specific WSI-level prediction task and compare various aggregation methods for that task. Finally, we conclude with a list of objectives and desirable attributes of aggregation methods in general, pros and cons of the various approaches, some recommendations and possible future directions.
An Asymptotically Optimal Bound for Covering Arrays of Higher Index

A covering array is an N × k array ( N rows, k columns) with each entry from a v -ary alphabet, and for every N × t subarray, all v t tuples of size t appear at least λ times. The covering array number is the smallest number N for which such an array exists. For λ = 1, the covering array number is asymptotically logarithmic in k , when v, t are ﬁxed. Godbole, Skipper, and Sunley proved a bound of the form log k + λ log log k for the covering array number for arbitrary λ and v, t constant. The author proved a similar bound via a diﬀerent technique, and conjectured that the log log k term can be removed. In this short note we answer the conjecture in the aﬃrmative with an asymptotically tight upper bound. In particular, we employ the probabilistic method in conjunction with the Lambert W function.
SleepyWheels: An Ensemble Model for Drowsiness Detection leading to Accident Prevention

Around 40 percent of accidents related to driving on highways in India occur due to the driver falling asleep behind the steering wheel. Several types of research are ongoing to detect driver drowsiness but they suffer from the complexity and cost of the models. In this paper, SleepyWheels a revolutionary method that uses a lightweight neural network in conjunction with facial landmark identification is proposed to identify driver fatigue in real time. SleepyWheels is successful in a wide range of test scenarios, including the lack of facial characteristics while covering the eye or mouth, the drivers varying skin tones, camera placements, and observational angles. It can work well when emulated to real time systems. SleepyWheels utilized EfficientNetV2 and a facial landmark detector for identifying drowsiness detection. The model is trained on a specially created dataset on driver sleepiness and it achieves an accuracy of 97 percent. The model is lightweight hence it can be further deployed as a mobile application for various platforms.
Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics

Well-calibrated probabilistic regression models are a crucial learning component in robotics applications as datasets grow rapidly and tasks become more complex. Unfortunately, classical regression models are usually either probabilistic kernel machines with a flexible structure that does not scale gracefully with data or deterministic and vastly scalable automata, albeit with a restrictive parametric form and poor regularization. In this paper, we consider a probabilistic hierarchical modeling paradigm that combines the benefits of both worlds to deliver computationally efficient representations with inherent complexity regularization. The presented approaches are probabilistic interpretations of local regression techniques that approximate nonlinear functions through a set of local linear or polynomial units. Importantly, we rely on principles from Bayesian nonparametrics to formulate flexible models that adapt their complexity to the data and can potentially encompass an infinite number of components. We derive two efficient variational inference techniques to learn these representations and highlight the advantages of hierarchical infinite local regression models, such as dealing with non-smooth functions, mitigating catastrophic forgetting, and enabling parameter sharing and fast predictions. Finally, we validate this approach on large inverse dynamics datasets and test the learned models in real-world control scenarios.
Balancing Utility and Fairness in Submodular Maximization

Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the utility is defined by a monotone submodular function. However, when the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across different groups. Although the \emph{utility} and \emph{fairness} objectives are both desirable, they might contradict each other, and, to the best of our knowledge, little attention has been paid to optimizing them jointly. To fill this gap, we propose a new problem called \emph{Bicriteria Submodular Maximization} (BSM) to balance utility and fairness. Specifically, it requires finding a fixed-size solution to maximize the utility function, subject to the value of the fairness function not being below a threshold. Since BSM is inapproximable within any constant factor, we focus on designing efficient instance-dependent approximation schemes. Our algorithmic proposal comprises two methods, with different approximation factors, obtained by converting a BSM instance into other submodular optimization problem instances. Using real-world and synthetic datasets, we showcase applications of our proposed methods in three submodular maximization problems: maximum coverage, influence maximization, and facility location.
The multi-Gaussian expansion method: a tool for building realistic photometric and kinematical models of stellar systems. I: The formalism

We introduce a new technique to build photometric and kinematical models of the stellar components of galaxies. This, so-called Multi-Gaussian Expansion method, generalizes the approach of Monnet et al. (1992). It provides accurate deconvolution of the observed surface brigthness for an arbitrary Point Spread Function, an analytical derivation of the spatial luminosity distribution for any assumed triaxial shape and an arbitrary line of view, and a semi-analytical derivation of the rotation and velocity dispersion laws for a large class of models with cylindrical rotation. Other applications (e. g. the virial mass) have been developed
Higher-order approximation for uncertainty quantification in time series analysis

For time series with high temporal correlation, the empirical process converges rather slowly to its limiting distribution. Many statistics in change-point analysis, goodness-of-fit testing and uncertainty quantification admit a representation as functionals of the empirical process and therefore inherit its slow convergence. Inference based on the asymptotic distribution of those quantities becomes highly impacted by relatively small sample sizes. We assess the quality of higher-order approximations of the empirical process by deriving the asymptotic distribution of the corresponding error terms. Based on the limiting distribution of the higher-order terms, we propose a novel approach to calculate confidence regions for statistical quantities such as the median. In a simulation study, we compare coverage rate and size of our confidence regions with those based on the asymptotic distribution of the empirical process and highlight some of our method's benefits.
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese

The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data are retrieved from publicly available datasets, and we pretrain Chinese CLIP models on the new dataset. We develop 5 Chinese CLIP models of multiple sizes, spanning from 77 to 958 million parameters. Furthermore, we propose a two-stage pretraining method, where the model is first trained with the image encoder frozen and then trained with all parameters being optimized, to achieve enhanced model performance. Our comprehensive experiments demonstrate that Chinese CLIP can achieve the state-of-the-art performance on MUGE, Flickr30K-CN, and COCO-CN in the setups of zero-shot learning and finetuning, and it is able to achieve competitive performance in zero-shot image classification based on the evaluation on the ELEVATER benchmark (Li et al., 2022). We have released our codes, models, and demos in https://github.com/OFA-Sys/Chinese-CLIP
Generation of Population Correlation Matrices with Specified Eigenvalues

Description and Purpose Given a set of n eigenvalues, the algorithm "randomly" generates an n x n population corelation matrix from the class of all correlation matrices having these same eigenvalues. The generated population correlation matrices can be used for performing Monte Carlo simulations or sampling experiments-see e.g. Bendel and Afifi (1977) and Lin and Perlman (1985). Let D be a diagonal matrix whose diagonal elements are the specified eigenvalues. The algorithm first generates a random orthgonal matrix A from the invariant Haar measure on the group of orthogonal matrices (see Heiberger, 1978; Tanner and Thisted, 1982) so that CO =ADA' is a random covariance matrix with eigenvalues given by D. Then the algorithm successively obtains n1 orthgonal matrix Pi so that C Pn-1Pn-2 . P2P1CoP'P21 . Pn-2Pn-1 is a corelation matrix having the same eigenvalues as CO (see Bendel and Mickey (1978)). Each matrix Pi is an elementary rotational matrix of the form:
Reinforcement Learning in Education: A Multi-Armed Bandit Approach

Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally perform a task within a given environment. Reinforcement leaning solves unsupervised problems where agents move through a state-action-reward loop to maximize the overall reward for the agent, which in turn optimizes the solving of a specific problem in a given environment. However, these algorithms are designed based on our understanding of actions that should be taken in a real-world environment to solve a specific problem. One such problem is the ability to identify, recommend and execute an action within a system where the users are the subject, such as in education. In recent years, the use of blended learning approaches integrating face-to-face learning with online learning in the education context, has in-creased. Additionally, online platforms used for education require the automation of certain functions such as the identification, recommendation or execution of actions that can benefit the user, in this sense, the student or learner. As promising as these scientific advances are, there is still a need to conduct research in a variety of different areas to ensure the successful deployment of these agents within education systems. Therefore, the aim of this study was to contextualise and simulate the cumulative reward within an environment for an intervention recommendation problem in the education context.
Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI

Early and accurate diagnosis of parkinsonian syndromes is critical to provide appropriate care to patients and for inclusion in therapeutic trials. The red nucleus is a structure of the midbrain that plays an important role in these disorders. It can be visualized using iron-sensitive magnetic resonance imaging (MRI) sequences. Different iron-sensitive contrasts can be produced with MRI. Combining such multimodal data has the potential to improve segmentation of the red nucleus. Current multimodal segmentation algorithms are computationally consuming, cannot deal with missing modalities and need annotations for all modalities. In this paper, we propose a new model that integrates prior knowledge from different contrasts for red nucleus segmentation. The method consists of three main stages. First, it disentangles the image into high-level information representing the brain structure, and low-frequency information representing the contrast. The high-frequency information is then fed into a network to learn anatomical features, while the list of multimodal low-frequency information is processed by another module. Finally, feature fusion is performed to complete the segmentation task. The proposed method was used with several iron-sensitive contrasts (iMag, QSM, R2*, SWI). Experiments demonstrate that our proposed model substantially outperforms a baseline UNet model when the training set size is very small.
Matrix Denoising with Partial Noise Statistics: Optimal Singular Value Shrinkage of Spiked F-Matrices

We study the problem of estimating a large, low-rank matrix corrupted by additive noise of unknown covariance, assuming one has access to additional side information in the form of noise-only measurements. We study the Whiten-Shrink-reColour (WSC) workflow, where a ‘noise covariance whitening’ transformation is applied to the observations, followed by appropriate singular value shrinkage and a ‘noise covariance re-colouring’ transformation. We show that under the mean square error loss, a unique, asymptotically optimal shrinkage nonlinearity exists for the WSC denoising workflow, and calculate it in closed form. To this end, we calculate the asymptotic eigenvector rotation of the random spiked F-matrix ensemble, a result which may be of independent interest. With sufficiently many pure-noise measurements, our optimally tuned WSC denoising workflow outperforms, in mean square error, matrix denoising algorithms based on optimal singular value shrinkage that do not make similar use of noise-only side information; numerical experiments show that our procedure’s relative performance is particularly strong in challenging statistical settings with high dimensionality and large degree of heteroscedasticity.
Gradient Descent and the Power Method: Exploiting their connection to find the leftmost eigen-pair and escape saddle points

This work shows that applying Gradient Descent (GD) with a ﬁxed step size to minimize a (possibly nonconvex) quadratic function is equivalent to running the Power Method (PM) on the gradients. The connection between GD with a ﬁxed step size and the PM, both with and without ﬁxed momentum, is thus established. Consequently, valuable eigen-information is available via GD. Recent examples show that GD with a ﬁxed step size, applied to locally quadratic nonconvex functions, can take exponential time to escape saddle points [6, 24]. Here, those examples are revisited and it is shown that eigenvalue information was missing, so that the examples may not provide a complete picture of the potential practical behaviour of GD. Thus, ongoing investigation of the behaviour of GD on nonconvex functions, possibly with an adaptive or variable step size, is warranted. It is shown that, in the special case of a quadratic in R 2 , if an eigenvalue is known, then GD with a ﬁxed step size will converge in two iterations, and a complete eigen-decomposition is available. By considering the dynamics of the gradients and iterates, new step size strategies are proposed to improve the practical performance of GD. Several numerical examples are presented, which demonstrate the advantages of exploiting the GD–PM connection.
Non Common Path Aberrations Correction

The primary goal of this thesis was the correction of Non-Common-Path-Aberrations in the SPHERE instrument for helping it meeting its contrast requirements. SPHERE's purpose is the search and characterization of giant exo-planets around nearby stars. The author implemented a method called Electric Field Conjugation that he tested in simulation as well as on the real system. A full week was booked in SPHERE schedule a few days before the second commissioning in June 2014. It gave the opportunity to the author to travel to the VLT in Chile and experiment directly on the system. The contrast gain objective of another order of magnitude in a medium-sized area has successfully been reached bringing SPHERE raw speckle contrast from about $10^{-6}$ to $10^{-7}$. The algorithm has therefore proven its value and will be further investigated and hopefully automated by the SPHERE team based on the codes developed by the author. However it is important to keep in mind that Electric Field Conjugation is more effective for follow-up studies in order to improve the quality of the observations. Indeed the area for a good correction is very limited. It can't be used for exo-planets discovery unless the corrected area is made big enough but the performance will be less.
Gas-rich, Field Ultra-diffuse Galaxies Host Few Gobular Clusters

We present Hubble Space Telescope imaging of 14 gas-rich, low-surface-brightness galaxies in the field at distances of 25–36 Mpc, with mean effective radii and g-band central surface brightnesses of 1.9 kpc and 24.2 mag arcsec−2. Nine meet the standard criteria to be considered ultra-diffuse galaxies (UDGs). An inspection of point-like sources brighter than the turnover magnitude of the globular cluster luminosity function and within twice the half-light radii of each galaxy reveals that, unlike those in denser environments, gas-rich, field UDGs host very few old globular clusters (GCs). Most of the targets (nine) have zero candidate GCs, with the remainder having one or two candidates each. These findings are broadly consistent with expectations for normal dwarf galaxies of similar stellar mass. This rules out gas-rich, field UDGs as potential progenitors of the GC-rich UDGs that are typically found in galaxy clusters. However, some in galaxy groups may be directly accreted from the field. In line with other recent results, this strongly suggests that there must be at least two distinct formation pathways for UDGs, and that this subpopulation is simply an extreme low surface brightness extension of the underlying dwarf galaxy population. The root cause of their diffuse stellar distributions remains unclear, but the formation mechanism appears to only impact the distribution of stars (and potentially dark matter), without strongly impacting the distribution of neutral gas, the overall stellar mass, or the number of GCs.
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia

Online encyclopedias, such as Wikipedia, have been well-developed and researched in the last two decades. One can find any attributes or other information of a wiki item on a wiki page edited by a community of volunteers. However, the traditional text, images and tables can hardly express some aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may care more about ``How to feed it'' or ``How to train it not to protect its food''. Currently, short-video platforms have become a hallmark in the online world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts, short-video apps have changed how we consume and create content today. Except for producing short videos for entertainment, we can find more and more authors sharing insightful knowledge widely across all walks of life. These short videos, which we call knowledge videos, can easily express any aspects (e.g. hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), and they can be systematically analyzed and organized like an online encyclopedia. In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China. We first collected items from multiple sources and mined user-centered aspects from millions of users' queries to build an item-aspect tree. Then we propose a new task called ``multi-modal item-aspect linking'' as an expansion of ``entity linking'' to link short videos into item-aspect pairs and build the whole short-video encyclopedia. Intrinsic evaluations show that our encyclopedia is of large scale and highly accurate. We also conduct sufficient extrinsic experiments to show how Kuaipedia can help fundamental applications such as entity typing and entity linking.