Miracle-dz/UniFAR-FTUs huggingface dataset:
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI

Early detection of dementia through specific biomarkers in MR images plays a critical role in developing support strategies proactively. Fazekas scale facilitates an accurate quantitative assessment of the severity of white matter lesions and hence the disease. Imaging Biomarkers of dementia are multiple and comprehensive documentation of them is time-consuming. Therefore, any effort to automatically extract these biomarkers will be of clinical value while reducing inter-rater discrepancies. To tackle this problem, we propose to classify the disease severity based on the Fazekas scale through the visual biomarkers, namely the Periventricular White Matter (PVWM) and the Deep White Matter (DWM) changes, in the real-world setting of thick-slice MRI. Small training sample size and weak supervision in form of assigning severity labels to the whole MRI stack are among the main challenges. To combat the mentioned issues, we have developed a deep learning pipeline that employs self-supervised representation learning, multiple instance learning, and appropriate pre-processing steps. We use pretext tasks such as non-linear transformation, local shuffling, in- and out-painting for self-supervised learning of useful features in this domain. Furthermore, an attention model is used to determine the relevance of each MRI slice for predicting the Fazekas scale in an unsupervised manner. We show the significant superiority of our method in distinguishing different classes of dementia compared to state-of-the-art methods in our mentioned setting, which improves the macro averaged F1-score of state-of-the-art from 61% to 76% in PVWM, and from 58% to 69.2% in DWM.
Deep Online Correction for Monocular Visual Odometry

In this work, we propose a novel deep online correction (DOC) framework for monocular visual odometry. The whole pipeline has two stages: First, depth maps and initial poses are obtained from convolutional neural networks (CNNs) trained in self-supervised manners. Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases. The benefits of our proposed method are twofold: 1) Different from online-learning methods, DOC does not need to calculate gradient propagation for parameters of CNNs. Thus, it saves more computation resources during inference phases. 2) Unlike hybrid methods that combine CNNs with traditional methods, DOC fully relies on deep learning (DL) frameworks. Though without complex back-end optimization modules, our method achieves outstanding performance with relative transform error (RTE) = 2.0% on KITTI Odometry benchmark for Seq. 09, which outperforms traditional monocular VO frameworks and is comparable to hybrid methods.
From Navigation to Racing: Reward Signal Design for Autonomous Racing

—The problem of autonomous navigation is to generate a set of navigation references which when followed move the vehicle from a starting position to and end goal location while avoiding obstacles. Autonomous racing complicates the navigation problem by adding the objective of minimising the time to complete a track. Solutions aiming for a minimum time solution require that the planner is concerned with the optimality of the trajectory according to the vehicle dynamics. Neural networks, trained from experience with reinforcement learning, have shown to be effective local planners which generate navigation references to follow a global plan and avoid obstacles. We address the problem designing a reward signal which can be used to train neural network-based local planners to race in a time-efﬁcient manner and avoid obstacles. The general challenge of reward signal design is to represent a desired behavior in an equation that can be calculated at each time step. The speciﬁc challenge of designing a reward signal for autonomous racing is to encode obstacle-free, time optimal racing trajectories in a clear signal We propose several methods of encoding ideal racing behavior based using a combination of the position and velocity of the vehicle and the actions taken by the network. The reward function candidates are expressed as equations and evaluated in the context of F1/10th autonomous racing. The results show that the best reward signal rewards velocity along, and punishes the lateral deviation from a precalculated, optimal reference trajectory.
Fast and High-Quality Blind Multi-Spectral Image Pansharpening

Blind pansharpening addresses the problem of generating a high spatial-resolution multi-spectral (HRMS) image given a low spatial-resolution multi-spectral (LRMS) image with the guidance of its associated spatially misaligned high spatial-resolution panchromatic (PAN) image without parametric side information. In this article, we propose a fast approach to blind pansharpening and achieve the state-of-the-art image reconstruction quality. Typical blind pansharpening algorithms are often computationally intensive since the blur kernel and the target HRMS image are often computed using iterative solvers and in an alternating fashion. To achieve fast blind pansharpening, we decouple the solution of the blur kernel and of the HRMS image. First, we estimate the blur kernel by computing the kernel coefficients with minimum total generalized variation that blur a downsampled version of the PAN image to approximate a linear combination of the LRMS image channels. Then, we estimate each channel of the HRMS image using local Laplacian prior (LLP) to regularize the relationship between each HRMS channel and the PAN image. Solving the HRMS image is accelerated by both parallelizing across the channels and by fast numerical algorithms for each channel. Due to the fast scheme and the powerful priors we used on the blur kernel coefficients (total generalized variation) and on the cross-channel relationship (LLP), numerical experiments demonstrate that our algorithm outperforms the state-of-the-art model-based counterparts in terms of both computational time and reconstruction quality of the HRMS images.
A robust Voronoi-tessellation-based approach for detection of coherent structures in sparsely-seeded ﬂows

A robust algorithm to detect coherent structures with sparse Lagrangian particle tracking data, using Voronoi tessellation and techniques from spectral graph theory, is proposed and tested. Neighbouring tracer particles are naturally identiﬁed through the Voronoi tessellation of the tracers’ distribution. The method examines the neighbouring time of tracer trajectories, deﬁned as the total ﬂow time two Voronoi cells share a common Voronoi edge, by converting this information into a Cartesian distance in the graph representation of the Voronoi diagram. Coherence is assigned to groups of Voronoi cells whose neighbouring time remains high throughout the time interval of analysis. The technique is ﬁrst tested on the two-dimensional synthetic data of a double-gyre ﬂow, and then with challenging, large-scale three-dimensional Lagrangian particle tracking data behind a bluﬀ body at high Reynolds number. The novel approach proves to be successful at identifying coherence with realistic and very sparse experimental data. Speciﬁcally, it is shown for the ﬁrst time that coherent structures are identiﬁable for mean inter-particle distances of the order of the largest length scales in the ﬂow.
Approximation for Probability Distributions by Wasserstein GAN

In this paper, we study Wasserstein Generative Adversarial Networks (WGAN) using GroupSort neural networks as discriminators. We show that the error bound of the approximation for the target distribution depends on both the width/depth (capacity) of generators and discriminators, as well as the number of samples in training. A quantiﬁed generalization bound is established for Wasserstein distance between the generated distribution and the target distribution. According to our theoretical results, WGAN has higher requirement for the capacity of discriminators than that of generators, which is consistent with some existing theories. More importantly, overly deep and wide (high capacity) generators may cause worse results than low capacity generators if discriminators are not strong enough. Moreover, we further develop a generalization error bound, which is free from curse of dimensionality w.r. t. numbers of training data. It reduces from (cid:101) O ( m − 1 /r + n − 1 /d ) to (cid:101) O (Pdim( F ◦ G ) · m − 1 / 2 + Pdim( F ) · n − 1 / 2 ). However, the latter seems to contradict to numerical observations. Compared with existing results, our results are suitable for general GroupSort WGAN without special architectures. Finally, numerical results on swiss roll and MNIST data sets conﬁrm our theoretical results.
A correlation-locking adaptive filtering technique for minimum variance integral control in adaptive optics

We propose the correlation-locking optimization scheme (CLOSE), a real-time adaptive filtering technique for adaptive optics (AO) systems controlled with integrators. CLOSE leverages the temporal autocorrelation of modal signals in the controller telemetry and drives the gains of the integral command law in a closed servo-loop. This supervisory loop is configured using only a few scalar parameters and automatically controls the modal gains to closely match transfer functions achieving minimum variance control. This optimization is proven to work throughout the range of noise and seeing conditions relevant to the AO system. This technique was designed while preparing the high-order AO systems for extremely large telescopes, in particular to tackle the optical gain (OG) phenomenon. This is a sensitivity reduction induced by on-sky residuals and is a prominent issue with pyramid wavefront sensors (PWFS). CLOSE follows upon the linear modal compensation approach to OG that was previously demonstrated to substantially improve AO correction with high-order PWFS systems. Operating on modal gains through multiplicative increments, CLOSE naturally compensates for the recurring issue of unaccounted sensitivity factors throughout the AO loop. We present end-to-end simulations of the MICADO instrument single-conjugate AO to demonstrate the performances and capabilities of CLOSE. We demonstrate that a single configuration provides an efficient and versatile optimization of the modal integrator while accounting for OG compensation and while providing significant robustness to transient effects impacting the PWFS sensitivity.
NGTS 15b, 16b, 17b, and 18b: four hot Jupiters from the Next-Generation Transit Survey

We report the discovery of four new hot Jupiters with the Next-Generation Transit Survey (NGTS). NGTS-15b, NGTS-16b, NGTS-17b, and NGTS-18b are short-period (P < 5 d) planets orbiting G-type main-sequence stars, with radii and masses between 1.10 and 1.30RJ and 0.41 and 0.76MJ, respectively. By considering the host star luminosities and the planets’ small orbital separations (0.039–0.052 au), we find that all four hot Jupiters are highly irradiated and therefore occupy a region of parameter space in which planetary inflation mechanisms become effective. Comparison with statistical studies and a consideration of the planets’ high incident fluxes reveal that NGTS-16b, NGTS-17b, and NGTS-18b are indeed likely inflated, although some disparities arise upon analysis with current Bayesian inflationary models. However, the underlying relationships that govern radius inflation remain poorly understood. We postulate that the inclusion of additional hyperparameters to describe latent factors such as heavy element fraction, as well as the addition of an updated catalogue of hot Jupiters, would refine inflationary models, thus furthering our understanding of the physical processes that give rise to inflated planets.
Thermal squeezeout of dark matter

We carry out a detailed study of the conﬁnement phase transition in a dark sector with a SU ( N ) gauge group and a single generation of dark heavy quark. We focus on heavy enough quarks such that their abundance freezes out before the phase transition and the phase transition is of ﬁrst-order. We ﬁnd that during this phase transition the quarks are trapped inside contracting pockets of the deconﬁned phase and are compressed enough to interact at a signiﬁcant rate, giving rise to a second stage of annihilation that can dramatically change the resulting dark matter abundance. As a result, the dark matter can be heavier than the often-quoted unitarity bound of ∼ 100 TeV. Our ﬁndings are almost completely independent of the details of the portal between the dark sector and the Standard Model. We comment brieﬂy on possible signals of such a sector. Our main ﬁndings are summarized in a companion letter, while here we provide further details on diﬀerent parts of the calculation.
Regularized Non-monotone Submodular Maximization

In this paper, we present a thorough study of maximizing a regularized non-monotone submodular function subject to various constraints, i.e., $\max \{ g(A) - \ell(A) : A \in \mathcal{F} \}$, where $g \colon 2^\Omega \to \mathbb{R}_+$ is a non-monotone submodular function, $\ell \colon 2^\Omega \to \mathbb{R}_+$ is a normalized modular function and $\mathcal{F}$ is the constraint set. Though the objective function $f := g - \ell$ is still submodular, the fact that $f$ could potentially take on negative values prevents the existing methods for submodular maximization from providing a constant approximation ratio for the regularized submodular maximization problem. To overcome the obstacle, we propose several algorithms which can provide a relatively weak approximation guarantee for maximizing regularized non-monotone submodular functions. More specifically, we propose a continuous greedy algorithm for the relaxation of maximizing $g - \ell$ subject to a matroid constraint. Then, the pipage rounding procedure can produce an integral solution $S$ such that $\mathbb{E} [g(S) - \ell(S)] \geq e^{-1}g(OPT) - \ell(OPT) - O(\epsilon)$. Moreover, we present a much faster algorithm for maximizing $g - \ell$ subject to a cardinality constraint, which can output a solution $S$ with $\mathbb{E} [g(S) - \ell(S)] \geq (e^{-1} - \epsilon) g(OPT) - \ell(OPT)$ using $O(\frac{n}{\epsilon^2} \ln \frac 1\epsilon)$ value oracle queries. We also consider the unconstrained maximization problem and give an algorithm which can return a solution $S$ with $\mathbb{E} [g(S) - \ell(S)] \geq e^{-1} g(OPT) - \ell(OPT)$ using $O(n)$ value oracle queries.
Joint Survey Processing. I. Compact Oddballs in the COSMOS Field—Low-luminosity Quasars at z > 6?

The faint-end slope of the quasar luminosity function at z ∼ 6 and its implication on the role of quasars in reionizing the intergalactic medium at early times has been an outstanding problem for some time. The identification of faint high-redshift quasars with luminosities of <1044.5 erg s−1 is challenging. They are rare (few per square degree), and the separation of these unresolved quasars from late-type stars and compact star-forming galaxies is difficult from ground-based observations alone. In addition, source confusion becomes significant at >25 mag, with ∼30% of sources having their flux contaminated by foreground objects when the seeing resolution is ∼0″. 7. We mitigate these issues by performing a pixel-level joint processing of ground and space-based data from Subaru/Hyper-SuprimeCam (HSC) and Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS). We create a deconfused catalog over the 1.64 deg2 of the COSMOS field, after accounting for spatial varying point-spread functions and astrometric differences between the two data sets. We identify twelve low-luminosity (M UV ∼ −21 mag) z > 6 quasar candidates through (i) their red color measured between ACS/F814W and HSC/i band and (ii) their compactness in the space-based data. Nondetections of our candidates in Hubble DASH data argues against contamination from late-type stars. Our constraints on the faint end of the quasar luminosity function at z ∼ 6.4 suggest a negligibly small contribution to reionization compared to the star-forming galaxy population. The confirmation of our candidates and the evolution of number density with redshift could provide better insights into how supermassive galaxies grew in the first billion years of cosmic time.
Graphs with unique zero forcing sets and Grundy dominating sets

The concept of zero forcing was introduced in the context of linear algebra, and was further studied by both graph theorists and linear algebraists. It is based on the process of activating vertices of a graph G starting from a set of vertices that are already active, and applying the rule that an active vertex with exactly one non-active neighbor forces that neighbor to become active. A set S ⊂ V (G) is called a zero forcing set of G if initially only vertices of S are active and the described process enforces all vertices of G to become active. The size of a minimum zero forcing set in G is called the zero forcing number of G. While a minimum zero forcing set can only be unique in edgeless graphs, we consider the weaker uniqueness condition, notably that for every two minimum zero forcing sets in a graph G there is an automorphism that maps one to the other. We characterize the class of trees that enjoy this condition by using properties of minimum path covers of trees. In addition, we investigate both variations of uniqueness for several concepts of Grundy domination, which first appeared in the context of domination games, yet they are also closely related to zero forcing. For each of the four variations of Grundy domination we characterize the graphs that have only one Grundy dominating set of the given type, and characterize those forests that enjoy the weaker (isomorphism based) condition of uniqueness. The latter characterizations lead to efficient algorithms for recognizing the corresponding
On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems

Recently, we have been witnessing an increasing use of machine learning methods in self-adaptive systems. Machine learning methods offer a variety of use cases for supporting self-adaptation, e.g., to keep runtime models up to date, reduce large adaptation spaces, or update adaptation rules. Yet, since machine learning methods apply in essence statistical methods, they may have an impact on the decisions made by a self-adaptive system. Given the wide use of formal approaches to provide guarantees for the decisions made by self-adaptive systems, it is important to investigate the impact of applying machine learning methods when such approaches are used. In this paper, we study one particular instance that combines linear regression to reduce the adaptation space of a self-adaptive system with statistical model checking to analyze the resulting adaptation options. We use computational learning theory to determine a theoretical bound on the impact of the machine learning method on the predictions made by the verifier. We illustrate and evaluate the theoretical result using a scenario of the DeltaIoT artifact. To conclude, we look at opportunities for future research in this area.
Error Analysis of Douglas-Rachford Algorithm for Linear Inverse Problems: Asymptotics of Proximity Operator for Squared Loss

—Proximal splitting-based convex optimization is a promising approach to linear inverse problems because we can use some prior knowledge of the unknown variables explicitly. In this paper, we ﬁrstly analyze the asymptotic property of the proximity operator for the squared loss function, which appears in the update equations of some proximal splitting methods for linear inverse problems. The analysis shows that the output of the proximity operator can be characterized with a scalar random variable in the large system limit. Moreover, we investigate the asymptotic behavior of the Douglas-Rachford algorithm, which is one of the famous proximal splitting methods. From the asymptotic result, we can predict the evolution of the mean-square-error (MSE) in the algorithm for large-scale linear inverse problems. Simulation results demonstrate that the MSE performance of the Douglas-Rachford algorithm can be well predicted by the analytical result in compressed sensing with the (cid:96) 1 optimization.
All NLP Tasks Are Generation Tasks: A General Pretraining Framework

There have been various types of pretraining architectures including autoregressive models (e.g., GPT), autoencoding models (e.g., BERT), and encoder-decoder models (e.g., T5). On the other hand, NLP tasks are different in nature, with three main categories being classification, unconditional generation, and conditional generation. However, none of the pretraining frameworks performs the best for all tasks, which introduces inconvenience for model development and selection. We propose a novel pretraining framework GLM (General Language Model) to address this challenge. Compared to previous work, our architecture has three major benefits: (1) it performs well on classification, unconditional generation, and conditional generation tasks with one single pretrained model; (2) it outperforms BERT-like models on classification due to improved pretrain-finetune consistency; (3) it naturally handles variable-length blank filling which is crucial for many downstream tasks. Empirically, GLM substantially outperforms BERT on the SuperGLUE natural language understanding benchmark with the same amount of pre-training data. Moreover, GLM with 1.25× parameters of BERTLarge achieves the best performance in NLU, conditional and unconditional generation at the same time, which demonstrates its generalizability to different downstream tasks.1 Equal contribution Department of Computer Science and Technology, Tsinghua Univerisity, Beijing, China Beijing Academy of Artificial Intelligence, Beijing, China Massachusetts Institute of Technology, Cambridge, U.S.A. Recurrent AI, Ltd. Correspondence to: Zhilin Yang <kimi_yang@rcrai.com>, Jie Tang <jietang@tsinghua.edu. cn>. The codes and pre-trained models are available at https: //github.com/THUDM/GLM All [START] NLP tasks are generation tasks All NLP tasks [END] are generation tasks
IRLI: Iterative Re-partitioning for Learning to Index

Neural models have transformed the fundamental information retrieval problem of mapping a query to a giant set of items. However, the need for efficient and low latency inference forces the community to reconsider efficient approximate near-neighbor search in the item space. To this end, learning to index is gaining much interest in recent times. Methods have to trade between obtaining high accuracy while maintaining load balance and scalability in distributed settings. We propose a novel approach called IRLI (pronounced `early'), which iteratively partitions the items by learning the relevant buckets directly from the query-item relevance data. Furthermore, IRLI employs a superior power-of-$k$-choices based load balancing strategy. We mathematically show that IRLI retrieves the correct item with high probability under very natural assumptions and provides superior load balancing. IRLI surpasses the best baseline's precision on multi-label classification while being $5x$ faster on inference. For near-neighbor search tasks, the same method outperforms the state-of-the-art Learned Hashing approach NeuralLSH by requiring only ~ {1/6}^th of the candidates for the same recall. IRLI is both data and model parallel, making it ideal for distributed GPU implementation. We demonstrate this advantage by indexing 100 million dense vectors and surpassing the popular FAISS library by >10% on recall.