Miracle-dz/UniFAR-FTUs huggingface dataset:
GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks

Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder: how does a GNN make such decisions, and can we trust its prediction with high confidence? When it comes to some critical fields, such as biomedicine, where making wrong decisions can have severe consequences, it is crucial to interpret the inner working mechanisms of GNNs before applying them. In this paper, we propose a model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model. More specifically, GNNInterpreter learns a probabilistic generative graph distribution that produces the most discriminative graph pattern the GNN tries to detect when making a certain prediction by optimizing a novel objective function specifically designed for the model-level explanation for GNNs. Compared to existing works, GNNInterpreter is more flexible and computationally efficient in generating explanation graphs with different types of node and edge features, without introducing another blackbox or requiring manually specified domain-specific rules. In addition, the experimental studies conducted on four different datasets demonstrate that the explanation graphs generated by GNNInterpreter match the desired graph pattern if the model is ideal; otherwise, potential model pitfalls can be revealed by the explanation. The official implementation can be found at https://github.com/yolandalalala/GNNInterpreter.
Truthful Generalized Linear Models

In this paper we study estimating Generalized Linear Models (GLMs) in the case where the agents (individuals) are strategic or self-interested and they concern about their privacy when reporting data. Compared with the classical setting, here we aim to design mechanisms that can both incentivize most agents to truthfully report their data and preserve the privacy of individuals' reports, while their outputs should also close to the underlying parameter. In the first part of the paper, we consider the case where the covariates are sub-Gaussian and the responses are heavy-tailed where they only have the finite fourth moments. First, motivated by the stationary condition of the maximizer of the likelihood function, we derive a novel private and closed form estimator. Based on the estimator, we propose a mechanism which has the following properties via some appropriate design of the computation and payment scheme for several canonical models such as linear regression, logistic regression and Poisson regression: (1) the mechanism is $o(1)$-jointly differentially private (with probability at least $1-o(1)$); (2) it is an $o(\frac{1}{n})$-approximate Bayes Nash equilibrium for a $(1-o(1))$-fraction of agents to truthfully report their data, where $n$ is the number of agents; (3) the output could achieve an error of $o(1)$ to the underlying parameter; (4) it is individually rational for a $(1-o(1))$ fraction of agents in the mechanism ; (5) the payment budget required from the analyst to run the mechanism is $o(1)$. In the second part, we consider the linear regression model under more general setting where both covariates and responses are heavy-tailed and only have finite fourth moments. By using an $\ell_4$-norm shrinkage operator, we propose a private estimator and payment scheme which have similar properties as in the sub-Gaussian case.
Plug-and-Play Regularization Using Linear Solvers

There has been tremendous research on the design of image regularizers over the years, from simple Tikhonov and Laplacian to sophisticated sparsity and CNN-based regularizers. Coupled with a model-based loss function, these are typically used for image reconstruction within an optimization framework. The technical challenge is to develop a regularizer that can accurately model realistic images and be optimized efficiently along with the loss function. Motivated by the recent plug-and-play paradigm for image regularization, we construct a quadratic regularizer whose reconstruction capability is competitive with state-of-the-art regularizers. The novelty of the regularizer is that, unlike classical regularizers, the quadratic objective function is derived from the observed data. Since the regularizer is quadratic, we can reduce the optimization to solving a linear system for applications such as superresolution, deblurring, inpainting, etc. In particular, we show that using iterative Krylov solvers, we can converge to the solution in few iterations, where each iteration requires an application of the forward operator and a linear denoiser. The surprising finding is that we can get close to deep learning methods in terms of reconstruction quality. To the best of our knowledge, the possibility of achieving near state-of-the-art performance using a linear solver is novel.
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning

Multi-task learning (MTL) jointly learns a set of tasks by sharing parameters among tasks. It is a promising approach for reducing storage costs while improving task accuracy for many computer vision tasks. The effective adoption of MTL faces two main challenges. The first challenge is to determine what parameters to share across tasks to optimize for both memory efficiency and task accuracy. The second challenge is to automatically apply MTL algorithms to an arbitrary CNN backbone without requiring time-consuming manual re-implementation and significant domain expertise. This paper addresses the challenges by developing the first programming framework AutoMTL that automates efficient MTL model development for vision tasks. AutoMTL takes as inputs an arbitrary backbone convolutional neural network (CNN) and a set of tasks to learn, and automatically produces a multi-task model that achieves high accuracy and small memory footprint simultaneously. Experiments on three popular MTL benchmarks (CityScapes, NYUv2, Tiny-Taskonomy) demonstrate the effectiveness of AutoMTL over state-of-the-art approaches as well as the generalizability of AutoMTL across CNNs. AutoMTL is open-sourced and available at https://github.com/zhanglijun95/AutoMTL.
Unconditional Regularity and Trace Results for the Isentropic Euler Equations with \({\boldsymbol{\gamma = 3}}\)

In this paper, we study the regularity properties of bounded entropy solutions to the isentropic Euler equations with $\gamma = 3$. First, we use a blow-up technique to obtain a new trace theorem for all such solutions. Second, we use a modified De Giorgi type iteration on the kinetic formulation to show a new partial regularity result on the Riemann invariants. We are able to conclude that in fact for any bounded entropy solution $u$, the density $\rho$ is almost everywhere upper semicontinuous away from vacuum. To our knowledge, this is the first example of a nonlinear hyperbolic system, which fails to be Temple class, but has the property that generic $L^\infty$ initial data give rise to bounded entropy solutions with a form of near classical regularity. This provides one example that $2\times 2$ hyperbolic systems can possess some of the more striking regularizing effects known to hold generically in the genuinely nonlinear, multidimensional scalar setting. While we are not able to use our regularity results to show unconditional uniqueness, the results substantially lower the likelihood that current methods of convex integration can be used in this setting.
On Condensates in Strongly Coupled Gauge Theories

We present a new perspective on the nature of quark and gluon condensates in quantum chromodynamics. We suggest that the spatial support of QCD condensates is restricted to the interiors of hadrons, since these condensates arise due to the interactions of confined quarks and gluons. An analogy is drawn with order parameters like the Cooper pair condensate and spontaneous magnetization experimentally measured in finite samples in condensed matter physics. Our picture explains the results of recent studies which find no significant signal for the vacuum gluon condensate. We also give a general discussion of condensates in asymptotically free vectorial and chiral gauge theories.
The Absolute Magnitudes of 1991T-like Supernovae

1991T-like supernovae are the luminous, slow-declining extreme of the Branch shallow-silicon (SS) subclass of Type Ia supernovae. They are distinguished by extremely weak Ca ii H & K and Si ii λ6355 and strong Fe iii absorption features in their optical spectra at pre-maximum phases, and have long been suspected to be over-luminous compared to normal Type Ia supernovae. In this paper, the pseudo-equivalent width of the Si ii λ6355 absorption obtained at light curve phases from ≤ +10 days is combined with the morphology of the i-band light curve to identify a sample of 1991T-like supernovae in the Carnegie Supernova Project II. Hubble diagram residuals show that, at optical as well as near-infrared wavelengths, these events are over-luminous by ∼0.1–0.5 mag with respect to the less extreme Branch SS (1999aa-like) and Branch core-normal supernovae with similar B-band light-curve decline rates.
Human-level Atari 200x faster

The task of building general agents that perform well over a wide range of tasks has been an importantgoal in reinforcement learning since its inception. The problem has been subject of research of alarge body of work, with performance frequently measured by observing scores over the wide rangeof environments contained in the Atari 57 benchmark. Agent57 was the first agent to surpass thehuman benchmark on all 57 games, but this came at the cost of poor data-efficiency, requiring nearly 80billion frames of experience to achieve. Taking Agent57 as a starting point, we employ a diverse set ofstrategies to achieve a 200-fold reduction of experience needed to outperform the human baseline. Weinvestigate a range of instabilities and bottlenecks we encountered while reducing the data regime, andpropose effective solutions to build a more robust and efficient agent. We also demonstrate competitiveperformance with high-performing methods such as Muesli and MuZero. The four key components toour approach are (1) an approximate trust region method which enables stable bootstrapping from theonline network, (2) a normalisation scheme for the loss and priorities which improves robustness whenlearning a set of value functions with a wide range of scales, (3) an improved architecture employingtechniques from NFNets in order to leverage deeper networks without the need for normalization layers,and (4) a policy distillation method which serves to smooth out the instantaneous greedy policy overtime.
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation

Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain. Although UDA is typically jointly trained on data from both domains, accessing the labeled source domain data is often restricted, due to concerns over patient data privacy or intellectual property. To sidestep this, we propose "off-the-shelf (OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor trained in a source domain to a target domain, in the absence of source domain data in adaptation. Toward this goal, we aim to develop a novel batch-wise normalization (BN) statistics adaptation framework. In particular, we gradually adapt the domain-specific low-order BN statistics, e.g., mean and variance, through an exponential momentum decay strategy, while explicitly enforcing the consistency of the domain shareable high-order BN statistics, e.g., scaling and shifting factors, via our optimization objective. We also adaptively quantify the channel-wise transferability to gauge the importance of each channel, via both low-order statistics divergence and a scaling factor. Furthermore, we incorporate unsupervised self-entropy minimization into our framework to boost performance alongside a novel queued, memory-consistent self-training strategy to utilize the reliable pseudo label for stable and efficient unsupervised adaptation. We evaluated our OSUDA-based framework on both cross-modality and cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks. Our experimental results showed that our memory consistent OSUDA performs better than existing source-relaxed UDA methods and yields similar performance to UDA methods with source data.
A Polarized Temporal Network Model to Study the Spread of Recurrent Epidemic Diseases in a Partially Vaccinated Population

Motivated by massive outbreaks of COVID-19 that occurred even in populations with high vaccine uptake, we propose a novel multi-population temporal network model for the spread of recurrent epidemic diseases. We study the effect of human behavior, testing, and vaccination campaigns on infection prevalence and local outbreak control. Our modeling framework decouples a vaccine's effectiveness in protecting against transmission and severe symptom development. Additionally, it captures the polarizing effect of vaccination decisions and homophily, i.e., people's tendency to interact with like-minded individuals. Through a mean-field approach, we analytically derive the epidemic threshold and, under further assumptions, we compute the endemic equilibrium. Our results show that while vaccination campaigns are highly beneficial in reducing pressure on hospitals, they may also facilitate resurgent outbreaks, particularly in the absence of effective testing campaigns. Subsequently, we employ numerical simulations to confirm and extend our theoretical findings to more realistic scenarios. Our analytical and numerical results demonstrate that vaccination programs are crucial, but as a sole control measure, they are not sufficient to achieve disease eradication without employing massive testing campaigns or relying on the population's responsibility. Furthermore, we show that homophily impedes local outbreak control, highlighting the peril of a polarized network structure.
Ample groupoids, topological full groups, algebraic K-theory spectra and infinite loop spaces

Inspired by work of Szymik and Wahl on the homology of Higman-Thompson groups, we establish a general connection between ample groupoids, topological full groups, algebraic K-theory spectra and inﬁnite loop spaces, based on the construction of small permutative categories of compact open bisections. This allows us to analyse homological invariants of topological full groups in terms of homology for ample groupoids. Applications include complete rational computations, general vanishing and acyclicity results for group homology of topological full groups as well as a proof of Matui’s AH-conjecture for all minimal, ample groupoids with comparison.
Category and generalized Hopf invariants

The Lusternik-Schnirelmann category of a topological space X is usually defined as follows: cat X =< n if X may be covered by n open sets each of which is contractible in X. The general reference for the properties of this homotopy invariant is [4]. It was observed by G. W. Whitehead in [9] that, for a certain class of spaces including polyhedra, this definition is equivalent to the one given below (Definition 2.1), which we make the starting point of our investigation. If we attach a cone CA to X by means of a map f:A X, it is trivial to verify with the original definition of categorymand easy to verify with ours-that cat Y =< cat X W 1, where Y X CA. Our interest centers in the problem of establishing conditions under which, in fact, cat Y -<_ cat X. We are motivated partly by the wish to compute the category of a 1-connected polyhedron as a function of the terms in a homology decomposition (see [3]) and partly by the observation that, in the important case n 2, our problem dualizes, in the sense of [2], a familiar problem of homotopy theory. Namely, if X admits a multiplication and Y is the fibre space over X induced by a map f’X -A, under what circumstances does Y admit a multiplication. Answers to this question, under certain restrictions, have been given by Copeland [1] and others in terms of the concept of primitivity of cohomology classes. As expected, our solution of the dual problem is primarily in terms of a concept of primitivity which we introduce for homotopy classes. Moreover if ve specialize A to be a Moore space K’(G, m 1), we get fairly complete results in which the primitivity property of the map f turns out to be equivalent to the vanishing of a generalized Hopf invariant which we define for elements of homotopy groups (with coefficients) of spaces of specified category. Just as in the dual situation, if X is a suspension space, all suspension elements of rm-l(G; X) are primitive, but the converse is false. We are thus enabled to construct spaces of category 2 which are not equivalent to suspensions, answering a question first raised by T. Ganea. The definition of category which we give suggests a related notion of weak category (Definition 2.2) which is a weaker hypothesis on a space X in that w cat X -< n if cat X _<_ n, but the converse is, in general, false. Nevertheless certain well-known properties of category generalize to weak category, includ-
The Effects of Space Weather on Flight Delays

Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those time-varying conditions in space caused by the sun are also called space weather, as the atmospheric conditions that can affect weather on the ground. It is known that aviation activities can be affected during space weather events, but the exact effects of space weather on aviation are still unclear. Especially how the flight delays, the top topic concerned by most people, will be affected by space weather has never been thoroughly researched. By analyzing huge amount of flight data (~5×106 records), for the first time, we demonstrate that space weather events could have systematically modulating effects on flight delays. The average arrival delay time and 30-minute delay rate during space weather events are significantly increased by 81.34% and 21.45% respectively compared to those during quiet periods. The evident negative correlation between the yearly flight regularity rate and the yearly mean total sunspot number during 22 years also confirms such delay effects. Further studies indicate that the interference in communication and navigation caused by geomagnetic field fluctuations and ionospheric disturbances associated with the space weather events will increase the flight delay time and delay rate. These results expand the traditional field of space weather research and could also provide us with brand new views for improving the flight delay predications.
Self-Supervised Learning of Phenotypic Representations from Cell Images with Weak Labels

We propose WS-DINO as a novel framework to use weak label information in learning phenotypic representations from high-content ﬂuorescent images of cells. Our model is based on a knowledge distillation approach with a vision transformer backbone (DINO), and we use this as a benchmark model for our study. Using WS-DINO, we ﬁne-tuned with weak label information available in high-content microscopy screens (treatment and compound) and achieve state-of-the-art performance in not-same-compound mechanism of action prediction on the BBBC021 dataset (98%), and not-same-compound-and-batch performance (96%) using the compound as the weak label. Our method bypasses single cell cropping as a pre-processing step, and using self-attention maps we show that the model learns structurally meaningful phenotypic proﬁles.
How to train your solver: Verification of boundary conditions for smoothed particle hydrodynamics

The weakly compressible smoothed particle hydrodynamics (WCSPH) method has been employed to simulate various physical phenomena involving fluids and solids. Various methods have been proposed to implement the solid wall, inlet/outlet, and other boundary conditions. However, error estimation and the formal rates of convergence for these methods have not been discussed or examined carefully. In this paper, we use the method of manufactured solution (MMS) to verify the convergence properties of a variety of commonly employed of various solid, inlet, and outlet boundary implementations. In order to perform this study, we propose various manufactured solutions for different domains. On the basis of the convergence offered by these methods, we systematically propose a convergent WCSPH scheme along with suitable methods for implementing the boundary conditions. We also demonstrate the accuracy of the proposed scheme by using it to solve the flow past a circular cylinder. Along with other recent developments in the use of adaptive resolution, this paves the way for accurate and efficient simulation of incompressible or weakly-compressible fluid flows using the SPH method.
DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement

Low-light image enhancement is a classical computer vision problem aiming to recover normal-exposure images from low-light images. However, convolutional neural networks commonly used in this ﬁeld are good at sampling low-frequency local structural features in the spatial domain, which leads to unclear texture details of the reconstructed images. To alleviate this problem, we propose a novel module using the Fourier coeﬃcients, which can recover high-quality texture details under the constraint of semantics in the frequency phase and supplement the spatial domain. In addition, we design a simple and eﬃcient module for the image spatial domain using dilated convolutions with diﬀerent receptive ﬁelds to alleviate the loss of detail caused by frequent downsampling. We integrate the above parts into an end-to-end dual branch network and design a novel loss committee and an adaptive fusion module to guide the network to ﬂexibly combine spatial and frequency domain features to generate more pleasing visual eﬀects. Finally, we evaluate the proposed network on public benchmarks. Extensive experimental results show that our method outperforms many existing state-of-the-art ones, showing outstanding performance and potential. We release our code at https://github.com/Zhuangyunliang/DPFNet.
ConvFormer: Closing the Gap Between CNN and Vision Transformers

: Vision transformers have shown excellent performance in computer vision tasks. However, the computation cost of their (local) self-attention mechanism is expensive. Comparatively, CNN is more eﬃcient with built-in inductive bias. Recent works show that CNN is promising to compete with vision transformers by learning their architecture design and training protocols. Nevertheless, existing methods either ignore multi-level features or lack dynamic prosperity, leading to sub-optimal performance. In this paper, we propose a novel attention mechanism named MCA, which captures diﬀerent patterns of input images by multiple kernel sizes and enables input-adaptive weights with a gating mechanism. Based on MCA, we present a neural network named ConvFormer. ConvFormer adopts the general architecture of vision transformers, while replacing the (local) self-attention mechanism with our proposed MCA. Extensive experimental results demonstrated that ConvFormer achieves state-of-the-art performance on ImageNet classiﬁcation, which outperforms similar-sized vision transformers(ViTs) and convolutional neural networks (CNNs). Moreover, for object detection on COCO and semantic segmentation tasks on ADE20K, ConvFormer also shows excellent performance compared with recently advanced methods. Code and models will be available.