Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Survey and Taxonomy on Image Dehazing Based on Deep Learning

With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challenges that can inspire the future research are pointed out. A collection of useful dehazing materials is available at https://github.com/Xiaofeng-life/AwesomeDehazing.
CoCa: Contrastive Captioners are Image-Text Foundation Models

Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby subsuming model capabilities from contrastive approaches like CLIP and generative methods like SimVLM. In contrast to standard encoder-decoder transformers where all decoder layers attend to encoder outputs, CoCa omits cross-attention in the first half of decoder layers to encode unimodal text representations, and cascades the remaining decoder layers which cross-attend to the image encoder for multimodal image-text representations. We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively. By sharing the same computational graph, the two training objectives are computed efficiently with minimal overhead. CoCa is pretrained end-to-end and from scratch on both web-scale alt-text data and annotated images by treating all labels simply as text, seamlessly unifying natural language supervision for representation learning. Empirically, CoCa achieves state-of-the-art performance with zero-shot transfer or minimal task-specific adaptation on a broad range of downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700, Moments-in-Time), crossmodal retrieval (MSCOCO, Flickr30K, MSR-VTT), multimodal understanding (VQA, SNLI-VE, NLVR2), and image captioning (MSCOCO, NoCaps). Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1 accuracy, 90.6% with a frozen encoder and learned classification head, and new state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder.
MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification

Most singer identification methods are processed in the frequency domain, which potentially leads to information loss during the spectral transformation. In this paper, instead of the frequency domain, we propose an end-to-end architecture that addresses this problem in the waveform domain. An en-coder based on Multi-scale Dilated Convolution Neural Networks (MDCNN) was introduced to generate wave embedding from the raw audio signal. Specifically, dilated convolution layers are used in the proposed method to enlarge the receptive field, aiming to extract song-level features. Furthermore, skip connection in the backbone network integrates the multi-resolution acoustic features learned by the stack of convolution layers. Then, the obtained wave embedding is passed into the following networks for singer identification. In experiments, the proposed method achieves comparable performance on the benchmark dataset of Artist20, which significantly improves related works.
Using virtual edges to extract keywords from texts modeled as complex networks

Detecting keywords in texts is important for many text mining applications. Graph-based methods have been commonly used to automatically find the key concepts in texts, however, relevant information provided by embeddings has not been widely used to enrich the graph structure. Here we modeled texts co-occurrence networks, where nodes are words and edges are established either by contextual or semantical similarity. We compared two embedding approaches -- Word2vec and BERT -- to check whether edges created via word embeddings can improve the quality of the keyword extraction method. We found that, in fact, the use of virtual edges can improve the discriminability of co-occurrence networks. The best performance was obtained when we considered low percentages of addition of virtual (embedding) edges. A comparative analysis of structural and dynamical network metrics revealed the degree, PageRank, and accessibility are the metrics displaying the best performance in the model enriched with virtual edges.
Towards Theory Development for Emergent E-Business Innovations: Using Convergent Interviewing to Explore the Adoption of XBRL in Australia

AbstrAct The eXtensible Business Reporting Language (XBRL) is an emerging XML-based standard which has the potential to significantly improve the efficiency and effectiveness of intra-and inter-organisational information supply chains in e-business. In this chapter, we present the case for using convergent interviews as an appropriate and efficient method for modelling factors impacting the adoption of emerging and under-researched innovations, such as XBRL. Using this method, we identify environmental, or-ganisational, and innovation-related factors as they apply to XBRL adoption and diffusion. Contentious factors, such as the role of government organisations, XBRL education and training, and the readiness of XBRL as an innovation, and its supporting software solutions are also examined in detail. Taken together, these discussions constitute an important step towards theory development for emergent e-business innovations. Practical adoptions strategies and their implications are also discussed.
SUBEXPONENTIALITY OF DENSITIES OF INFINITELY DIVISIBLE DISTRIBUTIONS

We show the equivalence of three properties for an inﬁnitely divisible distribution: the subexponentiality of the density, the subexponentiality of the density of its L´evy measure and the tail equivalence between the density and its L´evy measure density, under monotonic-type assump-tions on the L´evy measure. The key assumption is that tail of the L´evy measure density is asymptotic to a non-increasing function or is eventually non-increasing. Our conditions are novel and cover a rather wide class of inﬁnitely divisible distributions. Several signiﬁcant properties for analyzing the subexponentiality of densities have been derived such as closure properties of [ convolution, convolution roots and asymptotic equivalence ] and the factorization property. Moreover, we illustrate that the results are applicable for developing the statistical inference of subexponential inﬁnitely divisible distributions which are absolutely continuous.
Remark on Serre $C^*$-algebras

We study non-commutative algebraic geometry of Artin, Serre and Tate in terms of the operator algebras. Namely, we deﬁne the Serre C ∗ -algebra A X of a projective variety X as the norm-closure of a representation of the twisted homogeneous coordinate ring of X by the linear operators on a Hilbert space H . It is proved that X is homeomorphic to the space of all irreducible representations of the crossed product of A X by an automorphism of A X . The case of rational elliptic curves X is considered in detail.
Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images

Self-supervised learning (SSL) has become the new state of the art in several domain classification and segmentation tasks. One popular category of SSL are distillation networks, such as Bootstrap Your Own Latent (BYOL). This work proposes RS-BYOL, which builds on BYOL in the remote sensing (RS) domain where data are nontrivially different from natural RGB images. Since multispectral (MS) and synthetic aperture radar (SAR) sensors provide varied spectral and spatial resolution information, we utilize them as an implicit augmentation to learn invariant feature embeddings. In order to learn RS-based invariant features with SSL, we trained RS-BYOL in two ways, i.e., single channel feature learning and three channel feature learning. This work explores the usefulness of single channel feature learning from random 10 MS bands of 10–20 m resolution and VV-VH of SAR bands compared to the common notion of using three or more bands. In our linear probing evaluation, these single channel features reached a 0.92 F1 score on the EuroSAT classification task and 59.6 mIoU on the IEEE Data Fusion Contest segmentation task for certain single bands. We also compare our results with ImageNet weights and show that the RS-based SSL model outperforms the supervised ImageNet-based model. We further explore the usefulness of multimodal data compared to single modality data, and it is shown that utilizing MS and SAR data allows better invariant representations to be learnt than utilizing only MS data.
WeakSATD: Detecting Weak Self-admitted Technical Debt

Speeding up development may produce technical debt, i.e., not-quite-right code for which the effort to make it right increases with time as a sort of interest. Developers may be aware of the debt as they admit it in their code comments. Literature reports that such a self-admitted technical debt survives for a long time in a program, but it is not yet clear its impact on the quality of the code in the long term. We argue that self-admitted technical debt contains a number of different weaknesses that may affect the security of a program. Therefore, the longer a debt is not paid back the higher is the risk that the weaknesses can be exploited. To discuss our claim and rise the developers' awareness of the vulnerability of the self-admitted technical debt that is not paid back, we explore the self-admitted technical debt in the Chromium C-code to detect any known weaknesses. In this preliminary study, we first mine the Common Weakness Enumeration repository to define heuristics for the automatic detection and fix of weak code. Then, we parse the C-code to find self-admitted technical debt and the code block it refers to. Finally, we use the heuristics to find weak code snippets associated to self-admitted technical debt and recommend their potential mitigation to developers. Such knowledge can be used to prioritize self-admitted technical debt for repair. A prototype has been developed and applied to the Chromium code. Initial findings report that 55% of self-admitted technical debt code contains weak code of 14 different types.
Reinforcement Learning for Improved Random Access in Delay-Constrained Heterogeneous Wireless Networks

In this paper, we for the ﬁrst time investigate the random access problem for a delay-constrained heterogeneous wireless network. We begin with a simple two-device problem where two devices deliver delay-constrained trafﬁc to an access point (AP) via a common unreliable collision channel. By assuming that one device (called Device 1) adopts ALOHA, we aim to optimize the random access scheme of the other device (called Device 2). The most intriguing part of this problem is that Device 2 does not know the information of Device 1 but needs to maximize the system timely throughput. We ﬁrst propose a Markov Decision Process (MDP) formulation to derive a model-based upper bound so as to quantify the performance gap of certain random access schemes. We then utilize reinforcement learning (RL) to design an R-learning-based random access scheme, called tiny state-space R-learning random access (TSRA), which is subsequently extended for the tackling of the general multi-device problem. We carry out extensive simulations to show that the proposed TSRA simultaneously achieves higher timely throughput, lower computation complexity, and lower power consumption than the existing baseline— deep-reinforcement learning multiple access (DLMA). This indicates that our proposed TSRA scheme A preliminary version of this work was presented by IEEE GLOBECOM Workshop on Towards Native-AI Wireless Networks, 2021 [1]. is a promising means for efﬁcient random access over massive mobile devices with limited computation and battery capabilities.
Self-focusing virtual screening with active design space pruning

High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost of an exhaustive virtual screen may be prohibitive. Model-guided optimization has been employed to lower these costs through dramatic increases in sample efficiency compared to random selection. However, these techniques introduce new costs to the workflow through the surrogate model training and inference steps. In this study, we propose an extension to the framework of model-guided optimization that mitigates inference costs using a technique we refer to as design space pruning (DSP), which irreversibly removes poor-performing candidates from consideration. We study the application of DSP to a variety of optimization tasks and observe significant reductions in overhead costs while exhibiting similar performance to the baseline optimization. DSP represents an attractive extension of model-guided optimization that can limit overhead costs in optimization settings where these costs are non-negligible relative to objective costs, such as docking.
Thin current sheet formation: comparison between Earth's magnetotail and coronal streamers

Magnetic field line reconnection is a universal plasma process responsible for the magnetic field topology change and magnetic field energy dissipation into charged particle heating and acceleration. In many systems, the conditions leading to the magnetic reconnection are determined by the pre-reconnection configuration of a thin layer with intense currents -- otherwise known as the thin current sheet. In this study we investigate two such systems: Earth's magnetotail and helmet streamers in the solar corona. The pre-reconnection current sheet evolution has been intensely studied in the magnetotail, where in-situ spacecraft observations are available; but helmet streamer current sheets studies are fewer, due to lack of in-situ observations -- they are mostly investigated with numerical simulations and information that can be surmised from remote sensing instrumentation. Both systems exhibit qualitatively the same behavior, despite their largely different Mach numbers, much higher at the solar corona than at the magnetotail. Comparison of spacecraft data (from the magnetotail) with numerical simulations (for helmet streamers) shows that the pre-reconnection current sheet thinning, for both cases, is primarily controlled by plasma pressure gradients. Scaling laws of the current density, magnetic field, and pressure gradients are the same for both systems. We discuss how magnetotail observations and kinetic simulations can be utilized to improve our understanding and modeling of the helmet streamer current sheets.
ANUBIS: Review and Benchmark Skeleton-Based Action Recognition Methods with a New Dataset

—Skeleton-based action recognition, as a subarea of action recognition, is swiftly accumulating attention and popularity. The task is to recognize actions performed by human articulation points. Compared with other data modalities, 3D human skeleton representations have extensive unique desirable characteristics, including succinctness, robustness, racial-impartiality, and many more. We aim to provide a roadmap for new and existing researchers a on the landscapes of skeleton-based action recognition for new and existing researchers. To this end, we present a review in the form of a taxonomy on existing works of skeleton-based action recognition. We partition them into four major categories: (1) datasets; (2) extracting spatial features; (3) capturing temporal patterns; (4) improving signal quality. For each method, we provide concise yet informatively-sufﬁcient descriptions. To promote more fair and comprehensive evaluation on existing approaches of skeleton-based action recognition, we collect ANUBIS, a large-scale human skeleton dataset. Compared with previously collected dataset, ANUBIS are advantageous in the following four aspects: (1) employing more recently released sensors; (2) containing novel back view; (3) encouraging high enthusiasm of subjects; (4) including actions of the COVID pandemic era. Using ANUBIS, we comparably benchmark performance of current skeleton-based action recognizers. At the end of this paper, we outlook future development of skeleton-based action recognition by listing several new technical problems. We believe they are valuable to solve in order to commercialize skeleton-based action recognition in the near future. The dataset of ANUBIS is available at: http://hcc-workshop.anu. edu.au/webs/anu101/home.
Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment

In text-to-SQL tasks -- as in much of NLP -- compositional generalization is a major challenge: neural networks struggle with compositional generalization where training and test distributions differ. However, most recent attempts to improve this are based on word-level synthetic data or specific dataset splits to generate compositional biases. In this work, we propose a clause-level compositional example generation method. We first split the sentences in the Spider text-to-SQL dataset into sub-sentences, annotating each sub-sentence with its corresponding SQL clause, resulting in a new dataset Spider-SS. We then construct a further dataset, Spider-CG, by composing Spider-SS sub-sentences in different combinations, to test the ability of models to generalize compositionally. Experiments show that existing models suffer significant performance degradation when evaluated on Spider-CG, even though every sub-sentence is seen during training. To deal with this problem, we modify a number of state-of-the-art models to train on the segmented data of Spider-SS, and we show that this method improves the generalization performance.
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation

We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our knowledge, our system is among the first to utilize the camera pose information from SLAM to provide prior knowledge for tracking keypoints on symmetric objects - ensuring that new measurements are consistent with the current 3D scene. Moreover, our semantic key-point network is trained to predict the Gaussian covariance for the keypoints that captures the true error of the prediction, and thus is not only useful as a weight for the residuals in the system's optimization problems, but also as a means to detect harmful statistical outliers without choosing a manual threshold. Experiments show that our method provides competitive performance to the state of the art in 6DoF object pose estimation, and at a real-time speed. Our code, pre-trained models, and keypoint labels are available https://github.com/rpng/suo_slam.
Interpreting the Statistical Properties of High-z Extragalactic Sources Detected by the South Pole Telescope Survey

The results of the recently published spectroscopically complete survey of dusty star-forming galaxies detected by the South Pole Telescope over 2500 deg2 proved to be challenging for galaxy formation models that generally underpredict the observed abundance of high-z galaxies. In this paper we interpret these results in the light of a physically grounded model for the evolution of spheroidal galaxies. The model accurately reproduces the measured redshift distribution of galaxies without any adjustment of the parameters. The data do not support the indications of an excess of z > 4 dusty galaxies reported by some analyses of Herschel surveys.
Making SGD Parameter-Free

We develop an algorithm for parameter-free stochastic convex optimization (SCO) whose rate of convergence is only a double-logarithmic factor larger than the optimal rate for the corresponding known-parameter setting. In contrast, the best previously known rates for parameter-free SCO are based on online parameter-free regret bounds, which contain unavoidable excess logarithmic terms compared to their known-parameter counterparts. Our algorithm is conceptually simple, has high-probability guarantees, and is also partially adaptive to unknown gradient norms, smoothness, and strong convexity. At the heart of our results is a novel parameter-free certificate for SGD step size choice, and a time-uniform concentration result that assumes no a-priori bounds on SGD iterates.
Self-supervised learning unveils morphological clusters behind lung cancer types and prognosis

Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Characterizing and improving our understanding of phenotypes could reveal factors related to tumor progression and their underpinning biological processes, ultimately improving diagnosis and treatment. In recent years, the ﬁeld of histological deep learning applications has seen great progress, yet most of these applications focus on a supervised approach, relating tissue and associated sample annotations. Supervised approaches have their impact limited by two factors. Firstly, high-quality labels are expensive in time and effort, which makes them not easily scalable. Secondly, these methods focus on predicting annotations from histological images, fundamentally restricting the discovery of new tissue phenotypes. These limitations emphasize the importance of using new methods that can characterize tissue by the features enclosed in the image, without pre-deﬁned annotation or supervision. We present Phenotype Representation Learning (PRL), a methodology to extract histomorphological phenotypes through self-supervised learning and community detection. PRL creates phenotype clusters by identifying tissue patterns that share common morphological and cellular features, allowing to describe whole slide images (WSIs) through compositional representations of cluster contributions. We used this framework to analyze histopathology slides of adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) lung cancer subtypes from The Cancer Genome Atlas (TCGA) and New York University (NYU) cohorts. We show that PRL achieves a robust lung subtype prediction providing statistically relevant phenotypes for each lung subtype. We further demonstrate the signiﬁcance of these phenotypes in lung adenocarcinoma overall and recurrence free survival, relating clusters with patient outcomes, cell types, grown patterns, and omic-based immune signatures. Our framework provides a simple, interpretable and versatile method that can correlate histomorphological patterns with different clinical annotations, without the need of retraining for different prediction purposes.