pwc-archive/papers-with-abstracts huggingface dataset:
A Comprehensive Survey of Scene Graphs: Generation and Application
Authors: Xiaojun Chang, Pengzhen Ren, Pengfei Xu, Zhihui Li, Xiaojiang Chen, Alex Hauptmann
Date: 2021-03-17 00:00:00
Tasks: Image Captioning, Question Answering, Relationship Detection, Retrieval, Scene Understanding, Survey, Visual Question Answering, Visual Question Answering (VQA), Visual Relationship Detection

Scene graph is a structured representation of a scene that can clearly express the objects, attributes, and relationships between objects in the scene. As computer vision technology continues to develop, people are no longer satisfied with simply detecting and recognizing objects in images; instead, people look forward to a higher level of understanding and reasoning about visual scenes. For example, given an image, we want to not only detect and recognize objects in the image, but also know the relationship between objects (visual relationship detection), and generate a text description (image captioning) based on the image content. Alternatively, we might want the machine to tell us what the little girl in the image is doing (Visual Question Answering (VQA)), or even remove the dog from the image and find similar images (image editing and retrieval), etc. These tasks require a higher level of understanding and reasoning for image vision tasks. The scene graph is just such a powerful tool for scene understanding. Therefore, scene graphs have attracted the attention of a large number of researchers, and related research is often cross-modal, complex, and rapidly developing. However, no relatively systematic survey of scene graphs exists at present. To this end, this survey conducts a comprehensive investigation of the current scene graph research. More specifically, we first summarized the general definition of the scene graph, then conducted a comprehensive and systematic discussion on the generation method of the scene graph (SGG) and the SGG with the aid of prior knowledge. We then investigated the main applications of scene graphs and summarized the most commonly used datasets. Finally, we provide some insights into the future development of scene graphs. We believe this will be a very helpful foundation for future research on scene graphs.
Deep Manifold Learning for Dynamic MR Imaging
Authors: Ziwen Ke, Zhuo-Xu Cui, Wenqi Huang, Jing Cheng, Sen Jia, Haifeng Wang, Xin Liu, Hairong Zheng, Leslie Ying, Yanjie Zhu, Dong Liang
Date: 2021-03-09 00:00:00
Tasks: compressed sensing, Image Reconstruction, Rolling Shutter Correction

Purpose: To develop a deep learning method on a nonlinear manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data from highly undersampled measurements. Methods: Cardiac MR image reconstruction is modeled as general compressed sensing (CS) based optimization on a low-rank tensor manifold. The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals. Iterative procedures can be obtained by solving the optimization model on the manifold, including gradient calculation, projection of the gradient to tangent space, and retraction of the tangent space to the manifold. The iterative procedures on the manifold are unrolled to a neural network, dubbed as Manifold-Net. The Manifold-Net is trained using in vivo data with a retrospective electrocardiogram (ECG)-gated segmented bSSFP sequence. Results: Experimental results at high accelerations demonstrate that the proposed method can obtain improved reconstruction compared with a compressed sensing (CS) method k-t SLR and two state-of-the-art deep learning-based methods, DC-CNN and CRNN. Conclusion: This work represents the first study unrolling the optimization on manifolds into neural networks. Specifically, the designed low-rank manifold provides a new technical route for applying low-rank priors in dynamic MR imaging.
Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction
Authors: Mobarakol Islam, Navodini Wijethilake, Hongliang Ren
Date: 2021-03-17 00:00:00
Tasks: Decoder, Generative Adversarial Network, Prediction, Prognosis, Segmentation, Survival Prediction, Tumor Segmentation

The accurate prognosis of Glioblastoma Multiforme (GBM) plays an essential role in planning correlated surgeries and treatments. The conventional models of survival prediction rely on radiomic features using magnetic resonance imaging (MRI). In this paper, we propose a radiogenomic overall survival (OS) prediction approach by incorporating gene expression data with radiomic features such as shape, geometry, and clinical information. We exploit TCGA (The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities using a fully convolutional network (FCN) in a conditional Generative Adversarial Network (cGAN). Meanwhile, the same FCN architecture enables the tumor segmentation from the available and the synthesized MRI modalities. The proposed FCN architecture comprises octave convolution (OctConv) and a novel decoder, with skip connections in spatial and channel squeeze & excitation (skip-scSE) block. The OctConv can process low and high-frequency features individually and improve model efficiency by reducing channel-wise redundancy. Skip-scSE applies spatial and channel-wise excitation to signify the essential features and reduces the sparsity in deeper layers learning parameters using skip connections. The proposed approaches are evaluated by comparative experiments with state-of-the-art models in synthesis, segmentation, and overall survival (OS) prediction. We observe that adding missing MRI modality improves the segmentation prediction, and expression levels of gene markers have a high contribution in the GBM prognosis prediction, and fused radiogenomic features boost the OS estimation.
Multitask Learning of Negation and Speculation using Transformers
Authors: Aditya Khandelwal, Benita Kathleen Britto
Date: 2020-11-20 00:00:00
Tasks: Negation, Negation and Speculation Scope resolution, Negation Detection, Negation Scope Resolution, Speculation Detection, Speculation Scope Resolution

Detecting negation and speculation in language has been a task of considerable interest to the biomedical community, as it is a key component of Information Extraction systems from Biomedical documents. Prior work has individually addressed Negation Detection and Speculation Detection, and both have been addressed in the same way, using 2 stage pipelined approach: Cue Detection followed by Scope Resolution. In this paper, we propose Multitask learning approaches over 2 sets of tasks: Negation Cue Detection & Speculation Cue Detection, and Negation Scope Resolution & Speculation Scope Resolution. We utilise transformer-based architectures like BERT, XLNet and RoBERTa as our core model architecture, and finetune these using the Multitask learning approaches. We show that this Multitask Learning approach outperforms the single task learning approach, and report new state-of-the-art results on Negation and Speculation Scope Resolution on the BioScope Corpus and the SFU Review Corpus.
科学博士：注射新冠疫苗的4个理由，最后一个让人简直无法拒绝！COVID-19
Authors: 科学博士
Date: 2021-04-05 00:00:00

科学博士：注射新冠疫苗的4个理由，最后一个让人简直无法拒绝！
#新冠疫苗​ #新冠肺炎​ #注射理由​ #健康​ #COVID19​ #科学博士​

★推荐影片★
新冠病毒预防，为何要用肥皂洗手？肥皂不止洗涤还能杀灭病毒的原理！COVID-19
https://youtu.be/PRYXpZrQjec​

毕业季！如何一个月内写出高质量【博士论文】？方法在这里
https://youtu.be/KpvLQgbWEOI​

直博与硕博连读的区别，很重要！可能影响你的一生
https://youtu.be/QGZojkqnNKM​

太神奇了！水中剪玻璃？视频是真的吗？答案揭晓
https://youtu.be/zWhefpDgy1s​

新冠疫苗，为什么要打两针？打一针可以吗？性命攸关的重要问题！COVID-19
https://youtu.be/JQpdRHJnVt0​

三星堆文明的精髓，青铜纵目面具的含义：崇拜知识与好奇心
https://youtu.be/vVVEyrbgGgY​

“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？
https://youtu.be/AsP8capcf-k​

成都肖美丽女士劝止“渣男”室内吸烟，居然被骂神经病并且被泼油？北大博士有话说：为什么不能在室内公共场所吸烟！
https://youtu.be/lENcBBGxq_E
异形血液成分之谜揭晓！酸性比硫酸强10亿倍，世上只有一种材料可以防御它！
Authors: 科学博士
Date: 2021-04-05 00:00:00

异形血液成分之谜揭晓！酸性比硫酸强10亿倍，世上只有一种材料可以防御它！
#异形​ #血液​ #强酸​ #异形血液成分​ #世界最强酸​ #科学博士​

★推荐影片★
太神奇了！水中剪玻璃？视频是真的吗？答案揭晓
https://youtu.be/zWhefpDgy1s​

新冠疫苗，为什么要打两针？打一针可以吗？性命攸关的重要问题！COVID-19
https://youtu.be/JQpdRHJnVt0​

三星堆文明的精髓，青铜纵目面具的含义：崇拜知识与好奇心
https://youtu.be/vVVEyrbgGgY​

“长赐号”轮船卡住苏伊士运河, 14艘拖船都无法拖出，“罪魁祸首”居然是它？
https://youtu.be/AsP8capcf-k​

成都肖美丽女士劝止“渣男”室内吸烟，居然被骂神经病并且被泼油？北大博士有话说：为什么不能在室内公共场所吸烟！
https://youtu.be/lENcBBGxq_E​

科学博士：注射新冠疫苗的4个理由，最后一个让人简直无法拒绝！
https://youtu.be/oms3gP_Yzr0​

新冠病毒预防，为何要用肥皂洗手？肥皂不止洗涤还能杀灭病毒的原理！COVID-19
https://youtu.be/PRYXpZrQjec​

毕业季！如何一个月内写出高质量【博士论文】？方法在这里
https://youtu.be/KpvLQgbWEOI​

直博与硕博连读的区别，很重要！可能影响你的一生
https://youtu.be/QGZojkqnNKM
PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images
Authors: Dongnan Liu, Donghao Zhang, Yang song, Fan Zhang, Lauren O'Donnell, Heng Huang, Mei Chen, Weidong Cai
Date: 2020-09-11 00:00:00
Tasks: Domain Adaptation, Instance Segmentation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation, Unsupervised Instance Segmentation

In this work, we present an unsupervised domain adaptation (UDA) method, named Panoptic Domain Adaptive Mask R-CNN (PDAM), for unsupervised instance segmentation in microscopy images. Since there currently lack methods particularly for UDA instance segmentation, we first design a Domain Adaptive Mask R-CNN (DAM) as the baseline, with cross-domain feature alignment at the image and instance levels. In addition to the image- and instance-level domain discrepancy, there also exists domain bias at the semantic level in the contextual information. Next, we, therefore, design a semantic segmentation branch with a domain discriminator to bridge the domain gap at the contextual level. By integrating the semantic- and instance-level feature adaptation, our method aligns the cross-domain features at the panoptic level. Third, we propose a task re-weighting mechanism to assign trade-off weights for the detection and segmentation loss functions. The task re-weighting mechanism solves the domain bias issue by alleviating the task learning for some iterations when the features contain source-specific factors. Furthermore, we design a feature similarity maximization mechanism to facilitate instance-level feature adaptation from the perspective of representational learning. Different from the typical feature alignment methods, our feature similarity maximization mechanism separates the domain-invariant and domain-specific features by enlarging their feature distribution dependency. Experimental results on three UDA instance segmentation scenarios with five datasets demonstrate the effectiveness of our proposed PDAM method, which outperforms state-of-the-art UDA methods by a large margin.
Generative Locally Linear Embedding
Authors: Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley
Date: 2021-04-04 00:00:00
Tasks: Dimensionality Reduction, Variational Inference

Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embedding of points in the input space and embedding space, respectively. In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic. GLLE assumes that every data point is caused by its linear reconstruction weights as latent factors. The proposed GLLE algorithms can generate various LLE embeddings stochastically while all the generated embeddings relate to the original LLE embedding. We propose two versions for stochastic linear reconstruction, one using expectation maximization and another with direct sampling from a derived distribution by optimization. The proposed GLLE methods are closely related to and inspired by variational inference, factor analysis, and probabilistic principal component analysis. Our simulations show that the proposed GLLE methods work effectively in unfolding and generating submanifolds of data.
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
Authors: Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, Joongkyu Kim
Date: 2021-04-05 00:00:00
Proceeding: CVPR 2021 1
Tasks: Clustering, Few-Shot Semantic Segmentation, Segmentation

Prototype learning is extensively used for few-shot segmentation. Typically, a single prototype is obtained from the support feature by averaging the global object information. However, using one prototype to represent all the information may lead to ambiguities. In this paper, we propose two novel modules, named superpixel-guided clustering (SGC) and guided prototype allocation (GPA), for multiple prototype extraction and allocation. Specifically, SGC is a parameter-free and training-free approach, which extracts more representative prototypes by aggregating similar feature vectors, while GPA is able to select matched prototypes to provide more accurate guidance. By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation. In addition, our network can easily generalize to k-shot segmentation with substantial improvement and no additional computational cost. In particular, our evaluations on COCO demonstrate that ASGNet surpasses the state-of-the-art method by 5% in 5-shot segmentation.
Diff-TTS: A Denoising Diffusion Model for Text-to-Speech
Authors: Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon, Byoung Jin Choi, Nam Soo Kim
Date: 2021-04-03 00:00:00
Tasks: Denoising, GPU, Speech Synthesis, text-to-speech, Text to Speech

Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency. In this work, we propose a novel non-autoregressive TTS model, namely Diff-TTS, which achieves highly natural and efficient speech synthesis. Given the text, Diff-TTS exploits a denoising diffusion framework to transform the noise signal into a mel-spectrogram via diffusion time steps. In order to learn the mel-spectrogram distribution conditioned on the text, we present a likelihood-based optimization method for TTS. Furthermore, to boost up the inference speed, we leverage the accelerated sampling method that allows Diff-TTS to generate raw waveforms much faster without significantly degrading perceptual quality. Through experiments, we verified that Diff-TTS generates 28 times faster than the real-time with a single NVIDIA 2080Ti GPU.
Fingerspelling Detection in American Sign Language
Authors: Bowen Shi, Diane Brentari, Greg Shakhnarovich, Karen Livescu
Date: 2021-04-03 00:00:00
Proceeding: CVPR 2021 1
Tasks: Pose Estimation

Fingerspelling, in which words are signed letter by letter, is an important component of American Sign Language. Most previous work on automatic fingerspelling recognition has assumed that the boundaries of fingerspelling regions in signing videos are known beforehand. In this paper, we consider the task of fingerspelling detection in raw, untrimmed sign language videos. This is an important step towards building real-world fingerspelling recognition systems. We propose a benchmark and a suite of evaluation metrics, some of which reflect the effect of detection on the downstream fingerspelling recognition task. In addition, we propose a new model that learns to detect fingerspelling via multi-task training, incorporating pose estimation and fingerspelling recognition (transcription) along with detection, and compare this model to several alternatives. The model outperforms all alternative approaches across all metrics, establishing a state of the art on the benchmark.
A unified framework for non-negative matrix and tensor factorisations with a smoothed Wasserstein loss
Authors: Stephen Y. Zhang
Date: 2021-04-04 00:00:00

Non-negative matrix and tensor factorisations are a classical tool for finding low-dimensional representations of high-dimensional datasets. In applications such as imaging, datasets can be regarded as distributions supported on a space with metric structure. In such a setting, a loss function based on the Wasserstein distance of optimal transportation theory is a natural choice since it incorporates the underlying geometry of the data. We introduce a general mathematical framework for computing non-negative factorisations of both matrices and tensors with respect to an optimal transport loss. We derive an efficient computational method for its solution using a convex dual formulation, and demonstrate the applicability of this approach with several numerical illustrations with both matrix and tensor-valued data.
GUILeak: Tracing Privacy Policy Claims on User Input Data for Android Applications
Authors: Xiaoyin Wang，Mitra Bokaei Hosseini，Xue Qin，Rocky Slavin
Date: 2018-05-01 00:00:00

The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information.In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app’s privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.
STL Robustness Risk over Discrete-Time Stochastic Processes
Authors: Lars Lindemann, Nikolai Matni, George J. Pappas
Date: 2021-04-03 00:00:00

We present a framework to interpret signal temporal logic (STL) formulas over discrete-time stochastic processes in terms of the induced risk. Each realization of a stochastic process either satisfies or violates an STL formula. In fact, we can assign a robustness value to each realization that indicates how robustly this realization satisfies an STL formula. We then define the risk of a stochastic process not satisfying an STL formula robustly, referred to as the STL robustness risk. In our definition, we permit general classes of risk measures such as, but not limited to, the conditional value-at-risk. While in general hard to compute, we propose an approximation of the STL robustness risk. This approximation has the desirable property of being an upper bound of the STL robustness risk when the chosen risk measure is monotone, a property satisfied by most risk measures. Motivated by the interest in data-driven approaches, we present a sampling-based method for estimating the approximate STL robustness risk from data for the value-at-risk. While we consider the value-at-risk, we highlight that such sampling-based methods are viable for other risk measures.
Physics Informed Convex Artificial Neural Networks (PICANNs) for Optimal Transport based Density Estimation
Authors: Amanpreet Singh, Martin Bauer, Sarang Joshi
Date: 2021-04-02 00:00:00
Tasks: Density Estimation

Optimal Mass Transport (OMT) is a well studied problem with a variety of applications in a diverse set of fields ranging from Physics to Computer Vision and in particular Statistics and Data Science. Since the original formulation of Monge in 1781 significant theoretical progress been made on the existence, uniqueness and properties of the optimal transport maps. The actual numerical computation of the transport maps, particularly in high dimensions, remains a challenging problem. By Brenier's theorem, the continuous OMT problem can be reduced to that of solving a non-linear PDE of Monge-Ampere type whose solution is a convex function. In this paper, building on recent developments of input convex neural networks and physics informed neural networks for solving PDE's, we propose a Deep Learning approach to solve the continuous OMT problem. To demonstrate the versatility of our framework we focus on the ubiquitous density estimation and generative modeling tasks in statistics and machine learning. Finally as an example we show how our framework can be incorporated with an autoencoder to estimate an effective probabilistic generative model.
Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning
Authors: Jeong Min Lee, Milos Hauskrecht
Date: 2021-04-05 00:00:00
Tasks: Prediction

Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient's sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.
Evolution of Symbiosis in the Game of Life: Three Characteristics of Successful Symbiotes
Authors: Peter D. Turney
Date: 2021-04-02 00:00:00
Tasks: Management

In past work, we developed a computational model of the evolution of symbiotic entities (Model-S), based on Conway's Game of Life. In this article, we examine three trends that biologists have observed in the evolution of symbiotes. (1) Management: If one partner is able to control the symbiotic relation, this control can reduce conflict; thus, evolutionary selection favours symbiotes that have a manager. (2) Mutualism: Although partners in a symbiote often have conflicting needs, evolutionary selection favours symbiotes in which partners are better off together inside the symbiote than they would be as individuals outside of the symbiote. (3) Interaction: Repeated interaction among partners in symbiosis tends to promote increasing fitness due to evolutionary selection. We have added new components to Model-S that allow us to observe these three trends in runs of Model-S. The new components are analogous to the practice of staining cells in biology research, to reveal patterns that are not usually visible. When we measure the fitness of a symbiote by making it compete with other symbiotes, we find that fitter symbiotes have significantly more management, mutualism, and interaction than less fit symbiotes. These results confirm the trends observed in nature by biologists. Model-S allows biologists to study these evolutionary trends and other characteristics of symbiosis in ways that are not tractable with living organisms.