Reviewerly/exHarmony huggingface dataset:
Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels

Tissue-level semantic segmentation is a vital step in computational pathology. Fully-supervised models have already achieved outstanding performance with dense pixel-level annotations. However, drawing such labels on the giga-pixel whole slide images is extremely expensive and time-consuming. In this paper, we use only patch-level classification labels to achieve tissue semantic segmentation on histopathology images, finally reducing the annotation efforts. We propose a two-step model including a classification and a segmentation phases. In the classification phase, we propose a CAM-based model to generate pseudo masks by patch-level labels. In the segmentation phase, we achieve tissue semantic segmentation by our propose Multi-Layer Pseudo-Supervision. Several technical novelties have been proposed to reduce the information gap between pixel-level and patch-level annotations. As a part of this paper, we introduce a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). We conduct several experiments to evaluate our proposed model on two datasets. Our proposed model outperforms five state-of-the-art WSSS approaches. Note that we can achieve comparable quantitative and qualitative results with the fully-supervised model, with only around a 2% gap for MIoU and FwIoU. By comparing with manual labeling on a randomly sampled 100 patches dataset, patch-level labeling can greatly reduce the annotation time from hours to minutes. The source code and the released datasets are available at: https://github.com/ChuHan89/WSSS-Tissue.
Phase-Binarized Spin Hall Nano-Oscillator Arrays: Towards Spin Hall Ising Machines

Ising machines (IMs) are physical systems designed to find solutions to combinatorial optimization (CO) problems mapped onto the IM via the coupling strengths between its binary spins. Using its intrinsic dynamics and different annealing schemes, the IM relaxes over time to its lowest-energy state, which is the solution to the CO problem. IMs have been implemented on different platforms, and interacting nonlinear oscillators are particularly promising candidates. Here we demonstrate a pathway towards an oscillator-based IM using arrays of nanoconstriction spin Hall nano-oscillators (SHNOs). We show how SHNOs can be readily phase binarized and how their resulting microwave power corresponds to well-defined global phase states. To distinguish between degenerate states, we use phase-resolved Brillouin-light-scattering microscopy and directly observe the individual phase of each nanoconstriction. Micromagnetic simulations corroborate our experiments and confirm that our proposed IM platform can solve CO problems, showcased by how the phase states of a $2\ifmmode\times\else\texttimes\fi{}2$ SHNO array are solutions to a modified max-cut problem. Compared with the commercially available D-Wave ${\mathrm{Advantage}}^{\mathrm{TM}}$, our architecture holds significant promise for faster sampling, substantially reduced power consumption, and a dramatically smaller footprint.
Image encryption using spatial nonlinear optics

Abstract Optical technologies have been widely used in information security owing to its parallel and high-speed processing capability. However, the most critical problem with current optical encryption techniques is that the cyphertext is linearly related with the plaintext, leading to the possibility that one can crack the system by solving a set of linear equations with only two cyphertext from the same encryption machine. Many efforts have been taken in the last decade to resolve the linearity issue, but none of these offers a true nonlinear solution. Inspired by the recent advance in spatial nonlinear optics, here we demonstrate a true nonlinear optical encryption technique. We show that, owing to the self-phase modulation effect of the photorefractive crystal, the proposed nonlinear optical image encryption technique is robust against the known plaintext attack based on phase retrieval. This opens up a new avenue for optical encryption in the spatial nonlinear domain.
A survey on the interpretability of deep learning in medical diagnosis

Deep learning has demonstrated remarkable performance in the medical domain, with accuracy that rivals or even exceeds that of human experts. However, it has a significant problem that these models are “black-box” structures, which means they are opaque, non-intuitive, and difficult for people to understand. This creates a barrier to the application of deep learning models in clinical practice due to lack of interpretability, trust, and transparency. To overcome this problem, several studies on interpretability have been proposed. Therefore, in this paper, we comprehensively review the interpretability of deep learning in medical diagnosis based on the current literature, including some common interpretability methods used in the medical domain, various applications with interpretability for disease diagnosis, prevalent evaluation metrics, and several disease datasets. In addition, the challenges of interpretability and future research directions are also discussed here. To the best of our knowledge, this is the first time that various applications of interpretability methods for disease diagnosis have been summarized.
Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging

Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume help in diagnosing and monitoring neurological diseases. Several proposals have been designed throughout the years comprising conventional machine learning strategies as well as convolutional neural networks (CNN) approaches. In particular, in this paper, we analyse a sub-group of deep learning methods producing dense predictions. This branch, referred in the literature as Fully CNN (FCNN), is of interest as these architectures can process an input volume in less time than CNNs and local spatial dependencies may be encoded since several voxels are classified at once. Our study focuses on understanding architectural strengths and weaknesses of literature-like approaches. Hence, we implement eight FCNN architectures inspired by robust state-of-the-art methods on brain segmentation related tasks. We evaluate them using the IBSR18, MICCAI2012 and iSeg2017 datasets as they contain infant and adult data and exhibit varied voxel spacing, image quality, number of scans and available imaging modalities. The discussion is driven in three directions: comparison between 2D and 3D approaches, the importance of multiple modalities and overlapping as a sampling strategy for training and testing models. To encourage other researchers to explore the evaluation framework, a public version is accessible to download from our research website.
Learning-Based Tone Mapping Operator for Efficient Image Matching

In this paper, we propose a new framework to optimally tone map the high dynamic range (HDR) content for image matching under drastic illumination variations. Since tone mapping operators (TMO) have traditionally been used for displaying HDR scenes, their design is suboptimal when used for computer vision tasks, such as image matching. We address this suboptimality by proposing a two-step framework, consisting of: first, a luminance-invariant guidance model based on a support vector regressor (SVR) to optimally adapt the tone mapping function for image matching; and second, an energy maximization model to generate appropriate training samples for learning the SVR. At each step, we collectively address both stages of keypoint detection and descriptor extraction in the feature matching framework. By locally altering the intrinsic characteristics of the tone mapping function, the learned guidance model facilitates the extraction of local invariant features in the presence of illumination variations. We demonstrate that the proposed TMO significantly outperforms perceptually driven state-of-the-art TMOs on a dataset of HDR scenes characterized by challenging lighting variations, such as day/night transitions.
Graph-Based Joint Dequantization and Contrast Enhancement of Poorly Lit JPEG Images

JPEG images captured in poor lighting conditions suffer from both low luminance contrast and coarse quantization artifacts due to lossy compression. Performing dequantization and contrast enhancement in separate back-to-back steps would amplify the residual compression artifacts, resulting in low visual quality. Leveraging on recent development in graph signal processing (GSP), we propose to jointly dequantize and contrast-enhance such images in a single graph-signal restoration framework. Specifically, we separate each observed pixel patch into illumination and reflectance via Retinex theory, where we define generalized smoothness prior and signed graph smoothness prior according to their respective unique signal characteristics. Given only a transform-coded image patch, we compute robust edge weights for each graph via low-pass filtering in the dual graph domain. We compute the illumination and reflectance components for each patch alternately, adopting accelerated proximal gradient (APG) algorithms in the transform domain, with backtracking line search for further speedup. Experimental results show that our generated images outperform the state-of-the-art schemes noticeably in the subjective quality evaluation.
Unsupervised Compositionality Prediction of Nominal Compounds

Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.
Outsourced Decentralized Multi-Authority Attribute Based Signature and Its Application in IoT

IoT (Internet of things) devices often collect data and store the data in the cloud for sharing and further processing; This collection, sharing, and processing will inevitably encounter secure access and authentication issues. Attribute based signature (ABS), which utilizes the signer’s attributes to generate private keys, plays a competent role in data authentication and identity privacy preservation. In ABS, there are multiple authorities that issue different private keys for signers based on their various attributes, and a central authority is usually established to manage all these attribute authorities. However, one security concern is that if the central authority is compromised, the whole system will be broken. In this paper, we present an outsourced decentralized multi-authority attribute based signature (ODMA-ABS) scheme. The proposed ODMA-ABS achieves attribute privacy and stronger authority-corruption resistance than existing multi-authority attribute based signature schemes can achieve. In addition, the overhead to generate a signature is further reduced by outsourcing expensive computation to a signing cloud server. We present extensive security analysis and experimental simulation of the proposed scheme. We also propose an access control scheme that is based on ODMA-ABS.
Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation

Recently, a massive migration of enterprise applications to the cloud has been recorded in the IT world. One of the challenges of cloud computing is Quality-of-Service management, which includes the adoption of appropriate methods for allocating cloud-user applications to virtual resources, and virtual resources to the physical resources. The effective allocation of resources in cloud data centers is also one of the vital optimization problems in cloud computing, particularly when the cloud service infrastructures are built by lightweight computing devices. In this paper, we formulate and present the task allocation and virtual machine placement problems in a single cloud/fog computing environment, and propose a task allocation algorithmic solution and a Genetic Algorithm Based Virtual Machine Placement as solutions for the task allocation and virtual machine placement problem models. Finally, the experiments are carried out and the results show that the proposed solutions improve Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost.
Streaming Data Fusion for the Internet of Things

To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common approach to exploit streaming sensor data potential is to use machine learning techniques for predictive analytics in a way that is agnostic to the domain knowledge. Such an approach can be easily integrated in various use cases. In this paper, we propose a novel framework for data fusion of a set of heterogeneous data streams. The proposed framework enriches streaming sensor data with the contextual and historical information relevant for describing the underlying processes. The final result of the framework is a feature vector, ready to be used in a machine learning algorithm. The framework has been applied to a cloud and to an edge device. In the latter case, incremental learning capabilities have been demonstrated. The reported results illustrate a significant improvement of data-driven models, applied to sensor streams. Beside higher accuracy of the models the platform offers easy setup and thus fast prototyping capabilities in real-world applications.
The anisotropy of personal space

Violations of personal space are associated with discomfort. However, the exact function linking the magnitude of discomfort to interpersonal distance has not yet been specified. In this study, we explore whether interpersonal distance and discomfort are isotropic with respect to uncomfortably far or close distances. We also extend previous findings with regard to intrusions into personal space as well as maintenance of distances outside of personal space. We presented subjects with 15 interpersonal distances ranging from 40 to 250 cm and obtained verbal and joystick-based ratings of discomfort. Whereas discomfort rose immediately when personal space was entered, the gradient was less steep for distances that exceeded the comfort region of personal space. Thus, personal space is anisotropic with regard to experienced discomfort.
Automatically Identifying Complaints in Social Media

Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation.Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints.In this paper, we introduce the first systematic analysis of complaints in computational linguistics.We collect a new annotated data set of written complaints expressed in English on Twitter. 1 We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.
High-Fidelity Image Generation With Fewer Labels

Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels.
Wasserstein Dependency Measure for Representation Learning

Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as object recognition, speech recognition, and reinforcement learning. However, such approaches are fundamentally limited since a tight lower bound on mutual information requires sample size exponential in the mutual information. This limits the applicability of these approaches for prediction tasks with high mutual information, such as in video understanding or reinforcement learning. In these settings, such techniques are prone to overfit, both in theory and in practice, and capture only a few of the relevant factors of variation. This leads to incomplete representations that are not optimal for downstream tasks. In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks. To mitigate these problems we introduce the Wasserstein dependency measure, which learns more complete representations by using the Wasserstein distance instead of the KL divergence in the mutual information estimator. We show that a practical approximation to this theoretically motivated solution, constructed using Lipschitz constraint techniques from the GAN literature, achieves substantially improved results on tasks where incomplete representations are a major challenge.
Classifying cancer pathology reports with hierarchical self-attention networks

We introduce a deep learning architecture, hierarchical self-attention networks (HiSANs), designed for classifying pathology reports and show how its unique architecture leads to a new state-of-the-art in accuracy, faster training, and clear interpretability. We evaluate performance on a corpus of 374,899 pathology reports obtained from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) program. Each pathology report is associated with five clinical classification tasks – site, laterality, behavior, histology, and grade. We compare the performance of the HiSAN against other machine learning and deep learning approaches commonly used on medical text data – Naive Bayes, logistic regression, convolutional neural networks, and hierarchical attention networks (the previous state-of-the-art). We show that HiSANs are superior to other machine learning and deep learning text classifiers in both accuracy and macro F-score across all five classification tasks. Compared to the previous state-of-the-art, hierarchical attention networks, HiSANs not only are an order of magnitude faster to train, but also achieve about 1% better relative accuracy and 5% better relative macro F-score.
NeurAll: Towards a Unified Visual Perception Model for Automated Driving

Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently explored and modeled. In this paper, we propose a joint multi-task network design for learning several tasks simultaneously. Our main motivation is the computational efficiency achieved by sharing the expensive initial convolutional layers between all tasks. Indeed, the main bottleneck in automated driving systems is the limited processing power available on deployment hardware. There is also some evidence for other benefits in improving accuracy for some tasks and easing development effort. It also offers scalability to add more tasks leveraging existing features and achieving better generalization. We survey various CNN based solutions for visual perception tasks in automated driving. Then we propose a unified CNN model for the important tasks and discuss several advanced optimization and architecture design techniques to improve the baseline model. The paper is partly review and partly positional with demonstration of several preliminary results promising for future research. We first demonstrate results of multi-stream learning and auxiliary learning which are important ingredients to scale to a large multi-task model. Finally, we implement a two-stream three-task network which performs better in many cases compared to their corresponding single-task models, while maintaining network size.
Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model

This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in domains such as neurological and neuropsychiatric testing, advertising evaluation, pilot training, and automotive safety. Remote eye-tracking on a smartphone could enable the significant growth in the deployment of applications in these domains. Our system uses a 3D gaze-estimation model that enables accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features (pupil center and corneal reflections), the system uses Convolutional Neural Networks (CNNs) together with a novel center-of-mass output layer. The use of CNNs improves the system’s robustness to the significant variability in the appearance of eye-images found in handheld eye trackers. The system was tested with 8 subjects with the device free to move in their hands and produced a gaze bias of 0.72°. Our hybrid approach that uses artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor achieved an accuracy that is significantly (400%) better than current eye-tracking systems on smartphones that use natural illumination and machine-learning techniques to estimate the PoG.