Reviewerly/exHarmony huggingface dataset:
SAC-Net: Learning with weak and noisy labels in histopathology image segmentation

Deep convolutional neural networks have been highly effective in segmentation tasks. However, segmentation becomes more difficult when training images include many complex instances to segment, such as the task of nuclei segmentation in histopathology images. Weakly supervised learning can reduce the need for large-scale, high-quality ground truth annotations by involving non-expert annotators or algorithms to generate supervision information for segmentation. However, there is still a significant performance gap between weakly supervised learning and fully supervised learning approaches. In this work, we propose a weakly-supervised nuclei segmentation method in a two-stage training manner that only requires annotation of the nuclear centroids. First, we generate boundary and superpixel-based masks as pseudo ground truth labels to train our SAC-Net, which is a segmentation network enhanced by a constraint network and an attention network to effectively address the problems caused by noisy labels. Then, we refine the pseudo labels at the pixel level based on Confident Learning to train the network again. Our method shows highly competitive performance of cell nuclei segmentation in histopathology images on three public datasets. Code will be available at: https://github.com/RuoyuGuo/MaskGA_Net.
Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts

Abstract How do we make sense of the input from our sensory organs, and put the perceived information into context of our past experiences? The hippocampal-entorhinal complex plays a major role in the organization of memory and thought. The formation of and navigation in cognitive maps of arbitrary mental spaces via place and grid cells can serve as a representation of memories and experiences and their relations to each other. The multi-scale successor representation is proposed to be the mathematical principle underlying place and grid cell computations. Here, we present a neural network, which learns a cognitive map of a semantic space based on 32 different animal species encoded as feature vectors. The neural network successfully learns the similarities between different animal species, and constructs a cognitive map of ‘animal space’ based on the principle of successor representations with an accuracy of around 30% which is near to the theoretical maximum regarding the fact that all animal species have more than one possible successor, i.e. nearest neighbor in feature space. Furthermore, a hierarchical structure, i.e. different scales of cognitive maps, can be modeled based on multi-scale successor representations. We find that, in fine-grained cognitive maps, the animal vectors are evenly distributed in feature space. In contrast, in coarse-grained maps, animal vectors are highly clustered according to their biological class, i.e. amphibians, mammals and insects. This could be a putative mechanism enabling the emergence of new, abstract semantic concepts. Finally, even completely new or incomplete input can be represented by interpolation of the representations from the cognitive map with remarkable high accuracy of up to 95%. We conclude that the successor representation can serve as a weighted pointer to past memories and experiences, and may therefore be a crucial building block to include prior knowledge, and to derive context knowledge from novel input. Thus, our model provides a new tool to complement contemporary deep learning approaches on the road towards artificial general intelligence.
Efficient Hybrid Algorithm for Human Action Recognition

Recently, researchers have sought to find the ideal way to recognize human actions through video using artificial intelligence due to the multiplicity of applications that rely on it in many fields. In general, the methods have been divided into traditional methods and deep learning methods, which have provided a qualitative leap in the field of computer vision. Convolutional neural network CNN and recurrent neural network RNN are the most popular algorithms used with images and video. The researchers combined the two algorithms to search for the best results in a lot of research. In an attempt to obtain improved results in motion recognition through video, we present in this paper a combined algorithm, which is divided into two main parts, CNN and RNN. In the first part there is a preprocessing stage to make the video frame suitable for the input of both CNN networks which consist of a fusion of Inception-ResNet-V2 and GoogleNet to obtain activations, with the previously trained wights in Inception-ResNet-V2 and GoogleNet and then passed to a deep Gated Recurrent Units (GRU) connected to a fully connected SoftMax layer to recognize and distinguish the human action in the video. The results show that the proposed algorithm gives better accuracy of 97.97% with the UCF101 dataset and 73.12% in the hdmb51 data set compared to those present in the related literature.
Integrated Multiscale Domain Adaptive YOLO

The area of domain adaptation has been instrumental in addressing the domain shift problem encountered by many deep learning applications. This problem arises due to the difference between the distributions of source data used for training in comparison with target data used during realistic testing scenarios. In this paper, we introduce a novel MultiScale Domain Adaptive YOLO (MS-DAYOLO) framework that employs multiple domain adaptation paths and corresponding domain classifiers at different scales of the YOLOv4 object detector. Building on our baseline multiscale DAYOLO framework, we introduce three novel deep learning architectures for a Domain Adaptation Network (DAN) that generates domain-invariant features. In particular, we propose a Progressive Feature Reduction (PFR), a Unified Classifier (UC), and an Integrated architecture. We train and test our proposed DAN architectures in conjunction with YOLOv4 using popular datasets. Our experiments show significant improvements in object detection performance when training YOLOv4 using the proposed MS-DAYOLO architectures and when tested on target data for autonomous driving applications. Moreover, MS-DAYOLO framework achieves an order of magnitude real-time speed improvement relative to Faster R-CNN solutions while providing comparable object detection performance.
Discharge summary hospital course summarisation of in patient Electronic Health Record text with clinical concept guided deep pre-trained Transformer models

Brief Hospital Course (BHC) summaries are succinct summaries of an entire hospital encounter, embedded within discharge summaries, written by senior clinicians responsible for the overall care of a patient. Methods to automatically produce summaries from inpatient documentation would be invaluable in reducing clinician manual burden of summarising documents under high time-pressure to admit and discharge patients. Automatically producing these summaries from the inpatient course, is a complex, multi-document summarisation task, as source notes are written from various perspectives (e.g. nursing, doctor, radiology), during the course of the hospitalisation. We demonstrate a range of methods for BHC summarisation demonstrating the performance of deep learning summarisation models across extractive and abstractive summarisation scenarios. We also test a novel ensemble extractive and abstractive summarisation model that incorporates a medical concept ontology (SNOMED) as a clinical guidance signal and shows superior performance in 2 real-world clinical data sets.
Enhanced Security for Large-Scale 6G Cloud Computing: A Novel Approach to Identity based Encryption Key Generation

Cloud computing and 6G networks are in high demand at present due to their appealing features as well as the security of data stored in the cloud. There are various challenging methods that are computationally complicated that can be used in cloud security. Identity-based encryption (IBE) is the most widely used techniques for protecting data transmitted over the cloud. To prevent a malicious attack, it is an access policy that restricts access to legible data to only authorized users. The four stages of IBE are setup, key extraction or generation, decryption and encryption. Key generation is a necessary and time-consuming phase in the creation of a security key. The creation of uncrackable and non-derivable secure keys is a difficult computational and decisional task. In order to prevent user identities from being leaked, even if an opponent or attacker manages to encrypted material or to decode the key this study presents an advanced identity-based encryption technique with an equality test. The results of the experiments demonstrate that the proposed algorithm encrypts and decrypts data faster than the efficient selective-ID secure IBE strategy, a competitive approach. The proposed method's ability to conceal the identity of the user by utilizing the Lagrange coefficient, which is constituted of a polynomial interpolation function, is one of its most significant aspects.
Recurrent Affine Transformation for Text-to-image Synthesis

Text-to-image synthesis aims to generate realistic images conditioned on text descriptions. To fuse text information into synthesized images, conditional affine transformations (CATs), such as conditional batch normalization (CBN) and conditional instance normalization (CIN), are usually used to predict batch statistics of different layers. However, ordinary CAT blocks control the batch statistics independently disregarding the consistency among neighboring layers. To address the above issue, we propose a new fusion approach names recurrent affine transformation (RAT) for synthesizing images conditioned on text information. RAT connects all the CAT blocks with recurrent connections for explicitly fitting the temporal consistency between CAT blocks. To verify the effectiveness of RAT, we propose a novel visualization method to show how generative adversarial network (GAN) fuses conditional information. Our microscopic and macroscopic visualizations not only demonstrate the effectiveness of RAT but also turn out to be a useful perspective to analyze how GAN fuses conditional information. In addition, we propose a more stable spatial attention mechanism for the discriminator, which helps the text description to supervise the generator to synthesize more relevant image contents. Extensive experiments on the CUB, Oxford-102, and COCO datasets demonstrate the proposed model's superiority in comparison to state-of-the-art models. Our code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/senmaoy/RAT-GAN</uri> .
A hybrid approach based on linguistic analysis and fuzzy logic to ensure the surveillance of people having paranoid personality disorder towards Covid-19 on social media

This paper presents a supervised learning method for paranoid detection in French tweets. A classifier uses four groups of features (textual, linguistic, meta-data, timeline) that exploit a hybrid approach. This approach uses information obtained from the text of tweets by applying Natural Language Processing (NLP) techniques to analyse them, such as morphological analysis, syntactic analysis and sentence embedding. Thus, information about the user such as the number of followers and the number of shared posts. Besides, information about tweets such as the number of symbols and the number of hashtags. Moreover, information about the publication date of tweets such as the number of postings in the morning. Finally, statistical techniques to combine and filter the different types of features extracted from the previous steps in order to calculate the distance between the training corpus (the labelled data) and the test corpus (unlabelled data). In addition, the state mentioned statistical techniques are used for detecting the writing style of patients. In general, our method benefits from different types of features and preserves the principle of relativity by taking advantage of fuzzy logic. Our results are encouraging with an accuracy of 78% for the detection of paranoid people and 70% for the detection of the behaviour of these people towards Covid-19.
End-to-End page-Level assessment of handwritten text recognition

The evaluation of Handwritten Text Recognition (HTR) systems has traditionally used metrics based on the edit distance between HTR and ground truth (GT) transcripts, at both the character and word levels. This is very adequate when the experimental protocol assumes that both GT and HTR text lines are the same, which allows edit distances to be independently computed to each given line. Driven by recent advances in pattern recognition, HTR systems increasingly face the end-to-end page-level transcription of a document, where the precision of locating the different text lines and their corresponding reading order (RO) play a key role. In such a case, the standard metrics do not take into account the inconsistencies that might appear. In this paper, the problem of evaluating HTR systems at the page level is introduced in detail. We analyse the convenience of using a two-fold evaluation, where the transcription accuracy and the RO goodness are considered separately. Different alternatives are proposed, analysed and empirically compared both through partially simulated and through real, full end-to-end experiments. Results support the validity of the proposed two-fold evaluation approach. An important conclusion is that such an evaluation can be adequately achieved by just two simple and well-known metrics: the Word Error Rate (WER), that takes transcription sequentiality into account, and the here re-formulated Bag of Words Word Error Rate (bWER), that ignores order. While the latter directly and very accurately assess intrinsic word recognition errors, the difference between both metrics (ΔWER) gracefully correlates with the Normalised Spearman’s Foot Rule Distance (NSFD), a metric which explicitly measures RO errors associated with layout analysis flaws. To arrive to these conclusions, we have introduced another metric called Hungarian Word Word Rate (hWER), based on a here proposed regularised version of the Hungarian Algorithm. This metric is shown to be always almost identical to bWER and both bWER and hWER are also almost identical to WER whenever HTR transcripts and GT references are guarantee to be in the same RO.
Opportunities and Challenges of Synthetic Data Generation in Oncology

Widespread interest in artificial intelligence (AI) in health care has focused mainly on deductive systems that analyze available real-world data to discover patterns not otherwise visible. Generative adversarial network, a new type of inductive AI, has recently evolved to generate high-fidelity virtual synthetic data (SD) trained on relatively limited real-world information. The AI system is fed with a collection of real data, and it learns to generate new augmented data while maintaining the general characteristics of the original data set. The use of SD to enhance clinical research and protect patient privacy has drawn a lot of interest in medicine and in the complex field of oncology. This article summarizes the main characteristics of this innovative technology and critically discusses how it can be used to accelerate data access for secondary purposes, providing an overview of the opportunities and challenges of SD generation for clinical cancer research and health care.
Revealing Single Frame Bias for Video-and-Language Learning

Training an effective video-and-language model intuitively requires multiple frames as model inputs.However, it is unclear whether using multiple frames is beneficial to downstream tasks, and if yes, whether the performance gain is worth the drastically-increased computation and memory costs resulting from using more frames.In this work, we explore single-frame models for video-and-language learning.On a diverse set of video-andlanguage tasks (including text-to-video retrieval and video question answering), we show the surprising result that, with large-scale pretraining and a proper frame ensemble strategy at inference time, a single-frame trained model that does not consider temporal information can achieve better performance than existing methods that use multiple frames for training.This result reveals the existence of a strong "static appearance bias" in popular video-andlanguage datasets.Therefore, to allow for a more comprehensive evaluation of videoand-language models, we propose two new retrieval tasks based on existing fine-grained action recognition datasets that encourage temporal modeling.Our code is available at https: //github.com/jayleicn/singularity.
What Language Model to Train if You Have One Million GPU Hours?

The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners.Simple, well-motivated architectural variations can transfer across tasks and scale, increasing the impact of modeling research.However, with the emergence of stateof-the-art 100B+ parameters models, large language models are increasingly expensive to accurately design and train.Notably, it can be difficult to evaluate how modeling decisions may impact emergent capabilities, given that these capabilities arise mainly from sheer scale alone.In the process of building BLOOM-the Big Science Large Open-science Open-access Multilingual language model-our goal is to identify an architecture and training setup that makes the best use of our 1,000,000 A100-GPU-hours budget.Specifically, we perform an ablation study at the billion-parameter scale comparing different modeling practices and their impact on zero-shot generalization.In addition, we study the impact of various popular pretraining corpora on zero-shot generalization.We also study the performance of a multilingual model and how it compares to the Englishonly one.Finally, we consider the scaling behaviour of Transformers to choose the target model size, shape, and training setup.All our models and code are open-sourced at https: //huggingface.co/bigscience.
Demonstration of Quantum Energy Teleportation on Superconducting Quantum Hardware

Teleporting physical quantities to remote locations remains a key challenge for quantum information science and technology. Quantum teleportation has enabled the transfer of quantum information, but teleportation of quantum physical quantities has not yet been realized. Here we report the realization and observation of quantum energy teleportation on real superconducting quantum hardware. We achieve this by using several IBM superconducting quantum computers. The results are consistent with the exact solution of the theory and are improved by the mitigation of measurement error. Quantum energy teleportation requires only local operations and classical communication. Therefore, our results provide a realistic benchmark that is fully achievable with current quantum computing and communication technologies.
Spin-Flip Unitary Coupled Cluster Method: Toward Accurate Description of Strong Electron Correlation on Quantum Computers

Quantum computers have emerged as a promising platform to simulate strong electron correlation that is crucial to catalysis and photochemistry. However, owing to the choice of a trial wave function employed in the variational quantum eigensolver (VQE) algorithm, accurate simulation is restricted to certain classes of correlated phenomena. Herein, we combine the spin-flip (SF) formalism with the unitary coupled cluster with singles and doubles (UCCSD) method via the quantum equation-of-motion (qEOM) approach to allow for an efficient simulation of a large family of strongly correlated problems. We show that the developed qEOM-SF-UCCSD/VQE method outperforms its UCCSD/VQE counterpart for simulation of the cis–trans isomerization of ethylene, and the automerization of cyclobutadiene and the predicted qEOM-SF-UCCSD/VQE barrier heights are in a good agreement with the experimentally determined values. The developments presented herein will further stimulate the investigation of this approach for simulations of other types of correlated/entangled phenomena on quantum computers.
DLS-GAN: Generative Adversarial Nets for Defect Location Sensitive Data Augmentation

Limited data usually cause deep neural networks to hold poor performance after training, and many generative models are proposed to synthesize data to improve the performance of models. However, existing models ignore capturing the small defect details (e.g., features and locations), resulting in that most models cannot augment the Defect Location Sensitive Data (DLS data) in which the ratio of object size to the image size is small (e.g., 20%) and the locations of the defects are only on the object. In this paper, we propose a new augmentation model, named Defect Location Sensitive data augmentation GAN (DLS-GAN), to address DLS data augmentation problem. First, we modify the vanilla generator with two Encoder-Decoder models, and view the limited masked-images masked by labeling the defect-free pixels while remaining the defect pixels in defect images and many defect-free images as the input of the two models. The extracted feature map from the first Encoder-Decoder model provides the defect features and location information; the second one extracts the features of defect-free images, and integrates the two different features with a designed Defect Feature Transfer Module to synthesize images with desired defects. Second, we employ two discriminators to estimate the scores of both distribution matching degree and defect similarity between real data and generated ones. With the two modifications, we design a new loss function, and then prove that it makes our model get converged. Last, we conduct extensive experiments to demonstrate the significant performance improvement and generalizability of DLS-GAN on different types of DLS datasets. The experimental results show that our DLS-GAN outperforms the SOTA generative models in terms of synthesizing high quality images with desired defects. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Automated defect image detectors play an important role in the field of automated manufacturing. Training a detector with superior detection performance usually requires a large number of samples. However, it is difficult to collect many defect samples in practice. Although existing generative methods can synthesize realistic-like images, they cannot generate the Defect Location Sensitive Data (DLS Data) which refer to the samples that the defects appear at the specific locations in product objects, resulting in the synthesized images invalid. This paper proposes a new defect image generation model called DLS-GAN to address this problem, and validates its performance in different real-world industrial datasets ranging from DLS Data to Non-DLS Data. Such generated images can be adopted as useful resources for improving the detection performance of automated detector.