Reviewerly/exHarmony huggingface dataset:
Modeling the effects of perisaccadic attention on gaze statistics during scene viewing

Abstract How we perceive a visual scene depends critically on the selection of gaze positions. For this selection process, visual attention is known to play a key role in two ways. First, image-features attract visual attention, a fact that is captured well by time-independent fixation models. Second, millisecond-level attentional dynamics around the time of saccade drives our gaze from one position to the next. These two related research areas on attention are typically perceived as separate, both theoretically and experimentally. Here we link the two research areas by demonstrating that perisaccadic attentional dynamics improve predictions on scan path statistics. In a mathematical model, we integrated perisaccadic covert attention with dynamic scan path generation. Our model reproduces saccade amplitude distributions, angular statistics, intersaccadic turning angles, and their impact on fixation durations as well as inter-individual differences using Bayesian inference. Therefore, our result lend support to the relevance of perisaccadic attention to gaze statistics.
Galaxy morphology classification using neural ordinary differential equations

We introduce a continuous depth version of the Residual Network (ResNet) called Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification. We carry out a classification of galaxy images from the Galaxy Zoo 2 dataset, consisting of five distinct classes, and obtained an accuracy between 91%–95%, depending on the image class. We train NODE with different numerical techniques such as adjoint and Adaptive Checkpoint Adjoint (ACA) and compare them against ResNet. While ResNet has certain drawbacks, such as time consuming architecture selection (e.g. the number of layers) and the requirement of a large dataset needed for training, NODE can overcome these limitations. Through our results, we show that the accuracy of NODE is comparable to ResNet, and the number of parameters used is about one-third as compared to ResNet, thus leading to a smaller memory footprint, which would benefit next generation surveys.
Adapting a Language Model for Controlled Affective Text Generation

Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the state-of-the-art language generation models to generate affective (emotional) text. We posit a model capable of generating affect-driven and topic focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2. The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text. Previous attempts at modelling fine-grained emotions fall out on grammatical correctness at extreme intensities, but our model is resilient to this and delivers robust results at all intensities. We conduct automated evaluations and human studies to test the performance of our model, and provide a detailed comparison of the results with other models. In all evaluations, our model outperforms existing affective text generation models.
Video Action Recognition for Lane-Change Classification and Prediction of Surrounding Vehicles

In highway scenarios, an alert human driver will typically anticipate early cut-in/cut-out maneuvers of surrounding vehicles using visual cues mainly. Autonomous vehicles must anticipate these situations at an early stage too, to increase their safety and efficiency. In this work, lane-change recognition and prediction tasks are posed as video action recognition problems. Up to four different two-stream-based approaches, that have been successfully applied to address human action recognition, are adapted here by stacking visual cues from forward-looking video cameras to recognize and anticipate lane-changes of target vehicles. We study the influence of context and observation horizons on performance, and different prediction horizons are analyzed. The different models are trained and evaluated using the PREVENTION dataset. The obtained results clearly demonstrate the potential of these methodologies to serve as robust predictors of future lane-changes of surrounding vehicles proving an accuracy higher than 90% in time horizons of between 1-2 seconds.
Convolutional Neural Networks with Gated Recurrent Connections

The convolutional neural network (CNN) has become a basic model for solving many computer vision problems. In recent years, a new class of CNNs, recurrent convolution neural network (RCNN), inspired by abundant recurrent connections in the visual systems of animals, was proposed. The critical element of RCNN is the recurrent convolutional layer (RCL), which incorporates recurrent connections between neurons in the standard convolutional layer. With increasing number of recurrent computations, the receptive fields (RFs) of neurons in RCL expand unboundedly, which is inconsistent with biological facts. We propose to modulate the RFs of neurons by introducing gates to the recurrent connections. The gates control the amount of context information inputting to the neurons and the neurons' RFs therefore become adaptive. The resulting layer is called gated recurrent convolution layer (GRCL). Multiple GRCLs constitute a deep model called gated RCNN (GRCNN). The GRCNN was evaluated on several computer vision tasks including object recognition, scene text recognition and object detection, and obtained much better results than the RCNN. In addition, when combined with other adaptive RF techniques, the GRCNN demonstrated competitive performance to the state-of-the-art models on benchmark datasets for these tasks.
Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models

Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmentation, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power.
Feature-Guided CNN for Denoising Images From Portable Ultrasound Devices

As a non-invasive medical imaging scanning device, ultrasound has greatly increased the efficiency and accuracy of medical diagnosis. In recent years, portable ultrasound is being more widely used for its convenience and lower cost. Patients and physicians can receive the scanned images on their mobile phones at any time via a wireless network with low latency. However, it is difficult for portable ultrasound devices to capture images with the same quality as standard hospital ultrasound image acquisition systems. Usually, the images captured by portable ultrasound equipment have considerable noise. This noise undoubtedly affects the diagnosis of the physician. It is imperative to develop methods to remove the noise while preserving important information in the image. For this reason, we propose a novel denoising neural network model, called Feature-guided Denoising Convolutional Neural Network (FDCNN), to remove noise while retaining important feature information. In order to achieve high-quality denoising results, we employ a hierarchical denoising framework driven by a feature masking layer for medical images. Furthermore, we propose a feature extraction algorithm based on Explainable Artificial Intelligence (XAI) for medical images. Experimental results show that our medical image feature extraction method outperforms previous methods. Combined with the new denoising neural network architecture, portable ultrasound devices can now achieve better diagnostic performance.
Practical Quantum Computing

In the last few years, several quantum algorithms that try to address the problem of partial differential equation solving have been devised: on the one hand, “direct” quantum algorithms that aim at encoding the solution of the PDE by executing one large quantum circuit; on the other hand, variational algorithms that approximate the solution of the PDE by executing several small quantum circuits and making profit of classical optimisers. In this work, we propose an experimental study of the costs (in terms of gate number and execution time on a idealised hardware created from realistic gate data) associated with one of the “direct” quantum algorithm: the wave equation solver devised in [32]. We show that our implementation of the quantum wave equation solver agrees with the theoretical big-O complexity of the algorithm. We also explain in great detail the implementation steps and discuss some possibilities of improvements. Finally, our implementation proves experimentally that some PDE can be solved on a quantum computer, even if the direct quantum algorithm chosen will require error-corrected quantum chips, which are not believed to be available in the short-term.
Detecting Compromised Social Network Accounts Using Deep Learning for Behavior and Text Analyses

Social networks allow people to connect to one another. Over time, these accounts become an essential part of one's online identity. The account stores various personal data and contains one's network of acquaintances. Attackers seek to compromise user accounts for various malicious purposes, such as distributing spam, phishing, and much more. Timely detection of compromises becomes crucial for protecting users and social networks. This article proposes a novel system for detecting compromises of a social network account by considering both post behavior and textual content. A deep multi-layer perceptron-based autoencoder is leveraged to consolidate diverse features and extract underlying relationships. Experiments show that the proposed system outperforms previous techniques that considered only behavioral information. The authors believe that this work is well-timed, significant especially in the world that has been largely locked down by the COVID-19 pandemic and thus depends much more on reliable social networks to stay connected.
Fibonacci Anyons Versus Majorana Fermions: A Monte Carlo Approach to the Compilation of Braid Circuits in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>SU</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mn>2</mml:mn><mml:msub><mml:mo stretchy="false">)</mml:mo><mml:mi>k</mml:mi></mml:msub></mml:math> Anyon Models

An algorithmic approach to assess the potential of hybrid topological quantum computers is developed, allowing one to remove excess non-topological gates and reduce the impact of noise on the compiled braid circuits.
Constant-depth circuits for dynamic simulations of materials on quantum computers

Dynamic simulation of materials is a promising application for near-term quantum computers. Current algorithms for Hamiltonian simulation, however, produce circuits that grow in depth with increasing simulation time, limiting feasible simulations to short-time dynamics. Here, we present a method for generating circuits that are constant in depth with increasing simulation time for a subset of one-dimensional materials Hamiltonians, thereby enabling simulations out to arbitrarily long times. Furthermore, by removing the effective limit on the number of feasibly simulatable time-steps, the constant-depth circuits enable Trotter error to be made negligibly small by allowing simulations to be broken into arbitrarily many time-steps. Composed of two-qubit matchgates on nearest-neighbor qubits, these constant-depth circuits are constructed based on a set of multi-matchgate identity relationships. For an $N$-spin system, the constant-depth circuit contains only $\mathcal{O}(N^2)$ CNOT gates. When compared to standard Hamiltonian simulation algorithms, our method generates circuits with order-of-magnitude fewer gates, which allows us to successfully simulate the long-time dynamics of systems with up to 5 spins on available quantum hardware. This paves the way for simulations of long-time dynamics for scientifically and technologically relevant quantum materials, enabling the observation of interesting and important atomic-level physics.
Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data

Abstract Pathological images are easily accessible data with the potential of prognostic biomarkers. Moreover, integration of heterogeneous data types from multi-modality, such as pathological image and gene expression data, is invaluable to help predicting cancer patient survival. However, the analytical challenges are significant. Here, we take the hepatocellular carcinoma (HCC) pathological image features extracted by CellProfiler, and apply them as the input for Cox-nnet, a neural network-based prognosis prediction model. We compare this model with the conventional Cox proportional hazards (Cox-PH) model, CoxBoost, Random Survival Forests and DeepSurv, using C-index and log-rank P-values. The results show that Cox-nnet is significantly more accurate than Cox-PH and Random Survival Forests models and comparable with CoxBoost and DeepSurv models, on pathological image features. Further, to integrate pathological image and gene expression data of the same patients, we innovatively construct a two-stage Cox-nnet model, and compare it with another complex neural-network model called PAGE-Net. The two-stage Cox-nnet complex model combining histopathology image and transcriptomic RNA-seq data achieves much better prognosis prediction, with a median C-index of 0.75 and log-rank P-value of 6e−7 in the testing datasets, compared to PAGE-Net (median C-index of 0.68 and log-rank P-value of 0.03). Imaging features present additional predictive information to gene expression features, as the combined model is more accurate than the model with gene expression alone (median C-index 0.70). Pathological image features are correlated with gene expression, as genes correlated to top imaging features present known associations with HCC patient survival and morphogenesis of liver tissue. This work proposes two-stage Cox-nnet, a new class of biologically relevant and interpretable models, to integrate multiple types of heterogenous data for survival prediction.
Selectively steering photon spin angular momentum via electron-induced optical spin Hall effect

A deep subwavelength scale manipulation steers photon spin angular momentum via electron-induced optical spin Hall effect.
Deep learning based fusion strategies for personality prediction

Automated personality trait detection from text data has emerged and gained a great deal of attention in the subject area of affective computing and sentiment analysis. Most previous work has focused on features engineering such as linguistic styles and psycholinguistic databases which have correlations with personality. Recently, natural language processing has been affected significantly with transfer learning based on feature extraction and fine-tuning pre-trained language models. We propose a new deep learning-based model for personality prediction and classification using both data and classifier level fusion. The model gets benefit from, transfer learning in natural language processing through leading pre-trained language models namely Elmo, ULMFiT, and BERT. The proposed model demonstrates the powerfulness of the introduced method to be a promising personality prediction model. When evaluating the proposed method, results show a competitive and significant accuracy enhancement of about 1.25% and 3.12% in comparison to the most recent results for the two gold standard Essays and myPersonality datasets for personality detection.
Lifelong Teacher-Student Network Learning

A unique cognitive capability of humans consists in their ability to acquire new knowledge and skills from a sequence of experiences. Meanwhile, artificial intelligence systems are good at learning only the last given task without being able to remember the databases learnt in the past. We propose a novel lifelong learning methodology by employing a Teacher-Student network framework. While the Student module is trained with a new given database, the Teacher module would remind the Student about the information learnt in the past. The Teacher, implemented by a Generative Adversarial Network (GAN), is trained to preserve and replay past knowledge corresponding to the probabilistic representations of previously learnt databases. Meanwhile, the Student module is implemented by a Variational Autoencoder (VAE) which infers its latent variable representation from both the output of the Teacher module as well as from the newly available database. Moreover, the Student module is trained to capture both continuous and discrete underlying data representations across different domains. The proposed lifelong learning framework is applied in supervised, semi-supervised and unsupervised training.
A confidence prior for image dehazing

By sorting channel-minimized values in an ascending order, we individually put the values of several existing image dehazing priors on the curve of sorted values to propose a framework for unifying and understanding these priors. Then we propose a confidence ratio to specify the probability of each channel-minimized value within a range, and thus we can intuitively find a suitable point from the curve, which is actually defined as a novel prior. Although our novel prior and existing ones are perfectly unified under the same framework, our prior has an important advantage that it can freely control the suppression degree of outliers by directly adjusting the confidence ratio of channel-minimized values. In this way, we can remove influence of outliers in a controllable manner. To solve the problems caused by heterogeneity of pixel values and abrupt jumps of scene depths in hazy images, we adopt a regression method to adaptively learn the relationship between patch appearance and confidence ratios for all pixels. To further improve robustness, we use a Gaussian kernel to smooth the estimated confidence ratios for local consistency. Extensive experiments on both natural and synthetic images show that our confidence prior achieves significantly better performance than existing state-of-the-art methods.
The Challenges and Perspectives of the Integration Between Virtual and Augmented Reality and Manual Therapies

Virtual reality (VR) and augmented reality (AR) have been combined with physical rehabilitation and psychological treatments to improve patients' emotional reactions, body image, and physical function. Nonetheless, no detailed investigation assessed the relationship between VR or AR manual therapies (MTs), which are touch-based approaches that involve the manipulation of tissues for relieving pain and improving balance, postural stability and well-being in several pathological conditions. The present review attempts to explore whether and how VR and AR might be integrated with MTs to improve patient care, with particular attention to balance and to fields like chronic pain that need an approach that engages both mind and body. MTs rely essentially on touch to induce tactile, proprioceptive, and interoceptive stimulations, whereas VR and AR rely mainly on visual, auditory, and proprioceptive stimulations. MTs might increase patients' overall immersion in the virtual experience by inducing parasympathetic tone and relaxing the mind, thus enhancing VR and AR effects. VR and AR could help manual therapists overcome patients' negative beliefs about pain, address pain-related emotional issues, and educate them about functional posture and movements. VR and AR could also engage and change the sensorimotor neural maps that the brain uses to cope with environmental stressors. Hence, combining MTs with VR and AR could define a whole mind-body intervention that uses psychological, interoceptive, and exteroceptive stimulations for rebalancing sensorimotor integration, distorted perceptions, including visual, and body images. Regarding the technology needed to integrate VR and AR with MTs, head-mounted displays could be the most suitable devices due to being low-cost, also allowing patients to follow VR therapy at home. There is enough evidence to argue that integrating MTs with VR and AR could help manual therapists offer patients better and comprehensive treatments. However, therapists need valid tools to identify which patients would benefit from VR and AR to avoid potential adverse effects, and both therapists and patients have to be involved in the development of VR and AR applications to define truly patient-centered therapies. Furthermore, future studies should assess whether the integration between MTs and VR or AR is practically feasible, safe, and clinically useful.
RETRACTED ARTICLE: Blockchain for COVID-19: a comprehensive review

The rampant and sudden outbreak of the SARS-CoV-2 coronavirus also called COVID-19 and its uncontrollable spread have led to a global crisis. COVID-19 is a highly contagious disease and the only way to fight with it is to follow social distancing and Non-Pharmaceutical Interventions (NPIs). Moreover, this virus is increasing exponentially day-by-day and a huge amount of data from this disease is also generated at the fast pace. So, there is a need to store, manage, and analyze this huge amount of data efficiently to get meaningful insights from it, which further helps medical professionals to tackle this global pandemic situation. Moreover, this data is to be passed through an open channel, i.e., the Internet, which opens the doors for the intruders to perform some malicious activities. Blockchain (BC) emerges as a technology that can manage the data in an efficient, transparent manner and also preserve the privacy of all the stakeholders. It can also aid in transaction authorization and verification in the supply chain or payments. Motivated by these facts, in this paper, we present a comprehensive review on the adoption of BC to tackle COVID-19 situations. We also present a case study on BC-based digital vaccine passports and analyzed its complexity. Finally, we analyzed the research challenges and future directions in this emerging area.