Reviewerly/exHarmony huggingface dataset:
Pauses for Detection of Alzheimer’s Disease

Pauses, disfluencies and language problems in Alzheimer’s disease can be naturally modeled by fine-tuning Transformer-based pre-trained language models such as BERT and ERNIE. Using this method with pause-encoded transcripts, we achieved 89.6% accuracy on the test set of the ADReSS ( A lzheimer’s D ementia Re cognition through S pontaneous S peech) Challenge. The best accuracy was obtained with ERNIE, plus an encoding of pauses. Robustness is a challenge for large models and small training sets. Ensemble over many runs of BERT/ERNIE fine-tuning reduced variance and improved accuracy. We found that um was used much less frequently in Alzheimer’s speech, compared to uh . We discussed this interesting finding from linguistic and cognitive perspectives.
Case Report: Ambient Sensor Signals as Digital Biomarkers for Early Signs of Heart Failure Decompensation

Home monitoring systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. More recently, multimodal ambient sensor systems are also used to monitor digital biomarkers to detect clinically relevant health problems over longer time periods. Clinical signs of HF decompensation including increase of heart rate and respiration rate, decreased physical activity, reduced gait speed, increasing toilet use at night and deterioration of sleep quality have a great potential to be detected by non-intrusive contactless ambient sensor systems and negative changes of these parameters may be used to prevent further deterioration and hospitalization for HF decompensation. This is to our knowledge the first report about the potential of an affordable, contactless, and unobtrusive ambient sensor system for the detection of early signs of HF decompensation based on data with prospective data acquisition and retrospective correlation of the data with clinical events in a 91 year old senior with a serious heart problem over 1 year. The ambient sensor system detected an increase of respiration rate, heart rate, toilet use at night, toss, and turns in bed and a decrease of physical activity weeks before the decompensation. In view of the rapidly increasing prevalence of HF and the related costs for the health care systems and the societies, the real potential of our approach should be evaluated in larger populations of HF patients.
Interactive Audio Augmented Reality in Participatory Performance

Interactive Audio Augmented Reality (AAR) facilitates collaborative storytelling and human interaction in participatory performance. Spatial audio enhances the auditory environment and supports real-time control of media content and the experience. Nevertheless, AAR applied to interactive performance practices remains under-explored. This study examines how audio human-computer interaction can prompt and support actions, and how AAR can contribute to developing new kinds of interactions in participatory performance.This study investigates an AAR participatory performance based on the theater and performance practice by theater maker Augusto Boal. It draws from aspects of multi-player audio-only games and interactive storytelling. A user experience study of the performance shows that people are engaged with interactive content and interact and navigate within the spatial audio content using their whole body. Asymmetric audio cues, playing distinctive content for each participant, prompt verbal and non-verbal communication. The performative aspect was well-received and participants took on roles and responsibilities within their group during the experience.
PA-MVSNet: Sparse-to-Dense Multi-View Stereo With Pyramid Attention

Multi-view based 3D reconstruction aims to obtain 3D structure information of objects in space through two-dimensional images. In this paper, we propose a new multi-view stereo network that can robustly reconstruct the scene. To enhance the feature representation ability of Point-MVSNet, a pyramid attention module is introduced. Specifically, we exploit the attention mechanism for the multi-scale feature pyramid to capture larger receptive fields and richer information. Instead of constructing a feature pyramid as the input, results of the pyramid attention module at different scales are directly used for the next layer. The network eventually generates a high-quality depth estimation for 3D reconstruction from sparse to dense by an iterative refinement schedule. Experiments have been performed to evaluate 3D reconstruction quality by comparison with existing state-of-the-art methods on the DTU dataset. The experimental results indicate our method performs the best in overall quality compared with previous methods, proving the effectiveness of our method. In the end, we use the data collected by mobile devices to implement 3D reconstruction with a combination of traditional and learning-based methods, providing ideas for the 3D reconstruction technology on mobile devices.
Pseudo-Label Guided Collective Matrix Factorization for Multiview Clustering

Multiview clustering has aroused increasing attention in recent years since real-world data are always comprised of multiple features or views. Despite the existing clustering methods having achieved promising performance, there still remain some challenges to be solved: 1) most existing methods are unscalable to large-scale datasets due to the high computational burden of eigendecomposition or graph construction and 2) most methods learn latent representations and cluster structures separately. Such a two-step learning scheme neglects the correlation between the two learning stages and may obtain a suboptimal clustering result. To address these challenges, a pseudo-label guided collective matrix factorization (PLCMF) method that jointly learns latent representations and cluster structures is proposed in this article. The proposed PLCMF first performs clustering on each view separately to obtain pseudo-labels that reflect the intraview similarities of each view. Then, it adds a pseudo-label constraint on collective matrix factorization to learn unified latent representations, which preserve the intraview and interview similarities simultaneously. Finally, it intuitively incorporates latent representation learning and cluster structure learning into a joint framework to directly obtain clustering results. Besides, the weight of each view is learned adaptively according to data distribution in the joint framework. In particular, the joint learning problem can be solved with an efficient iterative updating method with linear complexity. Extensive experiments on six benchmark datasets indicate the superiority of the proposed method over state-of-the-art multiview clustering methods in both clustering accuracy and computational efficiency.
Quantum-limited discrimination of laser light and thermal light

Understanding the fundamental sensitivity limit of an optical sensor requires a full quantum mechanical description of the sensing task. In this work, we calculate the fundamental (quantum) limit for discriminating between pure laser light and thermal noise in a photon-starved regime. The Helstrom bound for discrimination error probability for single mode measurement is computed along with error probability bounds for direct detection, coherent homodyne detection and the Kennedy receiver. A generalized Kennedy (GK) receiver is shown to closely approach the Helstrom limit. We present an experimental demonstration of this sensing task and demonstrate $15.4$ dB improvement in discrimination sensitivity over direct detection using a GK receiver, and an improvement of $19.4\%$ in error probability over coherent detection.
One-shot three-dimensional measurement method with the color mapping of light direction

A one-shot three-dimensional measurement method of a material surface with a color mapping imaging system of the light direction extracted BRDF (bidirectional reflectance distribution function) is proposed here. The imaging system can measure light directions reflected from a material surface using a stripe pattern multicolor filter having a translational symmetry in one direction. Assuming that surface inclination angles are sufficiently small and that the surface has a flat peripheral boundary, a reconstruction method of a three-dimensional surface from light directions is derived theoretically on the basis of the geometrical optics. The method is experimentally validated using a round ridge fabricated on an aluminum flat plate.
A Novel Intelligent Approach for Dynamic Data Replication in Cloud Environment

In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio- inspired algorithms were proposed to improve both selection and placement of data replicas in the cloud environment. The suggested algorithms for dynamic data replication are multi-objective particle swarm optimization (MO-PSO) and ant colony optimization (MO-ACO). The first suggested algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., MO-PSO, is employed to obtain the best selected data replica depend on the most frequent one. However, the second suggested algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., MO-ACO, is employed to obtain the best data replica placement depend on the shortest distance, and the replicas availability. A simulation of the suggested strategy was carried out using CloudSim. Each data center (DC) includes hosts with set of virtual machines (VMs). The data replication order is determined at random from a thousand cloudlets. All replication files are randomly distributed in the proposed architecture. The performance of suggested techniques was evaluated against several approaches including: Adaptive Replica Dynamic Strategy (ARDS), Enhance Fast Spread (EFS), Genetic Algorithm (GA), Replica Selection and Placement (RSP), Popular File Replication First (PFRF), and Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S). The simulation results prove that MOPSO gives improved data replication compared against other algorithms. Additionally, MOACO realizes higher data availability, lower cost, and less bandwidth consumption compared with other algorithms.
AdaPrompt: Adaptive Prompt-based Finetuning for Relation Extraction

In this paper, we reformulate the relation extraction task as mask language modeling and propose a novel adaptive prompt-based finetuning approach. We propose an adaptive label words selection mechanism that scatters the relation label into variable number of label tokens to handle the complex multiple label space. We further introduce an auxiliary entity discriminator object to encourage the model to focus on context representation learning. Extensive experiments on benchmark datasets demonstrate that our approach can achieve better performance on both the few-shot and supervised setting.
COCO-Search18 fixation dataset for predicting goal-directed attention control

Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control - saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding [Formula: see text] 300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a person's intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.
Variational co-embedding learning for attributed network clustering

Recent works for attributed network clustering utilize graph convolution to obtain node embeddings and simultaneously perform clustering assignments on the embedding space. It is effective since graph convolution combines the structural and attributive information for node embedding learning. However, a major limitation of such works is that the graph convolution only incorporates the attribute information from the local neighborhood of nodes but fails to exploit the mutual affinities between nodes and attributes. In this regard, we propose a variational co-embedding learning model for attributed network clustering (VCLANC). VCLANC is composed of dual variational auto-encoders to simultaneously embed nodes and attributes. Relying on this, the mutual affinity information between nodes and attributes could be reconstructed from the embedding space and served as extra self-supervised knowledge for representation learning. At the same time, trainable Gaussian mixture model is used as priors to infer the node clustering assignments. To strengthen the performance of the inferred clusters, we use a mutual distance loss on the centers of the Gaussian priors and a clustering assignment hardening loss on the node embeddings. Experimental results on four real-world attributed network datasets demonstrate the effectiveness of the proposed VCLANC for attributed network clustering.
Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database and separated into training (N = 2,032) and internal validation (N = 492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N = 1,526). 62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to a regression-based models on time-updated data. The LSTM model performed better than discrete time regression-based models for both internal validation (AUROC 0.81 vs 0.75 vs 0.75; P < 0.001) and external validation (AUROC 0.65 vs 0.56 vs 0.56; P < 0.001). A LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.
Transient subdiffusion via disordered quantum walks

Transport phenomena play a crucial role in modern physics and applied sciences. Examples include the dissipation of energy across a large system, the distribution of quantum information in optical networks, and the timely modeling of spreading diseases. In this work, we experimentally prove the feasibility of disordered quantum walks to realize a quantum simulator that is able to model general subdiffusive phenomena, exhibiting a sublinear spreading in space over time. Our experiment simulates such phenomena by means of a finely controlled insertion of various levels of disorder during the evolution of the walker, enabled by the unique flexibility of our setup. This allows us to explore the full range of subdiffusive behaviors, ranging from anomalous Anderson localization to normal diffusion.
Development and evaluation of novel ophthalmology domain-specific neural word embeddings to predict visual prognosis

To develop and evaluate novel word embeddings (WEs) specific to ophthalmology, using text corpora from published literature and electronic health records (EHR). We trained ophthalmology-specific WEs using 121,740 PubMed abstracts and 89,282 EHR notes using word2vec continuous bag-of-words architecture. PubMed and EHR WEs were compared to general domain GloVe WEs and general biomedical domain BioWordVec embeddings using a novel ophthalmology-domain-specific 200-question analogy test and prediction of prognosis in 5547 low vision patients using EHR notes as inputs to a deep learning model. We found that many words representing important ophthalmic concepts in the EHR were missing from the general domain GloVe vocabulary, but covered in the ophthalmology abstract corpus. On ophthalmology analogy testing, PubMed WEs scored 95.0 %, outperforming EHR (86.0 %) and GloVe (91.0 %) but less than BioWordVec (99.5 %). On predicting low vision prognosis, PubMed and EHR WEs resulted in similar AUROC (0.830; 0.826), outperforming GloVe (0.778) and BioWordVec (0.784). We found that using ophthalmology domain-specific WEs improved performance in ophthalmology-related clinical prediction compared to general WEs. Deep learning models using clinical notes as inputs can predict the prognosis of visually impaired patients. This work provides a framework to improve predictive models using domain-specific WEs.
Incremental Text-to-Speech Synthesis Using Pseudo Lookahead With Large Pretrained Language Model

This letter presents an incremental text-to-speech (TTS) method that performs synthesis in small linguistic units while maintaining the naturalness of output speech. Incremental TTS is generally subject to a trade-off between latency and synthetic speech quality. It is challenging to produce high-quality speech with a low-latency setup that does not make much use of an unobserved future sentence (hereafter, "lookahead"). To resolve this issue, we propose an incremental TTS method that uses a pseudo lookahead generated with a language model to take the future contextual information into account without increasing latency. Our method can be regarded as imitating a human's incremental reading and uses pretrained GPT2, which accounts for the large-scale linguistic knowledge, for the lookahead generation. Evaluation results show that our method 1) achieves higher speech quality than the method taking only observed information into account and 2) achieves a speech quality equivalent to waiting for the future context observation.
A Privacy-preserving Covid-19 Updatable Test Result and Vaccination Provenance based on Blockchain and Smart contract

It is undisputable fact that Coronavirus pandemic will go into the annals of history as one of the devastating plagues.From the healthcare perspective, a lot of efforts are underway geared towards testing, management and vaccination whereas industry and research communities explore innovative solutions.Quite a number of solutions have emerged zooming in on contact tracing, combating misinformation, data aggregation and analysis as well as test result certification with blockchain technology been the core.Aside the reliance on centralized architectures and use of permissioned/consortium blockchain, conspicuously missing in existing solutions based on blockchain is the work around the immutability feature of the technology given the fact that a person's test result is not static but dynamic.In this paper, we propose a solution using blockchain and smart contract that allows for state changes to be made by authorized entities.We leverage distributed storage technology using InterPlanetary File System (IPFS) for storage of user encrypted records and subsequent retrieval for verification purposes.We extend our solution by incorporating vaccination status to provide comprehensive source of information and show proof of concept.The full code of our proposed solution is made publicly available on GitHub.
Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.
Research on Text Classification Method Based on LSTM Neural Network Model

Text classification is a process of automatically classifying test data according to given rules. Word embedding technology is based on neural probabilistic language model, which can get word vectors with rich semantic information. In the task of natural language processing, a set of excellent word vectors is the basis of all researches. In order to search and extract information from massive electronic texts, this paper constructs a LSTM neural network classification model to classify text information. LSTM can extract words and sentences with different contributions, and combine LSTM's region embedding technology to classify text. Experimental results show that, compared with traditional methods, this method has obvious improvement in performance and classification accuracy.