pwc-archive/papers-with-abstracts huggingface dataset:
Federated Meta Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things Underwater Acoustic Communications
Authors: Hao Zhao, Fei Ji, Quansheng Guan, Qiang Li, Shuai Wang, Hefeng Dong, Miaowen Wen
Date: 2021-05-24 00:00:00
Tasks: ARC, Federated Learning, Meta-Learning, Scheduling

Sixth-generation wireless communication (6G) will be an integrated architecture of "space, air, ground and sea". One of the most difficult part of this architecture is the underwater information acquisition which need to transmitt information cross the interface between water and air.In this senario, ocean of things (OoT) will play an important role, because it can serve as a hub connecting Internet of things (IoT) and Internet of underwater things (IoUT). OoT device not only can collect data through underwater methods, but also can utilize radio frequence over the air. For underwater communications, underwater acoustic communications (UWA COMMs) is the most effective way for OoT devices to exchange information, but it is always tormented by doppler shift and synchronization errors. In this paper, in order to overcome UWA tough conditions, a deep neural networks based receiver for underwater acoustic chirp communication, called C-DNN, is proposed. Moreover, to improve the performance of DL-model and solve the problem of model generalization, we also proposed a novel federated meta learning (FML) enhanced acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer. Particularly, tractable expressions are derived for the convergence rate of FML in a wireless setting, accounting for effects from both scheduling ratio, local epoch and the data amount on a single node.From our analysis and simulation results, it is shown that, the proposed C-DNN can provide a better BER performance and lower complexity than classical matched filter (MF) in underwater acoustic communications scenario. The ARC/FML framework has good convergence under a variety of channels than federated learning (FL). In summary, the proposed ARC/FML for OoT is a promising scheme for information exchange across water and air.
Protein folding simulations in the hydrophobic-polar model using a hybrid cuckoo search algorithm
Authors: Nabil Boumedine, Sadek Bouroubi
Date: 2021-05-22 00:00:00
Tasks: Protein Folding

A protein is a linear chain containing a set of amino acids, which folds on itself to create a specific native structure, also called the minimum energy conformation. It is the native structure that determines the functionality of each protein. The protein folding problem (PFP) remains one of the more difficult problems in computational and chemical biology. The principal challenge of PFP is to predict the optimal conformation of a given protein by considering only its amino acid sequence. As the conformational space contains a very large number of conformations, even when addressing short sequences, different simplified models have been developed and applied to make the PFP less complex. In the last few years, many computational approaches have been proposed to solve the PFP. They are based on simplified lattice models such as the hydrophobic-polar model. In this paper, we present a new Hybrid Cuckoo Search Algorithm (HCSA) to solve the 3D-HP protein folding optimization problem. Our proposed algorithm consists of combining the Cuckoo Search Algorithm (CSA) with the Hill Climbing (HC) algorithm. Simulation results on different benchmark sequences are presented and compared to the state-of-the-art algorithms.
Spatio-Temporal Investigation of Brain-Wide Sequences
Authors: Ohad Felsenstein, Moshe Abeles
Date: 2021-05-27 00:00:00

In "The Organization of Behavior" (Hebb, 1949), Hebb suggested that the propagation of activity between transiently grouped neurons plays an important role in behavior. Since then, multiple studies have provided evidence supporting Hebb's claim; however, most findings have been found locally in confined brain regions during unimodal tasks. Here we report on brain-wide behavioral-specific sequences in humans performing a multimodal task. To investigate the structure of these sequences, we used MEG to record brain activity in multiple brain regions simultaneously in participants performing a sensory-motor synchronization task. We detected local transient events corresponding to synchronously activating populations of pyramidal neurons and searched for their global organization as spatio-temporal patterns of activation sequences between distant neural populations. We focused our analysis on two types of spatio-temporal patterns: the most frequently repeating patterns and the most discriminative patterns, to concentrate on patterns with high relevancy to behavior. The findings revealed that global temporally precise sequences can be found and that these sequences have partially stereotypical characteristics, both temporally and spatially, with consistent properties across subjects. By implementing a simplistic single-trial decoding approach, we found that brain-wide sequences have a temporal precision of 17-31 milliseconds, which resembles the temporal precision found locally in neural assemblies.
Prenatal stress perturbs fetal iron homeostasis in a sex-specific manner
Authors: Peter Zimmermann, Marta C. Antonelli, Ritika Sharma, Alexander Müller, Camilla Zelgert, Bibiana Fabre, Natasha Wenzel, Hau-Tieng Wu, Martin G. Frasch, Silvia M. Lobmaier
Date: 2021-05-26 00:00:00

What is the influence of chronic maternal prenatal stress (PS) on fetal iron homeostasis? In a prospective case-control study in 164 pregnant women, we show that cord blood transferrin saturation is lower in male stressed neonates. The total effect of PS exposure on fetal ferritin revealed a decrease of 15.4% compared with controls. Electrocardiogram-based Fetal Stress Index (FSI) identified affected fetuses non-invasively during the third trimester of gestation. FSI-based timely detection of fetuses affected by PS can support early individualized iron supplementation and neurodevelopmental follow-up to prevent long-term sequelae due to PS-exacerbated impairment of the iron homeostasis.
Pouring Dynamics Estimation Using Gated Recurrent Units
Authors: Qi Zheng
Date: 2021-05-08 00:00:00

One of the most commonly performed manipulation in a human's daily life is pouring. Many factors have an effect on target accuracy, including pouring velocity, rotation angle, geometric of the source, and the receiving containers. This paper presents an approach to increase the repeatability and accuracy of the robotic manipulator by estimating the change in the amount of water of the pouring cup to a sequence of pouring actions using multiple layers of the deep recurrent neural network, especially gated recurrent units (GRU). The proposed GRU model achieved a validation mean squared error as low as 1e-4 (lbf) for the predicted value of weight f(t). This paper contains a comprehensive evaluation and analysis of numerous experiments with various designs of recurrent neural networks and hyperparameters fine-tuning.
Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction
Authors: Zhengjing Ma, Gang Mei, Salvatore Cuomo, Francesco Piccialli
Date: 2021-05-24 00:00:00

Heterogeneous data are commonly adopted as the inputs for some models that predict the future trends of some observations. Existing predictive models typically ignore the inconsistencies and imperfections in heterogeneous data while also failing to consider the (1) spatial correlations among monitoring points or (2) predictions for the entire study area. To address the above problems, this paper proposes a deep learning method for fusing heterogeneous data collected from multiple monitoring points using graph convolutional networks (GCNs) to predict the future trends of some observations and evaluates its effectiveness by applying it in an air quality predictions scenario. The essential idea behind the proposed method is to (1) fuse the collected heterogeneous data based on the locations of the monitoring points with regard to their spatial correlations and (2) perform prediction based on global information rather than local information. In the proposed method, first, we assemble a fusion matrix using the proposed RBF-based fusion approach; second, based on the fused data, we construct spatially and temporally correlated data as inputs for the predictive model; finally, we employ the spatiotemporal graph convolutional network (STGCN) to predict the future trends of some observations. In the application scenario of air quality prediction, it is observed that (1) the fused data derived from the RBF-based fusion approach achieve satisfactory consistency; (2) the performances of the prediction models based on fused data are better than those based on raw data; and (3) the STGCN model achieves the best performance when compared to those of all baseline models. The proposed method is applicable for similar scenarios where continuous heterogeneous data are collected from multiple monitoring points scattered across a study area.
UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning
Authors: Alireza Shamsoshoara, Fatemeh Afghah, Erik Blasch, Jonathan Ashdown, Mehdi Bennis
Date: 2021-04-02 00:00:00
Tasks: Imitation Learning, Scheduling

The damage to cellular towers during natural and man-made disasters can disturb the communication services for cellular users. One solution to the problem is using unmanned aerial vehicles to augment the desired communication network. The paper demonstrates the design of a UAV-Assisted Imitation Learning (UnVAIL) communication system that relays the cellular users' information to a neighbor base station. Since the user equipment (UEs) are equipped with buffers with limited capacity to hold packets, UnVAIL alternates between different UEs to reduce the chance of buffer overflow, positions itself optimally close to the selected UE to reduce service time, and uncovers a network pathway by acting as a relay node. UnVAIL utilizes Imitation Learning (IL) as a data-driven behavioral cloning approach to accomplish an optimal scheduling solution. Results demonstrate that UnVAIL performs similar to a human expert knowledge-based planning in communication timeliness, position accuracy, and energy consumption with an accuracy of 97.52% when evaluated on a developed simulator to train the UAV.
Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling
Authors: Sajiv Shah, Ayaan Haque, Fei Liu
Date: 2021-05-09 00:00:00

Modeling of non-rigid object launching and manipulation is complex considering the wide range of dynamics affecting trajectory, many of which may be unknown. Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing. Recently, advancements in data-powered artificial intelligence methods have allowed learnable models and systems to emerge. It is desirable to train a model for launch prediction on a robot, as deep neural networks can account for immeasurable dynamics. However, the inability to collect large amounts of experimental data decreases performance of deep neural networks. Through estimating force coefficients, the accepted physics models can be leveraged to produce adequate supplemental data to artificially increase the size of the training set, yielding improved neural networks. In this paper, we introduce a new framework for algorithmic estimation of force coefficients for non-rigid object launching, which can be generalized to other domains, in order to generate large datasets. We implement a novel training algorithm and objective for our deep neural network to accurately model launch trajectory of non-rigid objects and predict whether they will hit a series of targets. Our experimental results demonstrate the effectiveness of using simulated data from force coefficient estimation and shows the importance of simulated data for training an effective neural network.
GLAD: Groningen Lightweight Authorship Detection
Authors: Manuela Hürlimann, Benno Weck, Esther van den Berg, Simon Šuster, and Malvina Nissim
Date: 2015-09-06 00:00:00
Tasks: Authorship Verification, Position

We present a simple and effective approach to authorship verification for Dutch, English, Spanish and Greek, which can be easily ported to yet other languages.We train a binary linear classifier both on the features describing known and unknown documents individually, and on the joint features comparing these two types of documents. The list of feature types includes, among others, character n-grams, the lexical overlap, visual text properties and a compression measure. We obtain competitive results that outperform the baseline and position our system among the top PAN shared task participants.
Stance leg and surface stability modulate cortical activity during human single leg stance
Authors: Daniel Büchel1  · Tim Lehmann1  · Sarah Ullrich2  · John Cockcroft3  · Quinette Louw4  · Jochen Baumeister1, 4
Date: 2017-07-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Mobile Electroencephalography (EEG) provides insights into cortical contributions to postural control. Although changes in theta (4–8 Hz) and alpha frequency power (8–12 Hz) were shown to reflect attentional and sensorimotor processing during balance tasks, information about the effect of stance leg on cortical processing related to postural control is lacking. Therefore, the aim was to examine patterns of cortical activity during single-leg stance with varying surface stability. EEG and force plate data from 21 healthy males (22.43 ± 2.23 years) was recorded during unipedal stance (left/right) on a stable and unstable surface. Using source-space analysis, power spectral density was analyzed in the theta, alpha-1 (8–10 Hz) and alpha-2 (10–12 Hz) frequency bands. Repeated measures ANOVA with the factors leg and surface stability revealed significant interaction effects in the left (p = 0.045, ηp2 = 0.13) and right motor clusters (F = 16.156; p = 0.001, ηp2 = 0.41). Furthermore, significant main effects for surface stability were observed for the fronto-central cluster (theta), left and right motor (alpha-1), as well as for the right parieto-occipital cluster (alpha-1/alpha-2). Leg dependent changes in alpha-2 power may indicate lateralized patterns of cortical processing in motor areas during single-leg stance. Future studies may therefore consider lateralized patterns of cortical activity for the interpretation of postural deficiencies in unilateral lower limb injuries.
Fine-Tuning Self-Organizing Maps for Sentinel-2 Imagery: Separating Clouds from Bright Surfaces
Authors: Viktoria Kristollari, Vassilia Karathanassi
Date: 2020-06-14 00:00:00
Tasks: Cloud Detection

Removal of cloud interference is a crucial step for the exploitation of the spectral information stored in optical satellite images. Several cloud masking approaches have been developed through time, based on direct interpretation of the spectral and temporal properties of clouds through thresholds. The problem has also been tackled by machine learning methods with artificial neural networks being among the most recent ones. Detection of bright non-cloud objects is one of the most difficult tasks in cloud masking applications since spectral information alone often proves inadequate for their separation from clouds. Scientific attention has recently been redrawn on self-organizing maps (SOMs) because of their unique ability to preserve topologic relations, added to the advantage of faster training time and more interpretative behavior compared to other types of artificial neural networks. This study evaluated a SOM for cloud masking Sentinel-2 images and proposed a fine-tuning methodology to separate clouds from bright land areas. The fine-tuning process which is based on the output of the non-fine-tuned network, at first directly locates the neurons that correspond to the misclassified pixels. Then, the incorrect labels of the neurons are altered without applying further training. The fine-tuning method follows a general procedure, thus its applicability is broad and not confined only in the field of cloud-masking. The network was trained on the largest publicly available spectral database for Sentinel-2 cloud masking applications and was tested on a truly independent database of Sentinel-2 cloud masks. It was evaluated both qualitatively and quantitatively with the interpretation of its behavior through multiple visualization techniques being a main part of the evaluation. It was shown that the fine-tuned SOM successfully recognized the bright non-cloud areas and outperformed the state-of-the-art algorithms: Sen2Cor and Fmask, as well as the version that was not fine-tuned.
Artificial neural networks for cloud masking of Sentinel-2 ocean images with noise and sunglint
Authors: Viktoria Kristollari, Vassilia Karathanassi
Date: 2020-01-26 00:00:00
Tasks: Cloud Detection

Cloudy regions in optical satellite images prevent the extraction of valuable information by image processing techniques. Several threshold, multi-temporal and machine learning approaches have been developed for the separation of clouds in land and ocean applications, but this task still remains a challenge. Concerning deep water marine applications, the main difficulties are imposed in regions with high noise levels and sunglint. In this study, artificial neural networks (ANNs) with different configurations are evaluated for the detection of clouds in Sentinel-2 images depicting deep water regions with several noise levels. The ANNs are trained on a manual public dataset and on a manual dataset created for the needs of this study, which authors intend to make publicly available. Results are compared with the cloud masks produced by three state-of-the-art algorithms: Fmask, MAJA, and Sen2Cor. It was shown that the ANNs trained on the second dataset perform very favourably, in contrast to the ANNs trained on the first dataset that fails to adequately represent the spectra of the noisy Sentinel-2 images. This study further reinforces the value of the ‘cirrus’ band and indicates the bands that mitigate the influence of noisy spectra, by defining and examining an index that characterizes the importance of the bands according to the weights produced by the ANNs. Finally, the possibility of improving results by making predictions using the feature scaling parameters of the test set instead of those of the training set is also investigated in cases where the test set cannot be adequately represented by the training set.
Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery
Authors: Viktoria Kristollari, Vassilia Karathanassi
Date: 2020-08-26 00:00:00
Tasks: Cloud Detection, image-classification

Cloud contamination represents a large obstacle for mapping the earth’s surface using remotely sensed data. Therefore, cloudy pixels should be identified and eliminated before any further data processing can be achieved. Although several threshold, multi-temporal and machine learning applications have been developed to tackle this issue, it still remains a challenge. The main challenges are imposed by the difficulty to detect thin clouds and to separate bright clouds from bright non-cloud objects. Convolutional neural networks (CNNs) have proven to be one of the most promising methods for image classification tasks and their use is rapidly increasing in remote sensing problems. CNNs present interesting properties for image processing since they directly exploit not only the spectral information but also the spatial covariance of the data. In this work, we study the applicability of CNNs in cloud detection of Sentinel-2 imagery, a complex remote sensing problem with crucial spatial context. A patch-to-pixel CNN architecture consisting of three convolutional layers and two fully connected layers is trained on a recently available manually created public dataset. The results were evaluated both qualitatively and quantitatively through comparison with ground truth cloud masks and state-of-the art pixel-based algorithms (Fmask, Sen2Cor). It was shown that the proposed architecture even though simpler than the deep learning architectures proposed by recent literature, performs very favorably, especially in the challenging cases. Besides the evaluation of the results, feature maps where observed as an initial effort to extract the weights of the useful kernels for cloud masking applications.
Myofibroblast Forms Atherosclerotic Plaques
Authors: Xinggang Wang, Junbo Ge
Date: 2020-07-21 00:00:00

For decades, smooth muscle cells (SMCs) and macrophages are considered as the main contributors to atherosclerotic plaques. However, we found that in the human coronary atherosclerotic plaques, SMCs were few, while lots of myofibroblasts infiltrated in the intima near the lumen (fibrous cap) and their distribution was highly positive related to intimal thickness. In addition to lots of foam cells formation, collagen fibers were forming in the thickening intima near the lumen (fibrous cap), and denaturing or calcifying gradually far from the lumen, which evolved into various complex plaques. In vitro, myofibroblasts could actively take lots of low-density lipoprotein (LDL) to enhance proliferation. Lots of collagen fibers, foam cells and extracellular lipids accumulation emerged in myofibroblasts cultured with 5% FBS high glucose DMEM without adding modified LDL. It is consistent with the characteristics of human coronary atherosclerotic plaques. It is the first time that lipid rich plaques with lots of foam cells, extracellular lipids and collagen fibers formed in vitro. It demonstrated that myofibroblast should be the direct and main source of collagen fibers, foam cells and extracellular lipids. This suggests that atherosclerosis is not as complicated as previously considered, and it might be mainly a process of myofibroblast remodeling to vascular injury caused by various risk factors. This study made the pathogenesis of atherosclerosis clearer. It would provide a target cell for future treatments of atherosclerotic diseases. In vitro atherosclerotic plaques model formed by human myofibroblasts would be an efficient and convenient way to study atherosclerosis.
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention
Authors: João Pereira, Margarida Silveira
Date: 2018-12-17 00:00:00
Tasks: Anomaly Detection, Deep Attention, Representation Learning, Time Series, Time Series Analysis, Unsupervised Anomaly Detection

In the age of big data, time series are being generated in massive amounts. In the energy field, smart grids are enabling a unprecedented data acquisition with the integration of sensors and smart devices. In the context of renewable energies, there has been an increasing interest in solar photovoltaic energy generation. These installations are often integrated with smart sensors that measure the energy production. Such amount of data collected makes the quest for developing smart monitoring systems that can detect anomalous behaviour in these systems, trigger alerts and enable maintenance operations. In this paper, we propose a generic, unsupervised and scalable framework for anomaly detection in time series data, based on a variational recurrent autoencoder. Furthermore, we introduce attention in the model, by means of a variational self-attention mechanism (VSAM), to improve the performance of the encoding-decoding process. Afterwards, we perform anomaly detection based on the probabilistic reconstruction scores provided by our model. Our results on solar energy generation time series show the ability of the proposed approach to detect anomalous behaviour in time series data, while providing structured and expressive representations. Since it does not need labels to be trained, our methodology enables new applications for anomaly detection in energy time series data and beyond.