Showing papers by "Tampere University of Technology published in 2021"
TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
440 citations
TL;DR: In this article, the authors focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions.
Abstract: Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.
224 citations
TL;DR: A review of 3D and 4D printing technologies available for use in dentistry can be found in this paper, together with a critique on the materials available for 3D/4D printing.
121 citations
TL;DR: This article surveys recent advances in ultrasound haptic technology and discusses the fundamentals of this haptictechnology, how a variety of perceptible sensations are rendered, and how it is currently being used to enable novel interaction techniques.
Abstract: Ultrasound haptics is a contactless haptic technology that enables novel mid-air interactions with rich multisensory feedback. This article surveys recent advances in ultrasound haptic technology. We discuss the fundamentals of this haptic technology, how a variety of perceptible sensations are rendered, and how it is currently being used to enable novel interaction techniques. We summarize its strengths, weaknesses, and potential applications across various domains. We conclude with our perspective on key directions for this promising haptic technology.
83 citations
01 Apr 2021
TL;DR: Wang et al. as discussed by the authors proposed a method for joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps.
Abstract: Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.
53 citations
TL;DR: The studies on gameful interventions for older adults suggest that senior users may benefit from gamification and game-based interventions, especially in the health domain, however, due to methodological shortcomings and limited amount of research available, further work is called for.
Abstract: Background and objectives During past years, gamification has become a major trend in technology, and promising results of its effectiveness have been reported. However, prior research has predominantly focused on examining the effects of gamification among young adults, while other demographic groups such as older adults have received less attention. In this review, we synthesize existing scholarly work on the impact of gamification for older adults. Research design and methods A systematic search was conducted using 4 academic databases from inception through January 2019. A rigorous selection process was followed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results Twelve empirical peer-reviewed studies written in English, focusing on older adults aged ≥55, including a gameful intervention, and assessing subjective or objective outcomes were identified. Eleven of the 12 studies were conducted in the health domain. Randomized controlled study settings were reported in 8 studies. Positively oriented results were reported in 10 of 12 studies on visual attention rehabilitation, diabetes control, increasing positive emotions for patients with subthreshold depression, cognitive training and memory tests, engagement in training program, perceptions of self-efficacy, motivation and positive emotions of social gameplay conditions, increased physical activity and balancing ability, and increased learning performance and autonomy experiences. The results are, however, mostly weak indications of positive effects. Discussion and implications Overall, the studies on gameful interventions for older adults suggest that senior users may benefit from gamification and game-based interventions, especially in the health domain. However, due to methodological shortcomings and limited amount of research available, further work in the area is called for.
48 citations
TL;DR: Two alternatives of a direct model predictive control scheme for a three-phase two-level grid-connected converter with an $LCL$ filter are proposed, implying that a fixed modulation cycle akin to pulsewidth modulation (PWM) results.
Abstract: This article proposes two alternatives of a direct model predictive control (MPC) scheme for a three-phase two-level grid-connected converter with an $LCL$ filter. Although both approaches are implemented as direct control methods, i.e., they combine control and modulation in one computational stage, they operate the converter at a constant switching frequency and generate a discrete grid current harmonic spectrum. To achieve this, the first method allows for one switching transition per phase and sampling interval, implying that a fixed modulation cycle akin to pulsewidth modulation (PWM) results. Moreover, by appropriately designing the objective function of the optimization problem underlying MPC, grid current distortions similar to those of space vector modulation (SVM) are produced. As for the second approach, two phases are allowed to switch per sampling interval, emulating the behavior of discontinuous PWM. Consequently, due to the introduced formulations, harmonic limitations imposed by relevant grid codes can be met with the proposed methods. Furthermore, due to the multiple-input multiple-output (MIMO) nature of both approaches, all output variables of the system can be simultaneously controlled. Finally, the inherent full-state information of MPC renders an additional active damping loop unnecessary, further simplifying the controller design. The presented performance assessment highlights the potential benefits of both proposed MPC-based algorithms.
48 citations
TL;DR: CheXNet as mentioned in this paper uses convolutional support estimation network (CSEN) for classification of COVID-19 in X-ray images, achieving state-of-the-art performance in many classification tasks.
Abstract: Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.
47 citations
TL;DR: HIPPIE as mentioned in this paper is a soft X-ray beamline on the 3'GeV electron storage ring of the MAX'IV Laboratory, equipped with a novel ambient-pressure x-ray photoelectron spectroscopy (APXPS) instrument.
Abstract: HIPPIE is a soft X-ray beamline on the 3 GeV electron storage ring of the MAX IV Laboratory, equipped with a novel ambient-pressure X-ray photoelectron spectroscopy (APXPS) instrument. The endstation is dedicated to performing in situ and operando X-ray photoelectron spectroscopy experiments in the presence of a controlled gaseous atmosphere at pressures up to 30 mbar [1 mbar = 100 Pa] as well as under ultra-high-vacuum conditions. The photon energy range is 250 to 2200 eV in planar polarization and with photon fluxes >1012 photons s−1 (500 mA ring current) at a resolving power of greater than 10000 and up to a maximum of 32000. The endstation currently provides two sample environments: a catalysis cell and an electrochemical/liquid cell. The former allows APXPS measurements of solid samples in the presence of a gaseous atmosphere (with a mixture of up to eight gases and a vapour of a liquid) and simultaneous analysis of the inlet/outlet gas composition by online mass spectrometry. The latter is a more versatile setup primarily designed for APXPS at the solid–liquid (dip-and-pull setup) or liquid–gas (liquid microjet) interfaces under full electrochemical control, and it can also be used as an open port for ad hoc-designed non-standard APXPS experiments with different sample environments. The catalysis cell can be further equipped with an IR reflection–absorption spectrometer, allowing for simultaneous APXPS and IR spectroscopy of the samples. The endstation is set up to easily accommodate further sample environments.
44 citations
TL;DR: This paper examines pedestrian route choice preferences in San Francisco, California using a large, anonymized dataset of walking trajectories collected from an activity-based smartphone application and demonstrates how the estimated coefficients can be operationalized for policy and planning to describe pedestrian accessibility to BART stations inSan Francisco using ‘perceived distance’ as opposed to traversed distance.
Abstract: Big data from smartphone applications are enabling travel behavior studies at an unprecedented scale. In this paper, we examine pedestrian route choice preferences in San Francisco, California using a large, anonymized dataset of walking trajectories collected from an activity-based smartphone application. We study the impact of various street attributes known to affect pedestrian route choice from prior literature. Unlike most studies, where data has been constrained to a particular destination type (e.g. walking to transit stations) or limited in volume, a large number of actual trajectories presented here include a wide diversity of destinations and geographies, allowing us to describing typical pedestrians’ preferences in San Francisco as a whole. Other innovations presented in the paper include using a novel technique for generating alternative paths for route choice estimation and gathering previously hard-to-get route attribute information by computationally processing a large set of Google Street View images. We also demonstrate how the estimated coefficients can be operationalized for policy and planning to describe pedestrian accessibility to BART stations in San Francisco using ‘perceived distance’ as opposed to traversed distance.
40 citations
TL;DR: In this paper, the authors examine what aspects enable public organizations to develop AI capabilities and find that perceived financial costs, organizational innovativeness, perceived governmental pressure, government incentives, and regulatory support have an impact on the development of AI capabilities.
TL;DR: Self-ONN as discussed by the authors synthesizes novel nodal transformations on-the-fly as part of the learning process, thus eliminating the need for redundant training runs for operator search.
TL;DR: This study evaluates the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images and proposes a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task.
Abstract: Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.
TL;DR: A three-phase approach for early MI detection in low-quality echocardiography using a state-of-the-art deep learning model, analysis of the segmented LV wall by feature engineering, and the first public eChocardiographic dataset (HMC-QU) are introduced.
Abstract: Myocardial infarction (MI), or commonly known as heart attack , is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further heart tissue damages or death. The earliest and most reliable sign of ischemia is regional wall motion abnormality (RWMA) of the affected part of the ventricular muscle. Echocardiography can easily, inexpensively, and non-invasively exhibit the RWMA. In this article, we introduce a three-phase approach for early MI detection in low-quality echocardiography: 1) segmentation of the entire left ventricle (LV) wall using a state-of-the-art deep learning model, 2) analysis of the segmented LV wall by feature engineering, and 3) early MI detection. The main contributions of this study are highly accurate segmentation of the LV wall from low-quality echocardiography, pseudo labeling approach for ground-truth formation of the unannotated LV wall, and the first public echocardiographic dataset (HMC-QU) a MI detection. Furthermore, the outputs of the proposed approach can significantly help cardiologists for a better assessment of the LV wall characteristics. The proposed approach has achieved 95.72% sensitivity and 99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03% specificity, and 86.85% precision for MI detection on the HMC-QU dataset. a The benchmark HMC-QU dataset is publicly shared at the repository https://www.kaggle.com/aysendegerli/hmcqu-dataset.for
TL;DR: In this paper, a pragmatic mix-method study is conducted in the form of surveys, semi-structured interviews, and diary study spanning across 10 months of the COVID-19 pandemic, to examine self-reported insights on ERL challenges, experiences, and learning engagement of the students from Finland and India.
Abstract: COVID-19 pandemic has affected the entire world in many ways. It has sparked a prominent pedagogical shift for university level students, as it has changed the way students learn, attend classes, or communicate with teachers. Globally, every student is forced to adopt Emergency Remote Learning (ERL) as a result of immediate transformation of physical classes into remote education. This two-fold study investigated the differences between traditional distance, online, and virtual learning solutions and the new Emergency Remote Learning (ERL) method for the university level education. Furthermore, a pragmatic mix-method study is conducted in the form of surveys, semi-structured interviews, and diary study spanning across 10 months of pandemic, to examine self-reported insights on ERL challenges, experiences, and learning engagement of the students from Finland and India. Cumulative findings suggest that scheduling, distractions, pessimistic emotions, longer durations, and concentration were the highest challenges faced by the students which impacted their learning experiences and engagement. The study also found that the ERL specific factors like low-interactivity, technical limitations, non-structured, and non-standardized methods had a prominent impact on the effectiveness of remote education. Furthermore, the study has suggested guidelines for improving remote learning experience as a futuristic solution beyond COVID-19 pandemic. Supplementary Information The online version contains supplementary material available at 10.1007/s10639-021-10747-1.
TL;DR: In this article, a harmonically mode-locked Er-doped fiber laser with self-starting hybrid mode-locking triggered by frequency-shifting and nonlinear polarization evolution is presented.
Abstract: We experimentally demonstrate a harmonically mode-locked Er-doped fiber laser The distinctive feature of the laser is highly stable pulse trains generated via self-starting hybrid mode-locking triggered by frequency-shifting and nonlinear polarization evolution A intra-cavity tunable bandpass filter allows getting a pulse repetition rate up to 12 GHz with local adjustment of the wavelength
TL;DR: This article formulates the monostatic radar system model starting from the communication system model and shows the accuracy of the proposed system to measure the velocity of mobile objects at various speeds while the device is simultaneously served as a node to perform in-band bidirectional communication.
Abstract: In-band full-duplex (IBFD) technology is a promising solution to boost the throughput of wireless networks. To bring IBFD to reality, the modem has to cancel the self-interference (SI) signal, which includes the strong direct Tx leakage signal and the weaker reflected Tx signal from the surroundings. Adaptive analog and digital SI cancelation schemes have been proposed. It becomes then interesting to understand, although, how the echoed SI could be exploited for enabling radar functionality while reusing the waveform and the already-existing hardware. This article formulates the monostatic radar system model starting from the communication system model. Beside simulation-based assessment, the performance is also evaluated by an IBFD system prototype, which consists of both analog and digital SI canceller modules, enabling $>$ 85 dB Tx–Rx isolation. The system is enhanced with Doppler radar functionality, reusing as much as possible the existing IBFD functional blocks. The experimental result shows the accuracy of the proposed system to measure the velocity of mobile objects at various speeds between 0.2 and 1 m/s while the device is simultaneously served as a node to perform in-band bidirectional communication. This ability suits the proposed system for a broad spectrum of opportunistic remote sensing applications, such as body and hand gesture detection.
TL;DR: In this article, the authors argue that virtual technologies provide greater opportunities to influence consumer decisions than the present digital environment and suggest that virtual technology can potentially be used to nudge consumers towards sustainable consumption.
TL;DR: The first international evaluation on sound event localization and detection, organized as a task of the DCASE 2019 Challenge, was presented in this paper, where a large-scale realistic dataset of spatialized sound events was generated for training of learning-based approaches.
Abstract: Sound event localization and detection is a novel area of research that emerged from the combined interest of analyzing the acoustic scene in terms of the spatial and temporal activity of sounds of interest. This paper presents an overview of the first international evaluation on sound event localization and detection, organized as a task of the DCASE 2019 Challenge. A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset. The overview presents in detail how the systems were evaluated and ranked and the characteristics of the best-performing systems. Common strategies in terms of input features, model architectures, training approaches, exploitation of prior knowledge, and data augmentation are discussed. Since ranking in the challenge was based on individually evaluating localization and event classification performance, part of the overview focuses on presenting metrics for the joint measurement of the two, together with a reevaluation of submissions using these new metrics. The new analysis reveals submissions that performed better on the joint task of detecting the correct type of event close to its original location than some of the submissions that were ranked higher in the challenge. Consequently, ranking of submissions which performed strongly when evaluated separately on detection or localization, but not jointly on both, was affected negatively.
01 Jun 2021
TL;DR: In this paper, a machine learning based solution was proposed to boost positioning accuracy in urban areas by obtaining user equipment (UE) position from beamformed Radio Signal Strength (RSS) measurements and coherently fusing it with GNSS-based positioning data to enhance overall positioning performance.
Abstract: In modern radio networks with large antenna arrays and precise beamforming techniques, accurate user positioning plays a key role in enabling seamless mobility management, link optimization, navigation and safety control. In open and rural areas, Global Navigation Satellite Systems (GNSS) are able to provide high-accuracy and high-reliability positioning performance. However, in urban and densely built-up areas the GNSS performance is typically substantially degraded due to rich scattering and multipath propagation effects. In this paper, we propose a machine learning based solution to boost positioning accuracy in urban areas by (i) obtaining User Equipment (UE) position from beamformed Radio Signal Strength (RSS) measurements and (ii) coherently fusing it with GNSS-based positioning data to enhance overall positioning performance. Based on the obtained numerical results, we were able to achieve a meter-level accuracy with the proposed machine learning model utilizing the beamformed RSS measurements, and subsequently improve the positioning accuracy further via fusion with GNSS data.
05 Jan 2021
TL;DR: This work conducted semi-structured expert interviews in twelve German municipalities to examine perceived challenges of AI adoption from employee’s perspective and extended research regarding the Technology-Organization-Environment (TOE) framework.
Abstract: Artificial intelligence (AI) is becoming an increasingly important factor of everyday life. The progress of AI adoption continues to accelerate with increasing investments in AI techniques and applications worldwide. However, the use of AI is still not present in employee’s daily life of German municipalities. Since this technology has a promising potential that German municipalities can also take advantage of, it is important to facilitate the transition of municipalities to AI. For this reason, we have conducted semi-structured expert interviews in twelve German municipalities to examine perceived challenges of AI adoption from employee’s perspective. Using methods from Grounded Theory and Gioia we extended research regarding the Technology-Organization-Environment (TOE) framework. Our results proof six and identified four additional perceived challenges of AI adoption in municipalities. With these results, we are able to extend literature on the use of AI in the public sector introducing perceived challenges of AI adoption from employee’s perspective in municipalities extending the
TL;DR: In this article, a bilinear compatibility framework was employed to learn an acoustic-semantic projection between intermediate-level representations of audio instances and sound classes, i.e., acoustic embeddings and semantic embeddions.
Abstract: In this paper, we study zero-shot learning in audio classification via semantic embeddings extracted from textual labels and sentence descriptions of sound classes. Our goal is to obtain a classifier that is capable of recognizing audio instances of sound classes that have no available training samples, but only semantic side information. We employ a bilinear compatibility framework to learn an acoustic-semantic projection between intermediate-level representations of audio instances and sound classes, i.e., acoustic embeddings and semantic embeddings. We use VGGish to extract deep acoustic embeddings from audio clips, and pre-trained language models (Word2Vec, GloVe, BERT) to generate either label embeddings from textual labels or sentence embeddings from sentence descriptions of sound classes. Audio classification is performed by a linear compatibility function that measures how compatible an acoustic embedding and a semantic embedding are. We evaluate the proposed method on a small balanced dataset ESC-50 and a large-scale unbalanced audio subset of AudioSet. The experimental results show that classification performance is significantly improved by involving sound classes that are semantically close to the test classes in training. Meanwhile, we demonstrate that both label embeddings and sentence embeddings are useful for zero-shot learning. Classification performance is improved by concatenating label/sentence embeddings generated with different language models. With their hybrid concatenations, the results are improved further.
TL;DR: A Compressive Sensing (CS) based data encryption that is capable of both obfuscating selected sensitive parts of documents and compressively sampling, hence encrypting both sensitive and non-sensitive parts of the document is proposed.
Abstract: Recent advances in intelligent surveillance systems have enabled a new era of smart monitoring in a wide range of applications from health monitoring to homeland security. However, this boom in data gathering, analyzing and sharing brings in also significant privacy concerns. We propose a Compressive Sensing (CS) based data encryption that is capable of both obfuscating selected sensitive parts of documents and compressively sampling, hence encrypting both sensitive and non-sensitive parts of the document. The scheme uses a data hiding technique on CS-encrypted signal to preserve the one-time use obfuscation matrix. The proposed privacy-preserving approach offers a low-cost multi-tier encryption system that provides different levels of reconstruction quality for different classes of users, e.g., semi-authorized, full-authorized. As a case study, we develop a secure video surveillance system and analyze its performance.
TL;DR: Data analysis of iPSC-CM drug effects together with machine learning methods can create a very valuable and efficient platform to individualize medication in addition to drug screening and cardiotoxicity studies.
Abstract: Patient-specific induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer an attractive experimental platform to investigate cardiac diseases and therapeutic outcome. In this study, iPSC-CMs were utilized to study their calcium transient signals and drug effects by means of machine learning, a central part of artificial intelligence. Drug effects were assessed in six iPSC-lines carrying different mutations causing catecholaminergic polymorphic ventricular tachycardia (CPVT), a highly malignant inherited arrhythmogenic disorder. The antiarrhythmic effect of dantrolene, an inhibitor of sarcoplasmic calcium release, was studied in iPSC-CMs after adrenaline, an adrenergic agonist, stimulation by machine learning analysis of calcium transient signals. First, beats of transient signals were identified with our peak recognition algorithm previously developed. Then 12 peak variables were computed for every identified peak of a signal and by means of this data signals were classified into different classes corresponding to those affected by adrenaline or, thereafter, affected by a drug, dantrolene. The best classification accuracy was approximately 79% indicating that machine learning methods can be utilized in analysis of iPSC-CM drug effects. In the future, data analysis of iPSC-CM drug effects together with machine learning methods can create a very valuable and efficient platform to individualize medication in addition to drug screening and cardiotoxicity studies.
TL;DR: The novel coronavirus pandemic (COVID-19) has had far-reaching effects on public health around the world as discussed by the authors. Attempts to prevent the spread of the disease by quarantine have led to large-scale global...
Abstract: The novel coronavirus pandemic (COVID-19) has had far-reaching effects on public health around the world. Attempts to prevent the spread of the disease by quarantine have led to large-scale global ...
Institut de mécanique céleste et de calcul des éphémérides1, Massachusetts Institute of Technology2, Tampere University of Technology3, Astrophysics Research Institute4, Romanian Academy5, Jet Propulsion Laboratory6, European Space Research and Technology Centre7, Pontifical Catholic University of Peru8, European Southern Observatory9
TL;DR: In this article, the mass and shape of the M/Xe-type triple asteroid system (216) Kleopatra were estimated using the VLT/SPHERE/ZIMPOL camera.
Abstract: The recent estimates of the 3D shape of the M/Xe-type triple asteroid system (216) Kleopatra indicated a density of ∼5 g cm −3 , which is by far the highest for a small Solar System body. Such a high density implies a high metal content as well as a low porosity which is not easy to reconcile with its peculiar "dumbbell" shape. Given the unprecedented angular resolution of the VLT/SPHERE/ZIMPOL camera, here, we aim to constrain the mass (via the characterization of the orbits of the moons) and the shape of (216) Kleopatra with high accuracy, hence its density. We combined our new VLT/SPHERE observations of (216) Kleopatra recorded during two apparitions in 2017 and 2018 with archival data from the W. M. Keck Observatory, as well as lightcurve, occultation, and delay-Doppler images, to derive a model of its 3D shape using two different algorithms (ADAM, MPCD). Furthermore, an N-body dynamical model allowed us to retrieve the orbital elements of the two moons as explained in the accompanying paper. The shape of (216) Kleopatra is very close to an equilibrium dumbbell figure with two lobes and a thick neck. Its volume equivalent diameter (118.75 ± 1.40) km and mass (2.97 ± 0.32) × 10 18 kg (i.e., 56% lower than previously reported) imply a bulk density of (3.38 ± 0.50) g cm −3. Such a low density for a supposedly metal-rich body indicates a substantial porosity within the primary. This porous structure along with its near equilibrium shape is compatible with a formation scenario including a giant impact followed by reaccumulation. (216) Kleopatra's current rotation period and dumbbell shape imply that it is in a critically rotating state. The low effective gravity along the equator of the body, together with the equatorial orbits of the moons and possibly rubble-pile structure, opens the possibility that the moons formed via mass shedding. (216) Kleopatra is a puzzling multiple system due to the unique characteristics of the primary. This system certainly deserves particular attention in the future, with the Extremely Large Telescopes and possibly a dedicated space mission, to decipher its entire formation history.
TL;DR: The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.
TL;DR: Wang et al. as mentioned in this paper proposed a new storage solution that relies on bacterial nanonetworks properties, which allows digitally-encoded DNA to be stored into motility-restricted bacteria, which compose an archival architecture of clusters, and to be later retrieved by engineered motile bacteria, whenever reading operations are needed.
Abstract: Since the birth of computer and networks, fueled by pervasive computing, Internet of Things and ubiquitous connectivity, the amount of data stored and transmitted has exponentially grown through the years. Due to this demand, new storage solutions are needed. One promising media is the DNA as it provides numerous advantages, which includes the ability to store dense information while achieving long-term reliability. However, the question as to how the data can be retrieved from a DNA-based archive, still remains. In this paper, we aim to address this question by proposing a new storage solution that relies on bacterial nanonetworks properties. Our solution allows digitally-encoded DNA to be stored into motility-restricted bacteria, which compose an archival architecture of clusters, and to be later retrieved by engineered motile bacteria, whenever reading operations are needed. We conducted extensive simulations, in order to determine the reliability of data retrieval from motility-restricted storage clusters, placed spatially at different locations. Aiming to assess the feasibility of our solution, we have also conducted wet lab experiments that show how bacteria nanonetworks can effectively retrieve a simple message, such as “ Hello World, ” by conjugation with motility-restricted bacteria, and finally mobilize towards a target point for delivery.
TL;DR: This article constructs an offloading policy with interleaved exploration and exploitation epochs to solve the sequential FN selection problem and finds that the proposed policy is optimal in the sense that it achieves a regret with sublinear order.
Abstract: Fog computing provides computation and services to the edge of networks to support real-time applications. The latency performance is a crucial metric in fog computing. In this article, we consider a computation offloading problem in a fog network with unknown dynamics. In this network, mobile users can offload their computational tasks to neighborhood fog nodes (FNs) in each time slot. The queue of arrival tasks at each FN follows a Markov model with unknown statistics. In order to provide a satisfactory quality of experience, the network latency needs to be minimized. In this article, we construct an offloading policy with interleaved exploration and exploitation epochs to solve the sequential FN selection problem. An upper bound of regret is derived to show the effectiveness of the proposed method. The proposed policy is optimal in the sense that it achieves a regret with sublinear order. In addition, the proposed policy can be applied to both single-user setting and multiuser setting. Simulation results show that when compared with the existing offloading algorithms, the proposed algorithm can reduce the average latency by 7%–47% in the single-user setting, and 91% in the multiuser setting.
TL;DR: The authors examines how the credibility of the content of mis- or disinformation, as well as the believability of authors creating such information is assessed in online discussion, and concludes that the credibility and plausibility of the authors of false information is highly correlated.
Abstract: This study examines how the credibility of the content of mis- or disinformation, as well as the believability of authors creating such information is assessed in online discussion. More specifical...