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Showing papers by "Aalto University published in 2019"


Journal ArticleDOI
Kazunori Akiyama, Antxon Alberdi1, Walter Alef2, Keiichi Asada3  +403 moreInstitutions (82)
TL;DR: In this article, the Event Horizon Telescope was used to reconstruct event-horizon-scale images of the supermassive black hole candidate in the center of the giant elliptical galaxy M87.
Abstract: When surrounded by a transparent emission region, black holes are expected to reveal a dark shadow caused by gravitational light bending and photon capture at the event horizon. To image and study this phenomenon, we have assembled the Event Horizon Telescope, a global very long baseline interferometry array observing at a wavelength of 1.3 mm. This allows us to reconstruct event-horizon-scale images of the supermassive black hole candidate in the center of the giant elliptical galaxy M87. We have resolved the central compact radio source as an asymmetric bright emission ring with a diameter of 42 +/- 3 mu as, which is circular and encompasses a central depression in brightness with a flux ratio greater than or similar to 10: 1. The emission ring is recovered using different calibration and imaging schemes, with its diameter and width remaining stable over four different observations carried out in different days. Overall, the observed image is consistent with expectations for the shadow of a Kerr black hole as predicted by general relativity. The asymmetry in brightness in the ring can be explained in terms of relativistic beaming of the emission from a plasma rotating close to the speed of light around a black hole. We compare our images to an extensive library of ray-traced general-relativistic magnetohydrodynamic simulations of black holes and derive a central mass of M = (6.5 +/- 0.7) x 10(9) M-circle dot. Our radio-wave observations thus provide powerful evidence for the presence of supermassive black holes in centers of galaxies and as the central engines of active galactic nuclei. They also present a new tool to explore gravity in its most extreme limit and on a mass scale that was so far not accessible.

2,589 citations


Posted Content
TL;DR: This work redesigns the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images, and thereby redefines the state of the art in unconditional image modeling.
Abstract: The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

2,411 citations


Journal ArticleDOI
TL;DR: In this article, the authors provide an extensive review and an updated research agenda for the field, classified into nine main themes: understanding transitions; power, agency and politics; governing transitions; civil society, culture and social movements; businesses and industries; transitions in practice and everyday life; geography of transitions; ethical aspects; and methodologies.
Abstract: Research on sustainability transitions has expanded rapidly in the last ten years, diversified in terms of topics and geographical applications, and deepened with respect to theories and methods. This article provides an extensive review and an updated research agenda for the field, classified into nine main themes: understanding transitions; power, agency and politics; governing transitions; civil society, culture and social movements; businesses and industries; transitions in practice and everyday life; geography of transitions; ethical aspects; and methodologies. The review shows that the scope of sustainability transitions research has broadened and connections to established disciplines have grown stronger. At the same time, we see that the grand challenges related to sustainability remain unsolved, calling for continued efforts and an acceleration of ongoing transitions. Transition studies can play a key role in this regard by creating new perspectives, approaches and understanding and helping to move society in the direction of sustainability.

1,099 citations


Journal ArticleDOI
Kazunori Akiyama, Antxon Alberdi1, Walter Alef2, Keiichi Asada3  +251 moreInstitutions (56)
TL;DR: In this article, the authors present measurements of the properties of the central radio source in M87 using Event Horizon Telescope data obtained during the 2017 campaign, and find that >50% of the total flux at arcsecond scales comes from near the horizon and that the emission is dramatically suppressed interior to this region by a factor >10, providing direct evidence of the predicted shadow of a black hole.
Abstract: We present measurements of the properties of the central radio source in M87 using Event Horizon Telescope data obtained during the 2017 campaign. We develop and fit geometric crescent models (asymmetric rings with interior brightness depressions) using two independent sampling algorithms that consider distinct representations of the visibility data. We show that the crescent family of models is statistically preferred over other comparably complex geometric models that we explore. We calibrate the geometric model parameters using general relativistic magnetohydrodynamic (GRMHD) models of the emission region and estimate physical properties of the source. We further fit images generated from GRMHD models directly to the data. We compare the derived emission region and black hole parameters from these analyses with those recovered from reconstructed images. There is a remarkable consistency among all methods and data sets. We find that >50% of the total flux at arcsecond scales comes from near the horizon, and that the emission is dramatically suppressed interior to this region by a factor >10, providing direct evidence of the predicted shadow of a black hole. Across all methods, we measure a crescent diameter of 42 ± 3 μas and constrain its fractional width to be <0.5. Associating the crescent feature with the emission surrounding the black hole shadow, we infer an angular gravitational radius of GM/Dc2 = 3.8 ± 0.4 μas. Folding in a distance measurement of ${16.8}_{-0.7}^{+0.8}\,\mathrm{Mpc}$ gives a black hole mass of $M=6.5\pm 0.2{| }_{\mathrm{stat}}\pm 0.7{| }_{\mathrm{sys}}\times {10}^{9}\hspace{2pt}{M}_{\odot }$. This measurement from lensed emission near the event horizon is consistent with the presence of a central Kerr black hole, as predicted by the general theory of relativity.

1,024 citations


Journal ArticleDOI
Kazunori Akiyama, Antxon Alberdi1, Walter Alef2, Keiichi Asada3  +251 moreInstitutions (58)
TL;DR: In this article, the first Event Horizon Telescope (EHT) images of M87 were presented, using observations from April 2017 at 1.3 mm wavelength, showing a prominent ring with a diameter of ~40 μas, consistent with the size and shape of the lensed photon orbit encircling the "shadow" of a supermassive black hole.
Abstract: We present the first Event Horizon Telescope (EHT) images of M87, using observations from April 2017 at 1.3 mm wavelength. These images show a prominent ring with a diameter of ~40 μas, consistent with the size and shape of the lensed photon orbit encircling the "shadow" of a supermassive black hole. The ring is persistent across four observing nights and shows enhanced brightness in the south. To assess the reliability of these results, we implemented a two-stage imaging procedure. In the first stage, four teams, each blind to the others' work, produced images of M87 using both an established method (CLEAN) and a newer technique (regularized maximum likelihood). This stage allowed us to avoid shared human bias and to assess common features among independent reconstructions. In the second stage, we reconstructed synthetic data from a large survey of imaging parameters and then compared the results with the corresponding ground truth images. This stage allowed us to select parameters objectively to use when reconstructing images of M87. Across all tests in both stages, the ring diameter and asymmetry remained stable, insensitive to the choice of imaging technique. We describe the EHT imaging procedures, the primary image features in M87, and the dependence of these features on imaging assumptions.

952 citations


Journal ArticleDOI
TL;DR: In this article, the authors focus on the application of various phase change materials based on their thermophysical properties, in particular, the melting point, thermal energy storage density and thermal conductivity of the organic, inorganic and eutectic phases.

813 citations


Journal ArticleDOI
Kazunori Akiyama, Antxon Alberdi1, Walter Alef2, Keiichi Asada3  +259 moreInstitutions (62)
TL;DR: In this article, a large library of models based on general relativistic magnetohydrodynamic (GRMHD) simulations and synthetic images produced by GRS was constructed and compared with the observed visibilities.
Abstract: The Event Horizon Telescope (EHT) has mapped the central compact radio source of the elliptical galaxy M87 at 1.3 mm with unprecedented angular resolution. Here we consider the physical implications of the asymmetric ring seen in the 2017 EHT data. To this end, we construct a large library of models based on general relativistic magnetohydrodynamic (GRMHD) simulations and synthetic images produced by general relativistic ray tracing. We compare the observed visibilities with this library and confirm that the asymmetric ring is consistent with earlier predictions of strong gravitational lensing of synchrotron emission from a hot plasma orbiting near the black hole event horizon. The ring radius and ring asymmetry depend on black hole mass and spin, respectively, and both are therefore expected to be stable when observed in future EHT campaigns. Overall, the observed image is consistent with expectations for the shadow of a spinning Kerr black hole as predicted by general relativity. If the black hole spin and M87's large scale jet are aligned, then the black hole spin vector is pointed away from Earth. Models in our library of non-spinning black holes are inconsistent with the observations as they do not produce sufficiently powerful jets. At the same time, in those models that produce a sufficiently powerful jet, the latter is powered by extraction of black hole spin energy through mechanisms akin to the Blandford-Znajek process. We briefly consider alternatives to a black hole for the central compact object. Analysis of existing EHT polarization data and data taken simultaneously at other wavelengths will soon enable new tests of the GRMHD models, as will future EHT campaigns at 230 and 345 GHz.

808 citations


Journal ArticleDOI
Kazunori Akiyama, Antxon Alberdi1, Walter Alef2, Keiichi Asada3  +394 moreInstitutions (78)
TL;DR: The Event Horizon Telescope (EHT) as mentioned in this paper is a very long baseline interferometry (VLBI) array that comprises millimeter and submillimeter-wavelength telescopes separated by distances comparable to the diameter of the Earth.
Abstract: The Event Horizon Telescope (EHT) is a very long baseline interferometry (VLBI) array that comprises millimeter- and submillimeter-wavelength telescopes separated by distances comparable to the diameter of the Earth. At a nominal operating wavelength of ~1.3 mm, EHT angular resolution (λ/D) is ~25 μas, which is sufficient to resolve nearby supermassive black hole candidates on spatial and temporal scales that correspond to their event horizons. With this capability, the EHT scientific goals are to probe general relativistic effects in the strong-field regime and to study accretion and relativistic jet formation near the black hole boundary. In this Letter we describe the system design of the EHT, detail the technology and instrumentation that enable observations, and provide measures of its performance. Meeting the EHT science objectives has required several key developments that have facilitated the robust extension of the VLBI technique to EHT observing wavelengths and the production of instrumentation that can be deployed on a heterogeneous array of existing telescopes and facilities. To meet sensitivity requirements, high-bandwidth digital systems were developed that process data at rates of 64 gigabit s^(−1), exceeding those of currently operating cm-wavelength VLBI arrays by more than an order of magnitude. Associated improvements include the development of phasing systems at array facilities, new receiver installation at several sites, and the deployment of hydrogen maser frequency standards to ensure coherent data capture across the array. These efforts led to the coordination and execution of the first Global EHT observations in 2017 April, and to event-horizon-scale imaging of the supermassive black hole candidate in M87.

756 citations


Journal ArticleDOI
Kazunori Akiyama, Antxon Alberdi1, Walter Alef2, Keiichi Asada3  +243 moreInstitutions (60)
TL;DR: In this paper, the Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5-11 observing campaign are presented.
Abstract: We present the calibration and reduction of Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5–11 observing campaign. These global very long baseline interferometric observations include for the first time the highly sensitive Atacama Large Millimeter/submillimeter Array (ALMA); reaching an angular resolution of 25 μas, with characteristic sensitivity limits of ~1 mJy on baselines to ALMA and ~10 mJy on other baselines. The observations present challenges for existing data processing tools, arising from the rapid atmospheric phase fluctuations, wide recording bandwidth, and highly heterogeneous array. In response, we developed three independent pipelines for phase calibration and fringe detection, each tailored to the specific needs of the EHT. The final data products include calibrated total intensity amplitude and phase information. They are validated through a series of quality assurance tests that show consistency across pipelines and set limits on baseline systematic errors of 2% in amplitude and 1° in phase. The M87 data reveal the presence of two nulls in correlated flux density at ~3.4 and ~8.3 Gλ and temporal evolution in closure quantities, indicating intrinsic variability of compact structure on a timescale of days, or several light-crossing times for a few billion solar-mass black hole. These measurements provide the first opportunity to image horizon-scale structure in M87.

625 citations


Journal ArticleDOI
TL;DR: In this article, the authors propose an alternative definition of R2 for Bayesian fits, where the numerator can be larger than the denominator, which is a problem for Bayes fits.
Abstract: The usual definition of R2 (variance of the predicted values divided by the variance of the data) has a problem for Bayesian fits, as the numerator can be larger than the denominator. We propose an...

452 citations


Journal ArticleDOI
TL;DR: Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.
Abstract: Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.

Proceedings ArticleDOI
05 May 2019
TL;DR: In this article, a few-shot, unsupervised image-to-image translation algorithm is proposed that works on previously unseen target classes that are specified, at test time, only by a few example images.
Abstract: Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at https://github.com/NVlabs/FUNIT

Journal ArticleDOI
TL;DR: By "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster.
Abstract: Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase-change materials for memory devices; nanoparticle catalysts; and carbon-based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML-based interatomic potentials in diverse areas of materials research.

Journal ArticleDOI
TL;DR: A collection of quantile-based local efficiency measures, along with a practical approach for computing Monte Carlo error estimates for quantiles, are introduced and it is suggested that common trace plots should be replaced with rank plots from multiple chains.
Abstract: Markov chain Monte Carlo is a key computational tool in Bayesian statistics, but it can be challenging to monitor the convergence of an iterative stochastic algorithm. In this paper we show that the convergence diagnostic $\widehat{R}$ of Gelman and Rubin (1992) has serious flaws. Traditional $\widehat{R}$ will fail to correctly diagnose convergence failures when the chain has a heavy tail or when the variance varies across the chains. In this paper we propose an alternative rank-based diagnostic that fixes these problems. We also introduce a collection of quantile-based local efficiency measures, along with a practical approach for computing Monte Carlo error estimates for quantiles. We suggest that common trace plots should be replaced with rank plots from multiple chains. Finally, we give recommendations for how these methods should be used in practice.

Posted Content
TL;DR: This model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design, and verifies the effectiveness of the proposed framework through extensive experimental validation and comparisons to several baseline methods on benchmark datasets.
Abstract: Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human capability of picking up the essence of a novel object from a small number of examples and generalizing from there, we seek a few-shot, unsupervised image-to-image translation algorithm that works on previously unseen target classes that are specified, at test time, only by a few example images. Our model achieves this few-shot generation capability by coupling an adversarial training scheme with a novel network design. Through extensive experimental validation and comparisons to several baseline methods on benchmark datasets, we verify the effectiveness of the proposed framework. Our implementation and datasets are available at this https URL .

Journal ArticleDOI
TL;DR: The proposed convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3-D) space is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios.
Abstract: In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3-D) space. The proposed network takes a sequence of consecutive spectrogram time frames as input and maps it to two outputs in parallel. As the first output, the sound event detection (SED) is performed as a multi-label classification task on each time frame producing temporal activity for all the sound event classes. As the second output, localization is performed by estimating the 3-D Cartesian coordinates of the direction-of-arrival (DOA) for each sound event class using multi-output regression. The proposed method is able to associate multiple DOAs with respective sound event labels and further track this association with respect to time. The proposed method uses separately the phase and magnitude component of the spectrogram calculated on each audio channel as the feature, thereby avoiding any method- and array-specific feature extraction. The method is evaluated on five Ambisonic and two circular array format datasets with different overlapping sound events in anechoic, reverberant, and real-life scenarios. The proposed method is compared with two SED, three DOA estimation, and one SELD baselines. The results show that the proposed method is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios. The proposed method achieved a consistently higher recall of the estimated number of DOAs across datasets in comparison to the best baseline. Additionally, this recall was observed to be significantly better than the best baseline method for a higher number of overlapping sound events.

Journal ArticleDOI
TL;DR: In this paper, the authors provide a transition intermediary type typology that is sensitive to the emergence, neutrality and goals of intermediary actors as well as their context and level of action.

Journal ArticleDOI
TL;DR: A signal processing perspective of mm-wave JRC systems with an emphasis on waveform design is provided, to exploit opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads.
Abstract: Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improved performance, and safety due to enhanced information sharing. Today, millimeter-wave (mmwave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design.

Journal ArticleDOI
Nabila Aghanim1, Yashar Akrami2, Yashar Akrami3, Yashar Akrami4  +213 moreInstitutions (66)
TL;DR: The 2018 Planck CMB likelihoods were presented in this paper, following a hybrid approach similar to the 2015 one, with different approximations at low and high multipoles, and implementing several methodological and analysis refinements.
Abstract: This paper describes the 2018 Planck CMB likelihoods, following a hybrid approach similar to the 2015 one, with different approximations at low and high multipoles, and implementing several methodological and analysis refinements. With more realistic simulations, and better correction and modelling of systematics, we can now make full use of the High Frequency Instrument polarization data. The low-multipole 100x143 GHz EE cross-spectrum constrains the reionization optical-depth parameter $\tau$ to better than 15% (in combination with with the other low- and high-$\ell$ likelihoods). We also update the 2015 baseline low-$\ell$ joint TEB likelihood based on the Low Frequency Instrument data, which provides a weaker $\tau$ constraint. At high multipoles, a better model of the temperature-to-polarization leakage and corrections for the effective calibrations of the polarization channels (polarization efficiency or PE) allow us to fully use the polarization spectra, improving the constraints on the $\Lambda$CDM parameters by 20 to 30% compared to TT-only constraints. Tests on the modelling of the polarization demonstrate good consistency, with some residual modelling uncertainties, the accuracy of the PE modelling being the main limitation. Using our various tests, simulations, and comparison between different high-$\ell$ implementations, we estimate the consistency of the results to be better than the 0.5$\sigma$ level. Minor curiosities already present before (differences between $\ell$ 800 parameters or the preference for more smoothing of the $C_\ell$ peaks) are shown to be driven by the TT power spectrum and are not significantly modified by the inclusion of polarization. Overall, the legacy Planck CMB likelihoods provide a robust tool for constraining the cosmological model and represent a reference for future CMB observations. (Abridged)

Journal ArticleDOI
TL;DR: A comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats and the open challenges related to emerging SDN- and NFV-based security mechanisms.
Abstract: The explosive rise of Internet of Things (IoT) systems have notably increased the potential attack surfaces for cybercriminals. Accounting for the features and constraints of IoT devices, traditional security countermeasures can be inefficient in dynamic IoT environments. In this vein, the advantages introduced by software defined networking (SDN) and network function virtualization (NFV) have the potential to reshape the landscape of cybersecurity for IoT systems. To this aim, we provide a comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats. We also present lessons learned in the adoption of SDN/NFV-based protection approaches in IoT environments, comparing them with conventional security countermeasures. Finally, we deeply discuss the open challenges related to emerging SDN- and NFV-based security mechanisms, aiming to provide promising directives to conduct future research in this fervent area.

Journal ArticleDOI
TL;DR: In this article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data frastructures are discussed.
Abstract: Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data frastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

Journal ArticleDOI
TL;DR: In this paper, the authors examined the associations of the dark side of social media use and fake news sharing behavior among social media users and found that online trust, self-disclosure, fear of missing out, and social media fatigue are positively associated with the sharing fake news.


Journal ArticleDOI
06 Sep 2019-Science
TL;DR: It is shown that incident spectra can be computationally reconstructed from the different spectral response functions and measured photocurrents along the length of the nanowire, which is a practical step forward for the use of other light-sensitive nanomaterials for such ultra-miniaturized spectroscopy platforms.
Abstract: Spectrometers with ever-smaller footprints are sought after for a wide range of applications in which minimized size and weight are paramount, including emerging in situ characterization techniques. We report on an ultracompact microspectrometer design based on a single compositionally engineered nanowire. This platform is independent of the complex optical components or cavities that tend to constrain further miniaturization of current systems. We show that incident spectra can be computationally reconstructed from the different spectral response functions and measured photocurrents along the length of the nanowire. Our devices are capable of accurate, visible-range monochromatic and broadband light reconstruction, as well as spectral imaging from centimeter-scale focal planes down to lensless, single-cell-scale in situ mapping.

Journal ArticleDOI
TL;DR: In this article, a psychometrically valid and reliable instrument that measures different uses and gratifications behind the use of food delivery apps (FDAs) was developed and the association between different U&Gs and intentions to use FDAs were investigated.

Journal ArticleDOI
TL;DR: The expansion of the digital twin in including building life cycle management and the benefits and shortcomings of such implementation are studied and some of the technical shortcomings of the current Internet of Things systems are uncovered.
Abstract: The concept of a digital twin has been used in some industries where an accurate digital model of the equipment can be used for predictive maintenance. The use of a digital twin for performance is critical, and for capital-intensive equipment such as jet engines it proved to be successful in terms of cost savings and reliability improvements. In this paper, we aim to study the expansion of the digital twin in including building life cycle management and explore the benefits and shortcomings of such implementation. In four rounds of experimentation, more than 25,000 sensor reading instances were collected, analyzed, and utilized to create and test a limited digital twin of an office building facade element. This is performed to point out the method of implementation, highlight the benefits gained from digital twin, and to uncover some of the technical shortcomings of the current Internet of Things systems for this purpose.

Proceedings ArticleDOI
17 Jun 2019
TL;DR: In this article, the authors proposed a generic and effective detection of DNN model extraction attacks by generating synthetic queries and optimizing training hyperparameters, which outperformed state-of-the-art model extraction in terms of transferability of both targeted and non-targeted adversarial examples.
Abstract: Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantage to its owner, and (b) an adversary may use a stolen model to find transferable adversarial examples that can evade classification by the original model. Access to the model can be restricted to be only via well-defined prediction APIs. Nevertheless, prediction APIs still provide enough information to allow an adversary to mount model extraction attacks by sending repeated queries via the prediction API. In this paper, we describe new model extraction attacks using novel approaches for generating synthetic queries, and optimizing training hyperparameters. Our attacks outperform state-of-the-art model extraction in terms of transferability of both targeted and non-targeted adversarial examples (up to +29-44 percentage points, pp), and prediction accuracy (up to +46 pp) on two datasets. We provide take-aways on how to perform effective model extraction attacks. We then propose PRADA, the first step towards generic and effective detection of DNN model extraction attacks. It analyzes the distribution of consecutive API queries and raises an alarm when this distribution deviates from benign behavior. We show that PRADA can detect all prior model extraction attacks with no false positives.


Proceedings ArticleDOI
07 Jul 2019
Abstract: IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present DIoT, an autonomous self-learning distributed system for detecting compromised IoT devices. DIoT builds effectively on device-type-specific communication profiles without human intervention nor labeled data that are subsequently used to detect anomalous deviations in devices' communication behavior, potentially caused by malicious adversaries. DIoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DIoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that DIoT is highly effective (95.6% detection rate) and fast (257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DIoT reported no false alarms when evaluated in a real-world smart home deployment setting.

Journal ArticleDOI
TL;DR: A systematic literature survey was conducted by searching for two concepts: light and circadian rhythm, and it was indicated that a two-hour exposure to blue light in the evening suppresses melatonin, the maximum melatonin-suppressing effect being achieved at the shortest wavelengths.
Abstract: Light is necessary for life, and artificial light improves visual performance and safety, but there is an increasing concern of the potential health and environmental impacts of light. Findings from a number of studies suggest that mistimed light exposure disrupts the circadian rhythm in humans, potentially causing further health impacts. However, a variety of methods has been applied in individual experimental studies of light-induced circadian impacts, including definition of light exposure and outcomes. Thus, a systematic review is needed to synthesize the results. In addition, a review of the scientific evidence on the impacts of light on circadian rhythm is needed for developing an evaluation method of light pollution, i.e., the negative impacts of artificial light, in life cycle assessment (LCA). The current LCA practice does not have a method to evaluate the light pollution, neither in terms of human health nor the ecological impacts. The systematic literature survey was conducted by searching for two concepts: light and circadian rhythm. The circadian rhythm was searched with additional terms of melatonin and rapid-eye-movement (REM) sleep. The literature search resulted to 128 articles which were subjected to a data collection and analysis. Melatonin secretion was studied in 122 articles and REM sleep in 13 articles. The reports on melatonin secretion were divided into studies with specific light exposure (101 reports), usually in a controlled laboratory environment, and studies of prevailing light conditions typical at home or work environments (21 studies). Studies were generally conducted on adults in their twenties or thirties, but only very few studies experimented on children and elderly adults. Surprisingly many studies were conducted with a small sample size: 39 out of 128 studies were conducted with 10 or less subjects. The quality criteria of studies for more profound synthesis were a minimum sample size of 20 subjects and providing details of the light exposure (spectrum or wavelength; illuminance, irradiance or photon density). This resulted to 13 qualified studies on melatonin and 2 studies on REM sleep. Further analysis of these 15 reports indicated that a two-hour exposure to blue light (460 nm) in the evening suppresses melatonin, the maximum melatonin-suppressing effect being achieved at the shortest wavelengths (424 nm, violet). The melatonin concentration recovered rather rapidly, within 15 min from cessation of the exposure, suggesting a short-term or simultaneous impact of light exposure on the melatonin secretion. Melatonin secretion and suppression were reduced with age, but the light-induced circadian phase advance was not impaired with age. Light exposure in the evening, at night and in the morning affected the circadian phase of melatonin levels. In addition, even the longest wavelengths (631 nm, red) and intermittent light exposures induced circadian resetting responses, and exposure to low light levels (5-10 lux) at night when sleeping with eyes closed induced a circadian response. The review enables further development of an evaluation method of light pollution in LCA regarding the light-induced impacts on human circadian system.