39,329 research outputs found
Numerical modeling of modified Newtonian dynamics in galaxies : testing the external field effects
No full textGalaxies are natural laboratories for testing fundamental physics on the nature of the dark matter. MOdified Newtonian Dynamics (MOND) has been tested for over 20 years on small and large scales. While there are several versions of how MOND extrapolates to the large scales, and these versions are not yet fully successful, the original Bekenstein-Milgrom version of MOND is fully predictive and works very well on galaxy scales. However, little work has been done to explore this theory beyond fitting the rotation curves and Tully-Fisher relation of isolated disc galaxies. So far little is known of MONDian elliptical galaxies accelerating in any galaxy cluster.
A defining feature of MOND is that internal dynamics of the galaxy depends on the overall acceleration of the galaxy. The existence of cuspy triaxial equilibria for elliptical galaxies is the minimal requirement to MOND. With the PhD project here, I constructed and then further studied the evolution and stability of gravitationally bound systems resembling like cuspy elliptical galaxies, both in isolation and when embedded in a uniform external field. I also studied the escape speeds from spiral galaxies, in particular by comparing the potentials of the Milky Way Galaxy in the Cold Dark Matter (CDM) and MOND frameworks
Compton-thick AGN in the NuSTAR Era. IV. A deep NuSTAR and XMM-Newton view of the candidate compton-thick AGN in ESO 116-G018
No full textWe present a 2-78 keV spectral analysis of the deep NuSTAR and XMM-Newton observation of a nearby Seyfert 2 galaxy, ESO 116-G018, which is selected as a candidate Compton-thick (CT) active galactic nucleus (AGN) based on a previous Chandra-Swift-BAT study. Through our analysis, the source is, for the first time, confirmed to be a CT AGN at a >3 sigma confidence level, with the "line-of-sight" column density N-H,N-Z = [2.46-2.76] x 10(24) cm(-2). The "global average" column density of the obscuring torus is N-H,N-S = [0.46-0.62] x 10(24) cm(-2), which suggests a clumpy, rather than uniform, distribution of the obscuring material surrounding the accreting supermassive black hole. The excellent-quality data given by the combined NuSTAR and XMM-Newton observations enable us to produce a strong constraint on the covering factor of the torus of ESO 116-G018, which is found to be f(c) = [0.13-0.15]. We also estimate the bolometric luminosity from the broadband X-ray spectrum to be L-bol = [2.57-3.41] x 10(44) erg s(-1)
Physics-informed neural network based topology optimization through continuous adjoint
No full textIn this paper, we introduce a Physics-Informed Neural Networks (PINNs)-based Topology optimization method that is free from the usual finite element analysis and is applicable for both self-adjoint and non-self-adjoint problems. This approach leverages the continuous formulation of TO along with the continuous adjoint method to obtain sensitivity. Within this approach, the Deep Energy Method (DEM)-a variant of PINN-completely supersedes traditional PDE solution procedures such as a finite-element method (FEM) based solution process. We demonstrate the efficacy of the DEM-based TO framework through three benchmark TO problems: the design of a conduction-based heat sink, a compliant displacement inverter, and a compliant gripper. The results indicate that the DEM-based TO can generate optimal designs comparable to those produced by traditional FEM-based TO methods. Notably, our DEM-based TO process does not rely on FEM discretization for either state solution or sensitivity analysis. During DEM training, we obtain spatial derivatives based on Automatic Differentiation (AD) and dynamic sampling of collocation points, as opposed to the interpolated spatial derivatives from finite element shape functions or a static collocation point set. We demonstrate that, for the DEM method, when using AD to obtain spatial derivatives, an integration point set of fixed positions causes the energy loss function to be not lower-bounded. However, using a dynamically changing integration point set can resolve this issue. Additionally, we explore the impact of incorporating Fourier Feature input embedding to enhance the accuracy of DEM-based state analysis within the TO context. The source codes related to this study are available in the GitHub repository: https://github.com/xzhao399/DEM_TO.git
Top-quark effects in diphoton production through gluon fusion at next-to-leading order in QCD
No full textAt hadron colliders, the leading production mechanism for a pair of photons is from quark-antiquark annihilation at the tree level. However, due to large gluon-gluon luminosity, the loop-induced process gg→γγ provides a substantial contribution. In particular, the amplitudes mediated by the top quark become important at the tt threshold and above. In this paper we present the first complete computation of the next-to-leading order (NLO) corrections (up to αS3) to this process, including contributions from the top quark. These entail two-loop diagrams with massive propagators whose analytic expressions are unknown and have been evaluated numerically. We find that the NLO corrections to the top-quark induced terms are very large at low diphoton invariant mass m(γγ) and close to the tt threshold. The full result including five massless quarks and top quark contributions at NLO displays a much more pronounced change of slope in the m(γγ) distribution at tt threshold than at LO and an enhancement at high invariant mass with respect to the massless calculation
Microbiome and metabolome analyses of milk and feces from dairy cows with healthy, subclinical, and clinical mastitis
No full textMastitis is commonly recognized as a localized inflammatory udder disease induced by the infiltration of exogenous pathogens. In the present study, our objective was to discern fecal and milk variations in both microbiota composition and metabolite profiles among three distinct groups of cows: healthy cows, cows with subclinical mastitis and cows with clinical mastitis. The fecal microbial community of cows with clinical mastitis was significantly less rich and diverse than the one harbored by healthy cows. In parallel, mastitis caused a strong disturbance in milk microbiota. Metabolomic profiles showed that eleven and twenty-eight molecules exhibited significant differences among the three groups in feces and milk, respectively. Similarly, to microbiota profile, milk metabolome was affected by mastitis more extensively than fecal metabolome, with particular reference to amino acids and sugars. Pathway analysis revealed that amino acids metabolism and energy metabolism could be considered as the main pathways altered by mastitis. These findings underscore the notable distinctions of fecal and milk samples among groups, from microbiome and metabolomic points of view. This observation stands to enhance our comprehension of mastitis in dairy cows
Impact of metalloporphyrin-based porous coordination polymers on catalytic activities for the oxidation of alkylbenzene
No full textSeven metalloporphyrin-based porous coordination polymers: Feш (TZP)Poly (CP1), CoII (TZP)Poly (CP2), NiII (TZP)Poly (CP3), CuII (TZP)Poly (CP4), ZnII (TZP)Poly (CP5), MnII (TZP)Poly (CP6), PbII (TZP)Poly (CP7) (TZP = 5,10,15,20- tetrakis[4-(2,3,4,5-tetrazolylphenyl)] porphyrin) were prepared and characterized. CP1−CP7 are amorphous aggregation supported with lower crystallinity by scanning electron microscopy, Brunauer−Emmett−Teller and powder X-ray diffraction. These coordination polymers exhibit effective dye scavenging and catalytic activities toward the oxidation of alkylbenzene to ketones and can be reused by filtration with a slight decreasing of catalytic activities. Metal atoms metalloporphyrin polymers have a great influence on the catalytic activities of metalloporphyrin polymers
Thermal decomposition of ammonium perchlorate based mixture with fullerenes
No full text© 2007 Akadémiai Kiadó, BudapestX. Han, Y. L. Sun, T. F. Wang, Zh. K. Lin, Sh. F. Li, F. Q. Zhao, Z. R. Liu, J. H. Yi and X. N. Re
Fysiologisten signaalien mittaus ja huijauksen tunnistus kasvovideosta
No full textAbstractHuman faces contain rich biometric and physiological clues. Thus, identity recognition and physiological state monitoring from face videos are feasible. On one hand, subtle color changes in the facial skin can reveal important information about the heart pulse of individuals, which works as the base for remote photoplethysmography (rPPG) signal measurement. Benefitting from computer vision technology, physiological signals can be reconstructed from face videos under laboratory-controlled conditions. On the other hand, face anti-spoofing (FAS) is vital for biometric security as face recognition systems are vulnerable to various presentation attacks.In the first part of this thesis, three end-to-end spatio-temporal methods are presented for reliable rPPG signals recovery. To exploit efficient contextual clues from both spatial and temporal perspectives, several handcrafted and automatically searched spatio-temporal networks are proposed. Moreover, negative Pearson-based temporal loss and cross-entropy-based frequency constraints as well as rPPG-related auxiliary supervision (e.g., skin segmentation) are proposed for accurate rPPG signal recovery.In the second part of this thesis, seven deep learning based FAS methods are presented to resolve the issue of intrinsic spoof representation, which is crucial to real-world deployment under unseen scenarios and attack types. On one side, novel convolutional operators as well as the networks are designed for generalized, lightweight, and multi-modal FAS. On the other side, several material-based pixel-wise supervision signals (e.g., depth and reflection) are proposed with an advanced pyramid supervision strategy.Finally, with the evidence that the spoofings like a face mask cannot reflect live heart pulses, a novel facial rPPG-based method using a vision transformer is proposed to extract discriminative periodic liveness clues for challenging 3D mask attack detection.Original papersOriginal papers are not included in the electronic version of the dissertation.Yu, Z., Li, X., & Zhao, G. (2019). Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks. Proceedings of the British Machine Vision Conference (BMVC), 29.1-29.12. https://bmvc2019.org/wp-content/papers/0186.htmlSelf-archived versionYu, Z., Peng, W., Li, X., Hong, X., & Zhao, G. (2019). Remote heart rate measurement from highly compressed facial videos: An end-to-end deep learning solution with video enhancement. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 151–160. https://doi.org/10.1109/ICCV.2019.00024Self-archived versionYu, Z., Li, X., Niu, X., Shi, J., & Zhao, G. (2020). AutoHR: A strong end-to-end baseline for remote heart rate measurement with neural searching. IEEE Signal Processing Letters, 27, 1245–1249. https://doi.org/10.1109/LSP.2020.3007086Self-archived versionYu, Z., Zhao, C., Wang, Z., Qin, Y., Su, Z., Li, X., Zhou, F., & Zhao, G. (2020). Searching central difference convolutional networks for face anti-spoofing. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5294–5304. https://doi.org/10.1109/CVPR42600.2020.00534Self-archived versionYu, Z., Qin, Y., Li, X., Wang, Z., Zhao, C., Lei, Z., & Zhao, G. (2020). Multi-modal face anti-spoofing based on central difference networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2766–2774. https://doi.org/10.1109/CVPRW50498.2020.00333Self-archived versionYu, Z., Qin, Y., Xu, X., Zhao, C., Wang, Z., Lei, Z., & Zhao, G. (2020). Auto-Fas: Searching lightweight networks for face anti-spoofing. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 996–1000. https://doi.org/10.1109/ICASSP40776.2020.9053587Self-archived versionYu, Z., Li, X., Niu, X., Shi, J., & Zhao, G. (2020). Face anti-spoofing with human material perception. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 (Vol. 12352, pp. 557–575). Springer International Publishing. https://doi.org/10.1007/978-3-030-58571-6_33Self-archived versionYu, Z., Li, X., Shi, J., Xia, Z., & Zhao, G. (2021). Revisiting pixel-wise supervision for face anti-spoofing. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(3), 285–295. https://doi.org/10.1109/TBIOM.2021.3065526Self-archived versionYu, Z., Qin, Y., Zhao, H., Li, X., & Zhao, G. (2021). Dual-cross central difference network for face anti-spoofing. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 1281–1287. https://doi.org/10.24963/ijcai.2021/177Self-archived versionYu, Z., Li, X., Wang, P., & Zhao, G. (2021). TransRPPG: Remote photoplethysmography transformer for 3d mask face presentation attack detection. IEEE Signal Processing Letters, 28, 1290–1294. https://doi.org/10.1109/LSP.2021.3089908Self-archived versionTiivistelmäIhmiskasvot sisältävät runsaasti biometrisiä ja fysiologisia vihjeitä, mikä mahdollistaa identiteetin tunnistamisen ja fysiologisen tilan seurannan kasvovideosta. Toisaalta kasvojen ihon hienovaraiset värimuutokset voivat paljastaa tärkeää tietoa yksilöiden sydämen sykkeestä, jonka perusteella voidaan mitata signaaleja etäfotopletysmografian (rPPG) keinoin. Tietokonenäön avulla fysiologiset signaalit voidaan rekonstruoida kasvovideoista laboratorio-olosuhteissa. Toisaalta kasvojen väärentämisen torjunta (face anti-spoofing, FAS) on oleellista biometrisen-turvallisuuden kannalta, koska kasvojentunnistusjärjestelmät ovat alttiita väärien kasvokuvien käytölle.Opinnäytetyön ensimmäisessä osassa esitellään kolme päästä päähän ajallis-paikallista mallia rPPG-signaalien luotettavaa palautumista varten. Sekä paikan että ajan pohjalta saatavien tehokkaiden kontekstuaalisten vihjeiden hyödyntämiseksi ehdotetaan useita käsin laadittuja ja automaattisesti haettuja ajallis-paikallisia verkkoja. Lisäksi rPPG-signaalien tarkkaa palautumista varten ehdotetaan Pearsonin korrelaatiokertoimeen perustuvia negatiivisen ajallisen häviön ja ristientropiaan perustuvia taajuuden rajoitteita sekä rPPG-lukemiin perustuvaa lisävalvontaa (esim. ihon segmentointi).Opinnäytetyön toisessa osassa esitellään seitsemän syväoppimiseen perustuvaa FAS-menetelmää, joilla ratkaistaan sisäisten väärennöksen piirteiden ongelma, mikä on ratkaisevan tärkeää todellisessa käyttöönotossa ennennäkemättömissä tilanteissa ja hyökkäystyypeissä. Toisaalta uudet konvolutionaaliset operaattorit ja verkot on suunniteltu yleistetyille, kevyille ja multimodaalisille FAS-järjestelmille. Toisaalta ehdotetaan useiden materiaalipohjaisten, pikselikohtaisten valvontasignaalien (esim. syvyys ja heijastuminen) käyttöä kehittyneellä pyramidivalvontastrategialla.Lopuksi, koska on näyttöä siitä, että kasvomaskin kaltaiset väärennökset eivät voi heijastaa sydämen sykettä, ehdotetaan uudenlaista kasvojen rPPG-pohjaista menetelmää, jossa käytetään vision transformer -tietokonenäköteknologiaa, jotta voidaan irrottaa erottelevia, jaksoittaisia elävyysvihjeitä haastavien 3D-maskien avulla tehtyjen huijausyritysten havaitsemiseksi.OsajulkaisutOsajulkaisut eivät sisälly väitöskirjan elektroniseen versioon.Yu, Z., Li, X., & Zhao, G. (2019). Remote photoplethysmograph signal measurement from facial videos using spatio-temporal networks. Proceedings of the British Machine Vision Conference (BMVC), 29.1-29.12. https://bmvc2019.org/wp-content/papers/0186.htmlRinnakkaistallennettu versioYu, Z., Peng, W., Li, X., Hong, X., & Zhao, G. (2019). Remote heart rate measurement from highly compressed facial videos: An end-to-end deep learning solution with video enhancement. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 151–160. https://doi.org/10.1109/ICCV.2019.00024Rinnakkaistallennettu versioYu, Z., Li, X., Niu, X., Shi, J., & Zhao, G. (2020). AutoHR: A strong end-to-end baseline for remote heart rate measurement with neural searching. IEEE Signal Processing Letters, 27, 1245–1249. https://doi.org/10.1109/LSP.2020.3007086Rinnakkaistallennettu versioYu, Z., Zhao, C., Wang, Z., Qin, Y., Su, Z., Li, X., Zhou, F., & Zhao, G. (2020). Searching central difference convolutional networks for face anti-spoofing. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5294–5304. https://doi.org/10.1109/CVPR42600.2020.00534Rinnakkaistallennettu versioYu, Z., Qin, Y., Li, X., Wang, Z., Zhao, C., Lei, Z., & Zhao, G. (2020). Multi-modal face anti-spoofing based on central difference networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2766–2774. https://doi.org/10.1109/CVPRW50498.2020.00333Rinnakkaistallennettu versioYu, Z., Qin, Y., Xu, X., Zhao, C., Wang, Z., Lei, Z., & Zhao, G. (2020). Auto-Fas: Searching lightweight networks for face anti-spoofing. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 996–1000. https://doi.org/10.1109/ICASSP40776.2020.9053587Rinnakkaistallennettu versioYu, Z., Li, X., Niu, X., Shi, J., & Zhao, G. (2020). Face anti-spoofing with human material perception. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 (Vol. 12352, pp. 557–575). Springer International Publishing. https://doi.org/10.1007/978-3-030-58571-6_33Rinnakkaistallennettu versioYu, Z., Li, X., Shi, J., Xia, Z., & Zhao, G. (2021). Revisiting pixel-wise supervision for face anti-spoofing. IEEE Transactions on Biometrics, Behavior, and Identity Science, 3(3), 285–295. https://doi.org/10.1109/TBIOM.2021.3065526Rinnakkaistallennettu versioYu, Z., Qin, Y., Zhao, H., Li, X., & Zhao, G. (2021). Dual-cross central difference network for face anti-spoofing. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 1281–1287. https://doi.org/10.24963/ijcai.2021/177Rinnakkaistallennettu versioYu, Z., Li, X., Wang, P., & Zhao, G. (2021). TransRPPG: Remote photoplethysmography transformer for 3d mask face presentation attack detection. IEEE Signal Processing Letters, 28, 1290–1294. https://doi.org/10.1109/LSP.2021.3089908Rinnakkaistallennettu versioAcademic dissertation to be presented with the assent of the Doctoral Training Committee of Information Technology and Electrical Engineering of the University of Oulu for public defence in the OP auditorium (L10), Linnanmaa, on 11 March 2022, at 10 a.m.Abstract
Human faces contain rich biometric and physiological clues. Thus, identity recognition and physiological state monitoring from face videos are feasible. On one hand, subtle color changes in the facial skin can reveal important information about the heart pulse of individuals, which works as the base for remote photoplethysmography (rPPG) signal measurement. Benefitting from computer vision technology, physiological signals can be reconstructed from face videos under laboratory-controlled conditions. On the other hand, face anti-spoofing (FAS) is vital for biometric security as face recognition systems are vulnerable to various presentation attacks.
In the first part of this thesis, three end-to-end spatio-temporal methods are presented for reliable rPPG signals recovery. To exploit efficient contextual clues from both spatial and temporal perspectives, several handcrafted and automatically searched spatio-temporal networks are proposed. Moreover, negative Pearson-based temporal loss and cross-entropy-based frequency constraints as well as rPPG-related auxiliary supervision (e.g., skin segmentation) are proposed for accurate rPPG signal recovery.
In the second part of this thesis, seven deep learning based FAS methods are presented to resolve the issue of intrinsic spoof representation, which is crucial to real-world deployment under unseen scenarios and attack types. On one side, novel convolutional operators as well as the networks are designed for generalized, lightweight, and multi-modal FAS. On the other side, several material-based pixel-wise supervision signals (e.g., depth and reflection) are proposed with an advanced pyramid supervision strategy.
Finally, with the evidence that the spoofings like a face mask cannot reflect live heart pulses, a novel facial rPPG-based method using a vision transformer is proposed to extract discriminative periodic liveness clues for challenging 3D mask attack detection.Tiivistelmä
Ihmiskasvot sisältävät runsaasti biometrisiä ja fysiologisia vihjeitä, mikä mahdollistaa identiteetin tunnistamisen ja fysiologisen tilan seurannan kasvovideosta. Toisaalta kasvojen ihon hienovaraiset värimuutokset voivat paljastaa tärkeää tietoa yksilöiden sydämen sykkeestä, jonka perusteella voidaan mitata signaaleja etäfotopletysmografian (rPPG) keinoin. Tietokonenäön avulla fysiologiset signaalit voidaan rekonstruoida kasvovideoista laboratorio-olosuhteissa. Toisaalta kasvojen väärentämisen torjunta (face anti-spoofing, FAS) on oleellista biometrisen-turvallisuuden kannalta, koska kasvojentunnistusjärjestelmät ovat alttiita väärien kasvokuvien käytölle.
Opinnäytetyön ensimmäisessä osassa esitellään kolme päästä päähän ajallis-paikallista mallia rPPG-signaalien luotettavaa palautumista varten. Sekä paikan että ajan pohjalta saatavien tehokkaiden kontekstuaalisten vihjeiden hyödyntämiseksi ehdotetaan useita käsin laadittuja ja automaattisesti haettuja ajallis-paikallisia verkkoja. Lisäksi rPPG-signaalien tarkkaa palautumista varten ehdotetaan Pearsonin korrelaatiokertoimeen perustuvia negatiivisen ajallisen häviön ja ristientropiaan perustuvia taajuuden rajoitteita sekä rPPG-lukemiin perustuvaa lisävalvontaa (esim. ihon segmentointi).
Opinnäytetyön toisessa osassa esitellään seitsemän syväoppimiseen perustuvaa FAS-menetelmää, joilla ratkaistaan sisäisten väärennöksen piirteiden ongelma, mikä on ratkaisevan tärkeää todellisessa käyttöönotossa ennennäkemättömissä tilanteissa ja hyökkäystyypeissä. Toisaalta uudet konvolutionaaliset operaattorit ja verkot on suunniteltu yleistetyille, kevyille ja multimodaalisille FAS-järjestelmille. Toisaalta ehdotetaan useiden materiaalipohjaisten, pikselikohtaisten valvontasignaalien (esim. syvyys ja heijastuminen) käyttöä kehittyneellä pyramidivalvontastrategialla.
Lopuksi, koska on näyttöä siitä, että kasvomaskin kaltaiset väärennökset eivät voi heijastaa sydämen sykettä, ehdotetaan uudenlaista kasvojen rPPG-pohjaista menetelmää, jossa käytetään vision transformer -tietokonenäköteknologiaa, jotta voidaan irrottaa erottelevia, jaksoittaisia elävyysvihjeitä haastavien 3D-maskien avulla tehtyjen huijausyritysten havaitsemiseksi
Consequences of drying on the hydro-mechanical response of fibrous peats upon compression
No full textPeats are encountered in waterlogged deltaic areas, where degradation is delayed by favourable environmental conditions. The recent increase in frequency and severity of droughts is expected to accelerate peat degradation, in turn increasing subsidence and flood risk, urging better understanding of the response of peats to drying events. To this aim, compression tests on natural and reconstituted peat samples were performed, supported by X-ray micro-computed tomography. The peat fabric
was found to be the key factor in the response to drying, with fibres playing the most significant role. Drying in peats starts affecting the macro-fabric, with an irreversible reduction in volume and disruption of the fibrous network occurring under saturated conditions until a threshold void ratio is reached, below which desaturation occurs of the intra-fibres and intra-peds pores. The first drying stage dramatically decreases the compressibility, while the hydraulic conductivity is hardly affected due to the enlargement of macropores. Secondary compressibility is affected by the peat fabric besides the organic content. The total organic content does not change substantially during drying; hence, it is not the best proxy to describe the consequences of drying on the response of fibrous peats. The fibre content can be better used to serve the aim
Skyrmion-skyrmion and skyrmion-edge repulsions in skyrmion-based racetrack memory
No full textMagnetic skyrmions are promising for building next-generation magnetic memories and spintronic devices due to their stability, small size and the extremely low currents needed to move them. In particular, skyrmion-based racetrack memory is attractive for information technology, where skyrmions are used to store information as data bits instead of traditional domain walls. Here we numerically demonstrate the impacts of skyrmion-skyrmion and skyrmion-edge repulsions on the feasibility of skyrmion-based racetrack memory. The reliable and practicable spacing between consecutive skyrmionic bits on the racetrack as well as the ability to adjust it are investigated. Clogging of skyrmionic bits is found at the end of the racetrack, leading to the reduction of skyrmion size. Further, we demonstrate an effective and simple method to avoid the clogging of skyrmionic bits, which ensures the elimination of skyrmionic bits beyond the reading element. Our results give guidance for the design and development of future skyrmion-based racetrack memory
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