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Similar to the total execution time, the lower the total inference time the better.\r\n\tFLOPS: the performance capability of a CPU or GPU can be measured by counting the number of floating operation points (FLO) it can execute per second. Thus, the higher the FLOPS, the better.\r\n\tComputing resources issues/errors: Ideally, a better-performed setup will not encounter any computing resources issues/errors including but not limited to the Out-Of-Memory (OOM) error.\r\n\tBottlenecking: to put it simply, bottlenecking is a subpar performance that is caused by the inability of one component to keep up with the others, thus slowing down the overall ability of a setup to process data. Here, our primary concern is the bottlenecking between CPU and GPU. The bottlenecking factor is measured using an online tool: Bottleneck Calculator\r\n\r\n\r\n 4. Methods\r\n\r\n\r\n\tTo evaluate the hardware performance, two deep learning models are deployed for benchmarking purpose. The first model is a modified VGG19 based on a study by Deitsch et al. (Model A) [1], and the other model is a modified concatenated model proposed in a study from Rahimzadeh et al. (Model B) [2]. These models were previously implemented in Vo et al [3]. The model compilation, training and validation practices are similar to those mentioned in Vo et al [3]. Besides, several optimization practices such as mixed precision policy are applied for model training to make it run faster and consume less memory. The following datasets are used for benchmarking: the original MNIST dataset by LeCun et al., and the Zalando MNIST dataset by Xiao et al.\r\n\tOn the other hand, we also proposed another approach for benchmarking that is much simpler and quicker: evaluating the total execution time for a combination of basic operations. These basic operations include General Matrix to Matrix Multiplication (GEMM), 2D-Convolution (Convolve2D) and Recurrent Neural Network (RNN), and exist in almost all deep neural networks today [4]. We implemented our alternative approach based on the DeepBench work by Baidu [5]:\r\n\t\r\n\t\tIn DMM, we defined matrix C as a product of (MxN) and (NxK) matrices. For example, (3072,128,1024) means the resulting matrix is a product of (3072x128) and (128x1024) matrices. To benchmark, we implemented five different multiplications and measured the overall total execution time of these five. These multiplications included (3072,128,1024), (5124,9124,2560), (2560,64,2560), (7860,64,2560), and (1760,128,1760).\r\n\t\tIn SMM, we defined matrix C as a product of (MxN) and (NxK) matrices, and (100 - Dx100)% of the (MxN) matrix is omitted. For instance, (10752,1,3584,0.9) means the resulting matrix is a product of (10752x1) and (1x3584) matrices, while 10% of the (10752x1) matrix is omitted. To benchmark, we implemented four different multiplications and measured the overall total execution time of these five. These multiplications included (10752,1,3584,0.9), (7680,1500,2560,0.95), (7680,2,2560,0.95), and (7680,1,2560,0.95).\r\n\t\tIn Convolve2D, we defined a simple model containing only convolution layers and pooling layers and measured the resulting total execution time. The dataset used for this training this model is the Zalando MNIST by Xiao et al.\r\n\t\tWe did not implement the RNN due to several issues caused by the new version of Keras.\r\n\t\r\n\t\r\n\tTo evaluate total inference time, we loaded the already trained weights from our models (denoted as Model A-benchmarked and Model B-benchmarked, respectively) which has the best validation accuracy and conducted a prediction run on the test set from the Zalando MNIST. These files are available on Zenodo: Inference Models\r\n\r\n\r\n5.  References\r\n\r\n\r\n\t[1] S. Deitsch, V. Christlein, S. Berger, C. Buerhop-Lutz, A. Maier, F. Gallwitz, and C. Riess, “Automatic classification of defective photovoltaic module cells in electroluminescence images,” Solar Energy, vol. 185, p. 455–468, 06-2019\r\n\t[2] M. Rahimzadeh and A. Attar, “A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2,” Informatics in MedicineUnlocked, vol. 19, p. 100360, 2020.\r\n\t[3] H. Vo, “Realization and Verification of Deep Learning Models for FaultDetection and Diagnosis of Photovoltaic Modules,” Master’s Thesis, Aalto University. School of Electrical Engineering, 2021.\r\n\t[4] P. Warden, \"Why GEMM is at the heart of deep learning,\" Pete Warden's Blog, 2015. Available at: https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/\r\n\t[5] Baidu Research, \"Benchmarking Deep Learning operations on different hardware\". Available at: https://github.com/baidu-research/DeepBench\r\n\t[6] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, \"Gradient-based learning applied to document recognition,\" Proceedings of the IEEE, 1998.\r\n\t[7] Xiao, K. Rasul, and R. Vollgraf, “A Novel Image Dataset for Benchmarking Machine Learning Algorithms,” 2017. https://github.com/zalandoresearch/fashion-mnist\r\n\t[8] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion,O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vander-plas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay,“Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.\r\n\t[9] F. Chollet, “Keras,” 2015. Available at: https://github.com/fchollet/keras\r\n\t[10] ML Commons. Available at: https://mlcommons.org/en/\r\n\t[11] W. Dai and D. Berleant, “Benchmarking contemporary deep learning hardware and frameworks: A survey of qualitative metrics,” 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), Dec 2019."},"field_of_science":[{"id":"fd80ad86-b461-4ffb-abbd-888bc2ffd653","url":"http://www.yso.fi/onto/okm-tieteenala/ta213","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Electronic, automation and communications engineering, electronics","fi":"Sähkö-, automaatio- ja tietoliikennetekniikka, elektroniikka","sv":"El-, automations- och telekommunikationsteknik, elektronik"}}],"infrastructure":[],"issued":"1970-01-01","keyword":[],"language":[],"metadata_owner":{"id":"829302c3-f5d4-4d20-9ec3-5148bcf385cc","organization":"aalto.fi","admin_organization":"aalto.fi"},"other_identifiers":[],"persistent_identifier":"doi:10.5281/zenodo.4905213","pid_generated_by_fairdata":false,"projects":[],"provenance":[],"relation":[],"remote_resources":[{"title":{"en":"Data in remote location","fi":"Aineisto ulkoisessa palvelussa","sv":"Material i en extern tjänst","und":"Data in remote location"},"use_category":{"id":"1ad1b146-d147-45db-a2fd-1096abc94984","url":"http://uri.suomi.fi/codelist/fairdata/use_category/code/source","in_scheme":"http://uri.suomi.fi/codelist/fairdata/use_category","pref_label":{"en":"Source material","fi":"Lähdeaineisto"}},"download_url":"https://zenodo.org/record/4905213"}],"spatial":[],"state":"published","temporal":[],"theme":[],"title":{"en":"Hardware Benchmark for Deep Learning Capability"},"created":"2025-12-04T07:56:41Z","modified":"2026-03-26T06:39:42Z","dataset_versions":[{"id":"a42b6ae9-a324-49cf-99ff-86128efd7075","title":{"en":"Hardware Benchmark for Deep Learning Capability"},"persistent_identifier":"doi:10.5281/zenodo.4905213","state":"published","created":"2025-12-04T07:56:41Z","version":1}],"published_revision":2,"version":1,"api_version":3,"metadata_repository":"Fairdata","record_created":"2025-12-04T07:56:42Z","record_modified":"2026-03-26T06:39:43Z"},{"id":"835d552d-669f-47fa-a729-14ec3927f32f","access_rights":{"id":"c7923b1d-08d1-4987-bfb6-9d5eaa9fa9cf","license":[{"id":"add72c17-4fca-4d00-a2c7-b849c424c42f","url":"http://uri.suomi.fi/codelist/fairdata/license/code/CC-BY-4.0","in_scheme":"http://uri.suomi.fi/codelist/fairdata/license","pref_label":{"en":"Creative Commons Attribution 4.0 International (CC BY 4.0)","fi":"Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)"}}],"access_type":{"id":"b41462f7-00bf-4e50-935b-2bf1184453a7","url":"http://uri.suomi.fi/codelist/fairdata/access_type/code/open","in_scheme":"http://uri.suomi.fi/codelist/fairdata/access_type","pref_label":{"en":"Open","fi":"Avoin"}},"restriction_grounds":[]},"actors":[{"id":"a316aa0b-2716-4026-9cfe-b18c8574633f","roles":["creator"],"person":{"id":"5b621dcd-58b4-45e6-a4dd-11f89fc49341","name":"Heikki Muhli","external_identifier":"https://orcid.org/"},"organization":{"id":"219e2b5e-a291-4738-b49a-2fb600c43894","pref_label":{"en":"Department of Applied Physics","fi":"Department of Applied Physics","sv":"Department of Applied Physics","und":"Department of Applied Physics"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10076-T304","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization","parent":{"id":"2f9f9011-4061-4a93-96af-768471abada7","pref_label":{"en":"Aalto University","fi":"Aalto-yliopisto","sv":"Aalto-universitetet","und":"Aalto-yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10076","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}}},{"id":"d57b7091-e051-442d-b8dd-e9b288c057d6","roles":["contributor"],"person":{"id":"6705b4fe-ac0c-4073-a263-990c4e11891d","name":"Miguel A. 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The potential can be used to model general carbon systems, but is particularly geared towards modeling systems containing C60 molecules and graphitic carbons. It has been fitted with QUIP/GAP [1,2] by recomputing the a-C database of Deringer and Csányi [3] at the PBE level of theory [4] using the VASP code [5,6]. The a-C database from [3] has been augmented significantly to include other atomic structures, namely:\r\n\r\n\r\n\tDimers at closer interatomic separation than in [3]\r\n\tTrimers\r\n\tGraphite and graphene (including bilayer graphene) throughout the entire exfoliation curve at different levels of  biaxial strain\r\n\tGlassy carbon\r\n\tIsolated strained and distorted C60 molecules, interacting C60 dimers and colliding C60 dimers\r\n\tDifferent surface reconstructions of diamond\r\n\r\n\r\nThe main technical novelty introduced in this potential is van der Waals (vdW) corrections at different levels. It incorporates vdW corrections at the Tkatchenko-Scheffler (TS) level of theory [7] via a new machine learning based local parametrization of dispersion interactions. Environment independent (\"fixed C6\") vdW corrections can also be used (mostly for banchmarking, since the new TS approach is superior). Check the comments in the gap_files/carbon.gap file for details.\r\n\r\nFor the underlying PBE fit, this potential uses 2-body (distance_2b) and 3-body (angle_3b) descriptors [3] plus SOAP-type descriptors (soap_turbo) [8,9], as implemented in the TurboGAP code [10]. For the Hirshfeld volume fit, the potential reuses the soap_turbo descriptors for computational efficiency. The files can be used both with QUIP/GAP (compiled with the TurboGAP libraries) and TurboGAP.\r\n\r\nThe authors acknowledge funding from the Academy of Finland (grants 321713, 329483 and 330488) and computational resources from the Finnish Center for Scientific Computing (CSC) and Aalto University's Science IT project.\r\n\r\nMore details can be found in this paper:\r\n\r\n\r\nMachine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of  C60\r\nHeikki Muhli, Xi Chen, Albert P. Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, and Miguel A. Caro\r\nPhys. Rev. B 104, 054106 (2021)\r\n\r\n\r\nReferences\r\n\r\n\r\n\tA.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).\r\n\tLibAtoms: https://libatoms.github.io\r\n\tV.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).\r\n\tJ.P. Perdew, K. Burke, and M. Ernzerhof. Phys Rev. Lett. 77, 3865 (1996).\r\n\tVASP: http://vasp.at\r\n\tG. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).\r\n\tA. Tkatchenko and M. Scheffler. Phys. Rev. Lett. 102, 073005 (2009).\r\n\tA.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).\r\n\tM.A. Caro. Phys. Rev. B 100, 024112 (2019).\r\n\tTurboGAP: http://turbogap.fi\r\n\r\n\r\nContact\r\n\r\nMiguel A. Caro: mcaroba@gmail.com or miguel.caro@aalto.fi"},"field_of_science":[{"id":"f8d5f9ab-1c7d-4c5c-8ed5-e9ce964d798d","url":"http://www.yso.fi/onto/okm-tieteenala/ta114","in_scheme":"http://www.yso.fi/onto/okm-tieteenala/conceptscheme","pref_label":{"en":"Physical sciences","fi":"Fysiikka","sv":"Fysik"}}],"infrastructure":[],"issued":"1970-01-01","keyword":[],"language":[],"metadata_owner":{"id":"829302c3-f5d4-4d20-9ec3-5148bcf385cc","organization":"aalto.fi","admin_organization":"aalto.fi"},"other_identifiers":[],"persistent_identifier":"doi:10.5281/zenodo.4616343","pid_generated_by_fairdata":false,"projects":[],"provenance":[],"relation":[],"remote_resources":[{"title":{"en":"Data in remote location","fi":"Aineisto ulkoisessa palvelussa","sv":"Material i en extern tjänst","und":"Data in remote location"},"use_category":{"id":"1ad1b146-d147-45db-a2fd-1096abc94984","url":"http://uri.suomi.fi/codelist/fairdata/use_category/code/source","in_scheme":"http://uri.suomi.fi/codelist/fairdata/use_category","pref_label":{"en":"Source material","fi":"Lähdeaineisto"}},"download_url":"https://zenodo.org/record/4616343"}],"spatial":[],"state":"published","temporal":[],"theme":[],"title":{"en":"GAP interatomic potential for C60"},"created":"2025-12-04T07:56:39Z","modified":"2026-03-26T06:39:40Z","dataset_versions":[{"id":"835d552d-669f-47fa-a729-14ec3927f32f","title":{"en":"GAP interatomic potential for C60"},"persistent_identifier":"doi:10.5281/zenodo.4616343","state":"published","created":"2025-12-04T07:56:39Z","version":1}],"published_revision":2,"version":1,"api_version":3,"metadata_repository":"Fairdata","record_created":"2025-12-04T07:56:40Z","record_modified":"2026-03-26T06:39:41Z"},{"id":"5b0b1bb9-2215-4f90-a279-cf8dfd4cdddb","access_rights":{"id":"2d4ddba9-1192-4ba6-95c1-d6c8395bf830","license":[{"id":"add72c17-4fca-4d00-a2c7-b849c424c42f","url":"http://uri.suomi.fi/codelist/fairdata/license/code/CC-BY-4.0","in_scheme":"http://uri.suomi.fi/codelist/fairdata/license","pref_label":{"en":"Creative Commons Attribution 4.0 International (CC BY 4.0)","fi":"Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)"}}],"access_type":{"id":"b41462f7-00bf-4e50-935b-2bf1184453a7","url":"http://uri.suomi.fi/codelist/fairdata/access_type/code/open","in_scheme":"http://uri.suomi.fi/codelist/fairdata/access_type","pref_label":{"en":"Open","fi":"Avoin"}},"restriction_grounds":[]},"actors":[{"id":"44a37b56-d9fc-442f-8e8a-38bd436d6314","roles":["creator"],"person":{"id":"e43e198a-ef48-4ab4-b848-cb88471e992b","name":"Pauli Lehto","external_identifier":"https://orcid.org/0000-0002-4689-6063"},"organization":{"id":"e0554b6f-5e44-49c9-a76c-24f54c9f3bd4","pref_label":{"en":"Department of Energy and Mechanical Engineering","fi":"Department of Energy and Mechanical Engineering","sv":"Department of Energy and Mechanical Engineering","und":"Department of Energy and Mechanical Engineering"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10076-T212","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization","parent":{"id":"2f9f9011-4061-4a93-96af-768471abada7","pref_label":{"en":"Aalto University","fi":"Aalto-yliopisto","sv":"Aalto-universitetet","und":"Aalto-yliopisto"},"url":"http://uri.suomi.fi/codelist/fairdata/organization/code/10076","in_scheme":"http://uri.suomi.fi/codelist/fairdata/organization"}}},{"id":"b783143b-a9cc-45ef-8e1e-f64bd5b7c3d9","roles":["publisher"],"organization":{"id":"a407f1b5-e897-4929-acae-b66f2fb7e485","pref_label":{"en":"Zenodo","fi":"Zenodo","sv":"Zenodo"}}}],"cumulative_state":0,"data_catalog":"urn:nbn:fi:att:data-catalog-acris","description":{"en":"Example open access datasets for low-alloy structural steel weld metals, previously published in ref. [1]. The datasets are also related to the International Institute of Welding Commission C-IX, document nr. IX-L-1239-2021.\r\n\r\nFiles included:\r\n\r\n\r\n\tThe nomenclature of the files corresponds to what is used in ref. 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In cooperative TF-TF binding, two transcription factors bind onto a shared DNA binding site as a pair. Previous work has demonstrated pairwise TF-TF-DNA interactions with position weight matrices (PWMs), which may however not sufficiently take into account the complexity and flexibility of pairwise binding. Results We propose two random forest (RF) methods for joint TF-TF binding site prediction: ComBind and JointRF. We train models with previously published large-scale CAP-SELEX DNA libraries, which comprise DNA sequences enriched for binding of a selected TF pair. JointRF builds a random forest with sub-sequences selected from CAP-SELEX DNA reads with previously proposed pairwise PWM. JointRF outperforms (area under receiver operating characteristics curve, AUROC, 0.75) the current state-of-the-art method i.e. orientation and spacing specific pairwise PWMs (AUROC 0.59). Thus, JointRF may be utilized to improve prediction accuracy for pre-determined binding preferences. However, pairwise TF binding is currently considered flexible; a pair may bind DNA with different orientations and amounts of dinucleotide gaps or overlap between the two motifs. Thus, we developed ComBind, which utilizes random forests by considering simultaneously multiple orientations and spacings of the two factors. Our approach outperforms (AUROC 0.78) PWMs, as well as JointRF (p<0.00195). ComBind provides an approach for predicting TF-TF binding sites without prior knowledge on pairwise binding preferences. However, more research is needed to assess ComBind eligibility for practical applications. 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Opetus- ja kulttuuriministeriön osatukemassa hankkeessa kartoitettiin sopivinta mallintamistekniikkaa museon kokoelmissa oleville tanssipuvuille. Osana hanketta Aalto-yliopiston Rakennetun ympäristön mittauksen ja mallintamisen instituutti MeMo:ssa testattiin puvun mallintamista fotogrammetrialla, strukturoidun valon skannerilla sekä laserkeilaimella. Testausten perusteella arvioitiin, että fotogrammetria oli lupaavin metodi pukujen mallintamiseen. \r\nHankkeen toisessa vaiheessa museo osti mallintamispalvelua kaupalliselta toimijalta, ja yhteensä 15 pukua mallinnettiin fotogrammetrialla.\r\n\r\nTeatterimuseon hankeraportin voi ladata luettavaksi osoitteesta\r\nhttps://www.teatterimuseo.fi/fi/3D-malleja-Teatterimuseon-tanssipuvuista\r\n\r\nTeatterimuseon Sketchfab-tili https://sketchfab.com/Theatre_Museum .\r\n\r\nAineistossa esiintyvän puvun tiedot / Information on the modeled costume:\r\n\r\nBalettipuku, tutu / Ballet costume, tutu.\r\nTeos / Title: Prinsessa Ruusunen / Sleeping Beauty\r\nTuotanto / Production: Suomen kansallisooppera (nyk. Suomen kansallisooppera ja -baletti) / Finnish National Opera (now the Finnish National Opera and Ballet) 1999\r\nKoreografi / Choreographer: Rudolf Nurejev (Marius Petipan mukaan / According to Marius Petipa)\r\nSäveltäjä / Composer: Pjotr Tšaikovski / Pyotr Ilyich Tchaikovsky\r\nPukusuunnittelu / Costume design: Erika Turunen\r\nTanssijat / Dancers: Alexandra Campbell ja/and Asta Lindholm\r\nRooli / Role: Ruususen ystävätär / Aurora’s friend\r\n\r\nPrinsessa Ruusunen -baletin ensi-ilta oli 26.3.1999. Se oli yksi pukusuunnittelija Erika Turusen varhaisimmista oopperalle suunnittelemista teoksista. Tämän jälkeen hän on toiminut Suomen kansallisoopperan ja -baletin päätoimisena pukusuunnittelijana jo yli 20 vuotta. Hän on suunnitellut pukuja myös nykytanssiteoksiin. (https://www.erikaturunen.com)\r\nMallinnettu tutu on ollut myös Suomen Kansallisoopperan balettioppilaitoksen oppilaiden käytössä. Alun perin pukuun on kuulunut myös kullanvärinen, helmikoristeinen päähine. 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