@inproceedings{17378, abstract = {Generative Pre-trained Transformer models, known as GPT or OPT, set themselves apart through breakthrough performance across complex language modelling tasks, but also by their extremely high computational and storage costs. Specifically, due to their massive size, even inference for large, highly-accurate GPT models may require multiple performant GPUs, which limits the usability of such models. While there is emerging work on relieving this pressure via model compression, the applicability and performance of existing compression techniques is limited by the scale and complexity of GPT models. In this paper, we address this challenge, and propose OPTQ, a new one-shot weight quantization method based on approximate second-order information, that is both highly-accurate and highly-efficient. Specifically, OPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits per weight, with negligible accuracy degradation relative to the uncompressed baseline. Our method more than doubles the compression gains relative to previously-proposed one-shot quantization methods, preserving accuracy, allowing us for the first time to execute an 175 billion-parameter model inside a single GPU for generative inference. Moreover, we also show that our method can still provide reasonable accuracy in the extreme quantization regime, in which weights are quantized to 2-bit or even ternary quantization levels. We show experimentally that these improvements can be leveraged for end-to-end inference speedups over FP16, of around 3.25x when using high-end GPUs (NVIDIA A100) and 4.5x when using more cost-effective ones (NVIDIA A6000). The implementation is available at https://github.com/IST-DASLab/gptq.}, author = {Frantar, Elias and Ashkboos, Saleh and Hoefler, Torsten and Alistarh, Dan-Adrian}, booktitle = {11th International Conference on Learning Representations }, location = {Kigali, Rwanda}, publisher = {International Conference on Learning Representations}, title = {{OPTQ: Accurate post-training quantization for generative pre-trained transformers}}, year = {2023}, } @inproceedings{14458, abstract = {We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time. SparseGPT generalizes to semi-structured (2:4 and 4:8) patterns, and is compatible with weight quantization approaches. The code is available at: https://github.com/IST-DASLab/sparsegpt.}, author = {Frantar, Elias and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, issn = {2640-3498}, location = {Honolulu, Hawaii, HI, United States}, pages = {10323--10337}, publisher = {ML Research Press}, title = {{SparseGPT: Massive language models can be accurately pruned in one-shot}}, volume = {202}, year = {2023}, } @misc{14619, abstract = {Data underlying the publication "A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals" (DOI https://doi.org/10.1063/5.0173341).}, author = {Cheng, Bingqing}, publisher = {Zenodo}, title = {{BingqingCheng/solubility: V1.0}}, doi = {10.5281/ZENODO.8398094}, year = {2023}, } @article{12079, abstract = {We extend the recent rigorous convergence result of Abels and Moser (SIAM J Math Anal 54(1):114–172, 2022. https://doi.org/10.1137/21M1424925) concerning convergence rates for solutions of the Allen–Cahn equation with a nonlinear Robin boundary condition towards evolution by mean curvature flow with constant contact angle. More precisely, in the present work we manage to remove the perturbative assumption on the contact angle being close to 90∘. We establish under usual double-well type assumptions on the potential and for a certain class of boundary energy densities the sub-optimal convergence rate of order ε12 for general contact angles α∈(0,π). For a very specific form of the boundary energy density, we even obtain from our methods a sharp convergence rate of order ε; again for general contact angles α∈(0,π). Our proof deviates from the popular strategy based on rigorous asymptotic expansions and stability estimates for the linearized Allen–Cahn operator. Instead, we follow the recent approach by Fischer et al. (SIAM J Math Anal 52(6):6222–6233, 2020. https://doi.org/10.1137/20M1322182), thus relying on a relative entropy technique. We develop a careful adaptation of their approach in order to encode the constant contact angle condition. In fact, we perform this task at the level of the notion of gradient flow calibrations. This concept was recently introduced in the context of weak-strong uniqueness for multiphase mean curvature flow by Fischer et al. (arXiv:2003.05478v2).}, author = {Hensel, Sebastian and Moser, Maximilian}, issn = {1432-0835}, journal = {Calculus of Variations and Partial Differential Equations}, number = {6}, publisher = {Springer Nature}, title = {{Convergence rates for the Allen–Cahn equation with boundary contact energy: The non-perturbative regime}}, doi = {10.1007/s00526-022-02307-3}, volume = {61}, year = {2022}, } @article{12179, abstract = {We derive an accurate lower tail estimate on the lowest singular value σ1(X−z) of a real Gaussian (Ginibre) random matrix X shifted by a complex parameter z. Such shift effectively changes the upper tail behavior of the condition number κ(X−z) from the slower (κ(X−z)≥t)≲1/t decay typical for real Ginibre matrices to the faster 1/t2 decay seen for complex Ginibre matrices as long as z is away from the real axis. This sharpens and resolves a recent conjecture in [J. Banks et al., https://arxiv.org/abs/2005.08930, 2020] on the regularizing effect of the real Ginibre ensemble with a genuinely complex shift. As a consequence we obtain an improved upper bound on the eigenvalue condition numbers (known also as the eigenvector overlaps) for real Ginibre matrices. The main technical tool is a rigorous supersymmetric analysis from our earlier work [Probab. Math. Phys., 1 (2020), pp. 101--146].}, author = {Cipolloni, Giorgio and Erdös, László and Schröder, Dominik J}, issn = {1095-7162}, journal = {SIAM Journal on Matrix Analysis and Applications}, keywords = {Analysis}, number = {3}, pages = {1469--1487}, publisher = {Society for Industrial and Applied Mathematics}, title = {{On the condition number of the shifted real Ginibre ensemble}}, doi = {10.1137/21m1424408}, volume = {43}, year = {2022}, } @inproceedings{12302, abstract = {We propose a novel algorithm to decide the language inclusion between (nondeterministic) Büchi automata, a PSPACE-complete problem. Our approach, like others before, leverage a notion of quasiorder to prune the search for a counterexample by discarding candidates which are subsumed by others for the quasiorder. Discarded candidates are guaranteed to not compromise the completeness of the algorithm. The novelty of our work lies in the quasiorder used to discard candidates. We introduce FORQs (family of right quasiorders) that we obtain by adapting the notion of family of right congruences put forward by Maler and Staiger in 1993. We define a FORQ-based inclusion algorithm which we prove correct and instantiate it for a specific FORQ, called the structural FORQ, induced by the Büchi automaton to the right of the inclusion sign. The resulting implementation, called FORKLIFT, scales up better than the state-of-the-art on a variety of benchmarks including benchmarks from program verification and theorem proving for word combinatorics. Artifact: https://doi.org/10.5281/zenodo.6552870}, author = {Doveri, Kyveli and Ganty, Pierre and Mazzocchi, Nicolas Adrien}, booktitle = {Computer Aided Verification}, isbn = {9783031131875}, issn = {1611-3349}, location = {Haifa, Israel}, pages = {109--129}, publisher = {Springer Nature}, title = {{FORQ-based language inclusion formal testing}}, doi = {10.1007/978-3-031-13188-2_6}, volume = {13372}, year = {2022}, } @article{12495, abstract = {Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of machine learning with far-reaching societal impact. However, existing fair learning methods are vulnerable to accidental or malicious artifacts in the training data, which can cause them to unknowingly produce unfair classifiers. In this work we address the problem of fair learning from unreliable training data in the robust multisource setting, where the available training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm that identifies and suppresses those data sources that would have a negative impact on fairness or accuracy if they were used for training. As such, FLEA is not a replacement of prior fairness-aware learning methods but rather an augmentation that makes any of them robust against unreliable training data. We show the effectiveness of our approach by a diverse range of experiments on multiple datasets. Additionally, we prove formally that –given enough data– FLEA protects the learner against corruptions as long as the fraction of affected data sources is less than half. Our source code and documentation are available at https://github.com/ISTAustria-CVML/FLEA.}, author = {Iofinova, Eugenia B and Konstantinov, Nikola H and Lampert, Christoph}, issn = {2835-8856}, journal = {Transactions on Machine Learning Research}, publisher = {ML Research Press}, title = {{FLEA: Provably robust fair multisource learning from unreliable training data}}, year = {2022}, } @inproceedings{17088, abstract = {In this paper, we consider the problem of sparsifying BERT models, which are a key building block for natural language processing, in order to reduce their storage and computational cost. We introduce the Optimal BERT Surgeon (oBERT), an efficient and accurate pruning method based on approximate second-order information, which we show to yield state-of-the-art results in both stages of language tasks: pre-training and fine-tuning. Specifically, oBERT extends existing work on second-order pruning by allowing for pruning weight blocks, and is the first such method that is applicable at BERT scale. Second, we investigate compounding compression approaches to obtain highly compressed but accurate models for deployment on edge devices. These models significantly push boundaries of the current state-of-the-art sparse BERT models with respect to all metrics: model size, inference speed and task accuracy. For example, relative to the dense BERT-base, we obtain 10x model size compression with < 1% accuracy drop, 10x CPU-inference speedup with < 2% accuracy drop, and 29x CPU-inference speedup with < 7.5% accuracy drop. Our code, fully integrated with Transformers and SparseML, is available at https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT.}, author = {Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, location = {Abu Dhabi, United Arab Emirates}, pages = {4163--4181}, publisher = {Association for Computational Linguistics}, title = {{The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models}}, doi = {10.18653/v1/2022.emnlp-main.279}, year = {2022}, } @article{10769, abstract = {studiamos aspectos de Teoría Cuántica de Campos a densidad finita usando técnicas y conceptos de información cuántica. Nos enfocamos en fermiones de Dirac masivos con potencial químico en 1+1 dimensiones espacio-temporales. Usando la entropía de entrelazamiento en un intervalo, construimos la función c entrópica que es finita. Esta función c no es monótona, e incorpora el entrelazamiento de largo alcance proveniente de la superficie de Fermi. Motivados por trabajos previos de modelos en la red, calculamos numéricamente las entropías de Renyi y encontramos oscilaciones de Friedel. Seguidamente, analizamos la información mutua como una medida de correlación entre diferentes regiones. Usando una expansión de distancia grande desarrollada por Cardy, argumentamos que la información mutua detecta las correlaciones inducidas por la superficie de Fermi todavía al orden dominante en la expansión. Finalmente, analizamos la entropía relativa y sus generalizaciones de Renyi para distinguir estados con diferente carga. Encontramos que estados en diferentes sectores de superselección dan origen a un comportamiento super-extensivo en la entropía relativa.}, author = {Daguerre, L. and Torroba, G. and Medina Ramos, Raimel A and Solís, M.}, issn = {1850-1168}, journal = {Anales de la Asociacion Fisica Argentina}, number = {4}, pages = {93--98}, publisher = {Asociación Física Argentina}, title = {{Non relativistic quantum field theory: Dynamics and irreversibility}}, doi = {10.31527/analesafa.2021.32.4.93}, volume = {32}, year = {2022}, } @article{10797, abstract = {We consider symmetric partial exclusion and inclusion processes in a general graph in contact with reservoirs, where we allow both for edge disorder and well-chosen site disorder. We extend the classical dualities to this context and then we derive new orthogonal polynomial dualities. From the classical dualities, we derive the uniqueness of the non-equilibrium steady state and obtain correlation inequalities. Starting from the orthogonal polynomial dualities, we show universal properties of n-point correlation functions in the non-equilibrium steady state for systems with at most two different reservoir parameters, such as a chain with reservoirs at left and right ends.}, author = {Floreani, Simone and Redig, Frank and Sau, Federico}, issn = {0246-0203}, journal = {Annales de l'institut Henri Poincare (B) Probability and Statistics}, number = {1}, pages = {220--247}, publisher = {Institute of Mathematical Statistics}, title = {{Orthogonal polynomial duality of boundary driven particle systems and non-equilibrium correlations}}, doi = {10.1214/21-AIHP1163}, volume = {58}, year = {2022}, } @inproceedings{18229, abstract = {We present Self-Classifier – a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions (i.e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels. In our theoretical analysis, we prove that degenerate solutions are not in the set of optimal solutions of our approach. Self-Classifier is simple to implement and scalable. Unlike other popular unsupervised classification and contrastive representation learning approaches, it does not require any form of pre-training, expectation-maximization, pseudo-labeling, external clustering, a second network, stop-gradient operation, or negative pairs. Despite its simplicity, our approach sets a new state of the art for unsupervised classification of ImageNet; and even achieves comparable to state-of-the-art results for unsupervised representation learning. Code is available at https://github.com/elad-amrani/self-classifier.}, author = {Amrani, Elad and Karlinsky, Leonid and Bronstein, Alexander}, booktitle = {17th European Conference on Computer Vision}, isbn = {9783031198205}, issn = {1611-3349}, location = {Tel Aviv, Israel}, pages = {116--132}, publisher = {Springer Nature}, title = {{Self-supervised classification network}}, doi = {10.1007/978-3-031-19821-2_7}, volume = {13691}, year = {2022}, } @inproceedings{18231, abstract = {The success of learning with noisy labels (LNL) methods relies heavily on the success of a warm-up stage where standard supervised training is performed using the full (noisy) training set. In this paper, we identify a "warm-up obstacle": the inability of standard warm-up stages to train high quality feature extractors and avert memorization of noisy labels. We propose "Contrast to Divide" (C2D), a simple framework that solves this problem by pre-training the feature extractor in a self-supervised fashion. Using self-supervised pre-training boosts the performance of existing LNL approaches by drastically reducing the warm-up stage's susceptibility to noise level, shortening its duration, and improving extracted feature quality. C2D works out of the box with existing methods and demonstrates markedly improved performance, especially in the high noise regime, where we get a boost of more than 27% for CIFAR-100 with 90% noise over the previous state of the art. In real-life noise settings, C2D trained on mini-WebVision outperforms previous works both in WebVision and ImageNet validation sets by 3% top-1 accuracy. We perform an in-depth analysis of the framework, including investigating the performance of different pre-training approaches and estimating the effective upper bound of the LNL performance with semi-supervised learning. Code for reproducing our experiments is available at https://github.com/ContrastToDivide/C2D.}, author = {Zheltonozhskii, Evgenii and Baskin, Chaim and Mendelson, Avi and Bronstein, Alexander and Litany, Or}, booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision}, location = {Waikoloa, HI, United States}, pages = {387--397}, publisher = {Institute of Electrical and Electronics Engineers}, title = {{Contrast to divide: Self-supervised pre-training for learning with noisy labels}}, doi = {10.1109/wacv51458.2022.00046}, year = {2022}, } @inproceedings{11839, abstract = {It is a highly desirable property for deep networks to be robust against small input changes. One popular way to achieve this property is by designing networks with a small Lipschitz constant. In this work, we propose a new technique for constructing such Lipschitz networks that has a number of desirable properties: it can be applied to any linear network layer (fully-connected or convolutional), it provides formal guarantees on the Lipschitz constant, it is easy to implement and efficient to run, and it can be combined with any training objective and optimization method. In fact, our technique is the first one in the literature that achieves all of these properties simultaneously. Our main contribution is a rescaling-based weight matrix parametrization that guarantees each network layer to have a Lipschitz constant of at most 1 and results in the learned weight matrices to be close to orthogonal. Hence we call such layers almost-orthogonal Lipschitz (AOL). Experiments and ablation studies in the context of image classification with certified robust accuracy confirm that AOL layers achieve results that are on par with most existing methods. Yet, they are simpler to implement and more broadly applicable, because they do not require computationally expensive matrix orthogonalization or inversion steps as part of the network architecture. We provide code at https://github.com/berndprach/AOL.}, author = {Prach, Bernd and Lampert, Christoph}, booktitle = {Computer Vision – ECCV 2022}, isbn = {9783031198021}, location = {Tel Aviv, Israel}, pages = {350--365}, publisher = {Springer Nature}, title = {{Almost-orthogonal layers for efficient general-purpose Lipschitz networks}}, doi = {10.1007/978-3-031-19803-8_21}, volume = {13681}, year = {2022}, } @inproceedings{12299, abstract = {Transfer learning is a classic paradigm by which models pretrained on large “upstream” datasets are adapted to yield good results on “downstream” specialized datasets. Generally, more accurate models on the “upstream” dataset tend to provide better transfer accuracy “downstream”. In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have been pruned-that is, compressed by sparsifiying their connections. We consider transfer using unstructured pruned models obtained by applying several state-of-the-art pruning methods, including magnitude-based, second-order, regrowth, lottery-ticket, and regularization approaches, in the context of twelve standard transfer tasks. In a nutshell, our study shows that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities, and, while doing so, can lead to significant inference and even training speedups. At the same time, we observe and analyze significant differences in the behaviour of different pruning methods. The code is available at: https://github.com/IST-DASLab/sparse-imagenet-transfer.}, author = {Iofinova, Eugenia B and Peste, Elena-Alexandra and Kurtz, Mark and Alistarh, Dan-Adrian}, booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, issn = {2575-7075}, location = {New Orleans, LA, United States}, pages = {12256--12266}, publisher = {IEEE}, title = {{How well do sparse ImageNet models transfer?}}, doi = {10.1109/cvpr52688.2022.01195}, year = {2022}, } @inproceedings{11458, abstract = {The increasing computational requirements of deep neural networks (DNNs) have led to significant interest in obtaining DNN models that are sparse, yet accurate. Recent work has investigated the even harder case of sparse training, where the DNN weights are, for as much as possible, already sparse to reduce computational costs during training. Existing sparse training methods are often empirical and can have lower accuracy relative to the dense baseline. In this paper, we present a general approach called Alternating Compressed/DeCompressed (AC/DC) training of DNNs, demonstrate convergence for a variant of the algorithm, and show that AC/DC outperforms existing sparse training methods in accuracy at similar computational budgets; at high sparsity levels, AC/DC even outperforms existing methods that rely on accurate pre-trained dense models. An important property of AC/DC is that it allows co-training of dense and sparse models, yielding accurate sparse–dense model pairs at the end of the training process. This is useful in practice, where compressed variants may be desirable for deployment in resource-constrained settings without re-doing the entire training flow, and also provides us with insights into the accuracy gap between dense and compressed models. The code is available at: https://github.com/IST-DASLab/ACDC.}, author = {Peste, Elena-Alexandra and Iofinova, Eugenia B and Vladu, Adrian and Alistarh, Dan-Adrian}, booktitle = {35th Conference on Neural Information Processing Systems}, isbn = {9781713845393}, issn = {1049-5258}, location = {Virtual, Online}, pages = {8557--8570}, publisher = {Neural Information Processing Systems Foundation}, title = {{AC/DC: Alternating Compressed/DeCompressed training of deep neural networks}}, volume = {34}, year = {2021}, } @article{11604, abstract = {The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from ∼27 days to about 1 yr of continuous observations. This vast amount of data contains a wealth of information for variability, exoplanet, and stellar astrophysics studies but requires a number of processing steps before it can be fully utilized. In order to efficiently process all the TESS data and make it available to the wider scientific community, the TESS Data for Asteroseismology working group, as part of the TESS Asteroseismic Science Consortium, has created an automated open-source processing pipeline to produce light curves corrected for systematics from the short- and long-cadence raw photometry data and to classify these according to stellar variability type. We will process all stars down to a TESS magnitude of 15. This paper is the next in a series detailing how the pipeline works. Here, we present our methodology for the automatic variability classification of TESS photometry using an ensemble of supervised learners that are combined into a metaclassifier. We successfully validate our method using a carefully constructed labeled sample of Kepler Q9 light curves with a 27.4 days time span mimicking single-sector TESS observations, on which we obtain an overall accuracy of 94.9%. We demonstrate that our methodology can successfully classify stars outside of our labeled sample by applying it to all ∼167,000 stars observed in Q9 of the Kepler space mission.}, author = {Audenaert, J. and Kuszlewicz, J. S. and Handberg, R. and Tkachenko, A. and Armstrong, D. J. and Hon, M. and Kgoadi, R. and Lund, M. N. and Bell, K. J. and Bugnet, Lisa Annabelle and Bowman, D. M. and Johnston, C. and García, R. A. and Stello, D. and Molnár, L. and Plachy, E. and Buzasi, D. and Aerts, C.}, issn = {1538-3881}, journal = {The Astronomical Journal}, keywords = {Space and Planetary Science, Astronomy and Astrophysics}, number = {5}, publisher = {IOP Publishing}, title = {{TESS Data for Asteroseismology (T’DA) stellar variability classification pipeline: Setup and application to the Kepler Q9 data}}, doi = {10.3847/1538-3881/ac166a}, volume = {162}, year = {2021}, } @article{10545, abstract = {Classical models with complex energy landscapes represent a perspective avenue for the near-term application of quantum simulators. Until now, many theoretical works studied the performance of quantum algorithms for models with a unique ground state. However, when the classical problem is in a so-called clustering phase, the ground state manifold is highly degenerate. As an example, we consider a 3-XORSAT model defined on simple hypergraphs. The degeneracy of classical ground state manifold translates into the emergence of an extensive number of Z2 symmetries, which remain intact even in the presence of a quantum transverse magnetic field. We establish a general duality approach that restricts the quantum problem to a given sector of conserved Z2 charges and use it to study how the outcome of the quantum adiabatic algorithm depends on the hypergraph geometry. We show that the tree hypergraph which corresponds to a classically solvable instance of the 3-XORSAT problem features a constant gap, whereas the closed hypergraph encounters a second-order phase transition with a gap vanishing as a power-law in the problem size. The duality developed in this work provides a practical tool for studies of quantum models with classically degenerate energy manifold and reveals potential connections between glasses and gauge theories.}, author = {Medina Ramos, Raimel A and Serbyn, Maksym}, issn = {2469-9934}, journal = {Physical Review A}, number = {6}, publisher = {American Physical Society}, title = {{Duality approach to quantum annealing of the 3-variable exclusive-or satisfiability problem (3-XORSAT)}}, doi = {10.1103/physreva.104.062423}, volume = {104}, year = {2021}, } @misc{9291, abstract = {This .zip File contains the transport data for figures presented in the main text and supplementary material of "Enhancement of Proximity Induced Superconductivity in Planar Germanium" by K. Aggarwal, et. al. The measurements were done using Labber Software and the data is stored in the hdf5 file format. The files can be opened using either the Labber Log Browser (https://labber.org/overview/) or Labber Python API (http://labber.org/online-doc/api/LogFile.html).}, author = {Katsaros, Georgios}, publisher = {Institute of Science and Technology Austria}, title = {{Raw transport data for: Enhancement of proximity induced superconductivity in planar germanium}}, doi = {10.15479/AT:ISTA:9291}, year = {2021}, } @misc{9323, abstract = {This .zip File contains the data for figures presented in the main text and supplementary material of "A singlet triplet hole spin qubit in planar Ge" by D. Jirovec, et. al. The measurements were done using Labber Software and the data is stored in the hdf5 file format. The files can be opened using either the Labber Log Browser (https://labber.org/overview/) or Labber Python API (http://labber.org/online-doc/api/LogFile.html). A single file is acquired with QCodes and features the corresponding data type. XRD data are in .dat format and a code to open the data is provided. The code for simulations is as well provided in Python.}, author = {Jirovec, Daniel}, publisher = {Institute of Science and Technology Austria}, title = {{Research data for "A singlet-triplet hole spin qubit planar Ge"}}, doi = {10.15479/AT:ISTA:9323}, year = {2021}, } @misc{9327, abstract = {This archive contains the missing sweater mesh animations and displacement models for the code of "Mechanics-Aware Deformation of Yarn Pattern Geometry" Code Repository: https://git.ist.ac.at/gsperl/MADYPG}, author = {Sperl, Georg and Narain, Rahul and Wojtan, Christopher J}, publisher = {IST Austria}, title = {{Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data)}}, doi = {10.15479/AT:ISTA:9327}, year = {2021}, }