@inproceedings{17426, abstract = {The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently, the scientific community has focused on enhancing certifiable robustness guarantees by crafting 1-Lipschitz neural networks that leverage Lipschitz bounded dense and convolutional layers. Although different methods have been proposed in the literature to achieve this goal, understanding the performance of such methods is not straightforward, since different metrics can be relevant (e.g., training time, memory usage, accuracy, certifiable robustness) for different applications. For this reason, this work provides a thorough theoretical and empirical comparison between methods by evaluating them in terms of memory usage, speed, and certifiable robust accuracy. The paper also provides some guidelines and recommendations to support the user in selecting the methods that work best depending on the available resources. We provide code at https://github.com/berndprach/1LipschitzLayersCompared.}, author = {Prach, Bernd and Brau, Fabio and Buttazzo, Giorgio and Lampert, Christoph}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, location = {Seattle, WA, United States}, pages = {24574--24583}, publisher = {Computer Vision Foundation}, title = {{1-Lipschitz layers compared: Memory, speed, and certifiable robustness}}, doi = {10.1109/CVPR52733.2024.02320}, year = {2024}, } @inproceedings{18121, abstract = {It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity constraints, or impose sparsity after the fact, altering the network’s general behavior. In this paper, we demonstrate, for the first time, that sparsity can instead be incorporated into the interpretation process itself, as a sample-specific preprocessing step. Unlike previous work, this approach, which we call SPADE, does not place constraints on the trained model and does not affect its behavior during inference on the sample. Given a trained model and a target sample, SPADE uses sample-targeted pruning to provide a "trace" of the network’s execution on the sample, reducing the network to the most important connections prior to computing an interpretation. We demonstrate that preprocessing with SPADE significantly increases the accuracy of image saliency maps across several interpretability methods. Additionally, SPADE improves the usefulness of neuron visualizations, aiding humans in reasoning about network behavior. Our code is available at https://github.com/IST-DASLab/SPADE.}, author = {Moakhar, Arshia Soltani and Iofinova, Eugenia B and Frantar, Elias and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {45955--45987}, publisher = {ML Research Press}, title = {{SPADE: Sparsity-guided debugging for deep neural networks}}, volume = {235}, year = {2024}, } @unpublished{19063, abstract = {Instruction-tuned Large Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of computer science, particularly an explicit separation of instructions and data. This makes them vulnerable to manipulations such as indirect prompt injections and generally unsuitable for safety-critical tasks. Surprisingly, there is currently no established definition or benchmark to quantify this phenomenon. In this work, we close this gap by introducing a formal measure for instruction-data separation and an empirical variant that is calculable from a model's outputs. We also present a new dataset, SEP, that allows estimating the measure for real-world models. Our results on various LLMs show that the problem of instruction-data separation is real: all models fail to achieve high separation, and canonical mitigation techniques, such as prompt engineering and fine-tuning, either fail to substantially improve separation or reduce model utility. The source code and SEP dataset are openly accessible at https://github.com/egozverev/Shold-It-Be-Executed-Or-Processed. }, author = {Zverev, Egor and Abdelnabi, Sahar and Tabesh, Soroush and Fritz, Mario and Lampert, Christoph}, booktitle = {arXiv}, title = {{Can LLMs separate instructions from data? And what do we even mean by that?}}, doi = {10.48550/arXiv.2403.06833}, year = {2024}, } @article{11434, abstract = {The Indian summer monsoon rainfall (ISMR) has been declining since the 1950s. However, since 2002 it is reported to have revived. For these observed changes in the ISMR, several explanations have been reported. Among these explanations, however, the role of the eastern equatorial Indian Ocean (EEIO) is missing despite being one of the warmest regions in the Indian Ocean, and monotonously warming. A recent study reported that EEIO warming impacts the rainfall over northern India. Here we report that warming in the EEIO weakens the low-level Indian summer monsoon circulation and reduces ISMR. A warm EEIO drives easterly winds in the Indo–Pacific sector as a Gill response. The warm EEIO also enhances nocturnal convection offshore the western coast of Sumatra. The latent heating associated with the increased convection augments the Gill response and the resultant circulation opposes the monsoon low-level circulation and weakens the seasonal rainfall.}, author = {Goswami, Bidyut B}, issn = {1432-0894}, journal = {Climate Dynamics}, pages = {427--442}, publisher = {Springer Nature}, title = {{Role of the eastern equatorial Indian Ocean warming in the Indian summer monsoon rainfall trend}}, doi = {10.1007/s00382-022-06337-7}, volume = {60}, year = {2023}, } @article{12792, abstract = {In the physics literature the spectral form factor (SFF), the squared Fourier transform of the empirical eigenvalue density, is the most common tool to test universality for disordered quantum systems, yet previous mathematical results have been restricted only to two exactly solvable models (Forrester in J Stat Phys 183:33, 2021. https://doi.org/10.1007/s10955-021-02767-5, Commun Math Phys 387:215–235, 2021. https://doi.org/10.1007/s00220-021-04193-w). We rigorously prove the physics prediction on SFF up to an intermediate time scale for a large class of random matrices using a robust method, the multi-resolvent local laws. Beyond Wigner matrices we also consider the monoparametric ensemble and prove that universality of SFF can already be triggered by a single random parameter, supplementing the recently proven Wigner–Dyson universality (Cipolloni et al. in Probab Theory Relat Fields, 2021. https://doi.org/10.1007/s00440-022-01156-7) to larger spectral scales. Remarkably, extensive numerics indicates that our formulas correctly predict the SFF in the entire slope-dip-ramp regime, as customarily called in physics.}, author = {Cipolloni, Giorgio and Erdös, László and Schröder, Dominik J}, issn = {1432-0916}, journal = {Communications in Mathematical Physics}, pages = {1665--1700}, publisher = {Springer Nature}, title = {{On the spectral form factor for random matrices}}, doi = {10.1007/s00220-023-04692-y}, volume = {401}, year = {2023}, } @inbook{13052, abstract = {Imaging of the immunological synapse (IS) between dendritic cells (DCs) and T cells in suspension is hampered by suboptimal alignment of cell-cell contacts along the vertical imaging plane. This requires optical sectioning that often results in unsatisfactory resolution in time and space. Here, we present a workflow where DCs and T cells are confined between a layer of glass and polydimethylsiloxane (PDMS) that orients the cells along one, horizontal imaging plane, allowing for fast en-face-imaging of the DC-T cell IS.}, author = {Leithner, Alexander F and Merrin, Jack and Sixt, Michael K}, booktitle = {The Immune Synapse}, editor = {Baldari, Cosima and Dustin, Michael}, isbn = {9781071631348}, issn = {1940-6029}, pages = {137--147}, publisher = {Springer Nature}, title = {{En-Face Imaging of T Cell-Dendritic Cell Immunological Synapses}}, doi = {10.1007/978-1-0716-3135-5_9}, volume = {2654}, year = {2023}, } @inproceedings{13053, abstract = {Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior is stable under compression operations such as pruning. Thus, dense models trained via CrAM should be compressible post-training, in a single step, without significant accuracy loss. Experimental results on standard benchmarks, such as residual networks for ImageNet classification and BERT models for language modelling, show that CrAM produces dense models that can be more accurate than the standard SGD/Adam-based baselines, but which are stable under weight pruning: specifically, we can prune models in one-shot to 70-80% sparsity with almost no accuracy loss, and to 90% with reasonable (∼1%) accuracy loss, which is competitive with gradual compression methods. Additionally, CrAM can produce sparse models which perform well for transfer learning, and it also works for semi-structured 2:4 pruning patterns supported by GPU hardware. The code for reproducing the results is available at this https URL .}, author = {Peste, Elena-Alexandra and Vladu, Adrian and Kurtic, Eldar and Lampert, Christoph and Alistarh, Dan-Adrian}, booktitle = {11th International Conference on Learning Representations }, location = {Kigali, Rwanda }, publisher = {OpenReview}, title = {{CrAM: A Compression-Aware Minimizer}}, year = {2023}, } @article{14043, abstract = {Over the last two decades, a significant line of work in theoretical algorithms has made progress in solving linear systems of the form Lx=b, where L is the Laplacian matrix of a weighted graph with weights w(i,j)>0 on the edges. The solution x of the linear system can be interpreted as the potentials of an electrical flow in which the resistance on edge (i, j) is 1/w(i, j). Kelner et al. (in: Proceedings of the 45th Annual ACM Symposium on the Theory of Computing, pp 911–920, 2013. https://doi.org/10.1145/2488608.2488724) give a combinatorial, near-linear time algorithm that maintains the Kirchoff Current Law, and gradually enforces the Kirchoff Potential Law by updating flows around cycles (cycle toggling). In this paper, we consider a dual version of the algorithm that maintains the Kirchoff Potential Law, and gradually enforces the Kirchoff Current Law by cut toggling: each iteration updates all potentials on one side of a fundamental cut of a spanning tree by the same amount. We prove that this dual algorithm also runs in a near-linear number of iterations. We show, however, that if we abstract cut toggling as a natural data structure problem, this problem can be reduced to the online vector–matrix-vector problem, which has been conjectured to be difficult for dynamic algorithms (Henzinger et al., in: Proceedings of the 47th Annual ACM Symposium on the Theory of Computing, pp 21–30, 2015. https://doi.org/10.1145/2746539.2746609). The conjecture implies that the data structure does not have an O(n1−ϵ) time algorithm for any ϵ>0, and thus a straightforward implementation of the cut-toggling algorithm requires essentially linear time per iteration. To circumvent the lower bound, we batch update steps, and perform them simultaneously instead of sequentially. An appropriate choice of batching leads to an O˜(m1.5) time cut-toggling algorithm for solving Laplacian systems. Furthermore, we show that if we sparsify the graph and call our algorithm recursively on the Laplacian system implied by batching and sparsifying, we can reduce the running time to O(m1+ϵ) for any ϵ>0. Thus, the dual cut-toggling algorithm can achieve (almost) the same running time as its primal cycle-toggling counterpart.}, author = {Henzinger, Monika H and Jin, Billy and Peng, Richard and Williamson, David P.}, issn = {1432-0541}, journal = {Algorithmica}, pages = {2680--3716}, publisher = {Springer Nature}, title = {{A combinatorial cut-toggling algorithm for solving Laplacian linear systems}}, doi = {10.1007/s00453-023-01154-8}, volume = {85}, year = {2023}, } @article{14444, abstract = {We prove several results about substructures in Latin squares. First, we explain how to adapt our recent work on high-girth Steiner triple systems to the setting of Latin squares, resolving a conjecture of Linial that there exist Latin squares with arbitrarily high girth. As a consequence, we see that the number of order- n Latin squares with no intercalate (i.e., no 2×2 Latin subsquare) is at least (e−9/4n−o(n))n2. Equivalently, P[N=0]≥e−n2/4−o(n2)=e−(1+o(1))EN , where N is the number of intercalates in a uniformly random order- n Latin square. In fact, extending recent work of Kwan, Sah, and Sawhney, we resolve the general large-deviation problem for intercalates in random Latin squares, up to constant factors in the exponent: for any constant 0<δ≤1 we have P[N≤(1−δ)EN]=exp(−Θ(n2)) and for any constant δ>0 we have P[N≥(1+δ)EN]=exp(−Θ(n4/3logn)). Finally, as an application of some new general tools for studying substructures in random Latin squares, we show that in almost all order- n Latin squares, the number of cuboctahedra (i.e., the number of pairs of possibly degenerate 2×2 submatrices with the same arrangement of symbols) is of order n4, which is the minimum possible. As observed by Gowers and Long, this number can be interpreted as measuring ``how associative'' the quasigroup associated with the Latin square is.}, author = {Kwan, Matthew Alan and Sah, Ashwin and Sawhney, Mehtaab and Simkin, Michael}, issn = {1565-8511}, journal = {Israel Journal of Mathematics}, number = {2}, pages = {363--416}, publisher = {Springer Nature}, title = {{Substructures in Latin squares}}, doi = {10.1007/s11856-023-2513-9}, volume = {256}, year = {2023}, } @article{14453, abstract = {Squall lines are substantially influenced by the interaction of low-level shear with cold pools associated with convective downdrafts. Beyond an optimal shear amplitude, squall lines tend to orient themselves at an angle with respect to the low-level shear. While the mechanisms behind squall line orientation seem to be increasingly well understood, uncertainties remain on the implications of this orientation. Roca and Fiolleau (2020, https://doi.org/10.1038/s43247-020-00015-4) show that long lived mesoscale convective systems, including squall lines, are disproportionately involved in rainfall extremes in the tropics. This article investigates the influence of the interaction between low-level shear and squall line outflow on squall line generated precipitation extrema in the tropics. Using a cloud resolving model, simulated squall lines in radiative convective equilibrium amid a shear-dominated regime (super optimal), a balanced regime (optimal), and an outflow dominated regime (suboptimal). Our results show that precipitation extremes in squall lines are 40% more intense in the case of optimal shear and remain 30% superior in the superoptimal regime relative to a disorganized case. With a theoretical scaling of precipitation extremes (C. Muller & Takayabu, 2020, https://doi.org/10.1088/1748-9326/ab7130), we show that the condensation rates control the amplification of precipitation extremes in tropical squall lines, mainly due to its change in vertical mass flux (dynamic component). The reduction of dilution by entrainment explains half of this change, consistent with Mulholland et al. (2021, https://doi.org/10.1175/jas-d-20-0299.1). The other half is explained by increased cloud-base velocity intensity in optimal and superoptimal squall lines.}, author = {Abramian, Sophie and Muller, Caroline J and Risi, Camille}, issn = {1942-2466}, journal = {Journal of Advances in Modeling Earth Systems}, number = {10}, publisher = {Wiley}, title = {{Extreme precipitation in tropical squall lines}}, doi = {10.1029/2022MS003477}, volume = {15}, year = {2023}, } @misc{14472, abstract = {Data related to the following paper: "Stress granules plug and stabilize damaged endolysosomal membranes" (https://doi.org/10.1038/s41586-023-06726-w) Abstract: Endomembrane damage represents a form of stress that is detrimental for eukaryotic cells. To cope with this threat, cells possess mechanisms that repair the damage and restore cellular homeostasis. Endomembrane damage also results in organelle instability and the mechanisms by which cells stabilize damaged endomembranes to enable membrane repair remains unknown. In this work we use a minimal coarse-grained molecular dynamics system to explore how lipid vesicles undergoing poration in a protein-rich medium can be plugged and stabilised by condensate formation. The solution of proteins in and out of the vesicle is described by beads dispersed in implicit solvent. The membrane is described as a one-bead-thick fluid elastic layer of mechanical properties that mimic biological membranes. We tune the interactions between solution beads in the different compartments to capture the differences between the cytoplasmic and endosomal protein solutions and explore how the system responds to different degrees of membrane poration. We find that, in the right interaction regime, condensates form rapidly at the damage site upon solution mixing and act as a plug that prevents futher mixing and destabilisation of the vesicle. Further, when the condensate can interact with the membrane (wetting interactions) we find that it mediates pore sealing and membrane repair. This research is part of the work published in "Stress granules plug and stabilize damaged endolysosomal membranes", Bussi et al, Nature, 2023 - 10.1038/s41586-023-06726-w.}, author = {Vanhille-Campos, Christian Eduardo and Šarić, Anđela}, publisher = {Institute of Science and Technology Austria}, title = {{Stress granules plug and stabilize damaged endolysosomal membranes}}, doi = {10.15479/AT:ISTA:14472}, year = {2023}, } @article{14776, abstract = {Soluble chaperones residing in the endoplasmic reticulum (ER) play vitally important roles in folding and quality control of newly synthesized proteins that transiently pass through the ER en route to their final destinations. These soluble residents of the ER are themselves endowed with an ER retrieval signal that enables the cell to bring the escaped residents back from the Golgi. Here, by using purified proteins, we showed that Nicotiana tabacum phytaspase, a plant aspartate-specific protease, introduces two breaks at the C-terminus of the N. tabacum ER resident calreticulin-3. These cleavages resulted in removal of either a dipeptide or a hexapeptide from the C-terminus of calreticulin-3 encompassing part or all of the ER retrieval signal. Consistently, expression of the calreticulin-3 derivative mimicking the phytaspase cleavage product in Nicotiana benthamiana cells demonstrated loss of the ER accumulation of the protein. Notably, upon its escape from the ER, calreticulin-3 was further processed by an unknown protease(s) to generate the free N-terminal (N) domain of calreticulin-3, which was ultimately secreted into the apoplast. Our study thus identified a specific proteolytic enzyme capable of precise detachment of the ER retrieval signal from a plant ER resident protein, with implications for the further fate of the escaped resident.}, author = {Teplova, Anastasiia and Pigidanov, Artemii A. and Serebryakova, Marina V. and Golyshev, Sergei A. and Galiullina, Raisa A. and Chichkova, Nina V. and Vartapetian, Andrey B.}, issn = {1422-0067}, journal = {International Journal of Molecular Sciences}, keywords = {Inorganic Chemistry, Organic Chemistry, Physical and Theoretical Chemistry, Computer Science Applications, Spectroscopy, Molecular Biology, General Medicine, Catalysis}, number = {22}, publisher = {MDPI}, title = {{Phytaspase Is capable of detaching the endoplasmic reticulum retrieval signal from tobacco calreticulin-3}}, doi = {10.3390/ijms242216527}, volume = {24}, year = {2023}, } @misc{14812, abstract = {This repository contains the code and VCF files needed to conduct the analyses in our MS. Each folder contains a readMe document explaining the nature of each file and dataset and the results and analyses that they relate to. The same anlaysis code (but not VCF files) is also available at https://github.com/seanstankowski/Littorina_reproductive_mode}, author = {Stankowski, Sean}, publisher = {Zenodo}, title = {{Data and code for: The genetic architecture of a recent transition to live-bearing in marine snails}}, doi = {10.5281/ZENODO.8318995}, year = {2023}, } @misc{15027, abstract = {This data repository underpins the paper, published in PNAS (doi pending) and bioarxiv (doi: https://doi.org/10.1101/2023.07.05.547777).}, author = {Curk, Samo}, publisher = {Figshare}, title = {{aggregation_data}}, year = {2023}, } @article{17074, abstract = {We verify Bogoliubov's approximation for translation invariant Bose gases in the mean field regime, i.e. we prove that the ground state energy EN is given by EN=NeH+infσ(H)+oN→∞(1), where N is the number of particles, eH is the minimal Hartree energy and H is the Bogoliubov Hamiltonian. As an intermediate result we show the existence of approximate ground states ΨN, i.e. states satisfying ⟨HN⟩ΨN=EN+oN→∞(1), exhibiting complete Bose--Einstein condensation with respect to one of the Hartree minimizers.}, author = {Brooks, Morris and Seiringer, Robert}, issn = {2690-1005}, journal = {Probability and Mathematical Physics}, number = {4}, pages = {939--1000}, publisher = {Mathematical Sciences Publishers}, title = {{Validity of Bogoliubov’s approximation fortranslation-invariant Bose gases}}, doi = {10.2140/pmp.2022.3.939}, volume = {3}, year = {2023}, } @inproceedings{18218, abstract = {Deep neural networks are known to be susceptible to adversarial perturbations – small perturbations that alter the output of the network and exist under strict norm limitations. While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model’s output on a set of inputs. Universal perturbations present a more realistic case of adversarial attacks, as awareness of the model’s exact input is not required. In addition, the universal attack setting raises the subject of generalization to unseen data, where given a set of inputs, the universal perturbations aim to alter the model’s output on out-of-sample data. In this work, we study physical passive patch adversarial attacks on visual odometry-based autonomous navigation systems. A visual odometry system aims to infer the relative camera motion between two corresponding viewpoints, and is frequently used by vision-based autonomous navigation systems to estimate their state. For such navigation systems, a patch adversarial perturbation poses a severe security issue, as it can be used to mislead a system onto some collision course. To the best of our knowledge, we show for the first time that the error margin of a visual odometry model can be significantly increased by deploying patch adversarial attacks in the scene. We provide evaluation on synthetic closed-loop drone navigation data and demonstrate that a comparable vulnerability exists in real data. A reference implementation of the proposed method and the reported experiments is provided at https://github.com/patchadversarialattacks/patchadversarialattacks.}, author = {Nemcovsky, Yaniv and Jacoby, Matan and Bronstein, Alexander and Baskin, Chaim}, booktitle = {16th Asian Conference on Computer Vision}, isbn = {9783031262920}, issn = {1611-3349}, location = {Macao, China}, pages = {518--534}, publisher = {Springer Nature}, title = {{Physical passive patch adversarial attacks on visual odometry systems}}, doi = {10.1007/978-3-031-26293-7_31}, volume = {13847}, year = {2023}, } @unpublished{15039, abstract = {A crucial property for achieving secure, trustworthy and interpretable deep learning systems is their robustness: small changes to a system's inputs should not result in large changes to its outputs. Mathematically, this means one strives for networks with a small Lipschitz constant. Several recent works have focused on how to construct such Lipschitz networks, typically by imposing constraints on the weight matrices. In this work, we study an orthogonal aspect, namely the role of the activation function. We show that commonly used activation functions, such as MaxMin, as well as all piece-wise linear ones with two segments unnecessarily restrict the class of representable functions, even in the simplest one-dimensional setting. We furthermore introduce the new N-activation function that is provably more expressive than currently popular activation functions. We provide code at this https URL.}, author = {Prach, Bernd and Lampert, Christoph}, booktitle = {arXiv}, title = {{1-Lipschitz neural networks are more expressive with N-activations}}, doi = {10.48550/ARXIV.2311.06103}, year = {2023}, } @inproceedings{14771, abstract = {Pruning—that is, setting a significant subset of the parameters of a neural network to zero—is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or exacerbate bias in the output of the compressed model. Despite existing evidence for this phenomenon, the relationship between neural network pruning and induced bias is not well-understood. In this work, we systematically investigate and characterize this phenomenon in Convolutional Neural Networks for computer vision. First, we show that it is in fact possible to obtain highly-sparse models, e.g. with less than 10% remaining weights, which do not decrease in accuracy nor substantially increase in bias when compared to dense models. At the same time, we also find that, at higher sparsities, pruned models exhibit higher uncertainty in their outputs, as well as increased correlations, which we directly link to increased bias. We propose easy-to-use criteria which, based only on the uncompressed model, establish whether bias will increase with pruning, and identify the samples most susceptible to biased predictions post-compression. Our code can be found at https://github.com/IST-DASLab/pruned-vision-model-bias.}, author = {Iofinova, Eugenia B and Peste, Elena-Alexandra and Alistarh, Dan-Adrian}, booktitle = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, issn = {2575-7075}, location = {Vancouver, BC, Canada}, pages = {24364--24373}, publisher = {IEEE}, title = {{Bias in pruned vision models: In-depth analysis and countermeasures}}, doi = {10.1109/cvpr52729.2023.02334}, year = {2023}, } @article{18179, abstract = {Linnik type problems concern the distribution of projections of integral points on the unit sphere as their norm increases, and different generalizations of this phenomenon. Our work addresses a question of this type: we prove the uniform distribution of the projections of primitive Z2 points in the p-adic unit sphere, as their (real) norm tends to infinity. The proof is via counting lattice points in semi-simple S-arithmetic groups.}, author = {Guilloux, Antonin and Horesh, Tal}, issn = {2592-6616}, journal = {Publications mathématiques de Besançon - Algèbre et Théorie des nombres}, pages = {85--107}, publisher = {Presses Universitaires de Franche-Comté}, title = {{p-adic directions of primitive vectors}}, doi = {10.5802/pmb.50}, volume = {2023}, year = {2023}, } @article{13317, abstract = {We prove the Eigenstate Thermalisation Hypothesis (ETH) for local observables in a typical translation invariant system of quantum spins with L-body interactions, where L is the number of spins. This mathematically verifies the observation first made by Santos and Rigol (Phys Rev E 82(3):031130, 2010, https://doi.org/10.1103/PhysRevE.82.031130) that the ETH may hold for systems with additional translational symmetries for a naturally restricted class of observables. We also present numerical support for the same phenomenon for Hamiltonians with local interaction.}, author = {Sugimoto, Shoki and Henheik, Sven Joscha and Riabov, Volodymyr and Erdös, László}, issn = {1572-9613}, journal = {Journal of Statistical Physics}, number = {7}, publisher = {Springer Nature}, title = {{Eigenstate thermalisation hypothesis for translation invariant spin systems}}, doi = {10.1007/s10955-023-03132-4}, volume = {190}, year = {2023}, }