@article{18111, abstract = {Observations of tidal disruption events (TDEs) show signs of nitrogen enrichment reminiscent of other astrophysical sources such as active galactic nuclei and star-forming galaxies. Given that TDEs probe the gas from a single star, it is possible to test whether the observed enrichment is consistent with expectations from the CNO cycle by looking at the observed nitrogen/carbon (N/C) abundance ratios. Given that ≈20% of solar-mass stars (and an even larger fraction of more massive stars) live in close binaries, it is worthwhile to also consider what TDEs from stars influenced by binary evolution would look like. We show here that TDEs from stars stripped of their hydrogen-rich (and nitrogen-poor) envelopes through previous binary-induced mass loss can produce much higher observable N/C enhancements than even TDEs from massive stars. Additionally, we predict that the time dependence of the N/C abundance ratio in the mass fallback rate of stripped stars will follow the inverse behavior of main-sequence stars, enabling a more accurate characterization of the disrupted star.}, author = {Mockler, Brenna and Gallegos-Garcia, Monica and Götberg, Ylva Louise Linsdotter and Miller, Jon M. and Ramirez-Ruiz, Enrico}, issn = {2041-8213}, journal = {Astrophysical Journal Letters}, number = {1}, publisher = {IOP Publishing}, title = {{Tidal disruption events from stripped stars}}, doi = {10.3847/2041-8213/ad6c34}, volume = {973}, year = {2024}, } @inproceedings{18113, abstract = {The emergence of accurate open large language models (LLMs) has led to a race towards performant quantization techniques which can enable their execution on end-user devices. In this paper, we revisit the problem of “extreme” LLM compression—defined as targeting extremely low bit counts, such as 2 to 3 bits per parameter—from the point of view of classic methods in Multi-Codebook Quantization (MCQ). Our algorithm, called AQLM, generalizes the classic Additive Quantization (AQ) approach for information retrieval to advance the state-of-the-art in LLM compression, via two innovations: 1) learned additive quantization of weight matrices in input-adaptive fashion, and 2) joint optimization of codebook parameters across each transformer blocks. Broadly, AQLM is the first scheme that is Pareto optimal in terms of accuracy-vs-model-size when compressing to less than 3 bits per parameter, and significantly improves upon all known schemes in the extreme compression (2bit) regime. In addition, AQLM is practical: we provide fast GPU and CPU implementations of AQLM for token generation, which enable us to match or outperform optimized FP16 implementations for speed, while executing in a much smaller memory footprint.}, author = {Egiazarian, Vage and Panferov, Andrei and Kuznedelev, Denis and Frantar, Elias and Babenko, Artem and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {12284--12303}, publisher = {ML Research Press}, title = {{Extreme compression of large language models via additive quantization}}, volume = {235}, year = {2024}, } @inproceedings{18114, abstract = {This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations, revealing the underlying dynamics of data and enhancing interpretability and efficiency in data modeling. Central to our approach is a novel Relaxed Linear Programming Solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs. This integrates well with neural networks and surpasses the limitations of traditional ODE solvers enabling scalable GPU parallel processing. Overall, Mechanistic Neural Networks demonstrate their versatility for scientific machine learning applications, adeptly managing tasks from equation discovery to dynamic systems modeling. We prove their comprehensive capabilities in analyzing and interpreting complex scientific data across various applications, showing significant performance against specialized state-of-the-art methods. Source code is available at https://github.com/alpz/mech-nn.}, author = {Pervez, Adeel A and Locatello, Francesco and Gavves, Efstratios}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {40484--40501}, publisher = {ML Research Press}, title = {{Mechanistic neural networks for scientific machine learning}}, volume = {235}, year = {2024}, } @inproceedings{18115, abstract = {We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new data selection approach based on k-means clustering and sensitivity sampling. Assuming access to an embedding representation of the data with respect to which the model loss is Holder continuous, our approach provably allows selecting a set of “typical” k+1/ε2 elements whose average loss corresponds to the average loss of the whole dataset, up to a multiplicative (1±ε) factor and an additive ελΦk, where Φk represents the k-means cost for the input embeddings and λ is the Holder constant. We furthermore demonstrate the performance and scalability of our approach on fine-tuning foundation models and show that it outperforms state-of-the-art methods. We also show how it can be applied on linear regression, leading to a new sampling strategy that surprisingly matches the performance of leverage score sampling, while being conceptually simpler and more scalable.}, author = {Axiotis, Kyriakos and Cohen-Addad, Vincent and Henzinger, Monika H and Jerome, Sammy and Mirrokni, Vahab and Saulpic, David and Woodruff, David P. and Wunder, Michael}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {2086--2107}, publisher = {ML Research Press}, title = {{Data-efficient learning via clustering-based sensitivity sampling: Foundation models and beyond}}, volume = {235}, year = {2024}, } @inproceedings{18116, abstract = {As a staple of data analysis and unsupervised learning, the problem of private clustering has been widely studied, under various privacy models. Centralized differential privacy is the first of them, and the problem has also been studied for the local and the shuffle variation. In each case, the goal is to design an algorithm that computes privately a clustering, with the smallest possible error. The study of each variation gave rise to new algorithm: the landscape of private clustering algorithm is therefore quite intricate. In this paper, we show that a 20 year-old algorithm can be slightly modified to work for any of those models. This provides a unified picture: while matching almost all previously known results, it allows us to improve some of them, and extend to a new privacy model, the continual observation setting, where the input is changing over time and the algorithm must output a new solution at each time step.}, author = {La Tour, Max Dupré and Henzinger, Monika H and Saulpic, David}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {12046--12086}, publisher = {ML Research Press}, title = {{Making old things new: A unified algorithm for differentially private clustering}}, volume = {235}, year = {2024}, } @inproceedings{18117, abstract = {We investigate parameter-efficient fine-tuning (PEFT) methods that can provide good accuracy under limited computational and memory budgets in the context of large language models (LLMs). We present a new PEFT method called Robust Adaptation (RoSA) inspired by robust principal component analysis that jointly trains low-rank and highly-sparse components on top of a set of fixed pretrained weights to efficiently approximate the performance of a full-fine-tuning (FFT) solution. Across a series of challenging generative tasks such as grade-school math and SQL query generation, which require fine-tuning for good performance, we show that RoSA outperforms LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter budget, and can even recover the performance of FFT on some tasks. We provide system support for RoSA to complement the training algorithm, specifically in the form of sparse GPU kernels which enable memory- and computationally-efficient training, and show that it is also compatible with low-precision base weights, resulting in the first joint representation combining quantization, low-rank and sparse approximations. Our code is available at https://github.com/IST-DASLab/RoSA.}, author = {Nikdan, Mahdi and Tabesh, Soroush and Crncevic, Elvir and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {38187--38206}, publisher = {ML Research Press}, title = {{RoSA: Accurate parameter-efficient fine-tuning via robust adaptation}}, volume = {235}, year = {2024}, } @inproceedings{18118, abstract = {We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory. Its main advantage over previous work is that it allows for more flexibility in how the transfer of knowledge between tasks is realized. For previous approaches, this could only happen indirectly, by means of learning prior distributions over models. In contrast, the new generalization bounds that we prove express the process of meta-learning much more directly as learning the learning algorithm that should be used for future tasks. The flexibility of our framework makes it suitable to analyze a wide range of meta-learning mechanisms and even design new mechanisms. Other than our theoretical contributions we also show empirically that our framework improves the prediction quality in practical meta-learning mechanisms.}, author = {Zakerinia, Hossein and Behjati, Amin and Lampert, Christoph}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {58122--58139}, publisher = {ML Research Press}, title = {{More flexible PAC-Bayesian meta-learning by learning learning algorithms}}, volume = {235}, year = {2024}, } @article{18153, abstract = {Attention supports decision making by selecting the features that are relevant for decisions. Selective enhancement of the relevant features and inhibition of distractors has been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when relevance cannot be directly determined, and the attention signal needs to be internally constructed is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC) of mice in an attention-shifting task where relevance of stimulus modalities changed across blocks of trials. In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using this RNN model we predicted a correlation between contextual modulation of individual neurons and their stimulus drive, which we confirmed in ACC but not in V1.}, author = {Hajnal, Márton Albert and Tran, Duy and Szabó, Zsombor and Albert, Andrea and Safaryan, Karen and Einstein, Michael and Vallejo Martelo, Mauricio and Polack, Pierre-Olivier and Golshani, Peyman and Orbán, Gergő}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making}}, doi = {10.1038/s41467-024-49845-2}, volume = {15}, year = {2024}, } @inproceedings{18155, abstract = {We study the classical problem of verifying programs with respect to formal specifications given in the linear temporal logic (LTL). We first present novel sound and complete witnesses for LTL verification over imperative programs. Our witnesses are applicable to both verification (proving) and refutation (finding bugs) settings. We then consider LTL formulas in which atomic propositions can be polynomial constraints and turn our focus to polynomial arithmetic programs, i.e. programs in which every assignment and guard consists only of polynomial expressions. For this setting, we provide an efficient algorithm to automatically synthesize such LTL witnesses. Our synthesis procedure is both sound and semi-complete. Finally, we present experimental results demonstrating the effectiveness of our approach and that it can handle programs which were beyond the reach of previous state-of-the-art tools.}, author = {Chatterjee, Krishnendu and Goharshady, Amir Kafshdar and Goharshady, Ehsan and Karrabi, Mehrdad and Zikelic, Dorde}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, isbn = {9783031711619}, issn = {1611-3349}, location = {Milan, Italy}, pages = {600--619}, publisher = {Springer Nature}, title = {{Sound and complete witnesses for template-based verification of LTL properties on polynomial programs}}, doi = {10.1007/978-3-031-71162-6_31}, volume = {14933}, year = {2024}, } @inproceedings{18156, abstract = {Privately counting distinct elements in a stream is a fundamental data analysis problem with many applications in machine learning. In the turnstile model, Jain et al. [NeurIPS2023] initiated the study of this problem parameterized by the maximum flippancy of any element, i.e., the number of times that the count of an element changes from 0 to above 0 or vice versa. They give an item-level (ε,δ)-differentially private algorithm whose additive error is tight with respect to that parameterization. In this work, we show that a very simple algorithm based on the sparse vector technique achieves a tight additive error for item-level (ε,δ)-differential privacy and item-level ε-differential privacy with regards to a different parameterization, namely the sum of all flippancies. Our second result is a bound which shows that for a large class of algorithms, including all existing differentially private algorithms for this problem, the lower bound from item-level differential privacy extends to event-level differential privacy. This partially answers an open question by Jain et al. [NeurIPS2023].}, author = {Henzinger, Monika H and Sricharan, A. R. and Steiner, Teresa Anna}, booktitle = {International Conference on Approximation Algorithms for Combinatorial Optimization Problems }, isbn = {9783959773485}, issn = {1868-8969}, location = {London, United Kingdom}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{Private counting of distinct elements in the turnstile model and extensions}}, doi = {10.4230/LIPIcs.APPROX/RANDOM.2024.40}, volume = {317}, year = {2024}, } @article{18158, abstract = {We study the geometry of Poisson point processes from the point of view of optimal transport and Ricci lower bounds. We construct a Riemannian structure on the space of point processes and the associated distance W that corresponds to the Benamou–Brenier variational formula. Our main tool is a non-local continuity equation formulated with the difference operator. The closure of the domain of the relative entropy is a complete geodesic space, when endowed with W. The geometry of this non-local infinite-dimensional space is analogous to that of spaces with positive Ricci curvature. Among others: (a) the Ornstein–Uhlenbeck semi-group is the gradient flow of the relative entropy; (b) the Poisson space has an entropic Ricci curvature bounded from below by 1; (c) W satisfies an HWI inequality.}, author = {Dello Schiavo, Lorenzo and Herry, Ronan and Suzuki, Kohei}, issn = {2270-518X}, journal = {Journal de l'Ecole Polytechnique - Mathematiques}, pages = {957--1010}, publisher = {Ecole Polytechnique}, title = {{Wasserstein geometry and Ricci curvature bounds for Poisson spaces}}, doi = {10.5802/jep.270}, volume = {11}, year = {2024}, } @inproceedings{18159, abstract = {Markov Decision Processes (MDPs) are a classical model for decision making in the presence of uncertainty. Often they are viewed as state transformers with planning objectives defned with respect to paths over MDP states. An increasingly popular alternative is to view them as distribution transformers, giving rise to a sequence of probability distributions over MDP states. For instance, reachability and safety properties in modeling robot swarms or chemical reaction networks are naturally defned in terms of probability distributions over states. Verifying such distributional properties is known to be hard and often beyond the reach of classical state-based verifcation techniques. In this work, we consider the problems of certifed policy (i.e. controller) verifcation and synthesis in MDPs under distributional reach-avoidance specifcations. By certifed we mean that, along with a policy, we also aim to synthesize a (checkable) certifcate ensuring that the MDP indeed satisfes the property. Thus, given the target set of distributions and an unsafe set of distributions over MDP states, our goal is to either synthesize a certifcate for a given policy or synthesize a policy along with a certifcate, proving that the target distribution can be reached while avoiding unsafe distributions. To solve this problem, we introduce the novel notion of distributional reach-avoid certifcates and present automated procedures for (1) synthesizing a certifcate for a given policy, and (2) synthesizing a policy together with the certifcate, both providing formal guarantees on certifcate correctness. Our experimental evaluation demonstrates the ability of our method to solve several non-trivial examples, including a multi-agent robot-swarm model, to synthesize certifed policies and to certify existing policies. }, author = {Akshay, S and Chatterjee, Krishnendu and Meggendorfer, Tobias and Zikelic, Dorde}, booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence}, isbn = {9781956792041}, issn = {1045-0823}, location = {Jeju, Korea}, pages = {3--12}, publisher = {International Joint Conferences on Artificial Intelligence}, title = {{Certified policy verification and synthesis for MDPs under distributional reach-avoidance properties}}, doi = {10.24963/ijcai.2024/1}, year = {2024}, } @inproceedings{18160, abstract = {Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities into account. Robust Markov decision processes (RMDPs) address this shortcoming of MDPs by assigning to each transition an uncertainty set rather than a single probability value. In this work, we consider polytopic RMDPs in which all uncertainty sets are polytopes and study the problem of solving long-run average reward polytopic RMDPs. We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state and action spaces. This reduction allows us to derive several important consequences that were hitherto not known to hold for polytopic RMDPs. First, we derive new computational complexity bounds for solving long-run average reward polytopic RMDPs, showing for the first time that the threshold decision problem for them is in NP∩CONP and that they admit a randomized algorithm with sub-exponential expected runtime. Second, we present Robust Polytopic Policy Iteration (RPPI), a novel policy iteration algorithm for solving long-run average reward polytopic RMDPs. Our experimental evaluation shows that RPPI is much more efficient in solving long-run average reward polytopic RMDPs compared to state-of-the-art methods based on value iteration. }, author = {Chatterjee, Krishnendu and Kafshdar Goharshadi, Ehsan and Karrabi, Mehrdad and Novotný, Petr and Zikelic, Dorde}, booktitle = {33rd International Joint Conference on Artificial Intelligence}, isbn = {9781956792041}, issn = {1045-0823}, location = {Jeju, South Korea}, pages = {6707--6715}, publisher = {International Joint Conferences on Artificial Intelligence}, title = {{Solving long-run average reward robust MDPs via stochastic games}}, doi = {10.24963/ijcai.2024/741}, year = {2024}, } @inbook{18167, abstract = {Holdase chaperones are essential in the mitochondrial membrane-protein biogenesis as they stabilize preproteins and keep them in an import-competent state as they travel through the aqueous cytosol and intermembrane space. The small TIM chaperones of the mitochondrial intermembrane space function within a fine balance of client promiscuity and high affinity binding, while being also able to release their client proteins without significant energy barrier to the downstream insertases/translocases. The tendency of the preproteins to aggregate and the dynamic nature of the preprotein—chaperone complexes makes the preparation of these complexes challenging. Here we present two optimized methods for complex formation of highly hydrophobic precursor proteins and chaperones: a pull-down approach and an in-vitro translation strategy. In the former, attaching the client protein to an affinity resin keeps the individual client protein copies apart from each other and decreases the client self-aggregation probability, thereby favouring complex formation. In the latter approach, a purified chaperone, added to the cell-free protein synthesis, captures the nascent precursor protein. The choice of method will depend on the desired client-chaperone complex amount, or the need for specific labeling scheme.}, author = {Guillerm, Undina and Sučec, Iva and Schanda, Paul}, booktitle = {Methods in Enzymology}, issn = {0076-6879}, pages = {391--422}, publisher = {Elsevier}, title = {{Generation of TIM chaperone substrate complexes}}, doi = {10.1016/bs.mie.2024.07.051}, volume = {707}, year = {2024}, } @article{18168, abstract = {Despite the considerable interest in the recombinant production of synthetic spider silk fibers that possess mechanical properties similar to those of native spider silks, such as the cost-effectiveness, tunability, and scalability realization, is still lacking. To address this long-standing challenge, we have constructed an artificial spider silk gene using Golden Gate assembly for the recombinant bacterial production of dragline-mimicking silk, incorporating all the essential components: the N-terminal domain, a 33-residue-long major-ampullate-spidroin-inspired segment repeated 16 times, and the C-terminal domain (N16C). This designed silk-like protein was successfully expressed in Escherichia coli, purified, and cast into films from formic acid. We produced uniformly 13C–15N-labeled N16C films and employed solid-state magic-angle spinning nuclear magnetic resonance (NMR) for characterization. Thus, we could demonstrate that our bioengineered silk-like protein self-assembles into a film where, when hydrated, the solvent-exposed layer of the rigid, β-nanocrystalline polyalanine core undergoes a transition to an α-helical structure, gaining mobility to the extent that it fully dissolves in water and transforms into a highly dynamic random coil. This hydration-induced behavior induces chain dynamics in the glycine-rich amorphous soft segments on the microsecond time scale, contributing to the elasticity of the solid material. Our findings not only reveal the presence of structurally and dynamically distinct segments within the film’s superstructure but also highlight the complexity of the self-organization responsible for the exceptional mechanical properties observed in proteins that mimic dragline silk.}, author = {Wu, Dongqing and Koscic, Anamaria and Schneider, Sonja and Dubini, Romeo C. A. and Rodriguez Camargo, Diana C. and Schneider, Sabine and Rovo, Petra}, issn = {1526-4602}, journal = {Biomacromolecules}, number = {3}, pages = {1759--1774}, publisher = {American Chemical Society}, title = {{Unveiling the dynamic self-assembly of a recombinant dragline-silk-mimicking protein}}, doi = {10.1021/acs.biomac.3c01239}, volume = {25}, year = {2024}, } @article{18171, abstract = {Defense against pathogens and parasites requires substantial investment of energy and resources on part of the host. This makes the host immune function dependent on availability and accessibility of resources. A resource deprived host is therefore expected to be more susceptible to infections, although empirical results do not always align with this prediction. Limiting host access to resources can additionally impact within-host pathogen numbers, either directly by altering the amount of resources available to the pathogens for proliferation or indirectly by altering the efficiency of the host immune system. We tested for the effects of host starvation (complete deprivation of resources) on susceptibility to bacterial pathogens, and within-host pathogen proliferation, in Drosophila melanogaster females. Our results show that starvation increases post-infection mortality of the host, but in a pathogen-specific manner. This increase in mortality is always accompanied by increased within-host pathogen proliferation. We therefore propose that starvation compromises host resistance to bacterial infections in Drosophila melanogaster females thereby increasing susceptibility to infections.}, author = {Basu, Aabeer and Singh, Aparajita and Sehgal, Suhaas and Madaan, Tanvi and Prasad, Nagaraj Guru}, issn = {1096-0805}, journal = {Journal of Invertebrate Pathology}, number = {11}, publisher = {Elsevier}, title = {{Starvation increases susceptibility to bacterial infection and promotes systemic pathogen proliferation in Drosophila melanogaster females}}, doi = {10.1016/j.jip.2024.108209}, volume = {207}, year = {2024}, } @article{18172, abstract = {Red Giant stars host solar-like oscillations which have mixed character, being sensitive to conditions both in the outer convection zone and deep within the interior. The properties of these modes are sensitive to both core rotation and magnetic fields. While asteroseismic studies of the former have been done on a large scale, studies of the latter are currently limited to tens of stars. We aim to produce the first large catalogue of both magnetic and rotational perturbations. We jointly constrain these parameters by devising an automated method for fitting the power spectra directly. We successfully apply the method to 302 low-luminosity red giants. We find a clear bimodality in core rotation rate. The primary peak is at δνrot = 0.32 μHz, and the secondary at δνrot = 0.47 μHz. Combining our results with literature values, we find that the percentage of stars rotating much more rapidly than the population average increases with evolutionary state. We measure magnetic splittings of 2σ significance in 23 stars. While the most extreme magnetic splitting values appear in stars with masses > 1.1M⊙, implying they formerly hosted a convective core, a small but statistically significant magnetic splitting is measured at lower masses. Asymmetry between the frequencies of a rotationally split multiplet has previously been used to diagnose the presence of a magnetic perturbation. We find that of the stars with a significant detection of magnetic perturbation, 43\% do not show strong asymmetry. We find no strong evidence of correlation between the rotation and magnetic parameters.}, author = {Hatt, Emily J. and Ong, J. M.Joel and Nielsen, Martin B. and Chaplin, William J. and Davies, Guy R. and Deheuvels, Sébastien and Ballot, Jérôme and Li, Gang and Bugnet, Lisa Annabelle}, issn = {1365-2966}, journal = {Monthly Notices of the Royal Astronomical Society}, number = {2}, pages = {1060--1076}, publisher = {Oxford University Press}, title = {{Asteroseismic signatures of core magnetism and rotation in hundreds of low-luminosity red giants}}, doi = {10.1093/mnras/stae2053}, volume = {534}, year = {2024}, } @article{18174, abstract = {We investigate the phase ordering (pattern formation) of systems of two-dimensional core–shell particles using Monte Carlo (MC) computer simulations and classical density functional theory (DFT). The particles interact via a pair potential having a hard core and a repulsive square shoulder. Our simulations show that on cooling, the liquid state structure becomes increasingly characterized by long wavelength density modulations and on further cooling forms a variety of other phases, including clustered, striped, and other patterned phases. In DFT, the hard core part of the potential is treated using either fundamental measure theory or a simple local density approximation, whereas the soft shoulder is treated using the random phase approximation. The different DFTs are benchmarked using large-scale grand-canonical-MC and Gibbs-ensemble-MC simulations, demonstrating their predictive capabilities and shortcomings. We find that having the liquid state static structure factor S(k) for wavenumber k is sufficient to identify the Fourier modes governing both the liquid and solid phases. This allows us to identify from easier-to-obtain liquid state data the wavenumbers relevant to the periodic phases and to predict roughly where in the phase diagram these patterned phases arise.}, author = {Wassermair, Michael and Kahl, Gerhard and Roth, Roland and Archer, Andrew J.}, issn = {1089-7690}, journal = {The Journal of chemical physics}, number = {12}, publisher = {AIP Publishing}, title = {{Fingerprints of ordered self-assembled structures in the liquid phase of a hard-core, square-shoulder system}}, doi = {10.1063/5.0226954}, volume = {161}, year = {2024}, } @inproceedings{18175, abstract = {Large-scale software repositories are a source of insights for software engineering. They offer an unmatched window into the software development process at scale. Their sheer number and size holds the promise of broadly applicable results. At the same time, that very size presents practical challenges for scaling tools and algorithms to millions of projects. A reasonable approach is to limit studies to representative samples of the population of interest. Broadly applicable conclusions can then be obtained by generalizing to the entire population. The contribution of this paper is a standardized experimental design methodology for choosing the inputs of studies working with large-scale repositories. We advocate for a methodology that clearly lays out what the population of interest is, how to sample it, and that fosters reproducibility. Along the way, we discourage researchers from using extrinsic attributes of projects such as stars, that measure some unclear notion of popularity.}, author = {Maj, Petr and Muroya Lei, Stefanie and Siek, Konrad and Di Grazia, Luca and Vitek, Jan}, booktitle = {38th European Conference on Object-Oriented Programming}, isbn = {9783959773416}, issn = {1868-8969}, location = {Vienna, Austria}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik}, title = {{The fault in our stars: Designing reproducible large-scale code analysis experiments}}, doi = {10.4230/LIPIcs.ECOOP.2024.27}, volume = {313}, year = {2024}, } @article{18176, abstract = {Introducing a class of SU(2) invariant quantum unitary circuits generating chiral transport, we examine the role of broken space-reflection and time-reversal symmetries on spin-transport properties. Upon adjusting parameters of local unitary gates, the dynamics can be either chaotic or integrable. The latter corresponds to a generalization of the space-time discretized (Trotterized) higher-spin quantum Heisenberg chain. We demonstrate that breaking of space-reflection symmetry results in a drift in the dynamical spin susceptibility. Remarkably, we find a universal drift velocity given by a simple formula, which, at zero average magnetization, depends only on the values of SU(2) Casimir invariants associated with local spins. In the integrable case, the drift velocity formula is confirmed analytically based on the exact solution of thermodynamic Bethe ansatz equations. Finally, by inspecting the large fluctuations of the time-integrated current between two halves of the system in stationary maximum-entropy states, we demonstrate violation of the Gallavotti-Cohen symmetry, implying that such states cannot be regarded as equilibrium ones. We show that the scaled cumulant generating function of the time-integrated current instead obeys a generalized fluctuation relation.}, author = {Zadnik, Lenart and Ljubotina, Marko and Krajnik, Žiga and Ilievski, Enej and Prosen, Tomaž}, issn = {2691-3399}, journal = {PRX Quantum}, number = {3}, publisher = {American Physical Society}, title = {{Quantum many-body spin ratchets}}, doi = {10.1103/PRXQuantum.5.030356}, volume = {5}, year = {2024}, }