@phdthesis{22735, author = {Lin, Peipeng}, isbn = {978-3-99078-088-6}, issn = {2663-337X}, pages = {88}, publisher = {Institute of Science and Technology Austria}, title = {{Calibrating the teacher synapse: Mechanisms and functional significance of presynaptic inhibition at hippocampal mossy fiber synapses}}, doi = {10.15479/AT-ISTA-22735}, year = {2026}, } @phdthesis{22775, author = {Jamrichova, Silvia}, isbn = {978-3-99078-085-5}, issn = {2663-337X}, pages = {153}, publisher = {Institute of Science and Technology Austria}, title = {{Timing and strength of synaptic transmission at hippocampal mossy fiber synapses in mice and humans}}, doi = {10.15479/AT-ISTA-22775}, year = {2026}, } @article{22923, abstract = {We study the sensitivity of the eigenvectors of random matrices, showing that even small perturbations make the eigenvectors almost orthogonal. More precisely, we consider two deformed Wigner matrices 𝑊 +đ·1, 𝑊 +đ·2 and show that their bulk eigenvectors become asymptotically orthogonal as soon as Tr⁥(đ·1âˆ’đ·2)2 ≫1, or their respective energies are separated on a scale much bigger than the local eigenvalue spacing. Furthermore, we show that quadratic forms of eigenvectors of 𝑊 +đ·1, 𝑊 +đ·2 with any deterministic matrix 𝐮 ∈𝐂𝑁×𝑁 in a specific subspace of codimension one are of size 𝑁−1/2. This proves a generalization of the eigenstate thermalization hypothesis to eigenvectors belonging to two different spectral families.}, author = {Cipolloni, Giorgio and Erdös, LĂĄszlĂł and Henheik, Sven Joscha and Kolupaiev, Oleksii}, issn = {1050-5164}, journal = {Annals of Applied Probability}, keywords = {characteristic flow, Davis–Kahan theorem, Eigenstate thermalization, Eigenvector perturbation theory, Local law, zigzag strategy}, number = {4}, pages = {3707--3756}, publisher = {Institute of Mathematical Statistics}, title = {{Eigenvector decorrelation for random matrices}}, doi = {10.1214/26-AAP2318}, volume = {36}, year = {2026}, } @inbook{22925, abstract = {Hybridization Chain Reaction (HCR) enables highly sensitive and multiplexed detection of mRNA with subcellular spatial resolution. It employs fluorophore-tagged DNA hairpins that self-assemble on target-bound probe pairs, amplifying the signal without enzymatic reactions. Here, we describe a protocol for performing HCR fluorescence in situ hybridization (FISH) on Xenopus tissue sections. We also outline a semi-automated image analysis pipeline that enables per-cell quantification of probe expression and co-localization. This method provides a robust and quantitative approach for visualizing gene expression patterns in situ.}, author = {Vijatovic, David and Papadopoulos, Stavros and Dalla Vecchia, Marco and Sweeney, Lora Beatrice Jaeger}, booktitle = {Xenopus}, editor = {Beck, Caroline W.}, issn = {1940-6029}, keywords = {mRNA FISH, Hybridization chain reaction, In situ hybridization, Fluorescence microscopy}, pages = {245--259}, publisher = {Springer}, title = {{Fluorescent in situ mRNA hybridization (FISH) using Hybridization Chain Reaction (HCR) in Xenopus cryosections}}, doi = {10.1007/978-1-0716-5360-9_11}, volume = {3049}, year = {2026}, } @inproceedings{22920, abstract = {Two-player games on graphs are a classical framework for analyzing strategic decision making. In turn-based games, two players move a token along the edges of the graph, and the right to move the token is determined by the current vertex. In traditional bidding games - referred to as pure bidding games - the right to move the token is determined at each step through bidding; here we consider Richman bidding, where the winning player of a bid pays the losing player. The winner is decided based on a temporal or quantitative specification evaluated over the resulting infinite play. In this work, we combine turn-based games and pure bidding games into generalized bidding games, with player-1 vertices, player-2 vertices, and bidding vertices. This natural and simple generalization of bidding games has far-reaching consequences. First, we show that, as a model, generalized bidding games are more expressive than pure bidding games, and we provide several applications. Second, and most importantly, we show that generalized Richman bidding games are structurally equivalent to simple stochastic games, a well-studied model: they are linearly interreducible to each other. As was previously known, the special case of pure Richman bidding games corresponds to random-turn games. In other words, generalized bidding games extend pure bidding games in the same way that simple stochastic games extend random-turn games. We use this connection to solve generalized Richman bidding games for temporal (parity) and quantitative (mean-payoff and discounted-sum) specifications. From a computational perspective, we establish that generalized bidding games with parity and mean-payoff specifications retain the best known upper bounds for turn-based games and pure bidding games, namely NP∩coNP. Finally, we study a repair problem that asks whether bidding vertices can be assigned "owners" so as to bring the threshold budget required to win the game below a given target. This problem has direct applications in compositional policy synthesis for multi-objective settings, and we show it to be NP-complete.}, author = {Asadi, Ali and Henzinger, Thomas A and Kafshdar Goharshadi, Ehsan and Kebis, Pavol and Mallik, Kaushik}, booktitle = {37th International Conference on Concurrency Theory}, isbn = {9783959774475}, issn = {1868-8969}, keywords = {Bidding Games, Stochastic Games}, location = {Liverpool, United Kingdom}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fĂŒr Informatik}, title = {{Generalized bidding games: Where bidding and stochastic games meet}}, doi = {10.4230/LIPIcs.CONCUR.2026.13}, volume = {391}, year = {2026}, } @inproceedings{22919, abstract = {Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions. This difficulty also holds in turn-based stochastic games (TBSGs), where two adversarial players interact on a finite state space. In this work, we consider turn-based stochastic games with reachability objectives. For such settings, adversarial learning, in which players are adversarial even in the learning phase, is impossible. Therefore, the goal is to consider learning, in which both players learn the unknown model together. In this spirit, previous literature on PAC learning in TBSGs considers (a) public information shared by both players; and (b) centralized learning, which means that players share the same learning algorithm. In this work, our contribution is two-fold. First, we relax these strong assumptions and ensure learning: (i) with private information not shared with the other player; and (ii) decentralized learning where the players do not share the same learning algorithm. To the best of our knowledge, this work is the first positive result for decentralized and private information learning of TBSGs with reachability objectives. Second, we introduce a game-theoretic generalization of the Expected Conditional Distance (ECD) parameter, which measures the expected length of reaching the target set. We establish a polynomial-sample complexity bound with respect to the number of states, actions, ECD parameter, and inverses of error tolerance and failure probability.}, author = {Asadi, Ali and Chatterjee, Krishnendu and Kebis, Pavol}, booktitle = {37th International Conference on Concurrency Theory}, isbn = {9783959774475}, issn = {1868-8969}, keywords = {formal methods, games and logic, logical aspects of AI, model checking}, location = {Liverpool, United Kingdom}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fĂŒr Informatik}, title = {{PAC learning in turn-based stochastic games with reachability objectives: A decentralized private approach via expected conditional distance}}, doi = {10.4230/LIPIcs.CONCUR.2026.12}, volume = {391}, year = {2026}, } @article{22408, abstract = {Solitons—localized wave packets that travel without spreading—play a central role in understanding transport and properties of nonlinear systems. In quantum many-body systems, however, such robust excitations are typically destroyed by thermalization. Here, we theoretically demonstrate the existence of solitonic excitations in high-energy states of Rydberg atom chains in the regime of strong nearest-neighbor Rydberg blockade. These localized wave packets propagate directionally atop a special class of reviving initial states related to quantum many-body scars and are capable of carrying energy. Exhibiting long coherence times, these states constitute a form of non-ergodic quantum dynamics and can be efficiently implemented on Rydberg atom simulators. In this work, in addition to a phenomenological description of solitons, we identify their counterpart in a classical nonlinear dynamical system, demonstrate their potential use in quantum information transfer, and conjecture their relevance for anomalous energy transport reported in numerical studies of Rydberg atom arrays.}, author = {Kerschbaumer, Aron and Desaules, Jean-Yves Marc and Ljubotina, Marko and Serbyn, Maksym}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Quasi-solitons in Rydberg atom chains}}, doi = {10.1038/s41467-026-75598-1}, volume = {17}, year = {2026}, } @misc{21960, abstract = {Solitons - localized wave packets that travel without spreading - play a central role in understanding transport and properties of nonlinear systems. In quantum many-body systems, however, such robust excitations are typically destroyed by thermalization. Here, we theoretically demonstrate the existence of solitonic excitations in high-energy states of Rydberg atom chains in the regime of strong nearest-neighbor Rydberg blockade. These localized wave packets propagate directionally atop a special class of reviving initial states related to quantum many-body scars and are capable of carrying energy. Exhibiting long coherence times, these states constitute a form of non-ergodic quantum dynamics and can be efficiently implemented on Rydberg atom simulators. In this work, in addition to a phenomenological description of solitons, we identify their counterpart in a classical nonlinear dynamical system, demonstrate their potential use in quantum information transfer, and conjecture their relevance for anomalous energy transport reported in numerical studies of Rydberg atom arrays.}, author = {Kerschbaumer, Aron}, publisher = {Institute of Science and Technology Austria}, title = {{Research Data: "Quasi-solitons in Rydberg atom chains"}}, doi = {10.15479/AT-ISTA-21960}, year = {2026}, } @phdthesis{22808, abstract = {As automated decision-makers have become ubiquitous in many domains of life, their decisions have become increasingly consequential. Recent years have shown that such systems can exhibit discriminatory behaviour against individuals and social groups alike, thereby amplifying existing biases and entrenching socio-economic disparities over time. Algorithmic fairness addresses this problem by developing methods to quantify and mitigate unfair behaviour. However, much of the existing literature studies fairness in a static pre-deployment setting and, therefore, neglects that automated decision-makers are often deployed in dynamic environments, where their behaviour and the populations they affect may change over time. This thesis addresses this gap through the lens of runtime verification. Instead of treating fairness as a property of a classifier together with a fixed input distribution, it reframes fairness as a property of the interaction trace between the decision-maker and its deployment environment. To evaluate such sequential fairness properties, the thesis develops runtime monitors that observe the evolving interaction between the system and the environment and issue verdicts after each new observation. Because, these monitors are designed to detect unfair behaviour during deployment, they complement fair training, auditing, verification, and enforcement by providing an additional layer of mathematically rigorous fairness assurance. In summary, the thesis develops quantitative, trace-based analogues of classical group and individual fairness measures and constructs monitors for them. This includes monitors for long-run group fairness over Markovian traces, for the time-varying welfare of a changing population in a dynamical system, and for the individual fairness of an arbitrary system generating a trace of inputs and outputs. To achieve this, the monitors combine ideas from runtime verification, sequential statistics, and nearest-neighbour search. In the group-fairness settings, monitoring is primarily a sequential statistical estimation problem: the monitor must construct statistically sound interval estimates of fairness values from dependent and partially observed interactions. In the individual-fairness setting, the main challenge is computational efficiency: the monitor must detect individual fairness violations by efficiently comparing the current decision with all previously observed decisions. }, author = {Kueffner, Konstantin}, isbn = {978-3-99078-089-3}, issn = {2663-337X}, pages = {183}, publisher = {Institute of Science and Technology Austria}, title = {{Monitoring algorithmic fairness in sequential decision making}}, doi = {10.15479/AT-ISTA-22808}, year = {2026}, } @phdthesis{22873, author = {Ehrmann, Andreas}, isbn = {978-3-99078-092-3}, issn = {2663-337X}, keywords = {PhD Thesis, functional nanomachines, biological functionality, nanotechnology, energy delivery, target behavior, dynamics, design principles, optimization, differentiable statistical physics, machine learning}, pages = {168}, publisher = {Institute of Science and Technology Austria}, title = {{Biological functionality without biochemistry: Designing nanomachines for target behavior}}, doi = {10.15479/AT-ISTA-22873}, year = {2026}, } @phdthesis{22857, abstract = {Artificial intelligence and machine learning have undergone an unprecedented evolution in the past decade, motivating a research effort toward a theory able to capture the qualitative behavior of large-scale neural systems. A central puzzle has been the clear benefit of scaling architecture size and overfitting the training set in supervised learning tasks. This evidence, in apparent contradiction with classical statistical learning theory, pushed researchers to develop a new theory capturing the interplay between the algorithmic and architectural bias of training and the specific target function, differently from previous methods rooted in uniform stability. This approach has enabled a grounded understanding of novel learning regimes, typically through formal limits where the number of training samples $n$, data dimensions $d$, and model parameters $p$ grow to infinity at different rates. \\ In this thesis, we follow this approach, focusing on the trustworthiness of high-dimensional models: properties that are difficult to control during training or deployment and often emerge under unpredictable or adversarial conditions. In such settings, it is crucial to formally ensure a priori the reliability of machine learning systems. First, we study data memorization, both as label fitting and as the storage of private information about training samples in trained parameters. We prove that $p = \Omega(n)$ parameters are sufficient for a deep neural network to memorize a generic set of labels, and for a model to memorize spurious features across training data. We then give evidence that $p = \Omega(dn)$ parameters are instead necessary for an adversary to reconstruct the full training set from the trained parameters. Second, we study robustness, both to adversarial perturbations and to distribution shift. We first prove that $p = \Omega(dn)$ parameters can be sufficient for a class of neural networks to overfit the training data while guaranteeing robustness to adversarial perturbations. Then, we focus on spurious correlations learning in high-dimensional regression, studying the effect of the ridge regularization parameter in the proportional regime $n = \Theta(d)$, and connecting it via an equivalence argument to the role of over-parameterization $p = \Omega(n)$ in neural networks. We also investigate the architectural bias of attention-based networks, showing that they are sensitive to the replacement of individual words in an embedded sentence, allowing them to generalize on sentences where the contextual meaning depends on one or few words. Finally, we study differentially private optimization in high-dimensional regimes. We prove that standard private gradient methods do not suffer in the over-parameterized regime $p = \Omega(n)$, challenging the current wisdom based on stability-derived generalization bounds. We then consider linear regression in the proportional regime $n = \Theta(d)$, showing that standard private gradient descent can achieve optimal rates under appropriate hyper-parameter scaling, such as sufficiently small gradient clipping constants, whose role is still debated in practice.}, author = {Bombari, Simone}, isbn = {978-3-99078-091-6}, issn = {2663-337X}, keywords = {machine learning, high-dimensional statistics, deep learning theory, privacy, memorization, robustness}, pages = {446}, publisher = {Institute of Science and Technology Austria}, title = {{Trustworthy machine learning in high dimensions}}, doi = {10.15479/AT-ISTA-22857}, year = {2026}, } @inproceedings{22894, abstract = {Large-scale deep learning models are known to memorize parts of the training set. In machine learning theory, memorization is often framed as interpolation or label fitting, and classical results show that this can be achieved when the number of parameters p in the model is larger than the number of training samples n. In this work, we consider memorization from the perspective of data reconstruction, demonstrating that this can be achieved when p is larger than dn, where d is the dimensionality of the data. More specifically, we show that, in the random features model, when p ≫ dn, the subspace spanned by the training samples in feature space gives sufficient information to identify the individual samples in input space. Our analysis suggests an optimization method to reconstruct the dataset from the model parameters, and we demonstrate that this method performs well on various architectures (random features, two-layer fully-connected and deep residual networks). Our results reveal a law of data reconstruction, according to which the entire training dataset can be recovered as p exceeds the threshold dn. }, author = {Iurada, Leonardo and Bombari, Simone and Tommasi, Tatiana and Mondelli, Marco}, booktitle = {14th International Conference on Learning Representations}, isbn = {9798331339678}, location = {Rio de Janeiro, Brazil}, pages = {145275--145314}, publisher = {OpenReview}, title = {{A law of data reconstruction for random features (and beyond)}}, volume = {2026}, year = {2026}, } @inproceedings{22921, abstract = {Runtime monitoring of quantitative signals faces a fundamental trade-off between volatility and over-aggregation: instantaneous observations are noisy, while long-run averages obscure local structure. Localisation measures such as discounted averages offer a principled middle ground, yet remain poorly understood in runtime verification. This paper studies discounted sums from a monitoring perspective, in both deterministic and stochastic settings. We formalize the discounted monitoring problem and show that exact, sound monitoring of discounted sums cannot be achieved with finite memory. To overcome this impossibility, we introduce Δ-approximately sound monitoring, deriving explicit bounds on memory and observation requirements. We then extend the framework to stochastic processes via expected discounted sums, defining pointwise and uniform (Δ,ÎŽ)-soundness notions, establishing statistical optimality, and proving impossibility beyond a precision threshold. We also formalize the resource complexity of deterministic discounted monitoring via affine register machines and prove a tight worst-case lower bound. Finally, we present a specification language for arithmetic expressions over multiple discounted sums with synchronous and asynchronous semantics, and evaluate our approach on practical scenarios including algorithmic fairness.}, author = {Cano Cordoba, Filip and Henzinger, Thomas A and Kueffner, Konstantin and Sarac, Naci E}, booktitle = {37th International Conference on Concurrency Theory}, isbn = {9783959774475}, issn = {1868-8969}, keywords = {Runtime Verification, Probabilistic Systems, Quantitative Verification, Approximate Monitoring}, location = {Liverpool, United Kingdom}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fĂŒr Informatik}, title = {{Monitoring discounted sum properties}}, doi = {10.4230/LIPIcs.CONCUR.2026.22}, volume = {391}, year = {2026}, } @misc{22777, author = {Tong, Xin}, publisher = {Institute of Science and Technology Austria}, title = {{Towards a deeper understanding of meroblastic cleavage - supplementary movies}}, doi = {10.15479/AT-ISTA-22777}, year = {2026}, } @article{22957, abstract = {Linguistic inequality remains a silent barrier in contemporary science, particularly in rapidly evolving and interdisciplinary fields such as bioinformatics. This article examines how the Spanish-speaking community has responded to this gap through the creation of the Simposio de Estudiantes Hispanohablantes de BioinformĂĄtica y BiologĂ­a Computacional (SEH2Bioinfo), a collective initiative that transforms language into a tool for inclusion and collaboration. Drawing on a historical review of the use of Spanish in scientific communication and an analysis of the two editions of SEH2Bioinfo (2022 and 2025), complemented by surveys of more than 1500 participants, we explore the effects of language on confidence, participation, and academic visibility. The findings show that Spanish can serve as a space for training and mentorship without compromising rigor, while simultaneously strengthening international networks and promoting open science. The growth of SEH2Bioinfo, its collaborative approach, and its intercontinental reach establish the symposium as a model for linguistic equity in scientific practice. Its experience demonstrates that expanding linguistic diversity in bioinformatics is not only possible but essential to building a global scientific community that is fairer, more connected, and representative.}, author = {Padilla Franzotti, Carla L and OlguĂ­n-Orellana, Gabriel J and Palopoli, Nicolas and Urquiza-Zurich, SebastiĂĄn and Garcia-Moreno, Adrian and MonzĂłn, Sara and Cabrera-Pasadas, MĂłnica and Soler SĂĄez, Irene and Perpiñå-ClĂ©rigues, Carla and Puche Quiñonez, Rafael J and VĂ©lez Segura, Jennifer and Lovaco-Flores, Jose Abel and Osorio MogollĂłn, Cleidy M and Esquinas-RomĂĄn, Eva M and Miserendino, Maria C and Eranti, Pradeep and Castillo-Orozco, Ana and Cuesta-Astroz, Yesid and Parra, R Gonzalo}, issn = {2635-0041}, journal = {Bioinformatics Advances}, number = {1}, publisher = {Oxford University Press}, title = {{Bioinformatics in Español: Expanding open, diverse, and collaborative science through SEH2Bioinfo}}, doi = {10.1093/bioadv/vbag182}, volume = {6}, year = {2026}, } @phdthesis{21198, abstract = {In recent years there has been a massive increase in the amount of data generated in a decentralized manner. Ever more powerful edge devices, such as smartphones, have become ubiquitous in most societies on earth. Through text typed, photos taken and apps used, these devices, which we refer to as clients, generate enormous amounts of high quality and complex data. Moreover, the nature of these devices means the data they generate is often sensitive and privacy concerns prevent it being gathered and stored in a central location. This presents a challenge to the modern machine learning paradigm that requires central access to large amounts of data. Federated learning (FL) has emerged as one of the answers to this problem. Rather than bringing the data to the model, FL sends the model to the data. Model training takes place on device, with periodically synchronized updates, allowing data to remain locally stored. While this approach offers significant privacy advantages it comes with its own set of unique challenges. These include: data heterogeneity, the notion that different devices generate data in distinct ways which can negatively impact training dynamics; systems heterogeneity, meaning that different devices may have differing hardware specifications; high communication costs, which are induced by the repeated transferring of models over the network and low device computational power, which limits the use of larger models on device. In this thesis we present a range of methods for federated learning. We focus primarily on the challenge of data heterogeneity, though the methods presented are designed to be well adapted to the other challenges of a federated setting, such as the constraints of limited compute and communication overhead. We first present a method for explicitly modeling client data heterogeneity. The approach formulates clients as samples from a certain probability distribution and infers the parameters of this distribution from the available training clients. This learned distribution then represents the heterogeneity present among the clients and can be sampled from in order to create new simulated clients that are similar to the real clients we have observed so far. Following this we present two methods for directly dealing with data heterogeneity through personalization. Highly heterogeneous client data distributions can mean that learning a single global model becomes suboptimal, and some form of personalization of models to each individual client is required. Our approaches are based around hypernetworks, which we use to generate personalized model parameters without the need for additional training or finetuning. In the first approach we focus on generating full parameterizations of client models using learned embeddings of client data and labels, with a hypernetwork located on the central server. In the second approach we address the more challenging scenario where we want to generate a personalized model for a client without any label information. The hypernetwork is trained to generate a low dimensional representation of a client’s personalized model parameters, allowing it to be transferred to and run on the client devices. In our final presented method, we change our focus and rather than aim to directly address the challenge of data heterogeneity, we instead ensure we are unaffected by it. This is done in the context of k-means clustering and we present a method for federated clustering with a focus on added privacy guarantees.}, author = {Scott, Jonathan A}, issn = {2663-337X}, pages = {158}, publisher = {Institute of Science and Technology Austria}, title = {{Data heterogeneity and personalization in federated learning}}, doi = {10.15479/AT-ISTA-21198}, year = {2026}, } @phdthesis{21854, abstract = {As neural-network-based models grow both in size and popularity, interest has grown in making the models smaller and more efficient to train. To that end, many methods have been proposed to prune models by reducing their number of nonzero parameters. Additionally, parameter-efficient fine-tuning, in which a much smaller number of parameters than the total contained in the model is updated during training, has become very popular, especially in the space of Large Language Models. At the same time, the increasingly routine deployment of machine learning in real-world applications has spurred a drive to make them more trustworthy - in the sense of, among other things, being unbiased, interpretable, and editable. In this thesis, we examine the interplay between efficiency and trustworthiness. First, we analyze the effects of model pruning on bias in computer vision models, demonstrating that increased sparsity leads to greater bias, largely as a function of increased model uncertainty in marginal cases. Based on this observation, we propose several bias mitigation techniques. Then, we demonstrate that example-specific model pruning can improve model interpretation methods while improving pruning efficiency to make example-specific model pruning feasible in real time. Then, we investigate the effectiveness of parameter-efficient and data-efficient model personalization via fine-tuning, demonstrating that it is highly feasible with very small computational and data resources. Finally, we consider efficiency in editing model knowledge using a custom synthetic data framework, demonstrating that parameter-efficient, low-rank fine-tuning frequently outperforms full-rank fine-tuning, and, additionally, that restricting which model blocks are fine-tuned frequently improves results. Together, the results in this thesis provide new insights and techniques for combining trustworthiness and efficiency during neural network inference and training. }, author = {Iofinova, Eugenia B}, issn = {2663-337X}, pages = {237}, publisher = {Institute of Science and Technology Austria}, title = {{On the utility and effects of efficiency in artificial neural networks}}, doi = {10.15479/AT-ISTA-21854}, year = {2026}, } @article{21982, abstract = {A floating Leidenfrost droplet exhibits curvature inversion of its underside, due to the balance of vapor pressure and surface tension. Using interferometric imaging, we find different behavior for a levitated hydrogel sphere. Curvature inversion is observed briefly just after deposition, but quickly gives way to a steady state with no inversion. We show the essential role of vaporization in shaping the underbelly of the hydrogel, where changes due to direct mass loss are more significant than the balance of vapor pressure and elastic forces.}, author = {Diaz Melian, Vicente L and Lenton, Isaac C and Binysh, Jack and Souslov, Anton and Waitukaitis, Scott R}, issn = {2470-0053}, journal = {Physical Review E}, number = {5}, publisher = {American Physical Society}, title = {{Geometry of the vapor layer under a Leidenfrost hydrogel sphere}}, doi = {10.1103/m7gr-2t6j}, volume = {113}, year = {2026}, } @phdthesis{23005, abstract = {When a Leidenfrost droplet floats on its own vapor layer, the interplay between vapor pressure, surface tension, and gravity determines its shape, and in the stable regime the underside of the droplet exhibits curvature inversion. Using interferometric imaging, we observe a different behavior for a levitated hydrogel sphere. Curvature inversion appears briefly after deposition, but the continuous vaporization process removes this feature, leading first to an oscillatory regime and eventually to a steady state without curvature inversion. We demonstrate the essential role of vaporization in shaping the hydrogel underbelly, where direct mass loss has a stronger influence than the balance between vapor pressure and elastic forces.}, author = {Diaz Melian, Vicente L}, issn = {2663-337X}, pages = {114}, publisher = {Institute of Science and Technology Austria}, title = {{The morphology underneath a levitated hydrogel sphere}}, doi = {10.15479/AT-ISTA-23005}, year = {2026}, } @phdthesis{22984, author = {Teplova, Anastasiia}, isbn = {978-3-99078-093-0}, issn = {2663-337X }, pages = {190}, publisher = {Institute of Science and Technology Austria}, title = {{Chasing the elusive: Investigating nucleotide cyclase activity in gibberellin and strigolactone signalling; Cellular mechanisms of gravity sensing and response in roots}}, doi = {10.15479/AT-ISTA-22984}, year = {2026}, }