@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 für 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},
}

@inproceedings{22918,
  abstract     = {Given a weighted digraph G, a (t,g,μ)-DAG cover is a collection of g dominating DAGs D_1,… ,D_g such that all distances are approximately preserved: for every pair (u,v) of vertices, min_id_{D_i}(u,v) ≤ t⋅ d_G(u,v), and the total number of non-G edges is bounded by |(∪_i D_i)⧵ G| ≤ μ. Assadi, Hoppenworth, and Wein [STOC 25] and Filtser [SODA 26] studied DAG covers for general digraphs. This paper initiates the study of Steiner DAG cover, where the DAGs are allowed to contain Steiner points. 
We obtain Steiner DAG covers on the important classes of planar digraphs and low-treewidth digraphs. Specifically, we show that any digraph with treewidth tw admits a (1,2,Õ(n⋅tw))-Steiner DAG cover. For planar digraphs we provide a (1+ε,2,Õ_ε(n))-Steiner DAG cover.
We also demonstrate a stark difference between Steiner and non-Steiner DAG covers. As a lower bound, we show that any non-Steiner DAG cover for graphs with treewidth 1 with stretch t < 2 and sub-quadratic number of extra edges requires Ω(log n) DAGs.},
  author       = {Bhore, Sujoy and Chang, Hsien Chih and Conroy, Jonathan and Filtser, Arnold and Oh, Eunjin and Wein, Nicole and Zheng, Da Wei},
  booktitle    = {34th Annual European Symposium on Algorithms},
  isbn         = {9783959774451},
  issn         = {1868-8969},
  keywords     = {Directed graphs, DAG (directed acyclic graphs), distortion, metric embeddings, planar graph, treewidth},
  location     = {L’Aquila, Italy},
  publisher    = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
  title        = {{DAG covers for structured graphs: The Steiner point effect}},
  doi          = {10.4230/LIPIcs.ESA.2026.94},
  volume       = {388},
  year         = {2026},
}

@inproceedings{22917,
  abstract     = {A heap is a dynamic data structure that stores a set of labeled values under the following operations: pop returns the minimum value of the heap, Push(x_i) pushes a new value x_i onto the heap, and DecreaseKey(i, v) decreases the value x_i to v. A working-set heap is a heap that supports the x_i ← pop() operation in O(log Γ(x_i)) time where Γ(x_i) is the size of the working set: the number of elements that were pushed onto the heap while x_i was in the heap. The goal of working set heap design is to maintain the working set property while minimizing the overhead of the Push and DecreaseKey operations. On a word RAM, there exist working set heaps that support Push and DecreaseKey in amortized constant time. In this paper, we show via a simple construction that pointer machines, one of the most general and least-assuming computational models, support working set heaps that support Push in amortized constant time and DecreaseKey in inverse-Ackermann time. A by-product of this analysis is that Dijkstra’s shortest path algorithm can be near-universally optimal on a pointer machine - incurring only an additive O(m α(m)) overhead compared to the optimal running time for distance ordering, where m denotes the number of edges in the graph.},
  author       = {Van Der Hoog, Ivor and Iacono, John and Rotenberg, Eva and Rutschmann, Daniel P},
  booktitle    = {34th Annual European Symposium on Algorithms},
  isbn         = {9783959774451},
  issn         = {1868-8969},
  keywords     = {Data structures, graph algorithms, amortized analysis},
  location     = {L’Aquila, Italy},
  publisher    = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
  title        = {{Near-optimal working-set heaps and dijkstra on pointer machines}},
  doi          = {10.4230/LIPIcs.ESA.2026.45},
  volume       = {388},
  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},
}

@article{22941,
  abstract     = {Argonaute proteins mediate RNA interference in eukaryotes and nucleic‐acid defence in prokaryotes. Although these systems operate in distinct biological contexts and produce diverse downstream outcomes, they share a conserved structural architecture and mechanism of guide‐dependent target recognition. Here, we argue that Argonautes function as programmable surveillance platforms that kinetically sample nucleic acids, engage targets through sequential pairing and conformational checkpoints, and couple recognition to functional outputs through regulated structural transitions. Rather than representing distinct recognition mechanisms, the diverse targeting behaviours observed across Argonaute families reflect different implementations of this shared framework, in which guide biogenesis, target accessibility, pairing requirements and effector coupling are weighted differently according to biological context. Viewing Argonautes through this common operational logic reconciles apparent differences in specificity, mismatch tolerance and target selection across systems, while providing a unified conceptual framework for understanding their evolution and guiding the engineering of programmable nucleic‐acid recognition technologies.},
  author       = {Bravo, Jack Peter Kelly},
  issn         = {1873-3468},
  journal      = {FEBS Letters},
  publisher    = {Wiley},
  title        = {{‘Guide and Prejudice’ -  How Argonautes recognize targets across domains of life}},
  doi          = {10.1002/1873-3468.70450},
  year         = {2026},
}

@article{21501,
  abstract     = {Kinetically constrained models were originally introduced to capture slow relaxation in glassy systems, where dynamics are hindered by local constraints instead of energy barriers. Their quantum counterparts have recently drawn attention for exhibiting highly degenerate eigenstates at zero energy—known as zero modes—stemming from chiral symmetry. Yet, the structure and implications of these zero modes remain poorly understood. In this work, we focus on the properties of the zero mode subspace in quantum kinetically constrained models with a U(1) particle-conservation symmetry. We use the U(1) East, which lacks inversion symmetry, and the inversion-symmetric U(1) East-West models to illustrate our two main results. First, we observe that the simultaneous presence of constraints and chiral symmetry generally leads to a parametric increase in the number of zero modes due to the fragmentation of the many-body
Hilbert space into disconnected sectors. Second, we generalize the concept of compact localized states from single-particle physics and introduce the notion of collective bound states, a special kind of nonergodic eigenstates that are robust to enlarging the system size. We formulate sufficient criteria for their existence, arguing that the degenerate zero mode subspace plays a central role, and demonstrate bound states in both example models and in a two-dimensional model, the U(1) North-East, and in the pairflip model, a system without particle conservation. Our results motivate a systematic study of bound states and their relation to ergodicity breaking, transport, and other properties of quantum kinetically constrained
models. },
  author       = {Nicolau Jimenez, Eulalia and Ljubotina, Marko and Serbyn, Maksym},
  issn         = {2691-3399},
  journal      = {PRX Quantum},
  publisher    = {American Physical Society},
  title        = {{Fragmentation, zero modes, and collective bound states in constrained models}},
  doi          = {10.1103/sl79-1xgb},
  volume       = {7},
  year         = {2026},
}

@inproceedings{22245,
  abstract     = {A single-commodity congestion approximator for a graph is a compact data structure that approximately predicts the edge congestion required to route any set of single-commodity flow demands in a network. A hierarchical congestion approximator (HCA) consists of a laminar family of cuts in the graph and has numerous applications in approximating cut and flow problems in graphs, designing efficient routing schemes, and managing distributed networks.
There is a tradeoff between the running time for computing an HCA and its approximation quality. The best polynomial-time construction in an n-node graph gives an HCA with approximation quality O(log1.5n loglogn). Among near-linear time algorithms, the best previous result achieves approximation quality O(log4 n). We improve upon the latter result by giving the first near-linear time algorithm for computing an HCA with approximation quality O(log2 n loglogn). Additionally, our algorithm can be implemented in the parallel setting with polylogarithmic span and near-linear work, achieving the same approximation quality. This improves upon the best previous such algorithm, which has an O(log9n) approximation quality. We also present a lower bound of Ω(logn) for the approximation guarantee of hierarchical congestion approximators.
Crucial for achieving a near-linear running time is a new partitioning routine that, unlike previous such routines, manages to avoid recursing on large subgraphs. To achieve the improved approximation quality, we introduce the new concept of border routability of a cut and provide an improved sparsest cut oracle for general vertex weights.},
  author       = {Henzinger, Monika H and Münk, Robin and Räcke, Harald},
  booktitle    = {58th Annual ACM Symposium on Theory of Computing},
  isbn         = {9798400725364},
  issn         = {0737-8017},
  keywords     = {Congestion Approximators, Hierarchical Graph Decompositions},
  location     = {Salt Lake City, UT, United States},
  pages        = {1417--1428},
  publisher    = {ACM},
  title        = {{An improved quality hierarchical congestion approximator in near-linear time}},
  doi          = {10.1145/3798129.3800851},
  year         = {2026},
}

@inproceedings{21921,
  abstract     = {A variety of problems in geometry processing boil down to finding the most
parallel field relative to a connection. Instances of this prototypical problem
show up in computing direction fields and stripe patterns, quadrilateral
meshing, and visualization of fluid flows. When the class of allowed fields
includes those with topological defects, a relaxation is required to make
the problem well-posed. We observe that these problems can be viewed
as synchronization problems, which admit a natural semidefinite relaxation.
We propose a unified method of solving all these problems via the efficient
Burer-Monteiro factorization method. Geometrically, this amounts to lifting the field values to a higher-dimensional manifold, naturally resolving
the singular nature of defects. Practically, we show that our convex relaxation method achieves better and more reliable optima than previous work
employing alternative relaxations},
  author       = {Pacheco-Tallaj, Natalia and Couplet, Matteo and Chien, Edward and Palmer, David},
  booktitle    = {SIGGRAPH Conference Papers},
  location     = {Los Angeles, CA, United States},
  publisher    = {ACM},
  title        = {{Synchronizing fields with singularities}},
  doi          = {10.1145/3799902.3811225},
  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},
}

@unpublished{22893,
  abstract     = {Many biological machines function through controlled conformational transitions, yet designing synthetic nanostructures with prescribed dynamical behavior remains a major challenge. Here, we develop a modular inverse-design framework for bistable nanostructures whose function is controlled by an energy profile along a geometric reaction coordinate. Inspired by proteins with rigid domains connected by flexible hinges, we introduce a hinge-arm paradigm in which a small bistable hinge controls the energetics of a conformational transition, while rigid arms map this transition onto the separation between external binding sites. Specifically, we ask which features of a target energy profile can be programmed under different design constraints. We find that the energy barriers and the binding-site separations in the two metastable states can be readily designed, while controlling the location of the transition state or the full shape of the energy profile requires additional design freedom. Using a differentiable design framework, we find that some optimized solutions are numerically inexact but still display the functional behavior for which the target profile was selected, emphasizing the importance of function-based evaluation criteria. These results establish a practical hierarchy of designability for bistable nanostructures and provide a route toward synthetic nanomachines that couple conformational transitions to target behavior.},
  author       = {Ehrmann, Andreas and Krstić, Marija and Samadzadeh, Sahar and Goodrich, Carl Peter},
  booktitle    = {arXiv},
  title        = {{Designing bistable nanostructures for target behavior}},
  doi          = {10.48550/arXiv.2606.31620},
  year         = {2026},
}

@unpublished{22892,
  abstract     = {Countless biological processes are fueled by energy-rich molecules like ATP and GTP that supply energy with extreme efficiency. However, designing similar energy-delivery schemes from the bottom up, essential for the development of powered nanostructures and other \emph{de novo} machinery, presents a significant challenge: how can an energy-rich structure be stable in solution yet still deliver this energy at precisely the right time? In this paper, we present a purely physical mechanism that solves this challenge, facilitating energy transfer akin to ATP hydrolysis, yet occurring between synthetic nanostructures without any biochemical interactions. This targeted energy delivery is achieved by exploiting a differentiable state-based model to balance the energy profiles that govern the structural transitions in the two nanostructures, creating a coupled relaxation pathway with minimal barriers that facilitates energy delivery. We verify the effectiveness and robustness of this mechanism through Langevin Dynamics simulations, demonstrating that a bath of the high-energy structures can systematically and repeatedly drive the target structure out of equilibrium, enabling it to perform tasks. As the mechanism operates only through explicit physical forces without any biochemistry or internal state variables, our results present generic and far-reaching design principles, setting the stage for the next generation of synthetic nanomachines.},
  author       = {Ehrmann, Andreas and Goodrich, Carl Peter},
  booktitle    = {arXiv},
  title        = {{Controlling energy delivery with bistable nanostructures}},
  doi          = {10.48550/arXiv.2506.14266},
  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},
}

@article{22950,
  abstract     = {Little red dots (LRDs) are compact, red sources discovered by JWST at high redshift (z ≳ 4), marked by distinctive “V-shaped” spectral energy distributions (SEDs) and often interpreted as rapidly accreting active galactic nuclei (AGNs). Their true nature remains unclear though, and their evolutionary connection to their lower-redshift counterparts is still poorly constrained. Thus, we present WISEA J123635.56+621424.2 (here dubbed the Saguaro), a z = 2.0145 galaxy in GOODS-North, as a possible analog of high-redshift LRDs and a potential missing link in their evolutionary path toward lower-redshift systems. It features a compact LRD-like nucleus surrounded by a face-on spiral host. Its connections to LRDs include the following: (1) its nuclear spectrum shows a clear “V-shaped” SED, and (2) when redshifted to z = 7, surface brightness dimming makes the host undetectable, thus mimicking an LRD. This suggests that high-redshift LRDs may be embedded in extended hosts. To test this, we stack rest-frame UV images of 99 photometrically selected LRDs, revealing faint, diffuse emission. Stacking in redshift bins reveals mild radial growth, consistent with the expected galaxy size evolution. A simple analytic model confirms that surface brightness dimming alone can explain their compact appearance. Lastly, we show that the Saguaro is not unique by describing similar objects from the literature at z ≲ 3.5. Taken together, our results support a scenario in which LRDs may not be a distinct population, but could be the visible nuclei of galaxies undergoing a short-lived, (perhaps) AGN-dominated evolutionary phase, with their compact, red appearance driven largely by observational biases.},
  author       = {Rinaldi, Pierluigi and Rieke, George H. and Wu, Zihao and Gilbert, Carys J. E. and Pacucci, Fabio and Barchiesi, Luigi and Alberts, Stacey and Carniani, Stefano and Bunker, Andrew J. and Bhatawdekar, Rachana and D’Eugenio, Francesco and Ji, Zhiyuan and Johnson, Benjamin D. and Hainline, Kevin and Kokorev, Vasily and Kumari, Nimisha and Iani, Edoardo and Lyu, Jianwei and Maiolino, Roberto and Parlanti, Eleonora and Robertson, Brant E. and Sun, Yang and Vignali, Cristian and Williams, Christina C. and Willmer, Christopher N. A. and Zhu, Yongda},
  issn         = {1538-4357},
  journal      = {The Astrophysical Journal},
  number       = {2},
  publisher    = {IOP Publishing},
  title        = {{Beyond the dot: An LRD-like nucleus at the heart of an IR-bright galaxy and its implications for high-redshift LRDs}},
  doi          = {10.3847/1538-4357/ae80cd},
  volume       = {1006},
  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 ε-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 (ε,δ)-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 für Informatik},
  title        = {{Monitoring discounted sum properties}},
  doi          = {10.4230/LIPIcs.CONCUR.2026.22},
  volume       = {391},
  year         = {2026},
}

@inproceedings{22922,
  abstract     = {In this note, we recall the history and our motivation behind the development of Strategy Logic and we discuss some of the work that ensued from its introduction.},
  author       = {Chatterjee, Krishnendu and Henzinger, Thomas A and Piterman, Nir},
  booktitle    = {37th International Conference on Concurrency Theory},
  isbn         = {9783959774475},
  issn         = {1868-8969},
  keywords     = {Strategy Logic, Games, Automata},
  location     = {Liverpool, United Kingdom},
  publisher    = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
  title        = {{A look back at strategy logic}},
  doi          = {10.4230/LIPIcs.CONCUR.2026.6},
  volume       = {391},
  year         = {2026},
}

@article{22951,
  abstract     = {Anesthesia recovery is critical for resuming normal physiological and neuronal functions; however, the mechanisms involved remain elusive. Here, we identify a female-selective corticosterone-mediated microglia-neuron interaction during ketamine anesthesia recovery, absent in males. This microglia-neuron interaction induces plastic and functional neuronal changes, as evidenced by increased mEPSC frequency, which was occluded upon microglia depletion. We showed that this process is driven through up-regulation of the stress-responsive co-chaperone Fkbp5 mRNA and its protein, Fkbp51, in female microglia. Fkbp5/Fkbp51 is a key intermediary in a corticosteroid-induced stress response, and its involvement points toward a critical interface between endocrine signaling and microglia. To counteract the observed ketamine anesthesia-mediated increase in blood corticosterone during recovery, we removed the primary source of corticosterone by adrenalectomy. Close microglia-neuron interaction was reduced and increased again following corticosterone injection. Our findings identify a sex-specific microglia-mediated mechanism of neuronal plasticity during anesthesia recovery, driven by corticosterone, thereby enhancing our understanding of sex differences in brain function.},
  author       = {Venturino, Alessandro and Alam, Amin and Negrello, Thomas and Jin, Kelly and van Velthoven, Cindy T. J. and Cubero, Ryan J and Yeung, Jake and Koppensteiner, Peter and Tasic, Bosiljka and Siegert, Sandra},
  issn         = {2375-2548},
  journal      = {Science Advances},
  number       = {31},
  publisher    = {AAAS},
  title        = {{Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia}},
  doi          = {10.1126/sciadv.adz6517},
  volume       = {12},
  year         = {2026},
}

@article{22955,
  abstract     = {The regulation of 3D cell shape is a fundamental problem of life. In multicellular tissues, cell shape emerges through the balance of forces inside and outside the cell. In epithelia, the basement membrane (BM) is the first extracellular barrier that cells sense biochemically and mechanically. Despite this, little is known about how BM mechanical properties are regulated and how they impact cell shape. Through mathematical modeling, we show that the stress relaxation time of the BM can regulate cell shape. Using molecular dynamics simulations, we show that the stress relaxation time of a collagen IV network can be inferred from the lifetime of collagen IV molecules. To measure collagen IV lifetime in vivo, we develop a fluorescent timer reporter for collagen IV and show that perlecan modifies collagen IV lifetime. This cross-disciplinary approach establishes a multiscale framework to probe matrix turnover, and its regulation and function in cell shape control.},
  author       = {Barrientos, Ricardo and Meadowcroft, Billie and Sánchez-Sánchez, Besaiz J. and Stramer, Brian M. and Paluch, Ewa K. and Charras, Guillaume and Banerjee, Shiladitya and Šarić, Anđela and Mao, Yanlan},
  issn         = {1097-4172},
  journal      = {Cell},
  number       = {18},
  pages        = {5611--5624.e4},
  publisher    = {Elsevier},
  title        = {{Basement membrane turnover controls cell shape}},
  doi          = {10.1016/j.cell.2026.07.010},
  volume       = {189},
  year         = {2026},
}