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Full text linkWe define and study classes of ω-regular automata for which the nondeterminism can be resolved by a policy that uses a combination of memory and randomness on any input word, based solely on the prefix read so far. We examine two settings for providing the input word to an automaton. In the first setting, called adversarial resolvability, the input word is constructed letter-by-letter by an adversary, dependent on the resolver’s previous decisions. In the second setting, called stochastic resolvability, the adversary pre-commits to an infinite word and reveals it letter-by-letter. In each setting, we require the existence of an almost-sure resolver, i.e., a policy that ensures that as long as the adversary provides a word in the language of the underlying nondeterministic automaton, the run constructed by the policy is accepting with probability 1.
The class of automata that are adversarially resolvable is the well-studied class of history-deterministic automata. The case of stochastically resolvable automata, on the other hand, defines a novel class. Restricting the class of resolvers in both settings to stochastic policies without memory introduces two additional new classes of automata. We show that the new automata classes offer interesting trade-offs between succinctness, expressivity, and computational complexity, providing a fine gradation between deterministic automata and nondeterministic automata
PMLR
No full textWe study privately releasing column sums of a d-dimensional table with entries from a universe χ undergoing T row updates, called histogram under continual release. Our mechanisms give better additive ℓ∞-error than existing mechanisms for a large class of queries and input streams. Our first contribution is an output-sensitive mechanism in the insertions-only model (χ = {0, 1}) for maintaining (i) the histogram or (ii) queries that do not require maintaining the entire histogram, such as the maximum or minimum column sum, the median, or any quantiles. The mechanism has an additive error of O(d log2 (dq∗) + log T) whp, where q∗ is the maximum output value over all time steps on this dataset. The mechanism does not require q∗ as input. This breaks the Ω(d log T) bound of prior work when q∗ ≪ T. Our second contribution is a mechanism for the turnstile model that admits negative entry updates (χ = {−1, 0, 1}). This mechanism has an additive error of O(d log2(dK) + log T) whp, where K is the number of times two consecutive data rows differ, and the mechanism does not require K as input. This is useful when monitoring inputs that only vary under unusual circumstances. For d = 1 this gives the first
private mechanism with error O(log2 K + log T) for continual counting in the turnstile model, improving on the O(log2 n + log T) error bound by Dwork et al. (2015), where n is the number of ones in the stream, as well as allowing negative entries, while Dwork et al. (2015) can only handle nonnegative entries (χ = {0, 1})
On-demand single-microwave-photon source in a superconducting circuit with wideband frequency tunability
Full text linkIn this article, we propose a method for generating single microwave photons in superconducting circuits. We theoretically show that pure single microwave photons can be generated on demand and tuned over a large frequency band by making use of Landau-Zener transitions under a rapid sweep of a control parameter. We devise a protocol that enables fast control of the frequency of the emitted photon over two octaves, without requiring extensive calibration. Additionally, we make theoretical estimates of the generation efficiency, tunability, purity, and linewidth of the photons emitted using this method for both charge- and flux-qubit-based architectures. We also provide estimates of the optimal device parameters required for these architectures to realize the device
Saprotrophic-mycorrhizal divide in stable isotope composition throughout the whole fungus: From mycelium to hymenophore
No full textMycorrhizal and saprotrophic macromycetes contribute strongly to the carbon and nitrogen cycles of forest ecosystems, often studied by tracing stable isotope composition of carbon and nitrogen. The phenomenon of the saprotrophic-mycorrhizal divide highlights the difference in the stable isotope composition of fruiting bodies of mycorrhizal and saprotrophic fungi. Much less is known about the isotopic composition of the mycelium, which plays an important role in the formation of the soil organic matter and fuels the fungal trophic channel in soil food webs. In this study, we assessed whether the saprotrophic-mycorrhizal divide in the natural δ13С and δ15N values can be traced throughout entire fungal organisms. This hypothesis was tested using 16 species of ectomycorrhizal and six species of saprotrophic basidiomycetous fungi. We showed that not only fruiting bodies, but also the mycelium of ectomycorrhizal and saprotrophic fungi differs in the δ13C and δ15N values. In both ectomycorrhizal and saprotrophic fungi, the δ13C and δ15N values increased from mycelium to hymenophores and correlated positively with the total N content in the corresponding tissues. The differences between ectomycorrhizal and saprotrophic mycelium can be used to reconstruct the fungal-driven belowground carbon and nitrogen allocation, and the contribution of saprotrophic and mycorrhizal fungi to soil food webs
Phase behavior of Cacio e Pepe sauce
Full text link“Pasta alla Cacio e pepe” is a traditional Italian dish made with pasta, pecorino cheese, and pepper. Despite its simple ingredient list, achieving the perfect texture and creaminess of the sauce can be challenging. In this study, we systematically explore the phase behavior of Cacio e pepe sauce, focusing on its stability at increasing temperatures for various proportions of cheese, water, and starch. We identify starch concentration as the key factor influencing sauce stability, with direct implications for practical cooking. Specifically, we delineate a regime where starch concentrations below 1% (relative to cheese mass) lead to the formation of system-wide clumps, a condition determining what we term the “Mozzarella Phase” and corresponding to an unpleasant and separated sauce. Additionally, we examine the impact of cheese concentration relative to water at a fixed starch level, observing a lower critical solution temperature that we theoretically rationalized by means of a minimal effective free-energy model. We further analyze the effect of a less traditional stabilizer, trisodium citrate, and observe a sharp transition from the Mozzarella Phase to a completely smooth and stable sauce, in contrast to starch-stabilized mixtures, where the transition is more gradual. Finally, we present a scientifically optimized recipe based on our findings, enabling a consistently flawless execution of this classic dish
Rigid fibre transport in a periodic non-homogeneous geophysical turbulent flow
Full text linkFrom anthropogenic litter carried by ocean currents to plant stems travelling through the atmosphere, geophysical flows are often seeded with elongated, fibre-like particles. In this study, we used a large-scale laboratory model of a tidal current – representative of a widespread class of geophysical flows – to investigate the tumbling motion of long, slender and floating fibres in the complex turbulence generated by flow interactions with a tidal inlet. Despite the non-stationary, non-homogeneous and anisotropic nature of this turbulence, we find that long fibres statistically rotate at the same frequency as eddies of similar size, a phenomenon called scale selection, which is known to occur in ideal turbulence. Furthermore, we report that the signal of the instantaneous transverse velocity difference between the fibre ends changes significantly from the signal produced by the flow in the fibre surroundings, although the two are statistically equivalent. These observations have twofold implications. On the one hand, they confirm the reliability of using the end-to-end velocity signal of rigid fibres to probe the two-point transverse statistics of the flow, even under realistic conditions: oceanographers could exploit this observation to measure transverse velocity differences through elongated floats in the field, where superdiffusion complicates collecting sufficient data to probe two-point turbulence statistics at a fixed separation effectively. On the other hand, by addressing the dynamics of inertial range particles floating in the coastal zone, these observations are crucial to improving our ability to predict the fate of meso- and macro-litter, a size class that is currently understudied
Precipitation phase drives seasonal and decadal snowline changes in high mountain Asia
Full text linkSnow cover is of key importance for water resources in high mountain Asia (HMA) and is expected to undergo extensive changes in a warming climate. Past studies have quantified snow cover changes with satellite products of relatively low spatial resolution (∼500 m) which are hindered by the steep topography of this mountain region. We derive snowlines from Sentinel-2 and Landsat 5, 7 and 8 images, which, thanks to their higher spatial resolution, are less sensitive to the local topography. We calculate the snow line altitude (SLA) and its seasonality for all glacierized catchments of HMA and link these patterns to climate variables corrected for topographic biases. As such, the snowline changes provide a clear proxy for climatic changes. Our results highlight a strong spatial variability in mean SLA and in its seasonal changes, including across mountain chains and between the monsoon-dominated and the westerlies-dominated catchments. Over the period 1999–2019, the western regions of HMA (Pamir, Karakoram, Western Himalaya) have undergone increased snow coverage, expressed as seasonal SLA decrease, in spring and summer. This change is opposed to a widespread increase in SLA in autumn across the region, and especially the southeastern regions of HMA (Nyainqentanglha, Hengduan Shan, South–East Himalaya). Our results indicate that the diversity of seasonal snow dynamics across the region is controlled not by temperature or precipitation directly but by the timing and partitioning of solid precipitation. Decadal snowline changes (1999–2009 vs 2009–2019) seasonally precede temperature changes, suggesting that seasonal temperature changes in the Karakoram–Pamir and Eastern Nyainqentanglha regions may have responded to snow cover changes, rather than driving them
Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus
No full textStatus epilepticus (SE), seizures lasting beyond five minutes, is a medical emergency commonly treated with benzodiazepines which enhance GABAA receptor (GABAAR) conductance. Despite widespread use, benzodiazepines fail in over one-third of patients, potentially due to seizure-induced disruption of neuronal chloride (Cl−) homeostasis. Understanding these changes at a network level is crucial for improving clinical translation. Here, we address this using a large-scale spiking neural network model incorporating Cl− dynamics, informed by clinical EEG and experimental slice recordings. Our simulations confirm that the GABAAR reversal potential (EGABA) dictates the pro- or anti-seizure effect of GABAAR conductance modulation, with high EGABA rendering benzodiazepines ineffective or excitatory. We show SE-like activity and EGABA depend non-linearly on Cl− extrusion efficacy and GABAAR conductance. Critically, cell-type specific manipulations reveal that pyramidal cell, not interneuron, Cl− extrusion predominantly determines the severity of SE activity and the response to simulated benzodiazepines. Leveraging these mechanistic insights, we develop a predictive framework mapping network states to Cl− extrusion capacity and GABAergic load, yielding a proposed decision-making strategy to guide therapeutic interventions based on initial treatment response. This work identifies pyramidal cell Cl− handling as a key therapeutic target and demonstrates the utility of biophysically detailed network models for optimising SE treatment protocols
Prussian blue analogues as anode materials for battery applications: Complexities and horizons
No full textPrussian blue (PB) and Prussian blue analogues (PBAs) are a class of porous materials composed of transition metal cations, cyanide ligands, and alkali metal cations. Their ability to intercalate and deintercalate ions within their framework pores, coupled with the adaptability of their crystal structure to electrochemical changes, underpins their success in battery applications. PBAs with Fe or Co as the active site exhibit high redox potentials (vs SHE) and have been extensively explored as cathode materials, with well-documented chemistry, crystal structures, and electrochemical properties. In contrast, PBAs with Cr or Mn as the active site display lower redox potentials and remain significantly underexplored as anode materials. This gap has led to fewer reported compounds and a less comprehensive understanding of their structural and electrochemical behavior, leaving the field relatively opaque. In this perspective, we comprehensively analyze the challenges involved in producing and employing PBAs with low redox potentials as active battery materials. Conversely, we propose numerous horizons and ask fundamental questions that should pave the way for future research to advance the field
Unilateral incentive alignment in two-agent stochastic games
Full text linkMultiagent learning is challenging when agents face mixed-motivation interactions, where conflicts of interest arise as agents independently try to optimize their respective outcomes. Recent advancements in evolutionary game theory have identified a class of “zero-determinant” strategies, which confer an agent with significant unilateral control over outcomes in repeated games. Building on these insights, we present a comprehensive generalization of zero-determinant strategies to stochastic games, encompassing dynamic environments. We propose an algorithm that allows an agent to discover strategies enforcing predetermined linear (or approximately linear) payoff relationships. Of particular interest is the relationship in which both payoffs are equal, which serves as a proxy for fairness in symmetric games. We demonstrate that an agent can discover strategies enforcing such relationships through experience alone, without coordinating with an opponent. In finding and using such a strategy, an agent (“enforcer”) can incentivize optimal and equitable outcomes, circumventing potential exploitation. In particular, from the opponent’s viewpoint, the enforcer transforms a mixed-motivation problem into a cooperative problem, paving the way for more collaboration and fairness in multiagent systems