Search

The EU targets 10 Mt of green hydrogen production by 2030, but has not committed to targets for 2040. Green hydrogen competes with carbon capture and storage, biomass and imports in reaching emissions reductions; earlier studies have demonstrated the great uncertainty in future cost-optimal development of green hydrogen. In spite of this, we show that Europe risks little by setting green hydrogen production targets at around 25 Mt by 2040. Employing an extensive scenario analysis combined with novel near-optimal techniques, we find that this target results in systems that are within 10% of cost-optimal in most considered scenarios. Setting concrete targets is important in order to resolve significant uncertainty which hampers investments. Targeting green hydrogen reduces the dependence on carbon capture and storage and green fuel imports, making for a more robust European climate strategy., Comment: Main text: 15 pages, 6 figures, 2 tables. Supplementary information: 10 pages, 18 figures

In the context of medical Augmented Reality (AR) applications, object tracking is a key challenge and requires a significant amount of annotation masks. As segmentation foundation models like the Segment Anything Model (SAM) begin to emerge, zero-shot segmentation requires only minimal human participation obtaining high-quality object masks. We introduce a HoloLens-Object-Labeling (HOLa) Unity and Python application based on the SAM-Track algorithm that offers fully automatic single object annotation for HoloLens 2 while requiring minimal human participation. HOLa does not have to be adjusted to a specific image appearance and could thus alleviate AR research in any application field. We evaluate HOLa for different degrees of image complexity in open liver surgery and in medical phantom experiments. Using HOLa for image annotation can increase the labeling speed by more than 500 times while providing Dice scores between 0.875 and 0.982, which are comparable to human annotators. Our code is publicly available at: https://github.com/mschwimmbeck/HOLa, Comment: accepted by BMT 2024

Explaining why and how a tree $t$ structurally differs from another tree $t^*$ is a question that is encountered throughout computer science, including in understanding tree-structured data such as XML or JSON data. In this article, we explore how to learn explanations for structural differences between pairs of trees from sample data: suppose we are given a set $\{(t_1, t_1^*),\dots, (t_n, t_n^*)\}$ of pairs of labelled, ordered trees; is there a small set of rules that explains the structural differences between all pairs $(t_i, t_i^*)$? This raises two research questions: (i) what is a good notion of "rule" in this context?; and (ii) how can sets of rules explaining a data set be learnt algorithmically? We explore these questions from the perspective of database theory by (1) introducing a pattern-based specification language for tree transformations; (2) exploring the computational complexity of variants of the above algorithmic problem, e.g. showing NP-hardness for very restricted variants; and (3) discussing how to solve the problem for data from CS education research using SAT solvers.

Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises significant privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two, one at five centers from LFQ E.coli experiments and one at three centers from TMT human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled data, with completely negligible absolute differences no greater than $\text{$4 \times 10^{-12}$}$. In contrast, -log10(p-values) computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-27. FedProt is available as a web tool with detailed documentation as a FeatureCloud App., Comment: 52 pages, 16 figures, 12 tables. Last two authors listed are joint last authors

Oxygen deficient titanium dioxide (TiO$_{2-x}$) is a very attractive material for several applications ranging from photocatalysis to resistive switching. Oxygen vacancies turn insulating anatase titanium dioxide into a polaronic conductor, while creating a defect state band below the ultraviolet semiconducting gap. Here we employ a combination of broadband infrared (IR) reflectivity and THz-pump/IR-probe measurements to investigate the relationship between localized defect states and delocalized conducting polaronic states. We show that the THz pump allows to convert deeply localized electrons into metastable polarons with a lifetime in the ns range. These long-lived metastable states may find application in novel opto-electronic applications exploiting the interplay of dc resistivity, with terahertz and infrared signals.

Thanks to its tunable infrared band-gap and to its anisotropic conduction properties, black phosphorus represents a very unique 2D material, whose potential in the engineering of new devices still needs to be fully explored. We investigate here the nonlinear terahertz (THz) electrodynamics of black phosphorus along the more conducting armchair direction. Similarly to the case of other 2D systems like graphene and topological insulators, the THz saturable absorption properties of black phosphorus can be understood within a thermodynamic model by assuming a fast thermalization of the electron bath. While black phosphorus does not display the presence of massless fermions at ambient pressure and temperature, our analysis shows that its anomalous THz nonlinear properties can be accounted for by a relativistic massive Dirac dispersion, provided the Fermi temperature is low enough. An optimal tuning of the Fermi level therefore represents a strategy to engineer strong THz nonlinear response in other massive Dirac materials as in transition metal dichalchogenides or high-temperature superconductors., Comment: Accepted for publication in Physical Review Applied

Recent studies have found that fluctuations of magnetization transfer in integrable spin chains violate the central limit property. Here we revisit the problem of anomalous counting statistics in the Landau-Lifshitz field theory by specializing to two distinct anomalous regimes featuring a dynamical critical point. By performing optimized numerical simulations using an integrable space-time discretization we extract the algebraic growth exponents of time-dependent cumulants which attain their threshold values. The distinctly non-Gaussian statistics of magnetization transfer in the easy-axis regime is found to converge towards the universal distribution of charged single-file systems. At the isotropic point we infer a weakly non-Gaussian distribution, corroborating the view that superdiffusive spin transport in integrable spin chains does not belong to any known dynamical universality class., Comment: 4.5 pages, v2: Some rephrasing, results unchanged, v3: added coathor, reference, v4: added fluctuations in SU(3) model, v5: fixed typos, added two background references

Many AI-related reasoning problems are based on the problem of satisfiability of propositional formulas with some cardinality-minimality condition. While the complexity of the satisfiability problem (SAT) is well understood when considering systematically all fragments of propositional logic within Schaefer's framework (STOC 1978) this is not the case when such minimality condition is added. We consider the CardMinSat problem, which asks, given a formula F and an atom x, whether x is true in some cardinality-minimal model of F. We completely classify the computational complexity of the CardMinSat problem within Schaefer's framework, thus paving the way for a better understanding of the tractability frontier of many AI-related reasoning problems. To this end we use advanced algebraic tools developed by (Schnoor & Schnoor 2008) and (Lagerkvist 2014).

Introducing a general class of one-dimensional single-file systems (meaning that particle crossings are prohibited) of interacting hardcore particles with internal degrees of freedom (called charge), we exhibit a novel type of dynamical universality reflected in anomalous statistical properties of macroscopic fluctuating observables such as charge transfer. We find that stringent dynamical constraints lead to universal anomalous statistics of cumulative charge currents manifested both on the timescale characteristic of typical fluctuations and also in the rate function describing rare events. By computing the full counting statistics of net transferred charge between two extended subsystems, we establish a number of unorthodox dynamical properties in an analytic fashion. Most prominently, typical fluctuations in equilibrium are governed by a universal distribution that markedly deviates from the expected Gaussian statistics, whereas large fluctuations are described by an exotic large-deviation rate function featuring an exceptional triple critical point. Far from equilibrium, competition between dynamical phases leads to dynamical phase transitions of first and second order and spontaneous breaking of fluctuation symmetry of the univariate charge large-deviation function. The rich phenomenology of the outlined dynamical universality is exemplified on an exactly solvable classical cellular automaton of charged hardcore particles. We determine the dynamical phase diagram in the framework of Lee-Yang's theory of phase transitions and exhibit a hyper-dimensional diagram of distinct dynamical regimes. Our findings lead us to conclude that the conventional classification of dynamical universality classes based on the algebraic dynamical exponents and asymptotic scaling functions that characterize hydrodynamic relaxation of the dynamical structure factor is incomplete and calls for refinement., Comment: 30+15 pages, v2: new introduction, fixed typos, v3: corrected factors of 2 in Eqs. 62 and 63, v4: presentation redone, technical details moved to appendices, added references, v5: fixed typo in Eq. 58, up-to-date references

Liquid water is one of the most studied substances, yet many of its properties are difficult to rationalize. The uniqueness of water is rooted in the dynamic network of hydrogen-bonded molecules with relaxation time constants of about one picosecond. Terahertz fields oscillate on a picosecond timescale and are inherently suited to study water. Recent advances in non-linear terahertz spectroscopy have revealed large signals from water, which have been interpreted with different, sometimes competing, theoretical models. Here we show that the non-linear transmission of liquid water at ~1 THz is equal at 21 {\deg}C and 4 {\deg}C, thus suggesting that the most appropriate microscopic models should depend weakly on temperature. Among the different mechanisms proposed to date, the resonant reorientation of hydrogen-bonded water molecules might be the most appropriate to describe all of the currently available experimental results.

The current/height fluctuation statistics of Kardar-Parisi-Zhang (KPZ) universality in 1+1 dimensions are sensitive to the initial state. We find that the averages over the initial states exhibit universal and scale-invariant patterns when conditioning on fluctuations. To establish universality of our findings we demonstrate scale invariance at different times and heights using large-scale Monte-Carlo simulations of the totally asymmetric simple exclusion process (TASEP) which belongs to the KPZ universality class. Here we focus on current/height fluctuations in the steady state regime described by the Baik-Rains distribution. The conditioned probability distribution of an initial state order parameter shows a transition from uni- to bimodal. Bimodality occurs for negative current/height fluctuations that are dominated by super-diffusive shock dynamics. It is caused by two possible point-symmetric shock profiles and the KPZ mirror symmetry breakdown. Similar surprising relations between initial states and fluctuations might exist in other universality classes as well., Comment: 5 pages, 5 figures

We analytically compute the full counting statistics of charge transfer in a classical automaton of interacting charged particles. Deriving a closed-form expression for the moment generating function with respect to a stationary equilibrium state, we employ asymptotic analysis to infer the structure of charge current fluctuations for a continuous range of timescales. The solution exhibits several unorthodox features. Most prominently, on the timescale of typical fluctuations the probability distribution of the integrated charge current in a stationary ensemble without bias is distinctly non-Gaussian despite diffusive behavior of dynamical charge susceptibility. While inducing a charge imbalance is enough to recover Gaussian fluctuations, we find that higher cumulants grow indefinitely in time with different exponents, implying singular scaled cumulants. We associate this phenomenon with the lack of a regularity condition on moment generating functions and the onset of a dynamical critical point. In effect, the scaled cumulant generating function does not, irrespectively of charge bias, represent a faithful generating function of the scaled cumulants, yet the associated Legendre dual yields the correct large-deviation rate function. Our findings hint at novel types of dynamical universality classes in deterministic many-body systems., Comment: v2: 4.5 + 22 pages, minor corrections and fixed typos, new section on phase transition in SM; v3: slightly updated notation

US wind power generation has grown significantly over the last decades, in line with the number and average size of operating turbines. However, wind power density has declined, both measured in terms of wind power output per rotor swept area as well as per spacing area. To study this effect, we present a decomposition of US wind power generation data for the period 2001--2021 and examine how changes in input power density and system efficiency affected output power density. Here, input power density refers to the amount of wind available to turbines, system efficiency refers to the share of power in the wind flowing through rotor swept areas which is converted to electricity and output power density refers to the amount of wind power generated per rotor swept area. We show that, while power input available to turbines has increased in the period 2001--2021, system efficiency has decreased. In total, this has caused a decline in output power density in the last 10 years, explaining higher land-use requirements. The decrease in system efficiency is linked to the decrease in specific power, i.e. the ratio between the nameplate capacity of a turbine and its rotor swept area. Furthermore, we show that the wind available to turbines has increased substantially due to increases in the average hub height of turbines since 2001. However, site quality has slightly decreased in this period.