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Can we understand and predict the evolutionary pathways by which bacteria acquire multi-drug resistance (MDR)? These questions have substantial potential impact in basic biology and in applied approaches to address the global health challenge of antimicrobial resistance (AMR). Here, we review how a class of machine learning approaches called evolutionary accumulation modelling (EvAM) may help reveal these dynamics using genetic and/or phenotypic AMR datasets, without requiring longitudinal sampling. These approaches are well-established in cancer progression and evolutionary biology, but currently less used in AMR research. We discuss how EvAM can learn the evolutionary pathways by which drug resistances and AMR features are acquired as pathogens evolve, predict next evolutionary steps, identify influences between AMR features, and explore differences in MDR evolution between regions, demographics, and more. We demonstrate a case study on MDR evolution in Mycobacterium tuberculosis and discuss the strengths and weaknesses of these approaches, providing links to some approaches for implementation.

Asvin G and Andrew O'Desky recently introduced the graded algebra P$\Lambda$ of polysymmetric functions as a generalization of the algebra $\Lambda$ of symmetric functions. This article develops combinatorial formulas for some multiplication rules and transition matrix entries for P$\Lambda$ that are analogous to well-known classical formulas for $\Lambda$. In more detail, we consider pure tensor bases $\{s^{\otimes}_{\tau}\}$, $\{p^{\otimes}_{\tau}\}$, and $\{m^{\otimes}_{\tau}\}$ for P$\Lambda$ that arise as tensor products of the classical Schur basis, power-sum basis, and monomial basis for $\Lambda$. We find expansions in these bases of the non-pure bases $\{P_{\delta}\}$, $\{H_{\delta}\}$, $\{E^+_{\delta}\}$, and $\{E_{\delta}\}$ studied by Asvin G and O'Desky. The answers involve tableau-like structures generalizing semistandard tableaux, rim-hook tableaux, and the brick tabloids of E\u{g}ecio\u{g}lu and Remmel. These objects arise by iteration of new Pieri-type rules that give expansions of products such as $s^{\otimes}_{\sigma}H_{\delta}$, $p^{\otimes}_{\sigma}E_{\delta}$, etc., Comment: 30 pages, multiple in-line figures

For $0\leq k\leq n-1$, we introduce a family of $k$-skeletal paths which are counted by the $n$-th Catalan number for each $k$, and specialize to Dyck paths when $k=n-1$. We similarly introduce $k$-skeletal parking functions which are equinumerous with the spanning trees on $n+1$ vertices for each $k$, and specialize to classical parking functions for $k=n-1$. The preceding constructions are generalized to paths lying in a trapezoid with base $c > 0$ and southeastern diagonal of slope $1/m$; $c$ and $m$ need not be integers. We give bijections among these families when $k$ varies with $m$ and $c$ fixed. Our constructions are motivated by chip firing and have connections to combinatorial representation theory and tropical geometry., Comment: 29 pages, 9 figures

Gravitational interactions were studied by measuring the influence of small external field masses on a microwave resonator. It consisted of two spherical mirrors, which acted as independent pendulumsindividually suspended by strings. Two identical field masses weremoved along the axis of the resonator symmetrically and periodically betweena near and a far position. Their gravitational interaction altered the distance between the mirrors, changing the resonance frequency, which was measured and found consistent with Newton's law of gravity. The acceleration of a single mirror caused by the two field masses at the closest position varied from $5.4 \cdot 10^{-12} m/s^2$ to $259 \cdot 10^{-12}\ m/s^2$., Comment: 19 pages, 8 figures

We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. This label-wise perspective allows uncertainty to be quantified at the individual class level, thereby improving cost-sensitive decision-making and helping understand the sources of uncertainty. Furthermore, it allows to define total, aleatoric, and epistemic uncertainty on the basis of non-categorical measures such as variance, going beyond common entropy-based measures. In particular, variance-based measures address some of the limitations associated with established methods that have recently been discussed in the literature. We show that our proposed measures adhere to a number of desirable properties. Through empirical evaluation on a variety of benchmark data sets -- including applications in the medical domain where accurate uncertainty quantification is crucial -- we establish the effectiveness of label-wise uncertainty quantification., Comment: Uncertainty in Artificial Intelligence. arXiv admin note: substantial text overlap with arXiv:2401.00276

The azimuthal correlation angle, $\Delta\phi$, between the scattered lepton and the leading jet in deep inelastic $e^{\pm}p$ scattering at HERA has been studied using data collected with the ZEUS detector at a centre-of-mass energy of $\sqrt{s} = 318 \;\mathrm{GeV}$, corresponding to an integrated luminosity of $326 \;\mathrm{pb}^{-1}$. A measurement of jet cross sections in the laboratory frame was made in a fiducial region corresponding to photon virtuality $10 \;\mathrm{GeV}^2 < Q^2 < 350 \;\mathrm{GeV}^2$, inelasticity $0.04 < y < 0.7$, outgoing lepton energy $E_e > 10 \;\mathrm{GeV}$, lepton polar angle $140^\circ < \theta_e < 180^\circ$, jet transverse momentum $2.5 \;\mathrm{GeV} < p_\mathrm{T,jet} < 30 \;\mathrm{GeV}$, and jet pseudorapidity $-1.5 < \eta_\mathrm{jet} < 1.8$. Jets were reconstructed using the $k_\mathrm{T}$ algorithm with the radius parameter $R = 1$. The leading jet in an event is defined as the jet that carries the highest $p_\mathrm{T,jet}$. Differential cross sections, $d\sigma/d\Delta\phi$, were measured as a function of the azimuthal correlation angle in various ranges of leading-jet transverse momentum, photon virtuality and jet multiplicity. Perturbative calculations at $\mathcal{O}(\alpha_{s}^2)$ accuracy successfully describe the data within the fiducial region, although a lower level of agreement is observed near $\Delta\phi \rightarrow \pi$ for events with high jet multiplicity, due to limitations of the perturbative approach in describing soft phenomena in QCD. The data are equally well described by Monte Carlo predictions that supplement leading-order matrix elements with parton showering.

This paper reports on the integration of runtime monitoring into fully-electric aircraft designed by Volocopter, a German aircraft manufacturer of electric multi-rotor helicopters. The runtime monitor recognizes hazardous situations and system faults. Since the correct operation of the monitor is critical for the safety of the aircraft, the development of the monitor must follow strict aeronautical standards. This includes the integration of the monitor into different development environments, such as log-file analysis, hardware/software-in-the-loop testing, and test flights. We have used the stream-based monitoring framework RTLola to generate monitors for a range of requirements. In this paper, we present representative monitoring specifications and our lessons learned from integrating the generated monitors. Our main finding is that the specification and the integration need to be decoupled, because the specification remains stable throughout the development process, whereas the different development stages require a separate integration of the monitor into each environment. We achieve this decoupling with a novel abstraction layer in the monitoring framework that adapts the monitor to each environment without affecting the core component generated from the specification. The decoupling of the integration has also allowed us to react quickly to the frequent changes in the hardware and software environment of the monitor due to the fast-paced development of the aircraft in a startup company.

Programmable data planes allow for sophisticated applications that give operators the power to customize the functionality of their networks. Deploying these applications, however, often requires tedious and burdensome optimization of their layout and design, in which programmers must manually write, compile, and test an implementation, adjust the design, and repeat. In this paper we present Parasol, a framework that allows programmers to define general, parameterized network algorithms and automatically optimize their various parameters. The parameters of a Parasol program can represent a wide variety of implementation decisions, and may be optimized for arbitrary, high-level objectives defined by the programmer. Furthermore, optimization may be tailored to particular environments by providing a representative sample of traffic. We show how we implement the Parasol framework, which consists of a sketching language for writing parameterized programs, and a simulation-based optimizer for testing different parameter settings. We evaluate Parasol by implementing a suite of ten data-plane applications, and find that Parasol produces a solution with comparable performance to hand-optimized P4 code within a two-hour time budget.

Many computational chemistry and molecular simulation workflows can be expressed as graphs. This abstraction is useful to modularize and potentially reuse existing components, as well as provide parallelization and ease reproducibility. Existing tools represent the computation as a directed acyclic graph (DAG), thus allowing efficient execution by parallelization of concurrent branches. These systems can, however, generally not express cyclic and conditional workflows. We therefore developed Maize, a workflow manager for cyclic and conditional graphs based on the principles of flow-based programming. By running each node of the graph concurrently in separate processes and allowing communication at any time through dedicated inter-node channels, arbitrary graph structures can be executed. We demonstrate the effectiveness of the tool on a dynamic active learning task in computational drug design, involving the use of a small molecule generative model and an associated scoring system, and on a reactivity prediction pipeline using quantum-chemistry and semiempirical approaches.

With net-zero emission goals, low-carbon transitions enter a new phase of development, leading to new challenges for policymaking and research. Multiple transitions unfold in parallel across different sectors. This involves a broad range of technologies, while actors engage in increasingly complex discourses. Here, we study the discourses on hydrogen in Germany. Based on the analysis of 179 newspaper articles from 2016 to 2020, we find that a diverse set of actors, including many industry incumbents, speak favorably about hydrogen, emphasizing economic opportunities and its relevance for the energy transition, whereas skeptics highlight its low energy efficiency and expected scarcity. With the help of discourse network analysis, we identify three emerging conflicts around the use, production, and import of hydrogen. We explain these conflicts and the widespread support of incumbents with a conceptual framework that captures the complex interplay of sectoral contexts, specific technologies and actor interests.

Confocal fluorescence microscopy is widely applied for the study of point-like emitters such as biomolecules, material defects, and quantum light sources. Confocal techniques offer increased optical resolution, dramatic fluorescence background rejection and sub-nanometer localization, useful in super-resolution imaging of fluorescent biomarkers, single-molecule tracking, or the characterization of quantum emitters. However, rapid, noise-robust automated 3D focusing on point-like emitters has been missing for confocal microscopes. Here, we introduce FiND (Focusing in Noisy Domain), an imaging-free, non-trained 3D focusing framework that requires no hardware add-ons or modifications. FiND achieves focusing for signal-to-noise ratios down to 1, with a few-shot operation for signal-to-noise ratios above 5. FiND enables unsupervised, large-scale focusing on a heterogeneous set of quantum emitters. Additionally, we demonstrate the potential of FiND for real-time 3D tracking by following the drift trajectory of a single NV center indefinitely with a positional precision of < 10 nm. Our results show that FiND is a useful focusing framework for the scalable analysis of point-like emitters in biology, material science, and quantum optics., Comment: 17 pages, 7 figures

A new measurement of inclusive-jet cross sections in the Breit frame in neutral current deep inelastic scattering using the ZEUS detector at the HERA collider is presented. The data were taken in the years 2004 to 2007 at a centre-of-mass energy of $318\,\text{GeV}$ and correspond to an integrated luminosity of $347\,\text{pb}^{-1}$. Massless jets, reconstructed using the $k_t$-algorithm in the Breit reference frame, have been measured as a function of the squared momentum transfer, $Q^2$, and the transverse momentum of the jets in the Breit frame, $p_{\perp,\text{Breit}}$. The measured jet cross sections are compared to previous measurements and to perturbative QCD predictions. The measurement has been used in a next-to-next-to-leading-order QCD analysis to perform a simultaneous determination of parton distribution functions of the proton and the strong coupling, resulting in a value of $\alpha_s(M_Z^2) = 0.1142 \pm 0.0017~\text{(experimental/fit)}$ ${}^{+0.0006}_{-0.0007}~\text{(model/parameterisation)}$ ${}^{+0.0006}_{-0.0004}~\text{(scale)}$, whose accuracy is improved compared to similar measurements. In addition, the running of the strong coupling is demonstrated using data obtained at different scales., Comment: 42 pages, 10 figures

Multiple Sclerosis (MS) is a severe neurological disease characterized by inflammatory lesions in the central nervous system. Hence, predicting inflammatory disease activity is crucial for disease assessment and treatment. However, MS lesions can occur throughout the brain and vary in shape, size and total count among patients. The high variance in lesion load and locations makes it challenging for machine learning methods to learn a globally effective representation of whole-brain MRI scans to assess and predict disease. Technically it is non-trivial to incorporate essential biomarkers such as lesion load or spatial proximity. Our work represents the first attempt to utilize graph neural networks (GNN) to aggregate these biomarkers for a novel global representation. We propose a two-stage MS inflammatory disease activity prediction approach. First, a 3D segmentation network detects lesions, and a self-supervised algorithm extracts their image features. Second, the detected lesions are used to build a patient graph. The lesions act as nodes in the graph and are initialized with image features extracted in the first stage. Finally, the lesions are connected based on their spatial proximity and the inflammatory disease activity prediction is formulated as a graph classification task. Furthermore, we propose a self-pruning strategy to auto-select the most critical lesions for prediction. Our proposed method outperforms the existing baseline by a large margin (AUCs of 0.67 vs. 0.61 and 0.66 vs. 0.60 for one-year and two-year inflammatory disease activity, respectively). Finally, our proposed method enjoys inherent explainability by assigning an importance score to each lesion for the overall prediction. Code is available at https://github.com/chinmay5/ms_ida.git

Common data types like dates, addresses, phone numbers and tables can have multiple textual representations, and many heavily-used languages, such as SQL, come in several dialects. These variations can cause data to be misinterpreted, leading to silent data corruption, failure of data processing systems, or even security vulnerabilities. Saggitarius is a new language and system designed to help programmers reason about the format of data, by describing grammatical domains -- that is, sets of context-free grammars that describe the many possible representations of a datatype. We describe the design of Saggitarius via example and provide a relational semantics. We show how Saggitarius may be used to analyze a data set: given example data, it uses an algorithm based on semi-ring parsing and MaxSAT to infer which grammar in a given domain best matches that data. We evaluate the effectiveness of the algorithm on a benchmark suite of 110 example problems, and we demonstrate that our system typically returns a satisfying grammar within a few seconds with only a small number of examples. We also delve deeper into a more extensive case study on using Saggitarius for CSV dialect detection. Despite being general-purpose, we find that Saggitarius offers comparable results to hand-tuned, specialized tools; in the case of CSV, it infers grammars for 84% of benchmarks within 60 seconds, and has comparable accuracy to custom-built dialect detection tools., Comment: OOPSLA 2023

We develop a mathematically simple yet accurate model for the single-pulse non-resonant impulsive alignment of thermal ensembles of linear molecules. We find that our molecular alignment model not only provides a simplification for analytical and numerical calculations, but also establishes intuitive connections between system parameters, such as gas temperature and laser pulse intensity, and the resulting shape of the temporal molecular alignment.