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Relational network verification is a new approach to validating network changes. In contrast to traditional network verification, which analyzes specifications for a single network snapshot, relational network verification analyzes specifications concerning two network snapshots (e.g., pre- and post-change snapshots) and captures their similarities and differences. Relational change specifications are compact and precise because they specify the flows or paths that change between snapshots and then simply mandate that other behaviors of the network "stay the same", without enumerating them. To achieve similar guarantees, single-snapshot specifications need to enumerate all flow and path behaviors that are not expected to change, so we can check that nothing has accidentally changed. Thus, precise single-snapshot specifications are proportional to network size, which makes them impractical to generate for many real-world networks. To demonstrate the value of relational reasoning, we develop a high-level relational specification language and a tool called Rela to validate network changes. Rela first compiles input specifications and network snapshot representations to finite state transducers. It then checks compliance using decision procedures for automaton equivalence. Our experiments using data on complex changes to a global backbone (with over 10^3 routers) find that Rela specifications need fewer than 10 terms for 93% of them and it validates 80% of them within 20 minutes.

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.

We report the high-pressure synthesis (6 GPa, 1200 $^{\circ}$C) and ambient pressure characterization of hexagonal HP-BaCoO$_3$. The material (with the 2H crystal structure) has a short intrachain Co-Co distance of about 2.07 $\text{\r{A}}$. Our magnetization investigation revealed robust diamagnetic behavior below approximately 130 K when exposed to weak applied magnetic fields (10 Oe) and a distinct half-levitation phenomenon below that temperature, such as is often observed for superconductors. Its field-dependent magnetization profile, however, unveils the characteristics of ferromagnetism, marked by a substantial magnetic retentivity of 0.22(1) ${\mu}_B$/Co at a temperature of 2 K. Electrical resistivity measurements indicate that HP-BaCoO$_3$ is a ferromagnetic insulator, not a superconductor.

This document describes algorithms for generating general Morton and Hilbert orderings for three-dimensional data volumes.

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

This paper presents algorithms and pseudocode for encoding and decoding 3D Hilbert orderings.

Accurate solutions to the electronic Schr\"odinger equation can provide valuable insight for electron interactions within molecular systems, accelerating the molecular design and discovery processes in many different applications. However, the availability of such accurate solutions are limited to small molecular systems due to both the extremely high computational complexity and the challenge of operating and executing these workloads on high-performance compute clusters. This work presents a massively scalable cloud-based quantum chemistry platform by implementing a highly parallelizable quantum chemistry method that provides a polynomial-scaling approximation to full configuration interaction (FCI). Our platform orchestrates more than one million virtual CPUs on the cloud to analyze the bond-breaking behaviour of carbon-fluoride bonds of per- and polyfluoroalkyl substances (PFAS) with near-exact accuracy within the chosen basis set. This is the first quantum chemistry calculation utilizing more than one million virtual CPUs on the cloud and is the most accurate electronic structure computation of PFAS bond breaking to date.

Modern computer systems are characterized by deep memory hierarchies, composed of main memory, multiple layers of cache, and other specialized types of memory. In parallel and distributed systems, additional memory layers are added to this hierarchy. Achieving good performance for computational science applications, in terms of execution time, depends on the efficient use of this diverse and hierarchical memory. This paper revisits the use of space-filling curves to specify the ordering in memory of data structures used in representative scientific applications executing on parallel machines containing clusters of multicore CPUs with attached GPUs. This work examines the hypothesis that space-filling curves, such as Hilbert and Morton ordering, can improve data locality and hence result in more efficient data movement than row or column-based orderings. First, performance results are presented that show for what application parameterizations and machine characteristics this is the case, and are interpreted in terms of how an application interacts with the computer hardware and low-level software. This research particularly focuses on the use of stencil-based applications that form the basis of many scientific computations. Second, how space-filling curves impact data sharing in nearest-neighbour and stencil-based codes is considered.

Condensation models describe the equilibrium distribution of elements between coexisting mineral solid solutions, silicate liquid, and vapor in a closed chemical system, vapor phase always present, using equations of state of the phases involved at a fixed total P (< 1 bar) and temperature T. The VAPORS code uses a CaO-MgO-Al$_2$O$_3$-SiO$_2$ liquid model at T above the stability field of olivine and the MELTS algorithm at lower T. Quenched high-T crystal + liquid assemblages are preserved in meteorites as Type B Ca-, Al-rich inclusions (CAIs) and olivine-rich ferromagnesian chondrules. Experimental tests of compositional regions may clarify the nature of the phases present, the phase boundaries, and the partition of trace elements among these phases. Twenty-three Pt-loop equilibrium experiments in seven phase fields on twelve bulk compositions at specific T and dust enrichment factors tested the predicted stability fields of forsteritic olivine (Mg$_2$SiO$_4$), enstatite (MgSiO$_3$), Cr-bearing spinel (MgAl$_2$O$_4$), perovskite (CaTiO$_3$), melilite (Ca$_2$Al$_2$SiO$_7$ - Ca$_2$Mg$_2$Si$_2$O$_7$) and/or grossite (CaAl4O7) crystallizing from liquid. Experimental results for forsterite, enstatite, and grossite are in very good agreement with predictions, both in chemistry and phase abundances. On the other hand, the stability of spinel with olivine, and stability of perovskite and gehlenite are quite different from predictions. Perovskite is absent in all experiments. Even at low oxygen fugacity (IW-3.4), the most TiO$_2$-rich experiments do not crystallize Al-, Ti-bearing calcic pyroxene. The stability of spinel and olivine together is limited to a smaller phase field than is predicted. The melilite stability field is much larger than predicted, indicating a deficiency of current liquid or melilite activity models. In that respect, these experiments contribute to improving the data for calibrating thermodynamic models including MELTS., Comment: 17 pages, 5 figures, 5 tables

We determined the mineral-melt partition coefficients (Di's) and the compositional and/or temperature dependency between grossite, melilite, hibonite, olivine and Ca-, Al-inclusion (CAI)-type liquids for a number of light (LE), high field strength (HFSE), large ion lithophile (LILE), and rare earth (REE) elements including Li, Be, B, Sr, Zr, Nb, Ba, La, Ce, Eu, Dy, Ho, Yb, Hf, Ta, Th. A series of isothermal crystallization experiments was conducted at 5 kbar pressure and IW+1 in graphite capsules. The starting compositions were selected based on the calculated and experimentally confirmed phase relations during condensation in CI dust-enriched systems (Ebel and Grossman, 2000; Ebel, 2006; Ustunisik et al., 2014). Partition coefficients between melt and gehlenite, hibonite, and grossite show that the trace element budget of igneous CAIs is controlled by these three major Al-bearing phases in addition to pyroxene. In general, LE, LILE, REE, and HFSE partition coefficients (by mass) decrease in the order of Di(Gehlenite-Melt) > Di(Hibonite-Melt) > Di(Grossite-Melt). Results suggest that Di(Gehlenite-Melt) vary by a factor of 2-3 in different melt compositions at the same T (~1500 C). Increased melt Al and Ca, relative to earlier work, increases the compatibility of Di(Gehlenite-Melt), and also the compatibility of Di(Hibonite-Melt), especially for La and Ce. Olivine partitioning experiments confirm that olivine contribution to the trace element budget of CAIs is small due to the low Di(Olivine-Melt) at a range of temperatures while D-Eu, Yb(Olivine-Melt) are sensitive to changes in T and oxygen fugacity. The development of a predictive model for partitioning in CAI-type systems would require more experimental data and the use of analytical instruments capable of obtaining single phase analyses for crystals < 5 micron., Comment: 23 pages, 15 figures, 5 tables

We propose a robust and computationally efficient algorithm to generically construct first return maps of dynamical systems from time series without the need for embedding. Typically, a first return map is constructed using a heuristic convenience (maxima or zero-crossings of the time series, for example) or a computationally delicate geometric approach (explicitly constructing a Poincar\'e section from a hyper-surface normal to the flow and then interpolating to determine intersections with trajectories). Our approach relies on ordinal partitions of the time series and builds the first return map from successive intersections with particular ordinal sequences. Generically, we can obtain distinct first return maps for each ordinal sequence. We define entropy-based measures to guide our selection of the ordinal sequence for a ``good'' first return map and show that this method can robustly be applied to time series from classical chaotic systems to extract the underlying first return map dynamics. The results are shown on several well-known dynamical systems (Lorenz, R{\"o}ssler and Mackey-Glass in chaotic regimes).

Delay embedding methods are a staple tool in the field of time series analysis and prediction. However, the selection of embedding parameters can have a big impact on the resulting analysis. This has led to the creation of a large number of methods to optimise the selection of parameters such as embedding lag. This paper aims to provide a comprehensive overview of the fundamentals of embedding theory for readers who are new to the subject. We outline a collection of existing methods for selecting embedding lag in both uniform and non-uniform delay embedding cases. Highlighting the poor dynamical explainability of existing methods of selecting non-uniform lags, we provide an alternative method of selecting embedding lags that includes a mixture of both dynamical and topological arguments. The proposed method, {\em Significant Times on Persistent Strands} (SToPS), uses persistent homology to construct a characteristic time spectrum that quantifies the relative dynamical significance of each time lag. We test our method on periodic, chaotic and fast-slow time series and find that our method performs similar to existing automated non-uniform embedding methods. Additionally, $n$-step predictors trained on embeddings constructed with SToPS was found to outperform other embedding methods when predicting fast-slow time series.

Sr$_{2}$IrO$_{4}$ with strong spin-orbit coupling (SOC) and Hubbard repulsion (U) hosts Mott insulating states. The similar crystal structure, magnetic and electronic properties, particularly the $d$-wave gap observed in Sr$_{2}$IrO$_{4}$ enhanced the analogies to cuprate high-$T_{c}$ superconductor, La$_{2}$CuO$_{4}$. The incomplete analogy was due to the lack of broken inversion symmetry phases observed in Sr$_{2}$IrO$_{4}$. Here, under high pressure and high temperature conditions, we report a non-centrosymmetric Sr$_{2}$IrO$_{4}$. The crystal structure and its noncentrosymmetric character were determined by single crystal X-ray diffraction and high-resolution scanning transmission electron microscopy (HR-STEM). The magnetic characterization confirms the Ir$^{4+}$ with $S$ = 1/2 at low temperature in Sr$_{2}$IrO$_{4}$ with magnetic ordering occurred at around 86 K, where a larger moment is observed than the ambient pressure Sr$_{2}$IrO$_{4}$. Moreover, the resistivity measurement shows three-dimensional Mott variable-range hopping existed in the system. This non-centrosymmetric Sr$_{2}$IrO$_{4}$ phase appears to be a unique material to offer further understanding of high-$T_{c}$ superconductivity.

A lens is a single program that specifies two data transformations at once: one transformation converts data from source format to target format and a second transformation inverts the process. Over the past decade, researchers have developed many different kinds of lenses with different properties. One class of such languages operate over regular languages. In other words, these lenses convert strings drawn from one regular language to strings drawn from another regular language (and back again). In this paper, we define a more powerful language of lenses, which we call match-reference lenses, that is capable of translating between non-regular formats that contain repeated substrings, which is a primitive form of dependency. To define the non-regular formats themselves, we develop a new language, match-reference regular expressions, which are regular expressions that can bind variables to substrings and use those substrings repeatedly. These match-reference regular expressions are closely related to the familiar ``back-references" that can be found in traditional regular expression packages, but are redesigned to adhere to conventional programming language lexical scoping conventions and to interact smoothly with lens language infrastructure. We define the semantics of match-reference regular expressions and match-reference lenses. We also define a new kind of automaton, the match-reference regex automaton system (MRRAS), for deciding string membership in the language match-reference regular expressions. We illustrate our definitions with a variety of examples.