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The national forecasting competition WxChallenge, brainchild of Brad Illston at the University of Oklahoma in 2005, has become a cherished institution played across the United States each year. Participants include students, faculty, alumni, and industry professionals. However, forecasts are given as scalar values without expression of uncertainty, probabilities being a keystone of meteorological forecasting today, and previous attempts to add probabilistic elements to WxChallenge have failed partly due to challenges in making probability forecasting accessible to all, and inability to combine scores with different units while also appropriately rewarding forecasts using proper scoring rules. Much of the competition's maintenance relies on dedicated volunteers, highlighting need for more automation. Hence I propose three new features: (1) automated forecast problems based on morning ensemble guidance, forming prediction baselines, thresholds over which the players demonstrate skill in their later forecast; (2) a spread betting game, where the players allocate 100 confidence credits to the over-under for exceeding a percentile (e.g., 50pc) threshold of a variable (e.g., maximum temperature) derived from the ensemble baseline; and (3) a game where players distribute 100 confidence credits across bins of a continuous variable (e.g., accumulated precipitation) approximating a probability mass function. Forecasts are evaluated using Shannon information gained over the baseline forecast, yielding additive units of bits that allow score combinations of different variables and units. Information gain parallels the Brier Score and is likewise a sound measure of skill due its punishment of hedging. This proposal objective is to augment WxChallenge with two new probabilistic games that are accessible, scientifically sound, enjoyable, and optional., Comment: Manuscript is 10 pages and 2 figures. Originally submitted and rejected from Bulletin of American Meteorological Society. Future resubmission is expected

The Great Salt Lake (Utah, USA) is reducing in size, which raises several ecological concerns, including the effect of an increasing area of dry playa exposed by the retreating lake. This study focuses solely on concerns about the toxicity of metals in the dust blowing off the playa. Although considerable efforts have been made to understand aeolian dust in urban areas along the Wasatch Front, located just east and south of the Great Salt Lake, there is still a need to consolidate existing research and to conduct a compositional analysis of the dust found in these urban areas. We investigated the dust reaching urban monitoring sites around the Great Salt Lake that are managed by the Utah Division of Air Quality. By analyzing historical data, we found that the decrease in the Great Salt Lake's surface area has not led to a statistically significant increase in dust events in urban areas. Windrose plots align with prior research, indicating that heightened dust levels in urban areas coincide with winds originating from the south or west, passing over identified playas and deserts such as the Milford Flats, Sevier Dry Lake, Tule Dry Lake, Great Salt Lake Desert, Dugway Proving Grounds, and the West Desert of Utah. Metallic compositional analysis of urban dust was used to evaluate potential health risks associated with the dust using the hazard quotient, air regional screening levels, and cancer risk methods. This analysis revealed no significant increase in concentrations of toxic metals. However, this is not to preclude a risk of dust-related health concerns, especially due to pre-existing arsenic and lead levels.

Communicating forecast uncertainty effectively is a persistent challenge in predictive endeavours such as weather forecasting. This paper explores the application of possibility theory as a complementary approach to traditional probability in risk communication. Unlike probability, possibility theory allows for the representation of uncertain events as ranges of potential, distinguished by degrees of plausibility possibility and certainty necessity. Using a simplified fuzzy-logic inference system, we generate possibility forecasts of ozone-concentration forecasts from meteorological inputs. Observations are pre-processed as degrees of an adjective, e.g., 1 m/s wind speed may belong to the adjective "calm" at degree 0.8 (perhaps represented with an adverb as "substantially"). Aggregation of all rule activations yields a possibility distribution over the output range. As possibility is an upper bound of probability, possibilities provides most value for risk-averse stakeholders sensitive to the event of interest, especially near the event's predictability horizon when there is most uncertainty to remove. By setting the forecast challenge as the possibility of an event, we effectively extend the predictability horizon: trading some specificity for detection of fainter signals of the event's future occurrence. Possibility theory appreciates uncertainty's dual nature: inherent randomness (aleatoric) and knowledge deficiency (epistemic). While the unfamiliar nature of the theory requires more accessible language before operational use and public communication, possibility offers substantial practical benefits for assessment of uncertainty at the edge of predictability limits for vulnerable users., Comment: 9 Figures, 15 Pages, unsubmitted as of 28 Oct 2024

Fluid flows are intrinsically characterized via the topology and dynamics of underlying vortex lines. Turbulence in common fluids like water and air, mathematically described by the incompressible Navier-Stokes equations (INSE), engenders spontaneous self-stretching and twisting of vortex lines, generating a complex hierarchy of structures. While the INSE are routinely used to describe turbulence, their regularity remains unproven; the implicit assumption being that the self-stretching is ultimately regularized by viscosity, preventing any singularities. Here, we uncover an inviscid regularizing mechanism stemming from self-stretching itself, by analyzing the flow topology as perceived by an observer aligned with the vorticity vector undergoing amplification. While, initially, vorticity amplification occurs via increasing twisting of vortex lines, a regularizing anti-twist spontaneously emerges to prevent unbounded growth. By isolating a vortex, we additionally demonstrate the genericity of this self-regularizing anti-twist. Our work, directly linking dynamics of vortices to turbulence statistics, reveals how the Navier-Stokes dynamics avoids the development of singularities even without the aid of viscosity., Comment: 12 pages, 4 figures

Generative AI, such as OpenAI's GPT-4V large-language model, has rapidly entered mainstream discourse. Novel capabilities in image processing and natural-language communication may augment existing forecasting methods. Large language models further display potential to better communicate weather hazards in a style honed for diverse communities and different languages. This study evaluates GPT-4V's ability to interpret meteorological charts and communicate weather hazards appropriately to the user, despite challenges of hallucinations, where generative AI delivers coherent, confident, but incorrect responses. We assess GPT-4V's competence via its web interface ChatGPT in two tasks: (1) generating a severe-weather outlook from weather-chart analysis and conducting self-evaluation, revealing an outlook that corresponds well with a Storm Prediction Center human-issued forecast; and (2) producing hazard summaries in Spanish and English from weather charts. Responses in Spanish, however, resemble direct (not idiomatic) translations from English to Spanish, yielding poorly translated summaries that lose critical idiomatic precision required for optimal communication. Our findings advocate for cautious integration of tools like GPT-4V in meteorology, underscoring the necessity of human oversight and development of trustworthy, explainable AI., Comment: Supplementary material PDF attached. Submitted to Artificial Intelligence for the Earth Systems (American Meteorological Society) on 18 April 2024

We demonstrate an efficient and accurate, general-purpose first-principles blueprint for calculating anharmonic vibrational free energy and predicting structural phase transition temperatures of solids. Thermodynamic integration is performed without molecular dynamics using only interatomic force constants to model analogues of the true potential and generate their thermal ensembles. By replacing \textit{ab initio} molecular dynamics (AIMD) with statistical sampling of ensemble configurations and trading density-functional theory (DFT) energy calculations on each configuration for a set of matrix operations, our approach enables a faster thermodynamic integration by 4 orders of magnitude over the traditional route via AIMD. Experimental phase transition temperatures of a variety of strongly anharmonic materials with dynamical instabilities including shape-memory alloys are recovered to largely within 25% error. Such a combination of speed and accuracy enables the method to be deployed at a large-scale for predictive mapping of phase transition temperatures.

The secondary phase, such as Ni$_3$Al-based $L1_2$ $\gamma^\prime$, is crucially important for precipitation strengthening of superalloys. Composition-structure-property relations provide useful insights for guided alloy design. Here we use density functional theory combined with the multiple scattering theory to compute dependencies of the structural energies and equilibrium volumes versus composition for ternary Ni$_3$(Al$_{1-x}$X$_x$) alloys with X=(Ti, Zr, Hf; V, Nb, Ta; Cr, Mo, W) in $L1_2$, $D0_{24}$, and $D0_{19}$ phases with a homogeneous chemical disorder on the (Al$_{1-x}$X$_x$) sublattice. Our results provide a better understanding of the physics in Ni$_3$Al-based precipitates and facilitate design of next-generation nickel superalloys with precipitation strengthening., Comment: 12 pages, 5 figures

NiCoCr middle-entropy alloy is known for its exceptional strength at both low and elevated operating temperatures. Mechanical properties of NiCoCr-based alloys are affected by certain features of the energy landscape, such as the energy difference between the hcp and fcc phases (which is known to correlate with the stacking fault energy in the fcc phase) and curvature of the energy surface. We compute formation energies in the Ni-Co-Cr ternary and related quaternary systems and investigate dependences of the relative energies on composition. Such computed composition-structure-property relations can be useful for tuning composition and designing next-generation alloys with improved strength., Comment: 13 pages, 9 figures

UCGretina, a GEANT4 simulation of the GRETINA gamma-ray tracking array of highly-segmented high-purity germanium detectors is described. We have developed a model of the array, in particular of the Quad Module and the capsules, that gives good agreement between simulated and measured photopeak efficiencies over a broad range of gamma-ray energies and reproduces the shape of the measured Compton continuum. Both of these features are needed in order to accurately extract gamma-ray yields from spectra collected in in-beam gamma-ray spectroscopy measurements with beams traveling at $v/c \gtrsim 0.3$ at the National Superconducting Cyclotron Laboratory and the Facility for Rare Isotope Beams. In the process of developing the model, we determined that millimeter-scale layers of passive germanium surrounding the active volumes of the simulated crystals must be included in order to reproduce measured photopeak efficiencies. We adopted a simple model of effective passive layers and developed heuristic methods of determining passive-layer thicknesses by comparison of simulations and measurements for a single crystal and for the full array. Prospects for future development of the model are discussed., Comment: 26 pages, 12 figures

In a theoretical analysis, we generalise well known asymptotic results to obtain expressions for the rate of transfer of material from the surface of an arbitrary, rigid particle suspended in an open pathline flow at large P\'eclet number, $\textrm{Pe}$. The flow may be steady or periodic in time. We apply this result to numerically evaluate expressions for the surface flux to a freely suspended, axisymmetric ellipsoid (spheroid) in Stokes flow driven by a steady linear shear. We complement these analytical predictions with numerical simulations conducted over a range of $\textrm{Pe} = 10^1 - 10^4$ and confirm good agreement at large P\'eclet number. Our results allow us to examine the influence of particle shape upon the surface flux for a broad class of flows. When the background flow is irrotational, the surface flux is steady and is prescribed by three parameters only: the P\'eclet number, the particle aspect ratio and the strain topology. We observe that slender prolate spheroids tend to experience a higher surface flux compared to oblate spheroids with equivalent surface area. For rotational flows, particles may begin to spin or tumble, which may suppress or augment the convective transfer due to a realignment of the particle with respect to the strain field., Comment: Under consideration for publication in the Journal of Fluid Mechanics

Automatically tuning parallel compute kernels allows libraries and frameworks to achieve performance on a wide range of hardware, however these techniques are typically focused on finding optimal kernel parameters for particular input sizes and parameters. General purpose compute libraries must be able to cater to all inputs and parameters provided by a user, and so these techniques are of limited use. Additionally, parallel programming frameworks such as SYCL require that the kernels be deployed in a binary format embedded within the library. As such it is impractical to deploy a large number of possible kernel configurations without inflating the library size. Machine learning methods can be used to mitigate against both of these problems and provide performance for general purpose routines with a limited number of kernel configurations. We show that unsupervised clustering methods can be used to select a subset of the possible kernels that should be deployed and that simple classification methods can be trained to select from these kernels at runtime to give good performance. As these techniques are fully automated, relying only on benchmark data, the tuning process for new hardware or problems does not require any developer effort or expertise., Comment: 13 pages, 7 figures, 2 tables

We characterize mutation-finite cluster algebras of rank at least 3 using positive semi-definite quadratic forms. In particular, we associate with every unpunctured bordered surface a positive semi-definite quadratic space $V$, and with every triangulation a basis in $V$, such that any mutation of a cluster (i.e., a flip of a triangulation) transforms the corresponding bases into each other by partial reflections. Furthermore, every triangulation gives rise to an extended affine Weyl group of type $A$, which is invariant under flips. The construction is also extended to exceptional skew-symmetric mutation-finite cluster algebras of types $E$., Comment: 37 pages, many figures; v2: minor corrections. To appear in Transformations Groups, special issue in memory of E. B. Vinberg

Automated tuning of compute kernels is a popular area of research, mainly focused on finding optimal kernel parameters for a problem with fixed input sizes. This approach is good for deploying machine learning models, where the network topology is constant, but machine learning research often involves changing network topologies and hyperparameters. Traditional kernel auto-tuning has limited impact in this case; a more general selection of kernels is required for libraries to accelerate machine learning research. In this paper we present initial results using machine learning to select kernels in a case study deploying high performance SYCL kernels in libraries that target a range of heterogeneous devices from desktop GPUs to embedded accelerators. The techniques investigated apply more generally and could similarly be integrated with other heterogeneous programming systems. By combining auto-tuning and machine learning these kernel selection processes can be deployed with little developer effort to achieve high performance on new hardware., Comment: 4 pages, 4 figures, 1 table. Accepted to AsHES workshop at IPDPS 2020

Over recent years heterogeneous systems have become more prevalent across HPC systems, with over 100 supercomputers in the TOP500 incorporating GPUs or other accelerators. These hardware platforms have different performance characteristics and optimization requirements. In order to make the most of multiple accelerators a developer has to provide implementations of their algorithms tuned for each device. Hardware vendors provide libraries targeting their devices specifically, which provide good performance but frequently have different API designs, hampering portability. The SYCL programming model allows users to write heterogeneous programs using completely standard C++, and so developers have access to the power of C++ templates when developing compute kernels. In this paper we show that by writing highly parameterized kernels for matrix multiplies and convolutions we achieve performance competitive with vendor implementations across different architectures. Furthermore, tuning for new devices amounts to choosing the combinations of kernel parameters that perform best on the hardware., Comment: 11 pages, 9 figures, 4 tables

Over the past few years machine learning has seen a renewed explosion of interest, following a number of studies showing the effectiveness of neural networks in a range of tasks which had previously been considered incredibly hard. Neural networks' effectiveness in the fields of image recognition and natural language processing stems primarily from the vast amounts of data available to companies and researchers, coupled with the huge amounts of compute power available in modern accelerators such as GPUs, FPGAs and ASICs. There are a number of approaches available to developers for utilizing GPGPU technologies such as SYCL, OpenCL and CUDA, however many applications require the same low level mathematical routines. Libraries dedicated to accelerating these common routines allow developers to easily make full use of the available hardware without requiring low level knowledge of the hardware themselves, however such libraries are often provided by hardware manufacturers for specific hardware such as cuDNN for Nvidia hardware or MIOpen for AMD hardware. SYCL-DNN is a new open-source library dedicated to providing accelerated routines for neural network operations which are hardware and vendor agnostic. Built on top of the SYCL open standard and written entirely in standard C++, SYCL-DNN allows a user to easily accelerate neural network code for a wide range of hardware using a modern C++ interface. The library is tested on AMD's OpenCL for GPU, Intel's OpenCL for CPU and GPU, ARM's OpenCL for Mali GPUs as well as ComputeAorta's OpenCL for R-Car CV engine and host CPU. In this talk we will present performance figures for SYCL-DNN on this range of hardware, and discuss how high performance was achieved on such a varied set of accelerators with such different hardware features., Comment: 4 pages, 3 figures. In International Workshop on OpenCL (IWOCL '19), May 13-15, 2019, Boston