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The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high-performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real-world applications, we have created h5bench, a set of I/O kernels that exercise hierarchical data format version 5 (HDF5) I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library's heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (one-dimensional arrays, two-dimensional meshes, three-dimensional cubes), I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on existing supercomputers (Cori and Summit) and pre-exascale platforms (Perlmutter, Theta, and Polaris). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community.

Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like images. Lumbar spines of asymptomatic and low back pain (LBP) subjects were imaged at 3-Tesla with multi-echo ultrashort echo time (UTE) and field echo (FE) sequences followed by simple post-processing of averaging and inverting to depict spinal bones with a CT-like appearance. The contrast-to-noise ratio (CNR) for bone was determined to compare UTE vs. FE and single-echo vs. multi-echo data. Visually, both sequences depicted cortical bone with good contrast; UTE-processed sequences provided a flatter contrast for soft tissues that made them easy to distinguish from bone, while FE-processed images had better resolution and bone–muscle contrast, which are important for fracture detection. Additionally, multi-echo images provided significantly (p = 0.03) greater CNR compared with single-echo images. Using these techniques, progressive spondylolysis was detected in an LBP subject. This study demonstrates the feasibility of using spine bone MRI to yield CT-like contrast. Through the employment of multi-echo UTE and FE sequences combined with simple processing, we observe sufficient enhancements in image quality and contrast to detect pars fractures.

Large-scale multiphysics simulations are computationally challenging due to the coupling of multiple processes with widely disparate time scales. The advent of exascale computing systems exacerbates these challenges since these systems enable ever-increasing size and complexity. In recent years, there has been renewed interest in developing multirate methods as a means to handle the large range of time scales, as these methods may afford greater accuracy and efficiency than more traditional approaches of using implicit-explicit (IMEX) and low-order operator splitting schemes. However, to date there have been few performance studies that compare different classes of multirate integrators on complex application problems. In this work, we study the performance of several newly developed multirate infinitesimal (MRI) methods, implemented in the SUNDIALS solver package, on two reacting flow model problems built on structured mesh frameworks. The first model revisits prior work on a compressible reacting flow problem with complex chemistry that is implemented using BoxLib but where we now include comparisons between a new explicit MRI scheme with the multirate spectral deferred correction (SDC) methods in the original paper. The second problem uses the same complex chemistry as the first problem, combined with a simplified flow model, but runs at a large spatial scale where explicit methods become infeasible due to stability constraints. Two recently developed IMEX MRI multirate methods are tested. These methods rely on advanced features of the AMReX framework on which the model is built, such as multilevel grids and multilevel preconditioners. The results from these two problems show that MRI multirate methods can offer significant performance benefits on complex multiphysics application problems and that these methods may be combined with advanced spatial discretization to compound the advantages of both.

Predicting the evolution of fractured media is challenging due to coupled thermal, hydrological, chemical and mechanical processes that occur over a broad range of spatial scales, from the microscopic pore scale to field scale. We present a software framework and scientific workflow that couples the pore scale flow and reactive transport simulator Chombo-Crunch with the field scale geomechanics solver in GEOS to simulate fracture evolution in subsurface fluid-rock systems. This new multiphysics coupling capability comprises several novel features. An HDF5 data schema for coupling fracture positions between the two codes is employed and leverages the coarse resolution of the GEOS mechanics solver which limits the size of data coupled, and is, thus, not taxed by data resulting from the high resolution pore scale Chombo-Crunch solver. The coupling framework requires tracking of both before and after coarse nodal positions in GEOS as well as the resolved embedded boundary in Chombo-Crunch. We accomplished this by developing an approach to geometry generation that tracks the fracture interface between the two different methodologies. The GEOS quadrilateral mesh is converted to triangles which are organized into bins and an accessible tree structure; the nodes are then mapped to the Chombo representation using a continuous signed distance function that determines locations inside, on and outside of the fracture boundary. The GEOS positions are retained in memory on the Chombo-Crunch side of the coupling. The time stepping cadence for coupled multiphysics processes of flow, transport, reactions and mechanics is stable and demonstrates temporal reach to experimental time scales. The approach is validated by demonstration of 9 days of simulated time of a core flood experiment with fracture aperture evolution due to invasion of carbonated brine in wellbore-cement and sandstone. We also demonstrate usage of exascale computing resources by simulating a high resolution version of the validation problem on OLCF Frontier.

IntroductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).MethodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.ResultsOver 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p ​

The utility of a quantum computer is highly dependent on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor—in contrast to many established gate implementation strategies. In this work, we utilize a continuous control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly, with little to no additional optimization. Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.

The demand for specialized hardware to train AI models has increased in tandem with the increase in the model complexity over the recent years. Graphics processing unit (GPU) is one such hardware that is capable of parallelizing operations performed on a large chunk of data. Companies like Nvidia, AMD, and Google have been constantly scaling-up the hardware performance as fast as they can. Nevertheless, there is still a gap between the required processing power and processing capacity of the hardware. To increase the hardware utilization, the software has to be optimized too. In this paper, we present some general GPU optimization techniques we used to efficiently train the optiGAN model, a Generative Adversarial Network that is capable of generating multidimensional probability distributions of optical photons at the photodetector face in radiation detectors, on an 8GB Nvidia Quadro RTX 4000 GPU. We analyze and compare the performances of all the optimizations based on the execution time and the memory consumed using the Nvidia Nsight Systems profiler tool. The optimizations gave approximately a 4.5x increase in the runtime performance when compared to a naive training on the GPU, without compromising the model performance. Finally we discuss optiGANs future work and how we are planning to scale the model on GPUs.

Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactification of large deep neural networks to free up computational resources, in particular on edge devices. In this article, we consider proton-proton collisions at the High-Luminosity Large Hadron Collider (HL-LHC) and demonstrate a successful knowledge transfer from an event-level graph neural network (GNN) to a particle-level small deep neural network (DNN). Our algorithm, DistillNet, is a DNN that is trained to learn about the provenance of particles, as provided by the soft labels that are the GNN outputs, to predict whether or not a particle originates from the primary interaction vertex. The results indicate that for this problem, which is one of the main challenges at the HL-LHC, there is minimal loss during the transfer of knowledge to the small student network, while improving significantly the computational resource needs compared to the teacher. This is demonstrated for the distilled student network on a CPU, as well as for a quantized and pruned student network deployed on an field programmable gate array. Our study proves that knowledge transfer between networks of different complexity can be used for fast artificial intelligence (AI) in high-energy physics that improves the expressiveness of observables over non-AI-based reconstruction algorithms. Such an approach can become essential at the HL-LHC experiments, e.g. to comply with the resource budget of their trigger stages.

While digital computers rely on software-generated pseudo-random number generators, hardware-based true random number generators (TRNGs), which employ the natural physics of the underlying hardware, provide true stochasticity, and power and area efficiency. Research into TRNGs has extensively relied on the unpredictability in phase transitions, but such phase transitions are difficult to control given their often abrupt and narrow parameter ranges (e.g., occurring in a small temperature window). Here we demonstrate a TRNG based on self-oscillations in LaCoO3 that is electrically biased within its spin crossover regime. The LaCoO3 TRNG passes all standard tests of true stochasticity and uses only half the number of components compared to prior TRNGs. Assisted by phase field modeling, we show how spin crossovers are fundamentally better in producing true stochasticity compared to traditional phase transitions. As a validation, by probabilistically solving the NP-hard max-cut problem in a memristor crossbar array using our TRNG as a source of the required stochasticity, we demonstrate solution quality exceeding that using software-generated randomness.

We present an open-source software package, TRAVOLTA (Terrific Refinements to Accelerate, Validate, and Optimize Large Time-dependent Algorithms), for carrying out massively parallelized quantum optimal control calculations on GPUs. The TRAVOLTA software package is a significant overhaul of our previous NIC-CAGE algorithm and also includes algorithmic improvements to the gradient ascent procedure to enable faster convergence. We examine three different variants of GPU parallelization to assess their performance in constructing optimal control fields in a variety of quantum systems. In addition, we provide several examples with extensive benchmarks of our GPU-enhanced TRAVOLTA code to show that it generates the same results as previous CPU-based algorithms but with a speedup that is more than ten times faster. Our GPU enhancements and algorithmic improvements enable large quantum optimal control calculations that can be efficiently and routinely executed on modern multi-core computational hardware. Program summary: Program Title: TRAVOLTA CPC Library link to program files: https://doi.org/10.17632/grwppm37rn.1 Licensing provisions: GNU General Public License 3 Programming language: C++, openBLAS, and CUDA Supplementary material: Brief review of LU decomposition, raw numerical values used to generate Fig. 6 in the main text, and input examples for the TRAVOLTA software package. Nature of problem: The TRAVOLTA software package utilizes GPU accelerated routines and new algorithmic improvements to compute optimized electric fields that can drive a system from a known initial vibrational eigenstate to a specified final quantum state with a large (≈1) transition probability. Solution method: Quantum control, GPU acceleration, analytic gradients, Crank-Nicolson propagation, and gradient ascent optimization.

We present a workflow of practical calculations of electron-phonon (e-ph) coupling with many-electron correlation effects included using the GW perturbation theory (GWPT). This workflow combines BerkeleyGW, ABINIT, and EPW software packages to enable accurate e-ph calculations at the GW self-energy level, going beyond standard calculations based on density functional theory (DFT) and density-functional perturbation theory (DFPT). This workflow begins with DFT and DFPT calculations (ABINIT) as starting point, followed by GW and GWPT calculations (BerkeleyGW) for the quasiparticle band structures and e-ph matrix elements on coarse electron k- and phonon q-grids, which are then interpolated to finer grids through Wannier interpolation (EPW) for computations of various e-ph coupling determined physical quantities such as the electron self-energies or solutions of anisotropic Eliashberg equations, among others. A gauge-recovering symmetry unfolding technique is developed to reduce the computational cost of GWPT (as well as DFPT) while fulfilling the gauge consistency requirement for Wannier interpolation.

We present the GPU implementation efforts and challenges of the sparse solver package STRUMPACK. The code is made publicly available on github with a permissive BSD license. STRUMPACK implements an approximate multifrontal solver, a sparse LU factorization which makes use of compression methods to accelerate time to solution and reduce memory usage. Multiple compression schemes based on rank-structured and hierarchical matrix approximations are supported, including hierarchically semi-separable, hierarchically off-diagonal butterfly, and block low rank. In this paper, we present the GPU implementation of the block low rank (BLR) compression method within a multifrontal solver. Our GPU implementation relies on highly optimized vendor libraries such as cuBLAS and cuSOLVER for NVIDIA GPUs, rocBLAS and rocSOLVER for AMD GPUs and the Intel oneAPI Math Kernel Library (oneMKL) for Intel GPUs. Additionally, we rely on external open source libraries such as SLATE (Software for Linear Algebra Targeting Exascale), MAGMA (Matrix Algebra on GPU and Multi-core Architectures), and KBLAS (KAUST BLAS). SLATE is used as a GPU-capable ScaLAPACK replacement. From MAGMA we use variable sized batched dense linear algebra operations such as GEMM, TRSM and LU with partial pivoting. KBLAS provides efficient (batched) low rank matrix compression for NVIDIA GPUs using an adaptive randomized sampling scheme. The resulting sparse solver and preconditioner runs on NVIDIA, AMD and Intel GPUs. Interfaces are available from PETSc, Trilinos and MFEM, or the solver can be used directly in user code. We report results for a range of benchmark applications, using the Perlmutter system from NERSC, Frontier from ORNL, and Aurora from ALCF. For a high frequency wave equation on a regular mesh, using 32 Perlmutter compute nodes, the factorization phase of the exact GPU solver is about 6.5× faster compared to the CPU-only solver. The BLR-enabled GPU solver is about 13.8× faster than the CPU exact solver. For a collection of SuiteSparse matrices, the STRUMPACK exact factorization on a single GPU is on average 1.9× faster than NVIDIA’s cuDSS solver.

Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays, precisely locating and seamlessly averaging such particles across many tomograms present major challenges. Here, we developed TomoNet, a software package with a modern graphical user interface to carry out the entire pipeline of cryoET and subtomogram averaging to achieve high resolution. TomoNet features built-in automatic particle picking and three-dimensional (3D) classification functions and integrates commonly used packages to streamline high-resolution subtomogram averaging for structures in 1D, 2D, or 3D arrays. Automatic particle picking is accomplished in two complementary ways: one based on template matching and the other using deep learning. TomoNet's hierarchical file organization and visual display facilitate efficient data management as required for large cryoET datasets. Applications of TomoNet to three types of datasets demonstrate its capability of efficient and accurate particle picking on flexible and imperfect lattices to obtain high-resolution 3D biological structures: virus-like particles, bacterial surface layers within cellular lamellae, and membranes decorated with nuclear egress protein complexes. These results demonstrate TomoNet's potential for broad applications to various cryoET projects targeting high-resolution in situ structures.

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

Extracellular vesicles (EVs) are cell-secreted nanovesicles that play an important role in long-range cell-cell communication. Although EVs pose a promising alternative to cell-based therapy, targeted in vivo delivery still falls short. Many studies have explored the surface modification of EVs to enhance their targeting capabilities. However, to our knowledge, there are no standardized practices to confirm the successful surface modification of EVs or calculate the degree of conjugation on EV surfaces (conjugation efficiency). These pieces of information are essential in the reproducibility of targeted EV therapeutics and the determination of optimized conjugation conditions for EVs to see significant therapeutic effects in vitro and in vivo. This review will discuss the vast array of techniques adopted, technologies developed, and efficiency definitions made by studies that have calculated EV/nanoparticle surface conjugation efficiency and how differences between studies may contribute to differently reported conjugation efficiencies.

Chemical reactions are dynamical processes involving the correlated reorganization of atomic configurations, driving the conversion of an initial reactant into a result product. By virtue of the metastability of both the reactants and products, chemical reactions are rare events, proceeding fleetingly. Reaction pathways can be modelled probabilistically by using the notion of reactive density in the phase space of the molecular system. Such density is related to a function known as the committor function, which describes the likelihood of a configuration evolving to one of the nearby metastable regions. In theory, the committor function can be obtained by solving the backward Kolmogorov equation (BKE), which is a partial differential equation (PDE) defined in the full dimensional phase space. However, using traditional methods to solve this problem is not practical for high dimensional systems. In this work, we propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of states that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based PDE solver to obtain an approximation solution of a restricted BKE, even when the dimension of the problem is very high. The resulting solution provides an approximation for the committor function that encodes mechanistic information for the reaction, paving a new way for understanding of complex chemical reactions and evaluation of reaction rates.

High-Performance Computing (HPC) platforms are required to solve the most diverse large-scale scientific problems in various research areas, such as biology, chemistry, physics, and health sciences. Researchers use a multitude of scientific softwares, which have different requirements. These include input and output operations, which directly impact performance due to the existing difference in processing and data access speeds. Thus, supercomputers must efficiently handle mixed workload when storing data from the applications. Understanding the set of applications and their performance running in a supercomputer is paramount to understanding the storage system's usage, pinpointing possible bottlenecks, and guiding optimization techniques. This research proposes a methodology and visualization tool to evaluate a supercomputer's data storage infrastructure's performance, taking into account the diverse workload and demands of the system over a long period of operation. As a study case, we focus on the Santos Dumont supercomputer, identifying inefficient usage, problematic performance factors, and providing guidelines on how to tackle those issues.

Sparse symmetric positive definite systems of equations are ubiquitous in scientific workloads and applications. Parallel sparse Cholesky factorization is the method of choice for solving such linear systems. Therefore, the development of parallel sparse Cholesky codes that can efficiently run on today's large-scale heterogeneous distributed-memory platforms is of vital importance. Modern supercomputers offer nodes that contain a mix of CPUs and GPUs. To fully utilize the computing power of these nodes, scientific codes must be adapted to offload expensive computations to GPUs. We present symPACK, a GPU-capable parallel sparse Cholesky solver that uses one-sided communication primitives and remote procedure calls provided by the UPC++ library. We also utilize the UPC++ "memory kinds"feature to enable efficient communication of GPU-resident data. We show that on a number of large problems, symPACK outperforms comparable state-of-the-art GPU-capable Cholesky factorization codes by up to 14x on the NERSC Perlmutter supercomputer.

We present EZ, a novel current deposition algorithm for particle-in-cell (PIC) simulations. EZ calculates the current density on the electromagnetic grid due to macro-particle motion within a time step by solving the continuity equation of electrodynamics. Being a charge conserving hybridization of Esirkepov's method and ZigZag, we refer to it as “EZ” as shorthand for “Esirkepov meets ZigZag”. Simulations of a warm, relativistic plasma with PIConGPU show that EZ achieves the same level of charge conservation as the commonly used method by Esirkepov, yet reaches higher performance for macro-particle assignment-functions up to third-order. In addition to a detailed description of the functioning of EZ, reasons for the expected and observed performance increase are given, and guidelines for its implementation aiming at highest performance on GPUs are provided.

VORO++ is a software library written in C++ for computing the Voronoi tessellation, a technique in computational geometry that is widely used for analyzing systems of particles. VORO++ was released in 2009 and is based on computing the Voronoi cell for each particle individually. Here, we take advantage of modern computer hardware, and extend the original serial version to allow for multithreaded computation of Voronoi cells via the OpenMP application programming interface. We test the performance of the code, and demonstrate that it can achieve parallel efficiencies greater than 95% in many cases. The multithreaded extension follows standard OpenMP programming paradigms, allowing it to be incorporated into other programs. We provide an example of this using the VoroTop software library, performing a multithreaded Voronoi cell topology analysis of up to 102.4 million particles. Program summary: Program title: VORO++ CPC Library link to program files: https://doi.org/10.17632/tddc4w4zkk.1 Developer's repository link: https://github.com/chr1shr/voro Licensing provisions: BSD 3-clause (with LBNL modification) Programming language: C++ External routines/libraries: OpenMP Nature of problem: Multithreaded computation of the Voronoi tessellation in two and three dimensions Solution method: The VORO++ library is built around several C++ classes that can be incorporated into other programs. The two largest components are the container. classes that spatially sort input particles into a grid-based data structure, allowing for efficient searches of nearby particles, and the voronoicell. classes that represent a single Voronoi cell as an arbitrary convex polygon or polyhedron. The Voronoi cell for each particle is built by considering a sequence of plane cuts based on neighboring particles, after which many different statistics (e.g. volume, centroid, number of vertices) can be computed. Since each Voronoi cell is calculated individually, the Voronoi cells can be computed using multithreading via OpenMP.

We present a massively parallel, 3D phase-field simulation framework for modeling ferroelectric materials based scalable logic devices. This code package, FerroX, self-consistently solves the time-dependent Ginzburg Landau (TDGL) equation for ferroelectric polarization, Poisson's equation for electric potential, and charge equation for carrier densities in semiconductor regions. The algorithm is implemented using the AMReX software framework [1], which provides effective scalability on manycore and GPU-based supercomputing architectures. We demonstrate the performance of the algorithm with excellent scaling results on NERSC multicore and GPU systems, with a significant (15×) speedup on the GPU using a node-by-node comparison. We further demonstrate the applicability of the code in simulations of ferroelectric domain-wall induced negative capacitance (NC) effect in Metal-Ferroelectric-Insulator-Metal (MFIM) and Metal-Ferroelectric-Insulator-Semiconductor-Metal (MFISM) devices. The charge (Q) v.s. voltage (V) responses for these 3D structures clearly indicate stabilized negative capacitance with multidomain formation, which is corroborated by amplification of the voltage at the interface between the ferroelectric and dielectric layers.

Delays in legal proceedings significantly impact both corporate finances and individual livelihoods. Traditional methods for managing these delays typically rely on subjective assessments of what constitutes a reasonable process duration. This study explores a more precise approach by integrating machine learning and process mining techniques to enhance prediction of legal proceedings' overall time. Diverging from previous works that either utilized machine learning analysis or process mining in isolation, this research synergizes these approaches. We applied process mining clustering techniques to over 60,000 cases from Brazilian labor courts to segment cases based on their procedural movements, creating clusters. These clusters, along with other procedural characteristics, such as case subject, class, and digital status, were then incorporated into a feature set for regression modelling. We employed linear regression, support vector regressor, and gradient boosting based methods to develop models that predicted case duration. The gradient boosting model demonstrated the best performance with an R 2 -score of 0.87. Furthermore, our analysis identifies time bands where the model performs better and employs explainable AI techniques to elucidate key features influencing case durations. The clustering features emerged among the most significant for the task. The proposed combined approach offers a comprehensive method for analyzing and forecasting legal case timelines and also shows the potential of process mining clustering techniques to improve the analysis. [ABSTRACT FROM AUTHOR]

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. More accurate simulations with fast electron diffusion are even slower, requiring multiple hours of run time with parallel processing. The machine learning models use a database of 16,000+ GEN-RAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods implemented in πScope ensure that the database covers the range of 9 input parameters (ne0, Te0, Ip, Bt, R0, n∥︀, Ze f f, Vloop, PLHCD) with sufficient density in all regions of parameter space. The surrogate models reduce the computation time from minutes-hours to ms with high accuracy across the input parameter space. Data-driven surrogate models also allow for solving inverse and "lateral"problems. A surrogate model for the inverse problem maps from a desired current drive or power deposition profile to a set of input parameters that would result in such a profile, while a surrogate model for the lateral problem maps from a measured experimental quantity such as hard x-ray emission to a current drive or power deposition profile. The πScope database creation workflow is flexible and applicable to other RF simulation codes such as TORIC.

Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to 40 % on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.

Emerging quantum hardware provides new possibilities for quantum simulation.While much of the research has focused on simulating closed quantum systems,the real-world quantum systems are mostly open. Therefore, it is essential todevelop quantum algorithms that can effectively simulate open quantum systems.Here we present an adaptive variational quantum algorithm for simulating openquantum system dynamics described by the Lindblad equation. The algorithm isdesigned to build resource-efficient ansatze through the dynamical addition ofoperators by maintaining the simulation accuracy. We validate the effectivenessof our algorithm on both noiseless simulators and IBM quantum processors andobserve good quantitative and qualitative agreement with the exact solution. Wealso investigate the scaling of the required resources with system size andaccuracy and find polynomial behavior. Our results demonstrate that near-futurequantum processors are capable of simulating open quantum systems.

To alleviate bottlenecks in storing and accessing data on high-performance computing (HPC) systems, I/O libraries are enabling computation while data is in-transit, such as HDF5 filters. For scientific applications that commonly use floating-point data, error-bounded lossy compression methods are a critical technique to significantly reduce the storage and bandwidth requirements. Thus far, deciding when and where to schedule in-transit data transformations, such as compression, has been outside the scope of I/O libraries.In this paper, we introduce Runway, a runtime framework that enables computation on in-transit data with an object storage abstraction. Runway is designed to be extensible to execute user-defined functions at runtime. In this effort, we focus on studying methods to offload data compression operations to available processing units based on latency and throughput. We compare the performance of running compression on multi-core CPUs, as well as offloading it to a GPU and a Data Processing Unit (DPU). We implement a state-of-the-art error-bounded lossy compression algorithm, SZ3, as a Runway function with a variant optimized for DPUs. We propose dynamic modeling to guide scheduling decisions for in-transit data compression.

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.

Reacting flow simulations for combustion applications require extensive computing capabilities. Leveraging the AMReX library, the Pele suite of combustion simulation tools targets the largest supercomputers available and future exascale machines. We introduce PeleC, the compressible solver in the Pele suite, and detail its capabilities, including complex geometry representation, chemistry integration, and discretization. We present a comparison of development efforts using both OpenACC and AMReX’s C++ performance portability framework for execution on multiple GPU architectures. We discuss relevant details that have allowed PeleC to achieve high performance and scalability. PeleC’s performance characteristics are measured through relevant simulations on multiple supercomputers. The success of PeleC’s design for exascale is exhibited through demonstration of a 160 billion cell simulation and weak scaling onto 100% of Summit, an NVIDIA-based GPU supercomputer at Oak Ridge National Laboratory. Our results provide confidence that PeleC will enable future combustion science simulations with unprecedented fidelity.

We propose a GPU fine-grained load-balancing abstraction that decouples load balancing from work processing and aims to support both static and dynamic schedules with a programmable interface to implement new load-balancing schedules. Prior to our work, the only way to unleash the GPU's potential on irregular problems has been to workload-balance through application-specific, tightly coupled load-balancing techniques. With our open-source framework for load-balancing, we hope to improve programmers' productivity when developing irregular-parallel algorithms on the GPU, and also improve the overall performance characteristics for such applications by allowing a quick path to experimentation with a variety of existing load-balancing techniques. Consequently, we also hope that by separating the concerns of load-balancing from work processing within our abstraction, managing and extending existing code to future architectures becomes easier.

Heme is an essential cofactor for multiple cellular processes in most organisms. In developing erythroid cells, the demand for heme synthesis is high, but is significantly lower in non-erythroid cells. While the biosynthesis of heme in metazoans is well understood, the tissue-specific regulation of the pathway is less explored. To better understand this, we analyzed the mitochondrial heme metabolon in erythroid and non-erythroid cell lines from the perspective of ferrochelatase (FECH), the terminal enzyme in the heme biosynthetic pathway. Affinity purification of FLAG-tagged-FECH, together with mass spectrometric analysis, was carried out to identify putative protein partners in human and murine cell lines. Proteins involved in the heme biosynthetic process and mitochondrial organization were identified as the core components of the FECH interactome. Interestingly, in non-erythroid cell lines, the FECH interactome is highly enriched with proteins associated with the tricarboxylic acid (TCA) cycle. Overall, our study shows that the mitochondrial heme metabolon in erythroid and non-erythroid cells has similarities and differences, and suggests new roles for the mitochondrial heme metabolon and heme in regulating metabolic flux and key cellular processes.

The commonly supposed template-based format for RNA self-replication requires both duplex assembly and disassembly. This requisite binary provision presents a challenge to the development of a serviceable self-replication model since chemical reactions are thermochemically unidirectional. We submit that a solution to this problem lies in volcanic landmasses that engage in continuous cycles of wetting and drying and thus uniquely provide the twofold state required for self-replication. Moreover, they offer conditions that initiate chain branching, and thus furnish a path to autocatalytic self-replication. The foundations of this dual thermochemical landscape arise from the broad differences in the properties of the bulk water phase on the one hand, and the air/water interfacial regions that emerge in the evaporative stages on the other. With this reaction system as a basis and employing recognized thermochemical and kinetic parameters, we present simulations displaying the spontaneous and autocatalyzed conversion of racemic and unactivated RNA monomers to necessarily homochiral duplex structures over characteristic periods of years.

Autoencoders are the backbones of many imputation methods that aim to relieve the sparsity issue in single-cell RNA sequencing (scRNA-seq) data. The imputation performance of an autoencoder relies on both the neural network architecture and the hyperparameter choice. So far, literature in the single-cell field lacks a formal discussion on how to design the neural network and choose the hyperparameters. Here, we conducted an empirical study to answer this question. Our study used many real and simulated scRNA-seq datasets to examine the impacts of the neural network architecture, the activation function, and the regularization strategy on imputation accuracy and downstream analyses. Our results show that (i) deeper and narrower autoencoders generally lead to better imputation performance; (ii) the sigmoid and tanh activation functions consistently outperform other commonly used functions including ReLU; (iii) regularization improves the accuracy of imputation and downstream cell clustering and DE gene analyses. Notably, our results differ from common practices in the computer vision field regarding the activation function and the regularization strategy. Overall, our study offers practical guidance on how to optimize the autoencoder design for scRNA-seq data imputation.

BackgroundCo-use of tobacco and cannabis is highly prevalent among young US adults. Same-day co-use of tobacco and cannabis (ie, use of both substances on the same day) may increase the extent of use and negative health consequences among young adults. However, much remains unknown about same-day co-use of tobacco and cannabis, in part due to challenges in measuring this complex behavior. Nuanced understanding of tobacco and cannabis co-use in terms of specific products and intensity (ie, quantity of tobacco and cannabis use within a day) is critical to inform prevention and intervention efforts.ObjectiveWe used a daily-diary data collection method via smartphone to capture occurrence of tobacco and cannabis co-use within a day. We examined (1) whether the same route of administration would facilitate co-use of 2 substances on the same day and (2) whether participants would use more tobacco on a day when they use more cannabis.MethodsThis smartphone-based study collected 2891 daily assessments from 147 cigarette smokers (aged 18-26 years, n=76, 51.7% female) during 30 consecutive days. Daily assessments measured type (ie, cigarette, cigarillo, or e-cigarette) and intensity (ie, number of cigarettes or cigarillos smoked or number of times vaping e-cigarettes per day) of tobacco use and type (ie, combustible, vaporized, or edible) and intensity (ie, number of times used per day) of cannabis use. We estimated multilevel models to examine day-level associations between types of cannabis use and each type of tobacco use, as well as day-level associations between intensities of using cannabis and tobacco. All models controlled for demographic covariates, day-level alcohol use, and time effects (ie, study day and weekend vs weekday).ResultsSame-day co-use was reported in 989 of the total 2891 daily assessments (34.2%). Co-use of cigarettes and combustible cannabis (885 of the 2891 daily assessments; 30.6%) was most commonly reported. Participants had higher odds of using cigarettes (adjusted odds ratio [AOR] 1.92, 95% CI 1.31-2.81) and cigarillos (AOR 244.29, 95% CI 35.51-1680.62) on days when they used combustible cannabis. Notably, participants had higher odds of using e-cigarettes on days when they used vaporized cannabis (AOR 23.21, 95% CI 8.66-62.24). Participants reported a greater intensity of using cigarettes (AOR 1.35, 95% CI 1.23-1.48), cigarillos (AOR 2.04, 95% CI 1.70-2.46), and e-cigarettes (AOR 1.48, 95% CI 1.16-1.88) on days when they used more cannabis.ConclusionsTypes and intensities of tobacco and cannabis use within a day among young adult smokers were positively correlated, including co-use of vaporized products. Prevention and intervention efforts should address co-use and pay attention to all forms of use and timeframes of co-use (eg, within a day or at the same time), including co-use of e-cigarettes and vaporized cannabis, to reduce negative health outcomes.

Structured RNAs play crucial roles in viruses, exerting influence over both viral and host gene expression. However, the extensive diversity of structured RNAs and their ability to act in cis or trans positions pose challenges for predicting and assigning their functions. While comparative genomics approaches have successfully predicted candidate structured RNAs in microbes on a large scale, similar efforts for viruses have been lacking. In this study, we screened over 5 million DNA and RNA viral sequences, resulting in the prediction of 10,006 novel candidate structured RNAs. These predictions are widely distributed across taxonomy and ecosystem. We found transcriptional evidence for 206 of these candidate structured RNAs in the human fecal microbiome. These candidate RNAs exhibited evidence of nucleotide covariation, indicative of selective pressure maintaining the predicted secondary structures. Our analysis revealed a diverse repertoire of candidate structured RNAs, encompassing a substantial number of putative tRNAs or tRNA-like structures, Rho-independent transcription terminators, and potentially cis-regulatory structures consistently positioned upstream of genes. In summary, our findings shed light on the extensive diversity of structured RNAs in viruses, offering a valuable resource for further investigations into their functional roles and implications in viral gene expression and pave the way for a deeper understanding of the intricate interplay between viruses and their hosts at the molecular level.

We aimed to evaluate the impact of polypharmacy on the risk of having a fall in older persons with HIV (PWH). PWH at least 50 years of age who were seen at our institution from September 2012 to August 2017 were included. Unique participants were selected for either a case or control cohort depending on the presence of a documented fall during the study time period. Demographics, HIV-related measures, VACS score, number of medications, as well as the impact of taking benzodiazepines and opioids were compared between the two cohorts. Fall was documented for 637 patients compared to 1534 without a fall during the same time period. Multivariable logistic regression revealed that the total number of medications, having a higher VACS score, taking an opioid, being female sex assigned at birth, and having a lower nadir CD4 count were significantly associated with higher odds of having a fall. In this cohort of older PWH, taking a higher number of non-ARV medications significantly increased the odds of having a fall. In addition, taking an opioid resulted in the highest odds of having a fall. These results suggest the importance of deprescribing and addressing opioid use in reducing the risk of having a fall in older PWH.

High performance computing has a great potential to provide a range of significant benefits for investigating biological systems. These systems often present large modelling problems with many coupled subsystems, such as when studying colonies of bacteria cells. The aim to understand cell colonies has generated substantial interest as they can have strong economic and societal impacts through their roles in in industrial bioreactors and complex community structures, called biofilms, found in clinical settings. Investigating these communities through realistic models can rapidly exceed the capabilities of current serial software. Here, we introduce BMX, a software system developed for the high performance modelling of large cell communities by utilising GPU acceleration. BMX builds upon the AMRex adaptive mesh refinement package to efficiently model cell colony formation under realistic laboratory conditions. Using simple test scenarios with varying nutrient availability, we show that BMX is capable of correctly reproducing observed behavior of bacterial colonies on realistic time scales demonstrating a potential application of high performance computing to colony modelling. The open source software is available from the zenodo repository https://doi.org/10.5281/zenodo.8084270 under the BSD-2-Clause licence.

Traditionally, the brain has been regarded as a relatively insensitive late-reacting tissue, with radiologically detectable damage not being reported at doses < 60 Gy. When NASA proposed interplanetary exploration missions, it was required to conduct an intensive health and safety evaluation of cancer, cardiovascular, and cognitive risks associated with exposure to deep space radiation (SR). The SR dose that astronauts on a mission to Mars are predicted to receive is ~300 mGy. Even after correcting for the higher RBE of the SR particles, the biologically effective SR dose (

In this retrospective comparative case series at a teaching hospital, we reviewed adult patients with rhegmatogenous retinal detachment who underwent scleral buckling surgery with external drainage of subretinal fluid performed before versus after placement of the scleral buckle. Eight eyes in each group were roughly matched for age, sex, baseline visual acuity (VA), and detachment characteristics. The complication rate was 0% for the "before" group and 37% for the "after" group (p = 0.100). In the "after" group, two eyes (25%) developed iatrogenic retinal holes and one eye (12%) developed self-limited subretinal hemorrhage during external needle drainage. The duration of surgery was significantly shorter for the "before" group (mean 89 ± 16 min) compared to the "after" group (118 ± 20 min) (p = 0.008). The primary anatomic success rate was 100% for the "before" group and 75% for the "after" group (p = 0.233). Final VA was not significantly different between the groups or from baseline. In conclusion, while limited by our small sample size, this pilot study suggests that drainage of subretinal fluid before scleral buckle placement may be safer and more efficient compared to draining after buckle placement. Initial drainage may facilitate retinochoroid apposition to allow targeted cryopexy and precise buckle placement.

The number of procedures required to attain proficiency with new bronchoscopic biopsy technologies for peripheral pulmonary lesions (PPLs) is uncertain. A prospective, single-center study evaluated learning curves of two operators performing PPL biopsies using a novel, real-time, intraoperative tomographic imaging system in consecutive procedures in adults with CT-detected PPLs. Operators were considered "proficient" when they asked three or fewer questions of the manufacturer's clinical representative with no subsequent navigations in which they asked more than three questions. A total of 31 procedures were performed on 31 patients (Operator 1: 18, Operator 2: 13). Proficiency was achieved after an average of 10 procedures (Operator 1: 12, Operator 2: 8). From the learning curve to the post-learning curve period, the number of questions (median [IQR]: 23 [9.5-41.5] versus 0 [0-1], p < 0.001) and radiation dose (median [IQR]: 19.5 mGy/m2 [1.9-43.5] versus 1.5 mGy/m2 [0.7-3.3], p = 0.05) decreased significantly; procedure time decreased (median [IQR]: 12 min [7-20] versus 8 min [3-15], p = 0.29); and diagnostic yield increased significantly (13/20 cases [65%] to 11/11 cases [100%]), (p = 0.03). Based on this unique, clinically relevant method of assessing learning curve, proficiency with the Body Vision system was achieved at approximately the tenth procedure. These findings require validation in larger, diverse populations.

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

Abstract: Trophic analysis exposes the underlying hierarchies present in large complex systems. This allows one to use data to diagnose the sources, propagation paths, and basins of influence of shocks or information among variables or agents, which may be utilised to analyse dynamics in social, economic and historical data sets. Often, the analysis of static networks provides an aggregated picture of a dynamical process and explicit temporal information is typically missing or incomplete. Yet, for many networks, particularly historical ones, temporal information is often implicit, for example in the direction of edges in a network. In this paper, we show that the application of trophic analysis allows one to use the network structure to infer temporal information. We demonstrate this on a sociohistorical network derived from the study of hadith, which are narratives about the Prophet Muhammad’s actions and sayings that cite the people that transmitted the narratives from one generation to the next before they were systematically written down. We corroborate the results of the trophic analysis with a partially specified time labelling of a subset of the transmitters. The results correlate in a manner consistent with an observed history of information transmission flowing through the network. Thus, we show that one may reconstruct a temporal structure for a complex network in which information diffuses from one agent to another via social links and thus allows for the reconstruction of an event based temporal network from an aggregated static snapshot. Our paper demonstrates the utility of trophic analysis in revealing novel information from hierarchical structure, thus showing its potential for probing complex systems, particularly those with an inherent asymmetry.

Objectives: Current strategies for locating where offenders live either focus exclusively on individual suspects or generalize to entire neighborhoods. However, better estimates of where offenders are located may improve models of the ecological distribution of crime, and forecasts of the locations of future crime incidents. Methods: We propose a novel reverse spatial patterning (RSP) strategy that estimates where offenders may live based on the spatial locations of crime events. We rely on a distance decay function – based on the consistent finding that offenders do not travel far to commit crime – and Hipp's (2016) general theory of spatial crime patterns, to work backwards from the locations of actual crime events to make predictions about where offenders may live in subsequent years. We then use these estimates in models predicting crime locations. We create two versions of the RSP: one which assumes everyone is equally likely to offend, and another that creates an estimate assuming disproportionate offending across persons. Results: We test the effectiveness of our proposed strategy for these two measures using offense and arrest data from St. Petersburg, FL, and assess how well they predict the location of offenders (proxied by arrestees) and future crime events. We find consistent evidence that our RSP strategy provides better predictions of the locations of where offenders are located and also future crime incidents across a variety of crime types compared to existing strategies. Conclusion: The RSP approach is useful for creating estimates of where offenders live, which allow for better predictions of the locations of future crime incidents. These better forecasts will allow for more efficient allocation of police resources and targeted crime suppression efforts.