Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of a shared, common haplotype cosegregating with an SGCB c.544A>C mutation in Indian patients affected with sarcoglycanopathy

Background Sarcoglycanopathies (SG) is the most frequent form of autosomal recessive limb-girdle muscular dystrophies (LGMD) leading to progressive muscle wasting and weakness, predominantly characterized by limb-girdle weakness. LGMDR4 is caused by mutations in SGCB encoding for the beta-sarcoglycan proteins. In this study, we describe a shared, common haplotype cosegregating in 14 SG cases from 13 unrelated families with the likely pathogenic homozygous mutation c.544A>C (p.Thr182Pro) in SGCB. Methods The genotypes of five selected markers (rs10009426, rs6824707, rs2271046, rs35414474 and rs17611952) surrounding the c.544A>C (p.Thr182Pro) were extracted from the variant call format (VCF) generated from whole-exome sequencing (WES) of 14 cases and 14 related family members as controls. The linkage data file was constructed and linkage disequilibrium (LD) plots were generated using HaploView to visualize patterns of LD. Further, haplotype reconstructions based on the 6 markers were conducted using PLINK1.9. using the expectation-maximization (EM) algorithm, an iterative method to find maximum likelihood. Subsequently, the R programming language was used to determine and compare plots of the haplotype frequencies and percentages for both groups to infer the risk haplotypes. Results Four strong LD blocks were identified in control group: rs10009426 to rs6824707 (0.27 kb), rs6824707 to rs2271046 (41.6 kb), rs10009426 to rs2271046 (41.8 kb) and rs35414474 to rs17611952 (0.17 kb) which were absent in the case group. Similarly, a total of nine haplotypes were estimated in cases and controls of which haplotype H1= G, A, T, G, G, T showed significant statistical difference in the frequency between cases and controls. H1 is also observed to cosegregate with c.544A>C (p.Thr182Pro) in the pedigrees of all the cases. Conclusion The identification of c.544A>C (p.Thr182Pro) mutation in 14 cases from India indicates a probable event of founder effect. Further, the H1 haplotype, cosegregating with this mutation, convincingly sheds light on the recent developments in population genetics allowing insights into demographic and population history. This haplotype can also be used as a genetic marker to screen individuals with genetic susceptibility as carriers and provide genetically informed risk stratification and management in the prevention of SG.
Current Crowding in Nanoscale Superconductors within the Ginzburg-Landau Model

The current density in a superconductor with turnarounds or constrictions is non-uniform due to a geometrical current crowding effect. This effect reduces the critical current in the superconducting structure compared to a straight segment and is of importance when designing superconducting devices. We investigate the current crowding effect in numerical simulations within the generalized time-dependent Ginzburg-Landau (GTDGL) model. The results are validated experimentally by measuring the magnetic field dependence of the critical current in superconducting nanowire structures, similar to those employed in single-photon detector devices. Comparing the results with London theory, we conclude that the reduction in critical current is significantly smaller in the GTDGL model. This difference is attributed to the current redistribution effect, which reduces the current density in weak points of the superconductor and counteracts the current crowding effect. We numerically investigate the effect of fill factor on the critical current in a meander and conclude that the reduction of critical current is low enough to justify fill factors higher than $33\,\%$ for applications where detection efficiency is critical. Finally, we propose a novel meander design which can combine high fill factor and low current crowding.
Is Disaggregation possible for HPC Cognitive Simulation?

Cognitive simulation (CogSim) is an important and emerging workflow for HPC scientific exploration and scientific machine learning (SciML). One challenging workload for CogSim is the replacement of one component in a complex physical simulation with a fast, learned, surrogate model that is “inside” of the computational loop. The execution of this in-the-loop inference is particularly challenging because it requires frequent inference across multiple possible target models, can be on the simulation’s critical path (latency bound), is subject to requests from multiple MPI ranks, and typically contains a small number of samples per request. In this paper we explore the use of large, dedicated Deep Learning / AI accelerators that are disaggregated from compute nodes for this CogSim workload. We compare the trade-offs of using these accelerators versus the node-local GPU accelerators on leadership-class HPC systems.
Asymptotic nonlocality in non-Abelian gauge theories

Abstract Asymptotically nonlocal field theories represent a sequence of higher-derivative theories whose limit point is a ghost-free, infinite-derivative theory. Here, we extend previous work on pure scalar and Abelian gauge theories to asymptotically nonlocal non-Abelian theories. In particular, we confirm that there is a limit in which the Lee-Wick spectrum can be decoupled, but where the hierarchy problem is resolved via an emergent nonlocal scale that regulates loop diagrams and that is hierarchically smaller than the lightest Lee-Wick resonance.
Sketching as a Tool for Understanding and Accelerating Self-attention for Long Sequences

Transformer-based models are not efficient in processing long sequences due to the quadratic space and time complexity of the self-attention modules. To address this limitation, Linformer and Informer reduce the quadratic complexity to linear (modulo logarithmic factors) via low-dimensional projection and row selection, respectively. These two models are intrinsically connected, and to understand their connection we introduce a theoretical framework of matrix sketching. Based on the theoretical analysis, we propose Skeinformer to accelerate self-attention and further improve the accuracy of matrix approximation to self-attention with column sampling, adaptive row normalization and pilot sampling reutilization. Experiments on the Long Range Arena benchmark demonstrate that our methods outperform alternatives with a consistently smaller time/space footprint.
Spinning Language Models for Propaganda-As-A-Service

—We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to “spin” their outputs so as to support an adversary-chosen sentiment or point of view, but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model would output positive summaries of any text that mentions the name of some individual or organization. Model spinning enables propaganda-as-a-service. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy them to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. In technical terms, model spinning introduces a “meta-backdoor” into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary (e.g., positive sentiment). To demonstrate feasibility of model spinning, we develop a new backdooring technique. It stacks the adversarial meta-task (e.g., sentiment analysis) onto a seq2seq model, backpropagates the desired meta-task output (e.g., positive sentiment) to points in the word-embedding space we call “pseudo-words,” and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary’s meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models. Finally, we propose a black-box, meta-task-independent defense
Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images

Despite recent advances in deep-learning-based semantic segmentation, automatic building detection from remotely sensed imagery is still a challenging problem owing to large variability in the appearance of buildings across the globe. The errors occur mostly around the boundaries of the building footprints, in shadow areas, and when detecting buildings whose exterior surfaces have reflectivity properties that are very similar to those of the surrounding regions. To overcome these problems, we propose a generative adversarial network-based segmentation framework with uncertainty attention unit and refinement module embedded in the generator. The refinement module, composed of edge and reverse-attention units, is designed to refine the predicted building map. The edge attention enhances the boundary features to estimate building boundaries with greater precision, and the reverse attention allows the network to explore the features missing in the previously estimated regions. The uncertainty attention unit assists the network in resolving uncertainties in classification. As a measure of the power of our approach, as of December 4, 2021, it ranks at the second place on DeepGlobe’s public leaderboard despite the fact that main focus of our approach—refinement of the building edges—does not align exactly with the metrics used for leaderboard rankings. Our overall $F$1-score on DeepGlobe’s challenging dataset is 0.745. We also report improvements on the previous-best results for the challenging INRIA validation dataset for which our network achieves an overall IoU of 81.28% and an overall accuracy of 97.03%. Along the same lines, for the official INRIA test dataset, our network scores 77.86% and 96.41% in overall IoU and accuracy. We have also improved upon the previous best results on two other datasets: the WHU building dataset and the Massachusetts buildings dataset.
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation

Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like objectives (masked language modeling and image-text matching) during pretraining. Although they perform well in many understanding downstream tasks, e.g., visual question answering, image-text retrieval and visual entailment, they do not possess the ability to generate. To tackle this problem, we propose Unified multimodal pre-training for both Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generative tasks. We augment existing pretraining paradigms that only use random masks with causal masks, i.e., triangular masks that mask out future tokens, such that the pre-trained models can have autoregressive generation abilities by design. We formulate several previous understanding tasks as a text generation task and propose to use prompt-based method for fine-tuning on different downstream tasks. Our experiments show that there is a trade-off between understanding tasks and generation tasks while using the same model, and a feasible way to improve both tasks is to use more data. Our UniVL framework attains comparable performance to recent vision-language pre-training methods on both understanding tasks and generation tasks. Moreover, we demostrate that prompt-based finetuning is more data-efficient - it outperforms discriminative methods in few-shot scenarios.
Sequential Motifs in Observed Walks

The structure of complex networks can be characterized by counting and analysing network motifs. Motifs are small graph structures that occur repeatedly in a network, such as triangles or chains. Recent work has generalized motifs to temporal and dynamic network data. However, existing techniques do not generalize to sequential or trajectory data, which represent entities moving through the nodes of a network, such as passengers moving through transportation networks. The unit of observation in these data is fundamentally different since we analyse observations of trajectories (e.g. a trip from airport A to airport C through airport B), rather than independent observations of edges or snapshots of graphs over time. In this work, we define sequential motifs in trajectory data, which are small, directed and sequence-ordered graphs corresponding to patterns in observed sequences. We draw a connection between the counting and analysis of sequential motifs and Higher-Order Network (HON) models. We show that by mapping edges of a HON, specifically a $k$th-order DeBruijn graph, to sequential motifs, we can count and evaluate their importance in observed data. We test our methodology with two datasets: (1) passengers navigating an airport network and (2) people navigating the Wikipedia article network. We find that the most prevalent and important sequential motifs correspond to intuitive patterns of traversal in the real systems and show empirically that the heterogeneity of edge weights in an observed higher-order DeBruijn graph has implications for the distributions of sequential motifs we expect to see across our null models.
Large-scale economic dispatch by genetic algorithm

This paper presents a new genetic approach for solving the economic dispatch problem in large-scale power systems. A new encoding technique is developed. The chromosome contains only an encoding of the normalized system incremental cost in this encoding technique. Therefore, the total number of bits of chromosome is entirely independent of the number of units. The salient feature makes the proposed genetic approach attractive in large and complex systems which other methodologies may fail to achieve. Moreover, the approach can take network losses, ramp rate limits, and prohibited zone avoidance into account because of genetic algorithm's flexibility. Numerical results on an actual utility system of up to 40 units show that the proposed approach is faster and more robust than the well-known lambda-iteration method in large-scale systems.
MG-GAN: Supplementary Material

The supplementary material complements our work with the implementation details of MG-GAN in Appendix A. Furthermore, we provide details on the training procedure and additional experiments on the hyperparameters of MGGAN in Appendix B. In Appendix C, we explain how we compute Precision and Recall and describe the synthetic dataset in Appendix D. We discuss the performance of MGGAN on real-world datasets in Appendix E. Lastly, we investigate the benefits of multi-generator models in learning distinct modes on a toy dataset in Appendix F and add visualizations of predicted trajectories on the considered datasets in Appendix G.
Nonlinear elasticity of prestressed single crystals at high pressure and various elastic moduli

A general nonlinear theory for the elasticity of pre-stressed single crystals is presented. Various types of elastic moduli are defined, their importance is determined, and relationships between them are presented. In particular, B moduli are present in the relationship between the Jaumann objective time derivative of the Cauchy stress and deformation rate and are broadly used in computational algorithms in various finite-element codes. Possible applications to simplified linear solutions for complex nonlinear elasticity problems are outlined and illustrated for a superdislocation. The effect of finite rotations is fully taken into account and analyzed. Different types of the bulk and shear moduli under different constraints are defined and connected to the effective properties of polycrystalline aggregates. Expressions for elastic energy and stress-strain relationships for small distortions with respect to a pre-stressed configuration are derived in detail. Under initial hydrostatic load, general consistency conditions for elastic moduli and compliances are derived that follow from the existence of the generalized tensorial equation of state under hydrostatic loading obtained from single crystal or polycrystal. It is shown that B moduli can be found from the expression for the Gibbs energy. However, higher-order elastic moduli defined from the Gibbs energy do not have any meaning since they do not directly participate in any known equations, like stress-strain relationships and wave propagation equation. The deviatoric projection of B can also be found from the expression for the elastic energy for isochoric small strain increments, and the missing components of B can be found from the consistency conditions. Numerous inconsistencies and errors in the known works are analyzed.
METHODOLOGY FORMING FOR THE APPROACHES TO THE CYBER SECURITY OF INFORMATION SYSTEMS MANAGEMENT

This paper establishes the analogy of displaying an information security management system (ISMS) for information systems (IS) as a queuing system (QS). It became possible through the identification of structural and functional analogies between ISMS and QS. As a result of such analogy, it became possible to model the ISMS as a QS
A large-scale image dataset of wood surface defects for automated vision-based quality control processes [version 1; peer review: 1 approved with reservations]

The wood industry is facing many challenges. The high variability of raw material and the complexity of manufacturing processes results in a wide range of visible structure defects, which have to be controlled by trained specialists. These manual processes are not only tedious and biased, but also less effective. To overcome the drawbacks of the manual quality control processes, several automated vision-based systems have been proposed. Even though some conducted studies achieved a higher recognition rate than trained experts, researchers have to deal with a lack of large-scale databases and authentic data in this field. To address this issue, we performed a data acquisition experiment set in the industrial environment, where we were able to acquire an extensive set of authentic data from a production line. For this purpose, we designed and implemented a complex technical solution suitable for high-speed acquisition during harsh manufacturing conditions. In this data note, we present a large-scale dataset of high-resolution sawn timber surface images containing more than 43 000 labelled surface defects and covering 10 types of the most common wood defects. Moreover, with each image record, we provide two types of labels allowing researchers to perform semantic segmentation, as well as defect classification, and localization.
Deterministic particle flows for constraining stochastic nonlinear systems

Devising optimal interventions for constraining stochastic systems is a challenging endeavour that has to confront the interplay between randomness and nonlinearity. Existing methods for identifying the necessary dynamical adjustments resort either to space discretising solutions of ensuing partial differential equations, or to iterative stochastic path sampling schemes. Yet, both approaches become computationally demanding for increasing system dimension. Here, we propose a generally applicable and practically feasible non-iterative methodology for obtaining optimal dynamical interventions for diffusive nonlinear systems. We estimate the necessary controls from an interacting particle approximation to the logarithmic gradient of two forward probability flows evolved following deterministic particle dynamics. Applied to several biologically inspired models, we show that our method provides the necessary optimal controls in settings with terminal-, transient-, or generalised collective-state constraints and arbitrary system dynamics.
FLiMS: a Fast Lightweight 2-way Merge Sorter

—In this paper, we present FLiMS, a highly-efﬁcient and simple parallel algorithms for merging two sorted lists residing in banked and/or wide memory. On FPGAs, its implementation uses fewer hardware resources than the state-of-the-art alternatives, due to the reduced number of comparators and elimination of redundant logic found on prior attempts. In combination with the distributed nature of the selector stage, a higher performance is achieved for the same amount of parallelism or higher. This is useful in many applications such as in parallel merge trees to achieve high-throughput sorting, where the resource utilisation of the merger is critical for building larger trees and internalising the workload for faster computation. Also presented are efﬁcient variations of FLiMS for optimizing throughput for skewed datasets, achieving stable sorting or using fewer dequeue signals. FLiMS is also shown to perform well as conventional software on modern CPUs supporting single-instruction multiple-data (SIMD) instructions, surpassing the performance of some standard libraries for sorting.
Surrogate-data-enriched Physics-Aware Neural Networks

Neural networks can be used as surrogates for PDE models. They can be made physics-aware by penalizing underlying equations or the conservation of physical properties in the loss function during training. Current approaches allow to additionally respect data from numerical simulations or experiments in the training process. However, this data is frequently expensive to obtain and thus only scarcely available for complex models. In this work, we investigate how physics-aware models can be enriched with computationally cheaper, but inexact, data from other surrogate models like Reduced-Order Models (ROMs). In order to avoid trusting too-low-fidelity surrogate solutions, we develop an approach that is sensitive to the error in inexact data. As a proof of concept, we consider the one-dimensional wave equation and show that the training accuracy is increased by two orders of magnitude when inexact data from ROMs is incorporated.
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks

Gradient-based methods for the distributed training of residual networks (ResNets) typically require a forward pass of the input data, followed by back-propagating the error gradient to update model parameters, which becomes time-consuming as the network goes deeper. To break the algorithmic locking and exploit synchronous module parallelism in both the forward and backward modes, auxiliary-variable methods have attracted much interest lately but suffer from significant communication overhead and lack of data augmentation. In this work, a novel joint learning framework for training realistic ResNets across multiple compute devices is established by trading off the storage and recomputation of external auxiliary variables. More specifically, the input data of each independent processor is generated from its low-capacity auxiliary network (AuxNet), which permits the use of data augmentation and realizes forward unlocking. The backward passes are then executed in parallel, each with a local loss function that originates from the penalty or augmented Lagrangian (AL) methods. Finally, the proposed AuxNet is employed to reproduce the updated auxiliary variables through an end-to-end training process. We demonstrate the effectiveness of our methods on ResNets and WideResNets across CIFAR-10, CIFAR-100, and ImageNet datasets, achieving speedup over the traditional layer-serial training method while maintaining comparable testing accuracy.