Miracle-dz/UniFAR-FTUs huggingface dataset:
Reverse-Mode Automatic Differentiation and Optimization of GPU Kernels via Enzyme

Computing derivatives is key to many algorithms in scientific computing and machine learning such as optimization, uncertainty quantification, and stability analysis. Enzyme is a LL VM compiler plugin that performs reverse-mode automatic differentiation (AD) and thus generates high performance gradients of programs in languages including $\mathrm{C}/\mathrm{C}++$, Fortran, Julia, and Rust. Prior to this work, Enzyme and other AD tools were not capable of generating gradi-ents of GPU kernels. Our paper presents a combination of novel techniques that make Enzyme the first fully automatic reverse-mode AD tool to generate gradients of GPU kernels. Since unlike other tools Enzyme performs automatic differentiation within a general-purpose compiler, we are able to introduce several novel GPU and AD-specific optimizations. To show the generality and efficiency of our approach, we compute gradients office GPU-based HPC applications, executed on NVIDIA and AMD GPUs. All bench-marks run within an order of magnitude of the original program's execution time. Without GPU and AD-specific optimizations, gra-dients of GPU kernels either fail to run from a lack of resources or have infeasible overhead. Finally, we demonstrate that increasing the problem size by either increasing the number of threads or increasing the work per thread, does not substantially impact the overhead from differentiation.
Accelerating Large Scale de novo Metagenome Assembly Using GPUs

Metagenomic workflows involve studying uncultured microorganisms directly from the environment. These environmental samples when processed by modern sequencing machines yield large and complex datasets that exceed the capabilities of metagenomic software. The increasing sizes and complexities of datasets make a strong case for exascale-capable metagenome assemblers. However, the underlying algorithmic motifs are not well suited for GPUs. This poses a challenge since the majority of next-generation supercomputers will rely primarily on GPUs for computation. In this paper we present the first of its kind GPU-accelerated implementation of the local assembly approach that is an integral part of a widely used large-scale metagenome assembler, MetaHipMer. Local assembly uses algorithms that induce random memory accesses and non-deterministic workloads, which make GPU offloading a challenging task. Our GPU implementation outperforms the CPU version by about $7\mathrm{x}$ and boosts the performance of MetaHipMer by 42% when running on 64 Summit nodes.
Paths to OpenMP in the Kernel

OpenMP implementations make increasing demands on the kernel. We take the next step and consider bringing OpenMP into the kernel. Our vision is that the entire OpenMP application, run-time system, and a kernel framework is interwoven to become the kernel, allowing the OpenMP implementation to take full advantage of the hardware in a custom manner. We compare and contrast three approaches to achieving this goal. The first, runtime in kernel (RTK), ports the OpenMP runtime to the kernel, allowing any kernel code to use OpenMP pragmas. The second, process in kernel (PIK) adds a specialized process abstraction for running user-level OpenMP code within the kernel. The third, custom compilation for kernel (CCK), compiles OpenMP into a form that leverages the kernel framework without any intermediaries. We describe the design and implementation of these approaches, and evaluate them using NAS and other benchmarks.
Simurgh: A Fully Decentralized and Secure NVMM User Space File System

The availability of non-volatile main memory (NVMM) has started a new era for storage systems and NVMM specific file systems can support extremely high data and metadata rates, which are required by many HPC and data-intensive applications. Scaling metadata performance within NVMM file systems is nevertheless often restricted by the Linux kernel storage stack, while simply moving metadata management to the user space can compromise security or flexibility. This paper introduces Simurgh, a hardware-assisted user space file system with decentralized metadata management that allows secure metadata updates from within user space. Simurgh guarantees consistency, durability, and ordering of updates without sacrificing scalability. Security is enforced by only allowing NVMM access from protected user space functions, which can be implemented through two proposed instructions. Comparisons with other NVMM file systems show that Simurgh improves metadata performance up to 18x and application performance up to 89% compared to the second-fastest file system.
BAASH: Lightweight, Efficient, and Reliable Blockchain-As-A-Service for HPC Systems

Distributed resiliency becomes paramount to alleviate the growing costs of data movement and I/Os while preserving the data accuracy in HPC systems. This paper proposes to adopt blockchain-like decentralized protocols to achieve such distributed resiliency. The key challenge for such an adoption lies in the mismatch between blockchain's targeting systems (e.g., shared-nothing, loosely-coupled, TCP/IP stack) and HPC's unique design on storage subsystems, resource allocation, and programming models. We present BAASH, Blockchain-As-A-Service for HPC, deployable in a plug-n-play fashion. BAASH bridges the HPC-blockchain gap with two key components: (i) Lightweight consensus protocols for the HPC's shared-storage architecture, (ii) A new fault-tolerant mechanism compensating for the MPI to guarantee the distributed resiliency. We have implemented a prototype system and evaluated it with more than two million transactions on a 500-core HPC cluster. Results show that the prototype of the proposed techniques significantly outperforms vanilla blockchain systems and exhibits strong reliability with MPI.
Age-adapted percentiles of measured glomerular filtration in healthy individuals: extrapolation to living kidney donors over 65 years

Abstract Objectives Most data on glomerular filtration rate (GFR) originate from subjects <65 years old, complicating decision-making in elderly living kidney donors. In this retrospective multi-center study, we calculated percentiles of measured GFR (mGFR) in donors <65 years old and extrapolated these to donors ≥65 years old. Methods mGFR percentiles were calculated from a development cohort of French/Belgian living kidney donors <65 years (n=1,983), using quantiles modeled as cubic splines (two linear parts joining at 40 years). Percentiles were extrapolated and validated in an internal cohort of donors ≥65 years (n=147, France) and external cohort of donors and healthy subjects ≥65 years (n=329, Germany, Sweden, Norway, France, The Netherlands) by calculating percentages within the extrapolated 5th–95th percentile (P5–P95). Results Individuals in the development cohort had a higher mGFR (99.9 ± 16.4 vs. 86.4 ± 14 and 82.7 ± 15.5 mL/min/1.73 m2) compared to the individuals in the validation cohorts. In the internal validation cohort, none (0%) had mGFR below the extrapolated P5, 12 (8.2%) above P95 and 135 (91.8%) between P5–P95. In the external validation cohort, five subjects had mGFR below the extrapolated P5 (1.5%), 25 above P95 (7.6%) and 299 (90.9%) between P5–P95. Conclusions We demonstrate that extrapolation of mGFR from younger donors is possible and might aid with decision-making in elderly donors.
A seamless multiscale operator neural network for inferring bubble dynamics

Abstract Modelling multiscale systems from nanoscale to macroscale requires the use of atomistic and continuum methods and, correspondingly, different computer codes. Here, we develop a seamless method based on DeepONet, which is a composite deep neural network (a branch and a trunk network) for regressing operators. In particular, we consider bubble growth dynamics, and we model tiny bubbles of initial size from 100 nm to 10 $\mathrm {\mu }\textrm {m}$, modelled by the Rayleigh–Plesset equation in the continuum regime above 1 $\mathrm {\mu }\textrm {m}$ and the dissipative particle dynamics method for bubbles below 1 $\mathrm {\mu }\textrm {m}$ in the atomistic regime. After an offline training based on data from both regimes, DeepONet can make accurate predictions of bubble growth on-the-fly (within a fraction of a second) across four orders of magnitude difference in spatial scales and two orders of magnitude in temporal scales. The framework of DeepONet is general and can be used for unifying physical models of different scales in diverse multiscale applications.
REPLY:

We thank Zhang et al. for their interest in our article. (1) A large-scale registry database provides an exceptional opportunity to investigate BTC, a rare cancer. (1, 2) Although large databases, can provide a robust resource to investigate rare cancers, these analyses may still suffer from a lack of detailed clinical information, such as liver function and coagulation disorders, as well as from confounding due to compliance and over-counter-use of concomitant medications.
Liposomal Encapsulation of Polysaccharides (LEPS) as an Effective Vaccine Strategy to Protect Aged Hosts Against S. pneumoniae Infection

Despite the availability of licensed vaccines, pneumococcal disease caused by the bacteria Streptococcus pneumoniae (pneumococcus), remains a serious infectious disease threat globally. Disease manifestations include pneumonia, bacteremia, and meningitis, resulting in over a million deaths annually. Pneumococcal disease disproportionally impacts elderly individuals ≥65 years old. Interventions are complicated through a combination of complex disease progression and 100 different bacterial capsular polysaccharide serotypes. This has made it challenging to develop a broad vaccine against S. pneumoniae, with current options utilizing capsular polysaccharides as the primary antigenic content. However, current vaccines are substantially less effective in protecting the elderly. We previously developed a Liposomal Encapsulation of Polysaccharides (LEPS) vaccine platform, designed around limitations of current pneumococcal vaccines, that allowed the non-covalent coupling of polysaccharide and protein antigen content and protected young hosts against pneumococcal infection in murine models. In this study, we modified the formulation to make it more economical and tested the novel LEPS vaccine in aged hosts. We found that in young mice (2-3 months), LEPS elicited comparable responses to the pneumococcal conjugate vaccine Prevnar-13. Further, LEPS immunization of old mice (20-22 months) induced comparable antibody levels and improved antibody function compared to Prevnar-13. Importantly, LEPS protected old mice against both invasive and lung localized pneumococcal infections. In summary, LEPS is an alternative and effective vaccine strategy that protects aged hosts against different manifestations of pneumococcal disease.
Systematic analysis of membrane contact sites in Saccharomyces cerevisiae uncovers modulators of cellular lipid distribution

Actively maintained close appositions, or contact sites, between organelle membranes, enable the efficient transfer of biomolecules between the various cellular compartments. Several such sites have been described together with their tethering machinery. Despite these advances we are still far from a comprehensive understanding of the function and regulation of most contact sites. To systematically characterize the proteome of contact sites and support the discovery of new tethers and functional molecules, we established a high throughput screening approach in Saccharomyces cerevisiae based on co-localization imaging. We imaged split fluorescence reporters for six different contact sites, two of which have never been studied before, on the background of 1165 strains expressing a mCherry-tagged yeast protein that have a cellular punctate distribution (a hallmark of contact sites). By scoring both co-localization events and effects on reporter size and abundance, we discovered over 100 new potential contact site residents and effectors in yeast. Focusing on several of the newly identified residents, we identified one set of hits as previously unrecognized homologs to Vps13 and Atg2. These proteins share their lipid transport domain, thus expanding this family of lipid transporters. Analysis of another candidate, Ypr097w, which we now call Lec1 (Lipid-droplet Ergosterol Cortex 1), revealed that this previously uncharacterized protein dynamically shifts between lipid droplets and the cell cortex, and plays a role in regulation of ergosterol distribution in the cell.
EZTraits: A programmable tool to evaluate multi-site deterministic traits

The vast majority of human traits, including many disease phenotypes, are affected by alleles at numerous genomic loci. With a continually increasing set of variants with published clinical disease or biomarker associations, an easy-to-use tool for non-programmers to rapidly screen VCF files for risk alleles is needed. We have developed EZTraits as a tool to quickly evaluate genotype data (e.g., from microarrays) against a set of rules defined by the user. These rules can be defined directly in the scripting language Lua, for genotype calls using variant ID (RS number) or chromosomal position. Alternatively, EZTraits can parse simple and intuitive text including concepts like ‘any’ or ‘all’. Thus, EZTraits is designed to support rapid genetic analysis and hypothesis-testing by researchers, regardless of programming experience or technical background. The software is implemented in C++ and compiles and runs on Linux and MacOS. The source code is available under the MIT license from https://github.com/selfdecode/rd-eztraits Contact: manfred@selfdecode.com
Path Planning in Uncertain Ocean Currents using Ensemble Forecasts

We present a path planning framework for marine robots subject to uncertain ocean currents that exploits data from ensemble forecasting, which is a technique for current prediction used in oceanography. Ensemble forecasts represent a distribution of predicted currents as a set of flow fields that are considered to be equally likely. We show that the typical approach of computing the vector-wise mean and variance over this set can yield meaningless results, and propose an alternative approach that considers each flow field in the ensemble simultaneously. Our framework finds a sequence of vehicle controls that minimises the root-mean-square error distance (RMSE) over the full set of ensemble-induced trajectories. The key to achieving computational efficiency in this approach is our use of Monte Carlo tree search (MCTS) with a specialised heuristic that improves convergence rate while preserving asymptotic optimality and the anytime property. We demonstrate our results using real ensemble forecasts provided by the Australian Bureau of Meteorology, and provide comparisons with the deterministic mean-based approach where we observe RMSE reductions of 92% and 43% in two example scenarios. Further, we argue that the framework can be used in a plan-as-you-go manner where ensemble forecasts change over time. These results help to introduce ensemble forecasts as a viable source of data to improve path planning in marine robotics.
Video Processing using Deep learning Techniques: A Systematic Literature Review

Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.
Dual-Arm Needle Manipulation with the da Vinci® Surgical Robot Under Uncertainty

This paper proposes a path correction method for surgical robotic systems performing needle handoff manipulations as part of autonomous execution of surgical suturing. During handoff motions, the position and orientation of the needle is subject to perturbations from the idealized planned pose due to uncertainties in camera-robot calibration and needle localization. If, after a perturbation, the system needs to perform subsequent needle regrasp(s), but the robot still follows the originally planned trajectory out of the path planner [1], it has a lower chance of gripping the needle properly. In order to accommodate these unpredictable needle pose perturbations, the proposed path correction method works locally to direct the needle from the wrong pose to the original pose by partial replanning of the robot motion. The reliability of the proposed method is evaluated with three sets of experiments in a simulation environment.
Prediction of visceral fat area of bioelectrical impedance based on ensemble learning

Visceral fat as an important indicator of body composition largely reflects the health level of the human body. However, accurate measurement of visceral fat is still a big challenge. In this work, a new ensemble learning method named ByStepStack is proposed for visceral fat area prediction. The method is divided into two steps. In the first step, the mapping relationship model from the bioelectrical impedance to the body composition is obtained by Ridge regression. The second step is to predict the visceral fat area based on the body composition obtained from the previous step. The main innovation of the proposed method is using body composition to supervise feature representation, which can be a bridge between bioelectrical impedance and the visceral fat area. Finally, our method as well as the other state of the art ensemble learning methods is applied to predict visceral fat area on the same data set. The experimental results show that the proposed ByStepStack outperforms the other existing methods. Its relative error, average absolute error, mean square error and the determinable coefficients reach 0.0352, 2.237, 10. 7033, 0.9864 respectively.
TIMPANY - deTectIon of Model Poisoning Attacks usiNg accuracY

Nowadays, Federated Learning has widely been adopted for data security in the Industrial IoTs. With Federated Learning, local Industrial IoTs devices download the current machine learning model and update it on their own local Industrial IoTs devices. Then, local Industrial IoTs devices transmit these locally trained models back to the Industrial Server. The Industrial Server aggregates all the locally trained models into a single consolidated and enhanced global model. On one side, Federated Learning secures the data; on the other side, Federated Learning itself is vulnerable to one subtle yet severe attack: the model poisoning attack. Model poisoning attack is difficult to detect, especially in Industrial IoTs applications, for two reasons: a) neither the Industrial Server nor the local Industrial IoTs devices in Federated Learning is capable of identifying poisoned local models, and b) every iteration of Federated Learning consists of many Industrial IoTs devices, and therefore, verification of every single device is computationally expensive. Thus, this study proposes an effective and efficient framework for deTectIon of Model Poisoning Attacks usiNg AccuracY (TIMPANY). TIMPANY is the first detection framework for the model poisoning attack that utilizes accuracy as a detection measure. We performed theoretical analysis of TIMPANY with other detection solutions (for model poisoning attack) concerning communication and computational efficiency, security, and detection accuracy. Our thorough theoretical comparative analysis showed that TIMPANY efficiently addresses these open research challenges that previous studies failed to address. In our thorough experimental analysis, error analysis from the first iteration shows that TIMPANY results in 0% error, leading to a True Positive Rate and accuracy of 100% with 0% False Positive Rate. Thus, TIMPANY outperformed some of the existing detection solutions for model poisoning attacks against Federated Learning. We conclude that TIMPANY is effective and efficient against model poisoning attacks in Federated Learning, even for resource-constrained Industrial IoTs devices widely used in various industrial applications.
Unsupervised Learning of 3D Scene Flow from Monocular Camera*

Scene flow represents the motion of points in the 3D space, which is the counterpart of the optical flow that represents the motion of pixels in the 2D image. However, it is difficult to obtain the ground truth of scene flow in the real scenes, and recent studies are based on synthetic data for training. Therefore, how to train a scene flow network with unsupervised methods based on real-world data shows crucial significance. A novel unsupervised learning method for scene flow is proposed in this paper, which utilizes the images of two consecutive frames taken by monocular camera without the ground truth of scene flow for training. Our method realizes the goal that training scene flow network with real-world data, which bridges the gap between training data and test data and broadens the scope of available data for training. Unsupervised learning of scene flow in this paper mainly consists of two parts: (i) depth estimation and camera pose estimation, and (ii) scene flow estimation based on four different loss functions. Depth estimation and camera pose estimation obtain the depth maps and camera pose between two consecutive frames, which provide further information for the next scene flow estimation. After that, we used depth consistency loss, dynamic-static consistency loss, Chamfer loss, and Laplacian regularization loss to carry out unsupervised training of the scene flow network. To our knowledge, this is the first paper that realizes the unsupervised learning of 3D scene flow from monocular camera. The experiment results on KITTI show that our method for unsupervised learning of scene flow meets great performance compared to traditional methods Iterative Closest Point (ICP) and Fast Global Registration (FGR). The source code is available at: https://github.com/IRMVLab/3DUnMonoFlow.