Diffusion-based imputation methods, which impute missing features through the iterative propagation of observed features, have shown impressive performance in homogeneous graphs. However, these methods are not directly applicable to heterogeneous graphs, which have multiple types of nodes and edges, due to two key issues: (1) the presence of nodes with undefined features hinders diffusion-based imputation; (2) treating various edge types equally during diffusion does not fully utilize information contained in heterogeneous graphs. To address these challenges, this paper presents a novel imputation scheme that enables diffusion-based imputation in heterogeneous graphs. Our key idea involves (1) assigning a {\it virtual feature} to an undefined node feature and (2) determining the importance of each edge type during diffusion according to a new criterion. Through experiments, we demonstrate that our virtual feature scheme effectively serves as a bridge between existing diffusion-based methods and heterogeneous graphs, maintaining the advantages of these methods. Furthermore, we confirm that adjusting the importance of each edge type leads to significant performance gains on heterogeneous graphs. Extensive experimental results demonstrate the superiority of our scheme in both semi-supervised node classification and link prediction tasks on heterogeneous graphs with missing rates ranging from low to exceedingly high. The source code is available at https://github.com/daehoum1/hetgfd.

In this paper, we address learning tasks on graphs with missing features, enhancing the applicability of graph neural networks to real-world graph-structured data. We identify a critical limitation of existing imputation methods based on feature propagation: they produce channels with nearly identical values within each channel, and these low-variance channels contribute very little to performance in graph learning tasks. To overcome this issue, we introduce synthetic features that target the root cause of low-variance channel production, thereby increasing variance in these channels. By preventing propagation-based imputation methods from generating meaningless feature values shared across all nodes, our synthetic feature propagation scheme mitigates significant performance degradation, even under extreme missing rates. Extensive experiments demonstrate the effectiveness of our approach across various graph learning tasks with missing features, ranging from low to extremely high missing rates. Additionally, we provide both empirical evidence and theoretical proof to validate the low-variance problem. The source code is available at https://github.com/daehoum1/fisf.

Consistent checkpointing is a critical for in-memory databases (IMDBs) but its resource-intensive nature poses challenges for small- and medium-sized deployments in cloud environments, where memory utilization directly affects operational costs. Although traditional fork-based checkpointing offers merits in terms of performance and implementation simplicity, it incurs a considerable rise in memory footprint during checkpointing, particularly under update-intensive workloads. Memory provisioning emerges as a practical remedy to handle peak demands without compromising performance, albeit with potential concerns related to memory over-provisioning.In this article, we propose MDC+, a memory-efficient fork-based checkpointing scheme designed to maintain a reasonable memory footprint during checkpointing by leveraging collaboration among an IMDB, a user-level memory allocator, and the operating system. We explore two key techniques within the checkpointing scheme: (1) memory dump-based checkpointing, which enables early memory release, and (2) hint-based segregated memory allocation, which isolates immutable and updatable data to minimize page duplication. Our evaluation demonstrates that MDC+ significantly lowers peak memory footprint during checkpointing without affecting throughput or checkpointing time.

Generating 3D graphs of symmetry-group equivariance is of intriguing potential in broad applications from machine vision to molecular discovery. Emerging approaches adopt diffusion generative models (DGMs) with proper re-engineering to capture 3D graph distributions. In this paper, we raise an orthogonal and fundamental question of in what (latent) space we should diffuse 3D graphs. ❶ We motivate the study with theoretical analysis showing that the performance bound of 3D graph diffusion can be improved in a latent space versus the original space, provided that the latent space is of (i) low dimensionality yet (ii) high quality (i.e., low reconstruction error) and DGMs have (iii) symmetry preservation as an inductive bias. ❷ Guided by the theoretical guidelines, we propose to perform 3D graph diffusion in a low-dimensional latent space, which is learned through cascaded 2D–3D graph autoencoders for low-error reconstruction and symmetry-group invariance. The overall pipeline is dubbed latent 3D graph diffusion. ❸ Motivated by applications in molecular discovery, we further extend latent 3D graph diffusion to conditional generation given SE(3)-invariant attributes or equivariant 3D objects. ❹ We also demonstrate empirically that out-of-distribution conditional generation can be further improved by regularizing the latent space via graph self-supervised learning. We validate through comprehensive experiments that our method generates 3D molecules of higher validity / drug-likeliness and comparable or better conformations / energetics, while being an order of magnitude faster in training. Codes are released at https://github.com/Shen-Lab/LDM-3DG.

Ultra-wideband technology has the potential to provide precise real-world localization. However, due to Non-Line-of-Sight propagation, the transmitted packet can be incomplete or lost during the ranging, which may lead to communication failure. To minimize this, we propose the scheduled signal reception technique. The proposed approach is tested in a real-world environment with Qorvo DWM3001C modules. The experimental results verify its efficiency, offering a practical solution for UWB-based localization, particularly in dynamic circumstances.

How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world? In this paper, we address the question by focusing on a fundamental biochemical problem of generating 3D molecular conformers conditioned on molecular graphs in a multiscale manner. Our approach consists of two hierarchical stages: i) generation of coarse-grained fragment-level 3D structure from the molecular graph, and ii) generation of fine atomic details from the coarse-grained approximated structure while allowing the latter to be adjusted simultaneously. For the challenging second stage, which demands preserving coarse-grained information while ensuring SE(3) equivariance, we introduce a novel generative model termed Equivariant Blurring Diffusion (EBD), which defines a forward process that moves towards the fragment-level coarse-grained structure by blurring the fine atomic details of conformers, and a reverse process that performs the opposite operation using equivariant networks. We demonstrate the effectiveness of EBD by geometric and chemical comparison to state-of-the-art denoising diffusion models on a benchmark of drug-like molecules. Ablation studies draw insights on the design of EBD by thoroughly analyzing its architecture, which includes the design of the loss function and the data corruption process. Codes are released at https://github.com/Shen-Lab/EBD.

Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In this paper, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of un-labeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing auto-encoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations. Codes are available at https://github.com/junhocho/HGCAE.

In cloud ecosystems, distributed block storage systems are used to provide a persistent block storage service, which is the fundamental building block for operating cloud native services. However, existing distributed storage systems performed poorly for random write workloads in an all-NVMe storage configuration, becoming CPU-bottlenecked. Our roofline-based approach to performance analysis on a conventional distributed block storage system with NVMe SSDs reveals that the bottleneck does not lie in one specific software module, but across the entire software stack; (1) tightly coupled I/O processing, (2) inefficient threading architecture, and (3) local backend data store causing excessive CPU usage. To this end, we re-architect a modern distributed block storage system for improving random write performance. The key ingredients of our system are (1) decoupled operation processing using non-volatile memory, (2) prioritized thread control, and (3) CPU-efficient backend data store. Our system emphasizes low CPU overhead and high CPU efficiency to efficiently utilize NVMe SSDs in a distributed storage environment. We implement our system in Ceph. Compared to the native Ceph, our prototype system delivers more than 3x performance improvement for small random write I/Os in terms of both IOPS and latency by efficiently utilizing CPU cores.

As the size of data grows in modern applications, the efficient usage of limited resources is becoming crucial. In order to reorganize large data under memory limitations, many data-intensive applications utilize external sort as a critical component. To streamline external sort, a storage framework is especially needed since the entire dataset must be loaded and flushed a couple of times during the sorting process. Most existing frameworks have attempted to simplify the storage access pattern by associating each thread with a separate storage device. This prevents randomized and concurrent I/O requests, which impose a huge overhead for legacy drives in order to enable the parallelism needed for external sort. However, such regulations excessively restrain the capabilities of NVMe-based SSDs that deliver high throughput with abundant parallelism. In this article, we present a new framework for external sort that exploits both external and internal parallelism. Externally, any number of threads are mobilized to parallel external sort in a scalable way, even with one NVMe SSD. Meanwhile, some arbitration schemes, such as adaptive resource allocation and fairness control, are adopted to preserve the internal efficiency of storage devices. Our evaluation results demonstrate that our scheme can greatly improve both the I/O efficiency and scalability compared to the existing frameworks.

Most modern multi-core processors provide a shared last level cache (LLC) where data from all cores are placed to improve performance. However, this opens a new challenge for cache management, owing to cache pollution. With cache pollution, data with weak temporal locality can evict other data with strong temporal locality when both are mapped into the same cache set. In this article, we propose page reusability-based cache partitioning (PRCP) for multi-core systems to maximize cache utilization by minimizing cache pollution. To achieve this, PRCP divides pages into two groups: (1) highly-reused pages and (2) lowly-reused pages. The reusability of each page is collected online via periodic page table scans. PRCP then dynamically partitions the shared cache into two corresponding areas using page coloring technique. We have implemented PRCP in Linux kernel and evaluated it using SPEC CPU2006 benchmarks. The results show that our scheme can achieve comparable performance to the optimal offline MRC-guided process-based cache partitioning scheme without a priori knowledge of workloads.

In cloud computing, I/O-intensive workloads can be co-located with other applications or virtual machines on a single physical machine. In this case, copy-based I/O (buffered I/O) can lead to severe performance interference to other memoryintensive workloads. It is because that the buffered I/O consumes memory bandwidth during memory copy even though it benefits from caching. To address this problem, many zero-copy I/O schemes have been proposed but none of them provides both 1) transparent copy avoidance through read/write system calls and 2) benefits of kernel-level caching at the same time. To this end, this paper presents z-READ, an efficient and transparent zero-copy read I/O scheme based on page remapping and copy-on-write techniques. In our scheme, we introduce several optimizations that minimize the overheads of page remapping by reducing the number of remote TLB shootdown.We implement z- READ prototype in memory management of Linux kernel 4.12.9. Our experimental results show that the performance of the colocated memory-intensive workloads can be negatively affected by I/O-intensive workloads in the case of copy-based I/O (up to 1.96x slowdown in-memory configurations) while z-READ incurs only up to 1.07x slowdown for the respective configuration.

We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. In addition, a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks. The experimental results on clustering, link prediction and visualization tasks strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms.

Ultrawideband (UWB) based localization has the potential to be used in a variety of applications due to its high accuracy. For robust and high performance in real environment, the most challenging issue is the detection and mitigation of noise from non-line-of-sight (NLOS) signals. Current researches use channel state information, particle or Kalman filter, and statistics based approaches for the NLOS noise detection and mitigation. These solutions show high accuracy in some applications; however, they need additional hardware and work in static environment only. We propose an NLOS mitigation algorithm that does not need any additional hardware andworks in dynamic environment with mobile obstacles. The main idea of the algorithm are to estimate the NLOS bias by repetitively comparing the intersection of the hyperbolas with intersections of circles. The proposed approach is tested for Decawave UWB testbed. Experimental results show that the proposed scheme works well in different dynamic scenarios as compared to the localization scheme without NLOS noise mitigation.

A large number of cloud datastores have been developed to handle the cloud OLTP workload. Double caching problem where the same data resides both at the user buffer and the kernel buffer has been identified as one of the problems and has been largely solved by using direct I/O mode to bypass the kernel buffer. However, maintaining the caching layer only in user-level has the disadvantage that the user process may monopolize memory resources and that it is difficult to fully utilize the system memory due to the risks of the forced termination of the process or the unpredictable performance degradation in case of memory pressure. In this paper, we propose a new I/O mode, DBIO, to efficiently exploit OS kernel buffer as a victim cache for user-level file content cache, enjoying the strengths of kernel-level cache rather than just skipping it. DBIO provides the new file read/write function calls, which enable user programs to dynamically choose the right I/O behavior based on their context when issuing I/Os instead of when opening the file. On the cloud key-value store workloads and the traditional OLTP workloads with the modified version of MySQL/InnoDB, DBIO improves the in-memory cache hit ratio and the transaction performance compared to both buffered and direct I/O mode, fully utilizing the user buffer and the kernel buffer without double caching.

Building an ideal graph which reveals the exact intrinsic structure of the data is critical in graph-based clustering. There have been a lot of efforts to construct an affinity matrix satisfying such a need in terms of a similarity measure. A recent approach attracting attention is on using doubly stochastic normalization of the affinity matrix to improve the clustering performance. In this paper, we propose a novel method to build a high-quality affinity matrix via incorporating Davis-Kahan theorem of matrix perturbation theory in the doubly stochastic normalization problem. We interpret the goal of the doubly stochastic normalization problem as minimizing the relative distance between the eigenspaces of the corresponding matrices. Also, for the doubly stochastic normalization problem we include an additional constraint that each eigenvalue be on the unit interval to fully conform to the spectral graph theory. Experiments on our framework present superior performance over various datasets.

Recent demand and initiatives in brain research have driven significant interest toward developing chronically implantable neural interface systems with high spatiotemporal resolution and spatial coverage extending to the whole brain. Electroencephalography-based systems are noninvasive and cost efficient in monitoring neural activity across the brain, but suffer from fundamental limitations in spatiotemporal resolution. On the other hand, neural spike and local field potential (LFP) monitoring with penetrating electrodes offer higher resolution, but are highly invasive and inadequate for long-term use in humans due to unreliability in long-term data recording and risk for infection and inflammation. Alternatively, electrocorticography (ECoG) promises a minimally invasive, chronically implantable neural interface with resolution and spatial coverage capabilities that, with future technology scaling, may meet the needs of recently proposed brain initiatives. In this paper, we discuss the challenges and state-of-the-art technologies that are enabling next-generation fully implantable high-density ECoG interfaces, including details on electrodes, data acquisition front-ends, stimulation drivers, and circuits and antennas for wireless communications and power delivery. Along with state-of-the-art implantable ECoG interface systems, we introduce a modular ECoG system concept based on a fully encapsulated neural interfacing acquisition chip (ENIAC). Multiple ENIACs can be placed across the cortical surface, enabling dense coverage over wide area with high spatiotemporal resolution. The circuit and system level details of ENIAC are presented, along with measurement results.