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1. Understanding the Performance Implications of the Design Principles in Storage-Disaggregated Databases

   https://doi.org/10.1145/3654983  

   Pang, Xi; Wang, Jianguo (May 2024, Proceedings of the ACM on Management of Data)

   Storage-compute disaggregation has recently emerged as a novel architecture in modern data centers, particularly in the cloud. By decoupling compute from storage, this new architecture enables independent and elastic scaling of compute and storage resources, potentially increasing resource utilization and reducing overall costs. To best leverage the disaggregated architecture, a new breed of database systems termed storage-disaggregated databases has recently been developed, such as Amazon Aurora, Microsoft Socrates, Google AlloyDB, Alibaba PolarDB, and Huawei Taurus. However, little is known about the effectiveness of the design principles in these databases since they are typically developed by industry giants, and only the overall performance results are presented without detailing the impact of individual design principles. As a result, many critical research questions remain unclear, such as the performance impact of storage-disaggregation, the log-as-the-database design, shared-storage, and various log-replay methods. In this paper, we investigate the performance implications of the design principles that are widely adopted in storage-disaggregated databases for the first time. As these databases were usually not open-sourced, we have made a significant effort to implement a storage-disaggregated database prototype based on PostgreSQL v13.0. By fully controlling and instrumenting the codebase, we are able to selectively enable and disable individual optimizations and techniques to evaluate their impact on performance in various scenarios. Furthermore, we open-source our storage-disaggregated database prototype for use by the broader database research community, fostering collaboration and innovation in this field. 

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2. Digitized Phase‐Change Material Heterostack for Transmissive Diffractive Optical Neural Network

   https://doi.org/10.1002/adpr.202400201  

   Chen, Ruiyang; Fan, Jichao; Yu, Cunxi; Gao, Weilu (December 2024, Advanced Photonics Research)

   All‐optical and fully reconfigurable transmissive diffractive optical neural network (DONN) architectures emerge as high‐throughput and energy‐efficient machine learning (ML) hardware accelerators in broad applications. However, current device and system implementations have limited performance. In this work, a novel transmissive diffractive device architecture, a digitized phase‐change material (PCM) heterostack, which consists of multiple nonvolatile PCM layers with different thicknesses, is demonstrated. Through this architecture, the advantages of PCM electrical and optical properties can be leveraged and challenges associated with multilevel operations in a single PCM layer can be mitigated. Through proof‐of‐concept experiments, the electrical tuning of one PCM layer is demonstrated in a transmissive spatial light modulation device, and thermal analysis guides the design of multilayer devices and DONN systems to avoid thermal cross talk if individual heterostacks are assembled into an array. Further, a heterostack containing three PCM layers is designed based on experimental results to produce a large‐phase modulation range and uniform coverage, and the ML performance of DONN systems with the designed heterostack is evaluated. The developed device architecture is practically feasible and scalable for future energy‐efficient, fast‐reconfigured, and compact transmissive DONN systems. 

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3. EdgeGuard: Robust and Fault-Aware Design for Resilient Edge Computing AI Accelerators

   https://doi.org/10.1145/3716368.3735153  

   Ahmed, Sabrina; Hoque, Khaza Anuarul; Carrion_Schafer, Benjamin (June 2025, ACM)

   Smaller transistor feature sizes have made integrated circuits (ICs) more vulnerable to permanent faults. This leads to short lifetimes and increased risk of faults that lead to catastrophic errors. Fortunately, Artificial Neural Networks (ANNs) are error resilient as their accuracies can be maintained through e.g., fault-aware re-training. One of the problems though with previous work is that they require a re-design in the individual neuron processing element structure in order to efficiently deal with these faults. In this work, we propose a novel architecture combined with a design flow that performs a fault-aware weight re-assignment in order to minimize the effect of permanent faults on the accuracy of ANNs mapped to AI accelerator without the need of time-consuming fault-aware re-training nor neuron processing elements re-design. In particular, we deal with Tensor Processing Units (TPUs) although our proposed approach is also extensible to any other architecture. Experimental results show that our proposed approach and can be efficiently executed on a fast dedicated hardware re-binding unit or on software. 

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4. MAKER: 3D Pen Utilization in 3D Printing Practices

   Imeri, Astrit; Russell, Nicholas; Rust, James; Sahin, Serhat; Fidan, Ismail (June 2017, 2017 ASEE Annual Conference Proceedings)

   During the 2017 Engineering-a-Future activities at Tennessee Tech University, a set of 3D Pen activities has been organized for the female Middle School Students (5th and 6th grades) so that they learn more about the engineering fields and their practices in daily life. Various Additive Manufacturing technologies have been presented to students, and students have toured the lab facilities. During the 45-minute timeframe, students were grouped and constructed 3D art objects (butterfly, necklace, bird, and glasses) using the available 3D Pens. It was observed that the students had high enthusiasm about the new technology. This study will present the utilization of 3D Printing Pens in various practices, and report the current advantageous and disadvantageous of 3D Pen exercises detected through recent studies. 

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5. The Octocoral Trait Database: a global database of trait information for octocoral species

   https://doi.org/10.1038/s41597-024-04307-8  

   Gómez-Gras, D; Linares, C; Viladrich, N; Zentner, Y; Grinyó, J; Gori, A; McFadden, C S; Fabricius, K E; Madin, J S (December 2025, Scientific Data)

   Abstract Trait-based approaches are revolutionizing our understanding of high-diversity ecosystems by providing insights into the principles underlying key ecological processes, such as community assembly, species distribution, resilience, and the relationship between biodiversity and ecosystem functioning. In 2016, the Coral Trait Database advanced coral reef science by centralizing trait information for stony corals (i.e., Subphylum Anthozoa, Class Hexacorallia, Order Scleractinia). However, the absence of trait data for soft corals, gorgonians, and sea pens (i.e., Class Octocorallia) limits our understanding of ecosystems where these organisms are significant members and play pivotal roles. To address this gap, we introduce the Octocoral Trait Database, a global, open-source database of curated trait data for octocorals. This database houses species- and individual-level data, complemented by contextual information that provides a relevant framework for analyses. The inaugural dataset, OctocoralTraits v2.2, contains over 97,500 global trait observations across 98 traits and over 3,500 species. The database aims to evolve into a steadily growing, community-led resource that advances future marine science, with a particular emphasis on coral reef research. 

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