1. ProteinShake Building datasets and benchmarks for deep learning on protein structures 

   Kucera, Tim; Oliver, Carlos; Chen, Dexiong; et al. (2023)

   Advances in Neural Information Processing Systems

   We present ProteinShake, a Python software package that simplifies dataset creation and model evaluation for deep learning on protein structures. Users can create custom datasets or load an extensive set of pre-processed datasets from biological data repositories such as the Protein Data Bank (PDB) and AlphaFoldDB. Each dataset is associated with prediction tasks and evaluation functions covering a broad array of biological challenges. A ...

   Conference Paper

2. Electromagnets Under the Table: An Unobtrusive Magnetic Navigation System for Microsurgery 

   Schonewille, Adam; He, Changyan; Forbrigger, Cameron; et al. (2024)

   IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

   Miniature magnetic tools have the potential to enable minimally invasive surgical techniques to be applied to space-restricted surgical procedures in areas such as neurosurgery. However, typical magnetic navigation systems, which create the magnetic fields to drive such tools, either cannot generate large enough fields, or surround the patient in a way that obstructs surgeon access to the patient. This paper introduces the design of a ...

   Journal Article

3. Detecting Logic Bugs in Database Engines via Equivalent Expression Transformation 

   Jiang, Zu-Ming; Su, Zhendong (2024)

   PROCEEDINGS OF THE 18TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, OSDI 2024

   Database management systems (DBMSs) are crucial for storing and fetching data. To improve the reliability of such systems, approaches have been proposed to detect logic bugs that cause DBMSs to process data incorrectly. These approaches manipulate queries and check whether the query results produced by DBMSs follow the expectations. However, such query-level manipulation cannot handle complex query semantics and thus needs to limit the ...

   Conference Paper

4. Pecan: Cost-Efficient ML Data Preprocessing with Automatic Transformation Ordering and Hybrid Placement 

   Graur, Dan; Mraz, Oto; Li, Muyu; et al. (2024)

   PROCEEDINGS OF THE 2024 USENIX ANNUAL TECHNICAL CONFERENCE, ATC 2024

   Input data preprocessing is a common bottleneck in machine teaming (ML) jobs, that can significantly increase training time and cost as expensive GPUs or Till's idle waiting for input data. Previous Work has shown that offloading data preprocessing to remote CPU servers successfully alleviates data stalls and improves training time. However, remote CPU workers in disaggregated data processing systems comprise a significant fraction of ...

   Conference Paper

5. OSMOSIS: Enabling Multi-Tenancy in Datacenter SmartNICs 

   Khalilov, Mikhail; Chrapek, Marcin; Shen, Siyuan; et al. (2024)

   PROCEEDINGS OF THE 2024 USENIX ANNUAL TECHNICAL CONFERENCE, ATC 2024

   Multi-tenancy is essential for unleashing SmartNIC's potential in datacenters. Our systematic analysis in this work shows that existing on-path SmartNICs have resource multiplexing limitations. For example, existing solutions lack multi-tenancy capabilities such as performance isolation and QoS provisioning for compute and 10 resources. Compared to standard NIC data paths with a well-defined set of offloaded functions, unpredictable ...

   Conference Paper

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