Search for: All records

The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facilitate optimized software/hardware implementations by utilizing sparsity pattern- or target-aware data structures and layouts to enhance memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Additionally, because these frameworks represent formats using a limited set of per-dimension attributes, they lack the flexibility to accommodate numerous new variations of custom sparse data structures and layouts. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. Unlike the existing attribute-based frameworks, UniSparse decouples the logical representation of the sparse tensor (i.e., the data structure) from its low-level memory layout, enabling the customization of both. As a result, a rich set of format customizations can be succinctly expressed in a small set of well-defined query, mutation, and layout primitives. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats, and automatic code generation of format conversion and compute operations for heterogeneous architectures. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with specialized formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory (PIM) device.

 

Epigenetic variations contribute greatly to the phenotypic plasticity and diversity. Current functional studies on epialleles have predominantly focused on protein-coding genes, leaving the epialleles of non-coding RNA (ncRNA) genes largely understudied. Here, we uncover abundant DNA methylation variations of ncRNA genes and their significant correlations with plant adaptation among 1001 naturalArabidopsisaccessions. Through genome-wide association study (GWAS), we identify large numbers of methylation QTL (methylQTL) that are independent of known DNA methyltransferases and enriched in specific chromatin states. Proximal methylQTL closely located to ncRNA genes have a larger effect on DNA methylation than distal methylQTL. We ectopically tether a DNA methyltransferase MQ1v to miR157a by CRISPR-dCas9 and show de novo establishment of DNA methylation accompanied with decreased miR157a abundance and early flowering. These findings provide important insights into the genetic basis of epigenetic variations and highlight the contribution of epigenetic variations of ncRNA genes to plant phenotypes and diversity.

 

Plasmonic catalysis is uniquely positioned between photo/electrochemistry and thermal chemistry such that multiple factors may compete to dominate the reaction enhancement mechanism. The adoption of norms originating in both photochemistry and thermal chemistry has resulted in the use of language and methods of data analysis, which, in the context of plasmonic catalysis, may be implicitly contradictory. This article tracks several years of research towards understanding thermal and nonthermal effects in plasmonic catalysis and culminates with a discussion on how the choice of language and presentation of data can be tuned to avoid subtle yet significant contradictory implications. 

Plasmonic photocatalysis presents a promising method for light-to-matter conversion. However, most current studies focused on understanding the relative importance of thermal and nonthermal effects while their synergistic effects remained less studied. Here, we propose an index, termed Overall Light Effectiveness (OLE), to capture the combined impact of these light effects on reactions. By systematically varying the thickness of catalyst layers, we isolated thermal and nonthermal contributions and optimized them to achieve maximum light enhancement. We demonstrate the approach using carbon dioxide hydrogenation reaction on titania-supported rhodium nanoparticles as a model reaction system. It shows a generalizable potential in designing catalyst systems with optimum combinations of heating and light illumination, especially with broadband light illumination such as sunlight, for achieving the most economical light-to-matter conversion in plasmonic catalysis. 

Plasmonic photocatalysis is an emerging research field that holds promise for sustainable energy applications, particularly in solar energy conversion. In this study, we focus on the enhancement of broadband light absorption capabilities for plasmonic photocatalyst under white light illumination. By replacing parts of the catalyst with solar absorber, we can significantly improve the total reaction rate under mild heating conditions with less catalyst. Through careful comparison of reaction conditions and systematic optimization of the contributions from photothermal and non-thermal effects, we demonstrate a substantial enhancement in broadband light absorption capacity and overall light effectiveness, paving the way for advanced plasmonic photocatalysts with greater efficiency and practical applicability using solar light as the energy source. 

Pain is a significant global health issue, and the current treatment options for pain management have limitations in terms of effectiveness, side effects, and potential for addiction. There is a pressing need for improved pain treatments and the development of new drugs. Voltage-gated sodium channels, particularly Nav1.3, Nav1.7, Nav1.8, and Nav1.9, play a crucial role in neuronal excitability and are predominantly expressed in the peripheral nervous system. Targeting these channels may provide a means to treat pain while minimizing central and cardiac adverse effects. In this study, we construct protein–protein interaction (PPI) networks based on pain-related sodium channels and develop a corresponding drug–target interaction network to identify potential lead compounds for pain management. To ensure reliable machine learning predictions, we carefully select 111 inhibitor data sets from a pool of more than 1000 targets in the PPI network. We employ 3 distinct machine learning algorithms combined with advanced natural language processing (NLP)–based embeddings, specifically pretrained transformer and autoencoder representations. Through a systematic screening process, we evaluate the side effects and repurposing potential of more than 150,000 drug candidates targeting Nav1.7 and Nav1.8 sodium channels. In addition, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of these candidates to identify leads with near-optimal characteristics. Our strategy provides an innovative platform for the pharmacological development of pain treatments, offering the potential for improved efficacy and reduced side effects.

 

Query rewriting is often a prerequisite for effective query optimization, particularly for poorly-written queries. Prior work on query rewriting has relied on a set of "rules" based on syntactic pattern-matching. Whether relying on manual rules or auto-generated ones, rule-based query rewriters are inherently limited in their ability to handle new query patterns. Their success is limited by the quality and quantity of the rules provided to them. To our knowledge, we present the first synthesis-based query rewriting technique, SlabCity, capable of whole-query optimization without relying on any rewrite rules. SlabCity directly searches the space of SQL queries using a novel query synthesis algorithm that leverages a new concept called query dataflows. We evaluate SlabCity on four workloads, including a newly curated benchmark with more than 1000 real-life queries. We show that not only can SlabCity optimize more queries than state-of-the-art query rewriting techniques, but interestingly, it also leads to queries that are significantly faster than those generated by rule-based systems. 

For the conversion of CO 2 into fuels and chemical feedstocks, hybrid gas/liquid-fed electrochemical flow reactors provide advantages in selectivity and production rates over traditional liquid phase reactors. However, fundamental questions remain about how to optimize conditions to produce desired products. Using an alkaline electrolyte to suppress hydrogen formation and a gas diffusion electrode catalyst composed of copper nanoparticles on carbon nanospikes, we investigate how hydrocarbon product selectivity in the CO 2 reduction reaction in hybrid reactors depends on three experimentally controllable parameters: (1) supply of dry or humidified CO 2 gas, (2) applied potential, and (3) electrolyte temperature. Changing from dry to humidified CO 2 dramatically alters product selectivity from C 2 products ethanol and acetic acid to ethylene and C 1 products formic acid and methane. Water vapor evidently influences product selectivity of reactions that occur on the gas-facing side of the catalyst by adding a source of protons that alters reaction pathways and intermediates.