Proteins function through dynamic interactions with other proteins in cells, forming complex networks fundamental to cellular processes. While high-resolution and high-throughput methods have significantly advanced our understanding of how proteins interact with each other, the molecular details of many important protein-protein interactions are still poorly characterized, especially in non-mammalian species, including Drosophila. Recent advancements in deep learning techniques have enabled the prediction of molecular details in various cellular pathways at the network level. In this study, we used AlphaFold2 Multimer to examine and predict protein-protein interactions from both physical and functional datasets in Drosophila. We found that functional associations contribute significantly to high-confidence predictions. Through detailed structural analysis, we also found the importance of intrinsically disordered regions in the predicted high-confidence interactions. Our study highlights the importance of disordered regions in protein-protein interactions and demonstrates the importance of incorporating functional interactions in predicting physical interactions between proteins. We further compiled an interactive web interface to present these predictions, facilitating functional exploration, comparative analysis, and the generation of mechanistic hypotheses for future studies.

Existing tile size selection approaches are tightly coupled with compiler transformation pipelines, often leading to inaccurate modeling of cache behavior and limited effectiveness for non-rectangular tile shapes. This article presents TileMind , a decoupled analytical model that combines compile-time and runtime information for tile size selection in affine programs. It introduces a transformation-aware pre-tiling step that enables the decoupled selector to remain consistent with compiler transformations while extracting compile-time metadata. The extracted metadata is then combined with profiled runtime characteristics to construct a richer yet tractable feasible domain, within which a nonlinear objective for tile size selection is formulated. This objective is subsequently transformed into a binary product linearization problem, with its nonlinear constraints also linearized for efficient optimization. Finally, an intra-tile optimization aligns computation with data layout to enhance data reuse within tiles. Across two multi-core Intel CPUs, TileMind achieves 1.49× (sequential) and 1.33× (parallel) mean speedups on twenty PolyBench kernels, and 2.08–3.54× speedups on three deep learning workloads over the state-of-the-art analytical model Pluto-tss . Compared with TVM’s latest autotuner MetaSchedule, TileMind delivers 1.35–1.46× mean speedups while reducing tuning overhead by 2–4 orders of magnitude. While demonstrating effectiveness on selecting tile sizes for non-rectangular tile shapes and compatibility with PPCG, Pluto, and TVM, we further provide proof-of-concept results on GPUs, illustrating the potential portability of TileMind across architectures.

User-Defined Functions (UDFs) have long served as the standard method for extending the capabilities of data management systems. With the advent of WebAssembly (WASM), UDFs' dependencies, such as language runtimes and libraries, can be compiled into a WASM module, which is then instantiated to execute the UDF. This approach offers several key advantages: 1) it allows developers to write UDFs in their preferred programming language, rather than being limited to those natively supported by the database engine; 2) it isolates UDFs' dependencies within the WASM module, mitigating the risk of errors caused by conflicting dependencies on the same host; and 3) it promotes cross-platform compatibility, enabling seamless execution of UDFs across different engines, operating systems, and architectures. However, our analysis reveals that executing a WASM-based UDF incurs overhead due to data transfer between the database engine and the WASM runtime. This process involves data copying and data layout adjustments, which can significantly impact performance. To address these challenges, we present WAF, a WASM-based UDF execution environment. WAF leverages shared memory to eliminate data copying and shifts data layout adjustments from the execution phase to the compilation phase. Experimental results show that WAF reduces the execution overhead of WASM-based UDFs by 3.1x and achieves an 18.1x speedup compared to the container-based approach, eliminating nearly all data transfer delays.

High-sensitivity geoscience and remote sensing instruments employing large reflector antennas face a significant threat from radio frequency interferences (RFIs). In the presence of sufficiently strong RFI, the front-end components of the receiver are driven into nonlinear operation, potentially resulting in permanent damage to the receiver. In this article, we propose utilizing a wideband true-time delay active reconfigurable intelligent surface (WTTD-ARIS) to mitigate wideband RFI encroaching on the mainlobe of a large reflector antenna before it enters the RF front end of the receiver chain. The formulated optimization problem minimizes the sum of RFI power and noise power by jointly optimizing the refractive beamforming at the WTTD-ARIS and the wideband performance of the wireless channel. To tackle this high-dimensional mathematically intractable optimization problem, a two-stage scalable partition deep reinforcement learning (DRL) algorithm is proposed to reduce the computational complexity while achieving robust optimization of the coefficients of the WTTD-ARIS. First, the large-scale WTTD-ARIS is partitioned into several subarrays and a noise power minimization guided codebook design method is proposed to obtain the preliminary coefficients of each subarray. Then, a robust feature-domain partition graph attention reinforcement learning (FPGA-RL) algorithm is developed to correlate the inherent pattern of the coefficients and the dynamic environment to obtain the final coefficients. Finally, the experimental results demonstrate the effectiveness and robustness of the proposed technique to mitigate the mainlobe wideband RFI for a large reflector antenna before it enters the RF front end.

Large, single-dish radio telescopes with high sensitivities are ideal for detecting faint radio transients (RTs). However, single-dish radio telescopes possess a limited angular resolution, which limits their accuracy in localizing objects. In this article, we propose to improve the localization accuracy of the RT by exploring the 3-D focal field distributions (3DFFDs) of the dish reflector with a gradient-guided attentional network (GGAN). The LSTM-based attention block of the GGAN achieves the task-oriented adaptive recalibration of 3DFFD features by exploring the significant properties and spatial dependencies of 3DFFD. In addition, a gradient-guided approach is being developed to improve the attention block performance under varying incident angles. The proposed attention mechanism is applied to the convolutional neural network in order to reconstruct 3DFFDs and perceive RT positions based on the reconstructed results. Simulation results indicate that the technique can enable the precise localization of RTs. Moreover, the proposed solution improves the telescope’s instantaneous field of view (FOV) compared to a sky survey with the traditional cluster feed telescope.