Folded Polynomial Codes for Coded Distributed AA⊤AA^\top-Type Matrix Multiplication

In this paper, due to the important value in practical applications, we consider the coded distributed matrix multiplication problem of computing $AA^\top$ in a distributed computing system with $N$ worker nodes and a master node, where the input matrices $A$ and $A^\top$ are partitioned into $p$-by-$m$ and $m$-by-$p$ blocks of equal-size sub-matrices respectively. For effective straggler mitigation, we propose a novel computation strategy, named \emph{folded polynomial code}, which is obtained by modifying the entangled polynomial codes. Moreover, we characterize a lower bound on the optimal recovery threshold among all linear computation strategies when the underlying field is real number field, and our folded polynomial codes can achieve this bound in the case of $m=1$. Compared with all known computation strategies for coded distributed matrix multiplication, our folded polynomial codes outperform them in terms of recovery threshold, download cost and decoding complexity.Comment: 14 pages, 2 tabl

Event-Guided Procedure Planning from Instructional Videos with Text Supervision

In this work, we focus on the task of procedure planning from instructional videos with text supervision, where a model aims to predict an action sequence to transform the initial visual state into the goal visual state. A critical challenge of this task is the large semantic gap between observed visual states and unobserved intermediate actions, which is ignored by previous works. Specifically, this semantic gap refers to that the contents in the observed visual states are semantically different from the elements of some action text labels in a procedure. To bridge this semantic gap, we propose a novel event-guided paradigm, which first infers events from the observed states and then plans out actions based on both the states and predicted events. Our inspiration comes from that planning a procedure from an instructional video is to complete a specific event and a specific event usually involves specific actions. Based on the proposed paradigm, we contribute an Event-guided Prompting-based Procedure Planning (E3P) model, which encodes event information into the sequential modeling process to support procedure planning. To further consider the strong action associations within each event, our E3P adopts a mask-and-predict approach for relation mining, incorporating a probabilistic masking scheme for regularization. Extensive experiments on three datasets demonstrate the effectiveness of our proposed model.Comment: Accepted to ICCV 202

Urban metabolism and emergy of China’s cities

Unprecedented pace of urbanization and industrialization caused a massive increase in China’s urbanmetabolic pressure. The trend presents an urgent challenge for detailing the long-term changes anddisparities in urban metabolic performances in a wide range of cities. Here, we present empirical evidenceof 283 China’s cities from 2000 to 2018 based on emergy analysis indicating that China’s urbanmetabolic performance gradually becomes worse. For example, the environmental sustainability indexdecreased by 81.64% between 2000 and 2018. In addition, emergy-based performances among China’scities show considerable differences. Agricultural cities and light manufacturing cities have bettersustainability; energy production cities face high environmental pressure. Scenarios for 2025 show thattotal emergy use would experience slower growth; and most cities continue their decline in emergymetabolism. To ensure overall progress on urban metabolic performance, heavy manufacturing cities andenergy production cities should give more attention in adjusting emergy structure

Cannabinoid Receptor Subtype 2 (Cb2R) Agonist Gw405833 Reduces Agonist-Induced Ca2+ Oscillations In Mouse Pancreatic Acinar Cells

Emerging evidence demonstrates that the blockade of intracellular Ca 2+ signals may protect pancreatic acinar cells against Ca 2+ overload, intracellular protease activation, and necrosis. The activation of cannabinoid receptor subtype 2 (CB 2 R) prevents acinar cell pathogenesis in animal models of acute pancreatitis. However, whether CB 2 Rs modulate intracellular Ca 2+ signals in pancreatic acinar cells is largely unknown. We evaluated the roles of CB 2 R agonist, GW405833 (GW) in agonist-induced Ca 2+ oscillations in pancreatic acinar cells using multiple experimental approaches with acute dissociated pancreatic acinar cells prepared from wild type, CB 1 R-knockout (KO), and CB 2 R-KO mice. Immunohistochemical labeling revealed that CB 2 R protein was expressed in mouse pancreatic acinar cells. Electrophysiological experiments showed that activation of CB 2 Rs by GW reduced acetylcholine (ACh)-, but not cholecystokinin (CCK)-induced Ca 2+ oscillations in a concentration-dependent manner; this inhibition was prevented by a selective CB 2 R antagonist, AM630, or was absent in CB 2 R-KO but not CB 1 R-KO mice. In addition, GW eliminated L-arginine-induced enhancement of Ca 2+ oscillations, pancreatic amylase, and pulmonary myeloperoxidase. Collectively, we provide novel evidence that activation of CB 2 Rs eliminates ACh-induced Ca 2+ oscillations and L-arginine-induced enhancement of Ca 2+ signaling in mouse pancreatic acinar cells, which suggests a potential cellular mechanism of CB 2 R-mediated protection in acute pancreatitis

FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such data from multiple sensors to jointly optimize the prediction and planning tasks remains largely unexplored. In this paper, we present FusionAD, to the best of our knowledge, the first unified framework that fuse the information from two most critical sensors, camera and LiDAR, goes beyond perception task. Concretely, we first build a transformer based multi-modality fusion network to effectively produce fusion based features. In constrast to camera-based end-to-end method UniAD, we then establish a fusion aided modality-aware prediction and status-aware planning modules, dubbed FMSPnP that take advantages of multi-modality features. We conduct extensive experiments on commonly used benchmark nuScenes dataset, our FusionAD achieves state-of-the-art performance and surpassing baselines on average 15% on perception tasks like detection and tracking, 10% on occupancy prediction accuracy, reducing prediction error from 0.708 to 0.389 in ADE score and reduces the collision rate from 0.31% to only 0.12%

Fine-Tuning Stomatal Movement Through Small Signaling Peptides

As sessile organisms, plants are continuously exposed to a wide range of environmental stress. In addition to their crucial roles in plant growth and development, small signaling peptides are also implicated in sensing environmental stimuli. Notably, recent studies in plants have revealed that small signaling peptides are actively involved in controlling stomatal aperture to defend against biotic and abiotic stress. This review illustrates our growing knowledge of small signaling peptides in the modulation of stomatal aperture and highlights future challenges to decipher peptide signaling pathways in guard cells

RFID Spatio-Temporal Data Management

Radio-frequency Identification (RFID) technology promises to revolutionize the way we track items in supply chain, retail store, and asset management applications. The size and different characteristics of RFID data pose many interesting challenges in the current data management systems. In this paper, we provide a brief overview of RFID technology and highlight a few of the spatio-temporal data management challenges that we believe are suitable topics for exploratory research. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.221