Triangular Bézier sub-surfaces on a triangular Bézier surface

This paper considers the problem of computing the Bézier representation for a triangular sub-patch on a triangular Bézier surface. The triangular sub-patch is defined as a composition of the triangular surface and a domain surface that is also a triangular Bézier patch. Based on de Casteljau recursions and shifting operators, previous methods express the control points of the triangular sub-patch as linear combinations of the construction points that are constructed from the control points of the triangular Bézier surface. The construction points contain too many redundancies. This paper derives a simple explicit formula that computes the composite triangular sub-patch in terms of the blossoming points that correspond to distinct construction points and then an efficient algorithm is presented to calculate the control points of the sub-patch

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles using 3D Dubins Curves

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X − Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem

Spatio-Temporal Modeling for Flash Memory Channels Using Conditional Generative Nets

We propose a data-driven approach to modeling the spatio-temporal characteristics of NAND flash memory read voltages using conditional generative networks. The learned model reconstructs read voltages from an individual memory cell based on the program levels of the cell and its surrounding cells, as well as the specified program/erase (P/E) cycling time stamp. We evaluate the model over a range of time stamps using the cell read voltage distributions, the cell level error rates, and the relative frequency of errors for patterns most susceptible to inter-cell interference (ICI) effects. We conclude that the model accurately captures the spatial and temporal features of the flash memory channel

Screening soybean for adaptation to relay intercropping systems: associations between reproductive organ abscission and yield

The flower and pod abscission is one of the characteristics of soybean that severely limits yield, especially when intercropped with maize. Therefore, suitable soybean cultivars for intercropping are urgently needed to improve farmland productivity. We conducted a two-year field experiment to evaluate the flower and pod abscission, dry matter production, and yield advantages of 15 soybean cultivars. The results of the principal component analysis (PCA) and cluster analysis (CA) showed that 15 soybean cultivars were classified into three groups, i.e., high-yielding group (HYG), mid-yielding cultivars (MYG), and low-yielding cultivars (LYG). In the HYG group, ND12 and GX3 had characteristics of more flowers and pods and less abscission of flowers and pods. Moreover, the net assimilation rate (NAR) and relative growth rate (RGR) of HYG were significantly higher than the other. The HYG obtained a considerably higher partition ratio of 53% from biomass to seed than the other. Therefore, selecting and breeding cultivars with the characteristics of more flowers and pods and less abscission of flowers and pods can help to increase soybean yield in intercropping.This research was funded by the Program on Industrial Technology System of National Soybean (CARS-04-PS18), and the National Key Research and Development Program of China (2021YFF1000500). Qing Du was a recipient of a joint PhD scholarship supported by the China Scholarship Council (CSC) (No. 202106910037)

One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms

Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate and efficient workload prediction. Clustering-based methods for dynamic MT-ECP modeling often incur excessive costs due to the need to maintain numerous data clusters and models, which leads to excessive costs. Existing end-to-end time series prediction methods are challenging to provide consistent prediction performance in dynamic MT-ECP. In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP. Meticulously designed global pooling and information merging mechanisms can effectively identify and utilize global application patterns to drive local workload predictions. The integration of static content-aware mechanisms enhances model robustness in real-world scenarios. Through experiments on five real-world datasets, DynEformer achieved state-of-the-art in the dynamic scene of MT-ECP and provided a unified end-to-end prediction scheme for MT-ECP.Comment: 10 pages, 10 figure

An Energy Effective Adaptive Spatial Sampling Algorithm for WSNs

Abstract: The objective of environmental observation with WSNs (wireless sensor networks) is to extract the synoptic structures (spatio-temporal sequence) of the phenomena of ROI (region of interest) in order to make effective predictive and analytical characterizations. Energy limitation is one of the main obstacles to the universal application of WSNs and therefore there are a large mass of researches on energy conservation for WSNs. Among them, adaptive sampling strategy is regarded as a promising method to improve energy efficiency in recent years, therefore, many researches are concerning to different kinds of energy efficient sampling scheme for WSNs. In this paper, we dedicate to investigating how to schedule sensor nodes in the spatial region domain by our adaptive sampling scheme so as to reduce energy consumption of sensor nodes. The key idea of this paper is to schedule sensor nodes to achieve the desired level of accuracy by activating sensor system only when necessary to acquire a new set of samples and then prepare to power it off immediately afterwards. By adaptively sampling the region of interest, fewer sensors are activated at the same time. Moreover, only the necessary communications are remaining with this algorithm, so as to achieve significant energy conservation than before. The algorithm proposed in this literature is named as Adaptive Spatial Sampling (ASS) algorithm in short. The simulation results verified that ASS algorithm can outperform traditional fixed sampling strategy

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

gkab438Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.Peer reviewe

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.Peer reviewe

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values. This alignment requires a vast corpus of human feedback to learn a reward model, which is subsequently used to finetune language models. However, we have identified that the reward model often finds shortcuts to bypass its intended objectives, misleadingly assuming that humans prefer longer responses. The emergence of length bias often induces the model to favor longer outputs, yet it doesn't equate to an increase in helpful information within these outputs. In this paper, we propose an innovative solution, applying the Product-of-Experts (PoE) technique to separate reward modeling from the influence of sequence length. In our framework, the main expert concentrates on understanding human intents, while the biased expert targets the identification and capture of length bias. To further enhance the learning of bias, we introduce perturbations into the bias-focused expert, disrupting the flow of semantic information. Experimental results validate the effectiveness of our approach, indicating that language model performance is improved, irrespective of sequence length.Comment: EMNLP 2023 findings, Length Bias in RLHF, Mitigate bias in reward modelin