We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE, for the task of future predictions multiple interacting agents in dynamic scenes. DESIRE effectively predicts locations objects scenes by 1) accounting multi-modal nature prediction (i.e., given same context, may vary), 2) foreseeing potential outcomes and make strategic based on that, 3) reasoning not only from past motion history, but also scene context as well interactions among agents. achieves these single...

Pruning large neural networks while maintaining their performance is often desirable due to the reduced space and time complexity. In existing methods, pruning done within an iterative optimization procedure with either heuristically designed schedules or additional hyperparameters, undermining utility. this work, we present a new approach that prunes given network once at initialization prior training. To achieve this, introduce saliency criterion based on connection sensitivity identifies...

We develop predictive models of pedestrian dynamics by encoding the coupled nature multi-pedestrian interaction using game theory and deep learning-based visual analysis to estimate person-specific behavior parameters. focus on since they are important for developing interactive autonomous systems (e.g., cars, home robots, smart homes) that can understand different human pre-emptively respond future actions. Building interactions however, is very challenging due two reasons: (1) complex...

Predicting the trajectory of a wide receiver in game American football requires prior knowledge about (e.g., route trees, defensive formations) and an accurate model how environment will change over time opponent reaction strategies, motion attributes players). Our aim is to build computational receiver, which takes into account short-term predictive models time. While readily accessible, it quite challenging We propose several for predicting motions players generate dynamic input features...

The treatment of agricultural plastic waste is a critical source fine dust (PM2.5) emissions, contributing significantly to air pollution. Uncollected waste, predominantly subjected open-air incineration, exacerbates this issue, underscoring the need for comprehensive management strategies.This study aims predict PM2.5 emissions from processes and quantify contribution uncollected pollution, providing novel analysis relative environmental impact these two pathways. Using CAPSS model...

Landfills emit a variety of pollutants in both gaseous and liquid phases during the final disposal waste. Among pollutants, hydrogen sulfide (H2S), primarily generated anaerobic decomposition, oxidizes atmosphere to form sulfur oxides (SOx) contributes as precursor particulate matter (PM2.5) formation. While (H2S) can affect local air quality its atmospheric transport oxidation, there is lack research on quantitative evaluation movement oxidation processes emitted from landfills.Thus, this...

Achieving neutral limb alignment during total knee arthroplasty (TKA) has been considered an important determinant in the long-term prosthesis survival. The purpose of this study was to evaluate association between immediate postoperative mechanical lower and rate revision TKA by comparing acceptable axis group (within ± 3° from alignment) outlier (> deviation alignment).Between 2000 2006, clinical radiographic data 334 primary TKAs were retrospectively reviewed determine 10-year...

Network pruning is a promising avenue for compressing deep neural networks. A typical approach to starts by training model and then removing redundant parameters while minimizing the impact on what learned. Alternatively, recent shows that can be done at initialization prior training, based saliency criterion called connection sensitivity. However, it remains unclear exactly why an untrained, randomly initialized network effective. In this work, noting sensitivity as form of gradient, we...

We developed a new method of fabricating divalent copper ion (Cu2+) modified DNA thin film on glass substrate and studied its magnetic properties. evaluated the coercive field (Hc), remanent magnetization (Mr), susceptibility (χ) thermal variation with varying Cu2+ concentrations [Cu2+] resulting in films. Although thickness two dimensional dry state was extremely (0.6 nm), significant ferromagnetic signals were observed at room temperature. The films near 5 mM showed distinct S-shape...

We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built image classification. The takes as input the 2D feature vector maps which form intermediate representations of at different stages in CNN pipeline, and outputs a matrix scores each map. Standard are modified through incorporation this module, trained under constraint that convex combination vectors, parameterised by score matrices, must \textit{alone} be used Incentivised to amplify...

Concept bottleneck models (CBMs) are a class of interpretable neural network that predict the target response given input based on its high-level concepts. Unlike standard end-to-end models, CBMs enable domain experts to intervene predicted concepts and rectify any mistakes at test time, so more accurate task predictions can be made end. While such intervenability provides powerful avenue control, many aspects intervention procedure remain rather unexplored. In this work, we develop various...

The aim of this study was to assess the effect pre-aeration on sludge solubilization and behaviors nitrogen, dissolved sulfide, sulfate, siloxane. results showed that soluble chemical oxygen demand in sewage could be increased through pre-aeration. process resulted a higher methane yield compared anaerobic condition (blank). sludge, therefore, shown an effective method for enhancing digestibility sludge. In addition, result confirms prior its digestion accelerates growth methanogenic...

Here, we report on a new zeolite-based silicalite nanoparticle that can enhance the transfection efficiencies generated by poly ethylene imine-plasmid DNA (PEI-pDNA) complexes via sedimentation mechanism and of pDNA alone when surface functionalized with amine groups. The nanoparticles have mean size 55 nm. Functionalizing groups results in clear transition zeta potential from -25.9 ± 2.3 mV (pH 7.4) for unfunctionalized to 4.9 0.7 nanoparticles. We identify used promote non-viral vector...

Knowledge distillation is an effective method for training lightweight models, but it introduces a significant amount of computational overhead to the cost, as requires acquiring teacher supervisions on samples. This additional cost -- called most pronounced when we employ large-scale models such vision transformers (ViTs). We present MaskedKD, simple yet strategy that can significantly reduce distilling ViTs without sacrificing prediction accuracy student model. Specifically, MaskedKD...