The Contribution Of Occupancy Behavior To Energy Consumption In Low Income Residential Buildings

Energy consumption in residential buildings consumes 22% of the total US energy each year and is highly impacted by the occupant behavior. In order to model domestic demand profiles more accurately, it is important to understand occupancy behavior profile. Four low income houses in Texas are used as the test beds. The occupancy sensors are installed in every room. The real-life occupancy data from the occupancy sensors were compared with the American Time Use Survey (ATUS) data. The study period is from July 1 to August 31. The preliminary result shows that there is a similarity between ATUS data and actual occupancy profile. In addition, simulations in EnergyPlus were conducted to test how much energy consumption can be saved based on the thermostat control of real-life occupancy behavior patterns. The results show that such control can save cooling energy by 7%

Identify bottom contribution in non-photonic electron spectra and \vv\ from \AuAu collisions at RHIC

We present a study on the spectra and elliptic flow v2 for heavy flavor (charm and bottom) decayed electrons provided the relative contributions of charm and bottom hadrons from the PYTHIA calculations. We made a simultaneous fit to both measured non-photonic electron spectra and v2 distributions. The results suggest that the bottom contribution is not dominant for electron pt<5 GeV/c in the 200 GeV Au+Au collisions.Comment: 4 pages, 3 figures, proceedings for Hard Probe 2006, Asilomar; to be published in Nuclear Physics, Section

Quasi-static Soft Fixture Analysis of Rigid and Deformable Objects

We present a sampling-based approach to reasoning about the caging-based manipulation of rigid and a simplified class of deformable 3D objects subject to energy constraints. Towards this end, we propose the notion of soft fixtures extending earlier work on energy-bounded caging to include a broader set of energy function constraints and settings, such as gravitational and elastic potential energy of 3D deformable objects. Previous methods focused on establishing provably correct algorithms to compute lower bounds or analytically exact estimates of escape energy for a very restricted class of known objects with low-dimensional C-spaces, such as planar polygons. We instead propose a practical sampling-based approach that is applicable in higher-dimensional C-spaces but only produces a sequence of upper-bound estimates that, however, appear to converge rapidly to actual escape energy. We present 8 simulation experiments demonstrating the applicability of our approach to various complex quasi-static manipulation scenarios. Quantitative results indicate the effectiveness of our approach in providing upper-bound estimates for escape energy in quasi-static manipulation scenarios. Two real-world experiments also show that the computed normalized escape energy estimates appear to correlate strongly with the probability of escape of an object under randomized pose perturbation.Comment: Paper submitted to ICRA 202

Target-Grounded Graph-Aware Transformer for Aerial Vision-and-Dialog Navigation

This report details the method of the winning entry of the AVDN Challenge in ICCV 2023. The competition addresses the Aerial Navigation from Dialog History (ANDH) task, which requires a drone agent to associate dialog history with aerial observations to reach the destination. For better cross-modal grounding abilities of the drone agent, we propose a Target-Grounded Graph-Aware Transformer (TG-GAT) framework. Concretely, TG-GAT first leverages a graph-aware transformer to capture spatiotemporal dependency, which is beneficial for navigation state tracking and robust action planning. TG-GAT first leverages a graph-aware transformer to capture spatiotemporal dependencies for more robust action planning. In addition, an auxiliary visual grounding task is devised to boost the agent's awareness of referred landmarks. Moreover, a hybrid augmentation strategy based on large language models is utilized to mitigate data scarcity limitations. Our TG-GAT framework won the AVDN Challenge 2023, with 2.2% and 3.0% absolute improvements over the baseline on SPL and SR metrics, respectively. The code is available at https://github.com/yifeisu/avdn-challenge

AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization

Recent advances in deep learning research have shown remarkable achievements across many tasks in computer vision (CV) and natural language processing (NLP). At the intersection of CV and NLP is the problem of image captioning, where the related models' robustness against adversarial attacks has not been well studied. In this paper, we present a novel adversarial attack strategy, which we call AICAttack (Attention-based Image Captioning Attack), designed to attack image captioning models through subtle perturbations on images. Operating within a black-box attack scenario, our algorithm requires no access to the target model's architecture, parameters, or gradient information. We introduce an attention-based candidate selection mechanism that identifies the optimal pixels to attack, followed by Differential Evolution (DE) for perturbing pixels' RGB values. We demonstrate AICAttack's effectiveness through extensive experiments on benchmark datasets with multiple victim models. The experimental results demonstrate that our method surpasses current leading-edge techniques by effectively distributing the alignment and semantics of words in the output