Despite the impressive success of text-to-image (TTI) models, existing studies overlook the issue of whether these models accurately convey factual information. In this paper, we focus on the problem of image hallucination, where images created by TTI models fail to faithfully depict factual content. To address this, we introduce I-HallA (Image Hallucination evaluation with Question Answering), a novel automated evaluation metric that measures the factuality of generated images through visual question answering (VQA). We also introduce I-HallA v1.0, a curated benchmark dataset for this purpose. As part of this process, we develop a pipeline that generates high-quality question-answer pairs using multiple GPT-4 Omni-based agents, with human judgments to ensure accuracy. Our evaluation protocols measure image hallucination by testing if images from existing TTI models can correctly respond to these questions. The I-HallA v1.0 dataset comprises 1.2K diverse image-text pairs across nine categories with 1,000 rigorously curated questions covering various compositional challenges. We evaluate five TTI models using I-HallA and reveal that these state-of-the-art models often fail to accurately convey factual information. Moreover, we validate the reliability of our metric by demonstrating a strong Spearman correlation (ρ=0.95) with human judgments. Our benchmark dataset and metric can serve as a foundation for developing factually accurate TTI models.

Given a pretrained encoder-based language model, how can we accurately compress it without retraining? Retraining-free structured pruning algorithms are crucial in pretrained language model compression due to their significantly reduced pruning cost and capability to prune large language models. However, existing retraining-free algorithms encounter severe accuracy degradation, as they fail to handle pruning errors, especially at high compression rates. In this paper, we propose KPrune (Knowledge-preserving pruning), an accurate retraining-free structured pruning algorithm for pretrained encoder-based language models. KPrune focuses on preserving the useful knowledge of the pretrained model to minimize pruning errors through a carefully designed iterative pruning process composed of knowledge measurement, knowledge-preserving mask search, and knowledge-preserving weight-tuning. As a result, KPrune shows significant accuracy improvements up to 58.02%p higher F1 score compared to existing retraining-free pruning algorithms under a high compression rate of 80% on the SQuAD benchmark without any retraining process.

Drones suffer unpredictable battery performance that may cause crashes or fatal accidents during aerial missions. Users have reported that their drones unexpectedly shutoff even when they show more than 10% remaining battery capacity. After examining both the drones and battery sides, we discovered that the causes of these unexpected shutoffs to be unbalanced cell thermal degradation caused by thermal coupling between the drones and their battery cells. Drone heat dissipation differently affects battery cells, causes a different cell voltage drop, and results in unpredictable supply voltage and unexpected shutoffs. This paper describes the design and implementation of a thermal and battery-aware power management framework designed specifically for drones. Our framework transparently profiles each battery cell's resistance to capture the cell-level thermal degradation using novel discharging pulses, and estimates an accurate cell capacity, state-of-charge, and state-of-power in real-time to prevent unexpected shutoffs. We have implemented and deployed our framework on commercial drones without additional hardware or system modifications. We have evaluated its effectiveness across different drones and three different batteries demonstrating our framework generates accurate cell state-of-charge and prevents unexpected shutoffs.

The advent of large language models (LLMs) has opened up new opportunities in the field of mobile task automation. Their superior language understanding and reasoning capabilities allow users to automate complex and repetitive tasks. However, due to the inherent unreliability and high operational cost of LLMs, their practical applicability is quite limited. To address these issues, this paper introduces MobileGPT1, an innovative LLM-based mobile task automator equipped with a human-like app memory. MobileGPT emulates the cognitive process of humans interacting with a mobile app---explore, select, derive, and recall. This approach allows for a more precise and efficient learning of a task's procedure by breaking it down into smaller, modular sub-tasks that can be re-used, re-arranged, and adapted for various objectives. We implement MobileGPT using online LLMs services (GPT-3.5 and GPT-4) and evaluate its performance on a dataset of 185 tasks across 18 mobile apps. The results indicate that MobileGPT can automate and learn new tasks with 82.7% accuracy, and is able to adapt them to different contexts with near perfect (98.75%) accuracy while reducing both latency and cost by 62.5% and 68.8%, respectively, compared to the GPT-4 powered baseline.

Users have reported that their drones unexpectedly shutoff even when they show more than 10% remaining battery capacity. We discovered that the causes of these unexpected shutoffs to be significant thermal degradation of a cell caused by thermal coupling between the drones and their battery cells. This causes a large voltage drop for the cell affected by the drone heat dissipation, which leads to low supply voltage and unexpected shutoffs. This paper describes the design and implementation of a thermal and battery-aware power management framework designed specifically for drones. Our framework provides an accurate state-of-charge and state-of-power estimation for individual battery cells by accounting for their different thermal degradation. We have implemented our framework on commodity drones without additional hardware or system modification. We have evaluated its effectiveness using three different batteries demonstrating our framework generates accurate state-of-charge and prevents unexpected shutoffs.