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Non-maximum suppression (NMS) is an essential post-processing step for object detection. The de-facto standard for NMS, namely GreedyNMS, is not parallelizable and could thus be the performance bottleneck in object detection pipelines. MaxpoolNMS is ...

In today's information age, table images play a crucial role in storing structured information, making table image recognition technology an essential component in many fields. However, accurately recognizing the structure and text content of ...

Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant ...

Journaling offers significant benefits, including fostering self-reflection, enhancing writing skills, and aiding in mood monitoring. However, many people abandon the practice because traditional journaling is time-consuming, and detailed life events may ...

Time synchronization is a critical component in network operation and management, and it is also required by Ultra-Reliable, Low-Latency Communications (URLLC) in next-generation wireless systems such as those of 5G, 6G, and Open RAN. In this context, we ...

Recently quanta image sensors (QIS) – ultra-fast, zero-read-noise binary image sensors– have demonstrated remarkable imaging capabilities in many challenging scenarios. Despite their potential, the adoption of these sensors is severely ...

Interactive theorem provers such as Coq are powerful tools to formally guarantee the correctness of software. However, using these tools requires significant manual effort and expertise. While Large Language Models (LLMs) have shown promise in ...

Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain limited, especially in high-stakes domains. This paper introduces SQLucid, ...

Pathological image analysis is a crucial field in deep learning applications. However, training effective models demands large-scale annotated data, which faces challenges due to sampling and annotation scarcity. The rapid developing generative ...

The proliferation of mobile sensing technologies has enabled the study of various physiological and behavioural phenomena through unobtrusive data collection from smartphone sensors. This approach offers real-time insights into individuals' physical and ...

As mental health issues for young adults present a pressing public health concern, daily digital mood monitoring for early detection has become an important prospect. An active research area, digital phenotyping, involves collecting and analysing data ...

To satisfy the requirements of content distribution in computation-intensive and delay-sensitive services, this paper presents a novel joint task offloading and content caching (JTOCC) scheme in multi-cell multi-carrier non-orthogonal multiple-access (...

Structure-from-Motion (SfM) has become a ubiquitous tool for camera calibration and scene reconstruction with many downstream applications in computer vision and beyond. While the state-of-the-art SfM pipelines have reached a high level of ...

With the development of deep learning, thermal infrared target detection has a wide range of applications in various fields. However, most existing deep learning models have high computational complexity, sensitivity to hyperparameters, low timeliness, ...

As deep learning models become increasingly bigger and more complex, it is critical to improve model training and inference efficiency. Though a variety of highly optimized libraries and packages (known as DL kernels) have been developed, it is tedious ...

Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological relationships among users, thereby offering enhanced ...

Readability assessment for book-level long text is widely needed in real educational applications. However, most of the current researches focus on passage-level readability assessment and little work has been done to process ultra-long texts. In ...

Due to the high cost of training DNN models, how to protect the intellectual property of DNN models, especially when the models are deployed to users' devices, is becoming an important topic. One practical solution is to use Trusted Execution ...

Large Language Models (LLMs) have recently been widely used for code generation. Due to the complexity and opacity of LLMs, little is known about how these models generate code. We made the first attempt to bridge this knowledge gap by investigating ...