While the rapid exchange of messages in instant messaging (IM) is useful for maintaining convenient and continuous communication, it can simultaneously diminish recipients’ opportunities to reflect on and emotionally engage with individual messages. This paper introduces Adilet, a system that represents the process of composing and receiving delayed messages through a physical device, designed to enhance message awareness, perceived value, and reflective engagement. Adilet allows users to ensure the status of writing, sending, and receiving messages and approximate total writing time. A field study with four groups of two close partners demonstrated that Adilet improved message quality by increasing participants' awareness on both writing and reception. Furthermore, consistent awareness of message presence and clues about message content enhanced anticipation. Our findings suggest further implications for designing communication systems that support deeper and more valuable emotional exchange by improving message value and recognition in close relationships.

Low-rank adaptation (LoRA) has become the dominant method for parameter-efficient LLM fine-tuning, with LoRA-based quantization error compensation (LQEC) emerging as a powerful tool for recovering accuracy in compressed LLMs. However, LQEC has underperformed in sub-4-bit scenarios, with no prior investigation into understanding this limitation. We propose RILQ (Rank-Insensitive LoRA-based Quantization Error Compensation) to boost 2-bit LLM accuracy. Based on rank analysis revealing model-wise activation discrepancy loss's rank-insensitive nature, RILQ employs this loss to adjust adapters cooperatively across layers, enabling robust error compensation with low-rank adapters. Evaluations on LLaMA-2 and LLaMA-3 demonstrate RILQ's consistent improvements in 2-bit quantized inference across various state-of-the-art quantizers and enhanced accuracy in task-specific fine-tuning. RILQ maintains computational efficiency comparable to existing LoRA methods, enabling adapter-merged weight-quantized LLM inference with significantly enhanced accuracy, making it a promising approach for boosting 2-bit LLM performance.

As graphs are being used increasingly in various industries, a new standard of SQL (called SQL:2023) has incorporated SQL with Property Graph Queries (SQL/PGQ) as a core feature. While some approaches process graph queries within RDBMSs using graph view definition or materialized graph view, their performance is not good enough for interactive SQL/PGQ queries in terms of response time, throughput, and graph size. To address this problem, we propose a novel system design named Chimera , which features a dual-store architecture and a unified query planning called Traversal-Join(TJ). This design treats the topologies of a graph as first-class citizens rather than secondary elements overcoming the graph size limitations of the materialized graph view approach. It also generates an efficient, unified query plan that performs traversal and join in a mixed way, significantly enhancing both response time and throughput. Implemented on the open-source RDBMS, PostgreSQL, our extensive experiments with the LDBC SNB benchmark and microbenchmark show that Chimera significantly outperforms the existing approaches and GRDBMSs.