Designers often regard vagueness as an essential aspect of creative work, as it fosters diverse interpretations and helps prevent fixation. Although large language models (LLMs) are increasingly viewed as a promising creative partner, designers struggle to productively incorporate vagueness into AI-supported workflows. To address this challenge, we present QuerySwitch, an interactive prototype that enables fashion designers to manage vagueness by flexibly switching between two distinct query-output modes. Findings from a user study show that QuerySwitch helps fashion designers balance vagueness, enhances the usability of LLMs in design tasks, and promotes creative exploration. This work contributes to HCI by (1) foregrounding a critical construct in human–AI collaboration, (2) demonstrating how interaction mechanisms can scaffold designer agency in LLMs use, and (3) articulating design principles—structuring exploration and preserving key query formulations—that extend to creativity-driven domains.

BACKGROUND: With the exponential growth of public RNA-seq datasets, meta-analysis has become a crucial tool for integrating studies to increase statistical power and identify consistent biological patterns. However, conventional p value combination methods were not designed for large-scale integration and exhibit a critical flaw: as more datasets are added, the rate of false positives increases dramatically. This issue stems from the disproportionate influence of extremely low p values from just a few individual studies, which can create a misleading signal of overall significance and undermine the reliability of the findings. RESULTS: Here, we introduce a robust meta-analysis framework that incorporates p value capping, cutoff thresholding, and bagging to alleviate this issue. We implemented this framework using Stouffer’s method, termed the hybrid Stouffer (hStouffer) method. The proposed method demonstrates a reduced false positive rate while preserving high sensitivity. Furthermore, the validation showed that the DEGs identified by hStouffer accurately reflect the underlying biological phenomena, making it an essential tool for leveraging the full potential of expanding genomic databases to understand complex biological processes. CONCLUSIONS: The hStouffer method provides a statistically robust and biologically coherent solution for large-scale RNA-seq meta-analysis. By effectively controlling for technical artifacts and false discoveries, it enables researchers to extract more reliable and meaningful insights from complex, aggregated transcriptomic data.

In this paper, circular-shift order (CSO) is proposed as a subcarrier processing order of duplicated symbols in Wi-Fi. Non-high throughput (HT) duplicate (DUP) packets with the maximum bandwidth of 320 MHz for Wi-Fi 7 consist of sixteen duplicated 20 MHz subchannels of 64 subcarriers. Extremely high throughput (EHT) DUP mode is one of the new features of Wi-Fi 7 for the range extension with reliability. 320 MHz EHT DUP packets consist of two duplicated 160 MHz subchannels of 2048 subcarriers. For Wi-Fi symbols, outputs of an FFT core of decimation in frequency in bit-reversed order (BRO) are generally reordered into subcarriers in natural order (NO) using an FFT output reorder buffer. A 2048-depth memory is necessary for combining duplicated 160 MHz subchannels of 320 MHz EHT DUP mode in NO. On the other hand, the proposed CSO does not need a memory for combining duplicates because duplicated subcarriers are manipulated consecutively. In addition, we can achieve the required SNRs of 0.4~0.8 dB with decision-aided channel tracking in CSO for 320 MHz non-HT DUP packets with modulations except BPSK. Lastly, the ratio of minimum latency to FFT size for continuous reordered data for 320 MHz non-HT DUP packets decreases from 93.8 % to 77.4 %.

As the perspective on creativity shifts to “how it is expressed,” research has aimed to categorize it by problem-solving style. Since harnessing individual creative traits can positively impact creative performance, there has been an emphasis on designing computer systems that are tailored to personal problem-solving behaviors. AI-CST has opened up the potential to facilitate such customization. In this work, we consider two types of creativity—adaptors and innovators—based on problem-solving styles, and investigate AI-CST designs that both types could flexibly use according to the fashion design process. We identified two main AI-CST functions—determining design direction and receiving design inspiration—of the fashion design process, and developed CoCoStyle to map these functions. Through a user study with 30 fashion professionals (15 adaptors and 15 innovators), we found significant differences between the two groups from survey responses, system usage logs, and interviews. Based on the results, we discuss the theoretical and practical implications of AI and AI-CST where creativity is essential.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.