Despite numerous applications for fine-grained corpus analysis, researchers continue to rely on manual labeling, which does not scale, or statistical tools like topic modeling, which are difficult to control.We propose that LLMs have the potential to scale the nuanced analyses that researchers typically conduct manually to large text corpora.To this effect, inspired by qualitative research methods, we develop HICode 1 , a two-part pipeline that first inductively generates labels directly from analysis data and then hierarchically clusters them to surface emergent themes.We validate this approach across three diverse datasets by measuring alignment with human-constructed themes and demonstrating its robustness through automated and human evaluations.Finally, we conduct a case study of litigation documents related to the ongoing opioid crisis in the U.S., revealing aggressive marketing strategies employed by pharmaceutical companies and demonstrating HICode's potential for facilitating nuanced analyses in large-scale data.

Considering several sources that cause global position system (GPS) interference in civil aviation and the challenges faced by interference recognition algorithms in terms of efficiency and accuracy, we propose an improved You Only Look Once (YOLO)v7‐CHS algorithm (YOLOv7‐CHS) and investigate its effectiveness in identifying GPS signals and different types of interference signals. First, continuous wavelet transform (CWT) is introduced as a method for processing and analyzing signals in the time–frequency (TF) domain to effectively obtain their temporal and spectral characteristic information. Second, the ConvNeXt structure is integrated into the YOLOv7 backbone network to create a ConvNeXtBlock (CNeB) module to enhance the classification and recognition accuracy of interference signals. Additionally, an attention mechanism is introduced to further improve model recognition accuracy. To effectively improve the capability of signal feature extraction and mitigate the impact of background noise on TF feature suppression, we have integrated the efficient channel attention (ECA) channel attention module with the convolutional block attention module (CBAM) spatial attention module, thereby proposing a hybrid CBAM and ECA (HCE) attention module. Last, to address issues arising from accidental deletion of detection frames and multipath interference negatively affecting model recognition performance, we have employed the soft nonmaximum suppression (Soft‐NMS) algorithm while selecting an optimal loss function through comparative analysis. The comparative evaluation experimental results under different circumstances show that YOLOv7‐CHS achieves recognition accuracies of 98.0% and 99.6% for various types of signals, respectively. These values represent an increase of 1.7% and 1%, respectively, compared to YOLOv7. Moreover, in terms of lightweight indicators, YOLOv7‐CHS exhibits a significant improvement in performance: the frames per second (FPS) is increased by 75.1, the number of parameters (Params) was reduced by 4.75 M, and giga floating point operations per second (GFLOPs) were reduced by 65.9 G while effectively enhancing recognition capabilities. The proposed YOLOv7‐CHS not only improves signal recognition accuracy but also reduces model Params and computational complexity, achieving a lightweight model with promising application prospects in the rapid detection and recognition of GPS interference sources in civil aviation.

Decision-makers in the humanitarian sector rely on timely and exact information during crisis events. Knowing how many civilians were injured during an earthquake is vital to allocate aids properly. Information about such victim counts are however often only available within full-text event descriptions from newspapers and other reports. Extracting numbers from text is challenging: numbers have different formats and may require numeric reasoning. This renders purely tagging approaches insufficient. As a consequence, fine-grained counts of injured, displaced, or abused victims beyond fatalities are often not extracted and remain unseen. We cast victim count extraction as a question answering (QA) task with a regression or classification objective. We compare tagging approaches: regex, dependency parsing, semantic role labeling, and advanced text-to-text models. Beyond model accuracy, we analyze extraction reliability and robustness which are key for this sensitive task. In particular, we discuss model calibration and investigate out-of-distribution and few-shot performance. Ultimately, we make a comprehensive recommendation on which model to select for different desiderata and data domains. Our work is among the first to apply numeracy-focused large language models in a real-world use case with a positive impact.

The proliferation of connected cars globally has the potential to produce torrents of Big Data that will enable improvements in driver safety, new location based services, improvements in vehicle quality, and optimized vehicle designs. One aspect of connected car data involves driving behavior data and its use for Usage Based Insurance (UBI). UBI has become one of the most widely used applications of driving behavior data. Currently, the transmission and processing of high frequency driving behavior data from the connected car to the cloud is limited by wireless data costs and in-vehicle hardware complexity. To alleviate these issues, we detail the development of a machine learning framework utilizing a kinematic classification methodology applied to down sampled time series vehicle data sets for accurate imputation of driving behavior events in UBI applications. The down-sampled data, consisting of 5 second frames with data fields of timestamp, vehicle speed, and vehicle acceleration is classified into unique kinematic clusters to standardize any driving behavior data distributions. Subsequently, machine learning is used to impute harsh driving events for each 5 second frame in select kinematic clusters. This novel machine learning methodology reduced data set sizes by 75%, utilized a limited set of five attributes, and achieved an average precision and recall of 84.5% and 63.5% for two distinct connected car data sets with 1/5 Hz down-sampled data.