A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension

Pre-trained models have brought significant improvements to many NLP tasks and have been extensively analyzed. But little is known about the effect of fine-tuning on specific tasks. Intuitively, people may agree that a pre-trained model already learns semantic representations of words (e.g. synonyms are closer to each other) and fine-tuning further improves its capabilities which require more complicated reasoning (e.g. coreference resolution, entity boundary detection, etc). However, how to verify these arguments analytically and quantitatively is a challenging task and there are few works focus on this topic. In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e.g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task. Specifically, we identify five phenomena in MRC. According to pairwise probing tasks, we compare the performance of each layer's hidden representation of pre-trained and fine-tuned BERT. The proposed pairwise probe alleviates the problem of distraction from inaccurate model training and makes a robust and quantitative comparison. Our experimental analysis leads to highly confident conclusions: (1) Fine-tuning has little effect on the fundamental and low-level information and general semantic tasks. (2) For specific abilities required for downstream tasks, fine-tuned BERT is better than pre-trained BERT and such gaps are obvious after the fifth layer.Comment: e.g.: 4 pages, 1 figur

Data Governance in Multimodal Behavioral Research

In the digital era, multimodal behavioral research has emerged as a pivotal discipline, integrating diverse data sources to comprehensively understand human behavior. This paper defines and distinguishes data governance from mere data management within this context, highlighting its centrality in assuring data quality, ethical handling, and participant protection. Through a meticulous review of the literature and empirical experience, we identify key implementation strategies and elucidate the benefits and risks of data governance frameworks in multimodal research. A demonstrative case study illustrates the practical applications and challenges, revealing enhanced data reliability and research integrity as tangible outcomes. Our findings underscore the critical need for robust data governance, pointing to future advancements in the field, including the development of adaptive governance frameworks, innovative big data analytics solutions, and user-friendly tools. These enhancements are poised to amplify the utility of multimodal data, propelling behavioral science forward

SYNTHESIS AND CHARACTERIZATION OF SIDE GROUP-MODIFIED CYCLOTETRAPHOSPHAZENE DERIVATIVES

Two novel cyclotetraphosphazene derivatives were synthesized by the reaction of octachlorocyclotetraphosphazene with the potassium salt of 4-hydroxybenzaldehyde, and subsequent reduction of aldehyde groups to alcohol groups using sodium borohydride. The bromination reaction was carried out using PBr(3) to give N(4)P(4)(OC(6)H(4)-p-CH(2)Br)(8) (4). This compound was employed in reaction with imidazole or morpholine to produce eight-armed, star-branched title compounds. The target compounds were characterized by (1)H, (31)P, and (13)C NMR as well as IR and ESI-MS. The Cu complex of 5a was effective in oxidative cleavage reactions.Natural Science Foundation of China[20972143, 20602032, 20732004, 20972130

Improved GM-PHD Filter with Birth Intensity and Spawned Intensity Estimation Based on Trajectory Situation Feedback Control

The Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can effectively track multiple targets in a single scenario. However, for GM-PHD, unknown target behavior, e.g., target birth or target intersection, produces difficulties in terms of accurate estimation. First of all, GM-PHD assumes the model parameters about the birth target are prior information, which results in the inability to detect the birth target that occurs at random in complex scenarios. Then, since the measurements generated by the intersected targets overlap each other, GM-PHD cannot distinguish these targets, resulting in a biased estimation of the state and number of targets. To solve these problems, this paper proposes an improved GM-PHD filter with a birth intensity and spawned intensity updating method based on the trajectory situation feedback. In the filtering process, the trajectory initiation feedback formed by the rule-based correlation of Gaussian components is introduced to GM-PHD to adjust the birth intensity in real time, which is used to improve the detection of birth targets. Simultaneously, the analysis of trajectory situation is designed to determine the relative motion trend between targets. On this basis, the filter improves the recognition of the intersected targets by enhancing the spawned intensity. Simulation results demonstrate that the proposed algorithm achieves better performance on the state and number of targets in complex scenarios, and shows superiority to other GM-PHD filters

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

The existing thermal infrared (TIR) ship detection methods may suffer serious performance degradation in the situation of heavy sea clutter. To cope with this problem, a novel ship detection method based on morphological reconstruction and multi-feature analysis is proposed in this paper. Firstly, the TIR image is processed by opening- or closing-based gray-level morphological reconstruction (GMR) to smooth intricate background clutter while maintaining the intensity, shape, and contour features of ship target. Then, considering the intensity and contrast features, the fused saliency detection strategy including intensity foreground saliency map (IFSM) and brightness contrast saliency map (BCSM) is presented to highlight potential ship targets and suppress sea clutter. After that, an effective contour descriptor namely average eigenvalue measure of structure tensor (STAEM) is designed to characterize candidate ship targets, and the statistical shape knowledge is introduced to identify true ship targets from residual non-ship targets. Finally, the dual method is adopted to simultaneously detect both bright and dark ship targets in TIR image. Extensive experiments show that the proposed method outperforms the compared state-of-the-art methods, especially for infrared images with intricate sea clutter. Moreover, the proposed method can work stably for ship target with unknown brightness, variable quantities, sizes, and shapes

Scalable, Robust, Low-Cost, and Highly Thermally Conductive Anisotropic Nanocomposite Films for Safe and Efficient Thermal Management

Recently, soaring developments in microelectronics raise an urgent demand for thermal management materials to tackle their overheating concerns. Polymer nanocomposites are promising candidates but often suffer from their inability of mass production, high-cost, poor mechanical robustness, and even flammability. Hence, it is desirable to scalably fabricate low-cost, robust polymeric nanocomposites that are highly thermally conductive and fire-retardant to ensure safe and efficient thermal management. Herein, the scalable production of nacre-like anisotropic nanocomposite films using the layer-by-layer assembly of phenylphosphonic acid@graphene nanoplatelets (PPA@GNPs)-poly(vinyl alcohol) (PVA) layer and GNPs layers, is demonstrated. The PPA serves as interfacial modifiers and fire retardants for flammable PVA (film-forming agent) and GNPs (inexpensive conductive nanofillers) via hydrogen-bonding and π–π stacking. The resultant nanocomposite exhibits a high flexibility, high tensile strength of 259 MPa, and an ultrahigh in-plane thermal conductivity of 82.4 W m-1 K-1, making it effectively cool smartphone and high-power light emitting diode modules, outperforming commercial tinfoil counterparts. Moreover, the as-designed nanocomposites are intrinsically fire-retardant and can shield electromagnetic interference. This work offers a general strategy for mass production of thermally conductive nanocomposites holding great promise as thermal management materials in electronic, military, and aerospace fields