Signatures of Bose-Einstein condensation in an optical lattice

We discuss typical experimental signatures for the Bose-Einstein condensation (BEC) of an ultracold Bose gas in an inhomogeneous optical lattice at finite temperature. Applying the Hartree-Fock-Bogoliubov-Popov formalism, we calculate quantities such as the momentum-space density distribution, visibility and peak width as the system is tuned through the superfluid to normal phase transition. Different from previous studies, we consider systems with fixed total particle number, which is of direct experimental relevance. We show that the onset of BEC is accompanied by sharp features in all these signatures, which can be probed via typical time-of-flight imaging techniques. In particular, we find a two-platform structure in the peak width across the phase transition. We show that the onset of condensation is related to the emergence of the higher platform, which can be used as an effective experimental signature.Comment: 5 pages, 3 figure

Joint Event Extraction via Structural Semantic Matching

Event Extraction (EE) is one of the essential tasks in information extraction, which aims to detect event mentions from text and find the corresponding argument roles. The EE task can be abstracted as a process of matching the semantic definitions and argument structures of event types with the target text. This paper encodes the semantic features of event types and makes structural matching with target text. Specifically, Semantic Type Embedding (STE) and Dynamic Structure Encoder (DSE) modules are proposed. Also, the Joint Structural Semantic Matching (JSSM) model is built to jointly perform event detection and argument extraction tasks through a bidirectional attention layer. The experimental results on the ACE2005 dataset indicate that our model achieves a significant performance improvemen

Granular Estimation of User Cognitive Workload Using Multi-Modal Physiological Sensors

Mental workload (MWL) is a crucial area of study due to its significant influence on task performance and potential for significant operator error. However, measuring MWL presents challenges, as it is a multi-dimensional construct. Previous research on MWL models has focused on differentiating between two to three levels. Nonetheless, tasks can vary widely in their complexity, and little is known about how subtle variations in task difficulty influence workload indicators. To address this, we conducted an experiment inducing MWL in up to 5 levels, hypothesizing that our multi-modal metrics would be able to distinguish between each MWL stage. We measured the induced workload using task performance, subjective assessment, and physiological metrics. Our simulated task was designed to induce diverse MWL degrees, including five different math and three different verbal tiers. Our findings indicate that all investigated metrics successfully differentiated between various MWL levels induced by different tiers of math problems. Notably, performance metrics emerged as the most effective assessment, being the only metric capable of distinguishing all the levels. Some limitations were observed in the granularity of subjective and physiological metrics. Specifically, the subjective overall mental workload couldn\u27t distinguish lower levels of workload, while all physiological metrics could detect a shift from lower to higher levels, but did not distinguish between workload tiers at the higher or lower ends of the scale (e.g., between the easy and the easy-medium tiers). Despite these limitations, each pair of levels was effectively differentiated by one or more metrics. This suggests a promising avenue for future research, exploring the integration or combination of multiple metrics. The findings suggest that subtle differences in workload levels may be distinguishable using combinations of subjective and physiological metrics

Stability of scientific big data sharing mechanism based on two-way principal-agent

In the era of big data, facing the data-intensive scientific paradigm shift and the explosion of scientific big data, there is an urgent need for alliance cooperation between heterogeneous research groups to actively open and share scientific big data to support China's economic development, technological innovation and national security. Therefore, the study of scientific big data sharing mechanism has very important practical significance. We think science big data sharing is an ecosystem that is constantly evolving to higher-order ecological evolution. Based on the dual perspectives of psychological contract and contractual contract, the scientific big data sharing strategy evolution mechanism and sharing strategy incentive mechanism are explored.The research finds that the cooperation of scientific research groups is bound by psychological contract and contractual contract; stochastic evolutionary game has stronger explanatory power for sharing strategy evolution, complementarity is positive indicator, random interference and moral risk are negative indicators; two-way principal agent can describe Alliance members are mutually entrusted, and the shared strategy incentive contract consists of fixed wages and incentive wages, which are proportional to risk

Neural, Muscular, and Perceptual responses with shoulder exoskeleton use over Days

Passive shoulder exoskeletons have been widely introduced in the industry to aid upper extremity movements during repetitive overhead work. As an ergonomic intervention, it is important to understand how users adapt to these devices over time and if these induce external stress while working. The study evaluated the use of an exoskeleton over a period of 3 days by assessing the neural, physiological, and perceptual responses of twenty-four participants by comparing a physical task against the same task with an additional cognitive workload. Over days adaptation to task irrespective of task and group were identified. Electromyography (EMG) analysis of shoulder and back muscles reveals lower muscle activity in the exoskeleton group irrespective of task. Functional connectivity analysis using functional near infrared spectroscopy (fNIRS) reveals that exoskeletons benefit users by reducing task demands in the motor planning and execution regions. Sex-based differences were also identified in these neuromuscular assessments.Comment: Poster Abstract, Submitted to Neuroergonomics Conference and NYC Neuromodulation Conferences, July 28 to 31, 202

The Initial-Final Mass Relation among White Dwarfs in Wide Binaries

We present the initial-final mass relation derived from 10 white dwarfs in wide binaries that consist of a main sequence star and a white dwarf. The temperature and gravity of each white dwarf was measured by fitting theoretical model atmospheres to the observed spectrum using a $\chi^{2}$ fitting algorithm. The cooling time and mass was obtained using theoretical cooling tracks. The total age of each binary was estimated from the chromospheric activity of its main sequence component to an uncertainty of about 0.17 dex in log \textit{t} The difference between the total age and white dwarf cooling time is taken as the main sequence lifetime of each white dwarf. The initial mass of each white dwarf was then determined using stellar evolution tracks with a corresponding metallicity derived from spectra of their main sequence companions, thus yielding the initial-final mass relation. Most of the initial masses of the white dwarf components are between 1 - 2 M$_{\odot}$. Our results suggest a correlation between the metallicity of a white dwarf's progenitor and the amount of post-main-sequence mass loss it experiences - at least among progenitors with masses in the range of 1 - 2 M$_{\odot}$. A comparison of our observations to theoretical models suggests that low mass stars preferentially lose mass on the red giant branch.Comment: 28 pages, 8 figures, accepted for publication in Ap