Lay Rationalism and Inconsistency between Predicted Experience and Decision

Decision-makers are sometimes depicted as impulsive and overly influenced by ‘hot’, affective factors. The present research suggests that decision-makers may be too ‘cold’ and overly focus on rationalistic attributes, such as economic values, quantitative specifications, and functions. In support of this proposition, we find a systematic inconsistency between predicted experience and decision. That is, people are more likely to favor a rationalistically-superior option when they make a decision than when they predict experience. We discuss how this work contributes to research on predicted and decision utilities; we also discuss when decision-makers overweight hot factors and when they overweight cold factors. Copyright © 2003 John Wiley & Sons, Ltd

Observation of spin-tensor induced topological phase transitions of triply degenerate points with a trapped ion

Triply degenerate points (TDPs), which correspond to new types of topological semimetals, can support novel quasiparticles possessing effective integer spins while preserving Fermi statistics. Here by mapping the momentum space to the parameter space of a three-level system in a trapped ion, we experimentally explore the transitions between different types of TDPs driven by spin-tensor--momentum couplings. We observe the phase transitions between TDPs with different topological charges by measuring the Berry flux on a loop surrounding the gap-closing lines, and the jump of the Berry flux gives the jump of the topological charge (up to a $2\pi$ factor) across the transitions. For the Berry flux measurement, we employ a new method by examining the geometric rotations of both spin vectors and tensors, which lead to a generalized solid angle equal to the Berry flux. The controllability of multi-level ion offers a versatile platform to study high-spin physics and our work paves the way to explore novel topological phenomena therein.Comment: 9 pages, 10 figure

SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding

Temporal Interaction Graphs (TIGs) are widely employed to model intricate real-world systems such as financial systems and social networks. To capture the dynamism and interdependencies of nodes, existing TIG embedding models need to process edges sequentially and chronologically. However, this requirement prevents it from being processed in parallel and struggle to accommodate burgeoning data volumes to GPU. Consequently, many large-scale temporal interaction graphs are confined to CPU processing. Furthermore, a generalized GPU scaling and acceleration approach remains unavailable. To facilitate large-scale TIGs' implementation on GPUs for acceleration, we introduce a novel training approach namely Streaming Edge Partitioning and Parallel Acceleration for Temporal Interaction Graph Embedding (SPEED). The SPEED is comprised of a Streaming Edge Partitioning Component (SEP) which addresses space overhead issue by assigning fewer nodes to each GPU, and a Parallel Acceleration Component (PAC) which enables simultaneous training of different sub-graphs, addressing time overhead issue. Our method can achieve a good balance in computing resources, computing time, and downstream task performance. Empirical validation across 7 real-world datasets demonstrates the potential to expedite training speeds by a factor of up to 19.29x. Simultaneously, resource consumption of a single-GPU can be diminished by up to 69%, thus enabling the multiple GPU-based training and acceleration encompassing millions of nodes and billions of edges. Furthermore, our approach also maintains its competitiveness in downstream tasks.Comment: 13 pages, 8 figure

RDGSL: Dynamic Graph Representation Learning with Structure Learning

Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A cross-level investigation of the effects of leadership style of school principals on teachers' satisfaction and organizational citizenship behavior : the mediating role of trust-in-principal

This research presents a cross-level model of relationships between unit-level constructs (transformational leadership and paternalistic leadership climate) and individual-level constructs (trust, satisfaction with leader, and organizational citizenship behaviors). Hierarchical Linear Modeling (HLM) was used to test the model using a sample of 471 teachers working in 149 preliminary schools. Both Relationship-oriented and task-oriented transformational leadership climate were shown to be positively related to teachers' satisfaction with leader, but only relationship-oriented transformational leadership climate was found to significantly affect teacher OCBs. All three dimensions of paternalistic leadership climate were reported to significantly contribute to teachers' upward satisfaction but only benevolent and moral leadership was found to be significantly associated with teacher OCBs. All such relationships were found to be mediated by teachers' trust in leader. Both theoretical and practical implications are discussed. Key words: transformational leadership climate, paternalistic leadership climate, cross-level design, HL

Too-Much-Of-A-Good-Thing Effect of External Resource Investment—A Study on the Moderating Effect of Psychological Capital on the Contribution of Social Support to Work Engagement

Built on the job demands-resources model (JD-R) and self-determination theory, the present research proposed that the relationship between work resources (social support) and employees’ work engagement takes on an inverted U-shaped curve, and presents a model of the moderation of personal resources (psychological capital) on the relationship. The hypotheses were tested by hierarchical regression analysis and path analysis with 535 surveys collected in 19 enterprises. The findings demonstrated an inverted U-shaped curve relationship between enterprises’ social support and employees’ work engagement and further suggested that the predicting effect of social support on work engagement is influenced by employees’ psychological capital, that is to say, the transformation from social support to work engagement bears higher efficiency in employees with high psychological capital than in those with low psychological capital. However, psychological capital fails to display a moderating effect on the curve relationship between social support and work engagement. The present study, casting doubt on the assumption that enterprise supply must meet the needs of employees, argued that the effectiveness of enterprises’ resource support is influenced by the individual needs of employees

Preserving multi-level quantum coherence by dynamical decoupling

Quantum information processing with multi-level systems (qudits) provides additional features and applications than the two-level systems. However, qudits are more prone to dephasing and dynamical decoupling for qudits has never been experimentally demonstrated. Here, as a proof-of-principle demonstration, we experimentally apply dynamical decoupling to protect superpositions with three levels of a trapped $^9\rm{Be}^+$ ion from ambient noisy magnetic field, prolonging coherence by up to approximately an order of magnitude. Our demonstration, straightforwardly scalable to more levels, may open up a path toward long coherence quantum memory, metrology and information processing with qudits.Comment: 6 pages, 7 figure