Psychological contract’s effect on job mobility: Evidence from Chinese construction worker

The subject of this study is that the psychological contract (PC) approaches to job mobility within the construction industry with special reference to migrant construction workers in China. Using a semi-structured interview to elicit a full range of the PC’s con- tent of construction worker, we unravel the mechanism of such contract to influence the informal job mobility of workers through the lens of the evolutionary game framework. The results demonstrate that, in the case of fulfilling PC, the informal job mobility of workers is under control, and both workers and employers benefit from this situation. This study deepens the understanding of the PC’s effect on the job mobility of construction workers in China during the course of economic change. The theoretical and practical implications are discusse

DPF: Learning Dense Prediction Fields with Weak Supervision

Nowadays, many visual scene understanding problems are addressed by dense prediction networks. But pixel-wise dense annotations are very expensive (e.g., for scene parsing) or impossible (e.g., for intrinsic image decomposition), motivating us to leverage cheap point-level weak supervision. However, existing pointly-supervised methods still use the same architecture designed for full supervision. In stark contrast to them, we propose a new paradigm that makes predictions for point coordinate queries, as inspired by the recent success of implicit representations, like distance or radiance fields. As such, the method is named as dense prediction fields (DPFs). DPFs generate expressive intermediate features for continuous sub-pixel locations, thus allowing outputs of an arbitrary resolution. DPFs are naturally compatible with point-level supervision. We showcase the effectiveness of DPFs using two substantially different tasks: high-level semantic parsing and low-level intrinsic image decomposition. In these two cases, supervision comes in the form of single-point semantic category and two-point relative reflectance, respectively. As benchmarked by three large-scale public datasets PASCALContext, ADE20K and IIW, DPFs set new state-of-the-art performance on all of them with significant margins. Code can be accessed at https://github.com/cxx226/DPF

Overexpression of AtBMI1C, a Polycomb Group Protein Gene, Accelerates Flowering in Arabidopsis

Polycomb group protein (PcG)-mediated gene silencing is emerging as an essential developmental regulatory mechanism in eukaryotic organisms. PcGs inactivate or maintain the silenced state of their target chromatin by forming complexes, including Polycomb Repressive Complex 1 (PRC1) and 2 (PRC2). Three PRC2 complexes have been identified and characterized in Arabidopsis; of these, the EMF and VRN complexes suppress flowering by catalyzing the trimethylation of lysine 27 on histone H3 of FLOWER LOCUS T (FT) and FLOWER LOCUS C (FLC). However, little is known about the role of PRC1 in regulating the floral transition, although AtRING1A, AtRING1B, AtBMI1A, and AtBMI1B are believed to regulate shoot apical meristem and embryonic development as components of PRC1. Moreover, among the five RING finger PcGs in the Arabidopsis genome, four have been characterized. Here, we report that the fifth, AtBMI1C, is a novel, ubiquitously expressed nuclear PcG protein and part of PRC1, which is evolutionarily conserved with Psc and BMI1. Overexpression of AtBMI1C caused increased H2A monoubiquitination and flowering defects in Arabidopsis. Both the suppression of FLC and activation of FT were observed in AtBMI1C-overexpressing lines, resulting in early flowering. No change in the H3K27me3 level in FLC chromatin was detected in an AtBMI1C-overexpressing line. Our results suggest that AtBMI1C participates in flowering time control by regulating the expression of FLC; moreover, the repression of FLC by AtBMI1C is not due to the activity of PRC2. Instead, it is likely the result of PRC1 activity, into which AtBMI1C is integrated

Fully packed quantum loop model on the square lattice: phase diagram and application for Rydberg atoms

The quantum dimer and loop models attract great attentions, partially because the fundamental importance in the novel phases and phase transitions emerging in these prototypical constrained systems; and partially due to their intimate relevance towards the on-going experiments on Rydberg atom arrays in which the blockade mechanism naturally enforces the local constraint. Here we show, by means of the sweeping cluster quantum Monte Carlo method, the complete ground state phase diagram of the fully packed quantum loop model on the square lattice. We find between the lattice nematic (LN) phase with strong dimer attraction and the staggered phase (SP) with strong dimer repulsion, there emerges a resonating plaquette (RP) phase with off-diagonal translational symmetry breaking. Such a novel phase is separated from the LN via a first order transition and from the SP by the famous Rokhsar-Kivelson point. Our renormalization group analysis reveals the different flow directions, fully consistent with the order parameter histogram in Monte Carlo simulations. The realization and implication of our phase diagram in Rydberg experiments are proposed.Comment: 6+2 pages, 5+2 figure

Effects of Rock Fragments on the Soil Physicochemical Properties and Vegetation on the Northeastern Tibetan Plateau

Stony soils are very widely distributed and contain abundant rock fragments (>2 mm), which impose major effects on soil properties and plant growth. However, the role of rock fragments is still often neglected, which can lead to an inadequate understanding of the interaction between plants and soil. Undisturbed soil columns were collected from three alpine grasslands on the Qilian Mountain, and the X-ray computed tomography method was applied to investigate the characteristics of rock fragments. The results showed there was significant difference in number density, volumetric content and surface area density of rock fragment among the three grasslands, and followed the order of alpine meadow > alpine steppe > alpine desert steppe. In addition, the soil organic carbon, total nitrogen, total phosphorus, available phosphorus, N-NH4+, and N-NO3− contents in fine earth all increased with increasing number density, volumetric content and surface area density but to different degrees. Furthermore, positive correlations were observed between the rock shape factor and belowground biomass (R2 = 0.531, p < 0.05), between the rock volumetric content and aboveground biomass (R2 = 0.527, p < 0.05), and between number density and Simpson’s index (R2 = 0.875, p < 0.05). Our findings suggest that within a certain range, the increase in rock fragment content is conducive to soil nutrient accumulation and soil water storage and circulation and changes plant features, which contributes to the growth of plants. In addition, rock fragments should be given more consideration when investigating the relationships between soil and vegetation and their response to climate change in future studies

Classical model emerges in quantum entanglement: Quantum Monte Carlo study for an Ising-Heisenberg bilayer

By developing a cluster sampling of stochastic series expansion quantum Monte Carlo method, we investigate a spin-$1/2$ model on a bilayer square lattice with intra-layer ferromagnetic (FM) Ising coupling and inter-layer antiferromagnetic Heisenberg interaction. The continuous quantum phase transition which occurs at $g_c=3.045(2)$ between the FM Ising phase and the dimerized phase is studied via large scale simulations. From the analyzes of critical exponents we show that this phase transition belongs to the (2+1)-dimensional Ising universality class. Besides, the quantum entanglement is strong between the two layers, especially in dimerized phase. The effective Hamiltonian of single layer seems like a transverse field Ising model. However, we found the quantum entanglement Hamiltonian is a pure classical Ising model without any quantum fluctuations. Furthermore, we give a more general explanation about how a classical entanglement Hamiltonian emerges

Generative Retrieval with Semantic Tree-Structured Item Identifiers via Contrastive Learning

The retrieval phase is a vital component in recommendation systems, requiring the model to be effective and efficient. Recently, generative retrieval has become an emerging paradigm for document retrieval, showing notable performance. These methods enjoy merits like being end-to-end differentiable, suggesting their viability in recommendation. However, these methods fall short in efficiency and effectiveness for large-scale recommendations. To obtain efficiency and effectiveness, this paper introduces a generative retrieval framework, namely SEATER, which learns SEmAntic Tree-structured item identifiERs via contrastive learning. Specifically, we employ an encoder-decoder model to extract user interests from historical behaviors and retrieve candidates via tree-structured item identifiers. SEATER devises a balanced k-ary tree structure of item identifiers, allocating semantic space to each token individually. This strategy maintains semantic consistency within the same level, while distinct levels correlate to varying semantic granularities. This structure also maintains consistent and fast inference speed for all items. Considering the tree structure, SEATER learns identifier tokens' semantics, hierarchical relationships, and inter-token dependencies. To achieve this, we incorporate two contrastive learning tasks with the generation task to optimize both the model and identifiers. The infoNCE loss aligns the token embeddings based on their hierarchical positions. The triplet loss ranks similar identifiers in desired orders. In this way, SEATER achieves both efficiency and effectiveness. Extensive experiments on three public datasets and an industrial dataset have demonstrated that SEATER outperforms state-of-the-art models significantly.Comment: 8 main pages, 3 pages for appendi