Tailoring Non-Compact Spin Chains

We study three-point correlation functions of local operators in planar $\mathcal{N}=4$ SYM at weak coupling using integrability. We consider correlation functions involving two scalar BPS operators and an operator with spin, in the so called SL(2) sector. At tree level we derive the corresponding structure constant for any such operator. We also conjecture its one loop correction. To check our proposals we analyze the conformal partial wave decomposition of known four-point correlation functions of BPS operators. In perturbation theory, we extract from this decomposition sums of structure constants involving all primaries of a given spin and twist. On the other hand, in our integrable setup these sum rules are computed by summing over all solutions to the Bethe equations. A perfect match is found between the two approaches.Comment: 2 figure

Three Essays on the Economics of Education

This dissertation consists of three essays related to the field of economics of education. In chapter 2, using data from middle school students in China and exploiting the random assignment of students to classrooms within schools, I investigate the causal effect of peer groups on students’ scholastic achievement. I find that female student proportion in the classroom positively affects male students’ test scores and that the education level of peers’ parents improves the academic achievement of both male and female students. Students with highly-educated parents benefit more from classmates with higher parental education compared to students with relatively lower parental education. Investigation of mechanisms reveals that the peer effects can in part be explained by peers’ academic quality, classroom atmosphere, and behaviors of students’ classroom friends. Chapter 3 examines the causal impact of female education on fertility utilizing the Universal Primary Education (UPE) program in Malawi as a source of exogenous variation in schooling attainment. The results show that the UPE policy improved rural women’s educational attainment by 0.42 years and that an additional year of female education decreased women’s number of children ever born and living children by 0.39 and 0.33, respectively. An analysis of potential mechanisms suggests that the decreased fertility rates are driven by the reduction in women’s desired number of children, postponement of marriage and motherhood. There is no evidence that increased female education affects the characteristics of husband, women’s labor force participation, or modern contraceptive use. In chapter 4, I investigate the causal effect of maternal education on child mortality in Indonesia by using the one-time change in the length of the 1978 school year as a source of exogenous variation in education. The results show that the education reform increases women’s educational attainment by 0.82 years and an additional year of female education leads to a decrease in neonatal mortality by 0.8 percentage points. Mechanisms analysis suggests that higher female education postpones the timing of marriage and first birth, leads to higher quality of spouse and higher household wealth, and increases the use of prenatal health care and mass media

Intertwined charge and pair density orders in a monolayer high-Tc iron-based superconductor

Symmetry-breaking electronic phase in unconventional high-temperature (high-Tc) superconductors is a fascinating issue in condensed-matter physics, among which the most attractive phases are charge density wave (CDW) phase with four unit-cell periodicity in cuprates and nematic phase breaking the C4 rotational symmetry in iron-based superconductors (FeSCs). Recently, pair density wave (PDW), an exotic superconducting phase with non-zero momentum Cooper pairs, has been observed in high-Tc cuprates and the monolayer FeSC. However, the interplay between the CDW, PDW and nematic phase remains to be explored. Here, using scanning tunneling microscopy/spectroscopy, we detected commensurate CDW and CDW-induced PDW orders with the same period of lambda = 4aFe (aFe is the distance between neighboring Fe atoms) in a monolayer high-Tc Fe(Te,Se) film grown on SrTiO3(001) substrate. Further analyses demonstrate the observed CDW is a smectic order, which breaks both translation and C4 rotational symmetry. Moreover, the smecticity of the CDW order is strongest near the superconducting gap but weakens near defects and in an applied magnetic field, indicating the interplay between the smectic CDW and PDW orders. Our works provide a new platform to study the intertwined orders and their interactions in high-Tc superconductors

ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration

Image restoration aims to reconstruct degraded images, e.g., denoising or deblurring. Existing works focus on designing task-specific methods and there are inadequate attempts at universal methods. However, simply unifying multiple tasks into one universal architecture suffers from uncontrollable and undesired predictions. To address those issues, we explore prompt learning in universal architectures for image restoration tasks. In this paper, we present Degradation-aware Visual Prompts, which encode various types of image degradation, e.g., noise and blur, into unified visual prompts. These degradation-aware prompts provide control over image processing and allow weighted combinations for customized image restoration. We then leverage degradation-aware visual prompts to establish a controllable and universal model for image restoration, called ProRes, which is applicable to an extensive range of image restoration tasks. ProRes leverages the vanilla Vision Transformer (ViT) without any task-specific designs. Furthermore, the pre-trained ProRes can easily adapt to new tasks through efficient prompt tuning with only a few images. Without bells and whistles, ProRes achieves competitive performance compared to task-specific methods and experiments can demonstrate its ability for controllable restoration and adaptation for new tasks. The code and models will be released in \url{https://github.com/leonmakise/ProRes}

Discovery of a pair density wave state in a monolayer high-Tc iron-based superconductor

The pair density wave (PDW) is an extraordinary superconducting state where Cooper pairs carry nonzero momentum. It can emerge when the full condensation of zero momentum Cooper pairs is frustrated. Evidence for the existence of intrinsic PDW order in high-temperature (high-Tc) cuprate superconductors and kagome superconductors has emerged recently. However, the PDW order in iron-based high-Tc superconductors has not been observed experimentally. Here, using scanning tunneling microscopy/spectroscopy, we report the discovery of the PDW state in monolayer iron-based high-Tc Fe(Te,Se) films grown on SrTiO3(001) substrates. The PDW state with a period of {\lambda}~3.6a_Fe (a_Fe is the distance between neighboring Fe atoms) is observed at the domain walls by the spatial electronic modulations of the local density of states, superconducting gap, and the {\pi}-phase shift boundaries of the PDW around the dislocations of the intertwined charge density wave order. The discovery of the PDW state in the monolayer Fe(Te,Se) film provides a low-dimensional platform to study the interplay between the correlated electronic states and unconventional Cooper pairing in high-Tc superconductors

Symphonize 3D Semantic Scene Completion with Contextual Instance Queries

3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal task for autonomous driving, as it involves predicting per-voxel occupancy within a 3D scene from partial LiDAR or image inputs. Existing methods primarily focus on the voxel-wise feature aggregation, while neglecting the instance-centric semantics and broader context. In this paper, we present a novel paradigm termed Symphonies (Scene-from-Insts) for SSC, which completes the scene volume from a sparse set of instance queries derived from the input with context awareness. By incorporating the queries as the instance feature representations within the scene, Symphonies dynamically encodes the instance-centric semantics to interact with the image and volume features while avoiding the dense voxel-wise modeling. Simultaneously, it orchestrates a more comprehensive understanding of the scenario by capturing context throughout the entire scene, contributing to alleviating the geometric ambiguity derived from occlusion and perspective errors. Symphonies achieves a state-of-the-art result of 13.02 mIoU on the challenging SemanticKITTI dataset, outperforming existing methods and showcasing the promising advancements of the paradigm. The code is available at \url{https://github.com/hustvl/Symphonies}.Comment: Technical report. Code and models at: https://github.com/hustvl/Symphonie