84 research outputs found
Tailoring Non-Compact Spin Chains
We study three-point correlation functions of local operators in planar
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
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