413 research outputs found
How Do Heterogeneous Social Interactions Affect the Peer Effect in Rural-Urban Migration?: Empirical Evidence from China
In this paper, we use the "2002 Chinese Household Income Project Survey" (CHIP2002) data to examine how heterogeneous social interactions affect the peer effect in the rural-urban migration decision in China. We find that the peer effect, measured by the village migration ratio, significantly increases the individual probability of outward migration. We also find that the magnitude of the peer effect is nonlinear, depending on the strength and type of social interactions with other villagers. Interactions in information sharing can increase the magnitude of the peer effect, while interactions in mutual help in labor activities, such as help in housing construction, nursing and farm work in busy seasons, will impede the positive role of the peer effect. Being aware of the simultaneity bias caused by the two-way causality between social interaction strengths and migration, we utilize "historical family political identity in land reform" as an instrumental variable for social interactions. However, the hypothesis that probit and instrumental-variable probit results are not significantly different is not rejected. The existence of a nonlinear peer effect has rich policy implications. For policy makers to encourage rural-urban migration, it is feasible to increase education investment in rural areas or increase information sharing among rural residents. However, only an increase in the constant term in the regression, i.e., a "big push" in improving institutions for migration, can help rural Chinese residents escape the low equilibrium in migration.labor migration, urbanization, peer effect, social interaction, social multiplier
Weak Compactness Criterion in with an Existence Theorem of Minimizers
Nelson Dunford and Billy James Pettis [{\em Trans. Amer. Math. Soc.}, 47
(1940), pp. 323--392] proved that relatively weakly compact subsets of
coincide with equi-integrable families. We expand it to the case of
- the non-reflexive Sobolev space - by a tailor-made isometric operator. Herein
we extend an existence theorem of minimizers from reflexive Sobolev spaces to
non-reflexive ones
9-Isopropenyl-4-methyl-2H-thieno[2,3-h]chromen-2-one
The title compound, C15H12O2S, features three fused rings with a dihedral angle of 79.6 (2)° between the isopropenyl group and the thiophene ring. In the crystal, molecules are connected into a supramolecular helical chain via C—H⋯O contacts
Alteration-free and Model-agnostic Origin Attribution of Generated Images
Recently, there has been a growing attention in image generation models.
However, concerns have emerged regarding potential misuse and intellectual
property (IP) infringement associated with these models. Therefore, it is
necessary to analyze the origin of images by inferring if a specific image was
generated by a particular model, i.e., origin attribution. Existing methods are
limited in their applicability to specific types of generative models and
require additional steps during training or generation. This restricts their
use with pre-trained models that lack these specific operations and may
compromise the quality of image generation. To overcome this problem, we first
develop an alteration-free and model-agnostic origin attribution method via
input reverse-engineering on image generation models, i.e., inverting the input
of a particular model for a specific image. Given a particular model, we first
analyze the differences in the hardness of reverse-engineering tasks for the
generated images of the given model and other images. Based on our analysis, we
propose a method that utilizes the reconstruction loss of reverse-engineering
to infer the origin. Our proposed method effectively distinguishes between
generated images from a specific generative model and other images, including
those generated by different models and real images
How to Detect Unauthorized Data Usages in Text-to-image Diffusion Models
Recent text-to-image diffusion models have shown surprising performance in
generating high-quality images. However, concerns have arisen regarding the
unauthorized usage of data during the training process. One example is when a
model trainer collects a set of images created by a particular artist and
attempts to train a model capable of generating similar images without
obtaining permission from the artist. To address this issue, it becomes crucial
to detect unauthorized data usage. In this paper, we propose a method for
detecting such unauthorized data usage by planting injected memorization into
the text-to-image diffusion models trained on the protected dataset.
Specifically, we modify the protected image dataset by adding unique contents
on the images such as stealthy image wrapping functions that are imperceptible
to human vision but can be captured and memorized by diffusion models. By
analyzing whether the model has memorization for the injected content (i.e.,
whether the generated images are processed by the chosen post-processing
function), we can detect models that had illegally utilized the unauthorized
data. Our experiments conducted on Stable Diffusion and LoRA model demonstrate
the effectiveness of the proposed method in detecting unauthorized data usages
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons
With Deep Neural Network (DNN) being integrated into a growing number of
critical systems with far-reaching impacts on society, there are increasing
concerns on their ethical performance, such as fairness. Unfortunately, model
fairness and accuracy in many cases are contradictory goals to optimize. To
solve this issue, there has been a number of work trying to improve model
fairness by using an adversarial game in model level. This approach introduces
an adversary that evaluates the fairness of a model besides its prediction
accuracy on the main task, and performs joint-optimization to achieve a
balanced result. In this paper, we noticed that when performing backward
propagation based training, such contradictory phenomenon has shown on
individual neuron level. Based on this observation, we propose FairNeuron, a
DNN model automatic repairing tool, to mitigate fairness concerns and balance
the accuracy-fairness trade-off without introducing another model. It works on
detecting neurons with contradictory optimization directions from accuracy and
fairness training goals, and achieving a trade-off by selective dropout.
Comparing with state-of-the-art methods, our approach is lightweight, making it
scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron
can effectively improve all models' fairness while maintaining a stable
utility
CILIATE: Towards Fairer Class-based Incremental Learning by Dataset and Training Refinement
Due to the model aging problem, Deep Neural Networks (DNNs) need updates to
adjust them to new data distributions. The common practice leverages
incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that
updates output labels, to update the model with new data and a limited number
of old data. This avoids heavyweight training (from scratch) using conventional
methods and saves storage space by reducing the number of old data to store.
But it also leads to poor performance in fairness. In this paper, we show that
CIL suffers both dataset and algorithm bias problems, and existing solutions
can only partially solve the problem. We propose a novel framework, CILIATE,
that fixes both dataset and algorithm bias in CIL. It features a novel
differential analysis guided dataset and training refinement process that
identifies unique and important samples overlooked by existing CIL and enforces
the model to learn from them. Through this process, CILIATE improves the
fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art
methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three
popular datasets and widely used ResNet models
Mesh-MLP: An all-MLP Architecture for Mesh Classification and Semantic Segmentation
With the rapid development of geometric deep learning techniques, many
mesh-based convolutional operators have been proposed to bridge irregular mesh
structures and popular backbone networks. In this paper, we show that while
convolutions are helpful, a simple architecture based exclusively on
multi-layer perceptrons (MLPs) is competent enough to deal with mesh
classification and semantic segmentation. Our new network architecture, named
Mesh-MLP, takes mesh vertices equipped with the heat kernel signature (HKS) and
dihedral angles as the input, replaces the convolution module of a ResNet with
Multi-layer Perceptron (MLP), and utilizes layer normalization (LN) to perform
the normalization of the layers. The all-MLP architecture operates in an
end-to-end fashion and does not include a pooling module. Extensive
experimental results on the mesh classification/segmentation tasks validate the
effectiveness of the all-MLP architecture.Comment: 8 pages, 6 figure
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