6,449 research outputs found
Stabilization of Cr(III) wastes by C3S and C3S hydrated matrix : comparison of two incorporation methods
In the present study, the influence of Cr(III) on the properties of C3S and its stabilization in C3S hydrates was investigated by either direct incorporation as Cr2O3 during C3S preparation or introduced as nitrate salt during hydration. Levels of Cr used were from 0.1 to 3.0 wt% of C3S. The effect of Cr on the polymorph and hydration of C3S and its immobilization in the hydrates was detected by means of DTA/TG, XRD, isothermal calorimeter and ICP-AES, etc. When doped during sintering process, Cr caused a C3S polymorph transformation from T1 to T2 and led a decomposition of C3S into C2S and CaO resulting in high f-CaO content. Cr doping showed an obvious promotion effect on the hydration properties. The promotion effect decreased when the Cr addition increased to 3.0 wt%. When Cr was added as nitrate salt, Cr showed a retardation effect on the hydration of C3S due to the formation of Ca2Cr(OH)7 center dot 3H(2)O, which resulted in a high degree of Cr stabilization
MMF3: Neural Code Summarization Based on Multi-Modal Fine-Grained Feature Fusion
Background: Code summarization automatically generates the corresponding
natural language descriptions according to the input code. Comprehensiveness of
code representation is critical to code summarization task. However, most
existing approaches typically use coarse-grained fusion methods to integrate
multi-modal features. They generally represent different modalities of a piece
of code, such as an Abstract Syntax Tree (AST) and a token sequence, as two
embeddings and then fuse the two ones at the AST/code levels. Such a coarse
integration makes it difficult to learn the correlations between fine-grained
code elements across modalities effectively. Aims: This study intends to
improve the model's prediction performance for high-quality code summarization
by accurately aligning and fully fusing semantic and syntactic structure
information of source code at node/token levels. Method: This paper proposes a
Multi-Modal Fine-grained Feature Fusion approach (MMF3) for neural code
summarization. We introduce a novel fine-grained fusion method, which allows
fine-grained fusion of multiple code modalities at the token and node levels.
Specifically, we use this method to fuse information from both token and AST
modalities and apply the fused features to code summarization. Results: We
conduct experiments on one Java and one Python datasets, and evaluate generated
summaries using four metrics. The results show that: 1) the performance of our
model outperforms the current state-of-the-art models, and 2) the ablation
experiments show that our proposed fine-grained fusion method can effectively
improve the accuracy of generated summaries. Conclusion: MMF3 can mine the
relationships between crossmodal elements and perform accurate fine-grained
element-level alignment fusion accordingly. As a result, more clues can be
provided to improve the accuracy of the generated code summaries.Comment: 12 pages, 5 figure
Reconstructive Neuron Pruning for Backdoor Defense
Deep neural networks (DNNs) have been found to be vulnerable to backdoor
attacks, raising security concerns about their deployment in mission-critical
applications. While existing defense methods have demonstrated promising
results, it is still not clear how to effectively remove backdoor-associated
neurons in backdoored DNNs. In this paper, we propose a novel defense called
\emph{Reconstructive Neuron Pruning} (RNP) to expose and prune backdoor neurons
via an unlearning and then recovering process. Specifically, RNP first unlearns
the neurons by maximizing the model's error on a small subset of clean samples
and then recovers the neurons by minimizing the model's error on the same data.
In RNP, unlearning is operated at the neuron level while recovering is operated
at the filter level, forming an asymmetric reconstructive learning procedure.
We show that such an asymmetric process on only a few clean samples can
effectively expose and prune the backdoor neurons implanted by a wide range of
attacks, achieving a new state-of-the-art defense performance. Moreover, the
unlearned model at the intermediate step of our RNP can be directly used to
improve other backdoor defense tasks including backdoor removal, trigger
recovery, backdoor label detection, and backdoor sample detection. Code is
available at \url{https://github.com/bboylyg/RNP}.Comment: Accepted by ICML2
Large-scale Google Street View Images for Urban Change Detection
Urbanization has entered a new phase characterized by urban changes occurring at a micro-scale and “under the roof”, as opposed to external modifications. These changes, known as urban retrofitting, involve the incorporation of novel technologies or features into pre-existing systems to promote sustainability. Given the limitations of remote sensing images in identifying such urban changes, novel tools need to be developed for detecting urban retrofitting. In this study, we first build a pipeline to collect large-scale time-series urban street view images from Google Street View in Mecklenburg County, North Carolina. And we examine the feasibility of utilizing the acquired dataset to detect diverse forms of urban retrofitting, including re-building, re-greening and re-capital
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
Federated Learning over a Wireless Network: Distributed User Selection through Random Access
User selection has become crucial for decreasing the communication costs of
federated learning (FL) over wireless networks. However, centralized user
selection causes additional system complexity. This study proposes a network
intrinsic approach of distributed user selection that leverages the radio
resource competition mechanism in random access. Taking the carrier sensing
multiple access (CSMA) mechanism as an example of random access, we manipulate
the contention window (CW) size to prioritize certain users for obtaining radio
resources in each round of training. Training data bias is used as a target
scenario for FL with user selection. Prioritization is based on the distance
between the newly trained local model and the global model of the previous
round. To avoid excessive contribution by certain users, a counting mechanism
is used to ensure fairness. Simulations with various datasets demonstrate that
this method can rapidly achieve convergence similar to that of the centralized
user selection approach
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
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