226 research outputs found
STUDY ON THE STRUCTURAL CHANGE OF LIGNIN DURING AUTO-CATALYZED ETHANOL-WATER PULPING OF ASPEN BY 1H-NMR
This study concerns the structural change of lignin during auto-catalyzed ethanol-water pulping of aspen by 1H-NMR. The results showed that the linkages of alkyl-aryl ether of lignin, such as the α-ether linkages (α-O-4) and the β-ether linkages (β-O-4), were broken and the alkyl part formed carbenium at the Cα and Cβ of the aliphatic branch. Meanwhile, the aryl part of ether accepted one H+ and formed phenol. Because of the electronegative effect originating from the electron cloud of phenyl, partial carbenium of Cβ was rearranged. Due to its ether or hydroxyl linkage, rearranging to Cβ, the Cα was changed into carbenium and formed a new β-O-4 alkyl-aryl ether. The β-O-4 alkyl-aryl ether was not stable and broken further. So the large molecule of lignin was disintegrated into a smaller one and dissolved into ethanol. Finally, the α+ carbenium reformed α-O-4 linkages of ether with phenol
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Depending on how much information an adversary can access to, adversarial
attacks can be classified as white-box attack and black-box attack. For
white-box attack, optimization-based attack algorithms such as projected
gradient descent (PGD) can achieve relatively high attack success rates within
moderate iterates. However, they tend to generate adversarial examples near or
upon the boundary of the perturbation set, resulting in large distortion.
Furthermore, their corresponding black-box attack algorithms also suffer from
high query complexities, thereby limiting their practical usefulness. In this
paper, we focus on the problem of developing efficient and effective
optimization-based adversarial attack algorithms. In particular, we propose a
novel adversarial attack framework for both white-box and black-box settings
based on a variant of Frank-Wolfe algorithm. We show in theory that the
proposed attack algorithms are efficient with an convergence
rate. The empirical results of attacking the ImageNet and MNIST datasets also
verify the efficiency and effectiveness of the proposed algorithms. More
specifically, our proposed algorithms attain the best attack performances in
both white-box and black-box attacks among all baselines, and are more time and
query efficient than the state-of-the-art.Comment: 25 pages, 1 figure, 7 table
RA2: predicting simulation execution time for cloud-based design space explorations
Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running large-scale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds
UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding
In the era of Large Language Models (LLMs), tremendous strides have been made
in the field of multimodal understanding. However, existing advanced algorithms
are limited to effectively utilizing the immense representation capabilities
and rich world knowledge inherent to these large pre-trained models, and the
beneficial connections among tasks within the context of text-rich scenarios
have not been sufficiently explored. In this work, we introduce UniDoc, a novel
multimodal model equipped with text detection and recognition capabilities,
which are deficient in existing approaches. Moreover, UniDoc capitalizes on the
beneficial interactions among tasks to enhance the performance of each
individual task. To implement UniDoc, we perform unified multimodal instruct
tuning on the contributed large-scale instruction following datasets.
Quantitative and qualitative experimental results show that UniDoc sets
state-of-the-art scores across multiple challenging benchmarks. To the best of
our knowledge, this is the first large multimodal model capable of simultaneous
text detection, recognition, spotting, and understanding
- …