126 research outputs found
Channel Pruning Guided by Classification Loss and Feature Importance
In this work, we propose a new layer-by-layer channel pruning method called
Channel Pruning guided by classification Loss and feature Importance (CPLI). In
contrast to the existing layer-by-layer channel pruning approaches that only
consider how to reconstruct the features from the next layer, our approach
additionally take the classification loss into account in the channel pruning
process. We also observe that some reconstructed features will be removed at
the next pruning stage. So it is unnecessary to reconstruct these features. To
this end, we propose a new strategy to suppress the influence of unimportant
features (i.e., the features will be removed at the next pruning stage). Our
comprehensive experiments on three benchmark datasets, i.e., CIFAR-10,
ImageNet, and UCF-101, demonstrate the effectiveness of our CPLI method.Comment: AAAI202
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding
Visually-rich Document Understanding (VrDU) has attracted much research
attention over the past years. Pre-trained models on a large number of document
images with transformer-based backbones have led to significant performance
gains in this field. The major challenge is how to fusion the different
modalities (text, layout, and image) of the documents in a unified model with
different pre-training tasks. This paper focuses on improving text-layout
interactions and proposes a novel multi-modal pre-training model, LayoutMask.
LayoutMask uses local 1D position, instead of global 1D position, as layout
input and has two pre-training objectives: (1) Masked Language Modeling:
predicting masked tokens with two novel masking strategies; (2) Masked Position
Modeling: predicting masked 2D positions to improve layout representation
learning. LayoutMask can enhance the interactions between text and layout
modalities in a unified model and produce adaptive and robust multi-modal
representations for downstream tasks. Experimental results show that our
proposed method can achieve state-of-the-art results on a wide variety of VrDU
problems, including form understanding, receipt understanding, and document
image classification.Comment: Accepted by ACL 2023 main conferenc
RobustMQ: Benchmarking Robustness of Quantized Models
Quantization has emerged as an essential technique for deploying deep neural
networks (DNNs) on devices with limited resources. However, quantized models
exhibit vulnerabilities when exposed to various noises in real-world
applications. Despite the importance of evaluating the impact of quantization
on robustness, existing research on this topic is limited and often disregards
established principles of robustness evaluation, resulting in incomplete and
inconclusive findings. To address this gap, we thoroughly evaluated the
robustness of quantized models against various noises (adversarial attacks,
natural corruptions, and systematic noises) on ImageNet. The comprehensive
evaluation results empirically provide valuable insights into the robustness of
quantized models in various scenarios, for example: (1) quantized models
exhibit higher adversarial robustness than their floating-point counterparts,
but are more vulnerable to natural corruptions and systematic noises; (2) in
general, increasing the quantization bit-width results in a decrease in
adversarial robustness, an increase in natural robustness, and an increase in
systematic robustness; (3) among corruption methods, \textit{impulse noise} and
\textit{glass blur} are the most harmful to quantized models, while
\textit{brightness} has the least impact; (4) among systematic noises, the
\textit{nearest neighbor interpolation} has the highest impact, while bilinear
interpolation, cubic interpolation, and area interpolation are the three least
harmful. Our research contributes to advancing the robust quantization of
models and their deployment in real-world scenarios.Comment: 15 pages, 7 figure
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Quantization of transformer language models faces significant challenges due
to the existence of detrimental outliers in activations. We observe that these
outliers are asymmetric and concentrated in specific channels. To address this
issue, we propose the Outlier Suppression+ framework. First, we introduce
channel-wise shifting and scaling operations to eliminate asymmetric
presentation and scale down problematic channels. We demonstrate that these
operations can be seamlessly migrated into subsequent modules while maintaining
equivalence. Second, we quantitatively analyze the optimal values for shifting
and scaling, taking into account both the asymmetric property and quantization
errors of weights in the next layer. Our lightweight framework can incur
minimal performance degradation under static and standard post-training
quantization settings. Comprehensive results across various tasks and models
reveal that our approach achieves near-floating-point performance on both small
models, such as BERT, and large language models (LLMs) including OPTs, BLOOM,
and BLOOMZ at 8-bit and 6-bit settings. Furthermore, we establish a new state
of the art for 4-bit BERT
SRA Inhibition Improves Antitumor Potency of Antigen-Targeted Chaperone Vaccine
We Have Previously Demonstrated that Scavenger Receptor a (SRA) Acts as an Immunosuppressive Regulator of Dendritic Cell (DC) Function in Activating Antitumor T Cells. Here We Investigate the Potential of Inhibiting SRA Activity to Enhance DC-Targeted Chaperone Vaccines Including One that Was Recently Evaluated in Melanoma Patients. We Show that Short Hairpin RNA-Mediated SRA Silencing Significantly Enhances the Immunogenicity of DCs that Have Captured Chaperone Vaccines Designed to Target Melanoma (I.e., Hsp110-Gp100) and Breast Cancer (I.e., Hsp110-HER/Neu-ICD). SRA Downregulation Results in Heightened Activation of Antigen-Specific T Cells and Increased CD8+ T Cell-Dependent Tumor Inhibition. Additionally, Small Interfering RNA (SiRNA) Complexed with the Biodegradable, Biocompatible Chitosan as a Carrier Can Efficiently Reduce SRA Expression on CD11c+ DCs in Vitro and in Vivo. Our Proof-Of-Concept Study Shows that Direct Administration of the Chitosan-SiRNA Complex to Mice Promotes Chaperone Vaccine-Elicited Cytotoxic T Lymphocyte (CTL) Response, Culminating in Improved Eradication of Experimental Melanoma Metastases. Targeting SRA with This Chitosan-SiRNA Regimen Combined with the Chaperone Vaccine Also Leads to Reprogramming of the Tumor Environment, Indicated by Elevation of the Cytokine Genes (I.e., Ifng, Il12) Known to Skew Th1-Like Cellular Immunity and Increased Tumor Infiltration by IFN-Γ+CD8+ CTLs as Well as IL-12+CD11c+ DCs. Given the Promising Antitumor Activity and Safety Profile of Chaperone Vaccine in Cancer Patients, Further Optimization of the Chitosan-SiRNA Formulation to Potentially Broaden the Immunotherapeutic Benefits of Chaperone Vaccine is Warranted
C-Coll: Introducing Error-bounded Lossy Compression into MPI Collectives
With the ever-increasing computing power of supercomputers and the growing
scale of scientific applications, the efficiency of MPI collective
communications turns out to be a critical bottleneck in large-scale distributed
and parallel processing. Large message size in MPI collectives is a
particularly big concern because it may significantly delay the overall
parallel performance. To address this issue, prior research simply applies the
off-the-shelf fix-rate lossy compressors in the MPI collectives, leading to
suboptimal performance, limited generalizability, and unbounded errors. In this
paper, we propose a novel solution, called C-Coll, which leverages
error-bounded lossy compression to significantly reduce the message size,
resulting in a substantial reduction in communication cost. The key
contributions are three-fold. (1) We develop two general, optimized
lossy-compression-based frameworks for both types of MPI collectives
(collective data movement as well as collective computation), based on their
particular characteristics. Our framework not only reduces communication cost
but also preserves data accuracy. (2) We customize an optimized version based
on SZx, an ultra-fast error-bounded lossy compressor, which can meet the
specific needs of collective communication. (3) We integrate C-Coll into
multiple collectives, such as MPI_Allreduce, MPI_Scatter, and MPI_Bcast, and
perform a comprehensive evaluation based on real-world scientific datasets.
Experiments show that our solution outperforms the original MPI collectives as
well as multiple baselines and related efforts by 3.5-9.7X.Comment: 12 pages, 15 figures, 5 tables, submitted to SC '2
The Invasive MED/Q \u3cem\u3eBemisia tabaci\u3c/em\u3e Genome: A Tale of Gene Loss and Gene Gain
Background: Sweetpotato whitefly, Bemisia tabaci MED/Q and MEAM1/B, are two economically important invasive species that cause considerable damages to agriculture crops through direct feeding and indirect vectoring of plant pathogens. Recently, a draft genome of B. tabaci MED/Q has been assembled. In this study, we focus on the genomic comparison between MED/Q and MEAM1/B, with a special interest in MED/Q’s genomic signatures that may contribute to the highly invasive nature of this emerging insect pest.
Results: The genomes of both species share similarity in syntenic blocks, but have significant divergence in the gene coding sequence. Expansion of cytochrome P450 monooxygenases and UDP glycosyltransferases in MED/Q and MEAM1/B genome is functionally validated for mediating insecticide resistance in MED/Q using in vivo RNAi. The amino acid biosynthesis pathways in MED/Q genome are partitioned among the host and endosymbiont genomes in a manner distinct from other hemipterans. Evidence of horizontal gene transfer to the host genome may explain their obligate relationship. Putative loss-of-function in the immune deficiency-signaling pathway due to the gene loss is a shared ancestral trait among hemipteran insects.
Conclusions: The expansion of detoxification genes families, such as P450s, may contribute to the development of insecticide resistance traits and a broad host range in MED/Q and MEAM1/B, and facilitate species’ invasions into intensively managed cropping systems. Numerical and compositional changes in multiple gene families (gene loss and gene gain) in the MED/Q genome sets a foundation for future hypothesis testing that will advance our understanding of adaptation, viral transmission, symbiosis, and plant-insect-pathogen tritrophic interactions
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