109 research outputs found
Algorithm and Architecture for Path Metric Aided Bit-Flipping Decoding of Polar Codes
Polar codes attract more and more attention of researchers in recent years,
since its capacity achieving property. However, their error-correction
performance under successive cancellation (SC) decoding is inferior to other
modern channel codes at short or moderate blocklengths. SC-Flip (SCF) decoding
algorithm shows higher performance than SC decoding by identifying possibly
erroneous decisions made in initial SC decoding and flipping them in the
sequential decoding attempts. However, it performs not well when there are more
than one erroneous decisions in a codeword. In this paper, we propose a path
metric aided bit-flipping decoding algorithm to identify and correct more
errors efficiently. In this algorithm, the bit-flipping list is generated based
on both log likelihood ratio (LLR) based path metric and bit-flipping metric.
The path metric is used to verify the effectiveness of bit-flipping. In order
to reduce the decoding latency and computational complexity, its corresponding
pipeline architecture is designed. By applying these decoding algorithms and
pipeline architecture, an improvement on error-correction performance can be
got up to 0.25dB compared with SCF decoding at the frame error rate of
, with low average decoding latency.Comment: 6 pages, 6 figures, IEEE Wireless Communications and Networking
Conference (2019 WCNC
Robot Fleet Learning via Policy Merging
Fleets of robots ingest massive amounts of heterogeneous streaming data silos
generated by interacting with their environments, far more than what can be
stored or transmitted with ease. At the same time, teams of robots should
co-acquire diverse skills through their heterogeneous experiences in varied
settings. How can we enable such fleet-level learning without having to
transmit or centralize fleet-scale data? In this paper, we investigate policy
merging (PoMe) from such distributed heterogeneous datasets as a potential
solution. To efficiently merge policies in the fleet setting, we propose
FLEET-MERGE, an instantiation of distributed learning that accounts for the
permutation invariance that arises when parameterizing the control policies
with recurrent neural networks. We show that FLEET-MERGE consolidates the
behavior of policies trained on 50 tasks in the Meta-World environment, with
good performance on nearly all training tasks at test time. Moreover, we
introduce a novel robotic tool-use benchmark, FLEET-TOOLS, for fleet policy
learning in compositional and contact-rich robot manipulation tasks, to
validate the efficacy of FLEET-MERGE on the benchmark.Comment: See the code https://github.com/liruiw/Fleet-Tools for more detail
Necessary conditions for reaction-diffusion system with delay preserving positivity
We consider the reaction--diffusion system with delay
\begin{equation*}
\left\{\begin{aligned}
&\frac{\partial u}{\partial t}=A(t,x)\Delta u-\sum_{i=1}^{k}\gamma_{i}(t,x)\partial_{x_{i}}u +f(t,u_{t}) , &x\in \Omega; \\
&B(u)|_{\partial \Omega}=0.\\
\end{aligned}
\right.
\end{equation*}
We show that this system with delay preserves positivity if and only if its diffusion matrix and convection matrix are diagonal with non-negative elements and nonlinear delay term satisfies the normal tangential condition
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
Large language models (LLMs) have revolutionized natural language processing
tasks. However, their practical deployment is hindered by their immense memory
and computation requirements. Although recent post-training quantization (PTQ)
methods are effective in reducing memory footprint and improving the
computational efficiency of LLM, they hand-craft quantization parameters, which
leads to low performance and fails to deal with extremely low-bit quantization.
To tackle this issue, we introduce an Omnidirectionally calibrated Quantization
(OmniQuant) technique for LLMs, which achieves good performance in diverse
quantization settings while maintaining the computational efficiency of PTQ by
efficiently optimizing various quantization parameters. OmniQuant comprises two
innovative components including Learnable Weight Clipping (LWC) and Learnable
Equivalent Transformation (LET). LWC modulates the extreme values of weights by
optimizing the clipping threshold. Meanwhile, LET tackles activation outliers
by shifting the challenge of quantization from activations to weights through a
learnable equivalent transformation. Operating within a differentiable
framework using block-wise error minimization, OmniQuant can optimize the
quantization process efficiently for both weight-only and weight-activation
quantization. For instance, the LLaMA-2 model family with the size of 7-70B can
be processed with OmniQuant on a single A100-40G GPU within 1-16 hours using
128 samples. Extensive experiments validate OmniQuant's superior performance
across diverse quantization configurations such as W4A4, W6A6, W4A16, W3A16,
and W2A16. Additionally, OmniQuant demonstrates effectiveness in
instruction-tuned models and delivers notable improvements in inference speed
and memory reduction on real devices. Codes and models are available at
\url{https://github.com/OpenGVLab/OmniQuant}.Comment: Updated result with 2-bit quantization. A differentiable quantization
method for LL
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs
The ever-increasing large language models (LLMs), though opening a potential
path for the upcoming artificial general intelligence, sadly drops a daunting
obstacle on the way towards their on-device deployment. As one of the most
well-established pre-LLMs approaches in reducing model complexity, network
pruning appears to lag behind in the era of LLMs, due mostly to its costly
fine-tuning (or re-training) necessity under the massive volumes of model
parameter and training data. To close this industry-academia gap, we introduce
Dynamic Sparse No Training (DSnoT), a training-free fine-tuning approach that
slightly updates sparse LLMs without the expensive backpropagation and any
weight updates. Inspired by the Dynamic Sparse Training, DSnoT minimizes the
reconstruction error between the dense and sparse LLMs, in the fashion of
performing iterative weight pruning-and-growing on top of sparse LLMs. To
accomplish this purpose, DSnoT particularly takes into account the anticipated
reduction in reconstruction error for pruning and growing, as well as the
variance w.r.t. different input data for growing each weight. This practice can
be executed efficiently in linear time since its obviates the need of
backpropagation for fine-tuning LLMs. Extensive experiments on LLaMA-V1/V2,
Vicuna, and OPT across various benchmarks demonstrate the effectiveness of
DSnoT in enhancing the performance of sparse LLMs, especially at high sparsity
levels. For instance, DSnoT is able to outperform the state-of-the-art Wanda by
26.79 perplexity at 70% sparsity with LLaMA-7B. Our paper offers fresh insights
into how to fine-tune sparse LLMs in an efficient training-free manner and open
new venues to scale the great potential of sparsity to LLMs. Codes are
available at https://github.com/zyxxmu/DSnoT.Comment: Published as a conference paper at ICLR 202
Recent Progress on the Application of Electrochemical Technology in Food Detection
Electrochemical technology detects analytes based on their electrochemical signals. Due to its advantages of simple operation, low cost, high precision and sensitivity, the application of electrochemical technology in food detection and analysis has attracted much attention, and some research progress has been made. In this article, the types, basic principles and applications of electrochemical methods in the field of food detection are reviewed, and prospects for the application and development of electrochemical technology are discussed to provide a reference for promoting the application of electrochemical technology in the field of food detection and analysis in order to meet the development needs of rapid food detection and analysis
Gestational weight gain and pregnancy outcomes in Chinese women with type 2 diabetes mellitus: evidence from a tertiary hospital in Beijing
ObjectiveTo examine the effects of gestational weight gain on pregnancy outcomes and determine the optimal range of weight gain during pregnancy for Chinese women with type 2 diabetes mellitus.MethodsThis retrospective cohort study included 691 Chinese women with type 2 diabetes mellitus from 2012 to 2020. The study utilized a statistical-based approach to determine the optimal range of gestational weight gain. Additionally, multivariate logistic regression analysis was conducted to assess the impact of gestational weight gain on pregnancy outcomes.Results(1) In the obese subgroup, gestational weight gain below the recommendations was associated with decreased risks of large for gestational age (adjusted odds ratio [aOR] 0.19; 95% confidence interval [CI] 0.06-0.60) and macrosomia (aOR 0.18; 95% CI 0.05-0.69). In the normal weight subgroup, gestational weight gain below the recommendations of the Institute of Medicine was associated with decreased risks of preeclampsia (aOR 0.18; 95% CI 0.04-0.82) and neonatal hypoglycemia (aOR 0.38; 95% CI 0.15-0.97). (2) In the normal weight subgroup, gestational weight gain above the recommendations of the Institute of Medicine was associated with an increased risk of large for gestational age (aOR 4.56; 95% CI 1.54-13.46). In the obese subgroup, gestational weight gain above the recommendations was associated with an increased risk of preeclampsia (aOR 2.74; 95% CI 1.02, 7.38). (3) The optimal ranges of gestational weight gain, based on our study, were 9-16Â kg for underweight women, 9.5-14Â kg for normal weight women, 6.5-12Â kg for overweight women, and 3-10Â kg for obese women. (4) Using the optimal range of gestational weight gain identified in our study seemed to provide better prediction of adverse pregnancy outcomes.ConclusionFor Chinese women with type 2 diabetes, inappropriate gestational weight gain is associated with adverse pregnancy outcomes, and the optimal range of gestational weight gain may differ from the Institute of Medicine recommendations
A novel deep-learning based weighted feature fusion architecture for precise classification of pressure injury
Introduction: Precise classification has an important role in treatment of pressure injury (PI), while current machine-learning or deeplearning based methods of PI classification remain low accuracy.Methods: In this study, we developed a deeplearning based weighted feature fusion architecture for fine-grained classification, which combines a top-down and bottom-up pathway to fuse high-level semantic information and low-level detail representation. We validated it in our established database that consist of 1,519 images from multi-center clinical cohorts. ResNeXt was set as the backbone network.Results: We increased the accuracy of stage 3 PI from 60.3% to 76.2% by adding weighted feature pyramid network (wFPN). The accuracy for stage 1, 2, 4 PI were 0.870, 0.788, and 0.845 respectively. We found the overall accuracy, precision, recall, and F1-score of our network were 0.815, 0.808, 0.816, and 0.811 respectively. The area under the receiver operating characteristic curve was 0.940.Conclusions: Compared with current reported study, our network significantly increased the overall accuracy from 75% to 81.5% and showed great performance in predicting each stage. Upon further validation, our study will pave the path to the clinical application of our network in PI management
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