pwc-archive/papers-with-abstracts huggingface dataset:
Cryopreservation of seeds of blue waterlily (Nymphaea caerulea) using glutathione adding plant vitrification solution, PVS+
Authors: Chung-Hao Lee
Date: 2022-03-27 00:00:00

Nymphaea caerulea is a valuable freshwater aquatic plant, not only because of its ornamental value but also its extractions for chemical and medical uses. It is necessary to store its seeds as backup resources. Cryopreservation is the reliable, cost-effective method for long-term preservation of botanical genetic banks, especially for recalcitrant plants. In this study, we demonstrated that due to unable to tolerate desiccation and low-temperature, N. caerulea is recalcitrant. Since viability was lost before 6 months storage, N. caerulea seeds were not appropriate to long-term store by traditional storage method. The only way to long-term store N. caerulea seeds is cryopreservation. However, Plant Vitrification Solution 3 (PVS3; 50% w/v sucrose, 50% w/v glycerol), a commonly used plant vitrification solution, was ineffective on N. caerulea seeds cryopreservation. The maximum survival rate of cryopreserved seeds treated by PVS3 was only 23%. Oxidative stress induced by reactive oxygen species (ROS) accumulation within seeds was the reason to cause inefficiency of PVS3 treated seeds cryopreservation. We developed a new plant vitrification solution (PVS+), by adding glutathione (GSH) into PVS3. PVS+ rescued the inefficiency by decreasing ROS accumulation and elevated survival rate of cryopreserved seeds to 97%. Our results showed some inefficiencies of plant tissue cryopreservation may be caused by ROS-induced oxidative stress. Antioxidants can reduce ROS-induced oxidative stress and improve seeds survival after cryopreservation. In conclusion, N. caerulea seeds were identified as recalcitrant and successfully cryopreserved. Suppressing ROS accumulation during cryopreservation may be a potential strategy to improve survival rate of recalcitrant seeds after cryopreservation.
Implications of Mortality Displacement for Effect Modification and Selection Bias
Authors: Honghyok Kim, Jong-Tae Lee, Roger D. Peng, Kelvin C. Fong, Michelle L. Bell
Date: 2022-03-26 00:00:00
Tasks: Epidemiology, Selection bias, Time Series, Time Series Analysis

Mortality displacement is the concept that deaths are moved forward in time (e.g., a few days, several months, and years) by exposure from when they would occur without the exposure, which is common in environmental time-series studies. Using concepts of a frail population and loss of life expectancy, it is understood that mortality displacement may decrease rate ratio (RR). Such decreases are thought to be minimal or substantial depending on study populations. Environmental epidemiologists have interpreted RR considering mortality displacement. This theoretical paper reveals that mortality displacement can be formulated as a built-in selection bias of RR in Cox models due to unmeasured risk factors independent from exposure of interest, and mortality displacement can also be viewed as an effect modifier by integrating the concepts of rate and loss of life expectancy. Thus, depending on the framework through which we view bias, mortality displacement can be categorized as selection bias in the bias taxonomy of epidemiology, and simultaneously mortality displacement can be seen as an effect modifier. This dichotomy provides useful implications regarding policy, effect modification, exposure time-windows selection, and generalizability, specifically why research in epidemiology may produce unexpected and heterogeneous RR over different studies and sub-populations.
Using genome-wide expression compendia to study microorganisms
Authors: Alexandra J. Lee, Taylor Reiter, Georgia Doing, Julia Oh, Deborah A. Hogan, Casey S. Greene
Date: 2022-03-26 00:00:00
Tasks: Experimental Design

A gene expression compendium is a heterogeneous collection of gene expression experiments assembled from data collected for diverse purposes. The widely varied experimental conditions and genetic backgrounds across samples creates a tremendous opportunity for gaining a systems level understanding of the transcriptional responses that influence phenotypes. Variety in experimental design is particularly important for studying microbes, where the transcriptional responses integrate many signals and demonstrate plasticity across strains including response to what nutrients are available and what microbes are present. Advances in high-throughput measurement technology have made it feasible to construct compendia for many microbes. In this review we discuss how these compendia are constructed and analyzed to reveal transcriptional patterns.
Wind speed forecast using random forest learning method
Authors: G. V. Drisya, Valsaraj P., K. Asokan, K. Satheesh Kumar
Date: 2022-03-23 00:00:00
Tasks: Time Series, Time Series Analysis

Wind speed forecasting models and their application to wind farm operations are attaining remarkable attention in the literature because of its benefits as a clean energy source. In this paper, we suggested the time series machine learning approach called random forest regression for predicting wind speed variations. The computed values of mutual information and auto-correlation shows that wind speed values depend on the past data up to 12 hours. The random forest model was trained using ensemble from two weeks data with previous 12 hours values as input for every value. The computed root mean square error shows that model trained with two weeks data can be employed to make reliable short-term predictions up to three years ahead.
THE DCASE 2021 CHALLENGE TASK 6 SYSTEM: AUTOMATED AUDIO CAPTIONING WITH WEAKLY SUPERVISED PRE-TRAING AND WORD SELECTION METHODS
Authors: Weiqiang Yuan ∗, Qichen Han∗, Dong Liu, Xiang Li, Zhen Yang
Date: 2021-07-06 00:00:00
Proceeding: DCASE workshop 2021 7
Tasks: Audio captioning, Caption Generation, Decoder

This technical report describes the system participating to the De-
tection and Classification of Acoustic Scenes and Events
(DCASE) 2021 Challenge, Task 6: automated audio captioning.
We use encoder-decoder modeling framework for audio under-
standing and caption generation. Our solution focuses on solving
two problems in automated audio captioning: data insufficiency
and word selection indeterminacy. As limited audios with golden
captions are available, we collect large-scale weakly labeled da-
taset from Web with heuristic methods. Then we pre-train the en-
coder-decoder models with this dataset followed by fine-tuning
on Clotho dataset. To solve the word selection indeterminacy
problem, we use keywords extracted from captions of similar au-
dios and audio event tags produced by pre-trained models to guide
words generation in decoding stage. We tested our submissions
using the development-testing dataset. Our best submission
achieved 31.8 SPIDEr score where that of the baseline system is
5.4.
THE SJTU SYSTEM FOR DCASE2021 CHALLENGE TASK 6: AUDIO CAPTIONING BASED ON ENCODER PRE-TRAINING AND REINFORCEMENT LEARNING
Authors: Xuenan Xu, Zeyu Xie, Mengyue Wu, Kai Yu
Date: 2021-07-06 00:00:00
Proceeding: DCASE Challenge 2021 7
Tasks: Audio captioning, Audio Tagging, Decoder, reinforcement-learning, Reinforcement Learning (RL)

This report proposes an audio captioning system for the Detection
and Classification of Acoustic Scenes and Events (DCASE) 2021
challenge task Task 6. Our audio captioning system consists of a
10-layer convolution neural network (CNN) encoder and a tempo-
ral attentional single layer gated recurrent unit (GRU) decoder. In
this challenge, there is no restriction on the usage of external data
and pre-trained models. To better model the concepts in an audio
clip, we pre-train the CNN encoder with audio tagging on AudioSet.
After standard cross entropy based training, we further fine-tune the
model with reinforcement learning to directly optimize the evalua-
tion metric. Experiments show that our proposed system achieves a
SPIDEr of 28.6 on the public evaluation split without ensemble1.
Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 Dataset
Authors: Dmitry Rybolovlev, Maxim Goryunov, Andrey Matskevich
Date: 2020-01-01 00:00:00
Proceeding: Proceedings of the Institute for System Programming of RAS 2020 1
Tasks: Network Intrusion Detection

The paper deals with the construction and practical implementation of the model of computer attack detection based on machine learning methods. Among available public datasets one of the most relevant was chosen - CICIDS2017. For this dataset, the procedures of data preprocessing and sampling were developed in detail. In order to reduce computation time, the only class of computer attacks (brute force, XSS, SQL injection) was left in the training set. The procedure of feature space construction is described sequentially, which allowed to significantly reduce its dimensions - from 85 to 10 most important features. The quality assessment of ten most common machine learning models on the obtained pre-processed dataset was made. Among the models (algorithms) that demonstrated the best results (k-nearest neighbors, decision tree, random forest, AdaBoost, logistic regression), taking into account the minimum time of execution, the choice of random forest model was justified. А quasi-optimal selection of hyper parameters was carried out, which made it possible to improve the quality of the model in comparison with the previously published research results. The synthesized model of attack detection was tested on real network traffic. The model has shown its validity only under the condition of training on data collected in a specific network, since important features depend on the physical structure of the network and the settings of the equipment used. The conclusion was made that it is possible to use machine learning methods to detect computer attacks taking into account these limitations.
Wavelet-denoising on hardware devices with Perfect Reconstruction, low latency and adaptive thresholding
Authors: Dora M.Ballesteros L, Juan M.Moreno A
Date: 2013-05-01 00:00:00
Proceeding: Computers & Electrical Engineering 2013 5
Tasks: Denoising, Quantization

This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the biorthogonal base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.
An improved wavelet-based signal-denoising architecture with less hardware consumption
Authors: PankajGoel；MaheshChandraaAnkitaAnandbAsutoshKarc
Date: 2013-05-01 00:00:00
Proceeding: 2019年12月15日 2013 5
Tasks: Denoising, Quantization

This paper introduces a wavelet denoising architecture with adaptive thresholding for real-time 1D-systems and without the use of external memories for storing input data or wavelet coefficients. The Discrete Wavelet Transform (DWT) is executed sample-by-sample by a polyphase scheme of the biorthogonal base 5/3. Since the weights of the filters are represented by integer terms and the quantization error is quasi-zero, the principle of Perfect Reconstruction is satisfied. The adaptive threshold is based on a real-time sorting process which calculates the median of the detail coefficients. Simulations are presented to measure the delay, latency, quantization error and hardware cost. A comparison with related works is also provided in order to show the strengths of the current proposal. The good trade-off among reconstruction error, latency, delay and hardware cost permits to use the proposed architecture in a wide variety of signals that require good fidelity and prompt response.

In this paper, we propose the VLSI implementation of a wavelet packet transform-based architecture. Moreover, the proposed architecture is compared to the wavelet transform architecture in terms of their signal denoising capabilities, hardware resources used, maximum operating frequency and maximum combination path delay. The Wavelet packet transform architecture is designed and implemented for db2 and db3 wavelet types. For the proposed real-time VLSI implementation, the Xilinx Spartan-6 series FPGA platform is used. Simulation results show that the proposed architecture implementation results in an improved performance for ECG signals taken from the Fantasia Database as well as from the self-recorded database, as compared to the WT wavelet architectures which result in distorted outputs. Moreover, from the synthesis results, it is concluded that the proposed architecture implementation requires significantly less hardware as compared to the WT architectures considered. In terms of LUTs, the proposed architecture consumes 38.20 percent and 84.96 percent less resources than the WT-db2 and WT-db5 architectures, respectively. Similarly, in terms of slice registers, the proposed architecture consumes, respectively, 21.39 percent and 75.61 percent less resources than the WT-db2 and WT-db5 architectures. Furthermore, the maximum operating frequency of the proposed architecture is 180.278 MHz as compared to that of 195.496 MHz and 121.038 MHz for the WT-db2 and WT-db5 architectures respectively. The maximum combinational path delay of the proposed architecture is 48.109 ns, as compared to that of 13.784 ns and 15.202 ns for the WT-db2 and WT-db5 architectures, respectively.
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
Authors: Zhiheng Li, Martin Renqiang Min, Kai Li, Chenliang Xu
Date: 2022-03-29 00:00:00
Proceeding: CVPR 2022 1
Tasks: Attribute, Fairness, Image Generation, Vocal Bursts Intensity Prediction

Although progress has been made for text-to-image synthesis, previous methods fall short of generalizing to unseen or underrepresented attribute compositions in the input text. Lacking compositionality could have severe implications for robustness and fairness, e.g., inability to synthesize the face images of underrepresented demographic groups. In this paper, we introduce a new framework, StyleT2I, to improve the compositionality of text-to-image synthesis. Specifically, we propose a CLIP-guided Contrastive Loss to better distinguish different compositions among different sentences. To further improve the compositionality, we design a novel Semantic Matching Loss and a Spatial Constraint to identify attributes' latent directions for intended spatial region manipulations, leading to better disentangled latent representations of attributes. Based on the identified latent directions of attributes, we propose Compositional Attribute Adjustment to adjust the latent code, resulting in better compositionality of image synthesis. In addition, we leverage the $\ell_2$-norm regularization of identified latent directions (norm penalty) to strike a nice balance between image-text alignment and image fidelity. In the experiments, we devise a new dataset split and an evaluation metric to evaluate the compositionality of text-to-image synthesis models. The results show that StyleT2I outperforms previous approaches in terms of the consistency between the input text and synthesized images and achieves higher fidelity.
Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching
Authors: Dongkwon Jin, Jun-Tae Lee, Chang-Su Kim
Date: 2022-03-29 00:00:00
Proceeding: ECCV 2020 8
Tasks: Line Detection

A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror attention module. Then, through pairwise comparisons of extracted semantic lines, we iteratively select the most semantic line and remove redundant ones overlapping with the selected one. For the pairwise comparisons, we develop R-Net and M-Net in the Siamese architecture. Experiments demonstrate that the proposed algorithm outperforms the conventional semantic line detector significantly. Moreover, we apply the proposed algorithm to detect two important kinds of semantic lines successfully: dominant parallel lines and reflection symmetry axes. Our codes are available at https://github.com/dongkwonjin/Semantic-Line-DRM.
Category Guided Attention Network for Brain Tumor Segmentation in MRI
Authors: Jiangyun Li, Hong Yu, Chen Chen, Meng Ding, Sen Zha
Date: 2022-03-29 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Objective: Magnetic resonance imaging (MRI) has been widely used for the analysis and diagnosis of brain diseases. Accurate and automatic brain tumor segmentation is of paramount importance for radiation treatment. However, low tissue contrast in tumor regions makes it a challenging task.Approach: We propose a novel segmentation network named Category Guided Attention U-Net (CGA U-Net). In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost. Moreover, we propose an intra-class update approach to reconstruct feature maps by aggregating pixels of the same category. Main results: Experimental results on the BraTS 2019 datasets show that the proposed method outperformers the state-of-the-art algorithms in both segmentation performance and computational complexity. Significance: The CGA U-Net can effectively capture the global semantic information in the MRI image by using the SAM module, while significantly reducing the computational cost. Code is available at https://github.com/delugewalker/CGA-U-Net.
Affine Medical Image Registration with Coarse-to-Fine Vision Transformer
Authors: Tony C. W. Mok, Albert C. S. Chung
Date: 2022-03-29 00:00:00
Proceeding: CVPR 2022 1
Tasks: Image Registration, Medical Image Registration, Template Matching

Affine registration is indispensable in a comprehensive medical image registration pipeline. However, only a few studies focus on fast and robust affine registration algorithms. Most of these studies utilize convolutional neural networks (CNNs) to learn joint affine and non-parametric registration, while the standalone performance of the affine subnetwork is less explored. Moreover, existing CNN-based affine registration approaches focus either on the local misalignment or the global orientation and position of the input to predict the affine transformation matrix, which are sensitive to spatial initialization and exhibit limited generalizability apart from the training dataset. In this paper, we present a fast and robust learning-based algorithm, Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image registration. Our method naturally leverages the global connectivity and locality of the convolutional vision transformer and the multi-resolution strategy to learn the global affine registration. We evaluate our method on 3D brain atlas registration and template-matching normalization. Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods. The source code is available at https://github.com/cwmok/C2FViT.
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex
Authors: Chun-Hsien Lin, Pu-Jen Cheng
Date: 2022-03-29 00:00:00
Tasks: Document Classification, Machine Translation, Word Embeddings

Word embedding is a modern distributed word representations approach widely used in many natural language processing tasks. Converting the vocabulary in a legal document into a word embedding model facilitates subjecting legal documents to machine learning, deep learning, and other algorithms and subsequently performing the downstream tasks of natural language processing vis-\`a-vis, for instance, document classification, contract review, and machine translation. The most common and practical approach of accuracy evaluation with the word embedding model uses a benchmark set with linguistic rules or the relationship between words to perform analogy reasoning via algebraic calculation. This paper proposes establishing a 1,134 Legal Analogical Reasoning Questions Set (LARQS) from the 2,388 Chinese Codex corpus using five kinds of legal relations, which are then used to evaluate the accuracy of the Chinese word embedding model. Moreover, we discovered that legal relations might be ubiquitous in the word embedding model.
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
Authors: Minchan Kim, Myeonghun Jeong, Byoung Jin Choi, Sunghwan Ahn, Joun Yeop Lee, Nam Soo Kim
Date: 2022-03-29 00:00:00
Tasks: text-to-speech, Text to Speech, Transfer Learning, Zero-Shot Multi-Speaker TTS

Training a text-to-speech (TTS) model requires a large scale text labeled speech corpus, which is troublesome to collect. In this paper, we propose a transfer learning framework for TTS that utilizes a large amount of unlabeled speech dataset for pre-training. By leveraging wav2vec2.0 representation, unlabeled speech can highly improve performance, especially in the lack of labeled speech. We also extend the proposed method to zero-shot multi-speaker TTS (ZS-TTS). The experimental results verify the effectiveness of the proposed method in terms of naturalness, intelligibility, and speaker generalization. We highlight that the single speaker TTS model fine-tuned on the only 10 minutes of labeled dataset outperforms the other baselines, and the ZS-TTS model fine-tuned on the only 30 minutes of single speaker dataset can generate the voice of the arbitrary speaker, by pre-training on unlabeled multi-speaker speech corpus.