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IEEE International Symposium on Information Theory

作者： Shubhransh Singhvi Avital Boruchovsky Han Mao Kiah Eitan Yaakobi Signal Processing & Communications Research Center International Institute of Information Technology Hyderabad India Department of Computer Science Technion—Israel Institute of Technology Haifa Israel School of Physical and Mathematical Sciences Nanyang Technological University Singapore

We study a data-driven approach to the bee identification problem for DNA strands. The bee-identification problem, introduced by Tandon et al. (2019), requires one to identify M bees, each tagged by a unique barcode, via a set of M noisy measurements. Later, Chrisnata et al. (2022) extended the model to case where one observes N noisy measurements of each bee, and applied the model to address the unordered nature of DNA storage *** such systems, a unique address is typically prepended to each DNA data block to form a DNA strand, but the address may possibly be corrupted. While clustering is usually used to identify the address of a DNA strand, this requires ℳ 2 data comparisons (when ℳ is the number of reads). In contrast, the approach of Chrisnata et al. (2022) avoids data comparisons completely. In this work, we study an intermediate, data-driven approach to this identification *** the binary erasure channel, we first show that we can almost surely correctly identify all DNA strands under certain mild assumptions. Then we propose a data-driven pruning procedure and demonstrate that on average the procedure uses only a fraction of ℳ 2 data comparisons. Specifically, for ℳ = 2 n and erasure probability p, the expected number of data comparisons performed by the procedure is κℳ 2 , where ${\left( {\frac{{1 + 2p - {p^2}}}{2}} \right)^n} \leq \kappa \leq {\left( {\frac{{1 + p}}{2}} \right)^n}$.

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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection

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Remote Sensing 2024年第1期16卷

作者： Chang, Chein-I Chen, Shuhan Zhong, Shengwei Shi, Yidan Information and Technology College Dalian Maritime University Dalian116026 China Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland BaltimoreMD21250 United States Department of Electrical Engineering National Cheng Kung University Tainan70101 Taiwan Department of Electrical Engineering Zhejiang University Hangzhou310027 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Whether or not a hyperspectral anomaly detector is effective is determined by two crucial issues, anomaly detectability and background suppressibility (BS), both of which are very closely related to two factors, the datasets used for a selected hyperspectral anomaly detector and detection measures used for its performance evaluation. This paper explores how anomaly detectability and BS play key roles in hyperspectral anomaly detection (HAD). To address these two issues, we investigate three key elements attributed to HAD. One is a selected hyperspectral anomaly detector, and another is the datasets used for experiments. The third one is the detection measures used to evaluate the effectiveness of a hyperspectral anomaly detector. As for hyperspectral anomaly detectors, twelve commonly used anomaly detectors were evaluated and compared. To address the appropriate use of datasets for HAD, seven popular and widely used datasets were studied for HAD. As for the third issue, the traditional area under a receiver operating characteristic (ROC) curve of detection probability—PD versus false alarm probability, PF, (AUC(D,F))—was extended to 3D ROC analysis where a 3D ROC curve was developed to generate three 2D ROC curves from which eight detection measures could be derived to evaluate HAD in all round aspects, including anomaly detectability, BS and joint anomaly detectability and BS. Qualitative analysis showed that many works reported in the literature which claimed that their developed hyperspectral anomaly detectors performed better than other anomaly detectors are actually not true because they overlooked these two issues. Specifically, a comprehensive study via extensive experiments demonstrated that these 3D ROC curve-derived detection measures can be further used to address the various characterizations of different data scenes and also to provide explanations as to why certain data scenes are not suitable for HAD. © 2023 by the authors.

关键词： Curve fitting

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The Input and Output Entropies of the k-Deletion/Insertion Channel

arXiv

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arXiv 2022年

作者： Singhvi, Shubhransh Sabary, Omer Bar-Lev, Daniella Yaakobi, Eitan Signal Processing & Communications Research Center International Institute of Information Technology Hyderabad India Department of Computer Science Technion Israel Institute of Technology Haifa3200003 Israel

The channel output entropy of a transmitted word is the entropy of the possible channel outputs and similarly the input entropy of a received word is the entropy of all possible transmitted words. The goal of this work is to study these entropy values for the k-deletion, k-insertion channel, where exactly k symbols are deleted, inserted in the transmitted word, respectively. If all possible words are transmitted with the same probability then studying the input and output entropies is equivalent. For both the 1-insertion and 1-deletion channels, it is proved that among all words with a fixed number of runs, the input entropy is minimized for words with a skewed distribution of their run lengths and it is maximized for words with a balanced distribution of their run lengths. Among our results, we establish a conjecture by Atashpendar et al. which claims that for the binary 1-deletion, the input entropy is maximized for the alternating words. This conjecture is also verified for the 2-deletion channel, where it is proved that constant words with a single run minimize the input entropy. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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Data-Driven Bee Identification for DNA Strands

arXiv

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arXiv 2023年

作者： Singhvi, Shubhransh Boruchovsky, Avital Kiah, Han Mao Yaakobi, Eitan Signal Processing & Communications Research Center International Institute of Information Technology Hyderabad India Department of Computer Science Technion-Israel Institute of Technology Haifa3200003 Israel School of Physical and Mathematical Sciences Nanyang Technological University Singapore

We study a data-driven approach to the bee identification problem for DNA strands. The bee-identification problem, introduced by Tandon et al. (2019), requires one to identify M bees, each tagged by a unique barcode, via a set of M noisy measurements. Later, Chrisnata et al. (2022) extended the model to case where one observes N noisy measurements of each bee, and applied the model to address the unordered nature of DNA storage systems. In such systems, a unique address is typically prepended to each DNA data block to form a DNA strand, but the address may possibly be corrupted. While clustering is usually used to identify the address of a DNA strand, this requires M2 data comparisons (when M is the number of reads). In contrast, the approach of Chrisnata et al. (2022) avoids data comparisons completely. In this work, we study an intermediate, data-driven approach to this identification task. For the binary erasure channel, we first show that we can almost surely correctly identify all DNA strands under certain mild assumptions. Then we propose a data-driven pruning procedure and demonstrate that on average the procedure uses only a fraction of M2 data comparisons. Specifically, for M = 2n and erasure probability p, the expected number of data comparisons performed by the procedure is κM2, where (Equation presented). Copyright © 2023, The Authors. All rights reserved.

关键词： DNA

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Unified Source-Free Domain Adaptation

arXiv

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arXiv 2024年

作者： Tang, Song Su, Wenxin Ye, Mao Zhang, Jianwei Zhu, Xiatian The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The TAMS Group Department of Informatics Universität Hamburg Hamburg Germany The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China The Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

In the pursuit of transferring a source model to a target domain without access to the source training data, Source-Free Domain Adaptation (SFDA) has been extensively explored across various scenarios, including closed-set, open-set, partial-set, and generalized settings. Existing methods, focusing on specific scenarios, not only address only a subset of challenges but also necessitate prior knowledge of the target domain, significantly limiting their practical utility and deployability. In light of these considerations, we introduce a more practical yet challenging problem, termed unified SFDA, which comprehensively incorporates all specific scenarios in a unified manner. To tackle this unified SFDA problem, we propose a novel approach called Latent Causal Factors Discovery (LCFD). In contrast to previous alternatives that emphasize learning the statistical description of reality, we formulate LCFD from a causality perspective. The objective is to uncover the causal relationships between latent variables and model decisions, enhancing the reliability and robustness of the learned model against domain shifts. To integrate extensive world knowledge, we leverage a pre-trained vision-language model such as CLIP. This aids in the formation and discovery of latent causal factors in the absence of supervision in the variation of distribution and semantics, coupled with a newly designed information bottleneck with theoretical guarantees. Extensive experiments demonstrate that LCFD can achieve new state-of-the-art results in distinct SFDA settings, as well as source-free out-of-distribution generalization. Our code and data are available at https://***/tntek/source-free-domain-adaptation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Learning Assisted Approach for Electronic and Optical Characterization of Tin-Doped Titanium Oxide 2

A Learning Assisted Approach for Electronic and Optical Char...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Nforna, Edwin Akongnwi Kamsu, Brigitte Fotso Tchoffo, M. Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon Htttc Bambili University of Bamenda Materials Science Research Unit Department of Fundamental Sciences Cameroon Faculty of Science University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin Kyrenia10 Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we evaluate the effect of variation of mole fraction of Tin in doped titanium oxide on band gap. To achieve this, we resort to Matminer's "dielectric_constant"dataset and apply an algorithm to featurize the chemical formulae of the compounds in the dataset. Using k-fold cross-validation of random forest, we obtain a Machine learning model with R2 value of 0.81 MAE=0.45. Applying our model to predict the band gap of doped Titanium oxide over a range of 6 compositions, we find that the 50% doping level has a remarkably lower band gap than all other compositions which makes it a more suitable candidate for optical applications such as in solar cells. © 2022 IEEE.

关键词： Smart contract

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Synthesis Speech Based Data Augmentation for Low Resource Children ASR 23rd

Synthesis Speech Based Data Augmentation for Low Resource Ch...

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23rd International Conference on Speech and computer, SPECOM 2021

作者： Kadyan, Virender Kathania, Hemant Govil, Prajjval Kurimo, Mikko Speech and Language Research Centre School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Department of Electronics and Communication Engineering National Institute of Sikkim Ravangla India Department of Signal Processing and Acoustics Aalto University Espoo Finland

ISBN: (纸本)9783030878016

Successful speech recognition for children requires large training data with sufficient speaker variability. The collection of such a training database of children’s voices is challenging and very expensive for zero/low resource language like Punjabi. In this paper, the data scarcity issue of the low resourced language Punjabi is addressed through two levels of augmentation. The original training corpus is first augmented by modifying the prosody parameters for pitch and speaking rate. Our results show that the augmentation improves the system performance over the baseline system. Then the augmented data combined with original data and used to train the TTS system to generate synthesis data and extended dataset is further used for augmented by generating children’s utterances using text-to-speech synthesis and sampling the language model with methods that increase the acoustic and lexical diversity. The final speech recognition performance indicates a relative improvement of 50.10% with acoustic and 57.40% with language diversity based augmentation in comparison to that of the baseline system respectively. © 2021, Springer Nature Switzerland AG.

关键词： Speech synthesis

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Leveraging Pre-trained BERT for Audio Captioning

Leveraging Pre-trained BERT for Audio Captioning

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European signal processing Conference (EUSIPCO)

作者： Xubo Liu Xinhao Mei Qiushi Huang Jianyuan Sun Jinzheng Zhao Haohe Liu Mark D. Plumbley Volkan Kilic Wenwu Wang Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey UK Department of Computer Science University of Surrey UK Department of Electrical and Electronics Engineering Izmir Katip Celebi University Turkey

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

Audio captioning aims at using language to describe the content of an audio clip. Existing audio captioning systems are generally based on an encoder-decoder architecture, in which acoustic information is extracted by an audio encoder and then a language decoder is used to generate the captions. Training an audio captioning system often encounters the problem of data scarcity. Transferring knowledge from pre-trained audio models such as Pre-trained Audio Neural Networks (PANNs) have recently emerged as a useful method to mitigate this issue. However, there is less attention on exploiting pre-trained language models for the decoder, compared with the encoder. BERT is a pre-trained language model that has been extensively used in natural language processing tasks. Nevertheless, the potential of using BERT as the language decoder for audio captioning has not been investigated. In this study, we demonstrate the efficacy of the pre-trained BERT model for audio captioning. Specifically, we apply PANNs as the encoder and initialize the decoder from the publicly available pre-trained BERT models. We conduct an empirical study on the use of these BERT models for the decoder in the audio captioning model. Our models achieve competitive results with the existing audio captioning methods on the AudioCaps dataset.

关键词： Training Knowledge engineering Bit error rate Neural networks Europe signal processing Natural language processing

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Interactive Prototype Learning and Self-Learning for Few-Shot Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Song, Yuhui Xu, Chenchu Wang, Boyan Du, Xiuquan Chen, Jie Zhang, Yanping Li, Shuo School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230051 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Engineering Case Western Reserve University Cleveland44106 United States

Few-shot learning alleviates the heavy dependence of medical image segmentation tasks on large-scale labeled data, but it shows a strong performance gap when dealing with new targets and new tasks compared with traditional deep learning. Existing few-shot medical image segmentation models mainly realize segmentation by learning the semantic class knowledge of a few known (support) samples and extending it to unknown (query) samples. However, the neglect of the differences between known and unknown samples leads to a certain deviation in the transfer of class knowledge, and the trained model tends to overfit the class knowledge of training classes, which greatly reduces the segmentation and generalization performance of few-shot model. In this work, we propose an interactive prototype learning and self-learning network to alleviate the above problems. Specifically, we propose an interactive prototype learning network to promote feature consistency between support images and query images to improve the accuracy of prototype metric learning. In addition, to avoid overfitting the training class knowledge and reduce the over-dependence of query segmentation on the class knowledge, we introduce a query features-guided self-learning network to achieve the finer segmentation of query images through its rich feature information. Our model obtained the competitive segmentation results on three multi-organ and multi-modal public datasets that show a substantial improvement in generalization ability. © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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