pwc-archive/papers-with-abstracts huggingface dataset:
Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle
Authors: Junde Wu, Huihui Fang, Hoayi Xiong, Lixin Duan, Mingkui Tan, Weihua Yang, Huiying Liu, Yanwu Xu
Date: 2022-08-05 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to train and evaluate the deep learning models. When the annotations are collected from different graders, a common choice is majority vote. However such a strategy ignores the difference between the grader expertness. In this paper, we consider the task of predicting the segmentation with the calibrated inter-observer uncertainty. We note that in clinical practice, the medical image segmentation is usually used to assist the disease diagnosis. Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis First segmentation Framework (DiFF) is proposed to estimate diagnosis-first segmentation from the raw images.Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis First Ground-truth (DF-GT).Then, we further propose Take and Give Modelto segment DF-GT from the raw image. We verify the effectiveness of DiFF on three different medical segmentation tasks: OD/OC segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF is able to significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume
Authors: Wei Dai, Ziyao Zhang, Lixia Tian, Shengyuan Yu, Shuhui Wang, Zhao Dong, Hairong Zheng
Date: 2022-08-05 00:00:00
Tasks: Functional Connectivity, Time Series Analysis

In neuroimaging analysis, fMRI can well assess the function changes for brain diseases with no obvious structural lesions. To date, most deep-learning-based fMRI studies have employed functional connectivity (FC) as the basic feature for disease classification. However, FC is calculated on time series of predefined regions of interest and neglects detailed information contained in each voxel. Another drawback of using FC is the limited sample size for the training of deep models. The low representation ability of FC leads to poor performance in clinical practice, especially when dealing with multimodal medical data involving multiple types of visual signals and textual records for brain diseases. To overcome this bottleneck problem in the fMRI feature modality, we propose BrainFormer, an end-to-end functional interaction learning method for brain disease classification with single fMRI volume. Unlike traditional deep learning methods that construct convolution and transformers on FC, BrainFormer learns the functional interaction from fMRI signals, by modeling the local cues within each voxel with 3D convolutions and capturing the global correlations among distant regions with specially designed global attention mechanisms from shallow layers to deep layers. Meanwhile, BrainFormer can deal with multimodal medical data including fMRI volume, structural MRI, FC features and phenotypic data to achieve more comprehensive brain disease diagnosis. We evaluate BrainFormer on five independent multi-site datasets on autism, Alzheimer's disease, depression, attention deficit hyperactivity disorder and headache disorders. The results demonstrate its effectiveness and generalizability for multiple brain diseases diagnosis with multimodal features. BrainFormer may promote precision of neuroimaging-based diagnosis in clinical practice and motivate future studies on fMRI analysis.
Low-Light Hyperspectral Image Enhancement
Authors: Xuelong Li, Guanlin Li, Bin Zhao
Date: 2022-08-05 00:00:00
Tasks: Image Enhancement

Due to inadequate energy captured by the hyperspectral camera sensor in poor illumination conditions, low-light hyperspectral images (HSIs) usually suffer from low visibility, spectral distortion, and various noises. A range of HSI restoration methods have been developed, yet their effectiveness in enhancing low-light HSIs is constrained. This work focuses on the low-light HSI enhancement task, which aims to reveal the spatial-spectral information hidden in darkened areas. To facilitate the development of low-light HSI processing, we collect a low-light HSI (LHSI) dataset of both indoor and outdoor scenes. Based on Laplacian pyramid decomposition and reconstruction, we developed an end-to-end data-driven low-light HSI enhancement (HSIE) approach trained on the LHSI dataset. With the observation that illumination is related to the low-frequency component of HSI, while textural details are closely correlated to the high-frequency component, the proposed HSIE is designed to have two branches. The illumination enhancement branch is adopted to enlighten the low-frequency component with reduced resolution. The high-frequency refinement branch is utilized for refining the high-frequency component via a predicted mask. In addition, to improve information flow and boost performance, we introduce an effective channel attention block (CAB) with residual dense connection, which served as the basic block of the illumination enhancement branch. The effectiveness and efficiency of HSIE both in quantitative assessment measures and visual effects are demonstrated by experimental results on the LHSI dataset. According to the classification performance on the remote sensing Indian Pines dataset, downstream tasks benefit from the enhanced HSI. Datasets and codes are available: \href{https://github.com/guanguanboy/HSIE}{https://github.com/guanguanboy/HSIE}.
Sinusoidal Sensitivity Calculation for Line Segment Geometries
Authors: Luciano Vinas, Atchar Sudyadhom
Date: 2022-08-05 00:00:00
Tasks: Sensitivity

Purpose: Provide a closed-form solution to the sinusoidal coil sensitivity model proposed by Kern et al. Solution allows for the precise computations of varied, simulated bias fields which can be directly applied onto raw intensity datasets. Methods: Fourier distribution theory and standard integration techniques were used to calculate the Fourier transform of measured magnetic field produced from line segment sources. Results: A $L^1_{\rm loc}(\mathbb{R}^3)$ function is derived in full generality for arbitrary line segment geometries. Sampling criteria and equivalence to the original sinusoidal model are discussed. Lastly a CUDA accelerated implementation $\texttt{biasgen}$ is provided for on-demand sensitivity and bias generation. Conclusion: Given the modeling flexibility of the simulated procedure, practitioners will now have access to a more diverse ecosystem of simulated datasets which may be used to quantitatively compare prospective debiasing methods.
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations
Authors: Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein, Christian F. Baumgartner
Date: 2022-08-05 00:00:00
Tasks: Adversarial Robustness, Diagnostic, Image Reconstruction

Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled $k$-space data. However, these approaches currently have no guarantees for reconstruction quality and the reliability of such algorithms is only poorly understood. Adversarial attacks offer a valuable tool to understand possible failure modes and worst case performance of DL-based reconstruction algorithms. In this paper we describe adversarial attacks on multi-coil $k$-space measurements and evaluate them on the recently proposed E2E-VarNet and a simpler UNet-based model. In contrast to prior work, the attacks are targeted to specifically alter diagnostically relevant regions. Using two realistic attack models (adversarial $k$-space noise and adversarial rotations) we are able to show that current state-of-the-art DL-based reconstruction algorithms are indeed sensitive to such perturbations to a degree where relevant diagnostic information may be lost. Surprisingly, in our experiments the UNet and the more sophisticated E2E-VarNet were similarly sensitive to such attacks. Our findings add further to the evidence that caution must be exercised as DL-based methods move closer to clinical practice.
Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions
Authors: Germain Morilhat, Naomi Kifle, Sandra FinesilverSmith, Bram Ruijsink, Vittoria Vergani, Habtamu Tegegne Desita, Zerubabel Tegegne Desita, Esther Puyol-Anton, Aaron Carass, Andrew P. King
Date: 2022-08-05 00:00:00

In many low-to-middle income (LMIC) countries, ultrasound is used for assessment of pleural effusion. Typically, the extent of the effusion is manually measured by a sonographer, leading to significant intra-/inter-observer variability. In this work, we investigate the use of deep learning (DL) to automate the process of pleural effusion segmentation from ultrasound images. On two datasets acquired in a LMIC setting, we achieve median Dice Similarity Coefficients (DSCs) of 0.82 and 0.74 respectively using the nnU-net DL model. We also investigate the use of coordinate convolutions in the DL model and find that this results in a statistically significant improvement in the median DSC on the first dataset to 0.85, with no significant change on the second dataset. This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound in LMIC settings where there is often a lack of experienced radiologists to perform such tasks.
A Semantic Alignment System for Multilingual Query-Product Retrieval
Authors: Qi Zhang, Zijian Yang, Yilun Huang, Ze Chen, Zijian Cai, Kangxu Wang, Jiewen Zheng, Jiarong He, Jin Gao
Date: 2022-08-05 00:00:00
Tasks: Data Augmentation, Retrieval, Task 2

This paper mainly describes our winning solution (team name: www) to Amazon ESCI Challenge of KDD CUP 2022, which achieves a NDCG score of 0.9043 and wins the first place on task 1: the query-product ranking track. In this competition, participants are provided with a real-world large-scale multilingual shopping queries data set and it contains query-product pairs in English, Japanese and Spanish. Three different tasks are proposed in this competition, including ranking the results list as task 1, classifying the query/product pairs into Exact, Substitute, Complement, or Irrelevant (ESCI) categories as task 2 and identifying substitute products for a given query as task 3. We mainly focus on task 1 and propose a semantic alignment system for multilingual query-product retrieval. Pre-trained multilingual language models (LM) are adopted to get the semantic representation of queries and products. Our models are all trained with cross-entropy loss to classify the query-product pairs into ESCI 4 categories at first, and then we use weighted sum with the 4-class probabilities to get the score for ranking. To further boost the model, we also do elaborative data preprocessing, data augmentation by translation, specially handling English texts with English LMs, adversarial training with AWP and FGM, self distillation, pseudo labeling, label smoothing and ensemble. Finally, Our solution outperforms others both on public and private leaderboard.
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
Authors: Yongxiang Tang, Wentao Bai, Guilin Li, Xialong Liu, Yu Zhang
Date: 2022-08-05 00:00:00
Tasks: Recommendation Systems, Retrieval

In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, Recall@N, the frequency of positive samples being retrieved in the top N ranking, is widely used. However, most of the conventional loss functions for retrieval models such as softmax cross-entropy and pairwise comparison methods do not directly optimize Recall@N. Moreover, those conventional loss functions cannot be customized for the specific retrieval size N required by each application and thus may lead to sub-optimal performance. In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases. Furthermore, we develop the Lambda method, a gradient-based method that invites more flexibility and can further boost the system performance. We evaluate the proposed CROLoss on two public benchmark datasets. The results show that CROLoss achieves SOTA results over conventional loss functions for both datasets with various choices of retrieval size N. CROLoss has been deployed onto our online E-commerce advertising platform, where a fourteen-day online A/B test demonstrated that CROLoss contributes to a significant business revenue growth of 4.75%.
Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews
Authors: Danny Stax, Manel Slokom, Martha Larson
Date: 2022-08-05 00:00:00
Tasks: Position, Recommendation Systems

Recently, researchers have turned their attention to recommender systems that use only minimal necessary data. This trend is informed by the idea that recommender systems should use no more user interactions than are needed in order to provide users with useful recommendations. In this position paper, we make the case for applying the idea of minimal necessary data to recommender systems that use user reviews. We argue that the content of individual user reviews should be subject to minimization. Specifically, reviews used as training data to generate recommendations or reviews used to help users decide on purchases or consumption should be automatically edited to contain only the information that is needed.
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
Authors: Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee
Date: 2022-08-05 00:00:00
Tasks: Attribute, Recommendation Systems

Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while effectively guiding users to a more personalized recommendation. Perhaps surprisingly, conversational recommender systems can be plagued by popularity bias, much like traditional recommender systems. In this paper, we systematically study the problem of popularity bias in CRSs. We demonstrate the existence of popularity bias in existing state-of-the-art CRSs from an exposure rate, a success rate, and a conversational utility perspective, and propose a suite of popularity bias metrics designed specifically for the CRS setting. We then introduce a debiasing framework with three unique features: (i) Popularity-Aware Focused Learning to reduce the popularity-distorting impact on preference prediction; (ii) Cold-Start Item Embedding Reconstruction via Attribute Mapping, to improve the modeling of cold-start items; and (iii) Dual-Policy Learning, to better guide the CRS when dealing with either popular or unpopular items. Through extensive experiments on two frequently used CRS datasets, we find the proposed model-agnostic debiasing framework not only mitigates the popularity bias in state-of-the-art CRSs but also improves the overall recommendation performance.
Beware of Discarding Used SRAMs: Information is Stored Permanently
Authors: Joshua Hovanes, Yadi Zhong, Ujjwal Guin
Date: 2022-08-04 00:00:00
Tasks: Retrieval

Data recovery has long been a focus of the electronics industry for decades by security experts, focusing on hard disk recovery, a type of non-volatile memory. Unfortunately, none of the existing research, neither from academia, industry, or government, have ever considered data recovery from volatile memories. The data is lost when it is powered off, by definition. To the best of our knowledge, we are the first to present an approach to recovering data from a static random access memory. It is conventional wisdom that SRAM loses its contents whenever it turns off, and it is not required to protect sensitive information, e.g., the firmware code, secret encryption keys, etc., when an SRAM-based computing system retires. Unfortunately, the recycling of integrated circuits poses a severe threat to the protection of intellectual properties. In this paper, we present a novel concept to retrieve SRAM data as aging leads to a power-up state with an imprint of the stored values. We show that our proposed approaches can partially recover the previously used SRAM content. The accuracy of the recovered data can be further increased by incorporating multiple SRAM chips compared to a single one. It is impossible to retrieve the prior content of some stable SRAM cells, where aging shifts these cells towards stability. As the locations of these cells vary from chip to chip due to uncontrollable process variation, the same cell has a higher chance of being unstable or stable against aging in any of the chips, which helps us recover the content. Finally, majority voting is used to combine a set of SRAM chips' data to recover the stored data. We present our experimental result using commercial off-the-shelf SRAMs with stored binary image data before performing accelerated aging. We demonstrate the successful partial retrieval on SRAMs that are aged with as little as 4 hours of accelerated aging with 85C.
Models of signal processing in human hearing
Authors: Roland Sottek∗, Klaus Genuit
Date: 2005-06-09 00:00:00
Proceeding: AEU - International Journal of Electronics and Communications 2005 6

The paper presents a theoretical system model of the monaural signal processing in the human auditory system. Moreover,
it deals with the application of this hearing model to the calculation of just-noticeable amplitude differences and variations,
as well as elementary psychoacoustic parameters and a deconvolution algorithm for the detection of dominant spectral components based on the excitation pattern of the basilar membrane. The use of the hearing model for sound quality tasks allows evaluating the temporal and spectral patterns of a sound (“Relative Approach” analysis) where absolute level or loudness is often without significance.
Analysis of Thai Capital Market Linkages: Part I. Bivariate Copula Approach
Authors: Poomjai Nacaskul, Isariyaporn Sukcharoenchaikul, Pitikorn Khlaisamniang
Date: 2020-08-08 00:00:00
Proceeding: www.researchgate.net 2020 8
Tasks: Time Series, Time Series Analysis

Analytically thorough understanding of financial-market dynamics is fundamental to the promotion of capital-market innovation, efficiency, and resilience; innovative, efficient, and resilient capital market, in turn, is fundamental to the sustainable economic development of the nation and the robust financial stability of its economy. This paper uses Bivariate Copula (semi-parametric statistics) techniques to analyse probabilistic co-movement amongst 14 variables representing domestic (Thai) and international (US/Emerging Market/Asia) foreign-exchange, fixed-income, and equity market movements, as well as foreign portfolio-investment flows into Thai equity shares and bonds. In addition, by staggering paired time-series w/ time lag on one of the pairs, the resulting copula relationship is suggestive of information flow, similar in spirit to testing for Granger Causality. The methodology pipeline thus developed can be applied to any other time-series pairings of interest, not just those related to Thai financial markets.
Subsurface defect imaging in PZT ceramics using dual point contact excitation and detection
Authors: H. Mahawar, K. Agarwal, D. K. Prasad, F. Melandso, A. Habib
Date: 2019-12-04 00:00:00
Tasks: Denoising, Structural Health Monitoring

The application of piezoelectric materials, such as Lead Zirconate Titanate (ZrxTi1-x) O3 (PZT) is increasing in multiple dynamic industries such as structural health monitoring, wireless energy harvesting devices, measuring blood flow, etc.The main aim of this paper is to denoise the images generated by dual point excitation and detection method for subsurface damage detection. Nonetheless, these denoising schemes can be extended for other noisy images. In order to study effectively, the subsurface defects in a PZT ceramics and from its images the denoising schemes have been examined and a metric for the quantification of the noise is proposed which was previously non-existent. A delta pulse is used for excitation of the acoustic waves in PZT ceramics. The metric aids in calculating the energy of the noise been removed and also to verify the proficiency of the denoising technique been incorporated.
TransDSSL: Transformer based Depth Estimation via Self-Supervised Learning
Authors: Daechan Han, Jeongmin Shin, Namil Kim, Soomnim Hwang, Yukyung Choi
Date: 2022-08-05 00:00:00
Proceeding: journal 2022 8
Tasks: Decoder, Depth Estimation, Monocular Depth Estimation, Self-Supervised Learning, Unsupervised Monocular Depth Estimation

Recently, transformers have been widely adopted for various computer vision tasks and show promising results due to their ability to encode long-range spatial dependencies in an image effectively. However, very few studies on adopting transformers in self-supervised depth estimation have been conducted. When replacing the CNN architecture with the transformer in self-supervised learning of depth, we encounter several problems such as problematic multi-scale photometric loss function when used with transformers and, insuffcient ability to capture local details. In this paper, we propose an attention-based decoder module, Pixel-Wise Skip Attention (PWSA), to enhance fine details in feature maps while keeping global context from transformers. In addition, we propose utilizing self-distillation loss with single-scale photometric loss to alleviate the instability of transformer training by using correct training signals. We demonstrate that the proposed model performs accurate predictions on large objects and thin structures that require global context and local details. Our model achieves state-ofthe-art performance among the self-supervised monocular depth estimation methods on KITTI and DDAD benchmarks