pwc-archive/papers-with-abstracts huggingface dataset:
A standardised protocol for assessment of relative SARS-CoV-2 variant severity, with application to severity risk for COVID-19 cases infected with Omicron BA.1 compared to Delta variants in six European countries
Authors: Tommy Nyberg, Peter Bager, Ingrid Bech Svalgaard, Dritan Bejko, Nick Bundle, Josie Evans, Tyra Grove Krause, Jim McMenamin, Joël Mossong, Heather Mutch, Ajibola Omokanye, André Peralta-Santos, Pedro Pinto Leite, Jostein Starrfelt, Simon Thelwall, Lamprini Veneti, Robert Whittaker, John Wood, Richard Pebody, Anne M Presanis
Date: 2023-03-09 00:00:00
Tasks: ICU Admission

Several SARS-CoV-2 variants that evolved during the COVID-19 pandemic have appeared to differ in severity, based on analyses of single-country datasets. With decreased SARS-CoV-2 testing and sequencing, international collaborative studies will become increasingly important for timely assessment of the severity of newly emerged variants. The Joint WHO Regional Office for Europe and ECDC Infection Severity Working Group was formed to produce and pilot a standardised study protocol to estimate relative variant case-severity in settings with individual-level SARS-CoV-2 testing and COVID-19 outcome data during periods when two variants were co-circulating. To assess feasibility, the study protocol and its associated statistical analysis code was applied by local investigators in Denmark, England, Luxembourg, Norway, Portugal and Scotland to assess the case-severity of Omicron BA.1 relative to Delta cases. After pooling estimates using meta-analysis methods (random effects estimates), the risk of hospital admission (adjusted hazard ratio [aHR]=0.41, 95% CI 0.31-0.54), ICU admission (aHR=0.12, 95% CI 0.05-0.27), and death (aHR=0.31, 95% CI 0.28-0.35) was lower for Omicron BA.1 compared to Delta cases. The aHRs varied by age group and vaccination status. In conclusion, this study has demonstrated the feasibility of conducting variant severity analyses in a multinational collaborative framework. The results add further evidence for the reduced severity of the Omicron BA.1 variant.
The Financial Market of Indices of Socioeconomic Wellbeing
Authors: Thilini V. Mahanama, Abootaleb Shirvani, Svetlozar Rachev
Date: 2023-03-10 00:00:00

The financial industry should be involved in mitigating the risk of downturns in the financial wellbeing indices around the world by implementing well-developed financial tools such as insurance instruments on the underlying wellbeing indices. We define a new quantitative measure of the wellbeing of a country's population for those countries using the world development indicators provided by the World Bank. We monetize the indices of socioeconomic wellbeing, which serve as "risky assets," and consequently develop a global financial market for them, which serves as a "market of indices of socioeconomic wellbeing." Then, we compare the wellbeing of different countries using financial econometric analysis and dynamic asset pricing theory. We provide the optimal portfolio weight composition along with the efficient frontiers of the wellbeing socioeconomic indices with different risk-return measures. We derive insurance instruments, such as put options, which allow the financial industry to monitor, manage, and trade these indices, creating the funds for insurance against adverse movements of those indices. Our findings should help financial institutions to incorporate socioeconomic issues as an additional dimension to their "two-dimensional" risk-return adjusted optimal financial portfolios.
NFT Bubbles
Authors: Andrea Barbon, Angelo Ranaldo
Date: 2023-03-10 00:00:00

By investigating nonfungible tokens (NFTs), we provide the first systematic study of retail investor behavior through asset bubbles. Given that NFTs are recorded in public blockchains, we are able to track investor behavior over time, leading to the identification of numerous price run-ups and crashes. Our study reveals that agent-level variables, such as investor sophistication, heterogeneity, and wash trading, in addition to aggregate variables, such as volatility, price acceleration, and turnover, significantly predict bubble formation and price crashes. We find that sophisticated investors consistently outperform others and exhibit characteristics consistent with superior information and skills, supporting the narrative surrounding asset pricing bubbles.
Mixed noise reduction via sparse error constraint representation of high frequency image for wildlife image
Authors: Yuan Xu, Yaqin Zhao, Peng Lu
Date: 2022-05-31 00:00:00
Proceeding: Multimedia Tools and Applications 2022 5
Tasks: Denoising, Dictionary Learning

Wildlife image noise reduction is a difficult and challenging problem since the images are inevitably corrupted by the mixed noise in the complex field environments. Most of the existing denoising methods focused on the noise removal added manually to pure images. However, the noise of wildlife images taken in the field is random mixed noise; thus, the existing noise reduction algorithms are not suitable for the denoising of wildlife images. In this paper, we propose a novel mixed noise reduction method based on sparse error constraint representation for removing the wildlife image noise. Firstly, we use 2D-DCT method to decompose a noisy image into a high frequency image and a low frequency image, and then rank 2D-DCT coefficients based on Zig-zag sorting algorithm. As we know, the high frequency image contains more noise, so the dictionary learning model of high frequency image is established to recover the images corrupted by the mixed noise. The sparse error term describes the error between the sparse coefficients of the original image and those obtained by the error constraint method. And then we utilize the algorithm proximal alternating linearization minimization to solve the objective function due to the nonconvex and non-smooth minimization problem. In order to update the dictionary, we apply the lp- l1- norm term for sparse coding to obtain the optimal solution of sparse coefficients. The experiment results show that the proposed method has good noise reduction results for both the noisy images recorded in the wild and the images artificially corrupted by mixed noise while retain more details of the wildlife objects in the restored images.
Vessel-Promoted OCT to OCTA Image Translation by Heuristic Contextual Constraints
Authors: Shuhan LI, Dong Zhang, Xiaomeng Li, Chubin Ou, Lin An, Yanwu Xu, Kwang-Ting Cheng
Date: 2023-03-13 00:00:00
Tasks: SSIM, Translation

Optical Coherence Tomography Angiography (OCTA) is a crucial tool in the clinical screening of retinal diseases, allowing for accurate 3D imaging of blood vessels through non-invasive scanning. However, the hardware-based approach for acquiring OCTA images presents challenges due to the need for specialized sensors and expensive devices. In this paper, we introduce a novel method called TransPro, which can translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images without requiring any additional hardware modifications. Our TransPro method is primarily driven by two novel ideas that have been overlooked by prior work. The first idea is derived from a critical observation that the OCTA projection map is generated by averaging pixel values from its corresponding B-scans along the Z-axis. Hence, we introduce a hybrid architecture incorporating a 3D adversarial generative network and a novel Heuristic Contextual Guidance (HCG) module, which effectively maintains the consistency of the generated OCTA images between 3D volumes and projection maps. The second idea is to improve the vessel quality in the translated OCTA projection maps. As a result, we propose a novel Vessel Promoted Guidance (VPG) module to enhance the attention of network on retinal vessels. Experimental results on two datasets demonstrate that our TransPro outperforms state-of-the-art approaches, with relative improvements around 11.4% in MAE, 2.7% in PSNR, 2% in SSIM, 40% in VDE, and 9.1% in VDC compared to the baseline method. The code is available at: https://github.com/ustlsh/TransPro.
Universal Instance Perception as Object Discovery and Retrieval
Authors: Bin Yan, Yi Jiang, Jiannan Wu, Dong Wang, Ping Luo, Zehuan Yuan, Huchuan Lu
Date: 2023-03-12 00:00:00
Proceeding: CVPR 2023 1
Tasks: Described Object Detection, Generalized Referring Expression Comprehension, Instance Segmentation, Multi-Object Tracking and Segmentation, Multiple Object Tracking, Object, object-detection, Object Detection, Object Discovery, Object Tracking, Referring Expression, Referring Expression Comprehension, Referring Expression Segmentation, Referring Video Object Segmentation, Retrieval, Semantic Segmentation, Video Instance Segmentation, Visual Object Tracking, Visual Tracking, Zero Shot Segmentation

All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent subtasks. In this work, we present a universal instance perception model of the next generation, termed UNINEXT. UNINEXT reformulates diverse instance perception tasks into a unified object discovery and retrieval paradigm and can flexibly perceive different types of objects by simply changing the input prompts. This unified formulation brings the following benefits: (1) enormous data from different tasks and label vocabularies can be exploited for jointly training general instance-level representations, which is especially beneficial for tasks lacking in training data. (2) the unified model is parameter-efficient and can save redundant computation when handling multiple tasks simultaneously. UNINEXT shows superior performance on 20 challenging benchmarks from 10 instance-level tasks including classical image-level tasks (object detection and instance segmentation), vision-and-language tasks (referring expression comprehension and segmentation), and six video-level object tracking tasks. Code is available at https://github.com/MasterBin-IIAU/UNINEXT.
Range Resolution Enhanced Method with Spectral Properties for Hyperspectral Lidar
Authors: Yuhao Xia, Shilong Xu, Hui Shao, Ahui Hou, Jiajie Fang, Fei Han, Youlong Chen, Jiaqi Wen, Yuwei Chen, Yihua Hu
Date: 2023-03-03 00:00:00
Tasks: Position

Waveform decomposition is needed as a first step in the extraction of various types of geometric and spectral information from hyperspectral full-waveform LiDAR echoes. We present a new approach to deal with the "Pseudo-monopulse" waveform formed by the overlapped waveforms from multi-targets when they are very close. We use one single skew-normal distribution (SND) model to fit waveforms of all spectral channels first and count the geometric center position distribution of the echoes to decide whether it contains multi-targets. The geometric center position distribution of the "Pseudo-monopulse" presents aggregation and asymmetry with the change of wavelength, while such an asymmetric phenomenon cannot be found from the echoes of the single target. Both theoretical and experimental data verify the point. Based on such observation, we further propose a hyperspectral waveform decomposition method utilizing the SND mixture model with: 1) initializing new waveform component parameters and their ranges based on the distinction of the three characteristics (geometric center position, pulse width, and skew-coefficient) between the echo and fitted SND waveform and 2) conducting single-channel waveform decomposition for all channels and 3) setting thresholds to find outlier channels based on statistical parameters of all single-channel decomposition results (the standard deviation and the means of geometric center position) and 4) re-conducting single-channel waveform decomposition for these outlier channels. The proposed method significantly improves the range resolution from 60cm to 5cm at most for a 4ns width laser pulse and represents the state-of-the-art in "Pseudo-monopulse" waveform decomposition.
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection
Authors: Yingguang Yang, Renyu Yang, Hao Peng, Yangyang Li, Tong Li, Yong Liao, Pengyuan Zhou
Date: 2023-03-10 00:00:00
Tasks: Contrastive Learning, Knowledge Distillation

Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture makes it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge distillation mechanism for efficiently transferring knowledge of data distribution among clients. In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client's local model. We leverage local discriminator to enable customized model design and use local generator for data enhancement with hard-to-decide samples. Local training is conducted as multi-stage adversarial and contrastive learning to enable consistent feature spaces among clients and to constrain the optimization direction of local models, reducing the divergences between local and global models. Experiments demonstrate that FedACK outperforms the state-of-the-art approaches in terms of accuracy, communication efficiency, and feature space consistency.
Fine-grained Visual Classification with High-temperature Refinement and Background Suppression
Authors: Po-Yung Chou, Yu-Yung Kao, Cheng-Hung Lin
Date: 2023-03-11 00:00:00
Tasks: Fine-Grained Image Classification, Vocal Bursts Intensity Prediction

Fine-grained visual classification is a challenging task due to the high similarity between categories and distinct differences among data within one single category. To address the challenges, previous strategies have focused on localizing subtle discrepancies between categories and enhencing the discriminative features in them. However, the background also provides important information that can tell the model which features are unnecessary or even harmful for classification, and models that rely too heavily on subtle features may overlook global features and contextual information. In this paper, we propose a novel network called ``High-temperaturE Refinement and Background Suppression'' (HERBS), which consists of two modules, namely, the high-temperature refinement module and the background suppression module, for extracting discriminative features and suppressing background noise, respectively. The high-temperature refinement module allows the model to learn the appropriate feature scales by refining the features map at different scales and improving the learning of diverse features. And, the background suppression module first splits the features map into foreground and background using classification confidence scores and suppresses feature values in low-confidence areas while enhancing discriminative features. The experimental results show that the proposed HERBS effectively fuses features of varying scales, suppresses background noise, discriminative features at appropriate scales for fine-grained visual classification.The proposed method achieves state-of-the-art performance on the CUB-200-2011 and NABirds benchmarks, surpassing 93% accuracy on both datasets. Thus, HERBS presents a promising solution for improving the performance of fine-grained visual classification tasks. code: https://github.com/chou141253/FGVC-HERBS
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification
Authors: Ziqi He, Mengjia Xue, Yunhao Du, Zhicheng Zhao, Fei Su
Date: 2023-03-13 00:00:00
Tasks: Clustering, Computational Efficiency, Contrastive Learning, Person Re-Identification, Self-Supervised Learning, Unsupervised Person Re-Identification

Unsupervised Re-ID methods aim at learning robust and discriminative features from unlabeled data. However, existing methods often ignore the relationship between module parameters of Re-ID framework and feature distributions, which may lead to feature misalignment and hinder the model performance. To address this problem, we propose a dynamic clustering and cluster contrastive learning (DCCC) method. Specifically, we first design a dynamic clustering parameters scheduler (DCPS) which adjust the hyper-parameter of clustering to fit the variation of intra- and inter-class distances. Then, a dynamic cluster contrastive learning (DyCL) method is designed to match the cluster representation vectors' weights with the local feature association. Finally, a label smoothing soft contrastive loss ($L_{ss}$) is built to keep the balance between cluster contrastive learning and self-supervised learning with low computational consumption and high computational efficiency. Experiments on several widely used public datasets validate the effectiveness of our proposed DCCC which outperforms previous state-of-the-art methods by achieving the best performance.
Transcription free filler word detection with Neural semi-CRFs
Authors: Ge Zhu, Yujia Yan, Juan-Pablo Caceres, Zhiyao Duan
Date: 2023-03-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Non-linguistic filler words, such as "uh" or "um", are prevalent in spontaneous speech and serve as indicators for expressing hesitation or uncertainty. Previous works for detecting certain non-linguistic filler words are highly dependent on transcriptions from a well-established commercial automatic speech recognition (ASR) system. However, certain ASR systems are not universally accessible from many aspects, e.g., budget, target languages, and computational power. In this work, we investigate filler word detection system that does not depend on ASR systems. We show that, by using the structured state space sequence model (S4) and neural semi-Markov conditional random fields (semi-CRFs), we achieve an absolute F1 improvement of 6.4% (segment level) and 3.1% (event level) on the PodcastFillers dataset. We also conduct a qualitative analysis on the detected results to analyze the limitations of our proposed system.
TriDet: Temporal Action Detection with Relative Boundary Modeling
Authors: Dingfeng Shi, Yujie Zhong, Qiong Cao, Lin Ma, Jia Li, DaCheng Tao
Date: 2023-03-13 00:00:00
Proceeding: CVPR 2023 1
Tasks: Action Detection, Temporal Action Localization

In this paper, we present a one-stage framework TriDet for temporal action detection. Existing methods often suffer from imprecise boundary predictions due to the ambiguous action boundaries in videos. To alleviate this problem, we propose a novel Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. In the feature pyramid of TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layer to mitigate the rank loss problem of self-attention that takes place in the video features and aggregate information across different temporal granularities. Benefiting from the Trident-head and the SGP-based feature pyramid, TriDet achieves state-of-the-art performance on three challenging benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational costs, compared to previous methods. For example, TriDet hits an average mAP of $69.3\%$ on THUMOS14, outperforming the previous best by $2.5\%$, but with only $74.6\%$ of its latency. The code is released to https://github.com/sssste/TriDet.
NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects
Authors: Jiahui Fu, Yilun Du, Kurran Singh, Joshua B. Tenenbaum, John J. Leonard
Date: 2023-03-13 00:00:00
Tasks: Object, Object SLAM

We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for complete object models, encoding full shape information while transforming SE(3)-equivariantly in tandem with the object in the physical world. With NeuSE, relative frame transforms can be directly derived from inferred latent codes. Our proposed SLAM paradigm, using NeuSE for object shape and pose characterization, can operate independently or in conjunction with typical SLAM systems. It directly infers SE(3) camera pose constraints that are compatible with general SLAM pose graph optimization, while also maintaining a lightweight object-centric map that adapts to real-world changes. Our approach is evaluated on synthetic and real-world sequences featuring changed objects and shows improved localization accuracy and change-aware mapping capability, when working either standalone or jointly with a common SLAM pipeline.
Transformer Models for Acute Brain Dysfunction Prediction
Authors: Brandon Silva, Miguel Contreras, Tezcan Ozrazgat Baslanti, Yuanfang Ren, Guan Ziyuan, Kia Khezeli, Azra Bihorac, Parisa Rashidi
Date: 2023-03-13 00:00:00
Tasks: Binary Classification, Multi-class Classification, Prediction

Acute brain dysfunctions (ABD), which include coma and delirium, are prevalent in the ICU, especially among older patients. The current approach in manual assessment of ABD by care providers may be sporadic and subjective. Hence, there exists a need for a data-driven robust system automating the assessment and prediction of ABD. In this work, we develop a machine learning system for real-time prediction of ADB using Electronic Health Record (HER) data. Our data processing pipeline enables integration of static and temporal data, and extraction of features relevant to ABD. We train several state-of-the-art transformer models and baseline machine learning models including CatBoost and XGB on the data that was collected from patients admitted to the ICU at UF Shands Hospital. We demonstrate the efficacy of our system for tasks related to acute brain dysfunction including binary classification of brain acuity and multi-class classification (i.e., coma, delirium, death, or normal), achieving a mean AUROC of 0.953 on our Long-former implementation. Our system can then be deployed for real-time prediction of ADB in ICUs to reduce the number of incidents caused by ABD. Moreover, the real-time system has the potential to reduce costs, duration of patients stays in the ICU, and mortality among those afflicted.