pwc-archive/papers-with-abstracts huggingface dataset:
Occupational Retirement and Pension Reform: The Roles of Physical and Cognitive Health
Authors: Jiayi Wen
Date: 2023-08-25 00:00:00

Despite increasing cognitive demands of jobs, knowledge about the role of health in retirement has centered on its physical dimensions. This paper estimates a dynamic programming model of retirement that incorporates multiple health dimensions, allowing differential effects on labor supply across occupations. Results show that the effect of cognitive health surges exponentially after age 65, and it explains a notable share of employment declines in cognitively demanding occupations. Under pension reforms, physical constraint mainly impedes manual workers from delaying retirement, whereas cognitive constraint dampens the response of clerical and professional workers. Multidimensional health thus unevenly exacerbate welfare losses across occupations.
Efficient Learned Lossless JPEG Recompression
Authors: Lina Guo, Yuanyuan Wang, Tongda Xu, Jixiang Luo, Dailan He, Zhenjun Ji, Shanshan Wang, Yang Wang, Hongwei Qin
Date: 2023-08-25 00:00:00
Tasks: GPU, Image Compression, Quantization

JPEG is one of the most popular image compression methods. It is beneficial to compress those existing JPEG files without introducing additional distortion. In this paper, we propose a deep learning based method to further compress JPEG images losslessly. Specifically, we propose a Multi-Level Parallel Conditional Modeling (ML-PCM) architecture, which enables parallel decoding in different granularities. First, luma and chroma are processed independently to allow parallel coding. Second, we propose pipeline parallel context model (PPCM) and compressed checkerboard context model (CCCM) for the effective conditional modeling and efficient decoding within luma and chroma components. Our method has much lower latency while achieves better compression ratio compared with previous SOTA. After proper software optimization, we can obtain a good throughput of 57 FPS for 1080P images on NVIDIA T4 GPU. Furthermore, combined with quantization, our approach can also act as a lossy JPEG codec which has obvious advantage over SOTA lossy compression methods in high bit rate (bpp$>0.9$).
Head-Neck Dual-energy CT Contrast Media Reduction Using Diffusion Models
Authors: Qing Lyu, Josh Tan, Megan E. Lipford, Chuang Niu, Micheal E. Zapadka, Christopher M. Lack, Jonathan D. Clemente, Christopher T. Whitlow, Ge Wang
Date: 2023-08-24 00:00:00
Tasks: Denoising

Iodinated contrast media is essential for dual-energy computed tomography (DECT) angiography. Previous studies show that iodinated contrast media may cause side effects, and the interruption of the supply chain in 2022 led to a severe contrast media shortage in the US. Both factors justify the necessity of contrast media reduction in relevant clinical applications. In this study, we propose a diffusion model-based deep learning framework to address this challenge. First, we simulate different levels of low contrast dosage DECT scans from the standard normal contrast dosage DECT scans using material decomposition. Conditional denoising diffusion probabilistic models are then trained to enhance the contrast media and create contrast-enhanced images. Our results demonstrate that the proposed methods can generate high-quality contrast-enhanced results even for images obtained with as low as 12.5% of the normal contrast dosage. Furthermore, our method outperforms selected competing methods in a human reader study.
6G goal-oriented communications: How to coexist with legacy systems?
Authors: Mattia Merluzzi, Miltiadis C. Filippou, Leonardo Gomes Baltar, Markus D. Muek, Emilio Calvanese Strinati
Date: 2023-08-25 00:00:00

6G will connect heterogeneous intelligent agents to make them operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how AI and ML models behave depending on: i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper we investigate how to effectively share spectrum resources between a legacy communication system and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an eMBB service, i.e., a user uploading a video stream, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal-effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal-effectiveness, as well as that of goal cost . We argue and show, through numerical evaluations, that communication reliability and goal-effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal-effectiveness, a long-term optimization problem is formulated and solved via Lyapunov optimization tools, to guarantee the desired performance. Finally, our numerical results assess the advantages of the proposed optimization, and the superiority of the goal-oriented strategy against baseline 5G compliant legacy approaches, under both stationary and non-stationary environments.
Joint Band Assignment and Beam Management using Hierarchical Reinforcement Learning for Multi-Band Communication
Authors: Dohyun Kim, Miguel R. Castellanos, Robert W. Heath Jr
Date: 2023-08-25 00:00:00
Tasks: Hierarchical Reinforcement Learning, Management, Reinforcement Learning (RL)

Multi-band operation in wireless networks can improve data rates by leveraging the benefits of propagation in different frequency ranges. Distinctive beam management procedures in different bands complicate band assignment because they require considering not only the channel quality but also the associated beam management overhead. Reinforcement learning (RL) is a promising approach for multi-band operation as it enables the system to learn and adjust its behavior through environmental feedback. In this paper, we formulate a sequential decision problem to jointly perform band assignment and beam management. We propose a method based on hierarchical RL (HRL) to handle the complexity of the problem by separating the policies for band selection and beam management. We evaluate the proposed HRL-based algorithm on a realistic channel generated based on ray-tracing simulators. Our results show that the proposed approach outperforms traditional RL approaches in terms of reduced beam training overhead and increased data rates under a realistic vehicular channel.
Performance Comparison of Design Optimization and Deep Learning-based Inverse Design
Authors: Minyoung Jwa, Jihoon Kim, Seungyeon Shin, Ah-hyeon Jin, Dongju Shin, Namwoo Kang
Date: 2023-08-23 00:00:00
Tasks: Deep Learning

Surrogate model-based optimization has been increasingly used in the field of engineering design. It involves creating a surrogate model with objective functions or constraints based on the data obtained from simulations or real-world experiments, and then finding the optimal solution from the model using numerical optimization methods. Recent advancements in deep learning-based inverse design methods have made it possible to generate real-time optimal solutions for engineering design problems, eliminating the requirement for iterative optimization processes. Nevertheless, no comprehensive study has yet closely examined the specific advantages and disadvantages of this novel approach compared to the traditional design optimization method. The objective of this paper is to compare the performance of traditional design optimization methods with deep learning-based inverse design methods by employing benchmark problems across various scenarios. Based on the findings of this study, we provide guidelines that can be taken into account for the future utilization of deep learning-based inverse design. It is anticipated that these guidelines will enhance the practical applicability of this approach to real engineering design problems.
The role of PHF5A in cancer: A review and update
Authors: Patrick Diaba-Nuhoho
Date: 2023-08-24 00:00:00
Tasks: Management

PHF5A is a member of the zinc-finger proteins. To advance knowledge on their role in carcinogenesis, data from experimental studies, animal models and clinical studies in different tumorigenesis have been reviewed. Furthermore, PHF5A as an oncogenic function, is frequently expressed in tumor cells and a potential prognostic marker for different cancers. PHF5A is implicated in the regulation of cancer cell proliferation, invasion, migration and metastasis. Knockdown of PHF5A prevented the invasion and metastasis of tumor cells. Here, the role of PHF5A in different cancers and their possible mechanism in relation to recent literature is reviewed and discussed. However, there is an open promising perspective to their therapeutic management for different cancer types.
Robust Core-Periphery Constrained Transformer for Domain Adaptation
Authors: Xiaowei Yu, Dajiang Zhu, Tianming Liu
Date: 2023-08-25 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to learn transferable representation across domains. Recently a few UDA works have successfully applied Transformer-based methods and achieved state-of-the-art (SOTA) results. However, it remains challenging when there exists a large domain gap between the source and target domain. Inspired by humans' exceptional transferability abilities to adapt knowledge from familiar to uncharted domains, we try to apply the universally existing organizational structure in the human functional brain networks, i.e., the core-periphery principle to design the Transformer and improve its UDA performance. In this paper, we propose a novel brain-inspired robust core-periphery constrained transformer (RCCT) for unsupervised domain adaptation, which brings a large margin of performance improvement on various datasets. Specifically, in RCCT, the self-attention operation across image patches is rescheduled by an adaptively learned weighted graph with the Core-Periphery structure (CP graph), where the information communication and exchange between images patches are manipulated and controlled by the connection strength, i.e., edge weight of the learned weighted CP graph. Besides, since the data in domain adaptation tasks can be noisy, to improve the model robustness, we intentionally add perturbations to the patches in the latent space to ensure generating robust learned weighted core-periphery graphs. Extensive evaluations are conducted on several widely tested UDA benchmarks. Our proposed RCCT consistently performs best compared to existing works, including 88.3\% on Office-Home, 95.0\% on Office-31, 90.7\% on VisDA-2017, and 46.0\% on DomainNet.
Alternating Shrinking Higher-order Interactions for Sparse Neural Population Activity
Authors: Ulises Rodríguez-Domínguez, Hideaki Shimazaki
Date: 2023-08-25 00:00:00

Neurons in living things work cooperatively and efficiently to process incoming sensory information, often exhibiting sparse and widespread population activity involving structured higher-order interactions. While there are statistical models based on continuous probability distributions for neurons' sparse firing rates, how the spiking activities of a large number of interacting neurons result in the sparse and widespread population activity remains unknown. Here, for homogeneous (0,1) binary neurons, we provide sufficient conditions under which their spike-count population distribution converges to a sparse widespread distribution of the population spike rate in an infinitely large population of neurons. Following the conditions, we propose new models belonging to an exponential family distribution in which the sign and magnitude of neurons' higher-order interactions alternate and shrink as the order increases. The distributions exhibit parameter-dependent sparsity on a bounded support for the population firing rate. The theory serves as a building block for developing prior distributions and neurons' non-linearity for spike-based sparse coding.
UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
Authors: Haiwen Diao, Bo Wan, Ying Zhang, Xu Jia, Huchuan Lu, Long Chen
Date: 2023-08-28 00:00:00
Proceeding: CVPR 2024 1
Tasks: Question Answering, Retrieval, Text Retrieval, Transfer Learning, Video-Text Retrieval, Visual Grounding, Visual Question Answering

Parameter-efficient transfer learning (PETL), i.e., fine-tuning a small portion of parameters, is an effective strategy for adapting pre-trained models to downstream domains. To further reduce the memory demand, recent PETL works focus on the more valuable memory-efficient characteristic. In this paper, we argue that the scalability, adaptability, and generalizability of state-of-the-art methods are hindered by structural dependency and pertinency on specific pre-trained backbones. To this end, we propose a new memory-efficient PETL strategy, Universal Parallel Tuning (UniPT), to mitigate these weaknesses. Specifically, we facilitate the transfer process via a lightweight and learnable parallel network, which consists of: 1) A parallel interaction module that decouples the sequential connections and processes the intermediate activations detachedly from the pre-trained network. 2) A confidence aggregation module that learns optimal strategies adaptively for integrating cross-layer features. We evaluate UniPT with different backbones (e.g., T5, VSE$\infty$, CLIP4Clip, Clip-ViL, and MDETR) on various vision-and-language and pure NLP tasks. Extensive ablations on 18 datasets have validated that UniPT can not only dramatically reduce memory consumption and outperform the best competitor, but also achieve competitive performance over other plain PETL methods with lower training memory overhead. Our code is publicly available at: https://github.com/Paranioar/UniPT.
CPFES: Physical Fitness Evaluation Based on Canadian Agility and Movement Skill Assessment
Authors: Pengcheng Dong, Xiaojin Mao, Lixia Fan, Wenbo Wan, Jiande Sun
Date: 2023-08-28 00:00:00
Tasks: Pose Estimation

In recent years, the assessment of fundamental movement skills integrated with physical education has focused on both teaching practice and the feasibility of assessment. The object of assessment has shifted from multiple ages to subdivided ages, while the content of assessment has changed from complex and time-consuming to concise and efficient. Therefore, we apply deep learning to physical fitness evaluation, we propose a system based on the Canadian Agility and Movement Skill Assessment (CAMSA) Physical Fitness Evaluation System (CPFES), which evaluates children's physical fitness based on CAMSA, and gives recommendations based on the scores obtained by CPFES to help children grow. We have designed a landmark detection module and a pose estimation module, and we have also designed a pose evaluation module for the CAMSA criteria that can effectively evaluate the actions of the child being tested. Our experimental results demonstrate the high accuracy of the proposed system.
Improving the performance of object detection by preserving label distribution
Authors: Heewon Lee, Sangtae Ahn
Date: 2023-08-28 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection is a task that performs position identification and label classification of objects in images or videos. The information obtained through this process plays an essential role in various tasks in the field of computer vision. In object detection, the data utilized for training and validation typically originate from public datasets that are well-balanced in terms of the number of objects ascribed to each class in an image. However, in real-world scenarios, handling datasets with much greater class imbalance, i.e., very different numbers of objects for each class , is much more common, and this imbalance may reduce the performance of object detection when predicting unseen test images. In our study, thus, we propose a method that evenly distributes the classes in an image for training and validation, solving the class imbalance problem in object detection. Our proposed method aims to maintain a uniform class distribution through multi-label stratification. We tested our proposed method not only on public datasets that typically exhibit balanced class distribution but also on custom datasets that may have imbalanced class distribution. We found that our proposed method was more effective on datasets containing severe imbalance and less data. Our findings indicate that the proposed method can be effectively used on datasets with substantially imbalanced class distribution.
Improving Video Violence Recognition with Human Interaction Learning on 3D Skeleton Point Clouds
Authors: Yukun Su, Guosheng Lin, Qingyao Wu
Date: 2023-08-26 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Deep learning has proved to be very effective in video action recognition. Video violence recognition attempts to learn the human multi-dynamic behaviours in more complex scenarios. In this work, we develop a method for video violence recognition from a new perspective of skeleton points. Unlike the previous works, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. Specifically, we propose two types of Skeleton Points Interaction Learning (SPIL) strategies: (i) Local-SPIL: by constructing a specific weight distribution strategy between local regional points, Local-SPIL aims to selectively focus on the most relevant parts of them based on their features and spatial-temporal position information. In order to capture diverse types of relation information, a multi-head mechanism is designed to aggregate different features from independent heads to jointly handle different types of relationships between points. (ii) Global-SPIL: to better learn and refine the features of the unordered and unstructured skeleton points, Global-SPIL employs the self-attention layer that operates directly on the sampled points, which can help to make the output more permutation-invariant and well-suited for our task. Extensive experimental results validate the effectiveness of our approach and show that our model outperforms the existing networks and achieves new state-of-the-art performance on video violence datasets.
Bengali Document Layout Analysis with Detectron2
Authors: Md Ataullha, Mahedi Hassan Rabby, Mushfiqur Rahman, Tahsina Bintay Azam
Date: 2023-08-26 00:00:00
Tasks: Data Augmentation, Document Layout Analysis, Management, Optical Character Recognition, Optical Character Recognition (OCR)

Document digitization is vital for preserving historical records, efficient document management, and advancing OCR (Optical Character Recognition) research. Document Layout Analysis (DLA) involves segmenting documents into meaningful units like text boxes, paragraphs, images, and tables. Challenges arise when dealing with diverse layouts, historical documents, and unique scripts like Bengali, hindered by the lack of comprehensive Bengali DLA datasets. We improved the accuracy of the DLA model for Bengali documents by utilizing advanced Mask R-CNN models available in the Detectron2 library. Our evaluation involved three variants: Mask R-CNN R-50, R-101, and X-101, both with and without pretrained weights from PubLayNet, on the BaDLAD dataset, which contains human-annotated Bengali documents in four categories: text boxes, paragraphs, images, and tables. Results show the effectiveness of these models in accurately segmenting Bengali documents. We discuss speed-accuracy tradeoffs and underscore the significance of pretrained weights. Our findings expand the applicability of Mask R-CNN in document layout analysis, efficient document management, and OCR research while suggesting future avenues for fine-tuning and data augmentation.
MagicEdit: High-Fidelity and Temporally Coherent Video Editing
Authors: Jun Hao Liew, Hanshu Yan, Jianfeng Zhang, Zhongcong Xu, Jiashi Feng
Date: 2023-08-28 00:00:00
Tasks: Translation, Video Editing

In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task. We found that high-fidelity and temporally coherent video-to-video translation can be achieved by explicitly disentangling the learning of content, structure and motion signals during training. This is in contradict to most existing methods which attempt to jointly model both the appearance and temporal representation within a single framework, which we argue, would lead to degradation in per-frame quality. Despite its simplicity, we show that MagicEdit supports various downstream video editing tasks, including video stylization, local editing, video-MagicMix and video outpainting.
Price-Discrimination Game for Distributed Resource Management in Federated Learning
Authors: Han Zhang, Halvin Yang, Guopeng Zhang
Date: 2023-08-26 00:00:00
Tasks: Federated Learning, Management

In vanilla federated learning (FL) such as FedAvg, the parameter server (PS) and multiple distributed clients can form a typical buyer's market, where the number of PS/buyers of FL services is far less than the number of clients/sellers. In order to improve the performance of FL and reduce the cost of motivating clients to participate in FL, this paper proposes to differentiate the pricing for services provided by different clients rather than simply providing the same service pricing for different clients. The price is differentiated based on the performance improvements brought to FL and their heterogeneity in computing and communication capabilities. To this end, a price-discrimination game (PDG) is formulated to comprehensively address the distributed resource management problems in FL, including multi-objective trade-off, client selection, and incentive mechanism. As the PDG is a mixed-integer nonlinear programming (MINLP) problem, a distributed semi-heuristic algorithm with low computational complexity and low communication overhead is designed to solve it. The simulation result verifies the effectiveness of the proposed approach.
High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net
Authors: Zinuo Li, Xuhang Chen, Chi-Man Pun, Xiaodong Cun
Date: 2023-08-27 00:00:00
Proceeding: ICCV 2023 1
Tasks: Document Shadow Removal, Image Shadow Removal, Shadow Removal

Shadows often occur when we capture the documents with casual equipment, which influences the visual quality and readability of the digital copies. Different from the algorithms for natural shadow removal, the algorithms in document shadow removal need to preserve the details of fonts and figures in high-resolution input. Previous works ignore this problem and remove the shadows via approximate attention and small datasets, which might not work in real-world situations. We handle high-resolution document shadow removal directly via a larger-scale real-world dataset and a carefully designed frequency-aware network. As for the dataset, we acquire over 7k couples of high-resolution (2462 x 3699) images of real-world document pairs with various samples under different lighting circumstances, which is 10 times larger than existing datasets. As for the design of the network, we decouple the high-resolution images in the frequency domain, where the low-frequency details and high-frequency boundaries can be effectively learned via the carefully designed network structure. Powered by our network and dataset, the proposed method clearly shows a better performance than previous methods in terms of visual quality and numerical results. The code, models, and dataset are available at: https://github.com/CXH-Research/DocShadow-SD7K