pwc-archive/papers-with-abstracts huggingface dataset:
Bias and Diversity in Synthetic-based Face Recognition
Authors: Marco Huber, Anh Thi Luu, Fadi Boutros, Arjan Kuijper, Naser Damer
Date: 2023-11-07 00:00:00
Tasks: Attribute, Diversity, Face Recognition, Synthetic Face Recognition

Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensitive problem that face recognition systems are susceptible to bias, i.e. performance differences between different demographic and non-demographics attributes, which can lead to unfair decisions. In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data. To do this, we looked at the distribution of gender, ethnicity, age, and head position. Furthermore, we investigated the concrete bias of three recent synthetic-based face recognition models on the studied attributes in comparison to a baseline model trained on authentic data. Our results show that the generator generate a similar distribution as the used training data in terms of the different attributes. With regard to bias, it can be seen that the synthetic-based models share a similar bias behavior with the authentic-based models. However, with the uncovered lower intra-identity attribute consistency seems to be beneficial in reducing bias.
Fast Sun-aligned Outdoor Scene Relighting based on TensoRF
Authors: Yeonjin Chang, Yearim Kim, Seunghyeon Seo, Jung Yi, Nojun Kwak
Date: 2023-11-07 00:00:00
Tasks: NeRF

In this work, we introduce our method of outdoor scene relighting for Neural Radiance Fields (NeRF) named Sun-aligned Relighting TensoRF (SR-TensoRF). SR-TensoRF offers a lightweight and rapid pipeline aligned with the sun, thereby achieving a simplified workflow that eliminates the need for environment maps. Our sun-alignment strategy is motivated by the insight that shadows, unlike viewpoint-dependent albedo, are determined by light direction. We directly use the sun direction as an input during shadow generation, simplifying the requirements of the inference process significantly. Moreover, SR-TensoRF leverages the training efficiency of TensoRF by incorporating our proposed cubemap concept, resulting in notable acceleration in both training and rendering processes compared to existing methods.
Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining
Authors: Ugur Sahin, Hang Li, Qadeer Khan, Daniel Cremers, Volker Tresp
Date: 2023-11-07 00:00:00

Contemporary large-scale visual language models (VLMs) exhibit strong representation capacities, making them ubiquitous for enhancing image and text understanding tasks. They are often trained in a contrastive manner on a large and diverse corpus of images and corresponding text captions scraped from the internet. Despite this, VLMs often struggle with compositional reasoning tasks which require a fine-grained understanding of the complex interactions of objects and their attributes. This failure can be attributed to two main factors: 1) Contrastive approaches have traditionally focused on mining negative examples from existing datasets. However, the mined negative examples might not be difficult for the model to discriminate from the positive. An alternative to mining would be negative sample generation 2) But existing generative approaches primarily focus on generating hard negative texts associated with a given image. Mining in the other direction, i.e., generating negative image samples associated with a given text has been ignored. To overcome both these limitations, we propose a framework that not only mines in both directions but also generates challenging negative samples in both modalities, i.e., images and texts. Leveraging these generative hard negative samples, we significantly enhance VLMs' performance in tasks involving multimodal compositional reasoning. Our code and dataset are released at https://ugorsahin.github.io/enhancing-multimodal-compositional-reasoning-of-vlm.html.
Bubble Economics
Authors: Tomohiro Hirano, Alexis Akira Toda
Date: 2023-11-07 00:00:00

This article provides a self-contained overview of the theory of rational asset price bubbles. We cover topics from basic definitions, properties, and classical results to frontier research, with an emphasis on bubbles attached to real assets such as stocks, housing, and land. The main message is that bubbles attached to real assets are fundamentally nonstationary phenomena related to unbalanced growth. We present a bare-bones model and draw three new insights: (i) the emergence of asset price bubbles is a necessity, instead of a possibility; (ii) asset pricing implications are markedly different between balanced growth of stationary nature and unbalanced growth of nonstationary nature; and (iii) asset price bubbles occur within larger historical trends involving shifts in industrial structure driven by technological innovation, including the transition from the Malthusian economy to the modern economy.
A necessary and sufficient condition for the existence of chaotic dynamics in a neoclassical growth model with a pollution effect
Authors: Tomohiro Uchiyama
Date: 2023-11-06 00:00:00

In this paper, we study a neoclassical growth model with a (productivity inhibiting) pollution effect. In particular, we obtain a necessary and sufficient condition for the existence of a topological chaos. We investigate how the condition changes as the strength of the pollution effect changes. This is a new application of a recent result characterising the existence of a topological chaos for a unimodal interval map by Deng, Khan, Mitra (2022).
Optimizing Climate Policy through C-ROADS and En-ROADS Analysis
Authors: Iveena Mukherjee
Date: 2023-11-06 00:00:00

With the onset of climate change and the increasing need for effective policies, a multilateral approach is needed to make an impact on the growing threats facing the environment. Through the use of systematic analysis by way of C-ROADS and En-ROADS, numerous scenarios have been simulated to shed light on the most imperative policy factors to mitigate climate change. Within C-ROADS, it was determined that the impacts of the shrinking ice-albedo effect on global temperatures is significant, however differential sea ice melting between the poles may not impact human dwellings, as all regions are impacted by sea ice melt. Flood risks are also becoming more imminent, specifically in high population density areas. In terms of afforestation, China is the emerging leader, and if other countries follow suit, this can incur substantial dividends. Upon conducting a comprehensive analysis of global trends through En-ROADS, intriguing patterns appear between the length of a policy initiative, and its effectiveness. Quick policies with gradual increases in taxation proved successful. Government intervention was also favorable, however an optimized model is presented, with moderate subsidization of renewable energy. Through this systematic analysis of assumptions and policy for effective climate change mitigation efforts, an optimized, economically-favorable solution arises.
Improving the Effectiveness of Deep Generative Data
Authors: Ruyu Wang, Sabrina Schmedding, Marco F. Huber
Date: 2023-11-07 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification

Recent deep generative models (DGMs) such as generative adversarial networks (GANs) and diffusion probabilistic models (DPMs) have shown their impressive ability in generating high-fidelity photorealistic images. Although looking appealing to human eyes, training a model on purely synthetic images for downstream image processing tasks like image classification often results in an undesired performance drop compared to training on real data. Previous works have demonstrated that enhancing a real dataset with synthetic images from DGMs can be beneficial. However, the improvements were subjected to certain circumstances and yet were not comparable to adding the same number of real images. In this work, we propose a new taxonomy to describe factors contributing to this commonly observed phenomenon and investigate it on the popular CIFAR-10 dataset. We hypothesize that the Content Gap accounts for a large portion of the performance drop when using synthetic images from DGM and propose strategies to better utilize them in downstream tasks. Extensive experiments on multiple datasets showcase that our method outperforms baselines on downstream classification tasks both in case of training on synthetic only (Synthetic-to-Real) and training on a mix of real and synthetic data (Data Augmentation), particularly in the data-scarce scenario.
Institutional Screening and the Sustainability of Conditional Cooperation
Authors: Ethan Holdahl, Jiabin Wu
Date: 2023-11-06 00:00:00

This paper studies a preference evolution model in which a population of agents are matched to play a sequential prisoner's dilemma in an incomplete information environment. An institution can design an incentive-compatible screening scheme, such as a special zone that requires an entry fee, or a costly label for purchase, to segregate the conditional cooperators from the non-cooperators. We show that institutional intervention of this sort can help the conditional cooperators to prevail when the psychological benefit of cooperating for them is sufficiently strong and the membership of the special zone or the label is inheritable with a sufficiently high probability.
CLIP Guided Image-perceptive Prompt Learning for Image Enhancement
Authors: Weiwen Chen, Qiuhong Ke, Zinuo Li
Date: 2023-11-07 00:00:00
Tasks: Image Enhancement, Prompt Learning

Image enhancement is a significant research area in the fields of computer vision and image processing. In recent years, many learning-based methods for image enhancement have been developed, where the Look-up-table (LUT) has proven to be an effective tool. In this paper, we delve into the potential of Contrastive Language-Image Pre-Training (CLIP) Guided Prompt Learning, proposing a simple structure called CLIP-LUT for image enhancement. We found that the prior knowledge of CLIP can effectively discern the quality of degraded images, which can provide reliable guidance. To be specific, We initially learn image-perceptive prompts to distinguish between original and target images using CLIP model, in the meanwhile, we introduce a very simple network by incorporating a simple baseline to predict the weights of three different LUT as enhancement network. The obtained prompts are used to steer the enhancement network like a loss function and improve the performance of model. We demonstrate that by simply combining a straightforward method with CLIP, we can obtain satisfactory results.
The contribution of US broadband infrastructure subsidy and investment programs to GDP using input-output modeling
Authors: Matthew Sprintson, Edward Oughton
Date: 2023-11-04 00:00:00

More than one-fifth of the US population does not subscribe to a fixed broadband service despite broadband being a recognized merit good. For example, less than 4% of citizens earning more than US \$70k annually do not have broadband, compared to 26% of those earning below US \$20k annually. To address this, the federal government has undertaken one of the largest broadband investment programs ever via The Bipartisan Infrastructure Law, with the aim of addressing this disparity and expanding broadband connectivity to all citizens. We examine broadband availability, adoption, and need for each US state, and then construct an Input-Output model to explore the potential structural macroeconomic impacts of broadband spending on Gross Domestic Product (GDP) and supply chain linkages. In terms of macroeconomic impact, the total potential indirect contribution to US GDP by the program could be as high as US \$84.8 billion, \$32.7 billion, and \$9.78 billion for the Broadband Equity, Access, and Deployment program, the Affordable Connectivity Program, and additional programs, respectively. Thus, overall, the broadband allocations could expand US GDP by up to US \$127.3 billion (0.10% of annual US GDP over the next five years). Moreover, the broadband packages within the Bipartisan Infrastructure Law could create up to 230,000 jobs (0.14% labor market increase). We contribute one of the first economic impact assessments of the US Bipartisan Infrastructure Law to the literature.
Analysis of NaN Divergence in Training Monocular Depth Estimation Model
Authors: Bum Jun Kim, Hyeonah Jang, Sang Woo Kim
Date: 2023-11-07 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

The latest advances in deep learning have facilitated the development of highly accurate monocular depth estimation models. However, when training a monocular depth estimation network, practitioners and researchers have observed not a number (NaN) loss, which disrupts gradient descent optimization. Although several practitioners have reported the stochastic and mysterious occurrence of NaN loss that bothers training, its root cause is not discussed in the literature. This study conducted an in-depth analysis of NaN loss during training a monocular depth estimation network and identified three types of vulnerabilities that cause NaN loss: 1) the use of square root loss, which leads to an unstable gradient; 2) the log-sigmoid function, which exhibits numerical stability issues; and 3) certain variance implementations, which yield incorrect computations. Furthermore, for each vulnerability, the occurrence of NaN loss was demonstrated and practical guidelines to prevent NaN loss were presented. Experiments showed that both optimization stability and performance on monocular depth estimation could be improved by following our guidelines.
Beyond the Screen: Safeguarding Mental Health in the Digital Workplace Through Organizational Commitment and Ethical Environment
Authors: Ali Bai, Morteza Vahedian
Date: 2023-11-04 00:00:00
Tasks: Ethics

This research explores the intricate relationship between organizational commitment and nomophobia, illuminating the mediating influence of the ethical environment. Utilizing Meyer and Allen's three-component model, the study finds a significant inverse correlation between organizational commitment and nomophobia, highlighting how strong organizational ties can alleviate the anxiety of digital disconnection. The ethical environment further emerges as a significant mediator, indicating its dual role in promoting ethical behavior and mitigating nomophobia's psychological effects. The study's theoretical advancement lies in its empirical evidence on the seldom-explored nexus between organizational commitment and technology-induced stress. By integrating organizational ethics and technological impact, the research offers a novel perspective on managing digital dependence in the workplace. From a practical standpoint, this study serves as a catalyst for organizational leaders to reinforce affective and normative commitment, thereby reducing nomophobia. The findings underscore the necessity of ethical leadership and comprehensive ethical policies as foundations for employee well-being in the digital age. Conclusively, this study delineates the protective role of organizational commitment and the significance of ethical environments, guiding organizations to foster cultures that balance technological efficiency with employee welfare. As a contribution to both academic discourse and practical application, it emphasizes the importance of nurturing a supportive and ethically sound workplace in an era of pervasive digital integration.
FLORA: Fine-grained Low-Rank Architecture Search for Vision Transformer
Authors: Chi-Chih Chang, Yuan-Yao Sung, Shixing Yu, Ning-Chi Huang, Diana Marculescu, Kai-Chiang Wu
Date: 2023-11-07 00:00:00

Vision Transformers (ViT) have recently demonstrated success across a myriad of computer vision tasks. However, their elevated computational demands pose significant challenges for real-world deployment. While low-rank approximation stands out as a renowned method to reduce computational loads, efficiently automating the target rank selection in ViT remains a challenge. Drawing from the notable similarity and alignment between the processes of rank selection and One-Shot NAS, we introduce FLORA, an end-to-end automatic framework based on NAS. To overcome the design challenge of supernet posed by vast search space, FLORA employs a low-rank aware candidate filtering strategy. This method adeptly identifies and eliminates underperforming candidates, effectively alleviating potential undertraining and interference among subnetworks. To further enhance the quality of low-rank supernets, we design a low-rank specific training paradigm. First, we propose weight inheritance to construct supernet and enable gradient sharing among low-rank modules. Secondly, we adopt low-rank aware sampling to strategically allocate training resources, taking into account inherited information from pre-trained models. Empirical results underscore FLORA's efficacy. With our method, a more fine-grained rank configuration can be generated automatically and yield up to 33% extra FLOPs reduction compared to a simple uniform configuration. More specific, FLORA-DeiT-B/FLORA-Swin-B can save up to 55%/42% FLOPs almost without performance degradtion. Importantly, FLORA boasts both versatility and orthogonality, offering an extra 21%-26% FLOPs reduction when integrated with leading compression techniques or compact hybrid structures. Our code is publicly available at https://github.com/shadowpa0327/FLORA.
RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging Environments
Authors: Rui She, Qiyu Kang, Sijie Wang, Yuan-Rui Yang, Kai Zhao, Yang song, Wee Peng Tay
Date: 2023-11-07 00:00:00
Tasks: Autonomous Vehicles, Patch Matching

For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing. In various computer perception tasks for AVs, it may be helpful to match landmark patches taken by an onboard camera with other landmark patches captured at a different time or saved in a street scene image database. To perform matching under challenging driving environments caused by changing seasons, weather, and illumination, we utilize the spatial neighborhood information of each patch. We propose an approach, named RobustMat, which derives its robustness to perturbations from neural differential equations. A convolutional neural ODE diffusion module is used to learn the feature representation for the landmark patches. A graph neural PDE diffusion module then aggregates information from neighboring landmark patches in the street scene. Finally, feature similarity learning outputs the final matching score. Our approach is evaluated on several street scene datasets and demonstrated to achieve state-of-the-art matching results under environmental perturbations.
MeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy
Authors: Łukasz Struski, Tomasz Urbańczyk, Krzysztof Bucki, Bartłomiej Cupiał, Aneta Kaczyńska, Przemysław Spurek, Jacek Tabor
Date: 2023-11-07 00:00:00
Tasks: Generative Adversarial Network, Medical Procedure, Video Generation

Video generation is important, especially in medicine, as much data is given in this form. However, video generation of high-resolution data is a very demanding task for generative models, due to the large need for memory. In this paper, we propose Memory Efficient Video GAN (MeVGAN) - a Generative Adversarial Network (GAN) which uses plugin-type architecture. We use a pre-trained 2D-image GAN and only add a simple neural network to construct respective trajectories in the noise space, so that the trajectory forwarded through the GAN model constructs a real-life video. We apply MeVGAN in the task of generating colonoscopy videos. Colonoscopy is an important medical procedure, especially beneficial in screening and managing colorectal cancer. However, because colonoscopy is difficult and time-consuming to learn, colonoscopy simulators are widely used in educating young colonoscopists. We show that MeVGAN can produce good quality synthetic colonoscopy videos, which can be potentially used in virtual simulators.
Supervised domain adaptation for building extraction from off-nadir aerial images
Authors: Bipul Neupane, Jagannath Aryal, Abbas Rajabifard
Date: 2023-11-07 00:00:00
Tasks: Domain Adaptation, Earth Observation, Experimental Design, Knowledge Distillation, Model Compression, Semantic Segmentation, Transfer Learning

Building extraction $-$ needed for inventory management and planning of urban environment $-$ is affected by the misalignment between labels and off-nadir source imagery in training data. Teacher-Student learning of noise-tolerant convolutional neural networks (CNNs) is the existing solution, but the Student networks typically have lower accuracy and cannot surpass the Teacher's performance. This paper proposes a supervised domain adaptation (SDA) of encoder-decoder networks (EDNs) between noisy and clean datasets to tackle the problem. EDNs are configured with high-performing lightweight encoders such as EfficientNet, ResNeSt, and MobileViT. The proposed method is compared against the existing Teacher-Student learning methods like knowledge distillation (KD) and deep mutual learning (DML) with three newly developed datasets. The methods are evaluated for different urban buildings (low-rise, mid-rise, high-rise, and skyscrapers), where misalignment increases with the increase in building height and spatial resolution. For a robust experimental design, 43 lightweight CNNs, five optimisers, nine loss functions, and seven EDNs are benchmarked to obtain the best-performing EDN for SDA. The SDA of the best-performing EDN from our study significantly outperformed KD and DML with up to 0.943, 0.868, 0.912, and 0.697 F1 scores in the low-rise, mid-rise, high-rise, and skyscrapers respectively. The proposed method and the experimental findings will be beneficial in training robust CNNs for building extraction.
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
Authors: Iman Abbasnejad, Fabio Zambetta, Flora Salim, Timothy Wiley, Jeffrey Chan, Russell Gallagher, Ehsan Abbasnejad
Date: 2023-11-07 00:00:00
Tasks: Contrastive Learning, Diversity, Generative Adversarial Network, Image Generation, Image-to-Image Translation, Translation

SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images. Most current image-to-image translation approaches are devised as two mappings: a translation from the source to target domain and another to represent its inverse. While successful in many applications, these approaches may suffer from generating trivial solutions with limited diversity. That is because these methods learn more frequent associations rather than the scene structures. To mitigate the problem, we propose SCONE-GAN that utilises graph convolutional networks to learn the objects dependencies, maintain the image structure and preserve its semantics while transferring images into the target domain. For more realistic and diverse image generation we introduce style reference image. We enforce the model to maximize the mutual information between the style image and output. The proposed method explicitly maximizes the mutual information between the related patches, thus encouraging the generator to produce more diverse images. We validate the proposed algorithm for image-to-image translation and stylizing outdoor images. Both qualitative and quantitative results demonstrate the effectiveness of our approach on four dataset.