pwc-archive/papers-with-abstracts huggingface dataset:
A novel scaling approach for unbiased adjustment of risk estimators
Authors: Marcin Pitera, Thorsten Schmidt, Łukasz Stettner
Date: 2023-12-09 00:00:00

The assessment of risk based on historical data faces many challenges, in particular due to the limited amount of available data, lack of stationarity, and heavy tails. While estimation on a short-term horizon for less extreme percentiles tends to be reasonably accurate, extending it to longer time horizons or extreme percentiles poses significant difficulties. The application of theoretical risk scaling laws to address this issue has been extensively explored in the literature. This paper presents a novel approach to scaling a given risk estimator, ensuring that the estimated capital reserve is robust and conservatively estimates the risk. We develop a simple statistical framework that allows efficient risk scaling and has a direct link to backtesting performance. Our method allows time scaling beyond the conventional square-root-of-time rule, enables risk transfers, such as those involved in economic capital allocation, and could be used for unbiased risk estimation in small sample settings. To demonstrate the effectiveness of our approach, we provide various examples related to the estimation of value-at-risk and expected shortfall together with a short empirical study analysing the impact of our method.
Deep Learning for Dynamic NFT Valuation
Authors: Mingxuan He
Date: 2023-12-08 00:00:00
Tasks: Deep Learning

I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications.
Transformer and CNN Hybrid Deep Neural Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
Authors: Cheng Zhang; Wanshou Jiang; Yuan Zhang; Wei Wang; Qing Zhao; Chenjie Wang
Date: 2022-01-19 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 60) 2022 1
Tasks: Boundary Detection, Decoder, Image Segmentation, Segmentation, Semantic Segmentation

This article presents a transformer and convolutional neural network (CNN) hybrid deep neural network for semantic segmentation of very high resolution (VHR) remote sensing imagery. The model follows an encoder–decoder structure. The encoder module uses a new universal backbone Swin transformer to extract features to achieve better long-range spatial dependencies modeling. The decoder module draws on some effective blocks and successful strategies of CNN-based models in remote sensing image segmentation. In the middle of the framework, an atrous spatial pyramid pooling block based on depthwise separable convolution (SASPP) is applied to obtain a multiscale context. A U-shaped decoder is used to gradually restore the size of the feature maps. Three skip connections are built between the encoder and decoder feature maps of the same size to maintain the transmission of local details and enhance the communication of multiscale features. A squeeze-and-excitation (SE) channel attention block is added before segmentation for feature augmentation. An auxiliary boundary detection branch is combined to provide edge constraints for semantic segmentation. Extensive ablation experiments were conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam benchmarks to test the effectiveness of multiple components of the network. At the same time, the proposed method is compared with the current state-of-the-art methods on the two benchmarks. The proposed hybrid network achieved the second highest overall accuracy (OA) on both the Potsdam and Vaihingen benchmarks (code and models are available at https://github.com/zq7734509/mmsegmentation- multilayer).
Risk measures based on weak optimal transport
Authors: Michael Kupper, Max Nendel, Alessandro Sgarabottolo
Date: 2023-12-10 00:00:00

In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss computational aspects related to the nonlinear transform as well as approximations of the risk measures using, for example, neural networks. Our setup comprises a variety of examples, such as classical optimal transport penalties, parametric families of models, uncertainty on path spaces, moment constrains, and martingale constraints. In a last step, we show how to use the theoretical results for the numerical computation of worst-case losses in an insurance context and no-arbitrage prices of European contingent claims after quoted maturities in a model-free setting.
Prediction De-Correlated Inference: A safe approach for post-prediction inference
Authors: Feng Gan, Wanfeng Liang, Changliang Zou
Date: 2023-12-11 00:00:00
Tasks: Prediction

In modern data analysis, it is common to use machine learning methods to predict outcomes on unlabeled datasets and then use these pseudo-outcomes in subsequent statistical inference. Inference in this setting is often called post-prediction inference. We propose a novel assumption-lean framework for statistical inference under post-prediction setting, called Prediction De-Correlated Inference (PDC). Our approach is safe, in the sense that PDC can automatically adapt to any black-box machine-learning model and consistently outperform the supervised counterparts. The PDC framework also offers easy extensibility for accommodating multiple predictive models. Both numerical results and real-world data analysis demonstrate the superiority of PDC over the state-of-the-art methods.
Optimizing the Passenger Flow for Airport Security Check
Authors: Yuxin Wang, Fanfei Meng, Xiaotian Wang, Chaoyu Xie
Date: 2023-11-30 00:00:00

Due to the necessary security for the airport and flight, passengers are required to have strict security check before getting aboard. However, there are frequent complaints of wasting huge amount of time while waiting for the security check. This paper presents a potential solution aimed at optimizing gate setup procedures specifically tailored for Chicago OHare International Airport. By referring to queueing theory and performing Monte Carlo simulations, we propose an approach to significantly diminish the average waiting time to a more manageable level. Additionally, our study meticulously examines and identifies the influential factors contributing to this optimization, providing a comprehensive understanding of their impact.
Holistic Evaluation of GPT-4V for Biomedical Imaging
Authors: Zhengliang Liu, Hanqi Jiang, Tianyang Zhong, Zihao Wu, Chong Ma, Yiwei Li, Xiaowei Yu, Yutong Zhang, Yi Pan, Peng Shu, Yanjun Lyu, Lu Zhang, Junjie Yao, Peixin Dong, Chao Cao, Zhenxiang Xiao, Jiaqi Wang, Huan Zhao, Shaochen Xu, Yaonai Wei, Jingyuan Chen, Haixing Dai, Peilong Wang, Hao He, Zewei Wang, Xinyu Wang, Xu Zhang, Lin Zhao, Yiheng Liu, Kai Zhang, Liheng Yan, Lichao Sun, Jun Liu, Ning Qiang, Bao Ge, Xiaoyan Cai, Shijie Zhao, Xintao Hu, Yixuan Yuan, Gang Li, Shu Zhang, Xin Zhang, Xi Jiang, Tuo Zhang, Dinggang Shen, Quanzheng Li, Wei Liu, Xiang Li, Dajiang Zhu, Tianming Liu
Date: 2023-11-10 00:00:00
Tasks: Anatomy, Diagnostic, Image Captioning, Lesion Detection

In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain. We assess GPT-4V's performance across 16 medical imaging categories, including radiology, oncology, ophthalmology, pathology, and more. Tasks include modality recognition, anatomy localization, disease diagnosis, report generation, and lesion detection. The extensive experiments provide insights into GPT-4V's strengths and weaknesses. Results show GPT-4V's proficiency in modality and anatomy recognition but difficulty with disease diagnosis and localization. GPT-4V excels at diagnostic report generation, indicating strong image captioning skills. While promising for biomedical imaging AI, GPT-4V requires further enhancement and validation before clinical deployment. We emphasize responsible development and testing for trustworthy integration of biomedical AGI. This rigorous evaluation of GPT-4V on diverse medical images advances understanding of multimodal large language models (LLMs) and guides future work toward impactful healthcare applications.
Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
Authors: Siemen Herremans, Ali Anwar, Arne Troch, Ian Ravijts, Maarten Vangeneugden, Siegfried Mercelis, Peter Hellinckx
Date: 2023-11-17 00:00:00
Tasks: Model-based Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning

Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by countries such as Belgium and the Netherlands as an essential step towards a sustainable future. These environments pose unique challenges, since they can contain dynamic obstacles that do not broadcast their location, such as small vessels, kayaks or buoys. Therefore, this research proposes a navigational algorithm which can navigate an inland vessel in a wide variety of complex port scenarios using ranging sensors to observe the environment. The proposed methodology is based on a machine learning approach that has recently set benchmark results in various domains: model-based reinforcement learning. By randomizing the port environments during training, the trained model can navigate in scenarios that it never encountered during training. Furthermore, results show that our approach outperforms the commonly used dynamic window approach and a benchmark model-free reinforcement learning algorithm. This work is therefore a significant step towards vessels that can navigate autonomously in complex port scenarios.
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections
Authors: Supriya Sarker
Date: 2023-11-21 00:00:00
Tasks: Causal Inference, Management, Navigate, reinforcement-learning, Reinforcement Learning (RL)

In this report, we delve into two critical research inquiries. Firstly, we explore the extent to which Reinforcement Learning (RL) agents exhibit multimodal distributions in the context of stop-and-go traffic scenarios. Secondly, we investigate how RL-controlled Robot Vehicles (RVs) effectively navigate their direction and coordinate with other vehicles in complex traffic environments. Our analysis encompasses an examination of multimodality within queue length, outflow, and platoon size distributions for both Robot and Human-driven Vehicles (HVs). Additionally, we assess the Pearson coefficient correlation, shedding light on relationships between queue length and outflow, considering both identical and differing travel directions. Furthermore, we delve into causal inference models, shedding light on the factors influencing queue length across scenarios involving varying travel directions. Through these investigations, this report contributes valuable insights into the behaviors of mixed traffic (RVs and HVs) in traffic management and coordination.
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Authors: Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi
Date: 2023-02-08 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Time Series, Time Series Forecasting

Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings. These architectures take advantage of a graph structure and relational inductive biases to learn a single (global) inductive model to predict any number of the input time series, each associated with a graph node. Despite the gain achieved in computational and data efficiency w.r.t. fitting a set of local models, relying on a single global model can be a limitation whenever some of the time series are generated by a different spatiotemporal stochastic process. The main objective of this paper is to understand the interplay between globality and locality in graph-based spatiotemporal forecasting, while contextually proposing a methodological framework to rationalize the practice of including trainable node embeddings in such architectures. We ascribe to trainable node embeddings the role of amortizing the learning of specialized components. Moreover, embeddings allow for 1) effectively combining the advantages of shared message-passing layers with node-specific parameters and 2) efficiently transferring the learned model to new node sets. Supported by strong empirical evidence, we provide insights and guidelines for specializing graph-based models to the dynamics of each time series and show how this aspect plays a crucial role in obtaining accurate predictions.
Towards Free Data Selection with General-Purpose Models
Authors: Yichen Xie, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan
Date: 2023-09-29 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Active Learning

A desirable data selection algorithm can efficiently choose the most informative samples to maximize the utility of limited annotation budgets. However, current approaches, represented by active learning methods, typically follow a cumbersome pipeline that iterates the time-consuming model training and batch data selection repeatedly. In this paper, we challenge this status quo by designing a distinct data selection pipeline that utilizes existing general-purpose models to select data from various datasets with a single-pass inference without the need for additional training or supervision. A novel free data selection (FreeSel) method is proposed following this new pipeline. Specifically, we define semantic patterns extracted from inter-mediate features of the general-purpose model to capture subtle local information in each image. We then enable the selection of all data samples in a single pass through distance-based sampling at the fine-grained semantic pattern level. FreeSel bypasses the heavy batch selection process, achieving a significant improvement in efficiency and being 530x faster than existing active learning methods. Extensive experiments verify the effectiveness of FreeSel on various computer vision tasks. Our code is available at https://github.com/yichen928/FreeSel.
RegBN: Batch Normalization of Multimodal Data with Regularization
Authors: Morteza Ghahremani, Christian Wachinger
Date: 2023-10-01 00:00:00
Proceeding: NeurIPS 2023 11

Recent years have witnessed a surge of interest in integrating high-dimensional data captured by multisource sensors, driven by the impressive success of neural networks in the integration of multimodal data. However, the integration of heterogeneous multimodal data poses a significant challenge, as confounding effects and dependencies among such heterogeneous data sources introduce unwanted variability and bias, leading to suboptimal performance of multimodal models. Therefore, it becomes crucial to normalize the low- or high-level features extracted from data modalities before their fusion takes place. This paper introduces a novel approach for the normalization of multimodal data, called RegBN, that incorporates regularization. RegBN uses the Frobenius norm as a regularizer term to address the side effects of confounders and underlying dependencies among different data sources. The proposed method generalizes well across multiple modalities and eliminates the need for learnable parameters, simplifying training and inference. We validate the effectiveness of RegBN on eight databases from five research areas, encompassing diverse modalities such as language, audio, image, video, depth, tabular, and 3D MRI. The proposed method demonstrates broad applicability across different architectures such as multilayer perceptrons, convolutional neural networks, and vision transformers, enabling effective normalization of both low- and high-level features in multimodal neural networks. RegBN is available at \url{https://github.com/mogvision/regbn}.
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
Authors: Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato, Masashi Sugiyama
Date: 2020-11-23 00:00:00
Proceeding: NeurIPS 2023 11

Weight decay is a simple yet powerful regularization technique that has been very widely used in training of deep neural networks (DNNs). While weight decay has attracted much attention, previous studies fail to discover some overlooked pitfalls on large gradient norms resulted by weight decay. In this paper, we discover that, weight decay can unfortunately lead to large gradient norms at the final phase (or the terminated solution) of training, which often indicates bad convergence and poor generalization. To mitigate the gradient-norm-centered pitfalls, we present the first practical scheduler for weight decay, called the Scheduled Weight Decay (SWD) method that can dynamically adjust the weight decay strength according to the gradient norm and significantly penalize large gradient norms during training. Our experiments also support that SWD indeed mitigates large gradient norms and often significantly outperforms the conventional constant weight decay strategy for Adaptive Moment Estimation (Adam).
HYB-PARSIMONY: A hybrid approach combining Particle Swarm Optimization and Genetic Algorithms to find parsimonious models in high-dimensional datasets
Authors: J. Divasón, A. Pernia-Espinoza, F.J. Martinez-de-Pison
Date: 2023-12-01 00:00:00
Proceeding: Neurocomputing 2023 12

The PSO-PARSIMONY methodology (a heuristic for finding accurate and low-complexity models with particle swarm optimization (PSO)) allows obtaining machine learning models with a good balance between accuracy and complexity. However, when the datasets are of high dimensionality, the methodology does not sufficiently reduce the complexity of the models. This paper presents a new hybrid methodology, called HYB-PARSIMONY, that combines PSO with genetic algorithm (GA) based methods. In the early stages of the optimization process, GA methods have a preponderance to accelerate the search for parsimony. Later, PSO becomes more relevant to improve accuracy. This new methodology obtains significant improvements in the search for more accurate and low-complexity models in high-dimensional datasets.
PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections
Authors: J. Divasón, J.F. Ceniceros, A. Sanz-Garcia, A. Pernia-Espinoza, F.J. Martinez-de-Pison
Date: 2023-09-01 00:00:00
Proceeding: Neurocomputing 2023 9
Tasks: feature selection, Hyperparameter Optimization

We present PSO-PARSIMONY, a new methodology to search for parsimonious and highly accurate models by means of particle swarm optimization. PSO-PARSIMONY uses automatic hyperparameter optimization and feature selection to search for accurate models with low complexity. To evaluate the new proposal, a comparative study with multilayer perceptron algorithm was performed with public datasets and by applying it to predict two important parameters of the force–displacement curve in T-stub steel connections: initial stiffness and maximum strength. Models optimized with PSO-PARSIMONY showed an excellent trade-off between goodness-of-fit and parsimony. The new proposal was compared with GA-PARSIMONY, our previously published methodology that uses genetic algorithms in the optimization process. The new method needed more iterations and obtained slightly more complex individuals, but it performed better in the search for accurate models.
Artificial Intelligence Models for Assessing the Evaluation Process of Complex Student Projects
Authors: J. Divason, F.J. Martinez-de-Pison, A. Romero, E. Saenz-De-Cabezon
Date: 2023-02-20 00:00:00
Proceeding: IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2023 2

The evaluation of student projects is a difficult task, especially when they involve both a technical and a creative component. We propose an artificial intelligence (AI)-based methodology to help in the evaluation of complex projects in engineering and computer science courses. This methodology is intended to evaluate the assessment process itself allowing to analyze the influence of each variable in the final grade, to discover possible biases, inconsistencies and discrepancies, and to generate appropriate rubrics that help to avoid them. As an example of its application, we consider the evaluation of the projects submitted in an undergraduate introductory course on computer science. Using data collected from the evaluation during five academic years, we follow the proposed methodology to create AI models and analyze the main variables which are involved in the assessment of the projects. The proposed methodology can be applied to other courses and degrees, where both technical and creative components are considered to evaluate the projects.
Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency
Authors: E. Dulce-Chamorro, F.J. Martinez-de-Pison
Date: 2021-02-17 00:00:00
Proceeding: Logic Journal of the IGPL 2021 2
Tasks: feature selection, Model Selection

Of all the different types of public buildings, hospitals are the biggest energy consumers. Cooling systems for air conditioning and healthcare uses are particularly energy intensive. Forecasting hospital thermal-cooling demand is a remarkable and innovative method capable of improving the overall energy efficiency of an entire cooling system. Predictive models allow users to forecast the activity of water-cooled generators and adapt power generation to the real demand expected for the day ahead, while avoiding inefficient subcooling. In addition, the maintenance costs related to unnecessary starts and stops and power-generator breakdowns occurring over the long term can be reduced. This study is based on the operations of a real hospital facility and details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. Finally, a weighted combination of the three best models was created. The new operational method employed herein can be replicated in similar buildings with similar water-cooled generators.