pwc-archive/papers-with-abstracts huggingface dataset:
CVA Hedging by Risk-Averse Stochastic-Horizon Reinforcement Learning
Authors: Roberto Daluiso, Marco Pinciroli, Michele Trapletti, Edoardo Vittori
Date: 2023-12-21 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning

This work studies the dynamic risk management of the risk-neutral value of the potential credit losses on a portfolio of derivatives. Sensitivities-based hedging of such liability is sub-optimal because of bid-ask costs, pricing models which cannot be completely realistic, and a discontinuity at default time. We leverage recent advances on risk-averse Reinforcement Learning developed specifically for option hedging with an ad hoc practice-aligned objective function aware of pathwise volatility, generalizing them to stochastic horizons. We formalize accurately the evolution of the hedger's portfolio stressing such aspects. We showcase the efficacy of our approach by a numerical study for a portfolio composed of a single FX forward contract.
Implied CO$_{\textbf{2}}$-Price and Interest Rate of Carbon
Authors: Christian P. Fries
Date: 2023-12-20 00:00:00
Tasks: Relation

By its nature, the so-called social cost of carbon (SCC(t)) will likely not cover the cost induced by climate change (damage cost and abatement cost) if it is used as a CO$_2$-price. It is a marginal price only. We define an implied CO$_2$-price that covers the climate change-induced costs. The price can be interpreted as a \textit{polluter pays principle}. A numerical analysis using a classical DICE model reveals that the cost-implied CO$_2$ price is around 500 \$/tCO$_2$, while the corresponding price associated with the SCC is about 50 \$/tCO$_2$. In addition, we define the internal rate of return of carbon abatement and calculate it for the classical DICE model. This rate is much higher than the model's discount rate, which may suggest the advantage of financing abatement by loans.
Market-Adaptive Ratio for Portfolio Management
Authors: Ju-Hong Lee, Bayartsetseg Kalina, KwangTek Na
Date: 2023-12-21 00:00:00
Tasks: Management

Traditional risk-adjusted returns, such as the Treynor, Sharpe, Sortino, and Information ratios, have been pivotal in portfolio asset allocation, focusing on minimizing risk while maximizing profit. Nevertheless, these metrics often fail to account for the distinct characteristics of bull and bear markets, leading to sub-optimal investment decisions. This paper introduces a novel approach called the Market-adaptive Ratio, which was designed to adjust risk preferences dynamically in response to market conditions. By integrating the $\rho$ parameter, which differentiates between bull and bear markets, this new ratio enables a more adaptive portfolio management strategy. The $\rho$ parameter is derived from historical data and implemented within a reinforcement learning framework, allowing the method to learn and optimize portfolio allocations based on prevailing market trends. Empirical analysis showed that the Market-adaptive Ratio outperformed the Sharpe Ratio by providing more robust risk-adjusted returns tailored to the specific market environment. This advance enhances portfolio performance by aligning investment strategies with the inherent dynamics of bull and bear markets, optimizing risk and return outcomes.
Compositional Representation of Polymorphic Crystalline Materials
Authors: Namkyeong Lee, Heewoong Noh, Gyoung S. Na, Jimeng Sun, Tianfan Fu, Marinka Zitnik, Chanyoung Park
Date: 2023-11-17 00:00:00
Tasks: Representation Learning

Machine learning (ML) has seen promising developments in materials science, yet its efficacy largely depends on detailed crystal structural data, which are often complex and hard to obtain, limiting their applicability in real-world material synthesis processes. An alternative, using compositional descriptors, offers a simpler approach by indicating the elemental ratios of compounds without detailed structural insights. However, accurately representing materials solely with compositional descriptors presents challenges due to polymorphism, where a single composition can correspond to various structural arrangements, creating ambiguities in its representation. To this end, we introduce PCRL, a novel approach that employs probabilistic modeling of composition to capture the diverse polymorphs from available structural information. Extensive evaluations on sixteen datasets demonstrate the effectiveness of PCRL in learning compositional representation, and our analysis highlights its potential applicability of PCRL in material discovery. The source code for PCRL is available at https://github.com/Namkyeong/PCRL.
The GrassClover Image Dataset for Semantic and Hierarchical Species Understanding in Agriculture
Authors: Soren Skovsen, Mads Dyrmann, Anders K. Mortensen, Morten S. Laursen, Rene Gislum, Jorgen Eriksen, Sadaf Farkhani, Henrik Karstoft, Rasmus N. Jorgensen;
Date: 2019-06-25 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0 2019 6
Tasks: Semantic Segmentation

GrassClover is a diverse image and biomass dataset collected in an outdoor agricultural setting. The images contain dense populations of grass and clover mixtures with heavy occlusions and occurrences of weeds. Fertilization and treatment of mixed crops depend on the local species composition. Therefore, the overall challenge is related to predicting the species composition in the canopy image and in the biomass. The dataset is collected with three different acquisition systems with ground sampling distances of 4--8 px/mm. The observed mixed crops vary both in setting (field vs plot trial), seed compositions, yield, years since establishment and time of the season. Synthetic training images with pixel-wise hierarchical and instance labels are provided for supervised training. 31 600 unlabeled images are additionally provided for pre-training, semi-supervised training or unsupervised training. Furthermore, this paper provides challenges of semantic segmentation and prediction of the biomass compositions and a baseline model for this dataset.
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Authors: Yiheng Zhu, Jialu Wu, Chaowen Hu, Jiahuan Yan, Chang-Yu Hsieh, Tingjun Hou, Jian Wu
Date: 2023-02-08 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Bayesian Optimization, Diversity

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space. In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the diversity of candidates paramount. Computational methods have achieved initial success but still struggle with considering diversity in both objective and search space. To fill this gap, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. We further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. We empirically illustrate that HN-GFN has adequate capacity to generalize over preferences. Moreover, experiments in various real-world MOBO settings demonstrate that our framework predominantly outperforms existing methods in terms of candidate quality and sample efficiency. The code is available at https://github.com/violet-sto/HN-GFN.
PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection
Authors: Qiang Zhou, Weize Li, Lihan Jiang, Guoliang Wang, Guyue Zhou, Shanghang Zhang, Hao Zhao
Date: 2023-10-11 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: 4k, Anomaly Detection

Object anomaly detection is an important problem in the field of machine vision and has seen remarkable progress recently. However, two significant challenges hinder its research and application. First, existing datasets lack comprehensive visual information from various pose angles. They usually have an unrealistic assumption that the anomaly-free training dataset is pose-aligned, and the testing samples have the same pose as the training data. However, in practice, anomaly may exist in any regions on a object, the training and query samples may have different poses, calling for the study on pose-agnostic anomaly detection. Second, the absence of a consensus on experimental protocols for pose-agnostic anomaly detection leads to unfair comparisons of different methods, hindering the research on pose-agnostic anomaly detection. To address these issues, we develop Multi-pose Anomaly Detection (MAD) dataset and Pose-agnostic Anomaly Detection (PAD) benchmark, which takes the first step to address the pose-agnostic anomaly detection problem. Specifically, we build MAD using 20 complex-shaped LEGO toys including 4K views with various poses, and high-quality and diverse 3D anomalies in both simulated and real environments. Additionally, we propose a novel method OmniposeAD, trained using MAD, specifically designed for pose-agnostic anomaly detection. Through comprehensive evaluations, we demonstrate the relevance of our dataset and method. Furthermore, we provide an open-source benchmark library, including dataset and baseline methods that cover 8 anomaly detection paradigms, to facilitate future research and application in this domain. Code, data, and models are publicly available at https://github.com/EricLee0224/PAD.
Chatting Makes Perfect: Chat-based Image Retrieval
Authors: Matan Levy, Rami Ben-Ari, Nir Darshan, Dani Lischinski
Date: 2023-05-31 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Chat-based Image Retrieval, Image Description, Image Retrieval, Information Retrieval, Retrieval

Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. However, existing image retrieval approaches typically address the case of a single query-to-image round, and the use of chats for image retrieval has been mostly overlooked. In this work, we introduce ChatIR: a chat-based image retrieval system that engages in a conversation with the user to elicit information, in addition to an initial query, in order to clarify the user's search intent. Motivated by the capabilities of today's foundation models, we leverage Large Language Models to generate follow-up questions to an initial image description. These questions form a dialog with the user in order to retrieve the desired image from a large corpus. In this study, we explore the capabilities of such a system tested on a large dataset and reveal that engaging in a dialog yields significant gains in image retrieval. We start by building an evaluation pipeline from an existing manually generated dataset and explore different modules and training strategies for ChatIR. Our comparison includes strong baselines derived from related applications trained with Reinforcement Learning. Our system is capable of retrieving the target image from a pool of 50K images with over 78% success rate after 5 dialogue rounds, compared to 75% when questions are asked by humans, and 64% for a single shot text-to-image retrieval. Extensive evaluations reveal the strong capabilities and examine the limitations of CharIR under different settings. Project repository is available at https://github.com/levymsn/ChatIR.
Semantic Difference Guidance for the Uncertain Boundary Segmentation of CT Left Atrial Appendage
Authors: Xin You, Ming Ding, Minghui Zhang, Yangqian Wu, Yi Yu, Yun Gu, Jie Yang
Date: 2023-10-01 00:00:00
Proceeding: MICCAI 2023 10
Tasks: Segmentation

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, which is closely relevant to anatomical structures including the left atrium (LA) and the left atrial appendage (LAA). Thus, a thorough understanding of the LA and LAA is essential for the AF treatment. In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset. To deal with uncertain boundaries between the LA and LAA, we propose the semantic difference module (SDM) based on diffusion theory to refine features with enhanced boundary information. Besides, disconnections between the LA and LAA are frequently observed in the segmentation results due to uncertain boundaries of the LAA region and CT imaging noise. To address this issue, we devise another connectivity-refined network with the connectivity loss. The loss function exerts a distance regularization on coarse predictions from the first-stage network. Experiments demonstrate that our proposed model can achieve state-of-the-art segmentation performance compared with classic convolutional-neural-networks (CNNs) and recent Transformer-based models on this new dataset. Specifically, SDM can also outperform existing methods on refining uncertain boundaries. Codes are available at https://github.com/AlexYouXin/LA-LAA-segmentation.
A hybrid model based on CNN and Bi-LSTM for urban water demand prediction
Authors: Piao Hu, Jun Tong, Jingcheng Wang, Yue Yang, Luca de Oliveira Turci
Date: 2019-08-08 00:00:00
Proceeding: 2019 IEEE Congress on Evolutionary Computation (CEC) 2019 8

Water demand forecast is the basis of urban intelligent water supply, because the system is limited by nonlinear changes in the process of water consumption, the traditional prediction model has great impact in accuracy and stability. Even small changes in temperature and holidays periods can lead to abnormal changes in urban water use. To solve these problems, a hybrid model combining convolutional neural network and bidirectional long and short term memory network was adopted in this study. Corresponding corrective model is established for special situations such as weather natural changes and holidays. In order to extract the features of water quantity and climate data, these features are input into the Bi-LSTM network to predict the usage of urban water. This paper carries out a correlation analysis of historical water data and climatic factors that cause an impact on the usage of urban water. The previous five days water usage data and the daily maximum temperature were selected as the basis for the holiday correction model and the temperature correction model. Comparing the different models before and after correcting the deviation, the prediction results have been improved. The present work was compared with results of long-term and short-term memory networks (LSTM), bidirectional long-term memory networks (Bi-LSTM), CNN, sparse autoencoder (SAEs), and CNN-LSTM, hence the prediction error is reduced by using the CNN-Bi-LSTM model. Finally, under the same training period, the training time and convergence of the six models were analyzed. The training time of CNN-Bi-LSTM is less than LSTM, Bi-LSTM, CNN, and CNN-LSTM, but larger than SAEs. The training convergence of CNN-Bi-LSTM was set in 125 times, which is smaller than the training times of the other five models.
Image Restoration via Frequency Selection
Authors: Yuning Cui, Wenqi Ren, Xiaochun Cao, Alois Knoll
Date: 2023-11-06 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2023 11
Tasks: Deblurring, Denoising, Image Deblurring, Image Defocus Deblurring, Image Dehazing, Image Denoising, Image Restoration, Rain Removal

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain by considering the large discrepancy between spectra of sharp/degraded image pairs. However, these algorithms commonly utilize transformation tools, e.g. , wavelet transform, to split features into several frequency parts, which is not flexible enough to select the most informative frequency component to recover. In this paper, we exploit a multi-branch and content-aware module to decompose features into separate frequency subbands dynamically and locally, and then accentuate the useful ones via channel-wise attention weights. In addition, to handle large-scale degradation blurs, we propose an extremely simple decoupling and modulation module to enlarge the receptive field via global and window-based average pooling. Furthermore, we merge the paradigm of multi-stage networks into a single U-shaped network to pursue multi-scale receptive fields and improve efficiency. Finally, integrating the above designs into a convolutional backbone, the proposed Frequency Selection Network (FSNet) performs favorably against state-of-the-art algorithms on 20 different benchmark datasets for 6 representative image restoration tasks, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, image deraining, and image denoising.
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
Authors: Kai Zhao, Qiyu Kang, Yang song, Rui She, Sijie Wang, Wee Peng Tay
Date: 2023-10-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Adversarial Robustness

Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology. This paper investigates GNNs derived from diverse neural flows, concentrating on their connection to various stability notions such as BIBO stability, Lyapunov stability, structural stability, and conservative stability. We argue that Lyapunov stability, despite its common use, does not necessarily ensure adversarial robustness. Inspired by physics principles, we advocate for the use of conservative Hamiltonian neural flows to construct GNNs that are robust to adversarial attacks. The adversarial robustness of different neural flow GNNs is empirically compared on several benchmark datasets under a variety of adversarial attacks. Extensive numerical experiments demonstrate that GNNs leveraging conservative Hamiltonian flows with Lyapunov stability substantially improve robustness against adversarial perturbations. The implementation code of experiments is available at https://github.com/zknus/NeurIPS-2023-HANG-Robustness.
GENERATING 3D DATASET OF GAIT AND FULL BODY MOVEMENT OF CHILDREN WITH AUTISM SPECTRUM DISORDERS COLLECTED BY KINECT V2 CAMERA
Authors: Ahmed A. Al-Jubouri1, Israa Hadi Ali1, Yasen Rajihy1
Date: 2020-09-01 00:00:00
Proceeding: https://www.researchgate.net/publication/344044462_GENERATING_3D_DATASET_OF_GAIT_AND_FULL_BODY_MOVEMENT_OF_CHILDREN_WITH_AUTISM_SPECTRUM_DISORDERS_COLLECTED_BY_KINECT_V2_CAMERA 2020 9
Tasks: Diagnostic

Gait and body movement are a window to human brain which make these 
activities unique for each person. These activities are used to diagnose some 
disorders related to parts of brain which causes have not been known such as Autism 
Disorders (AD). The traditional diagnostic methods of AD are time-consuming and 
highly dependent on clinician’s judgment which is based on behaviour assessment. 
This approach leads to subjective interpretations that differ from doctor to another 
and affect by strengths and weaknesses of patient. Therefore this paper aims to 
diagnosis of AD based on gait and body movement analysis. At first, Kinect v2 uses 
to create a 3D dataset, which includes three dimensional joints positions, joints 
trajectories video, skeleton movement video captured by Kinect v2, and color videos 
captured by Samsung Note 9 camera. This paper also aims to classify children with 
autism from normal children by proposed system based on four stages: 
Augmentation of the database by using seven transformations to deal with small 
number of autism cases; Extracting features that we think play an important role in 
classification; Reducing data dimensions using Principal Component Analysis; using 
Rough Set to classify dataset. Results show that classification is 92% after 
augmentation.
Improve Temporal Action Proposals using Hierarchical Context
Authors: Qinying Liu, Zilei Wang, Shenghai Rong
Date: 2023-04-03 00:00:00
Proceeding: Pattern Recognition 2023 4
Tasks: Action Detection, Temporal Action Localization, Temporal Action Proposal Generation

Temporal action proposal (TAP) aims to generate accurate candidates of action instances in an untrimmed video. It has been proved that contexts are critically important to this task. In this paper, we propose a novel hierarchical context network (HCN) to further explore the snippet-level and proposal-level contexts, which are used to improve the representations of snippets and proposals, respectively. First, we pinpoint that different scales of snippet-level contexts are not equally important for different action instances. To this end, we incorporate a novel gating mechanism into the U-Net structure to capture the content-adaptive snippet-level contexts. Second, to exploit the proposal-level contexts, we propose a task-specific self-attention model with high efficiency. By stacking multiple attention models, we can deeply explore the proposal-level contexts in a wide range. Finally, to leverage both levels of context, we equip HCN with three branches to evaluate proposals from local to global perspectives. Our experiments on the ActivityNet-1.3 and THUMOS14 datasets show that HCN significantly outperforms previous TAP methods. Additionally, further experiments demonstrate that our method can substantially improve the state-of-the-art action detection performance when combined with existing action classifiers.
When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
Authors: Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, Chenxing Wang
Date: 2023-07-26 00:00:00
Proceeding: IEEE 39th International Conference on Data Engineering (ICDE) 2023 7
Tasks: Graph Attention, Traffic Prediction

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to the temporal changes and the dynamic spatial correlations of the traffic data. To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied. However, previous spatiotemporal networks are based on end-to-end training and thus fail to handle the distribution shift in the non-stationary traffic time series. On the other hand, the efficient and effective algorithm for modeling spatial correlations is still lacking in prior networks. In this paper, rather than proposing yet another end-to-end model, we aim to provide a novel disentangle fusion framework STWave to mitigate the distribution shift issue. The framework first decouples the complex traffic data into stable trends and fluctuating events, followed by a dual-channel spatio-temporal network to model trends and events, respectively. Finally, reasonable future traffic can be predicted through the fusion of trends and events. Besides, we incorporate a novel query sampling strategy and graph wavelet-based graph positional encoding into the full graph attention network to efficiently and effectively model dynamic spatial correlations. Extensive experiments on six traffic datasets show the superiority of our approach, i.e., the higher forecasting accuracy with lower computational cost.