pwc-archive/papers-with-abstracts huggingface dataset:
Modeling dynamic volatility under uncertain environment with fuzziness and randomness
Authors: Xianfei Hui, Baiqing Sun, Hui Jiang, Yan Zhou
Date: 2022-04-27 00:00:00
Tasks: Time Series Analysis

The problem related to predicting dynamic volatility in financial market plays a crucial role in many contexts. We build a new generalized Barndorff-Nielsen and Shephard (BN-S) model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Through the experiment of Dow Jones futures price, we find that compared with the classical model, this method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility has more ideal performance.
NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks
Authors: Shrey Jain, Camille Bruckmann, Chase McDougall
Date: 2022-04-18 00:00:00
Tasks: regression

In this paper we investigate the correlation between NFT valuations and various features from three primary categories: public market data, NFT metadata, and social trends data.
High-Frequency-Based Volatility Model with Network Structure
Authors: Huiling Yuan, Guodong Li, Junhui Wang
Date: 2022-04-14 00:00:00
Tasks: model, parameter estimation, Vocal Bursts Intensity Prediction

This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons.
Learning to Transfer Role Assignment Across Team Sizes
Authors: Dung Nguyen, Phuoc Nguyen, Svetha Venkatesh, Truyen Tran
Date: 2022-04-17 00:00:00
Tasks: Management, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

Multi-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive exploration over the exponentially large state-action space. A powerful approach is to decompose team works into roles, which are ideally assigned to agents with the relevant skills. Training agents to adaptively choose and play emerging roles in a team thus allows the team to scale to complex tasks and quickly adapt to changing environments. These promises, however, have not been fully realised by current role-based multi-agent reinforcement learning methods as they assume either a pre-defined role structure or a fixed team size. We propose a framework to learn role assignment and transfer across team sizes. In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment. We demonstrate that re-using the role-based credit assignment structure can foster the learning process of larger reinforcement learning teams to achieve tasks requiring different roles. Our proposal outperforms competing techniques in enriched role-enforcing Prey-Predator games and in new scenarios in the StarCraft II Micro-Management benchmark.
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Authors: Elvis Nava, John Z. Zhang, Mike Y. Michelis, Tao Du, Pingchuan Ma, Benjamin F. Grewe, Wojciech Matusik, Robert K. Katzschmann
Date: 2022-03-30 00:00:00
Tasks: Computational Efficiency

Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier-Stokes and finite elasticity is computationally expensive. Optimizing robotic swimmer design within such a system generally involves cumbersome, gradient-free procedures on top of the already costly simulation. To address this challenge we present a novel, fully differentiable hybrid approach to FSI that combines a 2D direct numerical simulation for the deformable solid structure of the swimmer and a physics-constrained neural network surrogate to capture hydrodynamic effects of the fluid. For the deformable solid simulation of the swimmer's body, we use state-of-the-art techniques from the field of computer graphics to speed up the finite-element method (FEM). For the fluid simulation, we use a U-Net architecture trained with a physics-based loss function to predict the flow field at each time step. The pressure and velocity field outputs from the neural network are sampled around the boundary of our swimmer using an immersed boundary method (IBM) to compute its swimming motion accurately and efficiently. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer. Due to differentiability, the simulator can be used for computational design of controls for soft bodies immersed in fluids via direct gradient-based optimization.
hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification
Authors: Mithun Das, Somnath Banerjee, Animesh Mukherjee
Date: 2022-03-25 00:00:00
Proceeding: DravidianLangTech (ACL) 2022 5
Tasks: Classification, Meme Classification

Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it. The situation is more complex for multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and models. We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022. We observe while the text-based model MURIL performs better for Non-troll meme classification, the image-based model VGG16 performs better for Troll-meme classification. Further fusing these two modalities help us achieve stable outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1 score and ranked second in this task.
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning
Authors: Zar Nawab Khan Swati, Qinghua Zhao3, Muhammad Kabir, Farman Ali, Ali Zakir, Saeed Ahmad, Jianfeng Lu
Date: 2019-01-04 00:00:00
Proceeding: journal 2019 1
Tasks: Content-Based Image Retrieval, Image Retrieval, Metric Learning, Retrieval, Self-Learning, Transfer Learning

This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors
on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR
systems for MR images is the semantic gap between the low-level visual information captured by the MRI
machine and the high-level information perceived by the human evaluator. The traditional feature extraction
methods focus only on low-level or high-level features and use some handcrafted features to reduce this gap.
It is necessary to design a feature extraction framework to reduce this gap without using handcrafted features
by encoding/combining low-level and high-level features. Deep learning is very powerful for feature
representation that can depict low-level and high-level information completely and embed the phase of feature
extraction in self-learning. Therefore, we propose a deep convolutional neural network (CNN) VGG19-based
novel feature extraction framework and apply closed-form metric learning (CFML) to measure the similarity
between the query image and database images. Furthermore, we adopt transfer learning and propose a blockwise fine-tuning strategy to enhance the retrieval performance. Extensive experiments are performed on a
publicly available CE-MRI dataset that consists of three types of brain tumors (i.e., glioma, meningioma, and
pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal
views. Our method is more generic, as we do not use any handcrafted features; it requires minimal
preprocessing, tested as robust on five-fold cross-validation, can achieve a five-fold mean average precision
(mAP) of 96.13% and outperforms state-of-the-art CBIR systems on the CE-MRI dataset.
A Dataset and Explorer for 3D Signed Distance Functions
Authors: Towaki Takikawa, Andrew Glassner, Morgan McGuire
Date: 2022-04-27 00:00:00
Proceeding: Journal of Computer Graphics Techniques 2022 4
Tasks: GPU

Reference datasets are a key tool in the creation of new algorithms. They allow us to compare different existing solutions and identify problems and weaknesses during the development of new algorithms. The signed distance function (SDF) is enjoying a renewed focus of research activity in computer graphics, but until now there has been no standard reference dataset of such functions. We present a database of 63 curated, optimized, and regularized functions of varying complexity. Our functions are provided as analytic expressions that can be efficiently evaluated on a GPU at any point in space. We also present a viewing and inspection tool and software for producing SDF samples appropriate for both traditional graphics and training neural networks.
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images
Authors: Justus Drögemüller, Carlos X. Garcia, Elena Gambaro, Michael Suppa, Jochen Steil, Màximo A. Roa
Date: 2021-12-07 00:00:00
Proceeding: IEEE Int. Conf. Advanced Robotics 2021 12
Tasks: Denoising, Grasp Generation, Grasp rectangle generation, Robotic Grasping, Stereo Matching, Synthetic Data Generation

This paper proposes a novel approach to automat-
ically generate labeled training data for predicting parallel-jaw
grasps from stereo-matched depth images. We generate realistic
depth images using Semi-Global Matching to compute disparity
maps from synthetic data, which allows producing images that
mimic the typical artifacts from real stereo matching in our
data, thus reducing the gap from simulation to real execu-
tion. Our pipeline automatically generates grasp annotations
for single or multiple objects on the synthetically rendered
scenes, avoiding any manual image pre-processing steps such as
inpainting or denoising. The labeled data is then used to train a
CNN-model that predicts parallel-jaw grasps, even in scenarios
with large amount of unknown depth values. We further show
that scene properties such as the presence of obstacles (a bin,
for instance) can be added to our pipeline, and the training
process results in grasp prediction success rates of up to 90%.
Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation
Authors: Zekang Chen, Jia Wei, Rui Li
Date: 2022-04-28 00:00:00
Tasks: Computed Tomography (CT), Image Registration, Image-to-Image Translation, Medical Image Registration, Translation

In clinical practice, well-aligned multi-modal images, such as Magnetic Resonance (MR) and Computed Tomography (CT), together can provide complementary information for image-guided therapies. Multi-modal image registration is essential for the accurate alignment of these multi-modal images. However, it remains a very challenging task due to complicated and unknown spatial correspondence between different modalities. In this paper, we propose a novel translation-based unsupervised deformable image registration approach to convert the multi-modal registration problem to a mono-modal one. Specifically, our approach incorporates a discriminator-free translation network to facilitate the training of the registration network and a patchwise contrastive loss to encourage the translation network to preserve object shapes. Furthermore, we propose to replace an adversarial loss, that is widely used in previous multi-modal image registration methods, with a pixel loss in order to integrate the output of translation into the target modality. This leads to an unsupervised method requiring no ground-truth deformation or pairs of aligned images for training. We evaluate four variants of our approach on the public Learn2Reg 2021 datasets \cite{hering2021learn2reg}. The experimental results demonstrate that the proposed architecture achieves state-of-the-art performance. Our code is available at https://github.com/heyblackC/DFMIR.
Toward Compositional Generalization in Object-Oriented World Modeling
Authors: Linfeng Zhao, Lingzhi Kong, Robin Walters, Lawson L. S. Wong
Date: 2022-04-28 00:00:00
Tasks: Decision Making, Object, reinforcement-learning, Reinforcement Learning (RL)

Compositional generalization is a critical ability in learning and decision-making. We focus on the setting of reinforcement learning in object-oriented environments to study compositional generalization in world modeling. We (1) formalize the compositional generalization problem with an algebraic approach and (2) study how a world model can achieve that. We introduce a conceptual environment, Object Library, and two instances, and deploy a principled pipeline to measure the generalization ability. Motivated by the formulation, we analyze several methods with exact or no compositional generalization ability using our framework, and design a differentiable approach, Homomorphic Object-oriented World Model (HOWM), that achieves soft but more efficient compositional generalization.
GRIT: General Robust Image Task Benchmark
Authors: Tanmay Gupta, Ryan Marten, Aniruddha Kembhavi, Derek Hoiem
Date: 2022-04-28 00:00:00
Tasks: Instance Segmentation, Keypoint Detection, Object Categorization, Object Localization, Question Answering, Referring Expression, Surface Normal Estimation, Surface Normals Estimation, Visual Question Answering, Visual Question Answering (VQA)

Computer vision models excel at making predictions when the test distribution closely resembles the training distribution. Such models have yet to match the ability of biological vision to learn from multiple sources and generalize to new data sources and tasks. To facilitate the development and evaluation of more general vision systems, we introduce the General Robust Image Task (GRIT) benchmark. GRIT evaluates the performance, robustness, and calibration of a vision system across a variety of image prediction tasks, concepts, and data sources. The seven tasks in GRIT are selected to cover a range of visual skills: object categorization, object localization, referring expression grounding, visual question answering, segmentation, human keypoint detection, and surface normal estimation. GRIT is carefully designed to enable the evaluation of robustness under image perturbations, image source distribution shift, and concept distribution shift. By providing a unified platform for thorough assessment of skills and concepts learned by a vision model, we hope GRIT catalyzes the development of performant and robust general purpose vision systems.
UniTE: Unified Translation Evaluation
Authors: Yu Wan, Dayiheng Liu, Baosong Yang, Haibo Zhang, Boxing Chen, Derek F. Wong, Lidia S. Chao
Date: 2022-04-28 00:00:00
Proceeding: ACL 2022 5
Tasks: Machine Translation, Multi-Task Learning, Translation

Translation quality evaluation plays a crucial role in machine translation. According to the input format, it is mainly separated into three tasks, i.e., reference-only, source-only and source-reference-combined. Recent methods, despite their promising results, are specifically designed and optimized on one of them. This limits the convenience of these methods, and overlooks the commonalities among tasks. In this paper, we propose UniTE, which is the first unified framework engaged with abilities to handle all three evaluation tasks. Concretely, we propose monotonic regional attention to control the interaction among input segments, and unified pretraining to better adapt multi-task learning. We testify our framework on WMT 2019 Metrics and WMT 2020 Quality Estimation benchmarks. Extensive analyses show that our \textit{single model} can universally surpass various state-of-the-art or winner methods across tasks. Both source code and associated models are available at https://github.com/NLP2CT/UniTE.
RoBLEURT Submission for the WMT2021 Metrics Task
Authors: Yu Wan, Dayiheng Liu, Baosong Yang, Tianchi Bi, Haibo Zhang, Boxing Chen, Weihua Luo, Derek F. Wong, Lidia S. Chao
Date: 2022-04-28 00:00:00
Tasks: Denoising

In this paper, we present our submission to Shared Metrics Task: RoBLEURT (Robustly Optimizing the training of BLEURT). After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy. Experimental results show that our model reaching state-of-the-art correlations with the WMT2020 human annotations upon 8 out of 10 to-English language pairs.
Attention Mechanism with Energy-Friendly Operations
Authors: Yu Wan, Baosong Yang, Dayiheng Liu, Rong Xiao, Derek F. Wong, Haibo Zhang, Boxing Chen, Lidia S. Chao
Date: 2022-04-28 00:00:00
Proceeding: Findings (ACL) 2022 5
Tasks: Machine Translation, Translation

Attention mechanism has become the dominant module in natural language processing models. It is computationally intensive and depends on massive power-hungry multiplications. In this paper, we rethink variants of attention mechanism from the energy consumption aspects. After reaching the conclusion that the energy costs of several energy-friendly operations are far less than their multiplication counterparts, we build a novel attention model by replacing multiplications with either selective operations or additions. Empirical results on three machine translation tasks demonstrate that the proposed model, against the vanilla one, achieves competitable accuracy while saving 99\% and 66\% energy during alignment calculation and the whole attention procedure. Code is available at: https://github.com/NLP2CT/E-Att.
Symmetric Transformer-based Network for Unsupervised Image Registration
Authors: Mingrui Ma, Lei Song, Yuanbo Xu, Guixia Liu
Date: 2022-04-28 00:00:00
Tasks: Decoder, Image Registration, Medical Image Analysis, Medical Image Registration, Unsupervised Image Registration

Medical image registration is a fundamental and critical task in medical image analysis. With the rapid development of deep learning, convolutional neural networks (CNN) have dominated the medical image registration field. Due to the disadvantage of the local receptive field of CNN, some recent registration methods have focused on using transformers for non-local registration. However, the standard Transformer has a vast number of parameters and high computational complexity, which causes Transformer can only be applied at the bottom of the registration models. As a result, only coarse information is available at the lowest resolution, limiting the contribution of Transformer in their models. To address these challenges, we propose a convolution-based efficient multi-head self-attention (CEMSA) block, which reduces the parameters of the traditional Transformer and captures local spatial context information for reducing semantic ambiguity in the attention mechanism. Based on the proposed CEMSA, we present a novel Symmetric Transformer-based model (SymTrans). SymTrans employs the Transformer blocks in the encoder and the decoder respectively to model the long-range spatial cross-image relevance. We apply SymTrans to the displacement field and diffeomorphic registration. Experimental results show that our proposed method achieves state-of-the-art performance in image registration. Our code is publicly available at \url{https://github.com/MingR-Ma/SymTrans}.
Bilinear value networks
Authors: Zhang-Wei Hong, Ge Yang, Pulkit Agrawal
Date: 2022-04-28 00:00:00
Tasks: Multi-Goal Reinforcement Learning

The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficiency is intimately connected with the generalization of the Q-function to new goals. The de-facto paradigm is to approximate Q(s, a, g) using monolithic neural networks. To improve the generalization of the Q-function, we propose a bilinear decomposition that represents the Q-value via a low-rank approximation in the form of a dot product between two vector fields. The first vector field, f(s, a), captures the environment's local dynamics at the state s; whereas the second component, {\phi}(s, g), captures the global relationship between the current state and the goal. We show that our bilinear decomposition scheme substantially improves data efficiency, and has superior transfer to out-of-distribution goals compared to prior methods. Empirical evidence is provided on the simulated Fetch robot task-suite and dexterous manipulation with a Shadow hand.
Linear Temporal Logic Modulo Theories over Finite Traces (Extended Version)
Authors: Luca Geatti, Alessandro Gianola, Nicola Gigante
Date: 2022-04-28 00:00:00

This paper studies Linear Temporal Logic over Finite Traces (LTLf) where proposition letters are replaced with first-order formulas interpreted over arbitrary theories, in the spirit of Satisfiability Modulo Theories. The resulting logic, called LTLf Modulo Theories (LTLfMT), is semi-decidable. Nevertheless, its high expressiveness comes useful in a number of use cases, such as model-checking of data-aware processes and data-aware planning. Despite the general undecidability of these problems, being able to solve satisfiable instances is a compromise worth studying. After motivating and describing such use cases, we provide a sound and complete semi-decision procedure for LTLfMT based on the SMT encoding of a one-pass tree-shaped tableau system. The algorithm is implemented in the BLACK satisfiability checking tool, and an experimental evaluation shows the feasibility of the approach on novel benchmarks.
Unified Simulation, Perception, and Generation of Human Behavior
Authors: Ye Yuan
Date: 2022-04-28 00:00:00

Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential aspects -- simulation, perception, and generation. Throughout the thesis, we show how the three aspects are deeply connected and how utilizing and improving one aspect can greatly benefit the other aspects. We also discuss the lessons learned and our vision for what is next for human behavior modeling.