pwc-archive/papers-with-abstracts huggingface dataset:
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design
Authors: Mohamed-Amine Chadi, Hajar Mousannif, Ahmed Aamouche
Date: 2023-09-24 00:00:00
Tasks: Diversity, Drug Design, Efficient Exploration, Navigate

In recent years, deep learning has demonstrated promising results in de novo drug design. However, the proposed techniques still lack an efficient exploration of the large chemical space. Most of these methods explore a small fragment of the chemical space of known drugs, if the desired molecules were not found, the process ends. In this work, we introduce a curiosity-driven method to force the model to navigate many parts of the chemical space, therefore, achieving higher desirability and diversity as well. At first, we train a recurrent neural network-based general molecular generator (G), then we fine-tune G to maximize curiosity and desirability. We define curiosity as the Tanimoto similarity between two generated molecules, a first molecule generated by G, and a second one generated by a copy of G (Gcopy). We only backpropagate the loss through G while keeping Gcopy unchanged. We benchmarked our approach against two desirable chemical properties related to drug-likeness and showed that the discovered chemical space can be significantly expanded, thus, discovering a higher number of desirable molecules with more diversity and potentially easier to synthesize. All Code and data used in this paper are available at https://github.com/amine179/Curiosity-RL-for-Drug-Design.
VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design
Authors: Khalid El-Awady
Date: 2023-12-25 00:00:00
Tasks: Deep Reinforcement Learning

We investigate the construction of generative models capable of encoding physical constraints that can be hard to express explicitly. For the problem of inverse material design, where one seeks to design a material with a prescribed set of properties, a significant challenge is ensuring synthetic viability of a proposed new material. We encode an implicit dataset relationships, namely that certain materials can be decomposed into other ones in the dataset, and present a VAE model capable of preserving this property in the latent space and generating new samples with the same. This is particularly useful in sequential inverse material design, an emergent research area that seeks to design a material with specific properties by sequentially adding (or removing) elements using policies trained through deep reinforcement learning.
Nonparametric Evaluation of Noisy ICA Solutions
Authors: Syamantak Kumar, Purnamrita Sarkar, Peter Bickel, Derek Bean
Date: 2024-01-16 00:00:00
Tasks: blind source separation, Diagnostic

Independent Component Analysis (ICA) was introduced in the 1980's as a model for Blind Source Separation (BSS), which refers to the process of recovering the sources underlying a mixture of signals, with little knowledge about the source signals or the mixing process. While there are many sophisticated algorithms for estimation, different methods have different shortcomings. In this paper, we develop a nonparametric score to adaptively pick the right algorithm for ICA with arbitrary Gaussian noise. The novelty of this score stems from the fact that it just assumes a finite second moment of the data and uses the characteristic function to evaluate the quality of the estimated mixing matrix without any knowledge of the parameters of the noise distribution. In addition, we propose some new contrast functions and algorithms that enjoy the same fast computability as existing algorithms like FASTICA and JADE but work in domains where the former may fail. While these also may have weaknesses, our proposed diagnostic, as shown by our simulations, can remedy them. Finally, we propose a theoretical framework to analyze the local and global convergence properties of our algorithms.
N-Adaptive Ritz Method: A Neural Network Enriched Partition of Unity for Boundary Value Problems
Authors: Jonghyuk Baek, Yanran Wang, J. S. Chen
Date: 2024-01-16 00:00:00
Tasks: Transfer Learning, Unity

Conventional finite element methods are known to be tedious in adaptive refinements due to their conformal regularity requirements. Further, the enrichment functions for adaptive refinements are often not readily available in general applications. This work introduces a novel neural network-enriched Partition of Unity (NN-PU) approach for solving boundary value problems via artificial neural networks with a potential energy-based loss function minimization. The flexibility and adaptivity of the NN function space are utilized to capture complex solution patterns that the conventional Galerkin methods fail to capture. The NN enrichment is constructed by combining pre-trained feature-encoded NN blocks with an additional untrained NN block. The pre-trained NN blocks learn specific local features during the offline stage, enabling efficient enrichment of the approximation space during the online stage through the Ritz-type energy minimization. The NN enrichment is introduced under the Partition of Unity (PU) framework, ensuring convergence of the proposed method. The proposed NN-PU approximation and feature-encoded transfer learning forms an adaptive approximation framework, termed the neural-refinement (n-refinement), for solving boundary value problems. Demonstrated by solving various elasticity problems, the proposed method offers accurate solutions while notably reducing the computational cost compared to the conventional adaptive refinement in the mesh-based methods.
ADVENT: Attack/Anomaly Detection in VANETs
Authors: Hamideh Baharlouei, Adetokunbo Makanju, Nur Zincir-Heywood
Date: 2024-01-16 00:00:00
Tasks: Anomaly Detection, Federated Learning

In the domain of Vehicular Ad hoc Networks (VANETs), where the imperative of having a real-world malicious detector capable of detecting attacks in real-time and unveiling their perpetrators is crucial, our study introduces a system with this goal. This system is designed for real-time detection of malicious behavior, addressing the critical need to first identify the onset of attacks and subsequently the responsible actors. Prior work in this area have never addressed both requirements, which we believe are necessary for real world deployment, simultaneously. By seamlessly integrating statistical and machine learning techniques, the proposed system prioritizes simplicity and efficiency. It excels in swiftly detecting attack onsets with a remarkable F1-score of 99.66%, subsequently identifying malicious vehicles with an average F1-score of approximately 97.85%. Incorporating federated learning in both stages enhances privacy and improves the efficiency of malicious node detection, effectively reducing the false negative rate.
Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation
Authors: Qichen He, Siying Xiao, Mao Ye, Xiatian Zhu, Ferrante Neri and Dongde Hou
Date: 2023-04-19 00:00:00
Proceeding: IJCAI 2023 4
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

Existing Unsupervised Domain Adaptation (UDA) methods typically attempt to perform knowledge transfer in a domain-invariant space explicitly or implicitly. In practice, however, the obtained features are often mixed with domain-specific information which causes performance degradation. To overcome this fundamental limitation, this article presents a novel independent feature decomposition and instance alignment method (IndUDA in short). Specifically, based on an invertible flow, we project the base feature into a decomposed latent space with domain-invariant and domain specific dimensions. To drive semantic decomposition independently, we then swap the domain invariant part across source and target domain samples with the same category and require their inverted features are consistent in class-level with the original features. By treating domain-specific information as noise, we replace it by Gaussian noise and further regularize source model training by instance alignment, i.e., requiring the base features close to the corresponding reconstructed features, respectively. Extensive experiment results demonstrate that our method achieves state-of-the-art performance on popular UDA benchmarks. The appendix and code are available at https://github.com/ayombeach/IndUDA.
ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors
Authors: Xiaoxuan Ma, Stephan P. Kaufhold, Jiajun Su, Wentao Zhu, Jack Terwilliger, Andres Meza, Yixin Zhu, Federico Rossano, Yizhou Wang
Date: 2023-10-25 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Action Detection, Pose Estimation

Understanding the behavior of non-human primates is crucial for improving animal welfare, modeling social behavior, and gaining insights into distinctively human and phylogenetically shared behaviors. However, the lack of datasets on non-human primate behavior hinders in-depth exploration of primate social interactions, posing challenges to research on our closest living relatives. To address these limitations, we present ChimpACT, a comprehensive dataset for quantifying the longitudinal behavior and social relations of chimpanzees within a social group. Spanning from 2015 to 2018, ChimpACT features videos of a group of over 20 chimpanzees residing at the Leipzig Zoo, Germany, with a particular focus on documenting the developmental trajectory of one young male, Azibo. ChimpACT is both comprehensive and challenging, consisting of 163 videos with a cumulative 160,500 frames, each richly annotated with detection, identification, pose estimation, and fine-grained spatiotemporal behavior labels. We benchmark representative methods of three tracks on ChimpACT: (i) tracking and identification, (ii) pose estimation, and (iii) spatiotemporal action detection of the chimpanzees. Our experiments reveal that ChimpACT offers ample opportunities for both devising new methods and adapting existing ones to solve fundamental computer vision tasks applied to chimpanzee groups, such as detection, pose estimation, and behavior analysis, ultimately deepening our comprehension of communication and sociality in non-human primates.
Expanding Small-Scale Datasets with Guided Imagination
Authors: Yifan Zhang, Daquan Zhou, Bryan Hooi, Kai Wang, Jiashi Feng
Date: 2022-11-25 00:00:00
Proceeding: NeurIPS 2023 11

The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming. To address this issue, we explore a new task, termed dataset expansion, aimed at expanding a ready-to-use small dataset by automatically creating new labeled samples. To this end, we present a Guided Imagination Framework (GIF) that leverages cutting-edge generative models like DALL-E2 and Stable Diffusion (SD) to "imagine" and create informative new data from the input seed data. Specifically, GIF conducts data imagination by optimizing the latent features of the seed data in the semantically meaningful space of the prior model, resulting in the creation of photo-realistic images with new content. To guide the imagination towards creating informative samples for model training, we introduce two key criteria, i.e., class-maintained information boosting and sample diversity promotion. These criteria are verified to be essential for effective dataset expansion: GIF-SD obtains 13.5% higher model accuracy on natural image datasets than unguided expansion with SD. With these essential criteria, GIF successfully expands small datasets in various scenarios, boosting model accuracy by 36.9% on average over six natural image datasets and by 13.5% on average over three medical datasets. The source code is available at https://github.com/Vanint/DatasetExpansion.
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints
Authors: Jingyi Gu, Wenlu Du, A M Muntasir Rahman, Guiling Wang
Date: 2023-11-25 00:00:00
Proceeding: The 4th ACM International Conference on AI in Finance 2023 11
Tasks: Deep Reinforcement Learning, Management, Portfolio Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the field of portfolio management using reinforcement learn- ing, existing approaches have mainly focused on cash-only trading, overlooking the potential benefits and risks of margin trading. Incor- porating margin accounts and their constraints, especially in short sale scenarios, is crucial yet often neglected. To address this gap, we make the first attempt to propose Margin Trader, an innovative and adaptive reinforcement learning framework designed for margin trading in the stock market. Margin Trader integrates margin ac- counts and constraints into a realistic trading environment for both long and short positions. The framework aims to balance profit maximization and risk management through the Margin Adjust- ment Module and the Maintenance Detection Module. Margin Trader supports various Deep Reinforcement Learning (DRL) algorithms and offers traders the flexibility to customize critical settings, such as equity allocation, margin ratios, and maintenance requirements, to suit diverse market conditions, individual preferences, and risk tolerance. Experimental results demonstrate that Margin Trader effectively learns profitable trading strategies and hedges risks in both bullish and bearish markets, outperforming other baseline models with the highest Sharpe ratio.
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
Authors: Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie
Date: 2024-01-16 00:00:00
Tasks: Image Generation

We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which allows for connecting two distributions in a more flexible way than standard diffusion models, makes possible a modular study of various design choices impacting generative models built on dynamical transport: learning in discrete or continuous time, the objective function, the interpolant that connects the distributions, and deterministic or stochastic sampling. By carefully introducing the above ingredients, SiT surpasses DiT uniformly across model sizes on the conditional ImageNet 256x256 and 512x512 benchmark using the exact same model structure, number of parameters, and GFLOPs. By exploring various diffusion coefficients, which can be tuned separately from learning, SiT achieves an FID-50K score of 2.06 and 2.62, respectively.
SAiD: Speech-driven Blendshape Facial Animation with Diffusion
Authors: Inkyu Park, Jaewoong Cho
Date: 2023-12-25 00:00:00

Speech-driven 3D facial animation is challenging due to the scarcity of large-scale visual-audio datasets despite extensive research. Most prior works, typically focused on learning regression models on a small dataset using the method of least squares, encounter difficulties generating diverse lip movements from speech and require substantial effort in refining the generated outputs. To address these issues, we propose a speech-driven 3D facial animation with a diffusion model (SAiD), a lightweight Transformer-based U-Net with a cross-modality alignment bias between audio and visual to enhance lip synchronization. Moreover, we introduce BlendVOCA, a benchmark dataset of pairs of speech audio and parameters of a blendshape facial model, to address the scarcity of public resources. Our experimental results demonstrate that the proposed approach achieves comparable or superior performance in lip synchronization to baselines, ensures more diverse lip movements, and streamlines the animation editing process.
EgoGen: An Egocentric Synthetic Data Generator
Authors: Gen Li, Kaifeng Zhao, Siwei Zhang, Xiaozhong Lyu, Mihai Dusmanu, Yan Zhang, Marc Pollefeys, Siyu Tang
Date: 2024-01-16 00:00:00
Proceeding: CVPR 2024 1
Tasks: Human Mesh Recovery, Motion Synthesis

Understanding the world in first-person view is fundamental in Augmented Reality (AR). This immersive perspective brings dramatic visual changes and unique challenges compared to third-person views. Synthetic data has empowered third-person-view vision models, but its application to embodied egocentric perception tasks remains largely unexplored. A critical challenge lies in simulating natural human movements and behaviors that effectively steer the embodied cameras to capture a faithful egocentric representation of the 3D world. To address this challenge, we introduce EgoGen, a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. Combined with collision-avoiding motion primitives and a two-stage reinforcement learning approach, our motion synthesis model offers a closed-loop solution where the embodied perception and movement of the virtual human are seamlessly coupled. Compared to previous works, our model eliminates the need for a pre-defined global path, and is directly applicable to dynamic environments. Combined with our easy-to-use and scalable data generation pipeline, we demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views. EgoGen will be fully open-sourced, offering a practical solution for creating realistic egocentric training data and aiming to serve as a useful tool for egocentric computer vision research. Refer to our project page: https://ego-gen.github.io/.
FIT-SLAM -- Fisher Information and Traversability estimation-based Active SLAM for exploration in 3D environments
Authors: Suchetan Saravanan, Corentin Chauffaut, Caroline Chanel, Damien Vivet
Date: 2024-01-17 00:00:00
Tasks: Efficient Exploration

Active visual SLAM finds a wide array of applications in GNSS-Denied sub-terrain environments and outdoor environments for ground robots. To achieve robust localization and mapping accuracy, it is imperative to incorporate the perception considerations in the goal selection and path planning towards the goal during an exploration mission. Through this work, we propose FIT-SLAM (Fisher Information and Traversability estimation-based Active SLAM), a new exploration method tailored for unmanned ground vehicles (UGVs) to explore 3D environments. This approach is devised with the dual objectives of sustaining an efficient exploration rate while optimizing SLAM accuracy. Initially, an estimation of a global traversability map is conducted, which accounts for the environmental constraints pertaining to traversability. Subsequently, we propose a goal candidate selection approach along with a path planning method towards this goal that takes into account the information provided by the landmarks used by the SLAM backend to achieve robust localization and successful path execution . The entire algorithm is tested and evaluated first in a simulated 3D world, followed by a real-world environment and is compared to pre-existing exploration methods. The results obtained during this evaluation demonstrate a significant increase in the exploration rate while effectively minimizing the localization covariance.
SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene Understanding
Authors: Baoxiong Jia, Yixin Chen, Huangyue Yu, Yan Wang, Xuesong Niu, Tengyu Liu, Qing Li, Siyuan Huang
Date: 2024-01-17 00:00:00
Tasks: 3D visual grounding, Scene Understanding, Visual Grounding

3D vision-language grounding, which focuses on aligning language with the 3D physical environment, stands as a cornerstone in the development of embodied agents. In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces several significant challenges: (i) the inherent complexity of 3D scenes due to the diverse object configurations, their rich attributes, and intricate relationships; (ii) the scarcity of paired 3D vision-language data to support grounded learning; and (iii) the absence of a unified learning framework to distill knowledge from grounded 3D data. In this work, we aim to address these three major challenges in 3D vision-language by examining the potential of systematically upscaling 3D vision-language learning in indoor environments. We introduce the first million-scale 3D vision-language dataset, SceneVerse, encompassing about 68K 3D indoor scenes and comprising 2.5M vision-language pairs derived from both human annotations and our scalable scene-graph-based generation approach. We demonstrate that this scaling allows for a unified pre-training framework, Grounded Pre-training for Scenes (GPS), for 3D vision-language learning. Through extensive experiments, we showcase the effectiveness of GPS by achieving state-of-the-art performance on all existing 3D visual grounding benchmarks. The vast potential of SceneVerse and GPS is unveiled through zero-shot transfer experiments in the challenging 3D vision-language tasks. Project website: https://scene-verse.github.io.
Incremento del precio de los combustibles y su incidencia en los productos de la canasta basica del canton el triunfo, provincia del guayas
Authors: Alvear Guzman Katherine, Campozano Buele Jenner, Duran Canarte Paulette, Holguin Cedeno Roger, Mejia Crespin Fernando
Date: 2023-11-13 00:00:00
Tasks: regression

The objective of this research was to analyze the impact of the increase in the price of fuels and its incidence on the products of the basic basket of the El Triunfo, the province of Guayas. In the present study, the non-experimental quantitative method was used. The study population was limited to the families of the town, seeking to determine how their level of consumption was impacted after the increase in fuels. Just 95 people were randomly taken. The study instrument that was used was surveys, with a focus on the purchasing power of families with respect to the basic basket after the increase in fuel prices. The results were processed through Cronbach's Alpha and reflected in pie charts. The independent and dependent variable that make up our study, were related through a simple linear regression, to determine if they correlate with each other. Keywords: Fuels, Basic basket, Linear regression, Subsidies, Inflation
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
Authors: Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, Ying Shan
Date: 2024-01-17 00:00:00
Proceeding: CVPR 2024 1
Tasks: Text-to-Video Generation, Video Generation

Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition.