pwc-archive/papers-with-abstracts huggingface dataset:
Ultimate ownership and bank competition
Authors: José Azar, Sahil Raina, Martin Schmalz
Date: 2021-06-15 00:00:00
Proceeding: Financial Management 2021 6
Tasks: Time Series

We document substantial time-series and cross-sectional variation in branch-level deposit account interest rates, maintenance fees, and fee thresholds, and examine whether variation in bank concentration helps explain variation in these prices. Herfindahl–Hirschman Index (HHI) alone is not correlatedwith any of the outcome variables. A “generalized HHI” (GHHI) capturing both common ownership (the degree to which banks are commonly owned by the same investors) and cross-ownership (the extent to which banks own shares in each other), is strongly correlated with all prices, even when we limit cross-sectional variation in bank ownership to only that predicted by the growth of index funds.
Confidence-aware Fusion using Dempster-Shafer Theory for Multispectral Pedestrian Detection
Authors: Qing Li; Changqing Zhang; Qinghua Hu; Huazhu Fu; Pengfei Zhu
Date: 2022-03-21 00:00:00
Proceeding: journal 2022 3
Tasks: Multispectral Object Detection, Pedestrian Detection

Multispectral pedestrian detection is an important and valuable task in many applications, which could provide a more accurate and reliable pedestrian detection result by using the complementary visual information from color and thermal images. However, it faces two open and difficult challenges: 1) how to effectively and dynamically integrate multispectral information according to the confidence of different modalities, and 2) how to produce a reliable prediction result. In this paper, we propose a novel confidence-aware multispectral pedestrian detection (CMPD) method, which flexibly learns the multispectral representation while simultaneously producing a reliable result with confidence estimation. Specifically, a dense fusion strategy is first proposed to extract the multilevel multispectral representation at the feature level. Then, an additional confidence subnetwork is utilized to dynamically estimate the detection confidence for each modality. Finally, Dempster's combination rule is introduced to fuse the results of different branches according to the rectified confidence. Our proposed CMPD method not only effectively integrates multimodal information but also provides a reliable prediction. Extensive experimental results demonstrate the efficiency of our algorithm compared with state-of-the-art methods.
UNBERT: User-News Matching BERT for News Recommendation
Authors: Qi Zhang, Jingjie Li, Qinglin Jia, Chuyuan Wang, Jieming Zhu, Zhaowei Wang, Xiuqiang He
Date: 2021-08-19 00:00:00
Proceeding: IJCAI 2021 8
Tasks: News Recommendation

Nowadays, news recommendation has become a popular channel for users to access news of their interests. How to represent rich textual contents of news and precisely match users' interests and candidate news lies in the core of news recommendation. However, existing recommendation methods merely learn textual representations from in-domain news data, which limits their generalization ability to new news that are common in cold-start scenarios. Meanwhile, many of these methods represent each user by aggregating the historically browsed news into a single vector and then compute the matching score with the candidate news vector, which may lose the low-level matching signals. In this paper, we explore the use of the successful BERT pre-training technique in NLP for news recommendation and propose a BERT-based user-news matching model, called UNBERT. In contrast to existing research, our UNBERT model not only leverages the pre-trained model with rich language knowledge to enhance textual representation, but also captures multi-grained user-news matching signals at both word-level and news-level. Extensive experiments on the Microsoft News Dataset (MIND) demonstrate that our approach constantly outperforms the state-of-the-art methods.
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
Authors: Karttikeya Mangalam, Raiymbek Akshulakov, Jitendra Malik
Date: 2023-08-17 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Diagnostic, EgoSchema, Form, Multiple-choice, Question Answering, Video Question Answering, Video Understanding, Zero-Shot Video Question Answer

We introduce EgoSchema, a very long-form video question-answering dataset, and benchmark to evaluate long video understanding capabilities of modern vision and language systems. Derived from Ego4D, EgoSchema consists of over 5000 human curated multiple choice question answer pairs, spanning over 250 hours of real video data, covering a very broad range of natural human activity and behavior. For each question, EgoSchema requires the correct answer to be selected between five given options based on a three-minute-long video clip. While some prior works have proposed video datasets with long clip lengths, we posit that merely the length of the video clip does not truly capture the temporal difficulty of the video task that is being considered. To remedy this, we introduce temporal certificate sets, a general notion for capturing the intrinsic temporal understanding length associated with a broad range of video understanding tasks & datasets. Based on this metric, we find EgoSchema to have intrinsic temporal lengths over 5.7x longer than the second closest dataset and 10x to 100x longer than any other video understanding dataset. Further, our evaluation of several current state-of-the-art video and language models shows them to be severely lacking in long-term video understanding capabilities. Even models with several billions of parameters achieve QA accuracy less than 33% (random is 20%) on the EgoSchema multi-choice question answering task, while humans achieve about 76% accuracy. We posit that \name{}{}, with its long intrinsic temporal structures and diverse complexity, would serve as a valuable evaluation probe for developing effective long-term video understanding systems in the future. Data and Zero-shot model evaluation code are open-sourced for both public and commercial use under the Ego4D license at http://egoschema.github.io
HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds
Authors: Gang Zhang, Junnan Chen, Guohuan Gao, Jianmin Li, Xiaolin Hu
Date: 2023-10-31 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: 3D Object Detection, Autonomous Driving, Decoder, Object, object-detection, Object Detection

3D object detection in point clouds is important for autonomous driving systems. A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene. Existing high-performance methods typically employ 3D sparse convolutional neural networks with small kernels to extract features. To reduce computational costs, these methods resort to submanifold sparse convolutions, which prevent the information exchange among spatially disconnected features. Some recent approaches have attempted to address this problem by introducing large-kernel convolutions or self-attention mechanisms, but they either achieve limited accuracy improvements or incur excessive computational costs. We propose HEDNet, a hierarchical encoder-decoder network for 3D object detection, which leverages encoder-decoder blocks to capture long-range dependencies among features in the spatial space, particularly for large and distant objects. We conducted extensive experiments on the Waymo Open and nuScenes datasets. HEDNet achieved superior detection accuracy on both datasets than previous state-of-the-art methods with competitive efficiency. The code is available at https://github.com/zhanggang001/HEDNet.
Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Authors: Beichen Zhang, Kun Zhou, Xilin Wei, Wayne Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen
Date: 2023-06-04 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Math

Chain-of-thought prompting~(CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models~(LLMs) to perform step-by-step reasoning on complex math-related tasks. However, most existing math reasoning datasets may be not able to fully evaluate and analyze the ability of LLMs in manipulating tools and performing reasoning, as they may only require very few invocations of tools or miss annotations for evaluating intermediate reasoning steps. To address the issue, we construct \textbf{CARP}, a new Chinese dataset consisting of 4,886 computation-intensive algebra problems with formulated annotations on intermediate steps. In CARP, we test four LLMs with CoT prompting, and find that they are all prone to make mistakes at the early steps of the solution, leading to wrong answers. Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}. In DELI, we first initialize a step-by-step solution based on retrieved exemplars, then iterate two deliberation procedures that check and refine the intermediate steps of the generated solution, from the perspectives of tool manipulation and natural language reasoning, until obtaining converged solutions or reaching the maximum turn. Experimental results on CARP and six other datasets show that the proposed DELI mostly outperforms competitive baselines, and can further boost the performance of existing CoT methods. Our data and code are available in \url{https://github.com/RUCAIBox/CARP}.
Evaluating Open-QA Evaluation
Authors: Cunxiang Wang, Sirui Cheng, Qipeng Guo, Yuanhao Yue, Bowen Ding, Zhikun Xu, Yidong Wang, Xiangkun Hu, Zheng Zhang, Yue Zhang
Date: 2023-05-21 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Question Answering

This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic evaluation methods have shown limitations, indicating that human evaluation still remains the most reliable approach. We introduce a new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset EVOUNA, designed to assess the accuracy of AI-generated answers in relation to standard answers within Open-QA. Our evaluation of these methods utilizes human-annotated results to measure their performance. Specifically, the work investigates methods that show high correlation with human evaluations, deeming them more reliable. We also discuss the pitfalls of current methods and methods to improve LLM-based evaluators. We believe this new QA-Eval task and corresponding dataset EVOUNA will facilitate the development of more effective automatic evaluation tools and prove valuable for future research in this area. All resources are available at \url{https://github.com/wangcunxiang/QA-Eval} and it is under the Apache-2.0 License.
CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care
Authors: Tong Xiang, Liangzhi Li, Wangyue Li, Mingbai Bai, Lu Wei, Bowen Wang, Noa Garcia
Date: 2023-07-04 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Misinformation

The recent advances in natural language processing (NLP), have led to a new trend of applying large language models (LLMs) to real-world scenarios. While the latest LLMs are astonishingly fluent when interacting with humans, they suffer from the misinformation problem by unintentionally generating factually false statements. This can lead to harmful consequences, especially when produced within sensitive contexts, such as healthcare. Yet few previous works have focused on evaluating misinformation in the long-form (LF) generation of LLMs, especially for knowledge-intensive topics. Moreover, although LLMs have been shown to perform well in different languages, misinformation evaluation has been mostly conducted in English. To this end, we present a benchmark, CARE-MI, for evaluating LLM misinformation in: 1) a sensitive topic, specifically the maternity and infant care domain; and 2) a language other than English, namely Chinese. Most importantly, we provide an innovative paradigm for building LF generation evaluation benchmarks that can be transferred to other knowledge-intensive domains and low-resourced languages. Our proposed benchmark fills the gap between the extensive usage of LLMs and the lack of datasets for assessing the misinformation generated by these models. It contains 1,612 expert-checked questions, accompanied with human-selected references. Using our benchmark, we conduct extensive experiments and found that current Chinese LLMs are far from perfect in the topic of maternity and infant care. In an effort to minimize the reliance on human resources for performance evaluation, we offer off-the-shelf judgment models for automatically assessing the LF output of LLMs given benchmark questions. Moreover, we compare potential solutions for LF generation evaluation and provide insights for building better automated metrics.
Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset
Authors: Saeid Naeini, Raeid Saqur, Mozhgan Saeidi, John Giorgi, Babak Taati
Date: 2023-06-19 00:00:00
Proceeding: NeurIPS 2023 12
Tasks: Only Connect Walls Dataset Task 1 (Grouping), Only Connect Walls Dataset Task 2 (Connections)

The quest for human imitative AI has been an enduring topic in AI research since its inception. The technical evolution and emerging capabilities of the latest cohort of large language models (LLMs) have reinvigorated the subject beyond academia to the cultural zeitgeist. While recent NLP evaluation benchmark tasks test some aspects of human-imitative behaviour (e.g., BIG-bench's 'human-like behavior' tasks), few, if not none, examine creative problem solving abilities. Creative problem solving in humans is a well-studied topic in cognitive neuroscience with standardized tests that predominantly use the ability to associate (heterogeneous) connections among clue words as a metric for creativity. Exposure to misleading stimuli - distractors dubbed red herrings - impede human performance in such tasks via the fixation effect and Einstellung paradigm. In cognitive neuroscience studies, such fixations are experimentally induced by pre-exposing participants to orthographically similar incorrect words to subsequent word-fragments or clues. The popular British quiz show Only Connect's Connecting Wall segment essentially mimics Mednick's Remote Associates Test (RAT) formulation with built-in, deliberate red herrings, which makes it an ideal proxy dataset to explore and study fixation effect and Einstellung paradigm from cognitive neuroscience in LLMs. In this paper we present the novel Only Connect Wall (OCW) dataset and report results from our evaluation of selected pre-trained language models and LLMs on creative problem solving tasks like grouping clue words by heterogeneous connections, and identifying correct open knowledge domain connections in respective groups. We synthetically generate two additional datasets: OCW-Randomized, OCW-WordNet to further analyze our red-herrings hypothesis in language models. The code and link to the dataset are available at https://github.com/TaatiTeam/OCW.
Automatic Labeling of Parkinson’s Disease Gait Videos with Weak Supervision
Authors: Mohsen Gholami, Rabab Ward, Ravneet Mahal, Maryam Mirian, Kevin Yen, Kye Won Park, Martin J. McKeown, Z. Jane Wang
Date: 2023-06-25 00:00:00
Proceeding: Medical Image Analysis 2023 6
Tasks: 3D Human Pose Estimation, Pose Estimation, Weakly-supervised 3D Human Pose Estimation

Motor dysfunction in Parkinson’s Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-consuming, expensive, semi-subjective, and may potentially result in conflicting labels across different raters. To address this problem, we propose an automatic, objective, and weakly-supervised method for labeling PD patients’ gait videos. The proposed method accepts videos of patients and classifies their gait scores as normal (Gait score in MDS-UPDRS = 0) or PD (MDS-UPDRS 
 1). Unlike previous work, the proposed method does not require a priori MDS-UPDRS ratings for training, utilizing only domain-specific knowledge obtained from neurologists. We propose several labeling functions that classify patients’ gait and use a generative model to learn the accuracy of each labeling function in a self-supervised manner. Since results depended upon the estimated values of the patients’ 3D poses, and existing pre-trained 3D pose estimators did not yield accurate results, we propose a weakly-supervised 3D human pose estimation method for fine-tuning pre-trained models in a clinical setting. Using leave-one-out evaluations, the proposed method obtains an accuracy of 89% on a dataset of 29 PD subjects – a significant improvement compared to previous work by 7%–10% depending upon the dataset. The method obtained state-of-the-art results on the Human3.6M dataset. Our results suggest that the use of labeling functions may provide a robust means to interpret and classify patient-oriented videos involving motor tasks.
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
Authors: Wenzhang Liu, Wenzhe Cai, Kun Jiang, Guangran Cheng, Yuanda Wang, Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun
Date: 2023-12-25 00:00:00
Tasks: CPU, Deep Reinforcement Learning, GPU, MuJoCo, reinforcement-learning, Reinforcement Learning

In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms, with the flexibility to easily incorporate new algorithms and environments. It is a versatile DRL library that supports CPU, GPU, and Ascend, and can be executed on various operating systems such as Ubuntu, Windows, MacOS, and EulerOS. Extensive benchmarks conducted on popular environments including MuJoCo, Atari, and StarCraftII multi-agent challenge demonstrate the library's impressive performance. XuanCe is open-source and can be accessed at https://github.com/agi-brain/xuance.git.
DiffusionGAN3D: Boosting Text-guided 3D Generation and Domain Adaptation by Combining 3D GANs and Diffusion Priors
Authors: Biwen Lei, Kai Yu, Mengyang Feng, Miaomiao Cui, Xuansong Xie
Date: 2023-12-28 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Generation, Domain Adaptation

Text-guided domain adaptation and generation of 3D-aware portraits find many applications in various fields. However, due to the lack of training data and the challenges in handling the high variety of geometry and appearance, the existing methods for these tasks suffer from issues like inflexibility, instability, and low fidelity. In this paper, we propose a novel framework DiffusionGAN3D, which boosts text-guided 3D domain adaptation and generation by combining 3D GANs and diffusion priors. Specifically, we integrate the pre-trained 3D generative models (e.g., EG3D) and text-to-image diffusion models. The former provides a strong foundation for stable and high-quality avatar generation from text. And the diffusion models in turn offer powerful priors and guide the 3D generator finetuning with informative direction to achieve flexible and efficient text-guided domain adaptation. To enhance the diversity in domain adaptation and the generation capability in text-to-avatar, we introduce the relative distance loss and case-specific learnable triplane respectively. Besides, we design a progressive texture refinement module to improve the texture quality for both tasks above. Extensive experiments demonstrate that the proposed framework achieves excellent results in both domain adaptation and text-to-avatar tasks, outperforming existing methods in terms of generation quality and efficiency. The project homepage is at https://younglbw.github.io/DiffusionGAN3D-homepage/.
How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation
Authors: Yang Xiao, Yi Cheng, Jinlan Fu, Jiashuo Wang, Wenjie Li, PengFei Liu
Date: 2023-12-28 00:00:00
Tasks: AI Agent, Language Modelling, Large Language Model

In recent years, AI has demonstrated remarkable capabilities in simulating human behaviors, particularly those implemented with large language models (LLMs). However, due to the lack of systematic evaluation of LLMs' simulated behaviors, the believability of LLMs among humans remains ambiguous, i.e., it is unclear which behaviors of LLMs are convincingly human-like and which need further improvements. In this work, we design SimulateBench to evaluate the believability of LLMs when simulating human behaviors. In specific, we evaluate the believability of LLMs based on two critical dimensions: 1) consistency: the extent to which LLMs can behave consistently with the given information of a human to simulate; and 2) robustness: the ability of LLMs' simulated behaviors to remain robust when faced with perturbations. SimulateBench includes 65 character profiles and a total of 8,400 questions to examine LLMs' simulated behaviors. Based on SimulateBench, we evaluate the performances of 10 widely used LLMs when simulating characters. The experimental results reveal that current LLMs struggle to align their behaviors with assigned characters and are vulnerable to perturbations in certain factors.
Evolutionary Swarm Robotics: Dynamic Subgoal-Based Path Formation and Task Allocation for Exploration and Navigation in Unknown Environments
Authors: Lavanya Ratnabala, Robinroy Peter, E. Y. A. Charles
Date: 2023-12-27 00:00:00

This research paper addresses the challenges of exploration and navigation in unknown environments from an evolutionary swarm robotics perspective. Path formation plays a crucial role in enabling cooperative swarm robots to accomplish these tasks. The paper presents a method called the sub-goal-based path formation, which establishes a path between two different locations by exploiting visually connected sub-goals. Simulation experiments conducted in the Argos simulator demonstrate the successful formation of paths in the majority of trials. Furthermore, the paper tackles the problem of inter-collision (traffic) among a large number of robots engaged in path formation, which negatively impacts the performance of the sub-goal-based method. To mitigate this issue, a task allocation strategy is proposed, leveraging local communication protocols and light signal-based communication. The strategy evaluates the distance between points and determines the required number of robots for the path formation task, reducing unwanted exploration and traffic congestion. The performance of the sub-goal-based path formation and task allocation strategy is evaluated by comparing path length, time, and resource reduction against the A* algorithm. The simulation experiments demonstrate promising results, showcasing the scalability, robustness, and fault tolerance characteristics of the proposed approach.