pwc-archive/papers-with-abstracts huggingface dataset:
ChatGPT as the Transportation Equity Information Source for Scientific Writing
Authors: Boniphace Kutela, Shoujia Li, Subasish Das, Jinli Liu
Date: 2023-03-10 00:00:00

Transportation equity is an interdisciplinary agenda that requires both transportation and social inputs. Traditionally, transportation equity information are sources from public libraries, conferences, televisions, social media, among other. Artificial intelligence (AI) tools including advanced language models such as ChatGPT are becoming favorite information sources. However, their credibility has not been well explored. This study explored the content and usefulness of ChatGPT-generated information related to transportation equity. It utilized 152 papers retrieved through the Web of Science (WoS) repository. The prompt was crafted for ChatGPT to provide an abstract given the title of the paper. The ChatGPT-based abstracts were then compared to human-written abstracts using statistical tools and unsupervised text mining. The results indicate that a weak similarity between ChatGPT and human-written abstracts. On average, the human-written abstracts and ChatGPT generated abstracts were about 58% similar, with a maximum and minimum of 97% and 1.4%, respectively. The keywords from the abstracts of papers with over the mean similarity score were more likely to be similar whereas those from below the average score were less likely to be similar. Themes with high similarity scores include access, public transit, and policy, among others. Further, clear differences in the key pattern of clusters for high and low similarity score abstracts was observed. Contrarily, the findings from collocated keywords were inconclusive. The study findings suggest that ChatGPT has the potential to be a source of transportation equity information. However, currently, a great amount of attention is needed before a user can utilize materials from ChatGPT
A Joint Model of Language and Perception for Grounded Attribute Learning
Authors: Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, Dieter Fox
Date: 2012-06-27 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling

As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them. Recently, this has led to study of the language grounding problem, where the goal is to extract representations of the meanings of natural language tied to perception and actuation in the physical world. In this paper, we present an approach for joint learning of language and perception models for grounded attribute induction. Our perception model includes attribute classifiers, for example to detect object color and shape, and the language model is based on a probabilistic categorial grammar that enables the construction of rich, compositional meaning representations. The approach is evaluated on the task of interpreting sentences that describe sets of objects in a physical workspace. We demonstrate accurate task performance and effective latent-variable concept induction in physical grounded scenes.
Weight-guided class complementing for long-tailed image recognition
Authors: Xinqiao Zhao, Jimin Xiao, Siyue Yu, Hui Li, Bingfeng Zhang
Date: 2023-02-03 00:00:00
Proceeding: Pattern Recognition 2023 2
Tasks: Long-tail Learning

Real-world data are often long-tailed distributed and have plenty classes. This characteristic leads to a significant performance drop for various models. One reason behind that is the gradient shift caused by unsampled classes in each training iteration. In this paper, we propose a Weight-Guided Class Complementing framework to address this issue. Specifically, this framework first complements the unsampled classes in each training iteration by using a dynamic updated data slot. Then, considering the over-fitting issue caused by class complementing, we utilize the classifier weights as learned knowledge and encourage the model to discover more class specific characteristics. Finally, we design a weight refining scheme to deal with the long-tailed bias existing in classifier weights. Experimental results show that our framework can be implemented upon different existing approaches effectively, achieving consistent improvements on various benchmarks with new state-of-the-art performances.
High-Frequency Preserving Image Downscaler
Authors: Jaehwan Kim, Soo Min Kang, Kwang Pyo Choi, Youngo Park, Chaeeun Lee, JongSeok Lee
Date: 2021-08-23 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2021 8
Tasks: Decoder, Vocal Bursts Intensity Prediction

The goal of downscaling an image is to reduce its resolution to a lower resolution, while maintaining the visual characteristics of the original image. Recent learning-based algorithms have shown great improvement over conventional methods in preserving high-frequency information. However, they continue to suffer from various artifacts due to the lack of true high-low resolution image pairs in their training datasets. In this paper, an unsupervised image downscaler that preserves the high frequency content of the original image based on an autoencoder is presented. Specifically, the image downscaler is obtained by extracting the decoder of the developed autoencoder. Furthermore, we propose a preprocessing step that enables image downscaler to any arbitrary scales. Experimental results on five benchmark datasets reveal the qualitative and quantitative superiority of the proposed method at various scales compared to other methods.
A multiscale graph cut approach to bright-field multiple cell image segmentation using a Bhattacharyya measure
Authors: Soo Min Kang, Justin W. L. Wan
Date: 2013-03-13 00:00:00
Proceeding: SPIE Medical Imaging 2013 3
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Automatic segmentation of bright-field cell images is important to cell biologists, but is difficult to achieve due to the complex nature of the cells in bright-field images (poor contrast, broken halo, missing boundaries). The standard segmentation techniques, such as the level set method and active contours, are not able to overcome these features of bright-field images. Consequently, poor segmentation results are produced. In this paper, we present a robust segmentation method, which combines the techniques of graph cut, multiresolution, and Bhattacharyya measure, performed in a multiscale framework, to locate multiple cells in bright-field images. The issue of low contrast in bright-field images is addressed by determining the difference in intensity profiles of the cells and the background. The resulting segmentation on the entire image frame provides global information. Then a local segmentation at different regions of interest is performed to obtain finer details of the segmentation result. We illustrate the effectiveness of the method by presenting the segmentation results of C2C12 (muscle) cells in bright-field images.
Computer Assisted Proofs and Automated Methods in Mathematics Education
Authors: Thierry Noah Dana-Picard
Date: 2023-03-10 00:00:00

This survey paper is an expanded version of an invited keynote at the ThEdu'22 workshop, August 2022, in Haifa (Israel). After a short introduction on the developments of CAS, DGS and other useful technologies, we show implications in Mathematics Education, and in the broader frame of STEAM Education. In particular, we discuss the transformation of Mathematics Education into exploration-discovery-conjecture-proof scheme, avoiding usage as a black box . This scheme fits well into the so-called 4 C's of 21st Century Education. Communication and Collaboration are emphasized not only between humans, but also between machines, and between man and machine. Specific characteristics of the outputs enhance the need of Critical Thinking. The usage of automated commands for exploration and discovery is discussed, with mention of limitations where they exist. We illustrate the topic with examples from parametric integrals (describing a "cognitive neighborhood" of a mathematical notion), plane geometry, and the study of plane curves (envelopes, isoptic curves). Some of the examples are fully worked out, others are explained and references are given.
Topic Modeling in Density Functional Theory on Citations of Condensed Matter Electronic Structure Packages
Authors: Marie Dumaz, Camila Romero-Bohorquez, Donald Adjeroh, Aldo H. Romero
Date: 2023-02-16 00:00:00
Tasks: Articles, Attribute, Word Similarity

With an increasing number of new scientific papers being released, it becomes harder for researchers to be aware of recent articles in their field of study. Accurately classifying papers is a first step in the direction of personalized catering and easy access to research of interest. The field of Density Functional Theory (DFT) in particular is a good example of a methodology used in very different studies, and interconnected disciplines, which has a very strong community publishing many research articles. We devise a new unsupervised method for classifying publications, based on topic modeling, and use a DFT-related selection of documents as a use case. We first create topics from word analysis and clustering of the abstracts from the publications, then attribute each publication/paper to a topic based on word similarity. We then make interesting observations by analyzing connections between the topics and publishers, journals, country or year of publication. The proposed approach is general, and can be applied to analyze publication and citation trends in other areas of study, beyond the field of Density Function Theory.
Three dimensional chaos game representation of protein sequences
Authors: Annie Thomas
Date: 2023-03-16 00:00:00

A new three dimensional approach to the chaos game representation of protein sequences is explored in this thesis. The basics of DNA, the synthesis of proteins from DNA, protein structure and functionality and sequence alignment techniques are presented. The mathematical background needed for understanding the chaos game representation and fractal analysis are briefly discussed. An account of the existing literature on the chaos game representation of DNA sequences and a detailed account of the chaos game representation of protein sequences in two dimensions with its advantages and limitations are presented. We explore a new three dimensional approach to the chaos game representation of protein sequences (3D-CGR) and study its ability a) to determine protein sequence similarity and differences, b) to study the effect of dinucleotide biases at amino acid level on the 3D-CGR derived protein homology, and c) to identify sequence similarity based on shuffled motifs that could be used for studying protein evolution due to exon shuffling.
A real-time blind quality-of-experience assessment metric for HTTP adaptive streaming
Authors: Chunyi Li, May Lim, Abdelhak Bentaleb, Roger Zimmermann
Date: 2023-03-17 00:00:00

In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm. An effective Quality of Experience (QoE) assessment metric can provide crucial feedback to an ABR algorithm. However, predicting such real-time QoE on the client side is challenging. The QoE prediction requires high consistency with the Human Visual System (HVS), low latency, and blind assessment, which are difficult to realize together. To address this challenge, we analyzed various characteristics of HAS systems and propose a non-uniform sampling metric to reduce time complexity. Furthermore, we design an effective QoE metric that integrates resolution and rebuffering time as the Quality of Service (QoS), as well as spatiotemporal output from a deep neural network and specific switching events as content information. These reward and penalty features are regressed into quality scores with a Support Vector Regression (SVR) model. Experimental results show that the accuracy of our metric outperforms the mainstream blind QoE metrics by 0.3, and its computing time is only 60\% of the video playback, indicating that the proposed metric is capable of providing real-time guidance to ABR algorithms and improving the overall performance of HAS.
Symbolic-Numeric Computation of Integrals in Successive Galerkin Approximation of Hamilton-Jacobi-Bellman Equation
Authors: Tomoyuki Iori
Date: 2023-03-17 00:00:00
Tasks: Numerical Integration

This paper proposes an efficient symbolic-numeric method to compute the integrals in the successive Galerkin approximation (SGA) of the Hamilton-Jacobi-Bellman (HJB) equation. A solution of the HJB equation is first approximated with a linear combination of the Hermite polynomials. The coefficients of the combination are then computed by iteratively solving a linear equation, which consists of the integrals of the Hermite polynomials multiplied by nonlinear functions. The recursive structure of the Hermite polynomials is inherited by the integrals, and their recurrence relations can be computed by using the symbolic computation of differential operators. By using the recurrence relations, all the integrals can be computed from a part of them that are numerically evaluated. A numerical example is provided to show the efficiency of the proposed method compared to a standard numerical integration method.
Preserving Derivative Information while Transforming Neuronal Curves
Authors: Thomas L. Athey, Daniel J. Tward, Ulrich Mueller, Laurent Younes, Joshua T. Vogelstein, Michael I. Miller
Date: 2023-03-16 00:00:00

The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit.
Improving Data Transfer Efficiency for AIs in the DareFightingICE using gRPC
Authors: Chollakorn Nimpattanavong, Ibrahim Khan, Thai Van Nguyen, Ruck Thawonmas, Worawat Choensawat, Kingkarn Sookhanaphibarn
Date: 2023-03-11 00:00:00

This paper presents a new communication interface for the DareFightingICE platform, a Java-based fighting game focused on implementing AI for controlling a non-player character. The interface uses an open-source remote procedure call, gRPC to improve the efficiency of data transfer between the game and the AI, reducing the time spent on receiving information from the game server. This is important because the main challenge of implementing AI in a fighting game is the need for the AI to select an action to perform within a short response time. The DareFightingICE platform has been integrated with Py4J, allowing developers to create AIs using Python. However, Py4J is less efficient at handling large amounts of data, resulting in excessive latency. In contrast, gRPC is well-suited for transmitting large amounts of data. To evaluate the effectiveness of the new communication interface, we conducted an experiment comparing the latency of gRPC and Py4J, using a rule-based AI that sends a kick command regardless of the information received from the game server. The experiment results showed not only a 65\% reduction in latency but also improved stability and eliminated missed frames compared to the current interface.
Experimental verification of an online traction parameter identification method
Authors: Alexander Kobelski, Pavel Osinenko, Stefan Streif
Date: 2023-03-17 00:00:00

Traction parameters, that characterize the ground-wheel contact dynamics, are the central factor in the energy efficiency of vehicles. To optimize fuel consumption, reduce wear of tires, increase productivity etc., knowledge of current traction parameters is unavoidable. Unfortunately, these parameters are difficult to measure and require expensive force and torque sensors. An alternative way is to use system identification to determine them. In this work, we validate such a method in field experiments with a mobile robot. The method is based on an adaptive Kalman filter. We show how it estimates the traction parameters online, during the motion on the field, and compare them to their values determined via a 6-directional force-torque sensor installed for verification. Data of adhesion slip ratio curves is recorded and compared to curves from literature for additional validation of the method. The results can establish a foundation for a number of optimal traction methods.
Vehicular Applications of Koopman Operator Theory -- A Survey
Authors: Waqas Manzoor, Samir Rawashdeh, Alireza Mohammadi
Date: 2023-03-18 00:00:00
Tasks: Survey

Koopman operator theory has proven to be a promising approach to nonlinear system identification and global linearization. For nearly a century, there had been no efficient means of calculating the Koopman operator for applied engineering purposes. The introduction of a recent computationally efficient method in the context of fluid dynamics, which is based on the system dynamics decomposition to a set of normal modes in descending order, has overcome this long-lasting computational obstacle. The purely data-driven nature of Koopman operators holds the promise of capturing unknown and complex dynamics for reduced-order model generation and system identification, through which the rich machinery of linear control techniques can be utilized. Given the ongoing development of this research area and the many existing open problems in the fields of smart mobility and vehicle engineering, a survey of techniques and open challenges of applying Koopman operator theory to this vibrant area is warranted. This review focuses on the various solutions of the Koopman operator which have emerged in recent years, particularly those focusing on mobility applications, ranging from characterization and component-level control operations to vehicle performance and fleet management. Moreover, this comprehensive review of over 100 research papers highlights the breadth of ways Koopman operator theory has been applied to various vehicular applications with a detailed categorization of the applied Koopman operator-based algorithm type. Furthermore, this review paper discusses theoretical aspects of Koopman operator theory that have been largely neglected by the smart mobility and vehicle engineering community and yet have large potential for contributing to solving open problems in these areas.
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition
Authors: Xiaoyu Yang, Qiujia Li, Chao Zhang, Philip C. Woodland
Date: 2023-03-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Knowledge Distillation, Self-Supervised Learning, speech-recognition, Speech Recognition

Although large foundation models pre-trained by self-supervised learning have achieved state-of-the-art performance in many tasks including automatic speech recognition (ASR), knowledge distillation (KD) is often required in practice to transfer the knowledge learned by large teacher models into much smaller student models with affordable computation and memory costs. This paper proposes a novel two-stage KD framework to distil the knowledge from multiple speech foundation models as teachers into a single student neural transducer model for ASR. In the first stage, the student model encoder is pre-trained using the embeddings extracted from multiple teacher models. In the second stage, the student encoder is fine-tuned with the audio-text pairs based on the ASR task. Experiments on the LibriSpeech 100-hour subset show that the proposed KD framework improves the performance of both streaming and non-streaming student models when using only one teacher. The performance of the student model can be further enhanced when multiple teachers are used jointly, achieving word error rate reductions (WERRs) of 17.5% and 10.6%. Our proposed framework can be combined with other existing KD methods to achieve further improvements. Further WERRs were obtained by incorporating extra unlabelled data during encoder pre-training, leading to a total relative WERR of 55.0% on the non-streaming student model.
The Graph feature fusion technique for speaker recognition based on wav2vec2.0 framework
Authors: Zirui Ge, Haiyan Guo, Zhen Yang
Date: 2023-03-19 00:00:00
Tasks: Graph Attention, Graph Neural Network, Speaker Recognition

Pre-trained wav2vec2.0 model has been proved its effectiveness for speaker recognition. However, current feature processing methods are focusing on classical pooling on the output features of the pre-trained wav2vec2.0 model, such as mean pooling, max pooling etc. That methods take the features as the independent and irrelevant units, ignoring the inter-relationship among all the features, and do not take the features as an overall representation of a speaker. Gated Recurrent Unit (GRU), as a feature fusion method, can also be considered as a complicated pooling technique, mainly focuses on the temporal information, which may show poor performance in some situations that the main information is not on the temporal dimension. In this paper, we investigate the graph neural network (GNN) as a backend processing module based on wav2vec2.0 framework to provide a solution for the mentioned matters. The GNN takes all the output features as the graph signal data and extracts the related graph structure information of features for speaker recognition. Specifically, we first give a simple proof that the GNN feature fusion method can outperform than the mean, max, random pooling methods and so on theoretically. Then, we model the output features of wav2vec2.0 as the vertices of a graph, and construct the graph adjacency matrix by graph attention network (GAT). Finally, we follow the message passing neural network (MPNN) to design our message function, vertex update function and readout function to transform the speaker features into the graph features. The experiments show our performance can provide a relative improvement compared to the baseline methods. Code is available at xxx.