pwc-archive/papers-with-abstracts huggingface dataset:
Synergizing Data Imputation and Electronic Health Records for Advancing Prostate Cancer Research: Challenges, and Practical Applications
Authors: Abderrahim Oussama Batouche, Eugen Czeizler, Miika Koskinen, Tuomas Mirtti, Antti Sakari Rannikko
Date: 2023-10-24 00:00:00
Tasks: Imputation, Retrieval

The presence of detailed clinical information in electronic health record (EHR) systems presents promising prospects for enhancing patient care through automated retrieval techniques. Nevertheless, it is widely acknowledged that accessing data within EHRs is hindered by various methodological challenges. Specifically, the clinical notes stored in EHRs are composed in a narrative form, making them prone to ambiguous formulations and highly unstructured data presentations, while structured reports commonly suffer from missing and/or erroneous data entries. This inherent complexity poses significant challenges when attempting automated large-scale medical knowledge extraction tasks, necessitating the application of advanced tools, such as natural language processing (NLP), as well as data audit techniques. This work aims to address these obstacles by creating and validating a novel pipeline designed to extract relevant data pertaining to prostate cancer patients. The objective is to exploit the inherent redundancies available within the integrated structured and unstructured data entries within EHRs in order to generate comprehensive and reliable medical databases, ready to be used in advanced research studies. Additionally, the study explores potential opportunities arising from these data, offering valuable prospects for advancing research in prostate cancer.
Preference Elicitation with Soft Attributes in Interactive Recommendation
Authors: Erdem Biyik, Fan Yao, Yinlam Chow, Alex Haig, Chih-Wei Hsu, Mohammad Ghavamzadeh, Craig Boutilier
Date: 2023-10-22 00:00:00
Tasks: Attribute, Interactive Recommendation, Recommendation Systems

Preference elicitation plays a central role in interactive recommender systems. Most preference elicitation approaches use either item queries that ask users to select preferred items from a slate, or attribute queries that ask them to express their preferences for item characteristics. Unfortunately, users often wish to describe their preferences using soft attributes for which no ground-truth semantics is given. Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation. Our techniques query users using both items and soft attributes to update the recommender system's belief about their preferences to improve recommendation quality. We demonstrate the effectiveness of our methods vis-a-vis competing approaches on both synthetic and real-world datasets.
ITEm: Unsupervised Image-Text Embedding Learning for eCommerce
Authors: Baohao Liao, Michael Kozielski, Sanjika Hewavitharana, Jiangbo Yuan, Shahram Khadivi, Tomer Lancewicki
Date: 2023-10-22 00:00:00

Product embedding serves as a cornerstone for a wide range of applications in eCommerce. The product embedding learned from multiple modalities shows significant improvement over that from a single modality, since different modalities provide complementary information. However, some modalities are more informatively dominant than others. How to teach a model to learn embedding from different modalities without neglecting information from the less dominant modality is challenging. We present an image-text embedding model (ITEm), an unsupervised learning method that is designed to better attend to image and text modalities. We extend BERT by (1) learning an embedding from text and image without knowing the regions of interest; (2) training a global representation to predict masked words and to construct masked image patches without their individual representations. We evaluate the pre-trained ITEm on two tasks: the search for extremely similar products and the prediction of product categories, showing substantial gains compared to strong baseline models.
MaRU: A Manga Retrieval and Understanding System Connecting Vision and Language
Authors: Conghao Tom Shen, Violet Yao, Yixin Liu
Date: 2023-10-22 00:00:00
Tasks: Decoder, object-detection, Object Detection, Retrieval

Manga, a widely celebrated Japanese comic art form, is renowned for its diverse narratives and distinct artistic styles. However, the inherently visual and intricate structure of Manga, which comprises images housing multiple panels, poses significant challenges for content retrieval. To address this, we present MaRU (Manga Retrieval and Understanding), a multi-staged system that connects vision and language to facilitate efficient search of both dialogues and scenes within Manga frames. The architecture of MaRU integrates an object detection model for identifying text and frame bounding boxes, a Vision Encoder-Decoder model for text recognition, a text encoder for embedding text, and a vision-text encoder that merges textual and visual information into a unified embedding space for scene retrieval. Rigorous evaluations reveal that MaRU excels in end-to-end dialogue retrieval and exhibits promising results for scene retrieval.
Semantic Modelling of Organizational Knowledge as a Basis for Enterprise Data Governance 4.0 -- Application to a Unified Clinical Data Model
Authors: Miguel AP Oliveira, Stephane Manara, Bruno Molé, Thomas Muller, Aurélien Guillouche, Lysann Hesske, Bruce Jordan, Gilles Hubert, Chinmay Kulkarni, Pralipta Jagdev, Cedric R. Berger
Date: 2023-10-20 00:00:00
Tasks: Management

Individuals and organizations cope with an always-growing amount of data, which is heterogeneous in its contents and formats. An adequate data management process yielding data quality and control over its lifecycle is a prerequisite to getting value out of this data and minimizing inherent risks related to multiple usages. Common data governance frameworks rely on people, policies, and processes that fall short of the overwhelming complexity of data. Yet, harnessing this complexity is necessary to achieve high-quality standards. The latter will condition any downstream data usage outcome, including generative artificial intelligence trained on this data. In this paper, we report our concrete experience establishing a simple, cost-efficient framework that enables metadata-driven, agile and (semi-)automated data governance (i.e. Data Governance 4.0). We explain how we implement and use this framework to integrate 25 years of clinical study data at an enterprise scale in a fully productive environment. The framework encompasses both methodologies and technologies leveraging semantic web principles. We built a knowledge graph describing avatars of data assets in their business context, including governance principles. Multiple ontologies articulated by an enterprise upper ontology enable key governance actions such as FAIRification, lifecycle management, definition of roles and responsibilities, lineage across transformations and provenance from source systems. This metadata model is the keystone to data governance 4.0: a semi-automatised data management process that considers the business context in an agile manner to adapt governance constraints to each use case and dynamically tune it based on business changes.
League of Legends: Real-Time Result Prediction
Authors: Jailson B. S. Junior, Claudio E. C. Campelo
Date: 2023-09-02 00:00:00
Tasks: Prediction

This paper presents a study on the prediction of outcomes in matches of the electronic game League of Legends (LoL) using machine learning techniques. With the aim of exploring the ability to predict real-time results, considering different variables and stages of the match, we highlight the use of unpublished data as a fundamental part of this process. With the increasing popularity of LoL and the emergence of tournaments, betting related to the game has also emerged, making the investigation in this area even more relevant. A variety of models were evaluated and the results were encouraging. A model based on LightGBM showed the best performance, achieving an average accuracy of 81.62\% in intermediate stages of the match when the percentage of elapsed time was between 60\% and 80\%. On the other hand, the Logistic Regression and Gradient Boosting models proved to be more effective in early stages of the game, with promising results. This study contributes to the field of machine learning applied to electronic games, providing valuable insights into real-time prediction in League of Legends. The results obtained may be relevant for both players seeking to improve their strategies and the betting industry related to the game.
TensorFlow based deep learning layer for Local Derivative Patterns
Authors: Devrim Akgun
Date: 2022-12-14 00:00:00
Proceeding: Software Impacts 2022 12
Tasks: Deep Learning

Deep learning architectures can extract features automatically via particular layers such as convolutions, recurrent networks, and pooling operations. In addition to the predefined layers in TensorFlow, developers can implement their custom layers based on the existing operators. This study presents a new library for using the Local Derivative Pattern (LDP) algorithm as a deep learning layer. The LDP layer has no trainable parameters and extracts the local patterns in four directions. The layer’s output provides the features to the following layers separately or combined. The developed library enables integrating LDP into any desired model that works with two-dimensional input data.
Factored-form Kalman-like implementations under maximum correntropy criterion
Authors: Maria V. Kulikova
Date: 2023-11-04 00:00:00
Tasks: Form

The maximum correntropy criterion (MCC) methodology is recognized to be a robust filtering strategy with respect to outliers and shown to outperform the classical Kalman filter (KF) for estimation accuracy in the presence of non-Gaussian noise. However, the numerical stability of the newly proposed MCC-KF estimators in finite precision arithmetic is seldom addressed. In this paper, a family of {\it factored-form} (square-root) algorithms is derived for the MCC-KF and its improved variant, respectively. The family traditionally consists of three factored-form implementations: (i) Cholesky factorization-based algorithms, (ii) modified Cholesky, i.e. UD-based methods, and (iii) the recently established SVD-based filtering. All these strategies are commonly recognized to enhance the numerical robustness of conventional filtering with respect to roundoff errors and, hence, they are the preferred implementations when solving applications with high reliability requirements. Previously, only Cholesky-based IMCC-KF algorithms have been designed. This paper enriches a factored-form family by introducing the UD- and SVD-based methods as well. A special attention is paid to {\it array} algorithms that are proved to be the most numerically stable and, additionally, suitable for parallel implementations. The theoretical properties are discussed and numerical comparison is presented for determining the most reliable implementations.
On the stable Cholesky factorization-based method for the maximum correntropy criterion Kalman filtering
Authors: Maria V. Kulikova
Date: 2023-11-04 00:00:00

This paper continues the research devoted to the design of numerically stable square-root implementations for the maximum correntropy criterion Kalman filtering (MCC-KF). In contrast to the previously obtained results, here we reveal the first robust (with respect to round-off errors) method within the Cholesky factorization-based approach. The method is formulated in terms of square-root factors of the {\it covariance} matrices, i.e. it belongs to the covariance-type filtering methodology. Additionally, a numerically stable orthogonal transformation is utilized at each iterate of the algorithm for accurate propagation of the Cholesky factors involved. The results of numerical experiments illustrate a superior performance of the novel MCC-KF implementation compared to both the conventional algorithm and its previously published Cholesky-based variant.
CARIS: Context-Augmented Referring Image Segmentation
Authors: Sun-Ao Liu, Yiheng Zhang, Zhaofan Qiu, Hongtao Xie, Yongdong Zhang, Ting Yao
Date: 2023-10-27 00:00:00
Proceeding: ACM MM 2023 10
Tasks: Decoder, Image Segmentation, Segmentation, Semantic Segmentation

Referring image segmentation aims to segment the target object described by a natural-language utterance. Recent approaches typically distinguish pixels by aligning pixel-wise visual features with linguistic features extracted from the referring description. Nevertheless, such a free-form description only specifies certain discriminative attributes of the target object or its relations to a limited number of objects, which fails to represent the rich visual context adequately. The stand-alone linguistic features are therefore unable to align with all visual concepts, resulting in inaccurate segmentation. In this paper, we propose to address this issue by incorporating rich visual context into linguistic features for sufficient vision-language alignment. Specifically, we present Context-Aware Referring Image Segmentation (CARIS), a novel architecture that enhances the contextual awareness of linguistic features via sequential vision-language attention and learnable prompts. Technically, CARIS develops a context-aware mask decoder with sequential bidirectional cross-modal attention to integrate the linguistic features with visual context, which are then aligned with pixel-wise visual features. Furthermore, two groups of learnable prompts are employed to delve into additional contextual information from the input image and facilitate the alignment with non-target pixels, respectively. Extensive experiments demonstrate that CARIS achieves new state-of-the-art performances on three public benchmarks. Code is available at https://github.com/lsa1997/CARIS.
Variational Autoencoders for Noise Reduction in Industrial LLRF Systems
Authors: J. P. Edelen, M. J. Henderson, J. Einstein-Curtis, C. C. Hall, J. A. Diaz Cruz, A. L. Edelen
Date: 2023-10-29 00:00:00

Industrial particle accelerators inherently operate in much dirtier environments than typical research accelerators. This leads to an increase in noise both in the RF system and in other electronic systems. Combined with the fact that industrial accelerators are mass produced, there is less attention given to optimizing the performance of an individual system. As a result, industrial systems tend to under perform considering their hardware hardware capabilities. With the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging, improving the signal processing of these machines will increase the margin for the deployment of these systems. Our work is focusing on using machine learning techniques to reduce the noise of RF signals used for pulse-to-pulse feedback in industrial accelerators. We will review our algorithms, simulation results, and results working with measured data. We will then discuss next steps for deployment and testing on an industrial system.
NeuSyRE: Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph Enrichment
Authors: M. Jaleed Khan, John Breslin, Edward Curry
Date: 2023-11-05 00:00:00
Proceeding: Semantic Web 2023 11
Tasks: Caption Generation, Common Sense Reasoning, Graph Generation, Image Captioning, Knowledge Graphs, object-detection, Object Detection, Scene Graph Detection, Scene Graph Generation, Scene Understanding, Visual Reasoning

Neuro-symbolic hybrid approaches are inevitable for seamless high-level understanding and reasoning about visual scenes. Scene Graph Generation (SGG) is a symbolic image representation approach based on deep neural networks (DNN) that involves predicting objects, their attributes, and pairwise visual relationships in images to create scene graphs, which are utilized in downstream visual reasoning. The crowdsourced training datasets used in SGG are highly imbalanced, which results in biased SGG results. The vast number of possible triplets makes it challenging to collect sufficient training samples for every visual concept or relationship. To address these challenges, we propose augmenting the typical data-driven SGG approach with common sense knowledge to enhance the expressiveness and autonomy of visual understanding and reasoning. We present a loosely-coupled neuro-symbolic visual understanding and reasoning framework that employs a DNN-based pipeline for object detection and multi-modal pairwise relationship prediction for scene graph generation and leverages common sense knowledge in heterogenous knowledge graphs to enrich scene graphs for improved downstream reasoning. A comprehensive evaluation is performed on multiple standard datasets, including Visual Genome and Microsoft COCO, in which the proposed approach outperformed the state-of-the-art SGG methods in terms of relationship recall scores, i.e. Recall@K and mean Recall@K, as well as the state-of-the-art scene graph-based image captioning methods in terms of SPICE and CIDEr scores with comparable BLEU, ROGUE and METEOR scores. As a result of enrichment, the qualitative results showed improved expressiveness of scene graphs, resulting in more intuitive and meaningful caption generation using scene graphs. Our results validate the effectiveness of enriching scene graphs with common sense knowledge using heterogeneous knowledge graphs. This work provides a baseline for future research in knowledge-enhanced visual understanding and reasoning.
Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies
Authors: Guangchen Lan
Date: 2023-11-05 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated learning (FL) is an emerging paradigm for training deep neural networks (DNNs) in distributed manners. Current FL approaches all suffer from high communication overhead and information leakage. In this work, we present a federated learning algorithm based on evolution strategies (FedES), a zeroth-order training method. Instead of transmitting model parameters, FedES only communicates loss values, and thus has very low communication overhead. Moreover, a third party is unable to estimate gradients without knowing the pre-shared seed, which protects data privacy. Experimental results demonstrate FedES can achieve the above benefits while keeping convergence performance the same as that with back propagation methods.
CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images
Authors: Chong Zhong, Yang Li, Danjuan Yang, Meiyan Li, Xingyao Zhou, Bo Fu, Catherine C. Liu, A. H. Welsh
Date: 2023-11-07 00:00:00
Tasks: regression

The ultra-widefield (UWF) fundus image is an attractive 3D biomarker in AI-aided myopia screening because it provides much richer myopia-related information. Though axial length (AL) has been acknowledged to be highly related to the two key targets of myopia screening, Spherical Equivalence (SE) measurement and high myopia diagnosis, its prediction based on the UWF fundus image is rarely considered. To save the high expense and time costs of measuring SE and AL, we propose the Copula-enhanced Convolutional Neural Network (CeCNN), a one-stop UWF-based ophthalmic AI framework to jointly predict SE, AL, and myopia status. The CeCNN formulates a multiresponse regression that relates multiple dependent discrete-continuous responses and the image covariate, where the nonlinearity of the association is modeled by a backbone CNN. To thoroughly describe the dependence structure among the responses, we model and incorporate the conditional dependence among responses in a CNN through a new copula-likelihood loss. We provide statistical interpretations of the conditional dependence among responses, and reveal that such dependence is beyond the dependence explained by the image covariate. We heuristically justify that the proposed loss can enhance the estimation efficiency of the CNN weights. We apply the CeCNN to the UWF dataset collected by us and demonstrate that the CeCNN sharply enhances the predictive capability of various backbone CNNs. Our study evidences the ophthalmology view that besides SE, AL is also an important measure to myopia.
Fine-tuning convergence model in Bengali speech recognition
Authors: Zhu Ruiying, Shen Meng
Date: 2023-11-07 00:00:00
Tasks: Automatic Speech Recognition, model, speech-recognition, Speech Recognition

Research on speech recognition has attracted considerable interest due to the difficult task of segmenting uninterrupted speech. Among various languages, Bengali features distinct rhythmic patterns and tones, making it particularly difficult to recognize and lacking an efficient commercial recognition method. In order to improve the automatic speech recognition model for Bengali, our team has chosen to utilize the wave2vec 2.0 pre-trained model, which has undergone convergence for fine-tuning. Regarding Word Error Rate (WER), the learning rate and dropout parameters were fine-tuned, and after the model training was stable, attempts were made to enlarge the training set ratio, which improved the model's performance. Consequently, there was a notable enhancement in the WER from 0.508 to 0.437 on the test set of the publicly listed official dataset. Afterwards, the training and validation sets were merged, creating a comprehensive dataset that was used as the training set, achieving a remarkable WER of 0.436.
A Method to Improve the Performance of Reinforcement Learning Based on the Y Operator for a Class of Stochastic Differential Equation-Based Child-Mother Systems
Authors: Cheng Yin, Yi Chen
Date: 2023-11-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This paper introduces a novel operator, termed the Y operator, to elevate control performance in Actor-Critic(AC) based reinforcement learning for systems governed by stochastic differential equations(SDEs). The Y operator ingeniously integrates the stochasticity of a class of child-mother system into the Critic network's loss function, yielding substantial advancements in the control performance of RL algorithms.Additionally, the Y operator elegantly reformulates the challenge of solving partial differential equations for the state-value function into a parallel problem for the drift and diffusion functions within the system's SDEs.A rigorous mathematical proof confirms the operator's validity.This transformation enables the Y Operator-based Reinforcement Learning(YORL) framework to efficiently tackle optimal control problems in both model-based and data-driven systems.The superiority of YORL is demonstrated through linear and nonlinear numerical examples showing its enhanced performance over existing methods post convergence.
An attempt to generate new bridge types from latent space of variational autoencoder
Authors: Hongjun Zhang
Date: 2023-11-02 00:00:00

Try to generate new bridge types using generative artificial intelligence technology. The grayscale images of the bridge facade with the change of component width was rendered by 3dsMax animation software, and then the OpenCV module performed an appropriate amount of geometric transformation (rotation, horizontal scale, vertical scale) to obtain the image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge. Based on Python programming language, TensorFlow and Keras deep learning platform framework, variational autoencoder was constructed and trained, and low-dimensional bridge-type latent space that is convenient for vector operations was obtained. Variational autoencoder can combine two bridge types on the basis of the original of human into one that is a new bridge type. Generative artificial intelligence technology can assist bridge designers in bridge-type innovation, and can be used as copilot.