pwc-archive/papers-with-abstracts huggingface dataset:
Multicriteria Optimization Techniques for Understanding the Case Mix Landscape of a Hospital
Authors: Robert L Burdett, Paul corry, Prasad Yarlagadda, David Cook, Sean Birgan
Date: 2023-07-31 00:00:00

Various medical and surgical units operate in a typical hospital and to treat their patients these units compete for infrastructure like operating rooms (OR) and ward beds. How that competition is regulated affects the capacity and output of a hospital. This article considers the impact of treating different patient case mix (PCM) in a hospital. As each case mix has an economic consequence and a unique profile of hospital resource usage, this consideration is important. To better understand the case mix landscape and to identify those which are optimal from a capacity utilisation perspective, an improved multicriteria optimization (MCO) approach is proposed. As there are many patient types in a typical hospital, the task of generating an archive of non-dominated (i.e., Pareto optimal) case mix is computationally challenging. To generate a better archive, an improved parallelised epsilon constraint method (ECM) is introduced. Our parallel random corrective approach is significantly faster than prior methods and is not restricted to evaluating points on a structured uniform mesh. As such we can generate more solutions. The application of KD-Trees is another new contribution. We use them to perform proximity testing and to store the high dimensional Pareto frontier (PF). For generating, viewing, navigating, and querying an archive, the development of a suitable decision support tool (DST) is proposed and demonstrated.
MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterization
Authors: Mehrdad Jalali, Matthias Mail, Rossella Aversa, Christian Kübel
Date: 2023-08-07 00:00:00

This paper introduces a new ontology for Materials Science Laboratory Equipment, termed MSLE. A fundamental issue with materials science laboratory (hereafter lab) equipment in the real world is that scientists work with various types of equipment with multiple specifications. For example, there are many electron microscopes with different parameters in chemical and physical labs. A critical development to unify the description is to build an equipment domain ontology as basic semantic knowledge and to guide the user to work with the equipment appropriately. Here, we propose to develop a consistent ontology for equipment, the MSLE ontology. In the MSLE, two main existing ontologies, the Semantic Sensor Network (SSN) and the Material Vocabulary (MatVoc), have been integrated into the MSLE core to build a coherent ontology. Since various acronyms and terms have been used for equipment, this paper proposes an approach to use a Simple Knowledge Organization System (SKOS) to represent the hierarchical structure of equipment terms. Equipment terms were collected in various languages and abbreviations and coded into the MSLE using the SKOS model. The ontology development was conducted in close collaboration with domain experts and focused on the large-scale devices for materials characterization available in our research group. Competency questions are expected to be addressed through the MSLE ontology. Constraints are modeled in the Shapes Query Language (SHACL); a prototype is shown and validated to show the value of the modeling constraints.
PokerKit: A Comprehensive Python Library for Fine-Grained Multi-Variant Poker Game Simulations
Authors: Juho Kim
Date: 2023-08-08 00:00:00

PokerKit is an open-source Python library designed to overcome the restrictions of existing poker game simulation and hand evaluation tools, which typically support only a handful of poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of poker variants and it provides a flexible architecture for users to define their custom games. This paper details the design and implementation of PokerKit, including its intuitive programmatic API, multi-variant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as poker AI development, tool creation, and online poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 99% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer poker, fostering future research and advanced AI development for a wide variety of poker games. The source code is available at https://github.com/uoftcprg/pokerkit
Learning to Predict by the Methods of Temporal Differences
Authors: Richard S. Sutton
Date: 1988-03-09 00:00:00
Proceeding: Machine Learning 1988 3
Tasks: Incremental Learning, Prediction

This article introduces a class of incremental learning procedures specialized for prediction that is, for using past experience with an incompletely known
system to predict its future behavior. Whereas conventional prediction-learning
methods assign credit by means of the difference between predicted and actual outcomes, tile new methods assign credit by means of the difference between temporally
successive predictions. Although such temporal-difference method~ have been used in
Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence
and optimality for special cases and relate them to supervised-learning methods. For
most real-world prediction problems, telnporal-differenee methods require less memory and less peak computation than conventional methods and they produce more
accurate predictions. We argue that most problems to which supervised learning
is currently applied are really prediction problems of the sort to which temporaldifference methods can be applied to advantage.
Physics-informed neural networks for highly compressible flows
Authors: T. Wagenaar
Date: 2023-09-15 00:00:00
Proceeding: TU Delft Education Repository 2023 9
Tasks: Physics-informed machine learning

While physics-informed neural networks have been shown to accurately solve a wide range of fluid dynamics problems, their effectivity on highly compressible flows is so far limited. In particular, they struggle with transonic and supersonic problems that involve discontinuities such as shocks. While there have been multiple efforts to alleviate the nonphysical phenomena that arise on such problems, current work does not identify and address the underlying failure modes sufficiently. This thesis shows that physics-informed neural networks struggle with highly compressible problems for two independent reasons. Firstly, the differential Euler equations conserve entropy along streamlines, so that physics-informed neural networks try to find an isentropic solution to a non-isentropic problem. Secondly, conventional slip boundary conditions form strong local minima that result in fictive objects that simplify the flow. In response to these failure modes, two new adaptations are introduced, namely a local viscosity method and a streamline output representation. The local viscosity method includes viscosity as an additional network output and adds a viscous loss term to the loss function, resulting in localized viscosity that facilitates the entropy change at shocks. Furthermore, the streamline output representation provides a more natural formulation of the slip boundary conditions, which prevents zero-velocity regions while promoting shock attachment. To the author's best knowledge, this thesis provides the first inviscid steady solutions of curved and detached shocks by physics-informed neural networks.
Online multi-object tracking via robust collaborative model and sample selection
Authors: Mohamed A. Naiel, M. Omair Ahmad, M.N.S. Swamy, Jongwoo Lim, Ming-Hsuan Yang
Date: 2017-01-01 00:00:00
Proceeding: Computer Vision and Image Understanding 2017 1
Tasks: Multi-Object Tracking, Object, object-detection, Object Detection, Object Tracking, Online Multi-Object Tracking

The past decade has witnessed significant progress in object detection and tracking in videos. In this paper, we present a collaborative model between a pre-trained object detector and a number of single-object online trackers within the particle filtering framework. For each frame, we construct an association between detections and trackers, and treat each detected image region as a key sample, for online update, if it is associated to a tracker. We present a motion model that incorporates the associated detections with object dynamics. Furthermore, we propose an effective sample selection scheme to update the appearance model of each tracker. We use discriminative and generative appearance models for the likelihood function and data association, respectively. Experimental results show that the proposed scheme generally outperforms state-of-the-art methods.
A Formal Analysis of Karn's Algorithm
Authors: Max von Hippel, Cristina Nita-Rotaru, Kenneth L. McMillan, Lenore Zuck
Date: 2023-07-01 00:00:00
Proceeding: International Conference on Networked Systems 2023 7

The stability of the Internet relies on timeouts. The timeout value, known as the Retransmission TimeOut (RTO), is constantly updated, based on sampling the Round Trip Time (RTT) of each packet as measured by its sender – that is, the time between when the sender transmits a packet and receives a corresponding acknowledgement. Many of the Internet protocols compute those
samples via the same sampling mechanism, known as Karn’s Algorithm.

We present a formal description of the algorithm, and study its properties. We prove the computed samples reflect the RTT of some packets, but it is not always possible to determine which. We then study some of the properties of RTO computations as described in the commonly used RFC6298. All properties are mechanically verified.
Modified Verhulst-Solow model for long-term population and economic growths
Authors: Iram Gleriaa, Sergio Da Silvab, Leon Brenig, Tarcısio M. Rocha Filho, Annibal Figueiredo
Date: 2023-08-16 00:00:00

In this study, we analyze the relationship between human population growth and economic dynamics. To do so, we present a modified version of the Verhulst model and the Solow model, which together simulate population dynamics and the role of economic variables in capital accumulation. The model incorporates support and foraging functions, which participate in the dynamic relationship between population growth and the creation and destruction of carrying capacity. The validity of the model is demonstrated using empirical data.
TeCH: Text-guided Reconstruction of Lifelike Clothed Humans
Authors: Yangyi Huang, Hongwei Yi, Yuliang Xiu, Tingting Liao, Jiaxiang Tang, Deng Cai, Justus Thies
Date: 2023-08-16 00:00:00
Tasks: Descriptive, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Despite recent research advancements in reconstructing clothed humans from a single image, accurately restoring the "unseen regions" with high-level details remains an unsolved challenge that lacks attention. Existing methods often generate overly smooth back-side surfaces with a blurry texture. But how to effectively capture all visual attributes of an individual from a single image, which are sufficient to reconstruct unseen areas (e.g., the back view)? Motivated by the power of foundation models, TeCH reconstructs the 3D human by leveraging 1) descriptive text prompts (e.g., garments, colors, hairstyles) which are automatically generated via a garment parsing model and Visual Question Answering (VQA), 2) a personalized fine-tuned Text-to-Image diffusion model (T2I) which learns the "indescribable" appearance. To represent high-resolution 3D clothed humans at an affordable cost, we propose a hybrid 3D representation based on DMTet, which consists of an explicit body shape grid and an implicit distance field. Guided by the descriptive prompts + personalized T2I diffusion model, the geometry and texture of the 3D humans are optimized through multi-view Score Distillation Sampling (SDS) and reconstruction losses based on the original observation. TeCH produces high-fidelity 3D clothed humans with consistent & delicate texture, and detailed full-body geometry. Quantitative and qualitative experiments demonstrate that TeCH outperforms the state-of-the-art methods in terms of reconstruction accuracy and rendering quality. The code will be publicly available for research purposes at https://huangyangyi.github.io/TeCH
Can Transformers Learn Optimal Filtering for Unknown Systems?
Authors: Haldun Balim, Zhe Du, Samet Oymak, Necmiye Ozay
Date: 2023-08-16 00:00:00

Transformer models have shown great success in natural language processing; however, their potential remains mostly unexplored for dynamical systems. In this work, we investigate the optimal output estimation problem using transformers, which generate output predictions using all the past ones. Particularly, we train the transformer using various distinct systems and then evaluate the performance on unseen systems with unknown dynamics. Empirically, the trained transformer adapts exceedingly well to different unseen systems and even matches the optimal performance given by the Kalman filter for linear systems. In more complex settings with non-i.i.d. noise, time-varying dynamics, and nonlinear dynamics like a quadrotor system with unknown parameters, transformers also demonstrate promising results. To support our experimental findings, we provide statistical guarantees that quantify the amount of training data required for the transformer to achieve a desired excess risk. Finally, we point out some limitations by identifying two classes of problems that lead to degraded performance, highlighting the need for caution when using transformers for control and estimation.
Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction
Authors: Yuhao Yang, Jun Wu, Yue Wang, Guangjian Zhang, Rong Xiong
Date: 2023-08-16 00:00:00
Tasks: 6D Pose Estimation using RGB, Pose Estimation

Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion. To address the problem,the paper proposes a bidirectional correspondence prediction network with a point-wise attention-aware mechanism. This network not only requires the model points to predict the correspondence but also explicitly models the geometric similarities between observations and the model prior. Our key insight is that the correlations between each model point and scene point provide essential information for learning point-pair matches. To further tackle the correlation noises brought by feature distribution divergence, we design a simple but effective pseudo-siamese network to improve feature homogeneity. Experimental results on the public datasets of LineMOD, YCB-Video, and Occ-LineMOD show that the proposed method achieves better performance than other state-of-the-art methods under the same evaluation criteria. Its robustness in estimating poses is greatly improved, especially in an environment with severe occlusions.
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
Authors: Munib Mesinovic, Peter Watkinson, Tingting Zhu
Date: 2023-08-16 00:00:00
Tasks: Decision Making, Explainable Models, Fairness, Prediction, Prognosis

Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regarding their opacity, potential biases, and the need for interpretability. To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial. Explainability is usually referred to as an AI system's ability to provide a robust interpretation of its decision-making logic or the decisions themselves to human stakeholders. In clinical risk prediction, other aspects of explainability like fairness, bias, trust, and transparency also represent important concepts beyond just interpretability. In this review, we address the relationship between these concepts as they are often used together or interchangeably. This review also discusses recent progress in developing explainable models for clinical risk prediction, highlighting the importance of quantitative and clinical evaluation and validation across multiple common modalities in clinical practice. It emphasizes the need for external validation and the combination of diverse interpretability methods to enhance trust and fairness. Adopting rigorous testing, such as using synthetic datasets with known generative factors, can further improve the reliability of explainability methods. Open access and code-sharing resources are essential for transparency and reproducibility, enabling the growth and trustworthiness of explainable research. While challenges exist, an end-to-end approach to explainability in clinical risk prediction, incorporating stakeholders from clinicians to developers, is essential for success.
PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing
Authors: Richard Nordsieck, André Schweizer, Michael Heider, Jörg Hähner
Date: 2023-08-16 00:00:00
Tasks: Knowledge Graphs

Procedural knowledge describes how to accomplish tasks and mitigate problems. Such knowledge is commonly held by domain experts, e.g. operators in manufacturing who adjust parameters to achieve quality targets. To the best of our knowledge, no real-world datasets containing process data and corresponding procedural knowledge are publicly available, possibly due to corporate apprehensions regarding the loss of knowledge advances. Therefore, we provide a framework to generate synthetic datasets that can be adapted to different domains. The design choices are inspired by two real-world datasets of procedural knowledge we have access to. Apart from containing representations of procedural knowledge in Resource Description Framework (RDF)-compliant knowledge graphs, the framework simulates parametrisation processes and provides consistent process data. We compare established embedding methods on the resulting knowledge graphs, detailing which out-of-the-box methods have the potential to represent procedural knowledge. This provides a baseline which can be used to increase the comparability of future work. Furthermore, we validate the overall characteristics of a synthesised dataset by comparing the results to those achievable on a real-world dataset. The framework and evaluation code, as well as the dataset used in the evaluation, are available open source.
Agglomerative Transformer for Human-Object Interaction Detection
Authors: Danyang Tu, Wei Sun, Guangtao Zhai, Wei Shen
Date: 2023-08-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: Clustering, Decoder, Human-Object Interaction Detection, Object

We propose an agglomerative Transformer (AGER) that enables Transformer-based human-object interaction (HOI) detectors to flexibly exploit extra instance-level cues in a single-stage and end-to-end manner for the first time. AGER acquires instance tokens by dynamically clustering patch tokens and aligning cluster centers to instances with textual guidance, thus enjoying two benefits: 1) Integrality: each instance token is encouraged to contain all discriminative feature regions of an instance, which demonstrates a significant improvement in the extraction of different instance-level cues and subsequently leads to a new state-of-the-art performance of HOI detection with 36.75 mAP on HICO-Det. 2) Efficiency: the dynamical clustering mechanism allows AGER to generate instance tokens jointly with the feature learning of the Transformer encoder, eliminating the need of an additional object detector or instance decoder in prior methods, thus allowing the extraction of desirable extra cues for HOI detection in a single-stage and end-to-end pipeline. Concretely, AGER reduces GFLOPs by 8.5% and improves FPS by 36%, even compared to a vanilla DETR-like pipeline without extra cue extraction.
Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?
Authors: Davide Buffelli, Ashish Gupta, Agnieszka Strzalka, Vassilis Plachouras
Date: 2023-08-16 00:00:00
Tasks: Deep Learning, Meta-Learning, Recommendation Systems, Representation Learning

Recommender systems have become fundamental building blocks of modern online products and services, and have a substantial impact on user experience. In the past few years, deep learning methods have attracted a lot of research, and are now heavily used in modern real-world recommender systems. Nevertheless, dealing with recommendations in the cold-start setting, e.g., when a user has done limited interactions in the system, is a problem that remains far from solved. Meta-learning techniques, and in particular optimization-based meta-learning, have recently become the most popular approaches in the academic research literature for tackling the cold-start problem in deep learning models for recommender systems. However, current meta-learning approaches are not practical for real-world recommender systems, which have billions of users and items, and strict latency requirements. In this paper we show that it is possible to obtaining similar, or higher, performance on commonly used benchmarks for the cold-start problem without using meta-learning techniques. In more detail, we show that, when tuned correctly, standard and widely adopted deep learning models perform just as well as newer meta-learning models. We further show that an extremely simple modular approach using common representation learning techniques, can perform comparably to meta-learning techniques specifically designed for the cold-start setting while being much more easily deployable in real-world applications.
Graph Out-of-Distribution Generalization with Controllable Data Augmentation
Authors: Bin Lu, Xiaoying Gan, Ze Zhao, Shiyu Liang, Luoyi Fu, Xinbing Wang, Chenghu Zhou
Date: 2023-08-16 00:00:00
Tasks: Data Augmentation, Graph Classification, Graph Neural Network, Out-of-Distribution Generalization, Selection bias

Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties. However, due to the selection bias of training and testing data (e.g., training on small graphs and testing on large graphs, or training on dense graphs and testing on sparse graphs), distribution deviation is widespread. More importantly, we often observe \emph{hybrid structure distribution shift} of both scale and density, despite of one-sided biased data partition. The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets. To alleviate this problem, we propose \texttt{OOD-GMixup} to jointly manipulate the training distribution with \emph{controllable data augmentation} in metric space. Specifically, we first extract the graph rationales to eliminate the spurious correlations due to irrelevant information. Secondly, we generate virtual samples with perturbation on graph rationale representation domain to obtain potential OOD training samples. Finally, we propose OOD calibration to measure the distribution deviation of virtual samples by leveraging Extreme Value Theory, and further actively control the training distribution by emphasizing the impact of virtual OOD samples. Extensive studies on several real-world datasets on graph classification demonstrate the superiority of our proposed method over state-of-the-art baselines.