pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Kernel for Kernel-Based Modal Statistical Methods
Authors: Ryoya Yamasaki, Toshiyuki Tanaka
Date: 2023-04-20 00:00:00
Tasks: Clustering, regression

Kernel-based modal statistical methods include mode estimation, regression, and clustering. Estimation accuracy of these methods depends on the kernel used as well as the bandwidth. We study effect of the selection of the kernel function to the estimation accuracy of these methods. In particular, we theoretically show a (multivariate) optimal kernel that minimizes its analytically-obtained asymptotic error criterion when using an optimal bandwidth, among a certain kernel class defined via the number of its sign changes.
Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks
Authors: Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, Nina Miolane
Date: 2023-04-20 00:00:00

The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein. Topological Deep Learning (TDL) provides a comprehensive framework to process and extract knowledge from data associated with these systems, such as predicting the social community to which an individual belongs or predicting whether a protein can be a reasonable target for drug development. TDL has demonstrated theoretical and practical advantages that hold the promise of breaking ground in the applied sciences and beyond. However, the rapid growth of the TDL literature for relational systems has also led to a lack of unification in notation and language across message-passing Topological Neural Network (TNN) architectures. This presents a real obstacle for building upon existing works and for deploying message-passing TNNs to new real-world problems. To address this issue, we provide an accessible introduction to TDL for relational systems, and compare the recently published message-passing TNNs using a unified mathematical and graphical notation. Through an intuitive and critical review of the emerging field of TDL, we extract valuable insights into current challenges and exciting opportunities for future development.
Too sick for surveillance: Can federal HIV service data improve federal HIV surveillance efforts?
Authors: Nick Williams
Date: 2023-04-20 00:00:00
Tasks: Ethics

Introduction: The value of integrating federal HIV services data with HIV surveillance is currently unknown. Upstream and complete case capture is essential in preventing future HIV transmission. Methods: This study integrated Ryan White, Social Security Disability Insurance, Medicare, Children Health Insurance Programs and Medicaid demographic aggregates from 2005 to 2018 for people living with HIV and compared them with Centers for Disease Control and Prevention HIV surveillance by demographic aggregate. Surveillance Unknown, Service Known (SUSK) candidate aggregates were identified from aggregates where services aggregate volumes exceeded surveillance aggregate volumes. A distribution approach and a deep learning model series were used to identify SUSK candidate aggregates where surveillance cases exceeded services cases in aggregate. Results: Medicare had the most candidate SUSK aggregates. Medicaid may have candidate SUSK aggregates where cases approach parity with surveillance. Deep learning was able to detect candidate SUSK aggregates even where surveillance cases exceed service cases. Conclusions: Integration of CMS case level records with HIV surveillance records can increase case discovery and life course model quality; especially for cases who die after seeking HIV services but before they become surveillance cases. The ethical implications for both the availability and reuse of clinical HIV Data without the knowledge and consent of the persons described remains an opportunity for the development of big data ethics in public health research. Future work should develop big data ethics to support researchers and assure their subjects that information which describes them is not misused.
Digital Twin Graph: Automated Domain-Agnostic Construction, Fusion, and Simulation of IoT-Enabled World
Authors: Jiadi Du, Tie Luo
Date: 2023-04-20 00:00:00
Tasks: Graph Learning

With the advances of IoT developments, copious sensor data are communicated through wireless networks and create the opportunity of building Digital Twins to mirror and simulate the complex physical world. Digital Twin has long been believed to rely heavily on domain knowledge, but we argue that this leads to a high barrier of entry and slow development due to the scarcity and cost of human experts. In this paper, we propose Digital Twin Graph (DTG), a general data structure associated with a processing framework that constructs digital twins in a fully automated and domain-agnostic manner. This work represents the first effort that takes a completely data-driven and (unconventional) graph learning approach to addresses key digital twin challenges.
Model Based Reinforcement Learning for Personalized Heparin Dosing
Authors: Qinyang He, Yonatan Mintz
Date: 2023-04-19 00:00:00
Tasks: Decision Making, model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Sequential Decision Making

A key challenge in sequential decision making is optimizing systems safely under partial information. While much of the literature has focused on the cases of either partially known states or partially known dynamics, it is further exacerbated in cases where both states and dynamics are partially known. Computing heparin doses for patients fits this paradigm since the concentration of heparin in the patient cannot be measured directly and the rates at which patients metabolize heparin vary greatly between individuals. While many proposed solutions are model free, they require complex models and have difficulty ensuring safety. However, if some of the structure of the dynamics is known, a model based approach can be leveraged to provide safe policies. In this paper we propose such a framework to address the challenge of optimizing personalized heparin doses. We use a predictive model parameterized individually by patient to predict future therapeutic effects. We then leverage this model using a scenario generation based approach that is capable of ensuring patient safety. We validate our models with numerical experiments by comparing the predictive capabilities of our model against existing machine learning techniques and demonstrating how our dosing algorithm can treat patients in a simulated ICU environment.
Data as voters: instance selection using approval-based multi-winner voting
Authors: Luis Sánchez-Fernández, Jesús A. Fisteus, Rafael López-Zaragoza
Date: 2023-04-19 00:00:00

We present a novel approach to the instance selection problem in machine learning (or data mining). Our approach is based on recent results on (proportional) representation in approval-based multi-winner elections. In our model, instances play a double role as voters and candidates. The approval set of each instance in the training set (acting as a voter) is defined from the concept of local set, which already exists in the literature. We then select the election winners by using a representative voting rule, and such winners are the data instances kept in the reduced training set. Our experiments show that, for KNN, the rule Simple 2-EJR (a variant of the Simple EJR voting rule that satisfies 2-EJR) outperforms all the state-of-the-art algorithms and all the baselines that we consider in this paper in terms of accuracy vs reduction. For SVMs, we have obtained slight increases in the average accuracy by using several voting rules that satisfy EJR or PJR compared to the results obtained with the original datasets.
CKmeans and FCKmeans : Two deterministic initialization procedures for Kmeans algorithm using a modified crowding distance
Authors: Abdesslem Layeb
Date: 2023-04-19 00:00:00
Tasks: Clustering

This paper presents two novel deterministic initialization procedures for K-means clustering based on a modified crowding distance. The procedures, named CKmeans and FCKmeans, use more crowded points as initial centroids. Experimental studies on multiple datasets demonstrate that the proposed approach outperforms Kmeans and Kmeans++ in terms of clustering accuracy. The effectiveness of CKmeans and FCKmeans is attributed to their ability to select better initial centroids based on the modified crowding distance. Overall, the proposed approach provides a promising alternative for improving K-means clustering.
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
Authors: Hongjing Xia, Joshua C. Chang, Sarah Nowak, Sonya Mahajan, Rohit Mahajan, Ted L. Chang, Carson C. Chow
Date: 2023-04-19 00:00:00
Tasks: Management, Survival Analysis

We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specific pitfall of applying machine learning to this problem, which is an inflated estimate of the effect of interventions, due to survivors bias -- where the magnitude of inflation may be conditional on heterogeneous confounders in the population. This bias arises simply because in order to receive an intervention after discharge, a person must not have been readmitted in the intervening period. After deriving an expression for this phantom effect, we controlled for this and other biases within an inherently interpretable Bayesian survival framework. We identified case management services as being the most impactful for reducing readmissions overall.
Adaptive Scheduling for Edge-Assisted DNN Serving
Authors: Jian He, Chenxi Yang, Zhaoyuan He, Ghufran Baig, Lili Qiu
Date: 2023-04-19 00:00:00
Tasks: Scheduling

Deep neural networks (DNNs) have been widely used in various video analytic tasks. These tasks demand real-time responses. Due to the limited processing power on mobile devices, a common way to support such real-time analytics is to offload the processing to an edge server. This paper examines how to speed up the edge server DNN processing for multiple clients. In particular, we observe batching multiple DNN requests significantly speeds up the processing time. Based on this observation, we first design a novel scheduling algorithm to exploit the batching benefits of all requests that run the same DNN. This is compelling since there are only a handful of DNNs and many requests tend to use the same DNN. Our algorithms are general and can support different objectives, such as minimizing the completion time or maximizing the on-time ratio. We then extend our algorithm to handle requests that use different DNNs with or without shared layers. Finally, we develop a collaborative approach to further improve performance by adaptively processing some of the requests or portions of the requests locally at the clients. This is especially useful when the network and/or server is congested. Our implementation shows the effectiveness of our approach under different request distributions (e.g., Poisson, Pareto, and Constant inter-arrivals).
Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline
Authors: Zhiyuan Wang, Mingyue Tang, Maria A. Larrazabal, Emma R. Toner, Mark Rucker, Congyu Wu, Bethany A. Teachman, Mehdi Boukhechba, Laura E. Barnes
Date: 2023-04-19 00:00:00
Tasks: Anxiety Detection

Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way.
Quantum Kernel Alignment with Stochastic Gradient Descent
Authors: Gian Gentinetta, David Sutter, Christa Zoufal, Bryce Fuller, Stefan Woerner
Date: 2023-04-19 00:00:00

Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantum kernels for a given data set -- a task called kernel alignment. In this paper we study this problem using the Pegasos algorithm, which is an algorithm that uses stochastic gradient descent to solve the support vector machine optimization problem. We extend Pegasos to the quantum case and and demonstrate its effectiveness for kernel alignment. Unlike previous work which performs kernel alignment by training a QSVM within an outer optimization loop, we show that using Pegasos it is possible to simultaneously train the support vector machine and align the kernel. Our experiments show that this approach is capable of aligning quantum feature maps with high accuracy, and outperforms existing quantum kernel alignment techniques. Specifically, we demonstrate that Pegasos is particularly effective for non-stationary data, which is an important challenge in real-world applications.
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing
Authors: Andy Li, Milan Markovic, Peter Edwards, Georgios Leontidis
Date: 2023-04-19 00:00:00
Tasks: Federated Learning, Network Pruning

Federated Learning (FL) presents a decentralized approach to model training in the agri-food sector and offers the potential for improved machine learning performance, while ensuring the safety and privacy of individual farms or data silos. However, the conventional FL approach has two major limitations. First, the heterogeneous data on individual silos can cause the global model to perform well for some clients but not all, as the update direction on some clients may hinder others after they are aggregated. Second, it is lacking with respect to the efficiency perspective concerning communication costs during FL and large model sizes. This paper proposes a new technical solution that utilizes network pruning on client models and aggregates the pruned models. This method enables local models to be tailored to their respective data distribution and mitigate the data heterogeneity present in agri-food data. Moreover, it allows for more compact models that consume less data during transmission. We experiment with a soybean yield forecasting dataset and find that this approach can improve inference performance by 15.5% to 20% compared to FedAvg, while reducing local model sizes by up to 84% and the data volume communicated between the clients and the server by 57.1% to 64.7%.
Conditional Generative Models for Learning Stochastic Processes
Authors: Salvatore Certo, Anh Pham, Nicolas Robles, Andrew Vlasic
Date: 2023-04-19 00:00:00
Tasks: Generative Adversarial Network

A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represent a more efficient state preparation procedure than current methods. This methodology has the potential to speed-up algorithms, such as Monte Carlo analysis. In particular, after demonstrating the effectiveness of the network in the learning task, the technique is applied to price Asian option derivatives, providing the foundation for further research on other path-dependent options.
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
Authors: Hannah Blocher, Georg Schollmeyer, Christoph Jansen, Malte Nalenz
Date: 2023-04-19 00:00:00
Tasks: Benchmarking, Descriptive

We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we analyze the distribution of different classifier performances over a sample of standard benchmark data sets. Our results promisingly demonstrate that our approach differs substantially from existing benchmarking approaches and, therefore, adds a new perspective to the vivid debate on the comparison of classifiers.
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation
Authors: Hao Chen, Peng Zheng, Xin Wang, Shu Hu, Bin Zhu, Jinrong Hu, Xi Wu, Siwei Lyu
Date: 2023-04-19 00:00:00
Tasks: Articles, Contrastive Learning, Misinformation, Self-Learning

As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent information. Although a plenty of studies have applied the supervised machine learning approaches to detect such content, the lack of gold standard training data has hindered the development. Analysing the single data format, either fake text description or fake image, is the mainstream direction for the current research. However, the misinformation in real-world scenario is commonly formed as a text-image pair where the news article/news title is described as text content, and usually followed by the related image. Given the strong ability of learning features without labelled data, contrastive learning, as a self-learning approach, has emerged and achieved success on the computer vision. In this paper, our goal is to explore the constrastive learning in the domain of misinformation identification. We developed a self-learning model and carried out the comprehensive experiments on a public data set named COSMOS. Comparing to the baseline classifier, our model shows the superior performance of non-matched image-text pair detection (approximately 10%) when the training data is insufficient. In addition, we observed the stability for contrsative learning and suggested the use of it offers large reductions in the number of training data, whilst maintaining comparable classification results.
Learning to Plan with Natural Language
Authors: Yiduo Guo, Yaobo Liang, Chenfei Wu, Wenshan Wu, Dongyan Zhao, Nan Duan
Date: 2023-04-20 00:00:00
Tasks: Transfer Learning

Large Language Models (LLMs) have shown remarkable performance in various basic natural language tasks. For completing the complex task, we still need a plan for the task to guide LLMs to generate the specific solutions step by step. LLMs can directly generate task plans, but these plans may still contain factual errors or are incomplete. A high-quality task plan contains correct step-by-step solutions for solving all situations and behavioral instructions for avoiding mistakes. To obtain it, we propose the Learning to Plan method, which involves two phases: (1) In the first learning task plan phase, it iteratively updates the task plan with new step-by-step solutions and behavioral instructions, which are obtained by prompting LLMs to derive from training error feedback. (2) In the subsequent test phase, the LLM uses the learned task plan to guide the inference of LLM on the test set. We demonstrate the effectiveness of our method on the five different reasoning type tasks (8 datasets). Further, our analysis experiment shows that the task plan learned by one LLM can directly guide another LLM to improve its performance, which reveals a new transfer learning paradigm. We release the code at \url{https://github.com/Eureka6174/LearnNLPlan}
Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health
Authors: Shaoxiong Ji, Tianlin Zhang, Kailai Yang, Sophia Ananiadou, Erik Cambria, Jörg Tiedemann
Date: 2023-04-20 00:00:00

Pretrained language models have been used in various natural language processing applications. In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions. Social posts, e.g., on Reddit, are usually long documents. However, there are no domain-specific pretrained models for long-sequence modeling in the mental health domain. This paper conducts domain-specific continued pretraining to capture the long context for mental health. Specifically, we train and release MentalXLNet and MentalLongformer based on XLNet and Longformer. We evaluate the mental health classification performance and the long-range ability of these two domain-specific pretrained models. Our models are released in HuggingFace.
Safety Assessment of Chinese Large Language Models
Authors: Hao Sun, Zhexin Zhang, Jiawen Deng, Jiale Cheng, Minlie Huang
Date: 2023-04-20 00:00:00

With the rapid popularity of large language models such as ChatGPT and GPT-4, a growing amount of attention is paid to their safety concerns. These models may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes such as fraud and dissemination of misleading information. Evaluating and enhancing their safety is particularly essential for the wide application of large language models (LLMs). To further promote the safe deployment of LLMs, we develop a Chinese LLM safety assessment benchmark. Our benchmark explores the comprehensive safety performance of LLMs from two perspectives: 8 kinds of typical safety scenarios and 6 types of more challenging instruction attacks. Our benchmark is based on a straightforward process in which it provides the test prompts and evaluates the safety of the generated responses from the evaluated model. In evaluation, we utilize the LLM's strong evaluation ability and develop it as a safety evaluator by prompting. On top of this benchmark, we conduct safety assessments and analyze 15 LLMs including the OpenAI GPT series and other well-known Chinese LLMs, where we observe some interesting findings. For example, we find that instruction attacks are more likely to expose safety issues of all LLMs. Moreover, to promote the development and deployment of safe, responsible, and ethical AI, we publicly release SafetyPrompts including 100k augmented prompts and responses by LLMs.