pwc-archive/papers-with-abstracts huggingface dataset:
Recent Advances in Novel Materials and Techniques for Developing Transparent Wound Dressings
Authors: Muzammil Kuddushi, Aatif Ali Shah, Cagri Ayranci, Xuehua Zhanga
Date: 2023-06-27 00:00:00

Optically transparent wound dressings offer a range of potential applications in the biomedical field, as they allow for the monitoring of wound healing progress without having to replace the dressing. These dressings must be impermeable to water and bacteria, yet permeable to moisture vapor and atmospheric gases in order to maintain a moist environment at the wound site. This review article provides a comprehensive overview of the types of wound dressings, novel wound dressing materials, advanced fabrication techniques for transparent wound dressing materials, and the key features and applications of transparent dressings for the healing process, as well as how it can improve healing outcomes. This review mainly focuses on representing specifications of transparent polymeric wound dressing materials, such as transparent electrospun nanofibers, transparent crosslinked hydrogels, and transparent composite films/membranes. Due to the advance properties of electrospun nanofiber such as large surface area, enable efficient incorporation of antibacterial molecules, a structure similar to the extracellular matrix, and high mechanical stability, is often used in wound dressing applications. We also highlight the hydrogels or films for wound healing applications, it's promote the healing process, provide a moisture environment, and offer pain relief with their cool, high-water content, excellent biocompatibility, and bio-biodegradability.
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
Authors: Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu
Date: 2023-06-09 00:00:00
Tasks: Drug Discovery

Generation of molecules with desired chemical and biological properties such as high drug-likeness, high binding affinity to target proteins, is critical for drug discovery. In this paper, we propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our model assumes an energy-based model (EBM) in the latent space. Conditional on the latent vector, the molecule and its properties are modeled by a molecule generation model and a property regression model respectively. To search for molecules with desired properties, we propose a sampling with gradual distribution shifting (SGDS) algorithm, so that after learning the model initially on the training data of existing molecules and their properties, the proposed algorithm gradually shifts the model distribution towards the region supported by molecules with desired values of properties. Our experiments show that our method achieves very strong performances on various molecule design tasks.
Dynamic functional connectivity: why the controversy?
Authors: Diego Vidaurre
Date: 2023-06-27 00:00:00
Tasks: Functional Connectivity

In principle, dynamic functional connectivity in fMRI is just a statistical measure. A passer-by might think it to be a specialist topic, but it continues to attract widespread attention and spark controversy. Why?
Does visual experience influence arm proprioception and its lateralization? Evidence from passive matching performance in congenitally-blind and sighted adults
Authors: Najib Abi Chebel, Florence Gaunet, Pascale Chavet, Christine Assaiante, Christophe Bourdin, Fabrice Sarlegna
Date: 2023-06-27 00:00:00

In humans, body segments' position and movement can be estimated from multiple senses such as vision and proprioception. It has been suggested that vision and proprioception can influence each other and that upper-limb proprioception is asymmetrical, with proprioception of the non-dominant arm being more accurate and/or precise than proprioception of the dominant arm. However, the mechanisms underlying the lateralization of proprioceptive perception are not yet understood. Here we tested the hypothesis that early visual experience influences the lateralization of arm proprioceptive perception by comparing 8 congenitally-blind and 8 matched, sighted right-handed adults. Their proprioceptive perception was assessed at the elbow and wrist joints of both arms using an ipsilateral passive matching task. Results support and extend the view that proprioceptive precision is better at the non-dominant arm for blindfolded sighted individuals. While this finding was rather systematic across sighted individuals, proprioceptive precision of congenitally-blind individuals was not lateralized as systematically, suggesting that lack of visual experience during ontogenesis influences the lateralization of arm proprioception.
Minimum information and guidelines for reporting a Multiplexed Assay of Variant Effect
Authors: Melina Claussnitzer, Victoria N. Parikh, Alex H. Wagner, Jeremy A. Arbesfeld, Carol J. Bult, Helen V. Firth, Lara A. Muffley, Alex N. Nguyen Ba, Kevin Riehle, Frederick P. Roth, Daniel Tabet, Benedetta Bolognesi, Andrew M. Glazer, Alan F. Rubin
Date: 2023-06-26 00:00:00

Multiplexed Assays of Variant Effect (MAVEs) have emerged as a powerful approach for interrogating thousands of genetic variants in a single experiment. The flexibility and widespread adoption of these techniques across diverse disciplines has led to a heterogeneous mix of data formats and descriptions, which complicates the downstream use of the resulting datasets. To address these issues and promote reproducibility and reuse of MAVE data, we define a set of minimum information standards for MAVE data and metadata and outline a controlled vocabulary aligned with established biomedical ontologies for describing these experimental designs.
Analysis of Control Measures for Vector-borne Diseases Using a Multistage Vector Model with Multi-Host Sub-populations
Authors: Francis G. T. Kamba, Leonard C. Eze, Jean Claude Kamgang, Christopher P. Thron
Date: 2023-06-24 00:00:00

We propose and analyze an epidemiological model for vector borne diseases that integrates a multi-stage vector population and several host sub-populations which may be characterized by a variety of compartmental model types: subpopulations all include Susceptible and Infected compartments, but may or may not include Exposed and/or Recovered compartments. The model was originally designed to evaluate the effectiveness of various prophylactic measures in malaria-endemic areas, but can be applied as well to other vector-borne diseases. This model is expressed as a system of several differential equations, where the number of equations depends on the particular assumptions of the model. We compute the basic reproduction number $\mathcal R_0$, and show that if $\mathcal R_0\leqslant 1$, the disease free equilibrium (DFE) is globally asymptotically stable (GAS) on the nonnegative orthant. If $\mathcal R_0>1$, the system admits a unique endemic equilibrium (EE) that is GAS. We analyze the sensitivity of $R_0$ and the EE to different system parameters, and based on this analysis we discuss the relative effectiveness of different control measures.
Vision-based interface for grasping intention detection and grip selection : towards intuitive upper-limb assistive devices
Authors: Etienne Moullet, François Bailly, Justin Carpentier, Christine Azevedo Coste
Date: 2023-06-27 00:00:00

Assistive devices for indivuals with upper-limb movement often lack controllability and intuitiveness, in particular for grasping function. In this work, we introduce a novel user interface for grasping movement control in which the user delegates the grasping task decisions to the device, only moving their (potentially prosthetic) hand toward the targeted object.
Robust Wasserstein Optimization and its Application in Mean-CVaR
Authors: Xin Hai, Kihun Nam
Date: 2023-06-27 00:00:00

We refer to recent inference methodology and formulate a framework for solving the distributionally robust optimization problem, where the true probability measure is inside a Wasserstein ball around the empirical measure and the radius of the Wasserstein ball is determined by the empirical data. We transform the robust optimization into a non-robust optimization with a penalty term and provide the selection of the Wasserstein ambiguity set's size. Moreover, we apply this framework to the robust mean-CVaR optimization problem and the numerical experiments of the US stock market show impressive results compared to other popular strategies.
Valuation of Equity Linked Securities with Guaranteed Return
Authors: David Xiao
Date: 2023-06-26 00:00:00

Equity-linked securities with a guaranteed return become very popular in financial markets ether as investment instruments or life insurance policies. The contract pays off a guaranteed amount plus a payment linked to the performance of a basket of equities averaged over a certain period. This paper presents a new model for valuing equity-linked securities. Our study shows that the security price can be replicated by the sum of the guaranteed amount plus the price of an Asian style option on the basket. Analytical formulas are derived for the security price and corresponding hedge ratios. The model appears to be accurate over a wide range of underlying security parameters according to numerical studies. Finally, we use our model to value a segregated fund with a guarantee at maturity.
A Cosine Similarity-based Method for Out-of-Distribution Detection
Authors: Nguyen Ngoc-Hieu, Nguyen Hung-Quang, The-Anh Ta, Thanh Nguyen-Tang, Khoa D Doan, Hoang Thanh-Tung
Date: 2023-06-23 00:00:00
Tasks: Out-of-Distribution Detection

The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine similarity scoring function. Extensive experiments on multiple benchmarks show that CTM outperforms existing post hoc OOD detection methods.
PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Models
Authors: Teo Susnjak
Date: 2023-06-15 00:00:00

With the proliferation of open-sourced Large Language Models (LLMs) and efficient finetuning techniques, we are on the cusp of the emergence of numerous domain-specific LLMs that have been finetuned for expertise across specialized fields and applications for which the current general-purpose LLMs are unsuitable. In academia, this technology has the potential to revolutionize the way we conduct systematic literature reviews (SLRs), access knowledge and generate new insights. This paper proposes an AI-enabled methodological framework that combines the power of LLMs with the rigorous reporting guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). By finetuning LLMs on domain-specific academic papers that have been selected as a result of a rigorous SLR process, the proposed PRISMA-DFLLM (for Domain-specific Finetuned LLMs) reporting guidelines offer the potential to achieve greater efficiency, reusability and scalability, while also opening the potential for conducting incremental living systematic reviews with the aid of LLMs. Additionally, the proposed approach for leveraging LLMs for SLRs enables the dissemination of finetuned models, empowering researchers to accelerate advancements and democratize cutting-edge research. This paper presents the case for the feasibility of finetuned LLMs to support rigorous SLRs and the technical requirements for realizing this. This work then proposes the extended PRISMA-DFLLM checklist of reporting guidelines as well as the advantages, challenges, and potential implications of implementing PRISMA-DFLLM. Finally, a future research roadmap to develop this line of AI-enabled SLRs is presented, paving the way for a new era of evidence synthesis and knowledge discovery.
"You might think about slightly revising the title": identifying hedges in peer-tutoring interactions
Authors: Yann Raphalen, Chloé Clavel, Justine Cassell
Date: 2023-06-18 00:00:00
Tasks: Management

Hedges play an important role in the management of conversational interaction. In peer tutoring, they are notably used by tutors in dyads (pairs of interlocutors) experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with students in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of such a hybrid model approach.
FSUIE: A Novel Fuzzy Span Mechanism for Universal Information Extraction
Authors: Tianshuo Peng, Zuchao Li, Lefei Zhang, Bo Du, Hai Zhao
Date: 2023-06-19 00:00:00
Tasks: UIE

Universal Information Extraction (UIE) has been introduced as a unified framework for various Information Extraction (IE) tasks and has achieved widespread success. Despite this, UIE models have limitations. For example, they rely heavily on span boundaries in the data during training, which does not reflect the reality of span annotation challenges. Slight adjustments to positions can also meet requirements. Additionally, UIE models lack attention to the limited span length feature in IE. To address these deficiencies, we propose the Fuzzy Span Universal Information Extraction (FSUIE) framework. Specifically, our contribution consists of two concepts: fuzzy span loss and fuzzy span attention. Our experimental results on a series of main IE tasks show significant improvement compared to the baseline, especially in terms of fast convergence and strong performance with small amounts of data and training epochs. These results demonstrate the effectiveness and generalization of FSUIE in different tasks, settings, and scenarios.
Clickbait Classification and Spoiling Using Natural Language Processing
Authors: Adhitya Thirumala, Elisa Ferracane
Date: 2023-06-16 00:00:00
Tasks: Articles, Classification, Language Modelling, Large Language Model, Question Answering, Task 2

Clickbait is the practice of engineering titles to incentivize readers to click through to articles. Such titles with sensationalized language reveal as little information as possible. Occasionally, clickbait will be intentionally misleading, so natural language processing (NLP) can scan the article and answer the question posed by the clickbait title, or spoil it. We tackle two tasks: classifying the clickbait into one of 3 types (Task 1), and spoiling the clickbait (Task 2). For Task 1, we propose two binary classifiers to determine the final spoiler type. For Task 2, we experiment with two approaches: using a question-answering model to identify the span of text of the spoiler, and using a large language model (LLM) to generate the spoiler. Because the spoiler is contained in the article, we frame the second task as a question-answering approach for identifying the starting and ending positions of the spoiler. We created models for Task 1 that were better than the baselines proposed by the dataset authors and engineered prompts for Task 2 that did not perform as well as the baselines proposed by the dataset authors due to the evaluation metric performing worse when the output text is from a generative model as opposed to an extractive model.
RoseTracker: A system for automated rose growth monitoring
Authors: Risa Shinoda, Ko Motoki, Kensho Hara, Hirokatsu Kataoka, Ryohei Nakano, Tetsuya Nakazaki, Ryozo Noguchi
Date: 2023-06-16 00:00:00
Proceeding: Smart Agricultural Technology 2023 6
Tasks: object-detection, Object Detection

In cut-flower cultivation, production planning is an important task because demand fluctuates throughout the year. For precise cultivation planning, understanding the cultivation status is necessary by the growing stage. However, manually counting all the roses in the greenhouse to determine the cultivation status is difficult without incurring considerable time and labor. Some studies have engaged in detecting the number of flowers, but these studies used close-up images and could not count flowers without omissions or overlapping in an entire farm. In addition, limited datasets for object detection based on cut-flower blooming stages are available. In this study, we propose the RoseBlooming dataset and an efficient rose-monitoring system called RoseTracker to bridge the gap between computer vision techniques and the horticulture cultivation industry. The RoseBlooming dataset is the innovative dataset of labeled images for cut flowers at the growing stage. RoseTracker can detect small roses from various angles while moving the camera, reduces detection omissions, and achieves an F1 score of 0.950, thereby outperforming conventional models. For application, we used overhead images captured under actual growing conditions. RoseTracker and the RoseBlooming dataset contribute to constructing the rose-growth monitoring system in high demand worldwide.
Deep learning for blocking in entity matching: a design space exploration
Authors: Saravanan Thirumuruganathan, Han Li, Nan Tang, Mourad Ouzzani, Yash Govind, Derek Paulsen, Glenn Fung, AnHai Doan
Date: 2021-07-01 00:00:00
Proceeding: Proceedings of the VLDB Endowment 2021 7
Tasks: Blocking

Entity matching (EM) finds data instances that refer to the same real-world entity. Most EM solutions perform blocking then matching. Many works have applied deep learning (DL) to matching, but far fewer works have applied DL to blocking. These blocking works are also limited in that they consider only a simple form of DL and some of them require labeled training data. In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM. We first define a large space of DL solutions for blocking, which contains solutions of varying complexity and subsumes most previous works. Next, we develop eight representative solutions in this space. These solutions do not require labeled training data and exploit recent advances in DL (e.g., sequence modeling, transformer, self supervision). We empirically determine which solutions perform best on what kind of datasets (structured, textual, or dirty). We show that the best solutions (among the above eight) outperform the best existing DL solution and the best existing non-DL solutions (including a state-of-the-art industrial non-DL solution), on dirty and textual data, and are comparable on structured data. Finally, we show that the combination of the best DL and non-DL solutions can perform even better, suggesting a new venue for research.
Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching
Authors: Derek Paulsen, Yash Govind, AnHai Doan
Date: 2023-04-20 00:00:00
Proceeding: Proceedings of the VLDB Endowment 2023 4
Tasks: Blocking

Blocking is a major task in entity matching. Numerous blocking solutions have been developed, but as far as we can tell, blocking using the well-known tf/idf measure has received virtually no attention. Yet, when we experimented with tf/idf blocking using Lucene, we found it did quite well. So in this paper we examine tf/idf blocking in depth. We develop Sparkly, which uses Lucene to perform top-k tf/idf blocking in a distributed share-nothing fashion on a Spark cluster. We develop techniques to identify good attributes and tokenizers that can be used to block on, making Sparkly completely automatic. We perform extensive experiments showing that Sparkly outperforms 8 state-of-the-art blockers. Finally, we provide an in-depth analysis of Sparkly's performance, regarding both recall/output size and runtime. Our findings suggest that (a) tf/idf blocking needs more attention, (b) Sparkly forms a strong baseline that future blocking work should compare against, and (c) future blocking work should seriously consider top-k blocking, which helps improve recall, and a distributed share-nothing architecture, which helps improve scalability, predictability, and extensibility.
A new adjusted maximum likelihood method for the Fay–Herriot small area model
Authors: Masayo Yoshimori ∗, Partha Lahiri
Date: 2013-11-20 00:00:00
Proceeding: Journal of Multivariate Analysis 2013 11
Tasks: regression

In the context of the Fay–Herriot model, a mixed regression model routinely used to
combine information from various sources in small area estimation, certain adjustments
to a standard likelihood (e.g., profile, residual, etc.) have been recently proposed in order
to produce strictly positive and consistent model variance estimators. These adjustments
protect the resulting empirical best linear unbiased prediction (EBLUP) estimator of a
small area mean from the possible over-shrinking to the regression estimator. However,
in certain cases, the existing adjusted likelihood methods can lead to high biases in the
estimation of both model variance and the associated shrinkage factors and can even
produce a negative second-order unbiased mean square error (MSE) estimate of an EBLUP.
In this paper, we propose a new adjustment factor that rectifies the above-mentioned
problems associated with the existing adjusted likelihood methods. In particular, we show
that our proposed adjusted residual maximum likelihood and profile maximum likelihood
estimators of the model variance and the shrinkage factors enjoy the same higher-order
asymptotic bias properties of the corresponding residual maximum likelihood and profile
maximum likelihood estimators, respectively. We compare performances of the proposed
method with the existing methods using Monte Carlo simulations.
Classifying the Ideological Orientation of User-Submitted Texts in Social Media
Authors: Kamalakkannan Ravi, Adan Ernesto Vela, Rickard Ewetz
Date: 2022-12-12 00:00:00
Proceeding: IEEE International Conference on Machine Learning and Applications (ICMLA) 2022 12
Tasks: Articles, Classification, News Classification

With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i.e., conservative or liberal). We first collect a balanced corpus of text articles posted to the online communities r/Liberal and r/Conservative from the social media website Reddit. Using the corpus, we develop and apply three classifiers. The baseline classifier is a Bayes model that accounts for each text article’s web domain, as such, classification is independent of content. Next, we develop a support vector machine (SVM) model with term frequency-inverse document frequency (TF-IDF) features; this approach highlight differences in language using a count-based feature-space to differentiate text articles. Last, we evaluate the context-based transformer (RoBERTa) model and discuss its under-performance relative to the baseline and SVM models.