pwc-archive/papers-with-abstracts huggingface dataset:
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
Authors: Jinsheng Li, Wei Cui, Xu Zhang
Date: 2024-03-14 00:00:00
Tasks: compressed sensing, Matrix Completion

Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utilize asymmetric factorization to achieve spectral compressed sensing. In this paper, we propose a novel nonconvex projected gradient descent method for spectral compressed sensing via symmetric factorization named Symmetric Hankel Projected Gradient Descent (SHGD), which updates only one matrix and avoids a balancing regularization term. SHGD reduces about half of the computation and storage costs compared to the prior gradient method based on asymmetric factorization. {Besides, the symmetric factorization employed in our work is completely novel to the prior low-rank factorization model, introducing a new factorization ambiguity under complex orthogonal transformation}. Novel distance metrics are designed for our factorization method and a linear convergence guarantee to the desired signal is established with $O(r^2\log(n))$ observations. Numerical simulations demonstrate the superior performance of the proposed SHGD method in phase transitions and computation efficiency compared to state-of-the-art methods.
Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling
Authors: Minghan Li, Eric Gaussier
Date: 2024-03-13 00:00:00
Tasks: Conversational Search, Domain Adaptation, Retrieval

Recent studies have demonstrated that the ability of dense retrieval models to generalize to target domains with different distributions is limited, which contrasts with the results obtained with interaction-based models. Prior attempts to mitigate this challenge involved leveraging adversarial learning and query generation approaches, but both approaches nevertheless resulted in limited improvements. In this paper, we propose to combine the query-generation approach with a self-supervision approach in which pseudo-relevance labels are automatically generated on the target domain. To accomplish this, a T5-3B model is utilized for pseudo-positive labeling, and meticulous hard negatives are chosen. We also apply this strategy on conversational dense retrieval model for conversational search. A similar pseudo-labeling approach is used, but with the addition of a query-rewriting module to rewrite conversational queries for subsequent labeling. This proposed approach enables a model's domain adaptation with real queries and documents from the target dataset. Experiments on standard dense retrieval and conversational dense retrieval models both demonstrate improvements on baseline models when they are fine-tuned on the pseudo-relevance labeled data.
Predicting the hydrogen release ability of LiBH4-based mixtures by ensemble machine learning
Authors: Zhao Dinga, B, Zhiqian Chenc, *, Tianyi Mad, Chang-Tien Luc, Wenhui Maa, Leon Shawb
Date: 2019-09-05 00:00:00
Proceeding: Energy Storage Materials 2019 9

The prediction of hydrogen release ability is indispensable to evaluating hydrogen storage performance of
 LiBH4-based mixtures before experimentation. To achieve this goal, ensemble machine learning is employed to
 automatically infer the relationship between factors (i.e., sample preparation, mixing conditions and operational
 variables) and target (H2 release amount), providing exceptional insight into hydrogen release ability. Specif
ically, the importance ranking of major variables for the hydrogen release of LiBH4 has been proposed for the first
 time based on the constructed uni-component catalysts database. We train our developed EoE model on 2,071 uni
component catalysts data and attempt to predict the hydrogen release amounts of LiBH4 doping with the unseen
 bi-component catalysts. The appealing results demonstrate the effectiveness and robustness of EoE. The procedure
 established in this study presents a novel approach for accelerating the research and development of hydrogen
 storage materials over various catalysts.
Adaptive Hinge Balance Loss for Document-Level Relation Extraction
Authors: Jize Wang, Xinyi Le, Xiaodi Peng, Cailian Chen
Date: 2023-12-06 00:00:00
Proceeding: EMNLP 2023 12
Tasks: Document-level Relation Extraction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Relation, Relation Extraction

Document-Level Relation Extraction aims at predicting relations between entities from multiple sentences. A common practice is to select multi-label classification thresholds to decide whether a relation exists between an entity pair. However, in the document-level task, most entity pairs do not express any relations, resulting in a highly imbalanced distribution between positive and negative classes. We argue that the imbalance problem affects threshold selection and may lead to incorrect "no-relation" predictions. In this paper, we propose to downweight the easy negatives by utilizing a distance between the classification threshold and
the predicted score of each relation. Our novel Adaptive Hinge Balance Loss measures the difficulty of each relation class with the distance, putting more focus on hard, misclassified relations, i.e. the minority positive relations. Experiment results on Re-DocRED demonstrate the superiority of our approach over other balancing methods. Source codes are available at https://github.com/Jize-W/HingeABL.
Financial time series forecasting with multi-modality graph neural network
Authors: Dawei Cheng, Fangzhou Yang, Sheng Xiang, Jin Liu
Date: 2022-01-01 00:00:00
Proceeding: Pattern Recognition 2022 1
Tasks: Graph Neural Network, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Financial time series analysis plays a central role in hedging market risks and optimizing investment decisions. This is a challenging task as the problems are always accompanied by multi-modality streams and lead-lag effects. For example, the price movements of stock are reflections of complicated market states in different diffusion speeds, including historical price series, media news, associated events, etc. Furthermore, the financial industry requires forecasting models to be interpretable and compliant. Therefore, in this paper, we propose a multi-modality graph neural network (MAGNN) to learn from these multimodal inputs for financial time series prediction. The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges. To ensure the model interpretability, we leverage a two-phase attention mechanism for joint optimization, allowing end-users to investigate the importance of inner-modality and inter-modality sources. Extensive experiments on real-world datasets demonstrate the superior performance of MAGNN in financial market prediction. Our method provides investors with a profitable as well as interpretable option and enables them to make informed investment decisions.
Competitive numerical analysis for stochastic HIV/AIDS epidemic model in a two-sex population
Authors: Ali Raza, Muhammad Rafiq, Dumitru Baleanu, Muhammad Shoaib Arif, Muhammad Naveed, Kaleem Ashraf
Date: 2019-07-11 00:00:00
Proceeding: IET Systems Biology 2019 7

This study is an attempt to explain a reliable numerical analysis of a stochastic HIV/AIDS model in a two-sex
population considering counselling and antiretroviral therapy (ART). The authors are comparing the solutions of the stochastic
and deterministic HIV/AIDS epidemic model. Here, an endeavour has been made to explain the stochastic HIV/AIDS epidemic
model is comparatively more pragmatic in contrast with the deterministic HIV/AIDS epidemic model. The effect of threshold
number H* holds on the stochastic HIV/AIDS epidemic model. If H* < 1 then condition helps us to control disease in a two-sex
human population while H* > 1 explains the persistence of disease in the two-sex human population. Lamentably, numerical
methods such as Euler–Maruyama, stochastic Euler, and stochastic Runge–Kutta do not work for large time step sizes. The
recommended structure preserving framework of the stochastic non-standard finite difference (SNSFD) scheme conserve all
vital characteristics such as positivity, boundedness, and dynamical consistency defined by Mickens. The effectiveness of
counselling and ART may control HIV/AIDS in a two-sex population
Veagle: Advancements in Multimodal Representation Learning
Authors: Rajat Chawla, Arkajit Datta, Tushar Verma, Adarsh Jha, Anmol Gautam, Ayush Vatsal, Sukrit Chaterjee, Mukunda NS, Ishaan Bhola
Date: 2024-01-18 00:00:00
Tasks: Image Captioning, Language Modelling, Question Answering, Representation Learning, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Lately, researchers in artificial intelligence have been really interested in how language and vision come together, giving rise to the development of multimodal models that aim to seamlessly integrate textual and visual information. Multimodal models, an extension of Large Language Models (LLMs), have exhibited remarkable capabilities in addressing a diverse array of tasks, ranging from image captioning and visual question answering (VQA) to visual grounding. While these models have showcased significant advancements, challenges persist in accurately interpreting images and answering the question, a common occurrence in real-world scenarios. This paper introduces a novel approach to enhance the multimodal capabilities of existing models. In response to the limitations observed in current Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs), our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Veagle leverages a dynamic mechanism to project encoded visual information directly into the language model. This dynamic approach allows for a more nuanced understanding of intricate details present in visual contexts. To validate the effectiveness of Veagle, we conduct comprehensive experiments on benchmark datasets, emphasizing tasks such as visual question answering and image understanding. Our results indicate a improvement of 5-6 \% in performance, with Veagle outperforming existing models by a notable margin. The outcomes underscore the model's versatility and applicability beyond traditional benchmarks.
Fault Detection and Tolerant Control for Aero2 2D0F Two-rotor Helicopter
Authors: Khalid Kabir Dandago, Long Zhang, Wei Pan
Date: 2024-03-14 00:00:00
Tasks: Fault Detection, Fault Diagnosis

Stability and satisfactory performance are critical control requirements for Unmanned Aerial Vehicle (UAV) applications. While conventional control systems for UAVs aim to ensure flight stability and safe operation while accomplishing tasks, UAVs may experience various flight faults that can degrade performance or, in severe cases, lead to instability. Unsatisfactory performance or instability of a UAV poses risks to lives, properties, and the flying environment. Therefore, it's essential to design a system capable of detecting faults, pinpointing their location, assessing their severity, and using this information to mitigate them, enabling the vehicle to continue operating satisfactorily. Despite the importance of analysing fault performance to select optimal fault detection and tolerance strategies, limited research has been conducted, especially with real systems. This study examines the performance of a 2-degree-of-freedom (2DOF) bi-rotor helicopter's control system in the presence of various actuator faults. Results from different fault conditions demonstrate that faults degrade the performance of conventional control systems on UAVs and introduce vibrations into the system, particularly when faults cause asymmetry or imbalance. However, additional experiments reveal that effective fault diagnosis and accommodation methods can help maintain satisfactory system performance despite the presence of faults.
Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms
Authors: Daniel Gusland, Jonas M. Christiansen, Børge Torvik, Francesco Fioranelli, Sevgi Z. Gurbuz, Matthew Ritchie
Date: 2021-05-07 00:00:00
Proceeding: IEEE Radar Conference 2021 5
Tasks: Benchmarking

In this paper, we discuss an "open radar initiative" aimed at promoting the sharing of radar datasets and a common framework for acquiring data. The framework is based on widely available and affordable short-range radar hardware (automotive FMCW radar transceivers). This framework and initiative are intended to create and promote access to a common shared dataset for the development and benchmarking of algorithms. While this is the norm for image processing and speech processing research, there has been reluctance in the radar community so far to create common datasets of shared data, often due to justified intellectual property or security classification reasons. Notable exceptions do exist, such as the MSTAR dataset of SAR images, which enabled great progress across the radar research community for a number of years. With this initiative, we hope to stimulate discussion and, with time, changes of practice in the radar research community. The main contribution of this work relative to previously shared datasets of radar data is that the proposed framework consists of a complete, integrated and replicable hardware and software pipeline, allowing users to not only download existing data, but also to acquire their own data with a compatible format that allows expansion and enrichment of the common dataset.
Stacking hybrid GARCH models for forecasting Bitcoin volatility
Authors: Serkan Aras
Date: 2021-07-15 00:00:00
Proceeding: Expert Systems with Applications 2021 7
Tasks: feature selection, Meta-Learning

Machine learning techniques have been used frequentlyfor volatility forecasting. However, previous studies have built these hybrid models in a form of a first-order GARCH(1,1) process by following general use for GARCH models. But the way of estimating parameters for GARCH and machine learning models differs considerably. Hence, we have investigated the effect of different model orders of the GARCH process on the volatility forecasts of Bitcoin obtained by the four most used machine learning models. Furthermore, we have proposed a stacking ensemble methodology based on GARCH hybrid models to improve the results further. The proposed stacking ensemble methodology utilizes the techniques of feature selection and feature extraction to reduce the dimension of the predictors before meta-learning. The results show that using higher model orders increases the accuracy of volatility forecasts for hybrid GARCH models. Also, the proposed stacking ensemble with LASSO produces forecasts superior to almost all hybrid models and better than the ordinary stacking ensemble.
Quantifying the effect of color processing on blood and damaged tissue detection in Whole Slide Images
Authors: Neel Kanwal; Saul Fuster; Farbod Khoraminia; Tahlita C.M. Zuiverloon; Chunming Rong; Kjersti Engan
Date: 2022-09-26 00:00:00
Proceeding: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) 2022 9
Tasks: Artifact Detection, Binary Classification, Blood Detection, Color Normalization, Damaged Tissue Detection, Diagnostic, Transfer Learning, whole slide images

Histological tissue examination has been a longstanding practice for cancer diagnosis where pathologists identify the presence of tumors on glass slides. Slides acquired from laboratory routine may contain unintentional artifacts due to complications in surgical resection. Blood and damaged tissue artifacts are two common problems associated with transurethral resection of the bladder tumor. Differences in histotechnical procedures among laboratories may also result in color variations and minor inconsistencies in outcome. A digitized version of a glass slide known as a whole slide image (WSI) holds enormous potential for automated diagnostics. The presence of irrelevant areas in a WSI undermines diagnostic value for pathologists as well as computational pathology (CPATH) systems. Therefore, automatic detection and exclusion of diagnostically irrelevant areas may lead to more reliable predictions. In this paper, we are detecting blood and damaged tissue against diagnostically relevant tissue. We gauge the effectiveness of transfer learning against training from scratch. Best models give 0.99 and 0.89 F1 scores for blood and damaged tissue detection. Since blood and damaged tissue have subtle color differences, we assess the impact of color processing methods on the binary classification performance of five well-known architectures. Finally, we remove the color to understand its importance against morphology on classification performance.
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
Authors: Neel Kanwal, Miguel López-Pérez, Umay Kiraz, Tahlita C.M. Zuiverloon, Rafael Molina, Kjersti Engan
Date: 2023-12-23 00:00:00
Proceeding: Computerized Medical Imaging and Graphics 2023 12
Tasks: Anomaly Detection, Artifact Detection, Defocus Blur Detection, Diagnostic, Folded Tissue Detection, Gaussian Processes, Transfer Learning, Uncertainty Quantification

Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions. This paper proposes an uncertainty-aware Deep Kernel Learning (DKL) model to detect blurry areas and folded tissues, two types of artifacts that can appear in WSIs. The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates. We achieved 0.996 and 0.938 F1 scores for blur and folded tissue detection on unseen data, respectively. In extensive experiments, we validated the DKL model on unseen data from external independent cohorts with different staining and tissue types, where it outperformed DCNNs. Interestingly, the DKL model is more confident in the correct predictions and less in the wrong ones. The proposed DKL model can be integrated into the preprocessing pipeline of CPATH systems to provide reliable predictions and possibly serve as a quality control tool.
Grey Correlation Analysis of Transportation Carbon Emissions under the Background of Carbon Peak and Carbon Neutrality
Authors: Yanming Sun
Date: 2022-04-22 00:00:00
Proceeding: Energies 2022 4
Tasks: Time Series

Transportation carbon emission reduction has become an important driving point for
China to achieve carbon peak and carbon neutrality. Based on the three-dimensional grey correlation
analysis model, taking the ﬁve factors affecting transportation carbon emissions, namely, population,
GDP, tertiary industry, energy structure and logistics scale, as the research object, the transportation
carbon emissions of China’s low-carbon pilot and nonpilot provinces from 2010 to 2019 are calculated
based on the Intergovernmental Panel on Climate Change (IPCC) carbon emission accounting method.
The time series grey correlation degree and regional grey correlation degree of each inﬂuencing factor
and trafﬁc carbon emission are obtained using the provincial data, so as to provide policy suggestions
for China to achieve the goal of “carbon peak and carbon neutrality” in the ﬁeld of transportation.
The results show that the descending order of the ﬁve inﬂuencing factors on transportation carbon
emissions is: energy structure, logistics scale, population, GDP and tertiary industry. From 2010 to
2019, the time series grey correlation degree between the ﬁve inﬂuencing factors and transportation
carbon emissions shows a ﬂuctuating downward trend, but the impact of demographic factors has
become more and more obvious in the past two years; According to the difference of grey correlation
degree in different regions, the trafﬁc development of various provinces in China is different, so it is
necessary to formulate relevant policies individually.
Governance of Generative Artificial Intelligence for Companies
Authors: Johannes Schneider, Pauline Kuss, Rene Abraham, Christian Meske
Date: 2024-02-05 00:00:00

Generative Artificial Intelligence (GenAI), specifically large language models like ChatGPT, has swiftly entered organizations without adequate governance, posing both opportunities and risks. Despite extensive debates on GenAI's transformative nature and regulatory measures, limited research addresses organizational governance, encompassing technical and business perspectives. Although numerous frameworks for governance of AI exist, it is not clear to what extent they apply to GenAI. Our review paper fills this gap by surveying recent works with the purpose of better understanding fundamental characteristics of GenAI and adjusting prior frameworks specifically towards GenAI governance within companies. To do so, it extends Nickerson's framework development processes to include prior conceptualizations. Our framework outlines the scope, objectives, and governance mechanisms tailored to harness business opportunities as well as mitigate risks associated with GenAI integration. Our research contributes a focused approach to GenAI governance, offering practical insights for companies navigating the challenges of GenAI adoption and highlighting research gaps.
Discussion of Loop Expansion and Introduction of Series Cutting Functions to Local Potential Approximation: Complexity Analysis Using Green's Functions, Cutting Of Nth-Order Social Interactions For Progressive Safety
Authors: Yasuko Kawahata
Date: 2024-01-21 00:00:00

In this study, we focus on the aforementioned paper, "Examination Kubo-Matsubara Green's Function Of The Edwards-Anderson Model: Extreme Value Information Flow Of Nth-Order Interpolated Extrapolation Of Zero Phenomena Using The Replica Method (2024)". This paper also applies theoretical physics methods to better understand the filter bubble phenomenon, focusing in particular on loop expansions and truncation functions. Using the loop expansion method, the complexity of social interactions during the occurrence of filter bubbles will be discussed in order to introduce series, express mathematically, and evaluate the impact of these interactions. We analyze the interactions between agents and their time evolution using a variety of Green's functions, including delayed Green's functions, advanced Green's functions, and causal Green's functions, to capture the dynamic response of the system through local potential approximations. In addition, we apply truncation functions and truncation techniques to ensure incremental safety and evaluate the long-term stability of the system. This approach will enable a better understanding of the mechanisms of filter bubble generation and dissolution, and discuss insights into their prevention and management. This research explores the possibilities of applying theoretical physics frameworks to social science problems and examines methods for analyzing the complex dynamics of information flow and opinion formation in digital society.This paper is partially an attempt to utilize "Generative AI" and was written with educational intent. There are currently no plans for it to become a peer-reviewed paper.