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Applications Of Sets And Functions By Using An Open Sets In Fuzzy Neutrosophic Topological Spaces, Basker P, Broumi Said, Vennila J

Neutrosophic Sets and Systems

The definitions provided by the authors of the current study are offered together with a discussion of the recent advances that they have contributed. We begin with an introduction to ���� Fr#bϱN , which includes the concepts of closed and open sets. We explore characteristics in ����-βd#bϱN and ����-℮bϱN (ϱN), and provide an idea of obtained results by adding the notion of FNbϱN OS and analyzing a few of their properties in ��������. We've researched the contrasts between the derived, exterior, and frontier notions that are provided. We also looked at the ideas of 〈��bϱN〉��-functions and Γ��-segregated functions and examined …

Modeling Influenced Criteria In Classifiers' Imbalanced Challenges Based On Trss Bolstered By The Vague Nature Of Neutrosophic Theory, Ibrahim El-Henawy, Shrouk El-Amir, Mona Mohamed, Florentin Smarandache

Neutrosophic Sets and Systems

Because of the advancements in technology, classification learning has become an essential activity in today's environment. Unfortunately, through the classification process, we noticed that the classifiers are unable to deal with the imbalanced data, which indicates there are many more instances (majority instances) in one class than in another. Identifying an appropriate classifier among the various candidates is a time-consuming and complex effort. Improper selection can hinder the classification model's ability to provide the right outcomes. Also, this operation requires preference among a set of alternatives by a set of criteria. Hence, multi-criteria decision making (MCDM) methodology is the appropriate …

New Fixed Point Results In Neutrosophic Metric Spaces, Umar Ishtiaq, Fahim Ud Din, Mureed Qasim, Lakhdar Ragoub, Khalil Javed

Neutrosophic Sets and Systems

In this manuscript, we give the generalization of banach’s, Kannan’s and Chatterjee’s fixed Point theorems in neutrosophic metric spaces by using new (TS-IF⍺) contractive mappings. Also, we establish common fixed point results in neutrosophic metric space by using Occasionally weakly compatible maps for integral type inequalities.

Exploring Neutrosophic Numeral System Algorithms For Handling Uncertainty And Ambiguity In Numerical Data: An Overview And Future Directions, A. A. Salama, Mahmoud Y. Shams, Sherif Elseuofi, Huda E. Khalid

Neutrosophic Sets and Systems

The Neutrosophic Numeral System Algorithms are a set of techniques designed to handle uncertainty and ambiguity in numerical data. These algorithms use Neutrosophic Set Theory, a mathematical framework that deals with incomplete, indeterminate, and inconsistent information. In this paper, we provide an overview of different approaches used in Neutrosophic Numeral System Algorithms, including Neutrosophic Binary System, Neutrosophic Decimal System, and Neutrosophic Octal System. These systems use different bases and representations to account for degrees of truth, indeterminacy, and falsity in numerical data. We also explore the relationship between Neutrosophic Numeral System Algorithms and Number Neutrosophic Systems, which are another type …

Neutrosophic Sets And Systems, Vol. 65,2024, Florentin Smarandache, Mohamed Abdel-Basset, Said Broumi

Neutrosophic Sets and Systems

No abstract provided.

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Multiobjective Stochastic Optimization: A Case Of Real-Time Matching In Ride-Sourcing Markets, Guodong Lyu, Wang Chi Cheung, Chung-Piaw Teo, Hai Wang

Research Collection School Of Computing and Information Systems

Problem Definition: The job of any marketplace is to facilitate the matching of supply with demand in real-time. Success is often measured using various metrics. The challenge is to design matching algorithms to balance the trade-offs among multiple objectives in a stochastic environment, to arrive at a “compromise” solution, which minimizes say the ℓp-norm-based distance function (for some 1 ≤p ≤∞) between the attained performance metrics and the target performances.Methodology/Results: We observe that the sample-average-approximation formulation of this multi-objective stochastic optimization problem can be solved by an online algorithm that uses only gradient information from “historical” (i.e., past) sample information, …

Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao

Research Collection School Of Computing and Information Systems

Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …

Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …

Test-Time Augmentation For 3d Point Cloud Classification And Segmentation, Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low point density, the performance of the downstream tasks drops significantly. This work explores test-time augmentation (TTA) for 3D point clouds. We are inspired by the recent revolution of learning implicit representation and point cloud upsampling, which can produce high-quality 3D surface reconstruction and proximity-to-surface, respectively. Our idea is to leverage the implicit field reconstruction or point cloud upsampling techniques as a systematic way …

Iterative Graph Self-Distillation, Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric Xing

Research Collection School Of Computing and Information Systems

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by …

Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

AdamW modifies Adam by adding a decoupled weight decay to decay network weights per training iteration. For adaptive algorithms, this decoupled weight decay does not affect specific optimization steps, and differs from the widely used ℓ2-regularizer which changes optimization steps via changing the first- and second-order gradient moments. Despite its great practical success, for AdamW, its convergence behavior and generalization improvement over Adam and ℓ2-regularized Adam (ℓ2-Adam) remain absent yet. To solve this issue, we prove the convergence of AdamW and justify its generalization advantages over Adam and ℓ2-Adam. Specifically, AdamW provably converges but minimizes a dynamically regularized loss that …

Attack As Detection: Using Adversarial Attack Methods To Detect Abnormal Examples, Zhe Zhao, Guangke Chen, Tong Liu, Taishan Li, Fu Song, Jingyi Wang, Jun Sun

Research Collection School Of Computing and Information Systems

As a new programming paradigm, deep learning (DL) has achieved impressive performance in areas such as image processing and speech recognition, and has expanded its application to solve many real-world problems. However, neural networks and DL are normally black-box systems; even worse, DL-based software are vulnerable to threats from abnormal examples, such as adversarial and backdoored examples constructed by attackers with malicious intentions as well as unintentionally mislabeled samples. Therefore, it is important and urgent to detect such abnormal examples. Although various detection approaches have been proposed respectively addressing some specific types of abnormal examples, they suffer from some limitations; …

Self-Admitted Technical Debts Identification: How Far Are We?, Hao Gu, Shichao Zhang, Qiao Huang, Zhifang Liao, Jiakun Liu, David Lo

Research Collection School Of Computing and Information Systems

Self-admitted technical debt (SATD) is a kind of technical debt that is already acknowledged by the developers and needs additional work or resources to address in the future. In recent years, though many methods have been proposed to detect SATDs, these methods have mainly focused on Java-type code comments published by Maldonado et al. It is unclear whether these methods trained on Maldonado's code comments dataset can find SATD in other programming languages or other software artifacts, such as issue trackers, pull requests, and commit messages effectively. In order to answer the above confusion and investigate how far our community …

Sustainability Forecasting For Deep Learning Packages, Junxiao Han, Yunkun Wang, Zhongxin Liu, Lingfeng Bao, Jiakun Liu, David Lo, Shuiguang Deng

Research Collection School Of Computing and Information Systems

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, …

Leveraging Multimodal Features And Item‑Level User Feedback For Bundle Construction, Yunshan Ma, Xiaohao Liu, Yinwei Wei, Zhulin Tao, Xiang Wang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of existing bundles. However, it is hard to acquire large-scale well-curated bundle dataset, especially for those platforms that have not offered bundle services before. Even for platforms with mature bundle services, there are still many items that are included in few or even zero bundles, which give rise to sparsity and cold-start challenges in the bundle construction models. To tackle these issues, we target at leveraging multimodal features, item-level user feedback signals, and the bundle composition information, …

Faster Rates For Compressed Federated Learning With Client-Variance Reduction, Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtarik

Research Collection School Of Computing and Information Systems

Due to the communication bottleneck in distributed and federated learning applications, algorithms using communication compression have attracted significant attention and are widely used in practice. Moreover, the huge number, high heterogeneity, and limited availability of clients result in high client -variance. This paper addresses these two issues together by proposing compressed and clientvariance reduced methods COFIG and FRECON. We prove an O( (1+\omega)3/2\surdN+ (1+\omega)N2/3 S\epsilon2 S\epsilon2 ) bound on the number of communication rounds of COFIG in the nonconvex setting, where N is the total number of clients, S is the number of clients participating in each round, \epsilon is …

Long Story Short, Hannah M. Varghese

Graphic Communication

Long Story Short and is a vintage up-cycling art brand. Our mission is to breathe new life into vintage pieces, crafting them into modern wearable art. As the name suggests, each creation tells a story while being revitalized for today’s fashion forward community.

Fast fashion and trend cycling has been a growing problem in today’s society. Things are going ‘in’ and ‘out’ quicker than they ever have. Trend cycling usually operates on a 20-year timeline. But, today’s sped up cycle encourages people to constantly over-consume in chase of the next cool thing. It’s unsustainable and an impossible chase. With a …

Enhancing Sequence With Quantum Key Distribution Protocols And An Intuitive User Interface, Blake Perkins

Theses and Dissertations

The rapidly growing domain of quantum networks necessitates advancements in associated software packages. This master’s thesis will detail, in part, new protocols added to extend the usefulness of SeQUeNCe. Notably, these added protocols were implemented to ensure compatibility and efficiency with the existing codebase. To complement this expansion in capability, the graphical user interface (GUI) was restructured. Updates to the GUI now allow users to initiate and operate these newly integrated protocols with ease, thereby expanding the accessibility of SeQUeNCe to a wider audience. By prioritizing the incorporation of these new protocols and refining the user interface, this research significantly …

Group Convolutional Decoders For Toric Codes, Jim Wang

Theses and Dissertations

Quantum Error Correction (QEC) enables both industrial and defense applications of quantum computing. Toric codes and other quantum Low-Density Parity-Check (LDPC) codes are promising and well-researched methods of QEC. However, their decoding cost increases exponentially with a computer’s qubit count. Neural Network (NN) decoders have been shown to decode a code’s error syndrome both accurately and fast enough for a real-time error correcting scheme. Recent key developments introduced Convolutional Neural Network (CNN) to implement a translationally equivariant decoder for a toric code. These CNN decoders both outperform NN decoders and require less training data. This research applies a Group Convolutional …

Level 2 Work Breakdown Structure Cost Growth Analysis, Kyle P. Marquis

Theses and Dissertations

This thesis analyzes first and second level WBS elements of 59 historical USAF Acquisition Category I RDT&E programs for accuracy and influence on the overall program cost growth. For the accuracy analysis portion, the mean, 15th, and 85th percentiles of common practice estimates at completion are compared with the same percentiles for distributions generated using the data. The results reveal a trend of substantial underestimation of risks associated with cost overruns. For the influence portion, this thesis analyzes the effect that cost growth trends of different second level WBS elements have on cost growth of the overall program through regression …

Temperature Forecasts For The Continental United States: A Deep Learning Approach Using Multidimensional Features, Jahangir Ali, Linyin Cheng

Geosciences Faculty Publications and Presentations

Accurate weather forecasts are critical for saving lives, emergency services, and future developments. Climate models such as numerical weather prediction models have made significant advancements in weather forecasts, but these models are computationally expensive and can be subject to inaccurate representations of complex natural interconnections. Alternatively, data-driven machine learning methods have provided new dimensions in assisting weather forecasts. In this study, we used convolutional neural networks (CNN) to assess how geopotential height at different levels of the troposphere may affect the predictability of extreme surface temperature (t2m) via two cases. Specifically, we analyzed temperature forecasts over the continental United States …

Trans-Disciplinary Communication In Collaborative Co-Design For Knowledge Sharing, James Lipuma, Cristo León

STEM for Success Resources

Explore the transformative power of Trans-Disciplinary Communication in Collaborative Co-Design for effective knowledge sharing.

Rights
All content in this magazine is licensed under a Creative Commons Attribution License. Attribution-Non-Commercial-Non-Derivatives 4.0 International (CC BY-NC-ND 4.0).

Euhforia Modelling Of The Sun-Earth Chain Of The Magnetic Cloud Of 28 June 2013, G Prete, A Niemela, B Schmieder, Nada Al-Haddad, Bin Zhuang, F Lepreti, V Carbone, S Poedts

Faculty Publications

Context. Predicting geomagnetic events starts with an understanding of the Sun-Earth chain phenomena in which (interplanetary) coronal mass ejections (CMEs) play an important role in bringing about intense geomagnetic storms. It is not always straightforward to determine the solar source of an interplanetary coronal mass ejection (ICME) detected at 1 au. Aims. The aim of this study is to test by a magnetohydrodynamic (MHD) simulation the chain of a series of CME events detected from L1 back to the Sun in order to determine the relationship between remote and in situ CMEs. Methods. We analysed both remote-sensing observations and in …

X-Ray Polarization Of The Black Hole X-Ray Binary 4u 1630–47 Challenges The Standard Thin Accretion Disk Scenario, Ajay Ratheesh, Michal Dovčiak, Henric Krawczynski, Jakub Podgorný, Lorenzo Marra, Alexandra Veledina, Valery F. Suleimanov, Nicole Rodriguez Cavero, James F. Steiner, Jiří Svoboda, Andrea Marinucci, Stefano Bianchi, Michela Negro, Giorgio Matt, Francesco Tombesi, Juri Poutanen, Adam Ingram, Roberto Taverna, Andrew West, Vladimir Karas, Francesco Ursini, Paolo Soffitta, Fiamma Capitanio, Domenico Viscolo, Alberto Manfreda, Fabio Muleri, Maxime Parra, Banafsheh Beheshtipour, Sohee Chun, Nicolò Cibrario, Niccolò Di Lalla, Sergio Fabiani, Kun Hu, Philip Kaaret, Vladislav Loktev, Romana Mikušincová, Tsunefumi Mizuno, Nicola Omodei, Pierre-Olivier Petrucci, Simonetta Puccetti, John Rankin, Silvia Zane, Sixuan Zhang, Iván Agudo, Lucio A. Antonelli, Matteo Bachetti, Luca Baldini, Wayne H. Baumgartner, Ronaldo Bellazzini, Stephen D. Bongiorno, Raffaella Bonino, Alessandro Brez, Niccolò Bucciantini, Simone Castellano, Elisabetta Cavazzuti, Chien-Ting Chen, Stefano Ciprini, Enrico Costa, Alessandra De Rosa, Ettore Del Monte, Laura Di Gesu, Alessandro Di Marco, Immacolata Donnarumma, Victor Doroshenko, Steven R. Ehlert, Teruaki Enoto, Yuri Evangelista, Riccardo Ferrazzoli, Javier A. Garcia, Shuichi Gunji, Kiyoshi Hayashida, Jeremy Heyl, Wataru Iwakiri, Svetlana G. Jorstad, Fabian Kislat, Takao Kitaguchi, Jeffery J. Kolodziejczak, Fabio La Monaca, Luca Latronico, Ioannis Liodakis, Simone Maldera, Frédéric Marin, Alan P. Marscher, Herman L. Marshall, Francesco Massaro, Ikuyuki Mitsuishi, Stephen C.-Y. Ng, Stephen L. O'Dell, Chiara Oppedisano, Alessandro Papitto, George G. Pavlov, Abel L. Peirson, Matteo Perri, Melissa Pesce-Rollins, Maura Pilia, Andrea Possenti, Brian D. Ramsey, Oliver J. Roberts, Roger W. Romani, Carmelo Sgrò, Patrick Slane, Gloria Spandre, Douglas A. Swartz, Toru Tamagawa, Fabrizio Tavecchio, Yuzuru Tawara, Allyn F. Tennant, Nicholas E. Thomas, Alessio Trois, Sergey S. Tsygankov, Roberto Turolla, Jacco Vink, Martin C. Weisskopf, Kinwah Wu, Fei Xie

Faculty Publications

A large energy-dependent X-ray polarization degree is detected by the Imaging X-ray Polarimetry Explorer (IXPE) in the high-soft emission state of the black hole X-ray binary 4U 1630–47. The highly significant detection (at ≈50σ confidence level) of an unexpectedly high polarization, rising from ∼6% at 2 keV to ∼10% at 8 keV, cannot be easily reconciled with standard models of thin accretion disks. In this work, we compare the predictions of different theoretical models with the IXPE data and conclude that the observed polarization properties are compatible with a scenario in which matter accretes onto the black hole through a …

Plasma Protein Signatures Of Adult Asthma, Gordon J Smilnak, Yura Lee, Abhijnan Chattopadhyay, Annah B Wyss, Julie D White, Sinjini Sikdar, Jianping Jin, Andrew J Grant, Alison A Motsinger-Reif, Jian-Liang Li, Mikyeong Lee, Bing Yu, Stephanie J London

Faculty, Staff and Student Publications

Background: Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

Methods: Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with …

Energy-Efficient Neuromorphic Architectures For Nuclear Radiation Detection Applications, Jorge I. Canales-Verdial, Jamison R. Wagner, Landon A. Schmucker, Mark Wetzel, Nathan J. Withers, Philippe Erol Proctor, Christof Teuscher, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.

High Frequency Principal Component Analysis Based On Correlation Matrix That Is Robust To Jumps, Microstructure Noise And Asynchronous Observation Times, Dachuan Chen

Research Collection School Of Economics

This paper developed the high frequency estimation for the principal component analysis (PCA) based on correlation matrix. This estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously, which is enabled by the newly proposed Truncated and Smoothed Two-Scales Realized Volatility (Truncated S-TSRV) estimator. The general framework of our methodology is constructed based on the estimation of realized spectral functions with respect to the spot correlation matrix. A new asymptotic representation for the element-wise estimation error of the spot correlation matrix estimate has been derived, resulting in a new bias correction term which is much more complex …

Systematic Evaluation Of Mri-Based Characterization Of Tumor-Associated Vascular Morphology And Hemodynamics Via A Dynamic Digital Phantom, Chengyue Wu, David A Hormuth, Ty Easley, Federico Pineda, Gregory S Karczmar, Thomas E Yankeelov

Faculty, Staff and Student Publications

Purpose: Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Approach: A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300μm/60s" role="presentation" style="box-sizing: inherit; display: inline-block; line-height: 0; font-size: 18.08px; font-size-adjust: none; overflow-wrap: normal; word-spacing: normal; text-wrap-mode: …

Mechanosensing And Anesthesia Of Single Internodal Cells Of Chara, Manya J. Rodgers, Mark P. Staves

Open Access Publishing Support Funded Articles

The giant (2–3 × 10⁻² m long) internodal cells of the aquatic plant, Chara, exhibit a rapid (>100 × 10⁻⁶ m s⁻¹) cyclic cytoplasmic streaming which stops in response to mechanical stimuli. Since the streaming – and the stopping of streaming upon stimulation – is easily visible with a stereomicroscope, these single cells are ideal tools to investigate mechanosensing in plant cells, as well as the potential for these cells to be anesthetized. We found that dropping a steel ball (0.88 × 10⁻³ kg, 6 × 10⁻³ m in diameter) through a …