pwc-archive/papers-with-abstracts huggingface dataset:
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
Authors: Hyunki Seong, David Hyunchul Shim
Date: 2024-02-21 00:00:00
Tasks: Autonomous Navigation, Decision Making, Explainable artificial intelligence, Specificity

We introduce MoNet, a novel functionally modular network for self-supervised and interpretable end-to-end learning. By leveraging its functional modularity with a latent-guided contrastive loss function, MoNet efficiently learns task-specific decision-making processes in latent space without requiring task-level supervision. Moreover, our method incorporates an online, post-hoc explainability approach that enhances the interpretability of end-to-end inferences without compromising sensorimotor control performance. In real-world indoor environments, MoNet demonstrates effective visual autonomous navigation, outperforming baseline models by 7% to 28% in task specificity analysis. We further explore the interpretability of our network through post-hoc analysis of perceptual saliency maps and latent decision vectors. This provides valuable insights into the incorporation of explainable artificial intelligence into robotic learning, encompassing both perceptual and behavioral perspectives. Supplementary materials are available at https://sites.google.com/view/monet-lgc.
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems
Authors: Ravi Chepuri, Dael Amzalag, Thomas Antonsen Jr., Michelle Girvan
Date: 2024-03-04 00:00:00
Tasks: Computational Efficiency, Time Series, Time Series Forecasting, Time Series Prediction

Reservoir computers (RCs) are powerful machine learning architectures for time series prediction. Recently, next generation reservoir computers (NGRCs) have been introduced, offering distinct advantages over RCs, such as reduced computational expense and lower training data requirements. However, NGRCs have their own practical difficulties, including sensitivity to sampling time and type of nonlinearities in the data. Here, we introduce a hybrid RC-NGRC approach for time series forecasting of dynamical systems. We show that our hybrid approach can produce accurate short term predictions and capture the long term statistics of chaotic dynamical systems in situations where the RC and NGRC components alone are insufficient, e.g., due to constraints from limited computational resources, sub-optimal hyperparameters, sparsely-sampled training data, etc. Under these conditions, we show for multiple model chaotic systems that the hybrid RC-NGRC method with a small reservoir can achieve prediction performance approaching that of a traditional RC with a much larger reservoir, illustrating that the hybrid approach can offer significant gains in computational efficiency over traditional RCs while simultaneously addressing some of the limitations of NGRCs. Our results suggest that hybrid RC-NGRC approach may be particularly beneficial in cases when computational efficiency is a high priority and an NGRC alone is not adequate.
A New Method for Sensorless Estimation of the Speed and Position in Brushed DC Motors Using Support Vector Machines
Authors: Ernesto Vazquez-Sanchez, Jaime Gomez-Gil, Jose-Carlos Gamazo-Real, Jose Fernando Diez-Higuera
Date: 2024-02-07 00:00:00
Tasks: Position

Currently, for many applications, it is necessary to know the speed and position of motors. This can be achieved using mechanical sensors coupled to the motor shaft or using sensorless techniques. The sensorless techniques in brushed dc motors can be classified into two types: 1) techniques based on the dynamic brushed dc motor model and 2) techniques based on the ripple component of the current. This paper presents a new method, based on the ripple component, for speed and position estimation in brushed dc motors, using support vector machines. The proposed method only measures the current and detects the pulses in this signal. The motor speed is estimated by using the inverse distance between the detected pulses, and the position is estimated by counting all detected pulses. The ability to detect ghost pulses and to discard false pulses is the main advantage of this method over other sensorless methods. The performed tests on two fractional horsepower brushed dc motors indicate that the method works correctly in a wide range of speeds and situations, in which the speed is constant or varies dynamically.
Feynman Diagrams as Computational Graphs
Authors: Pengcheng Hou, Tao Wang, Daniel Cerkoney, Xiansheng Cai, Zhiyi Li, Youjin Deng, Lei Wang, Kun Chen
Date: 2024-02-28 00:00:00

We propose a computational graph representation of high-order Feynman diagrams in Quantum Field Theory (QFT), applicable to any combination of spatial, temporal, momentum, and frequency domains. Utilizing the Dyson-Schwinger and parquet equations, our approach effectively organizes these diagrams into a fractal structure of tensor operations, significantly reducing computational redundancy. This approach not only streamlines the evaluation of complex diagrams but also facilitates an efficient implementation of the field-theoretic renormalization scheme, crucial for enhancing perturbative QFT calculations. Key to this advancement is the integration of Taylor-mode automatic differentiation, a key technique employed in machine learning packages to compute higher-order derivatives efficiently on computational graphs. To operationalize these concepts, we develop a Feynman diagram compiler that optimizes diagrams for various computational platforms, utilizing machine learning frameworks. Demonstrating this methodology's effectiveness, we apply it to the three-dimensional uniform electron gas problem, achieving unprecedented accuracy in calculating the quasiparticle effective mass at metal density. Our work demonstrates the synergy between QFT and machine learning, establishing a new avenue for applying AI techniques to complex quantum many-body problems.
Optimizing Wireless Networks with Deep Unfolding: Comparative Study on Two Deep Unfolding Mechanisms
Authors: Abuzar B. M. Adam, Mohammed A. M. Elhassan, Elhadj Moustapha Diallo
Date: 2024-02-17 00:00:00
Tasks: Deep Learning, Form

In this work, we conduct a comparative study on two deep unfolding mechanisms to efficiently perform power control in the next generation wireless networks. The power control problem is formulated as energy efficiency over multiple interference links. The problem is nonconvex. We employ fractional programming transformation to design two solutions for the problem. The first solution is a numerical solution while the second solution is a closed-form solution. Based on the first solution, we design a semi-unfolding deep learning model where we combine the domain knowledge of the wireless communications and the recent advances in the data-driven deep learning. Moreover, on the highlights of the closed-form solution, fully deep unfolded deep learning model is designed in which we fully leveraged the expressive closed-form power control solution and deep learning advances. In the simulation results, we compare the performance of the proposed deep learning models and the iterative solutions in terms of accuracy and inference speed to show their suitability for the real-time application in next generation networks.
Learning to Receive Help: Intervention-Aware Concept Embedding Models
Authors: Mateo Espinosa Zarlenga, Katherine M. Collins, Krishnamurthy Dvijotham, Adrian Weller, Zohreh Shams, Mateja Jamnik
Date: 2023-09-29 00:00:00
Proceeding: NeurIPS 2023 11

Concept Bottleneck Models (CBMs) tackle the opacity of neural architectures by constructing and explaining their predictions using a set of high-level concepts. A special property of these models is that they permit concept interventions, wherein users can correct mispredicted concepts and thus improve the model's performance. Recent work, however, has shown that intervention efficacy can be highly dependent on the order in which concepts are intervened on and on the model's architecture and training hyperparameters. We argue that this is rooted in a CBM's lack of train-time incentives for the model to be appropriately receptive to concept interventions. To address this, we propose Intervention-aware Concept Embedding models (IntCEMs), a novel CBM-based architecture and training paradigm that improves a model's receptiveness to test-time interventions. Our model learns a concept intervention policy in an end-to-end fashion from where it can sample meaningful intervention trajectories at train-time. This conditions IntCEMs to effectively select and receive concept interventions when deployed at test-time. Our experiments show that IntCEMs significantly outperform state-of-the-art concept-interpretable models when provided with test-time concept interventions, demonstrating the effectiveness of our approach.
Motion Prediction for Beating Heart Surgery with GRU
Authors: Yiyang Li, Bo Yang, Wanruo Zhang, Wenfeng Zheng, Chao Liu
Date: 2022-08-09 00:00:00
Proceeding: SSRN 2022 8
Tasks: motion prediction, Prediction

This work aims to predict the 3D coordinates of the point of interest (POI) on the surface of beating heart in dynamic minimally invasive surgery, which can improve the manoeuvrability of cardiac surgical robots and expand their functions. For accurate and robust POI motion prediction, a deep learning technique, the gated recurrent unit (GRU), is employed to learn the spatio-temporal (ST) correlation of the POI and its auxiliary points (APs) from their past trajectories. For reference, two neural network models that exploit only spatial and temporal correlations, respectively, are also investigated. The prediction accuracy and robustness of the above three models are verified based on the motion datasets of phantom and in vivo hearts collected by da Vinci robots. Source codes and motion data are shared on GitHub (https://github.com/oww-file/ST-GRU). The experimental results show that the proposed STGRU model is significantly better than the other two reference models, and it outperforms the state-of-the-art deep learning method on the same datasets
Orthogonal Dictionary Guided Shape Completion Network for Point Cloud
Authors: Pingping Cai, Deja Scott, Xiaoguang Li, Song Wang
Date: 2023-03-24 00:00:00
Proceeding: AAAI 2023 3
Tasks: Decoder, Point Cloud Completion

Point cloud shape completion, which aims to reconstruct the missing regions of the incomplete point clouds with plausible shapes, is an ill-posed and challenging task that benefits many downstream 3D applications. Prior approaches achieve this goal by employing a two-stage completion framework, generating a coarse yet complete seed point cloud through an encoder-decoder network, followed by refinement and upsampling. However, the encoded features suffer from information loss of the missing portion, leading to an inability of the decoder to reconstruct seed points with detailed geometric clues. To tackle this issue, we propose a novel Orthogonal Dictionary Guided Shape Completion Network (ODGNet). The proposed ODGNet consists of a Seed Generation U-Net, which leverages multi-level feature extraction and concatenation to significantly enhance the representation capability of seed points, and Orthogonal Dictionaries that can learn shape priors from training samples and thus compensate for the information loss of the missing portions during inference. Our design is simple but to the point, extensive experiment results indicate that the proposed method can reconstruct point clouds with more details and outperform previous state-of-the-art counterparts. The implementation code is available at https://github.com/corecai163/ODGNet.
Peristalsis of Nanofluids via an Inclined Asymmetric Channel with Hall Effects and Entropy Generation Analysis
Authors: Abdulwahed Muaybid A. Alrashdi
Date: 2023-11-02 00:00:00
Proceeding: MDPI 2023 11

This study deals with the entropy investigation of the peristalsis of a water–copper nanofluid through an asymmetric inclined channel. The asymmetric channel is anticipated to be filled with a uniform permeable medium, with a constant magnetic field impinging on the wall of the channel. The physical effects, such as Hall current, mixed convection, Ohmic heating, and heat generation/annihilation, are also considered. Mathematical modeling from the given physical description is formulated while employing the “long wavelength, low Reynolds number” approximations. Analytical and numerical procedures allow for the determination of flow behavior in the resulting system, the results of which are presented in the form of tables and graphs, in order to facilitate the physical analysis. The results indicate that the growth of nanoparticle volume fraction corresponds to a reduction in temperature, entropy generation, velocity, and pressure gradient. The enhanced Hall and Brinkman parameters reduce the entropy generation and temperature for such flows, whereas the enhanced permeability parameter reduces the velocity and pressure gradient considerably. Furthermore, a comparison of the heat transfer rates for the two results, for different physical parameters, indicates that these results agree well. The significance of the underlying study lies in the fact that it analyzes the peristalsis of a non-Newtonian nanofluid, where the rheological characteristics of the fluid are predicted using the Carreau-Yasuda model and by considering the various physical effects.
Keywords: peristalsis; nanofluid; permeable medium; mixed convection; asymmetric channel; entropy; homotopy
OAVA: the open audio-visual archives aggregator
Authors: Polychronis Charitidis, Sotirios Moschos, Chrysostomos Bakouras, Stavros Doropoulos, Giorgos Makris, Nikolas Mauropoulos, Ilias Nitsos, Sofia Zapounidou, Afrodite Malliari
Date: 2023-12-16 00:00:00
Proceeding: International Journal on Digital Libraries 2023 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Speech-to-Text

The purpose of the current article is to provide an overview of an open-access audiovisual aggregation and search service platform developed for Greek audiovisual content during the OAVA (Open Access AudioVisual Archive) project. The platform allows the search of audiovisual resources utilizing metadata descriptions, as well as full-text search utilizing content generated from automatic speech recognition (ASR) processes through deep learning models. A dataset containing reliable Greek audiovisual content providers and their resources (1710 in total) is created. Both providers and resources are reviewed according to specific criteria already established and used for content aggregation purposes, to ensure the quality of the content and to avoid copyright infringements. Well-known aggregation services and well-established schemas for audiovisual resources have been studied and considered regarding both aggregated content and metadata. Most Greek audiovisual content providers do not use established metadata schemas when publishing their content, nor technical cooperation with them is guaranteed. Thus, a model is developed for reconciliation and aggregation. To utilize audiovisual resources the OAVA platform makes use of the latest state-of-the-art ASR approaches. OAVA platform supports Greek and English speech-to-text models. Specifically for Greek, to mitigate the scarcity of available datasets, a large-scale ASR dataset is annotated to train and evaluate deep learning architectures. The result of the above-mentioned efforts, namely selection of content, metadata, development of appropriate ASR techniques, and aggregation and enrichment of content and metadata, is the OAVA platform. This unified search mechanism for Greek audiovisual content will serve teaching, research, and cultural activities. OAVA platform is available at: https://openvideoarchives.gr/.
DAN-NucNet: A dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions
Authors: Ibtihaj Ahmad, Yong Xia, Hengfei Cui, Zain Ul Islam
Date: 2023-03-01 00:00:00
Proceeding: Expert Systems with Applications 2023 3
Tasks: Instance Segmentation, Nuclei Classification, Segmentation, Semantic Segmentation

Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation in histology images is challenging in variable conditions (clinical wild), such as poor staining quality, stain variability, tissue variability, and conditions having higher morphological variability. Recently, some deep learning models have been proposed for nuclei segmentation. However, these models rarely solve the problems mentioned above simultaneously. Most of the information in Hematoxylin and Eosin (H&E) stained histology images is in its channel, and the remaining information is in the spatial domain. We observed that most problems could be solved by considering channel and spatial features simultaneously, e.g., the spatial and channel features provide the solution to the morphological variability and staining variability, respectively. Therefore, we propose a novel spatial-channel attention-based modified UNet architecture with ResNet blocks in encoder layers. The UNet baseline preserves coarse and fine features, thus proving the solution to the tissue variability. The proposed method significantly improves the segmentation performance compared to the state-of-the-art methods on three different benchmark datasets. We demonstrate that the proposed model is generalized for 20 cancer sites, more than any reported literature. The proposed model is less complex than most state-of-the-art models. The impact of the proposed model is that it will help improve further procedures such as nuclei instance segmentation, nuclei classification, and cancer grading.
Hyperspectral Blind Unmixing using a Double Deep Image Prior
Authors: Chao Zhou; Miguel R. D. Rodrigues
Date: 2023-08-21 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2023 8
Tasks: Hyperspectral Unmixing, Image Generation, Interpretable Machine Learning, Physics-informed machine learning

With the rise of machine learning, hyperspectral image (HSI) unmixing problems have been tackled using learning-based methods. However, physically meaningful unmixing results are not guaranteed without proper guidance. In this work, we propose an unsupervised framework inspired by deep image prior (DIP) that can be used for both linear and nonlinear blind unmixing models. The framework consists of three modules: 1) an Endmember estimation module using DIP (EDIP); 2) an Abundance estimation module using DIP (ADIP); and 3) a mixing module (MM). EDIP and ADIP modules generate endmembers and abundances, respectively, while MM produces a reconstruction of the HSI observations based on the postulated unmixing model. We introduce a composite loss function that applies to both linear and nonlinear unmixing models to generate meaningful unmixing results. In addition, we propose an adaptive loss weight strategy for better unmixing results in nonlinear mixing scenarios. The proposed methods outperform state-of-the-art unmixing algorithms in extensive experiments conducted on both synthetic and real datasets.
Credit Card Fraud Detection Using Machine Learning Approach
Authors: Kanal Soni1, Prof. Madhuri Chopade2, Prof. Rahul Vaghela
Date: 2021-10-19 00:00:00
Proceeding: Appl. Inf. Syst. Manage. 2021 10
Tasks: Fraud Detection

Using new spam technologies to carry out internet
banking fraud refers to shifting and withdrawing money from the
user’s balance account without it’s authorization. Credit card
fraud pops into the mind so far in the current scenario when the
concept of fraud bursts into some conversation. Credit card fraud
has escalated tremendously in recent times due to the incredible
growth in credit card purchases. In order to assess, identify or
prevent undesirable conduct, fraud detection requires tracking
the purchase behavior of users/customers. The purpose of this
project is to predict the genuine and fraud transactions with
respect to the amount of the transaction utilizing various machine
learning approaches like Logistic Regression, Decision Trees,
Support Vector Machine, Naïve Bayes, Random Forest and KNearest Neighbor. The model built who has greater accuracy and
precision is considered to be best fit for this system.
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
Authors: Yunhao Zhang, Junchi Yan
Date: 2023-02-02 00:00:00
Proceeding: ICLR 2023 2
Tasks: Decoder, Multivariate Time Series Forecasting, Time Series, Time Series Forecasting

Recently many deep models have been proposed for multivariate time series (MTS) forecasting. In particular, Transformer-based models have shown great potential because they can capture long-term dependency. However, existing Transformer-based models mainly focus on modeling the temporal dependency (cross-time dependency) yet often omit the dependency among different variables (cross-dimension dependency), which is critical for MTS forecasting. To fill the gap, we propose Crossformer, a Transformer-based model utilizing cross-dimension dependency for MTS forecasting. In Crossformer, the input MTS is embedded into a 2D vector array through the Dimension-Segment-Wise (DSW) embedding to preserve time and dimension information. Then the Two-Stage Attention (TSA) layer is proposed to efficiently capture the cross-time and cross-dimension dependency. Utilizing DSW embedding and TSA layer, Crossformer establishes a Hierarchical Encoder-Decoder (HED) to use the information at different scales for the final forecasting. Extensive experimental results on six real-world datasets show the effectiveness of Crossformer against previous state-of-the-arts.
SAM-dPCR: Real-Time and High-throughput Absolute Quantification of Biological Samples Using Zero-Shot Segment Anything Model
Authors: Yuanyuan Wei, Shanhang Luo, Changran Xu, Yingqi Fu, Qingyue Dong, Yi Zhang, Fuyang Qu, Guangyao Cheng, Yi-Ping Ho, Ho-Pui Ho, Wu Yuan
Date: 2024-01-22 00:00:00
Tasks: Self-Supervised Learning

Digital PCR (dPCR) has revolutionized nucleic acid diagnostics by enabling absolute quantification of rare mutations and target sequences. However, current detection methodologies face challenges, as flow cytometers are costly and complex, while fluorescence imaging methods, relying on software or manual counting, are time-consuming and prone to errors. To address these limitations, we present SAM-dPCR, a novel self-supervised learning-based pipeline that enables real-time and high-throughput absolute quantification of biological samples. Leveraging the zero-shot SAM model, SAM-dPCR efficiently analyzes diverse microreactors with over 97.7% accuracy within a rapid processing time of 3.16 seconds. By utilizing commonly available lab fluorescence microscopes, SAM-dPCR facilitates the quantification of sample concentrations. The accuracy of SAM-dPCR is validated by the strong linear relationship observed between known and inferred sample concentrations. Additionally, SAM-dPCR demonstrates versatility through comprehensive verification using various samples and reactor morphologies. This accessible, cost-effective tool transcends the limitations of traditional detection methods or fully supervised AI models, marking the first application of SAM in nucleic acid detection or molecular diagnostics. By eliminating the need for annotated training data, SAM-dPCR holds great application potential for nucleic acid quantification in resource-limited settings.