pwc-archive/papers-with-abstracts huggingface dataset:
Learning Diffractive Optical Communication Around Arbitrary Opaque Occlusions
Authors: Md Sadman Sakib Rahman, Tianyi Gan, Emir Arda Deger, Cagatay Isil, Mona Jarrahi, Aydogan Ozcan
Date: 2023-04-20 00:00:00
Tasks: Decoder

Free-space optical systems are emerging for high data rate communication and transfer of information in indoor and outdoor settings. However, free-space optical communication becomes challenging when an occlusion blocks the light path. Here, we demonstrate, for the first time, a direct communication scheme, passing optical information around a fully opaque, arbitrarily shaped obstacle that partially or entirely occludes the transmitter's field-of-view. In this scheme, an electronic neural network encoder and a diffractive optical network decoder are jointly trained using deep learning to transfer the optical information or message of interest around the opaque occlusion of an arbitrary shape. The diffractive decoder comprises successive spatially-engineered passive surfaces that process optical information through light-matter interactions. Following its training, the encoder-decoder pair can communicate any arbitrary optical information around opaque occlusions, where information decoding occurs at the speed of light propagation. For occlusions that change their size and/or shape as a function of time, the encoder neural network can be retrained to successfully communicate with the existing diffractive decoder, without changing the physical layer(s) already deployed. We also validate this framework experimentally in the terahertz spectrum using a 3D-printed diffractive decoder to communicate around a fully opaque occlusion. Scalable for operation in any wavelength regime, this scheme could be particularly useful in emerging high data-rate free-space communication systems.
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network
Authors: Lin Zhao, Haixing Dai, Zihao Wu, Dajiang Zhu, Tianming Liu
Date: 2023-03-27 00:00:00
Tasks: Neural Architecture Search

The evolution of convolutional neural networks (CNNs) can be largely attributed to the design of its architecture, i.e., the network wiring pattern. Neural architecture search (NAS) advances this by automating the search for the optimal network architecture, but the resulting network instance may not generalize well in different tasks. To overcome this, exploring network design principles that are generalizable across tasks is a more practical solution. In this study, We explore a novel brain-inspired design principle based on the core-periphery property of the human brain network to guide the design of CNNs. Our work draws inspiration from recent studies suggesting that artificial and biological neural networks may have common principles in optimizing network architecture. We implement the core-periphery principle in the design of network wiring patterns and the sparsification of the convolution operation. The resulting core-periphery principle guided CNNs (CP-CNNs) are evaluated on three different datasets. The experiments demonstrate the effectiveness and superiority compared to CNNs and ViT-based methods. Overall, our work contributes to the growing field of brain-inspired AI by incorporating insights from the human brain into the design of neural networks.
Projective Proximal Gradient Descent for A Class of Nonconvex Nonsmooth Optimization Problems: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property
Authors: Yingzhen Yang, Ping Li
Date: 2023-04-20 00:00:00

Nonconvex and nonsmooth optimization problems are important and challenging for statistics and machine learning. In this paper, we propose Projected Proximal Gradient Descent (PPGD) which solves a class of nonconvex and nonsmooth optimization problems, where the nonconvexity and nonsmoothness come from a nonsmooth regularization term which is nonconvex but piecewise convex. In contrast with existing convergence analysis of accelerated PGD methods for nonconvex and nonsmooth problems based on the Kurdyka-\L{}ojasiewicz (K\L{}) property, we provide a new theoretical analysis showing local fast convergence of PPGD. It is proved that PPGD achieves a fast convergence rate of $\cO(1/k^2)$ when the iteration number $k \ge k_0$ for a finite $k_0$ on a class of nonconvex and nonsmooth problems under mild assumptions, which is locally Nesterov's optimal convergence rate of first-order methods on smooth and convex objective function with Lipschitz continuous gradient. Experimental results demonstrate the effectiveness of PPGD.
Linear Convergence of Reshuffling Kaczmarz Methods With Sparse Constraints
Authors: Halyun Jeong, Deanna Needell
Date: 2023-04-20 00:00:00
Tasks: compressed sensing, Dimensionality Reduction

The Kaczmarz method (KZ) and its variants, which are types of stochastic gradient descent (SGD) methods, have been extensively studied due to their simplicity and efficiency in solving linear equation systems. The iterative thresholding (IHT) method has gained popularity in various research fields, including compressed sensing or sparse linear regression, machine learning with additional structure, and optimization with nonconvex constraints. Recently, a hybrid method called Kaczmarz-based IHT (KZIHT) has been proposed, combining the benefits of both approaches, but its theoretical guarantees are missing. In this paper, we provide the first theoretical convergence guarantees for KZIHT by showing that it converges linearly to the solution of a system with sparsity constraints up to optimal statistical bias when the reshuffling data sampling scheme is used. We also propose the Kaczmarz with periodic thresholding (KZPT) method, which generalizes KZIHT by applying the thresholding operation for every certain number of KZ iterations and by employing two different types of step sizes. We establish a linear convergence guarantee for KZPT for randomly subsampled bounded orthonormal systems (BOS) and mean-zero isotropic sub-Gaussian random matrices, which are most commonly used models in compressed sensing, dimension reduction, matrix sketching, and many inverse problems in neural networks. Our analysis shows that KZPT with an optimal thresholding period outperforms KZIHT. To support our theory, we include several numerical experiments.
Understanding Accelerated Gradient Methods: Lyapunov Analyses and Hamiltonian Assisted Interpretations
Authors: Penghui Fu, Zhiqiang Tan
Date: 2023-04-20 00:00:00

We formulate two classes of first-order algorithms more general than previously studied for minimizing smooth and strongly convex or, respectively, smooth and convex functions. We establish sufficient conditions, via new discrete Lyapunov analyses, for achieving accelerated convergence rates which match Nesterov's methods in the strongly and general convex settings. Next, we study the convergence of limiting ordinary differential equations (ODEs) and point out currently notable gaps between the convergence properties of the corresponding algorithms and ODEs. Finally, we propose a novel class of discrete algorithms, called the Hamiltonian assisted gradient method, directly based on a Hamiltonian function and several interpretable operations, and then demonstrate meaningful and unified interpretations of our acceleration conditions.
Identification and multiply robust estimation in causal mediation analysis across principal strata
Authors: Chao Cheng, Fan Li
Date: 2023-04-20 00:00:00

We consider assessing causal mediation in the presence of a post-treatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the post-treatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply robust estimators are consistent under four types of misspecifications and are efficient when all nuisance models are correctly specified. We also develop a nonparametric efficient estimator that leverages data-adaptive machine learners to achieve efficient inference and discuss sensitivity methods to address key identification assumptions. We illustrate our methods via simulations and two real data examples.
ZEBRA: Z-order Curve-based Event Retrieval Approach to Efficiently Explore Automotive Data
Authors: Christian Berger, Lukas Birkemeyer
Date: 2023-04-20 00:00:00
Tasks: Retrieval, Time Series

Evaluating the performance of software for automated vehicles is predominantly driven by data collected from the real world. While professional test drivers are supported with technical means to semi-automatically annotate driving maneuvers to allow better event identification, simple data loggers in large vehicle fleets typically lack automatic and detailed event classification and hence, extra effort is needed when post-processing such data. Yet, the data quality from professional test drivers is apparently higher than the one from large fleets where labels are missing, but the non-annotated data set from large vehicle fleets is much more representative for typical, realistic driving scenarios to be handled by automated vehicles. However, while growing the data from large fleets is relatively simple, adding valuable annotations during post-processing has become increasingly expensive. In this paper, we leverage Z-order space-filling curves to systematically reduce data dimensionality while preserving domain-specific data properties, which allows us to explore even large-scale field data sets to spot interesting events orders of magnitude faster than processing time-series data directly. Furthermore, the proposed concept is based on an analytical approach, which preserves explainability for the identified events.
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
Authors: Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, Jun Wang
Date: 2023-04-20 00:00:00
Tasks: Recommendation Systems

Recent regulations on the Right to be Forgotten have greatly influenced the way of running a recommender system, because users now have the right to withdraw their private data. Besides simply deleting the target data in the database, unlearning the associated data lineage e.g., the learned personal features and preferences in the model, is also necessary for data withdrawal. Existing unlearning methods are mainly devised for generalized machine learning models in classification tasks. In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i.e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items. To tackle the above issues, we propose an extra-efficient recommendation unlearning method based on Selective and Collaborative Influence Function (SCIF). Our proposed method can (i) avoid any kind of retraining which is computationally prohibitive for large-scale systems, (ii) further enhance efficiency by selectively updating user embedding and (iii) preserve the collaboration across the remaining users and items. Furthermore, in order to evaluate the unlearning completeness, we define a Membership Inference Oracle (MIO), which can justify whether the unlearned data points were in the training set of the model, i.e., whether a data point was completely unlearned. Extensive experiments on two benchmark datasets demonstrate that our proposed method can not only greatly enhance unlearning efficiency, but also achieve adequate unlearning completeness. More importantly, our proposed method outperforms the state-of-the-art unlearning method regarding comprehensive recommendation metrics.
Is ChatGPT a Good Recommender? A Preliminary Study
Authors: Junling Liu, Chao Liu, Peilin Zhou, Renjie Lv, Kang Zhou, Yan Zhang
Date: 2023-04-20 00:00:00
Tasks: Recommendation Systems, World Knowledge

Recommendation systems have witnessed significant advancements and have been widely used over the past decades. However, most traditional recommendation methods are task-specific and therefore lack efficient generalization ability. Recently, the emergence of ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. Nonetheless, the application of ChatGPT in the recommendation domain has not been thoroughly investigated. In this paper, we employ ChatGPT as a general-purpose recommendation model to explore its potential for transferring extensive linguistic and world knowledge acquired from large-scale corpora to recommendation scenarios. Specifically, we design a set of prompts and evaluate ChatGPT's performance on five recommendation scenarios. Unlike traditional recommendation methods, we do not fine-tune ChatGPT during the entire evaluation process, relying only on the prompts themselves to convert recommendation tasks into natural language tasks. Further, we explore the use of few-shot prompting to inject interaction information that contains user potential interest to help ChatGPT better understand user needs and interests. Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT has achieved promising results in certain tasks and is capable of reaching the baseline level in others. We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models. And the human evaluations show ChatGPT can truly understand the provided information and generate clearer and more reasonable results. We hope that our study can inspire researchers to further explore the potential of language models like ChatGPT to improve recommendation performance and contribute to the advancement of the recommendation systems field.
Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection.
Authors: J. Anitha Ruth, R. Uma, A. Meenakshi, P. Ramkumar
Date: 2022-05-05 00:00:00
Proceeding: Neural Processing Letters volume 54 2022 5
Tasks: Deep Learning, Specificity

The automatic detection of leaf disease is necessary to improve the quality and quantity of agricultural production. This paper proposes a framework based on optimal deep neural network (ODNN) to detect the plant leaf disease using the leaf images of healthy and diseased plants. The proposed work uses Convolutional Neural Network for feature extraction and ODNN is used for detection. In ODNN, a two level of weight optimization is employed to boost the performance of the traditional Deep Neural Network. A two level of weight optimization is done by Improved Butterfly Optimization Algorithm. Here the traditional Butterfly Optimization Algorithm is improved using Genetic Algorithm; this double weight updation improves the convergence rate to a considerable amount. Sensitivity, Accuracy and Specificity metrics are used for evaluating the performance of the proposed method. Experimental evaluations were done with the help of MATLAB. The proposed disease detection methodology achieves an overall accuracy of 99 percent. The experimental results give an indication that the proposed method turned out to be a better technique when compared to the existing techniques.
Patch-based Knowledge Distillation for Lifelong Person Re-Identification
Authors: Zhicheng Sun, Yadong Mu
Date: 2022-10-10 00:00:00
Proceeding: ACM Multimedia 2022 10
Tasks: Continual Learning, Knowledge Distillation, Lifelong learning, Person Re-Identification

The task of lifelong person re-identification aims to match a person across multiple cameras given continuous data streams. Similar to other lifelong learning tasks, it severely suffers from the so-called catastrophic forgetting problem, which refers to the notable performance degradation on previously-seen data after adapting the model to some newly incoming data. To alleviate it, a few existing methods have utilized knowledge distillation to enforce consistency between the original and adapted models. However, the effectiveness of such a strategy can be largely reduced facing the data distribution discrepancy between seen and new data. The hallmark of our work is using adaptively-chosen patches (rather than whole images as in other works) to pilot the forgetting-resistant distillation. Specifically, the technical contributions of our patch-based new solution are two-fold: first, a novel patch sampler is proposed. It is fully differentiable and trained to select a diverse set of image patches that stay crucial and discriminative under streaming data. Secondly, with those patches we curate a novel knowledge distillation framework. Valuable patch-level knowledge within individual patch features and mutual relations is well preserved by the two newly introduced distillation modules, further mitigating catastrophic forgetting. Extensive experiments on twelve person re-identification datasets clearly validate the superiority of our method over state-of-the-art competitors by large performance margins.
Steel Bar Counting from Images with Machine Learning
Authors: Ana Caren Hernández-Ruiz, Javier Alejandro Martínez-Nieto, and Julio David Buldain-Pérez
Date: 2021-02-07 00:00:00
Proceeding: Electronics 2021 2
Tasks: Clustering

Counting has become a fundamental task for data processing in areas such as microbiology, medicine, agriculture, and astrophysics. The proposed SA-CNN-DC (Scale Adaptive—Convolutional Neural Network—Distance Clustering) methodology in this paper is designed for the automated counting of steel bars from images. Its design consists of two Machine Learning techniques: Neural Networks and Clustering. The system has been trained to count round and squared steel bars, obtaining an average detection accuracy of 98.81% and 98.57%, respectively. In the steel industry, counting steel bars is a time-consuming task that highly relies on human labor and is prone to errors. Reduction of counting time and resources, safety and productivity of employees, and high confidence in the inventory are some of the advantages of the proposed methodology in a steel warehouse.
Selective Frequency Network for Image Restoration
Authors: Yuning Cui, Yi Tao, Zhenshan Bing, Wenqi Ren, Xinwei Gao, Xiaochun Cao, Kai Huang, Alois Knoll
Date: 2023-04-13 00:00:00
Proceeding: Conference 2023 4
Tasks: Deblurring, Image Defocus Deblurring, Image Dehazing, Image Restoration, Rain Removal, Single Image Deraining

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain in consideration of the large discrepancy between spectra of sharp/degraded image pairs. However, these works commonly utilize transformation tools, e.g., wavelet transform, to split features into several frequency parts, which is not flexible enough to select the most informative frequency component to recover. In this paper, we exploit a multi-branch and content-aware module to decompose features into separate frequency subbands dynamically and locally, and then accentuate the useful ones via channel-wise attention weights. In addition, to handle large-scale degradation blurs, we propose an extremely simple decoupling and modulation module to enlarge the receptive field via global and window-based average pooling. Integrating two developed modules into a U-Net backbone, the proposed Selective Frequency Network (SFNet) performs favorably against state-of-the-art algorithms on five image restoration tasks, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, and image deraining.
DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks
Authors: Rishal Aggarwal, Akash Gupta, Vineeth Chelur, C. V. Jawahar, and U. Deva Priyakumar
Date: 2021-08-10 00:00:00
Proceeding: Journal of Chemical Information and Modeling 2021 8
Tasks: Drug Design

A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilizes 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another data set SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 1st, 2018, until February 28th, 2020, for ligand binding site (LBS) detection. DeepPocket’s results on various binding site data sets and SC6K highlight its better performance over current state-of-the-art methods and good generalization ability over novel structures.
PUResNet: prediction of protein-ligand binding sites using deep residual neural network
Authors: Jeevan Kandel, Hilal Tayara, Kil To Chong
Date: 2021-09-08 00:00:00
Proceeding: Journal of Cheminformatics 2021 9
Tasks: Drug Discovery

Background
Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required.

Results
In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.