pwc-archive/papers-with-abstracts huggingface dataset:
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior
Authors: Xiaotong Chen, Zheming Zhou, Zhuo Deng, Omid Ghasemalizadeh, Min Sun, Cheng-Hao Kuo, Arnie Sen
Date: 2023-10-15 00:00:00
Tasks: 3D Reconstruction, Depth Completion, Optical Flow Estimation, Semantic Segmentation, Transparent objects

Reconstructing transparent objects using affordable RGB-D cameras is a persistent challenge in robotic perception due to inconsistent appearances across views in the RGB domain and inaccurate depth readings in each single-view. We introduce a two-stage pipeline for reconstructing transparent objects tailored for mobile platforms. In the first stage, off-the-shelf monocular object segmentation and depth completion networks are leveraged to predict the depth of transparent objects, furnishing single-view shape prior. Subsequently, we propose Epipolar-guided Optical Flow (EOF) to fuse several single-view shape priors from the first stage to a cross-view consistent 3D reconstruction given camera poses estimated from opaque part of the scene. Our key innovation lies in EOF which employs boundary-sensitive sampling and epipolar-line constraints into optical flow to accurately establish 2D correspondences across multiple views on transparent objects. Quantitative evaluations demonstrate that our pipeline significantly outperforms baseline methods in 3D reconstruction quality, paving the way for more adept robotic perception and interaction with transparent objects.
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification
Authors: Kanan Mahammadli, Abdullah Burkan Bereketoglu, Ayse Gul Kabakci
Date: 2023-10-15 00:00:00
Tasks: Cancer Classification, Data Augmentation, Transfer Learning

Breast Cancer is the most common cancer among women, which is also visible in men, and accounts for more than 1 in 10 new cancer diagnoses each year. It is also the second most common cause of women who die from cancer. Hence, it necessitates early detection and tailored treatment. Early detection can provide appropriate and patient-based therapeutic schedules. Moreover, early detection can also provide the type of cyst. This paper employs class-level data augmentation, addressing the undersampled classes and raising their detection rate. This approach suggests two key components: class-level data augmentation on structure-preserving stain normalization techniques to hematoxylin and eosin-stained images and transformer-based ViTNet architecture via transfer learning for multiclass classification of breast cancer images. This merger enables categorizing breast cancer images with advanced image processing and deep learning as either benign or as one of four distinct malignant subtypes by focusing on class-level augmentation and catering to unique characteristics of each class with increasing precision of classification on undersampled classes, which leads to lower mortality rates associated with breast cancer. The paper aims to ease the duties of the medical specialist by operating multiclass classification and categorizing the image into benign or one of four different malignant types of breast cancers.
SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation
Authors: Tan-Hanh Pham, Xianqi Li, Kim-Doang Nguyen
Date: 2023-10-16 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Automated medical image segmentation is becoming increasingly crucial to modern clinical practice, driven by the growing demand for precise diagnosis, the push towards personalized treatment plans, and the advancements in machine learning algorithms, especially the incorporation of deep learning methods. While convolutional neural networks (CNN) have been prevalent among these methods, the remarkable potential of Transformer-based models for computer vision tasks is gaining more acknowledgment. To harness the advantages of both CNN-based and Transformer-based models, we propose a simple yet effective UNet-Transformer (seUNet-Trans) model for medical image segmentation. In our approach, the UNet model is designed as a feature extractor to generate multiple feature maps from the input images, then the maps are propagated into a bridge layer, which is introduced to sequentially connect the UNet and the Transformer. In this stage, we approach the pixel-level embedding technique without position embedding vectors, aiming to make the model more efficient. Moreover, we apply spatial-reduction attention in the Transformer to reduce the computational/memory overhead. By leveraging the UNet architecture and the self-attention mechanism, our model not only retains the preservation of both local and global context information but also is capable of capturing long-range dependencies between input elements. The proposed model is extensively experimented on seven medical image segmentation datasets including polyp segmentation to demonstrate its efficacy. Comparison with several state-of-the-art segmentation models on these datasets shows the superior performance of our proposed seUNet-Trans network.
Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation
Authors: Shisheng Zhang, Ramtin Gharleghi, Sonit Singh, Arcot Sowmya, Susann Beier
Date: 2023-10-16 00:00:00
Tasks: Benchmarking, Coronary Artery Segmentation, Decoder, Diagnostic, Segmentation

Coronary artery diseases are among the leading causes of mortality worldwide. Timely and accurate diagnosis, facilitated by precise coronary artery segmentation, is pivotal in changing patient outcomes. In the realm of biomedical imaging, convolutional neural networks, especially the U-Net architecture, have revolutionised segmentation processes. However, one of the primary challenges remains the lack of benchmarking datasets specific to coronary arteries. However through the use of the recently published public dataset ASOCA, the potential of deep learning for accurate coronary segmentation can be improved. This paper delves deep into examining the performance of 25 distinct encoder-decoder combinations. Through analysis of the 40 cases provided to ASOCA participants, it is revealed that the EfficientNet-LinkNet combination, serving as encoder and decoder, stands out. It achieves a Dice coefficient of 0.882 and a 95th percentile Hausdorff distance of 4.753. These findings not only underscore the superiority of our model in comparison to those presented at the MICCAI 2020 challenge but also set the stage for future advancements in coronary artery segmentation, opening doors to enhanced diagnostic and treatment strategies.
Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition
Authors: Jun Zhang, Lipeng Zhu, Chao Wang, Shutao Li
Date: 2023-10-16 00:00:00
Tasks: valid

Integrating a low-spatial-resolution hyperspectral image (LR-HSI) with a high-spatial-resolution multispectral image (HR-MSI) is recognized as a valid method for acquiring HR-HSI. Among the current fusion approaches, the tensor ring (TR) decomposition-based method has received growing attention owing to its superior performance on preserving the spatial-spectral correlation. Furthermore, the low-rank property in some TR factors has been exploited via the matrix nuclear norm regularization along mode-2. On the other hand, the tensor nuclear norm (TNN)-based approaches have recently demonstrated to be more efficient on keeping high-dimensional low-rank structures in tensor recovery. Here, we study the low-rankness of TR factors from the TNN perspective and consider the mode-2 logarithmic TNN (LTNN) on each TR factor. A novel fusion model is proposed by incorporating this LTNN regularization and the weighted total variation which is to promote the continuity of HR-HSI in the spatial-spectral domain. Meanwhile, we have devised a highly efficient proximal alternating minimization algorithm to solve the proposed model. The experimental results indicate that our method improves the visual quality and exceeds the existing state-of-the-art fusion approaches with respect to various quantitative metrics.
A Framework for Inference Inspired by Human Memory Mechanisms
Authors: Xiangyu Zeng, Jie Lin, Piao Hu, Ruizheng Huang, Zhicheng Zhang
Date: 2023-10-01 00:00:00
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Question Answering

How humans and machines make sense of current inputs for relation reasoning and question-answering while putting the perceived information into context of our past memories, has been a challenging conundrum in cognitive science and artificial intelligence. Inspired by human brain's memory system and cognitive architectures, we propose a PMI framework that consists of perception, memory and inference components. Notably, the memory module comprises working and long-term memory, with the latter endowed with a higher-order structure to retain extensive and complex relational knowledge and experience. Through a differentiable competitive write access, current perceptions update working memory, which is later merged with long-term memory via outer product associations, reducing information conflicts and averting memory overflow. In the inference module, relevant information is retrieved from two separate memory origins and associatively integrated to attain a more comprehensive and precise interpretation of current perceptions. We exploratively apply our PMI to improve prevailing Transformers and CNN models on question-answering tasks like bAbI-20k and Sort-of-CLEVR datasets, as well as detecting equilateral triangles, language modeling and image classification tasks, and in each case, our PMI enhancements consistently outshine their original counterparts significantly. Visualization analyses reveal that relational memory consolidation, along with the interaction and integration of information from diverse memory sources, substantially contributes to the model effectiveness on inference tasks.
Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network
Authors: Zhenyu Tao, Wei Xu, Xiaohu You
Date: 2023-10-07 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Q-Learning

The proliferation of diverse wireless services in 5G and beyond has led to the emergence of network slicing technologies. Among these, admission control plays a crucial role in achieving service-oriented optimization goals through the selective acceptance of service requests. Although deep reinforcement learning (DRL) forms the foundation in many admission control approaches thanks to its effectiveness and flexibility, initial instability with excessive convergence delay of DRL models hinders their deployment in real-world networks. We propose a digital twin (DT) accelerated DRL solution to address this issue. Specifically, we first formulate the admission decision-making process as a semi-Markov decision process, which is subsequently simplified into an equivalent discrete-time Markov decision process to facilitate the implementation of DRL methods. A neural network-based DT is established with a customized output layer for queuing systems, trained through supervised learning, and then employed to assist the training phase of the DRL model. Extensive simulations show that the DT-accelerated DRL improves resource utilization by over 40% compared to the directly trained state-of-the-art dueling deep Q-learning model. This improvement is achieved while preserving the model's capability to optimize the long-term rewards of the admission process.
Topological Data Analysis in smart manufacturing: State of the art and futuredirections
Authors: Martin Uray, Barbara Giunti, Michael Kerber, Stefan Huber
Date: 2023-10-13 00:00:00
Tasks: Topological Data Analysis

Topological Data Analysis (TDA) is a discipline that applies algebraic topology techniques to analyze complex, multi-dimensional data. Although it is a relatively new field, TDA has been widely and successfully applied across various domains, such as medicine, materials science, and biology. This survey provides an overview of the state of the art of TDA within a dynamic and promising application area: industrial manufacturing and production, particularly within the Industry 4.0 context. We have conducted a rigorous and reproducible literature search focusing on TDA applications in industrial production and manufacturing settings. The identified works are categorized based on their application areas within the manufacturing process and the types of input data. We highlight the principal advantages of TDA tools in this context, address the challenges encountered and the future potential of the field. Furthermore, we identify TDA methods that are currently underexploited in specific industrial areas and discuss how their application could be beneficial, with the aim of stimulating further research in this field. This work seeks to bridge the theoretical advancements in TDA with the practical needs of industrial production. Our goal is to serve as a guide for practitioners and researchers applying TDA in industrial production and manufacturing systems. We advocate for the untapped potential of TDA in this domain and encourage continued exploration and research.
Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task
Authors: Maya Okawa, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka
Date: 2023-10-13 00:00:00
Proceeding: NeurIPS 2023 11

Modern generative models exhibit unprecedented capabilities to generate extremely realistic data. However, given the inherent compositionality of the real world, reliable use of these models in practical applications requires that they exhibit the capability to compose a novel set of concepts to generate outputs not seen in the training data set. Prior work demonstrates that recent diffusion models do exhibit intriguing compositional generalization abilities, but also fail unpredictably. Motivated by this, we perform a controlled study for understanding compositional generalization in conditional diffusion models in a synthetic setting, varying different attributes of the training data and measuring the model's ability to generate samples out-of-distribution. Our results show: (i) the order in which the ability to generate samples from a concept and compose them emerges is governed by the structure of the underlying data-generating process; (ii) performance on compositional tasks exhibits a sudden "emergence" due to multiplicative reliance on the performance of constituent tasks, partially explaining emergent phenomena seen in generative models; and (iii) composing concepts with lower frequency in the training data to generate out-of-distribution samples requires considerably more optimization steps compared to generating in-distribution samples. Overall, our study lays a foundation for understanding capabilities and compositionality in generative models from a data-centric perspective.
Uncertainty Quantification using Generative Approach
Authors: Yunsheng Zhang
Date: 2023-10-13 00:00:00
Tasks: Classification, regression, Uncertainty Quantification

We present the Incremental Generative Monte Carlo (IGMC) method, designed to measure uncertainty in deep neural networks using deep generative approaches. IGMC iteratively trains generative models, adding their output to the dataset, to compute the posterior distribution of the expectation of a random variable. We provide a theoretical guarantee of the convergence rate of IGMC relative to the sample size and sampling depth. Due to its compatibility with deep generative approaches, IGMC is adaptable to both neural network classification and regression tasks. We empirically study the behavior of IGMC on the MNIST digit classification task.
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
Authors: Debangshu Banerjee, Aditya Gopalan
Date: 2023-10-13 00:00:00
Tasks: Decision Making, Multi-Armed Bandits, Q-Learning

Parametric, feature-based reward models are employed by a variety of algorithms in decision-making settings such as bandits and Markov decision processes (MDPs). The typical assumption under which the algorithms are analysed is realizability, i.e., that the true values of actions are perfectly explained by some parametric model in the class. We are, however, interested in the situation where the true values are (significantly) misspecified with respect to the model class. For parameterized bandits, contextual bandits and MDPs, we identify structural conditions, depending on the problem instance and model class, under which basic algorithms such as $\epsilon$-greedy, LinUCB and fitted Q-learning provably learn optimal policies under even highly misspecified models. This is in contrast to existing worst-case results for, say misspecified bandits, which show regret bounds that scale linearly with time, and shows that there can be a nontrivially large set of bandit instances that are robust to misspecification.
Is Certifying $\ell_p$ Robustness Still Worthwhile?
Authors: Ravi Mangal, Klas Leino, Zifan Wang, Kai Hu, Weicheng Yu, Corina Pasareanu, Anupam Datta, Matt Fredrikson
Date: 2023-10-13 00:00:00

Over the years, researchers have developed myriad attacks that exploit the ubiquity of adversarial examples, as well as defenses that aim to guard against the security vulnerabilities posed by such attacks. Of particular interest to this paper are defenses that provide provable guarantees against the class of $\ell_p$-bounded attacks. Certified defenses have made significant progress, taking robustness certification from toy models and datasets to large-scale problems like ImageNet classification. While this is undoubtedly an interesting academic problem, as the field has matured, its impact in practice remains unclear, thus we find it useful to revisit the motivation for continuing this line of research. There are three layers to this inquiry, which we address in this paper: (1) why do we care about robustness research? (2) why do we care about the $\ell_p$-bounded threat model? And (3) why do we care about certification as opposed to empirical defenses? In brief, we take the position that local robustness certification indeed confers practical value to the field of machine learning. We focus especially on the latter two questions from above. With respect to the first of the two, we argue that the $\ell_p$-bounded threat model acts as a minimal requirement for safe application of models in security-critical domains, while at the same time, evidence has mounted suggesting that local robustness may lead to downstream external benefits not immediately related to robustness. As for the second, we argue that (i) certification provides a resolution to the cat-and-mouse game of adversarial attacks; and furthermore, that (ii) perhaps contrary to popular belief, there may not exist a fundamental trade-off between accuracy, robustness, and certifiability, while moreover, certified training techniques constitute a particularly promising way for learning robust models.
Machine Learning Estimation of Maximum Vertical Velocity from Radar
Authors: Randy J. Chase, Amy McGovern, Cameron Homeyer, Peter Marinescu, Corey Potvin
Date: 2023-10-13 00:00:00

The quantification of storm updrafts remains unavailable for operational forecasting despite their inherent importance to convection and its associated severe weather hazards. Updraft proxies, like overshooting top area from satellite images, have been linked to severe weather hazards but only relate to a limited portion of the total storm updraft. This study investigates if a machine learning model, namely U-Nets, can skillfully retrieve maximum vertical velocity and its areal extent from 3-dimensional gridded radar reflectivity alone. The machine learning model is trained using simulated radar reflectivity and vertical velocity from the National Severe Storm Laboratory's convection permitting Warn on Forecast System (WoFS). A parametric regression technique using the sinh-arcsinh-normal distribution is adapted to run with U-Nets, allowing for both deterministic and probabilistic predictions of maximum vertical velocity. The best models after hyperparameter search provided less than 50% root mean squared error, a coefficient of determination greater than 0.65 and an intersection over union (IoU) of more than 0.45 on the independent test set composed of WoFS data. Beyond the WoFS analysis, a case study was conducted using real radar data and corresponding dual-Doppler analyses of vertical velocity within a supercell. The U-Net consistently underestimates the dual-Doppler updraft speed estimates by 50$\%$. Meanwhile, the area of the 5 and 10 m s^-1 updraft cores show an IoU of 0.25. While the above statistics are not exceptional, the machine learning model enables quick distillation of 3D radar data that is related to the maximum vertical velocity which could be useful in assessing a storm's severe potential.
Identifiability of Product of Experts Models
Authors: Spencer L. Gordon, Manav Kant, Eric Ma, Leonard J. Schulman, Andrei Staicu
Date: 2023-10-13 00:00:00

Product of experts (PoE) are layered networks in which the value at each node is an AND (or product) of the values (possibly negated) at its inputs. These were introduced as a neural network architecture that can efficiently learn to generate high-dimensional data which satisfy many low-dimensional constraints -- thereby allowing each individual expert to perform a simple task. PoEs have found a variety of applications in learning. We study the problem of identifiability of a product of experts model having a layer of binary latent variables, and a layer of binary observables that are iid conditional on the latents. The previous best upper bound on the number of observables needed to identify the model was exponential in the number of parameters. We show: (a) When the latents are uniformly distributed, the model is identifiable with a number of observables equal to the number of parameters (and hence best possible). (b) In the more general case of arbitrarily distributed latents, the model is identifiable for a number of observables that is still linear in the number of parameters (and within a factor of two of best-possible). The proofs rely on root interlacing phenomena for some special three-term recurrences.
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations
Authors: Hardeep Bassi, Yuanran Zhu, Senwei Liang, Jia Yin, Cian C. Reeves, Vojtech Vlcek, Chao Yang
Date: 2023-10-13 00:00:00
Tasks: Numerical Integration

In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-differential equations (IDEs). The LSTM-RNN representation of the nonlinear integral operator allows us to turn a system of nonlinear integro-differential equations into a system of ordinary differential equations for which many efficient solvers are available. Furthermore, because the use of LSTM-RNN representation of the nonlinear integral operator in an IDE eliminates the need to perform a numerical integration in each numerical time evolution step, the overall temporal cost of the LSTM-RNN-based IDE solver can be reduced to $O(n_T)$ from $O(n_T^2)$ if a $n_T$-step trajectory is to be computed. We illustrate the efficiency and robustness of this LSTM-RNN-based numerical IDE solver with a model problem. Additionally, we highlight the generalizability of the learned integral operator by applying it to IDEs driven by different external forces. As a practical application, we show how this methodology can effectively solve the Dyson's equation for quantum many-body systems.