pwc-archive/papers-with-abstracts huggingface dataset:
Measurement of Individual Alteration in Perioperative ECGs During Elective Percutaneous Coronary Intervention
Authors: Philip Gemke, Theresa Bender, Ennio Idrobo-Avila, Henning Dathe, Dagmar Krefting, Tim Kacprowski, Nicolai Spicher
Date: 2023-02-10 00:00:00
Tasks: Electrocardiography (ECG)

The increasing availability of wearable electrocardiography (ECG) devices enables the continuous monitoring of individual ECG alterations. This could be beneficial for patients suffering from acute ischemia but with non-standard ECG findings that do not fit to the subject-independent and absolute thresholds defined in clinical guidelines. In this work, we evaluate the inter-patient magnitude of individual ECG alterations during ischemia. The freely available STAFF III database provides 12-lead ECG recordings of patients before, during, and after elective percutaneous coronary intervention(PCI), where a coronary vessel is widened with a balloon inflation. We compute individual alterations of ST-interval and T-wave amplitudes w.r.t. QRS amplitude over time for each patient and lead. We demonstrate that determining relative ST-interval/T-wave amplitudes and deriving individual alterations over time is feasible in standard and non-standard ECG recordings. To demonstrate clinical relevance, we use the features for differentiating N=54 STAFF III patients with atherosclerotic plaque in either the right coronary artery (RCA) or left ascending artery (LAD). Results show significant differences in 5 leads for ST-interval alterations and 3 leads for T-wave alterations, which are also suggested by clinical guidelines for ischemia detection. Clinical relevance- Assessing individual alterations over time could eventually close the gap in ECG evaluation of patients presenting with pre-existing heart conditions and non-standard ECGs.
Decentralized Exchanges: The Profitability Frontier of Constant Product Market Makers
Authors: Tobias Bitterli, Fabian Schär
Date: 2023-02-10 00:00:00

In this paper we analyze constant product market makers (CPMMs). We formalize the liquidity providers' profitability conditions and introduce a concept we call the profitability frontier in the xyk-space. We study the effect of mint and burn fees on the profitability frontier, consider various pool types, and compile a large data set from all Uniswap V2 transactions. We use this data to further study our theoretical framework and the profitability conditions. We show how the profitability of liquidity provision is severely affected by the costs of mint and burn events relative to the portfolio size and the characteristics of the trading pair.
Modelling Illiquid Stocks Using Quantum Stochastic Calculus: Asymptotic Methods
Authors: Will Hicks
Date: 2023-02-10 00:00:00

This article investigates the Fokker-Planck equations that arise from the application of quantum stochastic calculus to the modelling of illiquid financial markets, using asymptotic methods. We present a power series solution for quantum stochastic processes with a non-zero conservation process. Whilst the series in question are in general divergent, we show they can be used to approximate solutions for longer time frames, and provide estimates for the relative error on the higher order terms.
Modelling Illiquid Stocks Using Quantum Stochastic Calculus
Authors: Will Hicks
Date: 2023-02-10 00:00:00

Quantum Stochastic Calculus can be used as a means by which randomness can be introduced to observables acting on a Hilbert space. In this article we show how the mechanisms of Quantum Stochastic Calculus can be used to extend the classical Black-Scholes framework by incorporating a breakdown in the liquidity of a traded asset. This is captured via the widening of the bid offer spread, and the impact on the nature of the resulting probability distribution is modelled in this work.
Nemesis: Neural Mean Teacher Learning-Based Emotion-Centric Speaker
Authors: Aryan Yousefi, Kalpdrum Passi
Date: 2023-02-09 00:00:00
Proceeding: Algorithms Special Issue Machine Learning Algorithms for Big Data Analysis 2023 2
Tasks: Image Captioning

Image captioning is the multi-modal task of automatically describing a digital image based on its contents and their semantic relationship. This research area has gained increasing popularity over the past few years; however, most of the previous studies have been focused on purely objective content-based descriptions of the image scenes. In this study, efforts have been made to generate more engaging captions by leveraging human-like emotional responses. To achieve this task, a mean teacher learning-based method has been applied to the recently introduced ArtEmis dataset. ArtEmis is the first large-scale dataset for emotion-centric image captioning, containing 455K emotional descriptions of 80K artworks from WikiArt. This method includes a self-distillation relationship between memory-augmented language models with meshed connectivity. These language models are trained in a cross-entropy phase and then fine-tuned in a self-critical sequence training phase. According to various popular natural language processing metrics, such as BLEU, METEOR, ROUGE-L, and CIDEr, our proposed model has obtained a new state of the art on ArtEmis.
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
Authors: Yongqi Li, Yu Yu, Tieyun Qian
Date: 2023-02-13 00:00:00
Tasks: Contrastive Learning, Few-shot NER, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection stage and the inaccurate and unstable prototypes at the type classification stage remain to be challenging problems. In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems. We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references. Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance. Our code and data will be available at https://github.com/NLPWM-WHU/TadNER.
NephroNet: A Novel Program for Identifying Renal Cell Carcinoma and Generating Synthetic Training Images with Convolutional Neural Networks and Diffusion Models
Authors: Yashvir Sabharwal
Date: 2023-02-12 00:00:00
Tasks: image-classification, Image Classification, Image Generation

Renal cell carcinoma (RCC) is a type of cancer that originates in the kidneys and is the most common type of kidney cancer in adults. It can be classified into several subtypes, including clear cell RCC, papillary RCC, and chromophobe RCC. In this study, an artificial intelligence model was developed and trained for classifying different subtypes of RCC using ResNet-18, a convolutional neural network that has been widely used for image classification tasks. The model was trained on a dataset of RCC histopathology images, which consisted of digital images of RCC surgical resection slides that were annotated with the corresponding subtype labels. The performance of the trained model was evaluated using several metrics, including accuracy, precision, and recall. Additionally, in this research, a novel synthetic image generation tool, NephroNet, is developed on diffusion models that are used to generate original images of RCC surgical resection slides. Diffusion models are a class of generative models capable of synthesizing high-quality images from noise. Several diffusers such as Stable Diffusion, Dreambooth Text-to-Image, and Textual Inversion were trained on a dataset of RCC images and were used to generate a series of original images that resembled RCC surgical resection slides, all within the span of fewer than four seconds. The generated images were visually realistic and could be used for creating new training datasets, testing the performance of image analysis algorithms, and training medical professionals. NephroNet is provided as an open-source software package and contains files for data preprocessing, training, and visualization. Overall, this study demonstrates the potential of artificial intelligence and diffusion models for classifying and generating RCC images, respectively. These methods could be useful for improving the diagnosis and treatment of RCC and more.
Operation-level Progressive Differentiable Architecture Search
Authors: Xunyu Zhu, Jian Li, Yong liu, Weiping Wang
Date: 2023-02-11 00:00:00
Tasks: Neural Architecture Search

Differentiable Neural Architecture Search (DARTS) is becoming more and more popular among Neural Architecture Search (NAS) methods because of its high search efficiency and low compute cost. However, the stability of DARTS is very inferior, especially skip connections aggregation that leads to performance collapse. Though existing methods leverage Hessian eigenvalues to alleviate skip connections aggregation, they make DARTS unable to explore architectures with better performance. In the paper, we propose operation-level progressive differentiable neural architecture search (OPP-DARTS) to avoid skip connections aggregation and explore better architectures simultaneously. We first divide the search process into several stages during the search phase and increase candidate operations into the search space progressively at the beginning of each stage. It can effectively alleviate the unfair competition between operations during the search phase of DARTS by offsetting the inherent unfair advantage of the skip connection over other operations. Besides, to keep the competition between operations relatively fair and select the operation from the candidate operations set that makes training loss of the supernet largest. The experiment results indicate that our method is effective and efficient. Our method's performance on CIFAR-10 is superior to the architecture found by standard DARTS, and the transferability of our method also surpasses standard DARTS. We further demonstrate the robustness of our method on three simple search spaces, i.e., S2, S3, S4, and the results show us that our method is more robust than standard DARTS. Our code is available at https://github.com/zxunyu/OPP-DARTS.
Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity
Authors: Yang Janet Liu, Amir Zeldes
Date: 2023-02-13 00:00:00
Tasks: Cross-corpus, Discourse Parsing, Diversity

Recent advances in discourse parsing performance create the impression that, as in other NLP tasks, performance for high-resource languages such as English is finally becoming reliable. In this paper we demonstrate that this is not the case, and thoroughly investigate the impact of data diversity on RST parsing stability. We show that state-of-the-art architectures trained on the standard English newswire benchmark do not generalize well, even within the news domain. Using the two largest RST corpora of English with text from multiple genres, we quantify the impact of genre diversity in training data for achieving generalization to text types unseen during training. Our results show that a heterogeneous training regime is critical for stable and generalizable models, across parser architectures. We also provide error analyses of model outputs and out-of-domain performance. To our knowledge, this study is the first to fully evaluate cross-corpus RST parsing generalizability on complete trees, examine between-genre degradation within an RST corpus, and investigate the impact of genre diversity in training data composition.
Trainable Activations for Image Classification
Authors: Evgenii Pishchik
Date: 2023-01-26 00:00:00
Proceeding: Preprints 2023 1
Tasks: image-classification, Image Classification

Non-linear activation functions are one of the main parts of deep neural network architectures. The choice of the activation function can affect model speed, performance and convergence. Most popular activation functions don't have any trainable parameters and don't alter during the training. We propose different activation functions with and without trainable parameters. Said activation functions have a number of advantages and disadvantages. We'll be testing the performance of said activation functions and comparing the results with widely known activation function ReLU. We assume that the activation functions with trainable parameters can outperform functions without ones, because the trainable parameters allow the model to "select'' the type of each of the activation functions itself, however, this strongly depends on the architecture of the deep neural network and the activation function itself.
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
Authors: Yuanhao Wang, Qinghua Liu, Yu Bai, Chi Jin
Date: 2023-02-13 00:00:00
Tasks: Multi-agent Reinforcement Learning

A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where the description length of the game as well as the complexity of many existing learning algorithms scale exponentially with the number of agents. While recent works successfully address this challenge under the model of tabular Markov Games, their mechanisms critically rely on the number of states being finite and small, and do not extend to practical scenarios with enormous state spaces where function approximation must be used to approximate value functions or policies. This paper presents the first line of MARL algorithms that provably resolve the curse of multiagency under function approximation. We design a new decentralized algorithm -- V-Learning with Policy Replay, which gives the first polynomial sample complexity results for learning approximate Coarse Correlated Equilibria (CCEs) of Markov Games under decentralized linear function approximation. Our algorithm always outputs Markov CCEs, and achieves an optimal rate of $\widetilde{\mathcal{O}}(\epsilon^{-2})$ for finding $\epsilon$-optimal solutions. Also, when restricted to the tabular case, our result improves over the current best decentralized result $\widetilde{\mathcal{O}}(\epsilon^{-3})$ for finding Markov CCEs. We further present an alternative algorithm -- Decentralized Optimistic Policy Mirror Descent, which finds policy-class-restricted CCEs using a polynomial number of samples. In exchange for learning a weaker version of CCEs, this algorithm applies to a wider range of problems under generic function approximation, such as linear quadratic games and MARL problems with low ''marginal'' Eluder dimension.
ALAN: Autonomously Exploring Robotic Agents in the Real World
Authors: Russell Mendonca, Shikhar Bahl, Deepak Pathak
Date: 2023-02-13 00:00:00

Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While it is possible to build agents that can learn in such a manner without supervision, current methods struggle to scale to the real world. Thus, we propose ALAN, an autonomously exploring robotic agent, that can perform tasks in the real world with little training and interaction time. This is enabled by measuring environment change, which reflects object movement and ignores changes in the robot position. We use this metric directly as an environment-centric signal, and also maximize the uncertainty of predicted environment change, which provides agent-centric exploration signal. We evaluate our approach on two different real-world play kitchen settings, enabling a robot to efficiently explore and discover manipulation skills, and perform tasks specified via goal images. Website at https://robo-explorer.github.io/
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
Authors: Fares Fourati, Salma Kharrat, Vaneet Aggarwal, Mohamed-Slim Alouini, Marco Canini
Date: 2023-02-13 00:00:00
Tasks: Federated Learning

Federated learning, an emerging machine learning paradigm, enables clients to collaboratively train a model without exchanging local data. Clients participating in the training process significantly impact the convergence rate, learning efficiency, and model generalization. We propose a novel approach, client filtering, to improve model generalization and optimize client participation and training. The proposed method periodically filters available clients to identify a subset that maximizes a combinatorial objective function with an efficient greedy filtering algorithm. Thus, the clients are assessed as a combination rather than individually. We theoretically analyze the convergence of federated learning with client filtering in heterogeneous settings and evaluate its performance across diverse vision and language tasks, including realistic scenarios with time-varying client availability. Our empirical results demonstrate several benefits of our approach, including improved learning efficiency, faster convergence, and up to 10% higher test accuracy than training without client filtering.
Geometric Clifford Algebra Networks
Authors: David Ruhe, Jayesh K. Gupta, Steven de Keninck, Max Welling, Johannes Brandstetter
Date: 2023-02-13 00:00:00

We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical systems. GCANs are based on symmetry group transformations using geometric (Clifford) algebras. We first review the quintessence of modern (plane-based) geometric algebra, which builds on isometries encoded as elements of the $\mathrm{Pin}(p,q,r)$ group. We then propose the concept of group action layers, which linearly combine object transformations using pre-specified group actions. Together with a new activation and normalization scheme, these layers serve as adjustable $\textit{geometric templates}$ that can be refined via gradient descent. Theoretical advantages are strongly reflected in the modeling of three-dimensional rigid body transformations as well as large-scale fluid dynamics simulations, showing significantly improved performance over traditional methods.
Stitchable Neural Networks
Authors: Zizheng Pan, Jianfei Cai, Bohan Zhuang
Date: 2023-02-13 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Classification

The public model zoo containing enormous powerful pretrained model families (e.g., ResNet/DeiT) has reached an unprecedented scope than ever, which significantly contributes to the success of deep learning. As each model family consists of pretrained models with diverse scales (e.g., DeiT-Ti/S/B), it naturally arises a fundamental question of how to efficiently assemble these readily available models in a family for dynamic accuracy-efficiency trade-offs at runtime. To this end, we present Stitchable Neural Networks (SN-Net), a novel scalable and efficient framework for model deployment. It cheaply produces numerous networks with different complexity and performance trade-offs given a family of pretrained neural networks, which we call anchors. Specifically, SN-Net splits the anchors across the blocks/layers and then stitches them together with simple stitching layers to map the activations from one anchor to another. With only a few epochs of training, SN-Net effectively interpolates between the performance of anchors with varying scales. At runtime, SN-Net can instantly adapt to dynamic resource constraints by switching the stitching positions. Extensive experiments on ImageNet classification demonstrate that SN-Net can obtain on-par or even better performance than many individually trained networks while supporting diverse deployment scenarios. For example, by stitching Swin Transformers, we challenge hundreds of models in Timm model zoo with a single network. We believe this new elastic model framework can serve as a strong baseline for further research in wider communities.
Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography
Authors: Louis Blankemeier, Arjun Desai, Juan Manuel Zambrano Chaves, Andrew Wentland, Sally Yao, Eduardo Reis, Malte Jensen, Bhanushree Bahl, Khushboo Arora, Bhavik N. Patel, Leon Lenchik, Marc Willis, Robert D. Boutin, Akshay S. Chaudhari
Date: 2023-02-13 00:00:00
Tasks: Computed Tomography (CT)

Computed tomography (CT) is routinely used in clinical practice to evaluate a wide variety of medical conditions. While CT scans provide diagnoses, they also offer the ability to extract quantitative body composition metrics to analyze tissue volume and quality. Extracting quantitative body composition measures manually from CT scans is a cumbersome and time-consuming task. Proprietary software has been developed recently to automate this process, but the closed-source nature impedes widespread use. There is a growing need for fully automated body composition software that is more accessible and easier to use, especially for clinicians and researchers who are not experts in medical image processing. To this end, we have built Comp2Comp, an open-source Python package for rapid and automated body composition analysis of CT scans. This package offers models, post-processing heuristics, body composition metrics, automated batching, and polychromatic visualizations. Comp2Comp currently computes body composition measures for bone, skeletal muscle, visceral adipose tissue, and subcutaneous adipose tissue on CT scans of the abdomen. We have created two pipelines for this purpose. The first pipeline computes vertebral measures, as well as muscle and adipose tissue measures, at the T12 - L5 vertebral levels from abdominal CT scans. The second pipeline computes muscle and adipose tissue measures on user-specified 2D axial slices. In this guide, we discuss the architecture of the Comp2Comp pipelines, provide usage instructions, and report internal and external validation results to measure the quality of segmentations and body composition measures. Comp2Comp can be found at https://github.com/StanfordMIMI/Comp2Comp.
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Authors: Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu
Date: 2023-02-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Tomorrow's robots will need to distinguish useful information from noise when performing different tasks. A household robot for instance may continuously receive a plethora of information about the home, but needs to focus on just a small subset to successfully execute its current chore. Filtering distracting inputs that contain irrelevant data has received little attention in the reinforcement learning literature. To start resolving this, we formulate a problem setting in reinforcement learning called the $\textit{extremely noisy environment}$ (ENE), where up to $99\%$ of the input features are pure noise. Agents need to detect which features provide task-relevant information about the state of the environment. Consequently, we propose a new method termed $\textit{Automatic Noise Filtering}$ (ANF), which uses the principles of dynamic sparse training in synergy with various deep reinforcement learning algorithms. The sparse input layer learns to focus its connectivity on task-relevant features, such that ANF-SAC and ANF-TD3 outperform standard SAC and TD3 by a large margin, while using up to $95\%$ fewer weights. Furthermore, we devise a transfer learning setting for ENEs, by permuting all features of the environment after 1M timesteps to simulate the fact that other information sources can become relevant as the world evolves. Again, ANF surpasses the baselines in final performance and sample complexity. Our code is available at https://github.com/bramgrooten/automatic-noise-filtering