pwc-archive/papers-with-abstracts huggingface dataset:
Investigating the influence of growth arrest mechanisms on tumour responses to radiotherapy
Authors: Chloé Colson, Philip K. Maini, Helen M. Byrne
Date: 2023-02-26 00:00:00

Cancer is a heterogeneous disease and tumours of the same type can differ greatly at the genetic and phenotypic levels. Understanding how these differences impact sensitivity to treatment is an essential step towards patient-specific treatment design. In this paper, we investigate how two different mechanisms for growth control may affect tumour cell responses to fractionated radiotherapy (RT) by extending an existing ordinary differential equation model of tumour growth. In the absence of treatment, this model distinguishes between growth arrest due to nutrient insufficiency and competition for space and exhibits three growth regimes: nutrient-limited (NL), space limited (SL) and bistable (BS), where both mechanisms for growth arrest coexist. We study the effect of RT for tumours in each regime, finding that tumours in the SL regime typically respond best to RT, while tumours in the BS regime typically respond worst to RT. For tumours in each regime, we also identify the biological processes that may explain positive and negative treatment outcomes and the dosing regimen which maximises the reduction in tumour burden.
A collagen-based theranostic wound dressing with visual, long-lasting infection detection capability
Authors: Charles Brooker, Giuseppe Tronci
Date: 2023-02-26 00:00:00

Continuous wound monitoring is one strategy to minimise infection severity and inform prompt variations in therapeutic care following infection diagnosis. However, integration of this functionality in therapeutic wound dressings is still challenging. We hypothesised that a theranostic dressing could be realised by integrating a collagen-based wound contact layer with previously demonstrated wound healing capability, and a halochromic dye, i.e. bromothymol blue (BTB), undergoing colour change following infection-associated pH changes (pH: 5-6 --> >7). Two different BTB integration strategies, i.e. electrospinning and drop-casting, were pursued to introduce long-lasting visual infection detection capability through retention of BTB within the dressing. Both systems had an average BTB loading efficiency of 99 wt.% and displayed a colour change within one minute of contact with simulated wound fluid. Drop-cast samples retained up to 85 wt.% of BTB after 96 hours in a near-infected wound environment, in contrast to the fibre-bearing prototypes, which released over 80 wt.% of BTB over the same time period. An increase in collagen denaturation temperature (DSC) and red shifts (ATR-FTIR) suggests the formation of secondary interactions between the collagen-based hydrogel and the BTB, which are attributed to count for the long-lasting dye confinement and durable dressing colour change. Given the high L929 fibroblast viability in drop-cast sample extracts (92%, 7 days), the presented multiscale design is simple, cell- and regulatory-friendly, and compliant with industrial scale-up. This design, therefore, offers a new platform for the development of theranostic dressings enabling accelerated wound healing and prompt infection diagnosis.
A Tale of Tail Covariances (and Diversified Tails)
Authors: Jan Rosenzweig
Date: 2023-02-27 00:00:00
Tasks: Time Series, Time Series Analysis

This paper deals with tail diversification in financial time series through the concept of statistical independence by way of differential entropy and mutual information. By using moments as contrast functions to isolate the tails of the return distributions, we recover the tail covariance matrix, a specific two-dimensional slice of the mixed moment tensor, as a key driver of tail diversification. We further explore the links between the moment contrast approach and the original entropy formulation, and show an example of in- and out-of-sample diversification on a broad stock universe.
Detecting Rough Volatility: A Filtering Approach
Authors: Camilla Damian, Rüdiger Frey
Date: 2023-02-24 00:00:00
Tasks: parameter estimation, Time Series, Time Series Analysis

In this paper, we focus on the estimation of historical volatility of asset prices from high-frequency data. Stochastic volatility models pose a major statistical challenge: since in reality historical volatility is not observable, its current level and, possibly, the parameters governing its dynamics have to be estimated from the observable time series of asset prices. To complicate matters further, recent research has analyzed the rough behavior of volatility time series to challenge the common assumption that the volatility process is a Brownian semimartingale. In order to tackle the arising inferential task efficiently in this setting, we use the fact that a fractional Brownian motion can be represented as a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes) and we solve the filtering (and parameter estimation) problem by resorting to more standard techniques, such as particle methods.
Gambling the World Away: Myopic Investors
Authors: Bernhard K Meister
Date: 2023-02-27 00:00:00

Myopic investors are locally rational decision-makers but globally irrational. Their suboptimal portfolios lag the market. As a consequence, other market participants are provided with profit opportunities. Not subterfuge but constrained optimisation leads to disparities. Four overlapping examples are given. The first case centres on the difference between local and global optimisers and their respective Kelly fractions, the second on isolated versus combined optimisation, the third on the distinction between qualitative and quantitative investor, the fourth on the non-commutative nature of information and the resulting asymmetries.
Physical Momentum in the Indian Stock Market
Authors: Naresh Kumar Devulapally, Tulasi Narendra Das Tripurana
Date: 2023-02-26 00:00:00

Our study focuses on determining the presence of abnormal returns for physical momentum portfolios in the context of the Indian market. The physical momentum portfolios, comprising stocks from the NSE 500, are constructed for the daily, weekly, monthly, and yearly timescales. In the aforementioned timescales, we empirically evaluate the historical returns and varied risk profiles of these portfolios for the years 2014-2021. It has been observed that the best-performing physical momentum portfolios from each of the four timescales achieved higher returns and better risk measures when compared to the benchmark NIFTY 50 portfolio. We further find that the high-frequency daily time scale exhibits the strongest reversal in the physical momentum effect, wherein the portfolio yielded a 16-fold profit over the initial investment.
Exploring the Advantages of Transformers for High-Frequency Trading
Authors: Fazl Barez, Paul Bilokon, Arthur Gervais, Nikita Lisitsyn
Date: 2023-02-20 00:00:00
Tasks: Decoder, Position, Time Series, Time Series Analysis, Time Series Forecasting, Vocal Bursts Intensity Prediction

This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term Memory models. A hybrid Transformer model, called \textbf{HFformer}, is then introduced for time series forecasting which incorporates a Transformer encoder, linear decoder, spiking activations, and quantile loss function, and does not use position encoding. Furthermore, possible high-frequency trading strategies for use with the HFformer model are discussed, including trade sizing, trading signal aggregation, and minimal trading threshold. Ultimately, the performance of the HFformer and Long Short-Term Memory models are assessed and results indicate that the HFformer achieves a higher cumulative PnL than the LSTM when trading with multiple signals during backtesting.
Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey
Authors: Mostafa Hussien, Islam A. T. F. Taj-Eddin, Mohammed F. A. Ahmed, Ali Ranjha, Kim Khoa Nguyen, Mohamed Cheriet
Date: 2023-01-24 00:00:00

The last decade, (2012 - 2022), saw an unprecedented advance in machine learning (ML) techniques, particularly deep learning (DL). As a result of the proven capabilities of DL, a large amount of work has been presented and studied in almost every field. Since 2012, when the convolution neural networks have been reintroduced in the context of \textit{ImagNet} competition, DL continued to achieve superior performance in many challenging tasks and problems. Wireless communications, in general, and medium access control (MAC) techniques, in particular, were among the fields that were heavily affected by this improvement. MAC protocols play a critical role in defining the performance of wireless communication systems. At the same time, the community lacks a comprehensive survey that collects, analyses, and categorizes the recent work in ML-inspired MAC techniques. In this work, we fill this gap by surveying a long line of work in this era. We solidify the impact of machine learning on wireless MAC protocols. We provide a comprehensive background to the widely adopted MAC techniques, their design issues, and their taxonomy, in connection with the famous application domains. Furthermore, we provide an overview of the ML techniques that have been considered in this context. Finally, we augment our work by proposing some promising future research directions and open research questions that are worth further investigation.
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing
Authors: Alex Yahja, Saeed Kaviani, Bo Ryu, Jae H. Kim, Kevin A. Larson
Date: 2023-01-24 00:00:00
Tasks: Anomaly Detection, Deep Learning, Deep Reinforcement Learning

We developed DeepADMR, a novel neural anomaly detector for the deep reinforcement learning (DRL)-based DeepCQ+ MANET routing policy. The performance of DRL-based algorithms such as DeepCQ+ is only verified within the trained and tested environments, hence their deployment in the tactical domain induces high risks. DeepADMR monitors unexpected behavior of the DeepCQ+ policy based on the temporal difference errors (TD-errors) in real-time and detects anomaly scenarios with empirical and non-parametric cumulative-sum statistics. The DeepCQ+ design via multi-agent weight-sharing proximal policy optimization (PPO) is slightly modified to enable the real-time estimation of the TD-errors. We report the DeepADMR performance in the presence of channel disruptions, high mobility levels, and network sizes beyond the training environments, which shows its effectiveness.
Self-Supervised Surround-View Depth Estimation with Volumetric Feature Fusion
Authors: Jung-Hee Kim, Junwha Hur, Tien Phuoc Nguyen, Seong-Gyun Jeong
Date: 2022-11-29 00:00:00
Proceeding: NeurIPS 2022 11
Tasks: Autonomous Driving, Depth Estimation, Monocular Depth Estimation

We present a self-supervised depth estimation approach using a unified volumetric feature fusion for surround-view images. Given a set of surround-view images, our method constructs a volumetric feature map by extracting image feature maps from surround-view images and fuse the feature maps into a shared, unified 3D voxel space. The volumetric feature map then can be used for estimating a depth map at each surround view by projecting it into an image coordinate. A volumetric feature contains 3D information at its local voxel coordinate; thus our method can also synthesize a depth map at arbitrary rotated viewpoints by projecting the volumetric feature map into the target viewpoints. Furthermore, assuming static camera extrinsics in the multi-camera system, we propose to estimate a canonical camera motion from the volumetric feature map. Our method leverages 3D spatiotemporal context to learn metric-scale depth and the canonical camera motion in a self-supervised manner. Our method outperforms the prior arts on DDAD and nuScenes datasets, especially estimating more accurate metric-scale depth and consistent depth between neighboring views.
Hedging predictions in machine learning
Authors: Alexander Gammerman, Vladimir Vovk
Date: 2006-11-02 00:00:00
Tasks: valid

Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters. This paper describes a new technique for "hedging" the predictions output by many such algorithms, including support vector machines, kernel ridge regression, kernel nearest neighbours, and by many other state-of-the-art methods. The hedged predictions for the labels of new objects include quantitative measures of their own accuracy and reliability. These measures are provably valid under the assumption of randomness, traditional in machine learning: the objects and their labels are assumed to be generated independently from the same probability distribution. In particular, it becomes possible to control (up to statistical fluctuations) the number of erroneous predictions by selecting a suitable confidence level. Validity being achieved automatically, the remaining goal of hedged prediction is efficiency: taking full account of the new objects' features and other available information to produce as accurate predictions as possible. This can be done successfully using the powerful machinery of modern machine learning.
Leader-Following Consensus of High-Order Perturbed Multi-agent Systems under Multiple Time-Varying Delays
Authors: Milad Gholami
Date: 2023-02-01 00:00:00

Solving an output consensus problem in multi-agent systems is often hindered by multiple time-variant delays. To address such fundamental problems over time, we present a new optimal time-variant distributed control for linearly perturbed multi-agent systems by involving an integral sliding mode controller and a linear consensus scheme with constant wights under directed topology. Lyapunov-Krasovskii functionals along with linear matrix inequalities are jointly employed to demonstrate the associated closed-loop stability and convergence features. Maximum delays for the communicating networks are also estimated by linear matrix inequalities. Synchronizing a network of linear time-variant systems to the associated leader dynamics is additionally taken into account by developing an optimization algorithm to find the constant control gains.
Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network
Authors: Jingyi Gu, Fadi P. Deek, Guiling Wang
Date: 2023-02-27 00:00:00
Tasks: Prediction, Stock Market Prediction, Stock Prediction, Time Series Analysis

Predicting the Stock movement attracts much attention from both industry and academia. Despite such significant efforts, the results remain unsatisfactory due to the inherently complicated nature of the stock market driven by factors including supply and demand, the state of the economy, the political climate, and even irrational human behavior. Recently, Generative Adversarial Networks (GAN) have been extended for time series data; however, robust methods are primarily for synthetic series generation, which fall short for appropriate stock prediction. This is because existing GANs for stock applications suffer from mode collapse and only consider one-step prediction, thus underutilizing the potential of GAN. Furthermore, merging news and market volatility are neglected in current GANs. To address these issues, we exploit expert domain knowledge in finance and, for the first time, attempt to formulate stock movement prediction into a Wasserstein GAN framework for multi-step prediction. We propose IndexGAN, which includes deliberate designs for the inherent characteristics of the stock market, leverages news context learning to thoroughly investigate textual information and develop an attentive seq2seq learning network that captures the temporal dependency among stock prices, news, and market sentiment. We also utilize the critic to approximate the Wasserstein distance between actual and predicted sequences and develop a rolling strategy for deployment that mitigates noise from the financial market. Extensive experiments are conducted on real-world broad-based indices, demonstrating the superior performance of our architecture over other state-of-the-art baselines, also validating all its contributing components.
Deep Learning for Identifying Iran's Cultural Heritage Buildings in Need of Conservation Using Image Classification and Grad-CAM
Authors: Mahdi Bahrami, Amir Albadvi
Date: 2023-02-28 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification, Object Localization, Transfer Learning

The cultural heritage buildings (CHB), which are part of mankind's history and identity, are in constant danger of damage or in extreme situations total destruction. That being said, it's of utmost importance to preserve them by identifying the existent, or presumptive, defects using novel methods so that renovation processes can be done in a timely manner and with higher accuracy. The main goal of this research is to use new deep learning (DL) methods in the process of preserving CHBs (situated in Iran); a goal that has been neglected especially in developing countries such as Iran, as these countries still preserve their CHBs using manual, and even archaic, methods that need direct human supervision. Having proven their effectiveness and performance when it comes to processing images, the convolutional neural networks (CNN) are a staple in computer vision (CV) literacy and this paper is not exempt. When lacking enough CHB images, training a CNN from scratch would be very difficult and prone to overfitting; that's why we opted to use a technique called transfer learning (TL) in which we used pre-trained ResNet, MobileNet, and Inception networks, for classification. Even more, the Grad-CAM was utilized to localize the defects to some extent. The final results were very favorable based on those of similar research. The final proposed model can pave the way for moving from manual to unmanned CHB conservation, hence an increase in accuracy and a decrease in human-induced errors.
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Authors: Shun Lu, Yu Hu, Longxing Yang, Zihao Sun, Jilin Mei, Jianchao Tan, Chengru Song
Date: 2023-02-28 00:00:00
Proceeding: CVPR 2023 1

Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during training. And we further find that large gradient variance occurs during supernet training, which degrades the supernet ranking consistency. To mitigate this issue, we propose to explicitly minimize the gradient variance of the supernet training by jointly optimizing the sampling distributions of PAth and DAta (PA&DA). We theoretically derive the relationship between the gradient variance and the sampling distributions, and reveal that the optimal sampling probability is proportional to the normalized gradient norm of path and training data. Hence, we use the normalized gradient norm as the importance indicator for path and training data, and adopt an importance sampling strategy for the supernet training. Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS. We conduct comprehensive comparisons with other improved approaches in various search spaces. Results show that our method surpasses others with more reliable ranking performance and higher accuracy of searched architectures, showing the effectiveness of our method. Code is available at https://github.com/ShunLu91/PA-DA.
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data
Authors: Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma, Mido Assran, Ishan Misra, Licheng Yu, Sean Bell
Date: 2023-02-28 00:00:00
Tasks: Density Estimation, image-classification, Image Classification, Representation Learning

Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world unlabeled data that is uncurated, e.g., contains out-of-class data. We propose RoPAWS, a robust extension of PAWS that can work with real-world unlabeled data. We first reinterpret PAWS as a generative classifier that models densities using kernel density estimation. From this probabilistic perspective, we calibrate its prediction based on the densities of labeled and unlabeled data, which leads to a simple closed-form solution from the Bayes' rule. We demonstrate that RoPAWS significantly improves PAWS for uncurated Semi-iNat by +5.3% and curated ImageNet by +0.4%.
Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm
Authors: Yan Shu, Shaohui Liu, Honglei Xu, Feng Jiang
Date: 2023-02-28 00:00:00

Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment. Nonetheless, two major obstacles still obstruct its deployment to real-world applications. The first issue is that they rarely take the entire pipeline's speed into account. The second is that they are incapable of dealing with some low-quality images (i.e., meter breakage, blur, and uneven scale). In this paper, we propose a human-like alignment and recognition algorithm to overcome these problems. More specifically, a Spatial Transformed Module(STM) is proposed to obtain the front view of images in a self-autonomous way based on an improved Spatial Transformer Networks(STN). Meanwhile, a Value Acquisition Module(VAM) is proposed to infer accurate meter values by an end-to-end trained framework. In contrast to previous research, our model aligns and recognizes meters totally implemented by learnable processing, which mimics human's behaviours and thus achieves higher performances. Extensive results verify the good robustness of the proposed model in terms of the accuracy and efficiency.
EvoPrompting: Language Models for Code-Level Neural Architecture Search
Date: 2023-02-28 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Code Generation, Efficient Neural Network, Neural Architecture Search, Prompt Engineering

Given the recent impressive accomplishments of language models (LMs) for code generation, we explore the use of LMs as adaptive mutation and crossover operators for an evolutionary neural architecture search (NAS) algorithm. While NAS still proves too difficult a task for LMs to succeed at solely through prompting, we find that the combination of evolutionary prompt engineering with soft prompt-tuning, a method we term EvoPrompting, consistently finds diverse and high performing models. We first demonstrate that EvoPrompting is effective on the computationally efficient MNIST-1D dataset, where EvoPrompting produces convolutional architecture variants that outperform both those designed by human experts and naive few-shot prompting in terms of accuracy and model size. We then apply our method to searching for graph neural networks on the CLRS Algorithmic Reasoning Benchmark, where EvoPrompting is able to design novel architectures that outperform current state-of-the-art models on 21 out of 30 algorithmic reasoning tasks while maintaining similar model size. EvoPrompting is successful at designing accurate and efficient neural network architectures across a variety of machine learning tasks, while also being general enough for easy adaptation to other tasks beyond neural network design.