pwc-archive/papers-with-abstracts huggingface dataset:
Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation
Authors: Shuai Chen, Antonio Garcia-Uceda, Jiahang Su, Gijs van Tulder, Lennard Wolff, Theo van Walsum, Marleen de Bruijne
Date: 2022-09-14 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: 1) a model that generates synthetic structural errors, and 2) a label appearance simulation network that produces synthetic segmentations (with errors) that are similar in appearance to the real initial segmentations. Using these synthetic segmentations and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net and other previous refinement approaches. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method.
IMT to Satellite Stochastic Interference Modeling and Coexistence Analysis of Upper 6 GHz Band Service
Authors: Reza Aghazadeh Ayoubi, Dario Tagliaferri, Filippo Morandi, Luca Rinaldi, Laura Resteghini, Christian Mazzucco, Umberto Spagnolini
Date: 2022-09-14 00:00:00

The surging capacity demands of 5G networks and the limited coverage distance of high frequencies like millimeter wave (mmW) and sub-terahertz (THz) bands have led to consider the upper 6GHz (U6G) spectrum for radio access. However, due to the presence of the existing satellite (SAT) services in these bands, it is crucial to evaluate the impact of the interference of terrestrial U6G stations to SAT systems. A comprehensive study on the aggregated U6G-to-SAT interference is still missing in the literature. In this paper, we propose a stochastic model of interference (SMI) to evaluate the U6G-to-SAT interference, including the statistical characterization of array gain and clutter loss and considering different interference modes. Furthermore, we propose an approximate geometrical-based stochastic model of interference (GSMI) as an alternative method to SMI when the clutter-loss distribution is unavailable. Our results indicate that given the typical international mobile telecommunication (IMT) parameters, the aggregated interference power is well below the relevant protection criterion, and we prove numerically that the GSMI method overestimates the aggregated interference power with only 2dB compared to the SMI method.
Joint Communication and Sensing: 5G NR Compliant Ranging Using the Sounding Reference Signal
Authors: Michael Hofstadler, Reinhard Feger, Andreas Springer, Andreas Stelzer, Harald Pretl
Date: 2022-09-14 00:00:00

In this work, a proof of concept for 5G-compliant user-equipment side sensing is presented. It is based on orthogonal frequency division multiplexing radar-based ranging which is realized in this work by using the sounding reference signal from the 5G New Radio standard. The signal configuration and thus the corresponding waveform is generated in compliance with the existing 3rd Generation Partnership Project standard for 5G. It is an uplink physical signal and is originally intended, amongst others, for channel estimation. The used model is introduced, followed by the sounding reference signal. This leads to a first proof of concept by presenting simulation and measurement results. We show, that a range estimation error in the order of centimeters is achievable.
Lightweight 1-D CNN-based Timing Synchronization for OFDM Systems with CIR Uncertainty
Authors: Chaojin Qing, Shuhai Tang, Xi Cai, Jiafan Wang
Date: 2022-09-14 00:00:00
Tasks: compressed sensing

In this letter, a lightweight one-dimensional convolutional neural network (1-D CNN)-based timing synchronization (TS) method is proposed to reduce the computational complexity and processing delay and hold the timing accuracy in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the TS task is first transformed into a deep learning (DL)-based classification task, and then three iterations of the compressed sensing (CS)-based TS strategy are simplified to form a lightweight network, whose CNN layers are specially designed to highlight the classification features. Besides, to enhance the generalization performance of the proposed method against the channel impulse responses (CIR) uncertainty, the relaxed restriction for propagation delay is exploited to augment the completeness of training data. Numerical results reflect that the proposed 1-D CNN-based TS method effectively improves the TS accuracy, reduces the computational complexity and processing delay, and possesses a good generalization performance against the CIR uncertainty. The source codes of the proposed method are available at https://github.com/qingchj851/CNNTS.
Spline-Like Wavelet Filterbanks with Perfect Reconstruction on Arbitrary Graphs
Authors: Junxia You, Lihua Yang
Date: 2022-09-14 00:00:00
Tasks: Denoising

In this work, we propose a class of spline-like wavelet filterbanks for graph signals. These filterbanks possess the properties of critical sampling and perfect reconstruction. Besides, the analysis filters are localized in the graph domain because they are polynomials of the normalized adjacency matrix of the graph. We generalize the spline-like filters in the literature so that they have the ability to annihilate signals of some specified frequencies. Optimization problems are posed for the analysis filters to approximate desired responses. We conduct some experiments to demonstrate the good locality of the proposed filters and the good performance of the filterbank in the denoising task.
Classical Sequence Match is a Competitive Few-Shot One-Class Learner
Authors: Mengting Hu, Hang Gao, Yinhao Bai, Mingming Liu
Date: 2022-09-14 00:00:00
Proceeding: COLING 2022 10
Tasks: Meta-Learning

Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers. The models also show superiority even in the few-shot scenarios. In this paper, we revisit the classical methods and propose a new few-shot alternative. Specifically, we investigate the few-shot one-class problem, which actually takes a known sample as a reference to detect whether an unknown instance belongs to the same class. This problem can be studied from the perspective of sequence match. It is shown that with meta-learning, the classical sequence match method, i.e. Compare-Aggregate, significantly outperforms transformer ones. The classical approach requires much less training cost. Furthermore, we perform an empirical comparison between two kinds of sequence match approaches under simple fine-tuning and meta-learning. Meta-learning causes the transformer models' features to have high-correlation dimensions. The reason is closely related to the number of layers and heads of transformer models. Experimental codes and data are available at https://github.com/hmt2014/FewOne
Small Transformers Compute Universal Metric Embeddings
Authors: Anastasis Kratsios, Valentin Debarnot, Ivan Dokmanić
Date: 2022-09-14 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Memorization

We study representations of data from an arbitrary metric space $\mathcal{X}$ in the space of univariate Gaussian mixtures with a transport metric (Delon and Desolneux 2020). We derive embedding guarantees for feature maps implemented by small neural networks called \emph{probabilistic transformers}. Our guarantees are of memorization type: we prove that a probabilistic transformer of depth about $n\log(n)$ and width about $n^2$ can bi-H\"{o}lder embed any $n$-point dataset from $\mathcal{X}$ with low metric distortion, thus avoiding the curse of dimensionality. We further derive probabilistic bi-Lipschitz guarantees, which trade off the amount of distortion and the probability that a randomly chosen pair of points embeds with that distortion. If $\mathcal{X}$'s geometry is sufficiently regular, we obtain stronger, bi-Lipschitz guarantees for all points in the dataset. As applications, we derive neural embedding guarantees for datasets from Riemannian manifolds, metric trees, and certain types of combinatorial graphs. When instead embedding into multivariate Gaussian mixtures, we show that probabilistic transformers can compute bi-H\"{o}lder embeddings with arbitrarily small distortion.
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory
Authors: Sridhar Mahadevan
Date: 2022-09-13 00:00:00
Tasks: Causal Inference, reinforcement-learning, Reinforcement Learning (RL)

We present a unified formalism for structure discovery of causal models and predictive state representation (PSR) models in reinforcement learning (RL) using higher-order category theory. Specifically, we model structure discovery in both settings using simplicial objects, contravariant functors from the category of ordinal numbers into any category. Fragments of causal models that are equivalent under conditional independence -- defined as causal horns -- as well as subsequences of potential tests in a predictive state representation -- defined as predictive horns -- are both special cases of horns of a simplicial object, subsets resulting from the removal of the interior and the face opposite a particular vertex. Latent structure discovery in both settings involve the same fundamental mathematical problem of finding extensions of horns of simplicial objects through solving lifting problems in commutative diagrams, and exploiting weak homotopies that define higher-order symmetries. Solutions to the problem of filling "inner" vs "outer" horns leads to various notions of higher-order categories, including weak Kan complexes and quasicategories. We define the abstract problem of structure discovery in both settings in terms of adjoint functors between the category of universal causal models or universal decision models and its simplicial object representation.
Optimization without Backpropagation
Authors: Gabriel Belouze
Date: 2022-09-13 00:00:00

Forward gradients have been recently introduced to bypass backpropagation in autodifferentiation, while retaining unbiased estimators of true gradients. We derive an optimality condition to obtain best approximating forward gradients, which leads us to mathematical insights that suggest optimization in high dimension is challenging with forward gradients. Our extensive experiments on test functions support this claim.
A Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem
Authors: Luana P. Queiroz, Carine M. Rebello, Erber A. Costa, Vinicius V. Santana, Alirio E. Rodrigues, Ana M. Ribeiro, Idelfonso B. R. Nogueira
Date: 2022-09-13 00:00:00
Tasks: Multiobjective Optimization

Scientific machine learning (SciML) is a field of increasing interest in several different application fields. In an optimization context, SciML-based tools have enabled the development of more efficient optimization methods. However, implementing SciML tools for optimization must be rigorously evaluated and performed with caution. This work proposes the deductions of a robustness test that guarantees the robustness of multiobjective SciML-based optimization by showing that its results respect the universal approximator theorem. The test is applied in the framework of a novel methodology which is evaluated in a series of benchmarks illustrating its consistency. Moreover, the proposed methodology results are compared with feasible regions of rigorous optimization, which requires a significantly higher computational effort. Hence, this work provides a robustness test for guaranteed robustness in applying SciML tools in multiobjective optimization with lower computational effort than the existent alternative.
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
Authors: Vidhi Lalchand, Aditya Ravuri, Emma Dann, Natsuhiko Kumasaka, Dinithi Sumanaweera, Rik G. H. Lindeboom, Shaista Madad, Sarah A. Teichmann, Neil D. Lawrence
Date: 2022-09-14 00:00:00
Tasks: Data Integration, Dimensionality Reduction, Variational Inference

Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality reduction techniques are in need to disentangle biological variation in them, while accounting for technical and biological confounders. In this work, we extend a popular approach for probabilistic non-linear dimensionality reduction, the Gaussian process latent variable model, to scale to massive single-cell datasets while explicitly accounting for technical and biological confounders. The key idea is to use an augmented kernel which preserves the factorisability of the lower bound allowing for fast stochastic variational inference. We demonstrate its ability to reconstruct latent signatures of innate immunity recovered in Kumasaka et al. (2021) with 9x lower training time. We further analyze a COVID dataset and demonstrate across a cohort of 130 individuals, that this framework enables data integration while capturing interpretable signatures of infection. Specifically, we explore COVID severity as a latent dimension to refine patient stratification and capture disease-specific gene expression.
The pernicious danger of cortical brain maps
Authors: Benjamin Yost Hayden
Date: 2022-09-14 00:00:00

The parcellation of the primate cerebral cortex into numbered regions, based on cytoarchitecture, began with the pioneering research of neuroanatomist Kobrinian Brodmann. While the borders between regions have changed somewhat, and in some cases continue to be disputed, the idea of dividing the cortex into distinct numbered areas has become central to the goal of understanding brain function. And indeed, areal maps do provide a good starting point for functional parcellation. It is obvious, for example, that visual area V1 has a different function than primary motor cortex. However, as with anything good, one can take things too far. Indeed, cortical areas, while useful, have several pernicious side effects for neuroscientists interested in function, especially in prefrontal cortex.
Sneak peek at the tig sequences: useful sequences built from nucleic acid data
Authors: Camille Marchet
Date: 2022-09-13 00:00:00

This manuscript is a tutorial on tig sequences that emerged after the name "contig", and are of diverse purposes in sequence bioinformatics. We review these different sequences (unitigs, simplitigs, monotigs, omnitigs to cite a few), give intuitions of their construction and interest, and provide some examples of applications.
COVID-19 Regional Waves and Spread Risk Assessment through the Analysis of the Initial Outbreak in Guatemala
Authors: Juan Adolfo Ponciano, Juan Diego Chang, Mariela Abdalah, Kevin Facey, José Miguel Ponciano
Date: 2022-09-13 00:00:00

The initial surge of the COVID-19 pandemic hit Guatemala on March 2020. On a country scale, the epidemic has undergone a fairly well-known and distinguishable initial phase, reaching its peak on mid July 2020. However, the detailed picture is more involved and reflects inter-regional variations in the epidemic dynamics, presumably grounded on socio-demographic, connectivity, and human mobility factors. Classifying the regional epidemic curves and identifying the major hubs of regional COVID-19 spread can contribute towards defining an evidence-based risk map for future outbreaks of infectious diseases with similar transmissibility properties. In this work, we make a regional wave decomposition of the initial epidemic phase registered in Guatemala, and we use the Richards phenomenological model alongside multivariate ordination techniques of its estimated model parameters to draw a countrywide picture of the first epidemiological wave. By exploring similarities in the model space parameters, we traced routes for the disease spread across the country. We evaluated how well the proposed classification can help to define a regional risk hierarchy comprising early stage focal points, major hubs, and secondary regions of epidemic progression.
Predicting probability distributions for cancer therapy drug selection optimization
Authors: Jarek Duda
Date: 2022-09-13 00:00:00

Large variability between cell lines brings a difficult optimization problem of drug selection for cancer therapy. Standard approaches use prediction of value for this purpose, corresponding e.g. to expected value of their distribution. This article shows superiority of working on, predicting the entire probability distributions - proposing basic tools for this purpose. We are mostly interested in the best drug in their batch to be tested - proper optimization of their selection for extreme statistics requires knowledge of the entire probability distributions, which for distributions of drug properties among cell lines often turn out binomial, e.g. depending on corresponding gene. Hence for basic prediction mechanism there is proposed mixture of two Gaussians, trying to predict its weight based on additional information.
Clinicopathological correlation of p40/TTF1 co-expression in NSCLC and review of related literature
Authors: LiAn Yang, Ming Xiao, Xian Li, Ya-lan Wang
Date: 2022-09-14 00:00:00
Tasks: Prognosis, Specificity

TTF1 and {\Delta}Np63/p40 have been used to differentiate ADC and SQC in hypofractionated NSCLC because of their sensitivity and specificity. There are few cases where TTF1 and {\Delta}Np63/p40 are expressed together in the same tumour cells, and little is known about the clinicopathological features, treatment and prognosis of such cases. We investigated the electron microscopic features, immunohistochemical expression and molecular variation of a case of TTF1/p40 co-expressing NSCLC and reviewed and summarised the relevant literature. Our patient was a 58-year-old male with a CT showing a left-sided lung occupancy. As in all other cases reported in the literature, the tumour showed a solid growth pattern with polygonal cells, eosinophilic cytoplasm and clearly visible nuclear fission. Immunohistochemistry showed positive for TTF-1, p40, CK5/6, CK7, P63 and p53, and negative for NapsinA and ALK. Electron microscopy showed tumour cells characterised by bidirectional differentiation of adenocytes and squamous cells, consistent with previous reports. Second-generation sequencing suggested co-mutation of STK11/LKB1 and NF1 genes in this case. Mutations in STK11/LKB1 and NF1 genes have been found in ADC and SQC and are often associated with drug resistance and poor prognosis, but STK11/NF1 co-mutation has not been reported and more cases are needed to reveal the association. p40/TTF1 co-expression in NSCLC may be an under-recognised variant of NSCLC The origin may be a double positive stem cell-like basal cell located in the distal airway, with rapid clinical progression and poor prognosis.
Computing XVA for American basket derivatives by Machine Learning techniques
Authors: Ludovic Goudenege, Andrea Molent, Antonino Zanette
Date: 2022-09-14 00:00:00

Total value adjustment (XVA) is the change in value to be added to the price of a derivative to account for the bilateral default risk and the funding costs. In this paper, we compute such a premium for American basket derivatives whose payoff depends on multiple underlyings. In particular, in our model, those underlying are supposed to follow the multidimensional Black-Scholes stochastic model. In order to determine the XVA, we follow the approach introduced by Burgard and Kjaer \cite{burgard2010pde} and afterward applied by Arregui et al. \cite{arregui2017pde,arregui2019monte} for the one-dimensional American derivatives. The evaluation of the XVA for basket derivatives is particularly challenging as the presence of several underlings leads to a high-dimensional control problem. We tackle such an obstacle by resorting to Gaussian Process Regression, a machine learning technique that allows one to address the curse of dimensionality effectively. Moreover, the use of numerical techniques, such as control variates, turns out to be a powerful tool to improve the accuracy of the proposed methods. The paper includes the results of several numerical experiments that confirm the goodness of the proposed methodologies.
Statistical Learning of Value-at-Risk and Expected Shortfall
Authors: D Barrera, S Crépey, E Gobet, Hoang-Dung Nguyen, B Saadeddine
Date: 2022-09-14 00:00:00

We propose a non-asymptotic convergence analysis of a two-step approach to learn a conditional value-at-risk (VaR) and a conditional expected shortfall (ES) using Rademacher bounds, in a non-parametric setup allowing for heavy-tails on the financial loss. Our approach for the VaR is extended to the problem of learning at once multiple VaRs corresponding to different quantile levels. This results in efficient learning schemes based on neural network quantile and least-squares regressions. An a posteriori Monte Carlo procedure is introduced to estimate distances to the ground-truth VaR and ES. This is illustrated by numerical experiments in a Student-$t$ toy model and a financial case study where the objective is to learn a dynamic initial margin.
The Sign of Risk for Present Value of Future Losses
Authors: Brian P. Hanley, Steve Keen
Date: 2022-08-15 00:00:00

In the ongoing debate over discount rates and climate change, William Nordhaus has championed a higher discount rate to account for risk. Nicholas Stern has championed a lower rate. Here we prove that in the case of a stream of future losses, risk can only be represented by a lower discount rate, never a higher one.
ESG-valued discrete option pricing in complete markets
Authors: Yuan Hu, W. Brent Lindquist, Svetlozar T. Rachev
Date: 2022-09-13 00:00:00

We consider option pricing using replicating binomial trees, with a two fold purpose. The first is to introduce ESG valuation into option pricing. We explore this in a number of scenarios, including enhancement of yield due to trader information and the impact of the past history of a market driver. The second is to emphasize the use of discrete dynamic pricing, rather than continuum models, as the natural model that governs actual market practice. We further emphasize that discrete option pricing models must use discrete compounding (such as risk-free rate compounding of $1+r_f \Delta t$) rather than continuous compounding (such as $e^{r_f \Delta t})$.