pwc-archive/papers-with-abstracts huggingface dataset:
Three-Dimensional Ultrasound Matrix Imaging
Authors: Flavien Bureau, Justine Robin, Arthur Le Ber, William Lambert, Mathias Fink, Alexandre Aubry
Date: 2023-03-13 00:00:00
Tasks: 3D geometry, Seismic Imaging

Matrix imaging paves the way towards a next revolution in wave physics. Based on the response matrix recorded between a set of sensors, it enables an optimized compensation of aberration phenomena and multiple scattering events that usually drastically hinder the focusing process in heterogeneous media. Although it gave rise to spectacular results in optical microscopy or seismic imaging, the success of matrix imaging has been so far relatively limited with ultrasonic waves because wave control is generally only performed with a linear array of transducers. In this paper, we extend ultrasound matrix imaging to a 3D geometry. Switching from a 1D to a 2D probe enables a much sharper estimation of the transmission matrix that links each transducer and each medium voxel. Here, we first present an experimental proof of concept on a tissue-mimicking phantom through ex-vivo tissues and then, show the potential of 3D matrix imaging for transcranial applications.
Model scale versus domain knowledge in statistical forecasting of chaotic systems
Authors: William Gilpin
Date: 2023-03-13 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Chaos and unpredictability are traditionally synonymous, yet large-scale machine learning methods recently have demonstrated a surprising ability to forecast chaotic systems well beyond typical predictability horizons. However, recent works disagree on whether specialized methods grounded in dynamical systems theory, such as reservoir computers or neural ordinary differential equations, outperform general-purpose large-scale learning methods such as transformers or recurrent neural networks. These prior studies perform comparisons on few individually-chosen chaotic systems, thereby precluding robust quantification of how statistical modeling choices and dynamical invariants of different chaotic systems jointly determine empirical predictability. Here, we perform the largest to-date comparative study of forecasting methods on the classical problem of forecasting chaos: we benchmark 24 state-of-the-art forecasting methods on a crowdsourced database of 135 low-dimensional systems with 17 forecast metrics. We find that large-scale, domain-agnostic forecasting methods consistently produce predictions that remain accurate up to two dozen Lyapunov times, thereby accessing a new long-horizon forecasting regime well beyond classical methods. We find that, in this regime, accuracy decorrelates with classical invariant measures of predictability like the Lyapunov exponent. However, in data-limited settings outside the long-horizon regime, we find that physics-based hybrid methods retain a comparative advantage due to their strong inductive biases.
Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
Authors: Hosein Hashemi, Nikolai Hartmann, Sahand Sharifzadeh, James Kahn, Thomas Kuhr
Date: 2023-03-07 00:00:00
Tasks: Conditional Image Generation, Density Estimation, Inductive Bias, Metric Learning, Relational Reasoning, Self-Supervised Learning

Simulating high-resolution detector responses is a computationally intensive process that has long been challenging in Particle Physics. Despite the ability of generative models to streamline it, full ultra-high-granularity detector simulation still proves to be difficult as it contains correlated and fine-grained information. To overcome these limitations, we propose Intra-Event Aware Generative Adversarial Network (IEA-GAN). IEA-GAN presents a Relational Reasoning Module that approximates an event in detector simulation, generating contextualized high-resolution full detector responses with a proper relational inductive bias. IEA-GAN also introduces a Self-Supervised intra-event aware loss and Uniformity loss, significantly enhancing sample fidelity and diversity. We demonstrate IEA-GAN's application in generating sensor-dependent images for the ultra-high-granularity Pixel Vertex Detector (PXD), with more than 7.5 M information channels at the Belle II Experiment. Applications of this work span from Foundation Models for high-granularity detector simulation, such as at the HL-LHC (High Luminosity LHC), to simulation-based inference and fine-grained density estimation. To our knowledge, IEA-GAN is the first algorithm for faithful ultra-high-granularity full detector simulation with event-based reasoning.
Intensity-based dynamic speckle method using JPEG and JPEG2000 compression
Authors: Elena Stoykova, Blaga Blagoeva, Natalya Berberova-Buhova, Mikhail Levchenko, Dimana Nazarova, Lian Nedelchev, Joongki Park
Date: 2023-03-13 00:00:00

Statistical processing of speckle data enables observation of speed of processes. In intensity-based pointwise dynamic speckle analysis, a map related to speed's spatial distribution is extracted from a sequence of speckle patterns formed on an object under coherent light. Monitoring of time evolution of a process needs storage, transfer and processing of a large number of images. We have proposed lossy compression of these images using JPEG and JPEG2000 formats. We have compared the maps computed from non-compressed and decompressed synthetic and experimental images, and we have proven that both compression formats can be applied in the dynamic speckle analysis.
Identification- and many instrument-robust inference via invariant moment conditions
Authors: Tom Boot, Johannes W. Ligtenberg
Date: 2023-03-14 00:00:00

Identification-robust hypothesis tests are commonly based on the continuous updating GMM objective function. When the number of moment conditions grows proportionally with the sample size, the large-dimensional weighting matrix prohibits the use of conventional asymptotic approximations and the behavior of these tests remains unknown. We show that the structure of the weighting matrix opens up an alternative route to asymptotic results when, under the null hypothesis, the distribution of the moment conditions is reflection invariant. We provide several examples in which the invariance follows from standard assumptions. Our results show that existing tests will be asymptotically conservative, and we propose an adjustment to attain asymptotically nominal size. We illustrate our findings through simulations for various (non-)linear models, and an empirical application on the effect of the concentration of financial activities in banks on systemic risk.
A Commons-Compatible Implementation of the Sharing Economy: Blockchain-Based Open Source Mediation
Authors: Petra Tschuchnig, Manfred Mayr, Maximilian Tschuchnig, Peter Haber
Date: 2023-03-14 00:00:00
Tasks: Management

The network economical sharing economy, with direct exchange as a core characteristic, is implemented both, on a commons and platform economical basis. This is due to a gain in importance of trust, collaborative consumption and democratic management as well as technological progress, in the form of near zero marginal costs, open source contributions and digital transformation. Concurrent to these commons-based drivers, the grey area between commerce and private exchange is used to exploit work, safety and tax regulations by central platform economists. Instead of central intermediators, the blockchain technology makes decentralized consensus finding, using Proof-of-Work (PoW) within a self-sustaining Peer-to-Peer network, possible. Therefore, a blockchain-based open source mediation seems to offer a commons-compatible implementation of the sharing economy. This thesis is investigated through a qualitative case study of Sardex and Interlace with their blockchain application, based on expert interviews and a structured content analysis. To detect the most commons-compatible implementation, the different implementation options through conventional platform intermediators, an open source blockchain with PoW as well as Interlaces' permissioned blockchain approach, are compared. The following confrontation is based on deductive criteria, which illustrates the inherent characteristics of a commons-based sharing economy.
A Story of Consistency: Bridging the Gap between Bentham and Rawls Foundations
Authors: Stéphane Gonzalez, Nikolaos Pnevmatikos
Date: 2023-03-13 00:00:00
Tasks: Decision Making

The axiomatic foundations of Bentham and Rawls solutions are discussed within the broader domain of cardinal preferences. It is unveiled that both solution concepts share all four of the following axioms: Nonemptiness, Anonymity, Unanimity, and Continuity. In order to fully characterize the Bentham and Rawls solutions, three variations of a consistency criterion are introduced and their compatibility with the other axioms is assessed. Each expression of consistency can be interpreted as a property of decision-making in uncertain environments.
A Unified Theorem of the Alternative
Authors: Ian Ball
Date: 2023-03-13 00:00:00

This note presents a unified theorem of the alternative that explicitly allows for any combination of equality, componentwise inequality, weak dominance, strict dominance, and nonnegativity relations. The theorem nests 60 special cases, some of which have been stated as separate theorems.
Distributionally Robust Principal-Agent Problems and Optimality of Contracts
Authors: Peter Zhang
Date: 2023-03-13 00:00:00

We propose a distributionally robust principal agent formulation, which generalizes some common variants of worst-case and Bayesian principal agent problems. We construct a theoretical framework to certify whether any surjective contract family is optimal, and bound its sub-optimality. We then apply the framework to study the optimality of affine contracts. We show with geometric intuition that these simple contract families are optimal when the surplus function is convex and there exists a technology type that is simultaneously least productive and least efficient. We also provide succinct expressions to quantify the optimality gap of any surplus function, based on its concave biconjugate. This new framework complements the current literature in two ways: invention of a new toolset; understanding affine contracts' performance in a larger landscape. Our results also shed light on the technical roots of this question: why are there more positive results in the recent literature that show simple contracts' optimality in robust settings rather than stochastic settings? This phenomenon is related to two technical facts: the sum of quasi-concave functions is not quasi-concave, and the maximization and expectation operators do not commute.
Image Blending with Osmosis
Authors: Paul Bungert, Pascal Peter, Joachim Weickert
Date: 2023-03-14 00:00:00

Image blending is an integral part of many multi-image applications such as panorama stitching or remote image acquisition processes. In such scenarios, multiple images are connected at predefined boundaries to form a larger image. A convincing transition between these boundaries may be challenging, since each image might have been acquired under different conditions or even by different devices. We propose the first blending approach based on osmosis filters. These drift-diffusion processes define an image evolution with a non-trivial steady state. For our blending purposes, we explore several ways to compose drift vector fields based on the derivatives of our input images. These vector fields guide the evolution such that the steady state yields a convincing blended result. Our method benefits from the well-founded theoretical results for osmosis, which include useful invariances under multiplicative changes of the colour values. Experiments on real-world data show that this yields better quality than traditional gradient domain blending, especially under challenging illumination conditions.
Intelligent Reflecting Surfaces Positioning in 6G Networks
Authors: Mobasshir Mahbub, Raed M. Shubair
Date: 2023-03-14 00:00:00

The work analyzed the positioning of IRS over the coverage region of micro cell to derive optimal placement location to support cell-edge Internet of Things (IoT) devices with a favorable signal-to-interference plus noise ratio (SINR). Moreover, the work derived that the implementation of IRS significantly enhances energy efficiency notably reducing the transmit power of the micro cell base station.
Target-to-User Association in ISAC Systems With Vehicle-Lodged RIS
Authors: Marouan Mizmizi, Dario Tagliaferri, Damiano Badini, Umberto Spagnolini
Date: 2023-03-14 00:00:00
Tasks: Integrated sensing and communication, ISAC, Management

Target-to-user (T2U) association is a prerequisite to fully exploit the potential of the sensing function in communication-centric integrated sensing and communication (ISAC) systems, e.g., for beam and blockage management. This letter proposes to purposely mount a RIS on the roof of the vehicular user equipment (VUE), which can serve as an intentional back-reflector towards the base station. By controlling the reflection pattern over time, it is possible to transmit information to the sensing system, i.e., back-reflection as bit 1, no back-reflection as bit 0. The VUEs are configured to back-reflect a Hadamard code sequence, which enables T2U association. The numerical results confirm the validity of our proposal.
Robust Integrated Sensing and Communication Beamforming for Dual-functional Radar and Communications: Method and Insights
Authors: Ahmad Bazzi, Marwa Chafii
Date: 2023-03-14 00:00:00
Tasks: Integrated sensing and communication, ISAC, Joint Radar-Communication

This work presents a novel robust beamforming design dedicated for dual-functional radar and communication (DFRC) base stations (BSs) in the context of integrated sensing and communications (ISAC). The architecture is intended for circumstances with imperfect channel state information (CSI). Our suggested approach demonstrates several tradeoffs for joint radar-communication deployment. Due to the DFRC nature of the design, the beamformer can simultaneously point towards an intended target, while optimizing communication quality of service. We unveil several insights regarding closed form expressions, as well as optimality of the proposed beamformer. Lastly, simulation results demonstrate the effectiveness of the proposed ISAC beamformer.
Relative performance criteria of multiplicative form in complete markets
Authors: Anastasiya Tanana
Date: 2023-03-14 00:00:00
Tasks: Form

We consider existence and uniqueness of Nash equilibria in an $N$-player game of utility maximization under relative performance criteria of multiplicative form in complete semimartingale markets. For a large class of players' utility functions, a general characterization of Nash equilibria for a given initial wealth vector is provided in terms of invertibility of a map from $\mathbb{R}^N$ to $\mathbb{R}^N$. As a consequence of the general theorem, we derive existence and uniqueness of Nash equilibria for an arbitrary initial wealth vector, as well as their convergence, if either (i) players' utility functions are close to CRRA, or (ii) players' competition weights are small and relative risk aversions are bounded away from infinity.
QuTIE: Quantum optimization for Target Identification by Enzymes
Authors: Hoang M. Ngo, My T. Thai, Tamer Kahveci
Date: 2023-03-13 00:00:00

Target Identification by Enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is an NP-complete problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this paper, we consider the TIE problem for identifying drug targets in metabolic networks. We develop the first quantum optimization solution, called QuTIE (Quantum optimization for Target Identification by Enzymes), to this NP-complete problem. We do that by developing an equivalent formulation of the TIE problem in Quadratic Unconstrained Binary Optimization (QUBO) form, then mapping it to a logical graph, which is then embedded on a hardware graph on a quantum computer. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions which are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes.
Recovering Arrhythmic EEG Transients from Their Stochastic Interference
Authors: Javier Díaz, Hiroyasu Ando, GoEun Han, Olga Malyshevskaya, Xifang Hayashi, Juan-Carlos Letelier, Masashi Yanagisawa, Kaspar E. Vogt
Date: 2023-03-14 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Traditionally, the neuronal dynamics underlying electroencephalograms (EEG) have been understood as arising from \textit{rhythmic oscillators with varying degrees of synchronization}. This dominant metaphor employs frequency domain EEG analysis to identify the most prominent populations of neuronal current sources in terms of their frequency and spectral power. However, emerging perspectives on EEG highlight its arrhythmic nature, which is primarily inferred from broadband EEG properties like the ubiquitous $1/f$ spectrum. In the present study, we use an \textit{arrhythmic superposition of pulses} as a metaphor to explain the origin of EEG. This conceptualization has a fundamental problem because the interference produced by the superpositions of pulses generates colored Gaussian noise, masking the temporal profile of the generating pulse. We solved this problem by developing a mathematical method involving the derivative of the autocovariance function to recover excellent approximations of the underlying pulses, significantly extending the analysis of this type of stochastic processes. When the method is applied to spontaneous mouse EEG sampled at $5$ kHz during the sleep-wake cycle, specific patterns -- called $\Psi$-patterns -- characterizing NREM sleep, REM sleep, and wakefulness are revealed. $\Psi$-patterns can be understood theoretically as \textit{power density in the time domain} and correspond to combinations of generating pulses at different time scales. Remarkably, we report the first EEG wakefulness-specific feature, which corresponds to an ultra-fast ($\sim 1$ ms) transient component of the observed patterns. By shifting the paradigm of EEG genesis from oscillators to random pulse generators, our theoretical framework pushes the boundaries of traditional Fourier-based EEG analysis, paving the way for new insights into the arrhythmic components of neural dynamics.
Rigorous results of limiting behaviors of total tumor size under cyclic intermittent therapy for the system of reversible phenotype-switchable tumor cells
Authors: Jaewook Joo
Date: 2023-03-14 00:00:00
Tasks: Scheduling

We are keenly interested in finding the limiting behaviors of total tumor size when tumor cells are subject to the periodic repetition of therapy and rest periods, called intermittent cyclic therapy. We hypothesize that each tumor cell can take either therapy-sensitive or therapy-tolerant phenotype, its phenotype transition is mainly driven by the presence or absence of environmental stress, and such a transition is reversible. Even though those aforementioned hypotheses make the model system simple, most of prior papers attempted to numerically find the optimal therapeutic scheduling that minimizes total tumor size, and there is no rigorous proof of the limiting behaviors of total tumor size to my knowledge. Here we present such long-waited mathematically rigorous results. In the first part of the paper, we present the derivation of total tumor size reduction criterion and prove two theorems of two different limiting behaviors of total tumor size under two different therapy strategies, one leading to an asymptotic finite tumor size according to an iterated map method and anther leading to asymptotically diminishing of total tumor size. In the second part of the paper, we discuss the effects of the intratumoral competition between sensitive and tolerant phenotypes on the total tumor size reduction criterion.
Phylogenomic Models from Tree Symmetries
Authors: Elizabeth A. Allman, Colby Long, John A. Rhodes
Date: 2023-03-13 00:00:00

A model of genomic sequence evolution on a species tree should include not only a sequence substitution process, but also a coalescent process, since different sites may evolve on different gene trees due to incomplete lineage sorting. Chifman and Kubatko initiated the study of such models, leading to the development of the SVDquartets methods of species tree inference. A key observation was that symmetries in an ultrametric species tree led to symmetries in the joint distribution of bases at the taxa. In this work, we explore the implications of such symmetry more fully, defining new models incorporating only the symmetries of this distribution, regardless of the mechanism that might have produced them. The models are thus supermodels of many standard ones with mechanistic parameterizations. We study phylogenetic invariants for the models, and establish identifiability of species tree topologies using them.
LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer
Authors: Shan Tang, Shanjun Mao, Yangyang Chen, Falong Tan, Lihua Duan, Cong Pian, Xiangxiang Zeng
Date: 2023-03-13 00:00:00
Tasks: Diagnostic

Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the world. The diagnostic classification and etiological analysis of CRC are two critical issues worthy of attention. Many methods adopt gut microbiota to solve it, but few of them simultaneously take into account the complex interactions and individual heterogeneity of gut microbiota, which are two common and important issues in genetics and intestinal microbiology, especially in high-dimensional cases. In this paper, a novel method with a Binary matrix based on Logistic Regression (LRBmat) is proposed to deal with the above problem. The binary matrix can directly weakened or avoided the influence of heterogeneity, and also contain the information about gut microbial interactions with any order. Moreover, LRBmat has a powerful generalization, it can combine with any machine learning method and enhance them. The real data analysis on CRC validates the proposed method, which has the best classification performance compared with the state-of-the-art. Furthermore, the association rules extracted from the binary matrix of the real data align well with the biological properties and existing literatures, which are helpful for the etiological analysis of CRC. The source codes for LRBmat are available at https://github.com/tsnm1/LRBmat.
pracpac: Practical R Packaging with Docker
Authors: V. P. Nagraj, Stephen D. Turner
Date: 2023-03-13 00:00:00
Tasks: Management

R packages are the fundamental units of reproducible code in R, providing a mechanism for distributing user-developed code, documentation, and data. Docker is a virtualization technology that allows applications and their dependencies to be distributed and run reproducibly across platforms. The pracpac package provides an interface to create Docker images that contain custom R packages. The pracpac package leverages the renv package management tool to ensure reproducibility by building dependency packages inside the container image mirroring those installed on the developer's system. The pracpac package can be used to containerize any R package to deploy with other domain-specific non-R tools, Shiny applications, or entire data analysis pipelines. The pracpac package is available on CRAN (https://cran.r-project.org/package=pracpac), and source code is available under the MIT license on GitHub (https://github.com/signaturescience/pracpac).