pwc-archive/papers-with-abstracts huggingface dataset:
Finite Horizon Worst-Case Analysis of Linear Time-Varying Systems Applied to Launch Vehicle
Authors: Felix Biertümpfel, Nantiwat Pholdee, Samir Bennani, Harald Pfifer
Date: 2021-11-24 00:00:00

This paper presents an approach to compute the worst-case gain of the interconnection of a finite time horizon linear time-variant system and a perturbation. The input/output behavior of the uncertainty is described by integral quadratic constraints (IQCs). A condition for the worst-case gain of such an interconnection can be formulated using dissipation theory as a parameterized Riccati differential equation, which depends on the chosen IQC multiplier. A nonlinear optimization problem is formulated to minimize the upper bound of the worst-case gain over a set of admissible IQC multipliers. This problem can be efficiently solved with a custom-tailored logarithm scaled, adaptive differential evolution algorithm. It provides a fast alternative to similar approaches based on solving semidefinite programs. The algorithm is applied to the worst-case aerodynamic load analysis for an expendable launch vehicle (ELV). The worst-case load of the uncertain ELV is calculated under wind turbulence during the atmospheric ascend and compared to results from nonlinear simulation.
Native state of natural proteins optimises local entropy
Authors: Matteo Negri, Guido Tiana, Riccardo Zecchina
Date: 2021-11-25 00:00:00

The differing ability of polypeptide conformations to act as the native state of proteins has long been rationalized in terms of differing kinetic accessibility or thermodynamic stability. Building on the successful applications of physical concepts and sampling algorithms recently introduced in the study of disordered systems, in particular artificial neural networks, we quantitatively explore how well a quantity known as the local entropy describes the native state of model proteins. In lattice models and all-atom representations of proteins, we are able to efficiently sample high local entropy states and to provide a proof of concept of enhanced stability and folding rate. Our methods are based on simple and general statistical--mechanics arguments, and thus we expect that they are of very general use.
Multiscale modeling of brain network organization
Authors: Charley Presigny, Fabrizio De Vico Fallani
Date: 2021-11-26 00:00:00

A complete understanding of the brain requires an integrated description of the numerous scales of neural organization. It means studying the interplay of genes, synapses, and even whole brain regions which ultimately leads to different types of behavior, from perception to action, while asleep or awake. Yet, multiscale brain modeling is challenging, in part because of the difficulty to access simultaneously to information from multiple spatiotemporal scales. While some insights have been gained on the role of specific microcircuits (e.g., thalamocortical), a comprehensive characterization of how changes occurring at one scale can have an impact on other ones, remains poorly understood. Recent efforts to address this gap include the development of new analytical tools mostly adapted from network science and dynamical systems theory. These theoretical contributions provide a powerful framework to analyze and model interconnected complex systems exhibiting interactions within and between different scales, or layers. Here, we present recent advances for the characterization of the multiscale brain organization in terms of structure-function, oscillation frequencies and temporal evolution. Efforts are reviewed on the multilayer network properties underlying higher-order organization of neuronal assemblies, as well as on the identification of multimodal network-based biomarkers of brain pathologies, such as Alzheimer's disease. We conclude this Colloquium with a perspective discussion on how recent results from multilayer network theory, involving generative modeling, controllability and machine learning, could be adopted to address new questions in modern neuroscience.
Glass-box model representation of seismic failure mode prediction for conventional RC shear walls
Authors: Zeynep Tuna Deger, Gulsen Taskin Kaya
Date: 2021-11-12 00:00:00
Tasks: Classification, Decision Making

The recent surge in earthquake engineering is the use of machine learning methods to develop predictive models for structural behavior. Complex black-box models are typically used for decision making to achieve high accuracy; however, as important as high accuracy, it is essential for engineers to understand how the model makes the decision and verify that the model is physically meaningful. With this motivation, this study proposes a glass-box (interpretable) classification model to predict the seismic failure mode of conventional reinforced concrete shear (structural) walls. Reported experimental damage information of 176 conventional shear walls tested under reverse cyclic loading were designated as class-types, whereas key design properties (e.g. compressive strength of concrete, axial load ratio, and web reinforcement ratio) of shear walls were used as the basic classification features. The trade-off between model complexity and model interpretability was discussed using eight Machine Learning (ML) methods. The results showed that the Decision Tree method was a more convenient classifier with higher interpretability with a high classification accuracy than its counterparts. Also, to enhance the practicality of the model, a feature reduction was conducted to reduce the complexity of the proposed classifier with higher classification performance, and the most relevant features were identified, namely: compressive strength of concrete, wall aspect ratio, transverse boundary, and web reinforcement ratio. The ability of the final DT model to predict the failure modes was validated with a classification rate of around 90%. The proposed model aims to provide engineers interpretable, robust, and rapid prediction in seismic performance assessment.
Germany's current COVID-19 crisis is mainly driven by the unvaccinated
Authors: Benjamin F. Maier, Marc Wiedermann, Angelique Burdinski, Pascal Klamser, Mirjam A. Jenny, Cornelia Betsch, Dirk Brockmann
Date: 2021-11-24 00:00:00

Vaccines are the most powerful pharmaceutical tool to combat the COVID-19 pandemic. While the majority (about 65%) of the German population were fully vaccinated, incidence started growing exponentially in October 2021 with about 41% of recorded new cases aged twelve or above being symptomatic breakthrough infections, presumably also contributing to the dynamics. At the time, it (i) remains elusive how significant this contribution is and (ii) whether targeted non-pharmaceutical interventions (NPIs) may stop the amplification of the ongoing crisis. Here, we estimate that about 67%-76% of all new infections are caused by unvaccinated individuals, implying that only 24%-33% are caused by the vaccinated. Furthermore, we estimate 38%-51% of new infections to be caused by unvaccinated individuals infecting other unvaccinated individuals. In total, unvaccinated individuals are expected to be involved in 8-9 of 10 new infections. We further show that decreasing the transmissibility of the unvaccinated by, e.g. targeted NPIs, causes a steeper decrease in the effective reproduction number $\mathcal{R}$ than decreasing the transmissibility of vaccinated individuals, potentially leading to temporary epidemic control. Furthermore, reducing contacts between vaccinated and unvaccinated individuals serves to decrease $\mathcal R$ in a similar manner as increasing vaccine uptake. Taken together, our results contribute to the public discourse regarding policy changes in pandemic response and highlight the importance of combined measures, such as vaccination campaigns and contact reduction, to achieve epidemic control and preventing an overload of public health systems.
Difference in Differences and Ratio in Ratios for Limited Dependent Variables
Authors: Myoung-jae Lee, Sanghyeok Lee
Date: 2021-11-25 00:00:00

Difference in differences (DD) is widely used to find policy/treatment effects with observational data, but applying DD to limited dependent variables (LDV's) Y has been problematic. This paper addresses how to apply DD and related approaches (such as "ratio in ratios" or "ratio in odds ratios") to binary, count, fractional, multinomial or zero-censored Y under the unifying framework of `generalized linear models with link functions'. We evaluate DD and the related approaches with simulation and empirical studies, and recommend 'Poisson Quasi-MLE' for non-negative (such as count or zero-censored) Y and (multinomial) logit MLE for binary, fractional or multinomial Y.
The Macroeconomic Effects of Corporate Tax Reforms
Authors: Francesco Furno
Date: 2021-11-24 00:00:00

This paper extends a standard general equilibrium framework with a corporate tax code featuring two key elements: tax depreciation policy and the distinction between c-corporations and pass-through businesses. In the model, the stimulative effect of a tax rate cut on c-corporations is smaller when tax depreciation policy is accelerated, and is further diluted in the aggregate by the presence of pass-through entities. Because of a highly accelerated tax depreciation policy and a large share of pass-through activity in 2017, the model predicts small stimulus, large payouts to shareholders, and a dramatic loss of corporate tax revenues following the Tax Cuts and Jobs Act (TCJA-17). These predictions are consistent with novel micro- and macro-level evidence from professional forecasters and sectoral tax returns. At the same time, because of less-accelerated tax depreciation and a lower pass-through share in the early 1960s, the model predicts sizable stimulus in response to the Kennedy's corporate tax cuts - also supported by the data. The model-implied corporate tax multipliers for Trump's TCJA-17 and Kennedy's tax cuts are +0.6 and +2.5, respectively.
Securities Lending Haircuts and Indemnification Pricing
Authors: Wujiang Lou
Date: 2021-11-25 00:00:00

Securities borrowing and lending are critical to proper functioning of securities markets. To alleviate securities owners' exposure to borrower default risk, overcollateralization and indemnification are provided by the borrower and the lending agent respectively. Haircuts as the level of overcollateralization and the cost of indemnification are naturally interrelated: the higher haircut is, the lower cost shall become. This article presents a method of quantifying their relationship. Borrower dependent haircuts satisfying the lender's credit risk appetite are computed for US Treasuries and main equities by applying a repo haircut model to bilateral securities lending transactions. Indemnification is designed to fulfill a triple-A risk appetite when the transaction haircut fails to deliver. The cost of indemnification consists of a risk charge, a capital charge, and a funding charge, each corresponding to the expected loss, the economic capital, and the redundant fund needed to arrive at the triple-A haircut.
Coexistence of Centralized and Decentralized Markets
Authors: Berk Idem
Date: 2021-11-24 00:00:00

In this paper, I introduce a profit-maximizing centralized marketplace into a decentralized market with search frictions. Agents choose between the centralized marketplace and the decentralized bilateral trade. I characterize the optimal marketplace in this market choice game using a mechanism design approach. In the unique equilibrium, the centralized marketplace and the decentralized trade coexist. The profit of the marketplace decreases as the search frictions in the decentralized market are reduced. However, it is always higher than the half of the profit when the frictions are prohibitively high for decentralized trade. I also show that the ratio of the reduction in the profit depends only on the degree of search frictions and not on the distribution of valuations. The thickness of the centralized marketplace does not depend on the search frictions. I derive conditions under which, this equilibrium results in higher welfare than either institution on its own.
Diagnosis of sickle cell anemia using AutoML on UV-Vis absorbance spectroscopy data
Authors: Sarthak Srivastava, Radhika N. K., Rajesh Srinivasan, Nishanth K M Nambison, Sai Siva Gorthi
Date: 2021-11-24 00:00:00
Tasks: AutoML, Diagnostic, Specificity

Sickle cell anemia is a genetic disorder that is widespread in many regions of the world. Early diagnosis through screening and preventive treatments are known to reduce mortality in the case of sickle cell disease (SCD). In addition, the screening of individuals with the largely asymptomatic condition of sickle cell trait (SCT) is necessary to curtail the genetic propagation of the disease. However, the cost and complexity of conventional diagnostic methods limit the feasibility of early diagnosis of SCD and SCT in resource-limited areas worldwide. Recently, our group developed a low-cost UV-Vis absorbance spectroscopy based diagnostic test for SCD and SCT. Here, we propose an AutoML based approach to classify the raw spectra data obtained from the developed UV-Vis spectroscopy technique with high accuracy. The proposed approach can detect the presence of sickle hemoglobin with 100% sensitivity and 93.84% specificity. This study demonstrates the potential utility of the machine learning-based absorbance spectroscopy test for deployment in mass screening programs in resource-limited settings.
Fusion of Sensor Measurements and Target-Provided Information in Multitarget Tracking
Authors: Domenico Gaglione, Paolo Braca, Giovanni Soldi, Florian Meyer, Franz Hlawatsch, Moe Z. Win
Date: 2021-11-26 00:00:00

Tracking multiple time-varying states based on heterogeneous observations is a key problem in many applications. Here, we develop a statistical model and algorithm for tracking an unknown number of targets based on the probabilistic fusion of observations from two classes of data sources. The first class, referred to as target-independent perception systems (TIPSs), consists of sensors that periodically produce noisy measurements of targets without requiring target cooperation. The second class, referred to as target-dependent reporting systems (TDRSs), relies on cooperative targets that report noisy measurements of their state and their identity. We present a joint TIPS-TDRS observation model that accounts for observation-origin uncertainty, missed detections, false alarms, and asynchronicity. We then establish a factor graph that represents this observation model along with a state evolution model including target identities. Finally, by executing the sum-product algorithm on that factor graph, we obtain a scalable multitarget tracking algorithm with inherent TIPS-TDRS fusion. The performance of the proposed algorithm is evaluated using simulated data as well as real data from a maritime surveillance experiment.
Joint transmit and reflective beamforming for IRS-assisted integrated sensing and communication
Authors: Xianxin Song, Ding Zhao, Haocheng Hua, Tony Xiao Han, Xun Yang, Jie Xu
Date: 2021-11-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

This paper studies an intelligent reflecting surface (IRS)-assisted integrated sensing and communication (ISAC) system, in which one IRS is deployed to not only assist the wireless communication from a multi-antenna base station (BS) to a single-antenna communication user (CU), but also create virtual line-of-sight (LoS) links for sensing targets at areas with LoS links blocked. We consider that the BS transmits combined information and sensing signals for ISAC. Under this setup, we jointly optimize the transmit information and sensing beamforming at the BS and the reflective beamforming at the IRS, to maximize the IRS's minimum beampattern gain towards the desired sensing angles, subject to the minimum signal-to-noise ratio (SNR) requirement at the CU and the maximum transmit power constraint at the BS. Although the formulated SNR-constrained beampattern gain maximization problem is non-convex and difficult to solve, we present an efficient algorithm to obtain a high-quality solution using alternating optimization and semi-definite relaxation (SDR). Numerical results show that the proposed joint beamforming design achieves improved sensing performance while ensuring the communication requirement as compared to benchmarks without such joint optimization. It is also shown that the use of dedicated sensing beams is beneficial in enhancing the performance for IRS-assisted ISAC.
Short-time Fourier transform based on stimulated Brillouin scattering
Authors: Pengcheng Zuo, Dong Ma, Yang Chen
Date: 2021-11-26 00:00:00

In this paper, all-optical short-time Fourier transform (STFT) based on stimulated Brillouin scattering (SBS) is proposed and further used for real-time time-frequency analysis of different radio frequency (RF) signals. In the proposed all-optical STFT system, SBS not only provides a band-pass filter for implementing the window function in conjunction with a periodic frequency-sweep optical signal but also obtains the frequency domain information in different time windows through the generated waveform via frequency-to-time mapping (FTTM). A periodic frequency-sweep optical signal is generated and then modulated at a Mach-Zehnder modulator by the electrical signal under test (SUT). During different sweep periods, the fixed Brillouin gain functions as a bandpass filter to select a specific range of the spectrum, which is equivalent to applying a sliding window function to the corresponding section of the temporal signal with the help of the sweep optical signal. At the same time, after the optical signal is selectively amplified by the SBS gain and converted back to the electrical domain, SBS also implements the real-time FTTM, which can be utilized to obtain the frequency domain information corresponding to different time windows through the generated waveforms via the FTTM. The frequency domain information corresponding to different time windows is formed and spliced to analyze the time-frequency relationship of the SUT in real-time. An experiment is performed. STFTs of a variety of RF signals are carried out in a 12-GHz bandwidth limited only by the equipment, and the dynamic frequency resolution is better than 60 MHz.
Instrumented shoulder functional assessment using inertial measurement units for frozen shoulder
Authors: Ting-Yang Lu, Kai-Chun Liu, Chia-Yeh Hsieh, Chih-Ya Chang, Yu Tsao, Chia-Tai Chan
Date: 2021-11-26 00:00:00

Frozen shoulder (FS) is a shoulder condition that leads to pain and loss of shoulder range of motion. FS patients have difficulties in independently performing daily activities. Inertial measurement units (IMUs) have been developed to objectively measure upper limb range of motion (ROM) and shoulder function. In this work, we propose an IMU-based shoulder functional task assessment with kinematic parameters (e.g., smoothness, power, speed, and duration) in FS patients and analyze the functional performance on complete shoulder tasks and subtasks. Twenty FS patients and twenty healthy subjects were recruited in this study. Five shoulder functional tasks are performed by participants, such as washing hair (WH), washing upper back (WUB), washing lower back (WLB), placing an object on a high shelf (POH), and removing an object from back pocket (ROP). The results demonstrate that the used smoothness features can reflect the differences of movement fluency between FS patients and healthy controls (p < 0.05 and effect size > 0.8). Moreover, features of subtasks provided subtle information related to clinical conditions that have not been revealed in features of a complete task, especially the defined subtask 1 and 2 of each task.
On-Board Federated Learning for Dense LEO Constellations
Authors: Nasrin Razmi, Bho Matthiesen, Armin Dekorsy, Petar Popovski
Date: 2021-11-24 00:00:00
Tasks: Earth Observation, Federated Learning

Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and deployed by various private and public entities. While global connectivity is the main rationale, these constellations also offer the potential to gather immense amounts of data, e.g., for Earth observation. Power and bandwidth constraints together with motives like privacy, limiting delay, or resiliency make it desirable to process this data directly within the constellation. We consider the implementation of on-board federated learning (FL) orchestrated by an out-of-constellation parameter server (PS) and propose a novel communication scheme tailored to support FL. It leverages intra-orbit inter-satellite links, the predictability of satellite movements and partial aggregating to massively reduce the training time and communication costs. In particular, for a constellation with 40 satellites equally distributed among five low Earth orbits and the PS in medium Earth orbit, we observe a 29x speed-up in the training process time and a 8x traffic reduction at the PS over the baseline.
The Parameter Sensitivities of a Jump-diffusion Process in Basic Credit Risk Analysis
Authors: Bin Xie, Weiping Li
Date: 2021-11-26 00:00:00

We detect the parameter sensitivities of bond pricing which is driven by a Brownian motion and a compound Poisson process as the discontinuous case in credit risk research. The strict mathematical deductions are given theoretically due to the explicit call price formula. Furthermore, we illustrate Matlab simulation to verify these conclusions.
Optimal Design of Experiments for Simulation-Based Inference of Mechanistic Acyclic Biological Networks
Authors: Vincent Zaballa, Elliot Hui
Date: 2021-11-26 00:00:00

Biological signaling pathways based upon proteins binding to one another to relay a signal for genetic expression, such as the Bone Morphogenetic Protein (BMP) signaling pathway, can be modeled by mass action kinetics and conservation laws that result in non-closed form polynomial equations. Accurately determining parameters of biological pathways that represent physically relevant features, such as binding affinity of proteins and their associated uncertainty, presents a challenge for biological models lacking an explicit likelihood function. Additionally, parameterizing non-closed form biological models requires copious amounts of data from expensive perturbation-response experiments to fit model parameters. We present an algorithm (SBIDOEMAN) for determining optimal experiments and parameters of systems biology models with implicit likelihoods. We evaluate our algorithm using simulations of held-out true parameter values and demonstrate an improvement in the rate of accurate parameter inference over random and equidistant experimental designs when evaluated on two simple models of the BMP signaling pathway with an implicit likelihood function.