pwc-archive/papers-with-abstracts huggingface dataset:
Synergistic effect of shear and ADP on platelet growth on ZTA and Ti6Al4V surfaces
Authors: Anjana Jayaraman, Junhyuk Kang, Megan A. Jamiolkowski, William R. Wagner, Brian J. Kirby, James F. Antaki
Date: 2020-12-10 00:00:00

Continuous-flow ventricular assist devices (VADs) have been an increasingly common, life-saving therapy for advanced heart-failure patients, but elevate the risk of thrombosis due to a combination of non-physiological hemodynamics and synthetic biomaterials. Limited work has been done to address platelet adhesion and aggregation on artificial surfaces under flow with sub-threshold concentrations of weak agonists. We perfused a blood analog containing hemoglobin-depleted red blood cells and fluorescently labeled platelets across a titanium alloy (Ti6Al4V) and zirconia-toughened alumina (ZTA) surface at shear rates of 400 and 1000 s-1. Upstream of the specimen, sub-threshold concentrations of ADP were uniformly introduced at concentrations of 0, 5, and 10 nM. Time-lapse videos of depositing platelets were recorded, and the percentage of the surface covered was quantified. Surface coverage percentages at 400 s-1and 1000 s-1were compared for each concentration of ADP and material surface combination. We observed a threshold concentration of ADP that expedites platelet deposition that is dependent on both shear and material surface chemistry. Additionally, we observed embolization when thrombus areas exceeded 300{\mu}m2, which was dependent on the combination of shear, ADP concentration, and material surface. This work is the first to simultaneously examine the three key contributing factors leading to thrombotic events. Our findings assist in considering alternative material choices constituting VADs and the need to address material reactivity in assessing antiplatelet agent tests.
Analysis of multipath channel delay estimation using subspace fitting
Authors: Tarik Kazaz, Jac Romme, Gerard J. M. Janssen, Alle-Jan van der Veen
Date: 2020-12-10 00:00:00

The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.
Arabisc: Context-Sensitive Neural Spelling Checker
Authors: Yasmin Moslem, Rejwanul Haque, Andy Way
Date: 2020-12-01 00:00:00
Tasks: Language Modelling, Sentence, Spelling Correction

Traditional statistical approaches to spelling correction usually consist of two consecutive processes — error detection and correction — and they are generally computationally intensive. Current state-of-the-art neural spelling correction models usually attempt to correct spelling errors directly over an entire sentence, which, as a consequence, lacks control of the process, e.g. they are prone to overcorrection. In recent years, recurrent neural networks (RNNs), in particular long short-term memory (LSTM) hidden units, have proven increasingly popular and powerful models for many natural language processing (NLP) problems. Accordingly, we made use of a bidirectional LSTM language model (LM) for our context-sensitive spelling detection and correction model which is shown to have much control over the correction process. While the use of LMs for spelling checking and correction is not new to this line of NLP research, our proposed approach makes better use of the rich neighbouring context, not only from before the word to be corrected, but also after it, via a dual-input deep LSTM network. Although in theory our proposed approach can be applied to any language, we carried out our experiments on Arabic, which we believe adds additional value given the fact that there are limited linguistic resources readily available in Arabic in comparison to many languages. Our experimental results demonstrate that the proposed methods are effective in both improving the quality of correction suggestions and minimising overcorrection.
Modeling, Control and Human-In-The-Loop Stability Analysis of an Elastic Quadrotor
Authors: Emre Eraslan, Yildiray Yildiz
Date: 2020-12-10 00:00:00

This paper introduces an analytical framework for the derivation of hybrid equations of motion of a flexible quadrotor. This approach helps obtain rigid and elastic equations of motion simultaneously, in a decoupled form, which facilitates the controller design. A delay-dependent stability condition is obtained for the overall system dynamics, including the operator with reaction time delay, the adaptive controller and the flexible quadrotor dynamics. Two different adaptive controllers are implemented to address the uncertainties. It is demonstrated via simulations that the flexible arm tip oscillations are mitigated when a closed loop reference model adaptive controller is used, compared to a conventional model reference adaptive controller.
Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models
Authors: Yu Yao, Klaas E. Stephan
Date: 2020-12-10 00:00:00
Tasks: Clustering

In this paper, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject-wise generative models. Specifically, we focus on the case where the subject-wise generative model is a dynamic causal model (DCM) for fMRI and clusters are defined in terms of effective brain connectivity. While an attractive approach for detecting mechanistically interpretable subgroups in heterogeneous populations, inverting such a hierarchical model represents a particularly challenging case, since DCM is often characterized by high posterior correlations between its parameters. In this context, standard MCMC schemes exhibit poor performance and extremely slow convergence. In this paper, we investigate the properties of hierarchical clustering which lead to the observed failure of standard MCMC schemes and propose a solution designed to improve convergence but preserve computational complexity. Specifically, we introduce a class of proposal distributions which aims to capture the interdependencies between the parameters of the clustering and subject-wise generative models and helps to reduce random walk behaviour of the MCMC scheme. Critically, these proposal distributions only introduce a single hyperparameter that needs to be tuned to achieve good performance. For validation, we apply our proposed solution to synthetic and real-world datasets and also compare it, in terms of computational complexity and performance, to Hamiltonian Monte Carlo (HMC), a state-of-the-art Monte Carlo. Our results indicate that, for the specific application domain considered here, our proposed solution shows good convergence performance and superior runtime compared to HMC.
Peer-to-Peer Localization for Single-Antenna Devices
Authors: Xianan Zhang, Wei Wang, Xuedou Xiao, Hang Yang, Xinyu Zhang, Tao Jiang
Date: 2020-12-10 00:00:00
Tasks: Indoor Localization, TAG

Some important indoor localization applications, such as localizing a lost kid in a shopping mall, call for a new peer-to-peer localization technique that can localize an individual's smartphone or wearables by directly using another's on-body devices in unknown indoor environments. However, current localization solutions either require pre-deployed infrastructures or multiple antennas in both transceivers, impending their wide-scale application. In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy. P2PLocate leverages the multipath variations intentionally created by an on-body backscatter tag, coupled with spatial information offered by user movements, to accomplish this objective without relying on any pre-deployed infrastructures or pre-training. P2PLocate incorporates novel algorithms to address two major challenges: (i) interference with strong direct-path signal while extracting multipath variations, and (ii) lack of direction information while using single-antenna transceivers. We implement P2PLocate on commercial off-the-shelf Google Nexus 6p, Intel 5300 WiFi card, and Raspberry Pi B4. Real-world experiments reveal that P2PLocate can localize both static and mobile targets with a median accuracy of 0.88 m.
Relational Communication
Authors: Anton Kolotilin, Hongyi Li
Date: 2019-01-17 00:00:00
Tasks: Decision Making

We study a communication game between an informed sender and an uninformed receiver with repeated interactions and voluntary transfers. Transfers motivate the receiver's decision-making and signal the sender's information. Although full separation can always be supported in equilibrium, partial or complete pooling is optimal if the receiver's decision-making is highly responsive to information. In this case, the receiver's decision-making is disciplined by pooling extreme states where she is most tempted to defect.
Complementary Dynamics
Authors: Jiayi Eris Zhang, Seungbae Bang, David I.W. Levin, Alec Jacobson
Date: 2020-12-01 00:00:00

We present a novel approach to enrich arbitrary rig animations with elastodynamic secondary effects. Unlike previous methods which pit rig displacements and physical forces as adversaries against each other, we advocate that physics should complement artists’ intentions. We propose optimizing for elastodynamic displacements in the subspace orthogonal to displacements that can be created by the rig. This ensures that the additional dynamic motions do not undo the rig animation. The complementary space is high dimensional, algebraically constructed without manual oversight, and capable of rich high-frequency dynamics. Unlike prior tracking methods, we do not require extra painted weights, segmentation into fixed and free regions or tracking clusters. Our method is agnostic to the physical model and plugs into non-linear FEM simulations, geometric as-rigid-as-possible energies, or mass-spring models. Our method does not require a particular type of rig and adds secondary effects to skeletal animations, cage-based deformations, wire deformers, motion capture data, and rigid-body simulations.
Internet of Buoys: An Internet of Things Implementation at Sea
Authors: Michiel Sandra, Sara Gunnarsson, Anders J Johansson
Date: 2020-12-10 00:00:00

Internet of Things (IoT) applications are emerging in many different areas, including maritime environments. One of the applications in this area is the monitoring of buoys at sea. To realize wireless tracking of buoys, an accurate prediction of the path loss in an open-sea environment is essential. So far, channel measurements at sea have mainly been conducted with antennas placed a couple of meters above the sea surface, which is higher than the buoys themselves. Therefore, we investigated the validity of the published channel models at sea by means of path loss measurements using a LoRa link with a transmitter antenna height of 0.35 m and a base station antenna height of 2.65 m and 5.2 m. Our results show that the round earth loss model is not accurate at these antenna heights. The ITU-R P.2001-3 model and a model by Bullington show a better agreement with our measurements. However, the difference between our two measurement campaigns shows that more investigation is needed on the dependence of the path loss on the sea state. Additionally, the availability of Sigfox, Narrowband Internet of Things (NB-IoT), and The Things Network at sea has been explored. We found that NB-IoT and Sigfox can be used for IoT applications in the tested area at low antenna heights.
Control Analysis and Synthesis of Data-Driven Learning: A Kalman State-Space Approach
Authors: Deyuan Meng
Date: 2020-12-10 00:00:00

This paper aims to deal with the control analysis and synthesis problem of data-driven learning, regardless of unknown plant models and iteration-varying uncertainties. For the tracking of any desired target, a Kalman state-space approach is presented to transform it into two robust stability problems, which bridges a connection between data-driven control and model-based control. This approach also makes it possible to employ the extended state observer (ESO) in the design of data-driven learning to overcome the effect of iteration-varying uncertainties. It is shown that ESO-based data-driven learning ensures model-free systems to achieve the tracking of any desired target. In particular, our results apply to iterative learning control, which is verified by an example.
Towards Secure Localization in Randomly Deployed Wireless Networks
Authors: Marko Beko, Slavisa Tomic
Date: 2020-09-04 00:00:00

Being able to accurately locate wireless devices, while guaranteeing high-level of security against spoofing attacks, benefits all participants in the localization chain (e.g., end users, network operators, and location service providers). On the one hand, most of existing localization systems are designed for innocuous environments, where no malicious adversaries are present. This makes them highly susceptible to security threats coming from interferers, attacks or even unintentional errors (malfunctions) and thus, practically futile in hostile settings. On the other hand, existing secure localization solutions make certain (favorable) assumptions regarding the network topology (e.g., that the target device lies within a convex hull formed by reference points), which restrict their applicability. Therefore, this work addresses the problem of target localization in randomly deployed wireless networks in the presence of malicious attackers, whose goal is to manipulate (spoof) the estimation process and disable accurate localization. We propose a low-complex solution based on clustering and weighted central mass to detect attackers, using only the bare minimum of reference points, after which we solve the localization problem by a bisection procedure. The proposed method is studied from both localization accuracy and success in attacker detection point of views, where closed-form expressions for upper and lower bounds on the probability of attacker detection are derived. Its performance is validated through computer simulations, which corroborate the effectiveness of the proposed scheme, outperforming the state-of-the-art methods.
A Formalism for Modelling Traction forces and Cell Shape Evolution during Cell Migration in Various Biomedical Processes
Authors: Qiyao Peng, Fred Vermolen, Daphne Weihs
Date: 2020-12-10 00:00:00

The phenomenological model for cell shape deformation and cell migration (Chen et.al. 2018; Vermolen and Gefen 2012) is extended with the incorporation of cell traction forces and the evolution of cell equilibrium shapes as a result of cell differentiation. Plastic deformations of the extracellular matrix are modelled using morphoelasticity theory. The resulting partial differential differential equations are solved by the use of the finite element method. The paper treats various biological scenarios that entail cell migration and cell shape evolution. The experimental observations in Mak et.al. (2013), where transmigration of cancer cells through narrow apertures is studied, are reproduced using a Monte Carlo framework.
Quantum Technology for Economists
Authors: Isaiah Hull, Or Sattath, Eleni Diamanti, Göran Wendin
Date: 2020-12-08 00:00:00
Tasks: Misconceptions

Research on quantum technology spans multiple disciplines: physics, computer science, engineering, and mathematics. The objective of this manuscript is to provide an accessible introduction to this emerging field for economists that is centered around quantum computing and quantum money. We proceed in three steps. First, we discuss basic concepts in quantum computing and quantum communication, assuming knowledge of linear algebra and statistics, but not of computer science or physics. This covers fundamental topics, such as qubits, superposition, entanglement, quantum circuits, oracles, and the no-cloning theorem. Second, we provide an overview of quantum money, an early invention of the quantum communication literature that has recently been partially implemented in an experimental setting. One form of quantum money offers the privacy and anonymity of physical cash, the option to transact without the involvement of a third party, and the efficiency and convenience of a debit card payment. Such features cannot be achieved in combination with any other form of money. Finally, we review all existing quantum speedups that have been identified for algorithms used to solve and estimate economic models. This includes function approximation, linear systems analysis, Monte Carlo simulation, matrix inversion, principal component analysis, linear regression, interpolation, numerical differentiation, and true random number generation. We also discuss the difficulty of achieving quantum speedups and comment on common misconceptions about what is achievable with quantum computing.
Performance Analysis of Model-Free Adaptive Control
Authors: Feilong Zhang
Date: 2020-09-07 00:00:00
Tasks: model

We analyzed model-free adaptive control (MFAC) law through closed-loop function to widen its application range.
Geometric algorithms for sampling the flux space of metabolic networks
Authors: Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas, Haris Zafeiropoulos
Date: 2020-12-10 00:00:00

Systems Biology is a fundamental field and paradigm that introduces a new era in Biology. The crux of its functionality and usefulness relies on metabolic networks that model the reactions occurring inside an organism and provide the means to understand the underlying mechanisms that govern biological systems. Even more, metabolic networks have a broader impact that ranges from resolution of ecosystems to personalized medicine.The analysis of metabolic networks is a computational geometry oriented field as one of the main operations they depend on is sampling uniformly points from polytopes; the latter provides a representation of the steady states of the metabolic networks. However, the polytopes that result from biological data are of very high dimension (to the order of thousands) and in most, if not all, the cases are considerably skinny. Therefore, to perform uniform random sampling efficiently in this setting, we need a novel algorithmic and computational framework specially tailored for the properties of metabolic networks.We present a complete software framework to handle sampling in metabolic networks. Its backbone is a Multiphase Monte Carlo Sampling (MMCS) algorithm that unifies rounding and sampling in one pass, obtaining both upon termination. It exploits an improved variant of the Billiard Walk that enjoys faster arithmetic complexity per step. We demonstrate the efficiency of our approach by performing extensive experiments on various metabolic networks. Notably, sampling on the most complicated human metabolic network accessible today, Recon3D, corresponding to a polytope of dimension 5 335 took less than 30 hours. To our knowledge, that is out of reach for existing software.
The Retina as a Dynamical System
Authors: Bruno Cessac
Date: 2020-12-10 00:00:00

Considering the retina as a high dimensional, non autonomous, dynamical system, layered and structured, with non stationary and spatially inhomogeneous entries (visual scenes), we present several examples where dynamical systems-, bifurcations-, and ergodic-theory provide useful insights on retinal behaviour and dynamics.
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR
Authors: S Chakraborty, E Bisong, S Bhatt, T Wagner, R Elliott, F Mosconi
Date: 2020-12-10 00:00:00
Tasks: Articles, Information Retrieval, Language Modeling, Language Modelling, Question Answering, Retrieval

The SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical research. However, as the number of biomedical research papers continue to increase, the task of finding relevant articles to answer pressing questions has become significant. In this work, we propose a textual data mining tool that supports literature search to accelerate the work of researchers in the biomedical domain. We achieve this by building a neural-based deep contextual understanding model for Question-Answering (QA) and Information Retrieval (IR) tasks. We also leverage the new BREATHE dataset which is one of the largest available datasets of biomedical research literature, containing abstracts and full-text articles from ten different biomedical literature sources on which we pre-train our BioMedBERT model. Our work achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. In addition, we observe superior relevant results when BioMedBERT embeddings are used with Elasticsearch for the Information Retrieval task on the intelligently formulated BioASQ dataset. We believe our diverse dataset and our unique model architecture are what led us to achieve the state-of-the-art results for QA and IR tasks.
Spatially Conditioned Graphs for Detecting Human-Object Interactions
Authors: Frederic Z. Zhang, Dylan Campbell, Stephen Gould
Date: 2020-12-11 00:00:00
Proceeding: ICCV 2021 10
Tasks: Human-Object Interaction Detection, Object

We address the problem of detecting human-object interactions in images using graphical neural networks. Unlike conventional methods, where nodes send scaled but otherwise identical messages to each of their neighbours, we propose to condition messages between pairs of nodes on their spatial relationships, resulting in different messages going to neighbours of the same node. To this end, we explore various ways of applying spatial conditioning under a multi-branch structure. Through extensive experimentation we demonstrate the advantages of spatial conditioning for the computation of the adjacency structure, messages and the refined graph features. In particular, we empirically show that as the quality of the bounding boxes increases, their coarse appearance features contribute relatively less to the disambiguation of interactions compared to the spatial information. Our method achieves an mAP of 31.33% on HICO-DET and 54.2% on V-COCO, significantly outperforming state-of-the-art on fine-tuned detections.