pwc-archive/papers-with-abstracts huggingface dataset:
3D Super-Resolution Ultrasound with Adaptive Weight-Based Beamforming
Authors: Jipeng Yan, Bingxue Wang, Kai Riemer, Joseph Hansen-Shearer, Marcelo Lerendegui, Matthieu Toulemonde, Christopher J Rowlands, Peter D. Weinberg, Meng-Xing Tang
Date: 2022-08-25 00:00:00
Tasks: Super-Resolution

Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where large main lobe and significant side lobes make separation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes. This study aims to improve 3D SRUS by implementing a low-cost 3D coherence beamformer based on channel signal variance, as well as two other adaptive weight-based coherence beamformers: nonlinear beamforming with p-th root compression and coherence factor. The 3D coherence beamformers, together with DAS, are compared in computer simulation, on a microflow phantom, and in vivo. Simulation results demonstrate that the adaptive weight-based beamformers can significantly narrow the main lobe and suppress the side lobes for modest computational cost. Significantly improved 3D SR images of microflow phantom and a rabbit kidney are obtained through the adaptive weight-based beamformers. The proposed variance-based beamformer performs best in simulations and experiments.
What Impulse Response Do Instrumental Variables Identify?
Authors: Bonsoo Koo, Seojeong Lee, Myung Hwan Seo
Date: 2022-08-25 00:00:00

Macro shocks are often composites, yet overlooked in the impulse response analysis. When an instrumental variable (IV) is used to identify a composite shock, it violates the common IV exclusion restriction. We show that the Local Projection-IV estimand is represented as a weighted average of component-wise impulse responses but with possibly negative weights, which occur when the IV and shock components have opposite correlations. We further develop alternative (set-) identification strategies for the LP-IV based on sign restrictions or additional granular information. Our applications confirm the composite nature of monetary policy shocks and reveal a non-defense spending multiplier exceeding one.
Optimal Delegation in a Multidimensional World
Authors: Andreas Kleiner
Date: 2022-08-25 00:00:00

We study a model of delegation in which a principal takes a multidimensional action and an agent has private information about a multidimensional state of the world. The principal can design any direct mechanism, including stochastic ones. We provide necessary and sufficient conditions for an arbitrary mechanism to maximize the principal's expected payoff. We also discuss simple conditions which ensure that some convex delegation set is optimal. A key step of our analysis shows that a mechanism is incentive compatible if and only if its induced indirect utility is convex and lies below the agent's first-best payoff.
Towards Sustainable Internet of Underwater Things: UAV-aided Energy Efficient Wake-up Solutions
Authors: Muhammad Muzzammil, Nour Kouzayha, Nasir Saeed, Tareq Y. Al-Naffouri
Date: 2022-08-25 00:00:00

With the advancements in underwater wireless communications, internet of underwater things (IoUT) realization is inevitable to enable many practical applications, such as exploring ocean resources, ocean monitoring, underwater navigation, and surveillance. The IoUT network comprises battery-operated sensor nodes, and replacing or charging such batteries is challenging due to the harsh ocean environment. Hence, an energy-efficient IoUT network development becomes vital to improve the network lifetime. Therefore, this paper proposes unmanned aerial vehicle (UAV)-aided energy-efficient wake-up designs to activate the underwater IoT nodes on-demand and reduce their energy consumption. Specifically, the UAV communicates with water surface nodes, i.e., buoys, to send wake-up signals to activate the IoUT sensor nodes from sleep mode. We present three different technologies to enable underwater wake-up: acoustic, optical, and magnetic induction-based solutions. Moreover, we verify the significance of each technology through simulations using the performance metrics of received power and lifetime. Also, the results of the proposed on-demand wake-up approach are compared to conventional duty cycling, showing the superior performance of the proposed schemes. Finally, we present some exciting research challenges and future directions.
A Novel SCA-Based Method for Beamforming Optimization in IRS/RIS-Assisted MU-MISO Downlink
Authors: Vaibhav Kumar, Rui Zhang, Marco Di Renzo, Le-Nam Tran
Date: 2022-08-25 00:00:00

In this letter, we consider the fundamental problem of jointly designing the transmit beamformers and the phase-shifts of the intelligent reflecting surface (IRS) / reconfigurable intelligent surface (RIS) to minimize the transmit power, subject to quality-of-service constraints at individual users in an IRS-assisted multiuser multiple-input single-output downlink communication system. In particular, we propose a new successive convex approximation based second-order cone programming approach in which all the optimization variables are simultaneously updated in each iteration. Our proposed scheme achieves superior performance compared to state-of-the-art benchmark solutions. In addition, the complexity of the proposed scheme is $O(N_{\mathrm s}^{3.5})$, while that of state-of-the-art benchmark schemes is $O(N_{\mathrm s}^{7})$, where $N_{\mathrm s}$ denotes the number of reflecting elements at the IRS.
Laser-Powered UAVs for Wireless Communication Coverage: A Large-Scale Deployment Strategy
Authors: Mohamed-Amine Lahmeri, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2022-08-25 00:00:00

The use of unmanned aerial vehicles (UAVs) is strongly advocated for sixth-generation (6G) networks, as the 6G standard will not be limited to improving broadband services, but will also target the extension of the geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. That being said, it is important to underline that although UAVs are characterized by their high mobility and their ability to establish line-of-sight (LOS) links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage. To this end, we use tractable tools from stochastic geometry to model the complex communication system. We analyze the user's connectivity profile under different laser charging capabilities and in different type of environments. We show a decrease in the coverage probability by more than 12% in moderate-to-strong turbulence conditions compared to low turbulence conditions. We also show how the connection rate to the aerial network significantly decreases in favor of the terrestrial network for short laser charging ranges. We conclude that laser-powered drones are considered interesting alternatives when placed in LOS with users, in low-to-moderate optical turbulence, and at reasonable ranges from the charging stations.
Intelligent Random Access Framework for Massive and Ultra-Reliable Low Latency IoT Communications
Authors: Muhammad Waleed Aftab, Ishtiaq Ahmad
Date: 2022-08-25 00:00:00

The Internet of Things (IoT) enables smart cities to achieve the vision of connecting everything by smartly linking gadgets without the need for human interaction. However, due to the rapid proliferation of IoT devices, the amount of data produced accounts for a significant share of all communication services. Hence, 6G-enabled specifications for wireless networks are required to enable both massive and ultra-reliable low-latency access to such a hybrid IoT network. In this article, we propose a smart hybrid random access (SH-RA) scheme for massive connections and ultra-reliable low-latency access in the network architecture of IoT communications. According to numerical results, compared to the other baseline schemes, the SH-RA framework enormously enhances the total access probability and fulfills the quality-of-service (QoS) requirements.
Distributed Spatio-Temporal Information Based Cooperative 3D Positioning in GNSS-Denied Environments
Authors: Yue Cao, Shaoshi Yang, Zhiyong Feng, Lihua Wang, Lajos Hanzo
Date: 2022-08-25 00:00:00

A distributed spatio-temporal information based cooperative positioning (STICP) algorithm is proposed for wireless networks that require three-dimensional (3D) coordinates and operate in the global navigation satellite system (GNSS) denied environments. Our algorithm supports any type of ranging measurements that can determine the distance between nodes. We first utilize a finite symmetric sampling based scaled unscented transform (SUT) method for approximating the nonlinear terms of the messages passing on the associated factor graph (FG) with high precision, despite relying on a small number of samples. Then, we propose an enhanced anchor upgrading mechanism to avoid any redundant iterations. Our simulation results and analysis show that the proposed STICP has a lower computational complexity than the state-of-the-art belief propagation based localizer, despite achieving an even more competitive positioning performance.
Data-driven Discovery of Chemotactic Migration of Bacteria via Machine Learning
Authors: Yorgos M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee, Sujit S. Datta, Juan M. Bello-Rivas, Ioannis G. Kevrekidis
Date: 2022-08-25 00:00:00

E. coli chemotactic motion in the presence of a chemoattractant field has been extensively studied using wet laboratory experiments, stochastic computational models as well as partial differential equation-based models (PDEs). The most challenging step in bridging these approaches, is establishing a closed form of the so-called chemotactic term, which describes how bacteria bias their motion up chemonutrient concentration gradients, as a result of a cascade of biochemical processes. Data-driven models can be used to learn the entire evolution operator of the chemotactic PDEs (black box models), or, in a more targeted fashion, to learn just the chemotactic term (gray box models). In this work, data-driven Machine Learning approaches for learning the underlying model PDEs are (a) validated through the use of simulation data from established continuum models and (b) used to infer chemotactic PDEs from experimental data. Even when the data at hand are sparse (coarse in space and/or time), noisy (due to inherent stochasticity in measurements) or partial (e.g. lack of measurements of the associated chemoattractant field), we can attempt to learn the right-hand-side of a closed PDE for an evolving bacterial density. In fact we show that data-driven PDEs including a short history of the bacterial density field (e.g. in the form of higher-order in time PDEs in terms of the measurable bacterial density) can be successful in predicting further bacterial density evolution, and even possibly recovering estimates of the unmeasured chemonutrient field. The main tool in this effort is the effective low-dimensionality of the dynamics (in the spirit of the Whitney and Takens embedding theorems). The resulting data-driven PDE can then be simulated to reproduce/predict computational or experimental bacterial density profile data, and estimate the underlying (unmeasured) chemonutrient field evolution.
Controlling collective dynamical states of mesoscale brain networks with local perturbations
Authors: Lia Papadopoulos, Demian Battaglia, Dani S. Bassett
Date: 2022-08-25 00:00:00
Tasks: Functional Connectivity

Oscillatory synchrony is hypothesized to support the flow of information between brain regions, with different phase-locked configurations enabling activation of different effective interactions. Along these lines, past work has proposed multistable phase-locking as a means for hardwired brain networks to flexibly support multiple functional patterns, without having to reconfigure their anatomical connections. Given the potential link between interareal communication and phase-locked states, it is thus important to understand how those states might be controlled to achieve rapid alteration of functional connectivity in interareal circuits. Here, we study functional state control in small networks of coupled neural masses that display collective multistability under determinstic conditions, and that display more biologically-realistic irregular oscillations and transient phase-locking when conditions are stochastic. In particular, we investigate the global responses of these mesoscale circuits to external signals that target only a single subunit. Focusing mainly on the more realistic scenario wherein network dynamics are stochastic, we identify conditions under which local inputs (i) can trigger fast transitions to topologically distinct functional connectivity motifs that are temporarily stable ("state switching"), (ii) can smoothly adjust the spatial pattern of phase-relations for a particular set of lead-lag relationships ("state morphing"), and (iii) fail to regulate global phase-locking states. In total, our results add to a growing literature highlighting that the modulation of multistable, interareal coherence patterns could provide a basis for flexible brain network operation.
Population recordings of human motor units often display 'onion skin' discharge patterns -- implications for voluntary motor control
Authors: Gregory EP Pearcey, W Zev Rymer
Date: 2022-08-24 00:00:00

Over the past two decades, there has been a radical transformation in our ability to extract useful biological signals from the surface electromyogram (EMG). Advances in EMG electrode design and signal processing techniques have resulted in an extraordinary capacity to identify motor unit spike trains from the surface of a muscle. These EMG grid, or high-density surface EMG (HDsEMG), recordings now provide accurate depictions of as many as 20-30 motor unit spike trains simultaneously during isometric contractions, even at high forces. Such multi-unit recordings often display an unexpected feature known as onion skin behavior, in which multiple motor unit spike trains show essentially parallel and organized increases in discharge rate with increases in voluntary force, such that the earliest recruited units reach the highest discharge rates, while higher threshold units display more modest rate increases. This sequence results in an orderly pattern of discharge resembling the layers of an onion, in which discharge rate trajectories stay largely parallel and rarely cross. Our objective in this review is to explain why this pattern of discharge rates is unexpected, why it does not accurately reflect our current understanding of motoneuron electrophysiology, and why it may potentially lead to unpredicted disruption in muscle force generation. This review is aimed at the practicing clinician, or the clinician scientist. More advanced descriptions of potential electrophysiological mechanisms associated with onion skin characteristics targeting the research scientist will be provided as reference material.
Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations
Authors: Mehrad Sarmashghi, Shantanu P. Jadhav, Uri T. Eden
Date: 2022-07-25 00:00:00
Tasks: Computational Efficiency, Hippocampus

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses; however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region.
SCN 1A /Na V 1.1 channelopathies: Mechanisms in expression systems, animal models, and human iPSC models
Authors: Massimo Mantegazza, Vania Broccoli
Date: 2022-08-25 00:00:00

Pathogenic SCN1A/NaV1.1 mutations cause well defined epilepsies, including Genetic Epilepsy with Febrile Seizures Plus (GEFS+) and the severe epileptic encephalopathy Dravet syndrome. In addition, they cause a severe form of migraine with aura, Familial Hemiplegic Migraine. Moreover, SCN1A/NaV1.1 variants have been inferred as risk factors in other types of epilepsy. We review here the advancements obtained studying pathological mechanisms of SCN1A/NaV1.1 mutations with experimental systems. We present results gained with in vitro expression systems, gene targeted animal models and the iPSC technology, highlighting advantages, limits and pitfalls for each of these systems. Overall, the results obtained in the last two decades confirm that the initial pathological mechanism of epileptogenic SCN1A/NaV1.1 mutations is loss-of-function of NaV1.1 leading to hypoexcitability of at least some types of GABAergic neurons (including cortical and hippocampal parvalbumin- and somatostatin-positive ones). Conversely, more limited results point to NaV1.1 gain-of-function for FHM mutations. Behind these relatively simple pathological mechanisms, an unexpected complexity has been observed, in part generated by technical issues in experimental studies and in part related to intrinsically complex pathophysiological responses and remodeling, which yet remain to be fully disentangled.
Pricing Stocks with Trading Volumes
Authors: Ben Duan, Yutian Li, Dawei Lu, Yang Lu, Ran Zhang
Date: 2022-08-25 00:00:00

The present paper proposes a new framework for describing the stock price dynamics. In the traditional geometric Brownian motion model and its variants, volatility plays a vital role. The modern studies of asset pricing expand around volatility, trying to improve the understanding of it and remove the gap between the theory and market data. Unlike this, we propose to replace volatility with trading volume in stock pricing models. This pricing strategy is based on two hypotheses: a price-volume relation with an idea borrowed from fluid flows and a white-noise hypothesis for the price rate of change (ROC) that is verified via statistic testing on actual market data. The new framework can be easily adopted to local volume and stochastic volume models for the option pricing problem, which will point out a new possible direction for this central problem in quantitative finance.
A statistical test of market efficiency based on information theory
Authors: Xavier Brouty, Matthieu Garcin
Date: 2022-08-25 00:00:00
Tasks: Time Series, Time Series Analysis

We determine the amount of information contained in a time series of price returns at a given time scale, by using a widespread tool of the information theory, namely the Shannon entropy, applied to a symbolic representation of this time series. By deriving the exact and the asymptotic distribution of this market information indicator in the case where the efficient market hypothesis holds, we develop a statistical test of market efficiency. We apply it to a real dataset of stock indices, single stock, and cryptocurrency, for which we are able to determine at each date whether the efficient market hypothesis is to be rejected, with respect to a given confidence level.
ProTstab – predictor for cellular protein stability
Authors: Yang Yang, Xuesong Ding, Guanchen Zhu, Abhishek Niroula, Qiang Lv & Mauno Vihinen
Date: 2019-11-04 00:00:00
Proceeding: BMC Genomics 2019 11

Background
Stability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples.

Results
We took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities.

Conclusions
The Pearson’s correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.
Learning from Time-Changing Data with Adaptive Windowing
Authors: Albert Bifet, Ricard Gavalda
Date: 2007-04-01 00:00:00
Proceeding: Proceedings of the Seventh SIAM International Conference on Data Mining 2007 4

We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, instead of being fixed a priori, is recomputed online according to the rate of change observed from the data in the window itself. This delivers the user or programmer from having to guess a time-scale for change. Contrary to many related works, we provide rigorous guarantees of performance, as bounds on the rates of false positives and false negatives. Using ideas from data stream algorithmics, we develop a time-and memory-efficient version of this algorithm, called ADWIN2. We show how to combine ADWIN2 with the Naïve Bayes (NB) predictor, in two ways: one, using it to monitor the error rate of the current model and declare when revision is necessary and, two, putting it inside the NB predictor to maintain up-to-date estimations of conditional probabilities in the data. We test our approach using synthetic and real data streams and compare them to both fixed-size and variable-size window strategies with good results.
Continuous Inspection Schemes (CUSUM)
Authors: E. S. Page
Date: 1954-06-01 00:00:00
Proceeding: Biometrika 1954 6
Tasks: Change Detection

Paper does not publish an official abstract.

CUSUM (or cumulative sum control chart) is a method from the field of statistical process control. Developed by E.S. Page of the University of Cambridge, it is used for monitoring change detection.
Inference about the change-point from cumulative sum tests (Page-Hinkley test)
Authors: D. V. Hinkley
Date: 1971-12-01 00:00:00
Proceeding: Biometrika 1971 12
Tasks: Relation

Commonly known as the Page-Hinkley test. The point of change in mean in a sequence of normal random variables can be estimated
from a cumulative sum test scheme. The asymptotic distribution of this estimate and
associated test statistics are derived and numerical results given. The relation to likelihood
inference is emphasized. Asymptotic results are compared with empirical sequential
results, and some practical implications are discussed.