pwc-archive/papers-with-abstracts huggingface dataset:
Memory retrieval dynamics and storage capacity of a modular network model of association cortex with featural decomposition
Authors: Carlo Fulvi Mari
Date: 2021-04-23 00:00:00
Tasks: Retrieval

The primate heteromodal cortex presents an evident functional modularity at a mesoscopic level, with physiological and anatomical evidence pointing to it as likely substrate of long-term memory. In order to investigate some of its properties, a model of multimodular autoassociator is studied. Each of the many modules represents a neocortical functional ensemble of recurrently connected neurons and operates as a Hebbian autoassociator, storing a number of local features which it can recall upon cue. The global memory patterns are made of combinations of features sparsely distributed across the modules. Intermodular connections are modelled as a finite-connectivity random graph. Any pair of features in any respective pair of modules is allowed to be involved in several memory patterns; the coarse-grained modular network dynamics is defined in such a way as to overcome the consequent ambiguity of associations. Effects of long-range homeostatic synaptic scaling on network performance are also assessed. The dynamical process of cued retrieval almost saturates a natural upper bound while producing negligible spurious activation. The extent of finite-size effects on storage capacity is quantitatively evaluated. In the limit of infinite size, the functional relationship between storage capacity and number of features per module reduces to that which other authors found by methods from equilibrium statistical mechanics, which suggests that the origin of the functional form is of a combinatorial nature. In contrast with its apparent inevitability at intramodular level, long-range synaptic scaling results to be of minor relevance to both retrieval and storage capacity, casting doubt on its existence in the neocortex. A conjecture is also posited about how statistical fluctuation of connectivity across the network may underpin spontaneous emergence of semantic hierarchies through learning.
Managing mental & psychological wellbeing amidst COVID-19 pandemic: Positive psychology interventions
Authors: Maria Tresita Paul V., N. Uma Devi
Date: 2021-04-23 00:00:00

COVID-19 pandemic has shaken the roots of healthcare facilities worldwide, with the US being one of the most affected countries irrespective of being a superpower. Along with the current pandemic, COVID-19 can cause a secondary crisis of mental health pandemic if left unignored. Various studies from past epidemics, financial turmoil and pandemic, especially SARS and MERS, have shown a steep increase in mental and psychological issues like depression, low quality of life, self-harm and suicidal tendencies among general populations. The most venerable being the individuals infected and cured due to social discrimination. The government is taking steps to contain and prevent further infections of COVID-19. However, the mental and psychological wellbeing of people is still left ignored in developing countries like India. There is a significant gap in India concerning mental and psychological health still being stigmatized and considered 'non-existent'. This study's effort is to highlight the importance of mental and psychological health and to suggest interventions based on positive psychology literature. These interventions can support the wellbeing of people acting as a psychological first aid. Keywords: COVID-19, Coronavirus, Pandemic, Mental wellbeing, Psychological Wellbeing, Positive Psychology Interventions. KEYWORDS - COVID-19, Coronavirus, Pandemic, Wellbeing, Positive Psychology, Interventions, PPI.
If it Looks like a Human and Speaks like a Human ... Dialogue and cooperation in human-robot interactions
Authors: Mario A. Maggioni, Domenico Rossignoli
Date: 2021-04-23 00:00:00

The paper presents the results of a behavioral experiment conducted between February 2020 and March 2021 at Universit\`a Cattolica del Sacro Cuore, Milan Campus in which students were matched to either a human or a humanoid robotic partner to play an iterated Prisoner's Dilemma. The results of a Logit estimation procedure show that subjects are more likely to cooperate with human rather robotic partners; that are more likely to cooperate after receiving a dialogic verbal reaction following the realization of a sub-obtimal social outcome; that the effect of the verbal reaction is independent on the nature of the partner. Our findings provide new evidence on the effect of verbal communication in strategic frameworks. Results are robust to the exclusion of students of Economics related subjects, to the inclusion of a set of psychological and behavioral controls, to the way subjects perceive robots' behavior and to potential gender biases in human-human interactions.
The Crowd Classification Problem: Social Dynamics of Binary Choice Accuracy
Authors: Joshua Becker, Douglas Guilbeault, Ned Smith
Date: 2021-04-22 00:00:00
Tasks: Classification, Decision Making, General Classification

Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision-making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary choice judgments, also known as classification tasks--e.g. yes/no or build/buy decisions--social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially inaccurate groups become increasingly inaccurate after information exchange even as they signal stronger support. We term this dynamic the "crowd classification problem." Using both a novel dataset as well as a reanalysis of three previous datasets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary choice vote can become less accurate even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision-making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence.
Does home advantage without crowd exist in American football?
Authors: Dávid Zoltán Szabó, Diego Andrés Pérez
Date: 2021-04-22 00:00:00

It is well-known that home team has an inherent advantage against visiting teams when playing team sports. One of the most obvious underlying reasons, the presence of supporting fans has mostly disappeared in major leagues with the emergence of COVID-19 pandemic. This paper investigates with the help of historical National Football League (NFL) data, how much effect spectators have on the game outcome. Our findings reveal that under no allowance of spectators the home teams' performance is substantially lower than under normal circumstances, even performing slightly worse than the visiting teams. On the other hand, when a limited amount of spectators are allowed to the game, the home teams' performance is no longer significantly different than what we observe with full stadiums. This suggests that from a psychological point of view the effect of crowd support is already induced by a fraction of regular fans.
Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields
Authors: Albert W. Reed, Hyojin Kim, Rushil Anirudh, K. Aditya Mohan, Kyle Champley, Jingu Kang, Suren Jayasuriya
Date: 2021-04-23 00:00:00
Proceeding: ICCV 2021 10
Tasks: CT Reconstruction

Reconstructing dynamic, time-varying scenes with computed tomography (4D-CT) is a challenging and ill-posed problem common to industrial and medical settings. Existing 4D-CT reconstructions are designed for sparse sampling schemes that require fast CT scanners to capture multiple, rapid revolutions around the scene in order to generate high quality results. However, if the scene is moving too fast, then the sampling occurs along a limited view and is difficult to reconstruct due to spatiotemporal ambiguities. In this work, we design a reconstruction pipeline using implicit neural representations coupled with a novel parametric motion field warping to perform limited view 4D-CT reconstruction of rapidly deforming scenes. Importantly, we utilize a differentiable analysis-by-synthesis approach to compare with captured x-ray sinogram data in a self-supervised fashion. Thus, our resulting optimization method requires no training data to reconstruct the scene. We demonstrate that our proposed system robustly reconstructs scenes containing deformable and periodic motion and validate against state-of-the-art baselines. Further, we demonstrate an ability to reconstruct continuous spatiotemporal representations of our scenes and upsample them to arbitrary volumes and frame rates post-optimization. This research opens a new avenue for implicit neural representations in computed tomography reconstruction in general.
Non-Stationary Wireless Channel Modeling Approach Based on Extreme Value Theory for Ultra-Reliable Communications
Authors: Niloofar Mehrnia, Sinem Coleri
Date: 2021-04-23 00:00:00

A proper channel modeling methodology that characterizes the statistics of extreme events is key in the design of a system at an ultra-reliable regime of operation. The strict constraint of ultra-reliability corresponds to the packet error rate (PER) in the range of $10^{-9}-10^{-5}$ within the acceptable latency on the order of milliseconds. Extreme value theory (EVT) is a robust framework for modeling the statistical behavior of extreme events in the channel data. In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation. This methodology includes techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the generalized Pareto distribution (GPD). The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Finally, the proposed approach chooses the best model with minimum complexity that represents the time variation behavior of the non-stationary channel data sequence. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate the capability of the proposed methodology in providing the best fit to the extremes of the non-stationary data, significantly outperforming the channel modeling approach in characterizing the extreme events with the stationary channel assumption.
Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges
Authors: Yuanhao Cui, Fan Liu, Xiaojun Jing, Junsheng Mu
Date: 2021-04-23 00:00:00
Tasks: ISAC

Recent advances in wireless communication and solid-state circuits together with the enormous demands of sensing ability have given rise to a new enabling technology, integrated sensing and communications (ISAC). The ISAC captures two main advantages over dedicated sensing and communication functionalities: 1) Integration gain to efficiently utilize congested resources, and even, 2) Coordination gain to balance dual-functional performance or/and perform mutual assistance. Meanwhile, triggered by ISAC, we are also witnessing a paradigm shift in the ubiquitous IoT architecture, in which the sensing and communication layers are tending to converge into a new layer, namely, the signaling layer. In this paper, we first attempt to introduce a definition of ISAC, analyze the various influencing forces, and present several novel use cases. Then, we complement the understanding of the signaling layer by presenting several key benefits in the IoT era. We classify existing dominant ISAC solutions based on the layers in which integration is applied. Finally, several challenges and opportunities are discussed. We hope that this overview article will serve as a primary starting point for new researchers and offer a bird's-eye view of the existing ISAC-related advances from academia and industry, ranging from solid-state circuitry, signal processing, and wireless communication to mobile computing.
Real-time NLOS/LOS Identification for Smartphone-based Indoor Positioning System using WiFi RTT and RSS
Authors: Yinhuan Dong, Tugrul Arslan
Date: 2021-04-22 00:00:00
Tasks: Time Series, Time Series Analysis

The accuracy of smartphone-based positioning methods using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several statistical features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time of getting enough samples is quite long. Therefore, this paper proposes a new real-time NLOS/LOS identification method for smartphone-based indoor positioning system using WiFi RTT and RSS. Based on our extensive analysis of RSS and RTT features, a machine learning-based method using random forest was chosen and developed to separate the samples for NLOS/LOS conditions. Experiments in different environments show that our method achieves a discrimination accuracy of about 94% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.
On the Optimal Power Allocation and User Pairing for Uplink Non-Orthogonal Multiple Access Networks
Authors: Mouktar Bello, Anrsenia Chorti, Inbar Fijalkow
Date: 2021-04-20 00:00:00
Tasks: 2k

In this paper, we derive the optimal user pairing and power allocation in uplink non orthogonal multiple access (NOMA) networks. The optimal power allocation that maximizes the sum rate is found for two user NOMA networks, while ensuring that the individual rates of NOMA users are at least equal to those they would achieve with orthogonal multiple access (OMA). Next, we prove that in a 2K user network and when the optimal power allocation is used, the optimal pairing reduces to pairing the user with index k to the user with index 2K-k+1, i.e., the user with the best channel condition and the one with the worst channel condition are paired together, the second best with the second worst and so on. Finally, the expressions of the corresponding optimal NOMA power coefficients are derived analytically for networks with more than two users. With these expressions at hand, our simulation results validate the superiority of NOMA over OMA in terms of sum rates.
Towards rational glyco-engineering in CHO: from data to predictive models
Authors: Jerneja Štor, David E. Ruckerbauer, Diana Széliova, Jürgen Zanghellini, Nicole Borth
Date: 2021-04-23 00:00:00
Tasks: parameter estimation

Metabolic modeling strives to develop modeling approaches that are robust and highly predictive. To achieve this, various modeling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments.
A primer on the oxDNA model of DNA: When to use it, how to simulate it and how to interpret the results
Authors: Aditya Sengar, Thomas E. Ouldridge, Oliver Henrich, Lorenzo Rovigatti, Petr Sulc
Date: 2021-04-23 00:00:00

The oxDNA model of DNA has been applied widely to systems in biology, biophysics and nanotechnology. It is currently available via two independent open source packages. Here we present a set of clearly-documented exemplar simulations that simultaneously provide both an introduction to simulating the model, and a review of the model's fundamental properties. We outline how simulation results can be interpreted in terms of -- and feed into our understanding of -- less detailed models that operate at larger length scales, and provide guidance on whether simulating a system with oxDNA is worthwhile.
Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review
Authors: Fan Zhang, Alessandro Daducci, Yong He, Simona Schiavi, Caio Seguin, Robert Smith, Chun-Hung Yeh, Tengda Zhao, Lauren J. O'Donnell
Date: 2021-04-23 00:00:00
Tasks: Diffusion  MRI

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
Pricing Asian Options with Correlators
Authors: Silvia Lavagnini
Date: 2021-04-23 00:00:00

We derive a series expansion by Hermite polynomials for the price of an arithmetic Asian option. This series requires the computation of moments and correlators of the underlying price process, but for a polynomial jump-diffusion, these are given in closed form, hence no numerical simulation is required to evaluate the series. This allows, for example, for the explicit computation of Greeks. The weight function defining the Hermite polynomials is a Gaussian density with scale $b$. We find that the rate of convergence for the series depends on $b$, for which we prove a lower bound to guarantee convergence. Numerical examples show that the series expansion is accurate but unstable for initial values of the underlying process far from zero, mainly due to rounding errors.
Dynamic investment portfolio optimization using a Multivariate Merton Model with Correlated Jump Risk
Authors: Bahareh Afhami, Mohsen Rezapour, Mohsen Madadi, Vahed Maroufy
Date: 2021-04-22 00:00:00
Tasks: Portfolio Optimization

In this paper, we are concerned with the optimization of a dynamic investment portfolio when the securities which follow a multivariate Merton model with dependent jumps are periodically invested and proceed by approximating the Condition-Value-at-Risk (CVaR) by comonotonic bounds and maximize the expected terminal wealth. Numerical studies as well as applications of our results to real datasets are also provided.
Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer
Authors: Deniz Kavi
Date: 2020-08-21 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Sentiment Analysis, text-classification, Text Classification

Text classification has seen an increased use in both academic and industry settings. Though rule based methods have been fairly successful, supervised machine learning has been shown to be most successful for most languages, where most research was done on English. In this article, the success of lexicon analysis, support vector machines, and extreme gradient boosting for the task of text classification and sentiment analysis are evaluated in Turkish and a pretrained transformer based classifier is proposed, outperforming previous methods for Turkish text classification. In the context of text classification, all machine learning models proposed in the article are domain-independent and do not require any task-specific modifications.
Distributed Collaborative 3D-Deployment of UAV Base Stations for On-Demand Coverage
Authors: Tatsuaki Kimura and Masaki Oguray
Date: 2020-09-06 00:00:00
Tasks: Density Estimation

Deployment of unmanned aerial vehicles (UAVs) performing as flying aerial base stations (BSs) has a great potential of adaptively serving ground users during temporary events, such as major disasters and massive events. However, planning an efficient, dynamic, and 3D deployment of UAVs in adaptation to dynamically and spatially varying ground users is a highly complicated problem due to the complexity in air-toground channels and interference among UAVs. In this paper, we propose a novel distributed 3D deployment method for UAVBSs in a downlink network for on-demand coverage. Our method consists mainly of the following two parts: sensing-aided crowd density estimation and distributed push-sum 
algorithm. The first part estimates the ground user density from its observation through on-ground sensors, thereby allowing us to avoid the computationally intensive process of obtaining the positions of all the ground users. On the basis of the estimated user density, in the second part, each UAV dynamically updates its 3D position in collaboration with its neighboring UAVs for maximizing the total coverage. We prove the convergence of our distributed algorithm by employing a distributed push-sum algorithm framework. Simulation results demonstrate that our method can improve the overall coverage with a limited number of ground sensors. We also demonstrate that our method can be applied to a dynamic network in which the density of ground users varies temporally.
心态决定成败！如何塑造强大的高考心态| 北大博士对话高考状元
Authors: 科学博士
Date: 2021-04-25 00:00:00

心态决定成败！如何塑造强大的高考心态| 北大博士对话高考状元
#高考心态​ #考试心态​ #北大博士对话高考状元​ #科学博士​ #心态​

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