pwc-archive/papers-with-abstracts huggingface dataset:
Herd Behavior in Crypto Asset Market and Effect of Financial Information on Herd Behavior
Authors: Üzeyir Aydin, Büşra Ağan, Ömer Aydin
Date: 2021-04-01 00:00:00

The initial purpose of the study is to search whether the market exhibits herd behaviour or not by examining the crypto-asset market in the context of behavioural finance. And the second purpose of the study is to measure whether the financial information stimulates the herd behaviour or not. Within this frame, the announcements of the Federal Open Market Committee (FOMC), Governing Council of European Central Bank (ECB) and Policy Board of Bank of Japan (BOJ) for interest change, and S&P 500, Nikkei 225, FTSE 100 and GOLD SPOT indices data were used. In the study, the analyses were made over 100 cryptocurrencies with the highest trading volume by the use of the 2014:5 - 2019:12 period. For the analysis, the Markov Switching approach, as well as loads of empiric models developed by Chang et al. (2000), were used. According to the results obtained, the presence of herd behaviour in the crypto-asset market was determined in the relevant period. But it was found that interest rate announcements and stock exchange performances had no effect on herd behaviour.
On the Generation of Long Binary Sequences with Record-Breaking PSL Values
Authors: Miroslav Dimitrov, Tsonka Baitcheva, Nikolay Nikolov
Date: 2021-04-02 00:00:00

Binary sequences are widely used in various practical fields, such as telecommunications, radar technology, navigation, cryptography, measurement sciences, biology or industry. In this paper, a method to generate long binary sequences (LBS) with low peak sidelobe level (PSL) value is proposed. Having an LBS with length $n$, both the time and memory complexities of the proposed algorithm are $\mathcal{O}(n)$. During our experiments, we repeatedly reach better PSL values than the currently known state of art constructions, such as Legendre sequences, with or without rotations, Rudin-Shapiro sequences or m-sequences, with or without rotations, by always reaching a record-breaking PSL values strictly less than $\sqrt{n}$. Furthermore, the efficiency and simplicity of the proposed method are particularly beneficial to the lightweightness of the implementation, which allowed us to reach record-breaking PSL values for less than a second.
An Algorithm for Sensor Data Uncertainty Quantification
Authors: James T. Meech, Phillip Stanley-Marbell
Date: 2020-12-27 00:00:00
Tasks: Uncertainty Quantification

This article presents an algorithm for reducing measurement uncertainty of one physical quantity when given oversampled measurements of two physical quantities with correlated noise. The algorithm assumes that the aleatoric measurement uncertainty in both physical quantities follows a Gaussian distribution and relies on sampling faster than it is possible for the measurand (the true value of the physical quantity that we are trying to measure) to change (due to the system thermal time constant) to calculate the parameters of the noise distribution. In contrast to the Kalman and particle filters, which respectively require state update equations and a map of one physical quality, our algorithm requires only the oversampled sensor measurements. When applied to temperature-compensated humidity sensors, it provides reduced uncertainty in humidity estimates from correlated temperature and humidity measurements. In an experimental evaluation, the algorithm achieves average uncertainty reduction of 10.3 %. The algorithm incurs an execution time overhead of 5.3 % when compared to the minimum algorithm required to measure and calculate the uncertainty. Detailed instruction-level emulation of a C-language implementation compiled to the RISC-V architecture shows that the uncertainty reduction program required 0.05 % more instructions per iteration than the minimum operations required to calculate the uncertainty.
Channel Estimation for MIMO Space Time Coded OTFS under Doubly Selective Channels
Authors: Roberto Bomfin, Marwa Chafii, Ahmad Nimr, Gerhard Fettweis
Date: 2021-04-02 00:00:00
Tasks: Diversity

In this paper, we present a unique word (UW)-based channel estimation approach for multiple-input multiple-output (MIMO) systems under doubly dispersive channels, which is applied to orthogonal time frequency space (OTFS) with space time coding (STC). The OTFS modulation has been recently proposed as a robust technique under time varying channels due to its property of spreading the data symbols over time and frequency. Yet another relevant aspect is the employment of multiple antennas at the transmitter and receiver. Therefore, we consider an STC MIMO system with cyclic delay diversity at the transmitter and maximum ratio combining at the receiver, where we develop a UW-based channel estimation scheme for multiple transmit antennas. We show a recently proposed frame optimization scheme for SISO is directly applicable to MIMO. In addition, we evaluate numerically the frame error rate (FER) of OTFS and OFDM with 2x2 and 4x4 MIMO, where the time varying channel is estimated using the UW-based approach. The FER results reveal that OTFS becomes more advantageous than OFDM for MIMO-STC systems with higher order modulation and code rate.
Synchronized SCUBA: D2D Communication for Out-of-Sync Devices
Authors: Vishnu Rajendran Chandrika, Gautham Prasad, Lutz Lampe, Gus Vos
Date: 2021-04-01 00:00:00

Device-to-device (D2D) communication is an essential component enabling connectivity for the Internet-of-Things (IoT). SCUBA, which stands for Sidelink Communication on Unlicensed Bands, is a novel medium access control protocol that facilitates D2D communications on the sidelink for IoT and machine-type communication (MTC) cellular devices. SCUBA includes support for direct peer-to-peer communication on the unlicensed bands by operating in a time division multiplexed manner to coexist with the underlying primary radio access technology, e.g., long term evolution - MTC (LTE-M). A fundamental requirement in the current version of SCUBA is that the communicating devices are to be synchronized with each other so that timing occasions of the devices can be accurately estimated by each other. However, when the devices are out of sync with each other, which may be caused due to one or more of the devices being out of cellular coverage region or are being served by different base stations, typically observed in mobile devices, operation of legacy SCUBA fails. To this end, we design synchronization methods to establish successful SCUBA links between devices that are out-of-sync with each other. Due to the inherent timing discovery embedded in our method, our solution also extends the operating range of SCUBA by eliminating its reliance on timing-agnostic communication. We analyze and compare the performance of our methods in terms of power consumption and the resultant impact on device battery life to show the potential of our solutions.
How do we generalize?
Authors: Jessica Elizabeth Taylor, Aurelio Cortese, Helen C. Barron, Xiaochuan Pan, Masamichi Sakagami, Dagmar Zeithamova
Date: 2021-04-02 00:00:00
Tasks: valid

Humans and animals are able to generalize or transfer information from previous experience so that they can behave appropriately in novel situations. What mechanisms--computations, representations, and neural systems--give rise to this remarkable ability? The members of this Generative Adversarial Collaboration (GAC) come from a range of academic backgrounds but are all interested in uncovering the mechanisms of generalization. We started out this GAC with the aim of arbitrating between two alternative conceptual accounts: (1) generalization stems from integration of multiple experiences into summary representations that reflect generalized knowledge, and (2) generalization is computed on-the-fly using separately stored individual memories. Across the course of this collaboration, we found that--despite using different terminology and techniques, and although some of our specific papers may provide evidence one way or the other--we in fact largely agree that both of these broad accounts (as well as several others) are likely valid. We believe that future research and theoretical synthesis across multiple lines of research is necessary to help determine the degree to which different candidate generalization mechanisms may operate simultaneously, operate on different scales, or be employed under distinct conditions. Here, as the first step, we introduce some of these candidate mechanisms and we discuss the issues currently hindering better synthesis of generalization research. Finally, we introduce some of our own research questions that have arisen over the course of this GAC, that we believe would benefit from future collaborative efforts.
Defining the lead time of wastewater-based epidemiology for COVID-19
Authors: Scott W. Olesen, Maxim Imakaev, Claire Duvallet
Date: 2021-04-01 00:00:00
Tasks: Epidemiology

Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may shed the virus in stool before developing symptoms, suggesting that measurements of SARS-CoV-2 concentrations in wastewater could be a "leading indicator" of COVID-19 prevalence. Multiple studies have corroborated the leading indicator concept by showing that the correlation between wastewater measurements and COVID-19 case counts is maximized when case counts are lagged. However, the meaning of "leading indicator" will depend on the specific application of wastewater-based epidemiology, and the correlation analysis is not relevant for all applications. In fact, the quantification of a leading indicator will depend on epidemiological, biological, and health systems factors. Thus, there is no single "lead time" for wastewater-based COVID-19 monitoring. To illustrate this complexity, we enumerate three different applications of wastewater-based epidemiology for COVID-19: a qualitative "early warning" system; an independent, quantitative estimate of disease prevalence; and a quantitative alert of bursts of disease incidence. The leading indicator concept has different definitions and utility in each application.
Diverse ATPase proteins in mobilomes constitute a large potential sink for prokaryotic host ATP
Authors: Hyunjin Shim, Haridha Shivram, Shufei Lei, Jennifer A. Doudna, Jillian F. Banfield
Date: 2021-04-02 00:00:00

Prokaryote mobilome genomes rely on host machineries for survival and replication. Given that mobile genetic elements (MGEs) derive their energy from host cells, we investigated the diversity of ATP-utilizing proteins in MGE genomes to determine whether they might be associated with proteins that could suppress related host proteins that consume host energy. A comprehensive search of 353 huge phage genomes revealed that up to 9% of the proteins have ATPase domains. For example, ATPase proteins constitute ~3% of the genomes of Lak phages with ~550 kbp genomes that occur in the microbiomes of humans and other animals. Statistical analysis shows the number of ATPase proteins increases linearly with genome length, consistent with a large sink for host ATP during replication of megaphages. Using metagenomic data from diverse environments, we found 505 mobilome proteins with ATPase domains fused to diverse functional domains. Among these composite ATPase proteins, 61.6% have known functional domains that could contribute to host energy diversion during the mobilome life cycle. As many have domains that are known to interact with nucleic acids and proteins, we infer that numerous ATPase proteins are used during replication and for protection from host immune systems. We found a set of uncharacterized ATPase proteins with nuclease and protease activities, displaying unique domain architectures that are energy intensive based on the presence of multiple ATPase domains. In many cases, these composite ATPase proteins genomically co-localize with small proteins in genomic contexts that are reminiscent of toxin-antitoxin systems. Small proteins that function as inhibitors may be a common strategy for control of cellular processes, thus could inspire the development of new nucleic acid and protein manipulation tools, with diverse biotechnological applications.
Back to the roots: A discrete Kermack-McKendrick model adapted to Covid-19
Authors: Matthias Kreck, Erhard Scholz
Date: 2021-04-01 00:00:00

A widely used tool for analysing the Covid-19 pandemic is the standard SIR model. It seems often to be used as a black box, not taking into account that this model was derived as a special case of the seminal Kermack-McKendrick theory from 1927. This is our starting point. We explain the setup of the Kermack-McKendrick theory (passing to a discrete approach) and use medical information for specializing to a model which we call {\em adapted K-McK-model}. This includes effects of vaccination, mass testing and mutants. We demonstrate the use of the model by applying it to the development in Germany. As a striking application we demonstrate that a comparatively mild intervention reducing the time until quarantine by one day leads to a drastic improvement. A similar effect can be obtained by certain mass testings as we will demonstrate. We discuss possibilities to apply the model both for predictions and as an analysis tool. We compare the adapted K-McK-model to the standard SIR model and observe condiderable differences if the contact rates are not constant. Finally we compare the model reproduction rate with the empirical reproduction rate determined by the Robert Koch-Institut.
Impact of climate change on West Nile virus distribution in South America
Authors: Camila Lorenz, Thiago Salomao de Azevedo, Francisco Chiaravalloti-Neto
Date: 2021-04-01 00:00:00
Tasks: Epidemiology

West Nile virus (WNV) is a vector-borne pathogen of global relevance and is currently the most widely distributed flavivirus of encephalitis worldwide. This virus infects birds, humans, horses, and other mammals, and its transmission cycle occurs in urban and rural areas. Climate conditions have direct and indirect impacts on vector abundance and virus dynamics within the mosquito. The significance of environmental variables as drivers in WNV epidemiology is increasing under the current climate change scenario. In this study, we used a machine learning algorithm to model WNV distributions in South America. Our model evaluated eight environmental variables (type of biome, annual temperature, seasonality of temperature, daytime temperature variation, thermal amplitude, seasonality of precipitation, annual rainfall, and elevation) for their contribution to the occurrence of WNV since its introduction in South America (2004). Our results showed that environmental variables can directly alter the occurrence of WNV, with lower precipitation and higher temperatures associated with increased virus incidence. High-risk areas may be modified in the coming years, becoming more evident with high greenhouse gas emission levels. Countries such as Bolivia and Paraguay will be greatly affected, drastically changing their current WNV distribution. Several Brazilian areas will also increase the likelihood of presenting WNV, mainly in the Northeast and Midwest regions and the Pantanal biome. The Galapagos Islands will also probably increase their geographic range suitable for WNV occurrence. It is necessary to develop preventive policies to minimize potential WNV infection in humans and enhance active epidemiological surveillance in birds, humans, and other mammals before it becomes a more significant public health problem in South America.
Direct laser writing for cardiac tissue engineering: a microfluidic heart on a chip with integrated transducers
Authors: Rachael K. Jayne, M. Çağatay Karakan, Kehan Zhang, Noelle Pierce, Christos Michas, David J. Bishop, Christopher S. Chen, Kamil L. Ekinci, Alice E. White
Date: 2021-04-02 00:00:00

We have designed and fabricated a microfluidic-based platform for sensing mechanical forces generated by cardiac microtissues in a highly-controlled microenvironment. Our fabrication approach combines Direct Laser Writing (DLW) lithography with soft lithography. At the center of our platform is a cylindrical volume, divided into two chambers by a cylindrical polydimethylsiloxane (PDMS) shell. Cells are seeded into the inner chamber from a top opening, and the microtissue assembles onto tailor-made attachment sites on the inner walls of the cylindrical shell. The outer chamber is electrically and fluidically isolated from the inner one by the cylindrical shell and is designed for actuation and sensing purposes. Externally applied pressure waves to the outer chamber deform parts of the cylindrical shell and thus allow us to exert time-dependent forces on the microtissue. Oscillatory forces generated by the microtissue similarly deform the cylindrical shell and change the volume of the outer chamber, resulting in measurable electrical conductance changes. We have used this platform to study the response of cardiac microtissues derived from human induced pluripotent stem cells (hiPSC) under prescribed mechanical loading and pacing.
Influence of risk tolerance on long-term investments: A Malliavin calculus approach
Authors: Hyungbin Park
Date: 2021-04-02 00:00:00

This study investigates the influence of risk tolerance on the expected utility in the long run. We estimate the extent to which the expected utility of optimal portfolios is affected by small changes in the risk tolerance. For this purpose, we adopt the Malliavin calculus method and the Hansen--Scheinkman decomposition, through which the expected utility is expressed in terms of the eigenvalues and eigenfunctions of an operator. We conclude that the influence of risk aversion on the expected utility is determined by these eigenvalues and eigenfunctions in the long run.
Perpetual callable American volatility options in a mean-reverting volatility model
Authors: Hsuan-Ku Liu
Date: 2021-04-02 00:00:00

This paper investigates problems associated with the valuation of callable American volatility put options. Our approach involves modeling volatility dynamics as a mean-reverting 3/2 volatility process. We first propose a pricing formula for the perpetual American knock-out put. Under the given conditions, the value of perpetual callable American volatility put options is discussed.
Geodesic B-Score for Improved Assessment of Knee Osteoarthritis
Authors: Felix Ambellan, Stefan Zachow, Christoph von Tycowicz
Date: 2021-03-12 00:00:00

Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions.
AI Fairness via Domain Adaptation
Authors: Neil Joshi, Phil Burlina
Date: 2021-03-15 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Fairness

While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployment, including AI privacy, domain generalization, and fairness. This last challenge is addressed in this study. Here we look at a novel method for ensuring AI fairness with respect to protected or sensitive factors. This method uses domain adaptation via training set enhancement to tackle bias-causing training data imbalance. More specifically, it uses generative models that allow the generation of more synthetic training samples for underrepresented populations. This paper applies this method to the use case of detection of age related macular degeneration (AMD). Our experiments show that starting with an originally biased AMD diagnostics model the method has the ability to improve fairness.
Group Collaborative Learning for Co-Salient Object Detection
Authors: Qi Fan, Deng-Ping Fan, Huazhu Fu, Chi Keung Tang, Ling Shao, Yu-Wing Tai
Date: 2021-03-15 00:00:00
Proceeding: CVPR 2021 1
Tasks: Co-Salient Object Detection, Object, object-detection, Object Detection, Salient Object Detection

We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus. To learn a better embedding space without extra computational overhead, we explicitly employ auxiliary classification supervision. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that our simple GCoNet outperforms 10 cutting-edge models and achieves the new state-of-the-art. We demonstrate this paper's new technical contributions on a number of important downstream computer vision applications including content aware co-segmentation, co-localization based automatic thumbnails, etc.
NAS-TC: Neural Architecture Search on Temporal Convolutions for Complex Action Recognition
Authors: Pengzhen Ren, Gang Xiao, Xiaojun Chang, Yun Xiao, Zhihui Li, Xiaojiang Chen
Date: 2021-03-17 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, GPU, Neural Architecture Search

In the field of complex action recognition in videos, the quality of the designed model plays a crucial role in the final performance. However, artificially designed network structures often rely heavily on the researchers' knowledge and experience. Accordingly, because of the automated design of its network structure, Neural architecture search (NAS) has achieved great success in the image processing field and attracted substantial research attention in recent years. Although some NAS methods have reduced the number of GPU search days required to single digits in the image field, directly using 3D convolution to extend NAS to the video field is still likely to produce a surge in computing volume. To address this challenge, we propose a new processing framework called Neural Architecture Search- Temporal Convolutional (NAS-TC). Our proposed framework is divided into two phases. In the first phase, the classical CNN network is used as the backbone network to complete the computationally intensive feature extraction task. In the second stage, a simple stitching search to the cell is used to complete the relatively lightweight long-range temporal-dependent information extraction. This ensures our method will have more reasonable parameter assignments and can handle minute-level videos. Finally, we conduct sufficient experiments on multiple benchmark datasets and obtain competitive recognition accuracy.