pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Quantized Consensus of General Linear Multi-agent Systems under Denial-of-Service Attacks
Authors: Shuai Feng, Hideaki Ishii
Date: 2020-04-28 00:00:00
Tasks: Quantization

In this paper, we study multi-agent consensus problems under Denial-of-Service (DoS) attacks with data rate constraints. We first consider the leaderless consensus problem and after that we briefly present the analysis of leader-follower consensus. The dynamics of the agents take general forms modeled as homogeneous linear time-invariant systems. In our analysis, we derive lower bounds on the data rate for the multi-agent systems to achieve leaderless and leader-follower consensus in the presence of DoS attacks, under which the issue of overflow of quantizer is prevented. The main contribution of the paper is the characterization of the trade-off between the tolerable DoS attack levels for leaderless and leader-follower consensus and the required data rates for the quantizers during the communication attempts among the agents. To mitigate the influence of DoS attacks, we employ dynamic quantization with zooming-in and zooming-out capabilities for avoiding quantizer saturation.
US Equity Risk Premiums during the COVID-19 Pandemic
Authors: Alan L. Lewis
Date: 2020-04-28 00:00:00

We study equity risk premiums in the United States during the COVID-19 pandemic.
Automated Physics-Derived Code Generation for Sensor Fusion and State Estimation
Date: 2020-04-28 00:00:00
Tasks: C++ code, Code Generation, Sensor Fusion, State Estimation

We present a new method for automatically generating the implementation of
state-estimation algorithms from a machine-readable specification of the
physics of a sensing system and physics of its signals and signal constraints.
We implement the new state-estimator code generation method as a backend for a
physics specification language and we apply the backend to generate complete C
code implementations of state estimators for both linear systems (Kalman
filters) and non-linear systems (extended Kalman filters). The state estimator
code generation from physics specification is completely automated and requires
no manual intervention. The generated filters can incorporate an Automatic
Differentiation technique which combines function evaluation and
differentiation in a single process. Using the description of physical system
of a range of complexities, we generate extended Kalman filters, which we
evaluate in terms of prediction accuracy using simulation traces. The results
show that our automatically-generated sensor fusion and state estimation
implementations provide state estimation within the same error bound as the
human-written hand-optimized counterparts. We additionally quantify the code
size and dynamic instruction count requirements of the generated state
estimator implementations on the RISC-V architecture. The results show that our
synthesized state estimation implementation employing Automatic Differentiation
leads to an average improvement in the dynamic instruction count of the
generated Kalman filter of 7%-16% compared to the standard differentiation
technique. This is improvement comes at the limited cost of an average 4.5%
increase in the code size of the generated filters.
End-to-end NILM System Using High Frequency Data and Neural Networks
Date: 2020-04-29 00:00:00
Tasks: Non-Intrusive Load Monitoring, Time Series, Time Series Analysis

Improving energy efficiency is a necessity in the fight against climate
change. Non Intrusive Load Monitoring (NILM) systems give important information
about the household consumption that can be used by the electric utility or the
end users. In this work the implementation of an end-to-end NILM system is
presented, which comprises a custom high frequency meter and neural-network
based algorithms. The present article presents a novel way to include high
frequency information as input of neural network models by means of
multivariate time series that include carefully selected features. Furthermore,
it provides a detailed assessment of the generalization error and shows that
this class of models generalize well to new instances of seen-in-training
appliances. An evaluation database formed of measurements in two Uruguayan
homes is collected and discussion on general unsupervised approaches is
provided.
Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description
Authors: Anuraag Singh, Giorgio Triulzi, Christopher L. Magee
Date: 2020-04-29 00:00:00
Tasks: All, Management

In this work, we attempt to provide a comprehensive granular account of the pace of technological change. More specifically, we survey estimated yearly performance improvement rates for nearly all definable technologies for the first time. We do this by creating a correspondence of all patents within the US patent system to a set of technology domains. A technology domain is a body of patented inventions achieving the same technological function using the same knowledge and scientific principles. We obtain a set of 1757 domains using an extension of the previously defined classification overlap method (COM). These domains contain 97.14% of all patents within the entire US patent system. From the identified patent sets, we calculated the average centrality of the patents in each domain to estimate their improvement rates, following a methodology tested in prior work. The estimated improvement rates vary from a low of 1.9% per year for the Mechanical Skin treatment - Hair Removal and wrinkles domain to a high of 228.8% per year for the Network management - client-server applications domain. We developed a one-line descriptor identifying the technological function achieved and the underlying knowledge base for the largest 50, fastest 20 as well as slowest 20 of these domains, which cover more than forty percent of the patent system. In general, the rates of improvement were not a strong function of the patent set size and the fastest improving domains are predominantly software-based. We make available an online system that allows for automated searching for domains and improvement rates corresponding to any technology of interest to researchers, strategists and policy formulators.
Deep Learning for High Speed Optical Coherence Elastography
Date: 2020-04-29 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

Mechanical properties of tissue provide valuable information for identifying
lesions. One approach to obtain quantitative estimates of elastic properties is
shear wave elastography with optical coherence elastography (OCE). However,
given the shear wave velocity, it is still difficult to estimate elastic
properties. Hence, we propose deep learning to directly predict elastic tissue
properties from OCE data. We acquire 2D images with a frame rate of 30 kHz and
use convolutional neural networks to predict gelatin concentration, which we
use as a surrogate for tissue elasticity. We compare our deep learning approach
to predictions from conventional regression models, using the shear wave
velocity as a feature. Mean absolut prediction errors for the conventional
approaches range from 1.32$\pm$0.98 p.p. to 1.57$\pm$1.30 p.p. whereas we
report an error of 0.90$\pm$0.84 p.p for the convolutional neural network with
3D spatio-temporal input. Our results indicate that deep learning on
spatio-temporal data outperforms elastography based on explicit shear wave
velocity estimation.
Determined BSS based on time-frequency masking and its application to harmonic vector analysis
Authors: Kohei Yatabe, Daichi Kitamura
Date: 2020-04-29 00:00:00
Tasks: blind source separation

This paper proposes harmonic vector analysis (HVA) based on a general algorithmic framework of audio blind source separation (BSS) that is also presented in this paper. BSS for a convolutive audio mixture is usually performed by multichannel linear filtering when the numbers of microphones and sources are equal (determined situation). This paper addresses such determined BSS based on batch processing. To estimate the demixing filters, effective modeling of the source signals is important. One successful example is independent vector analysis (IVA) that models the signals via co-occurrence among the frequency components in each source. To give more freedom to the source modeling, a general framework of determined BSS is presented in this paper. It is based on the plug-and-play scheme using a primal-dual splitting algorithm and enables us to model the source signals implicitly through a time-frequency mask. By using the proposed framework, determined BSS algorithms can be developed by designing masks that enhance the source signals. As an example of its application, we propose HVA by defining a time-frequency mask that enhances the harmonic structure of audio signals via sparsity of cepstrum. The experiments showed that HVA outperforms IVA and independent low-rank matrix analysis (ILRMA) for both speech and music signals. A MATLAB code is provided along with the paper for a reference ( https://doi.org/10.24433/CO.9507820.v1 ).
Decoding SARS-CoV-2 transmission, evolution and ramification on COVID-19 diagnosis, vaccine, and medicine
Authors: Rui Wang, Yuta Hozumi, Changchuan Yin, Guo-Wei Wei
Date: 2020-04-29 00:00:00
Tasks: COVID-19 Diagnosis, Diagnostic

Tremendous effort has been given to the development of diagnostic tests, preventive vaccines, and therapeutic medicines for coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Much of this development has been based on the reference genome collected on January 5, 2020. Based on the genotyping of 6156 genome samples collected up to April 24, 2020, we report that SARS-CoV-2 has had 4459 alarmingly mutations which can be clustered into five subtypes. We introduce mutation ratio and mutation $h$-index to characterize the protein conservativeness and unveil that SARS-CoV-2 envelope protein, main protease, and endoribonuclease protein are relatively conservative, while SARS-CoV-2 nucleocapsid protein, spike protein, and papain-like protease are relatively non-conservative. In particular, the nucleocapsid protein has more than half its genes changed in the past few months, signaling devastating impacts on the ongoing development of COVID-19 diagnosis, vaccines, and drugs.
Supervised MPC control of large-scale electricity networks via clustering methods
Date: 2020-04-29 00:00:00
Tasks: Clustering, Distributed Optimization

This paper describes a control approach for large-scale electricity networks,
with the goal of efficiently coordinating distributed generators to balance
unexpected load variations with respect to nominal forecasts. To mitigate the
difficulties due to the size of the problem, the proposed methodology is
divided in two steps. First, the network is partitioned into clusters, composed
of several dispatchable and non dispatchable generators, storage systems, and
loads. A clustering algorithm is designed with the aim of obtaining clusters
with the following characteristics: (i) they must be compact, keeping the
distance between generators and loads as small as possible; (ii) they must be
able to internally balance load variations to the maximum possible extent. Once
the network clustering has been completed, a two layer control system is
designed. At the lower layer, a local Model Predictive Controller is associated
to each cluster for managing the available generation and storage elements to
compensate local load variations. If the local sources are not sufficient to
balance the cluster's load variations, a power request is sent to the
supervisory layer, which optimally distributes additional resources available
from the other clusters of the network. To enhance the scalability of the
approach, the supervisor is implemented relying on a fully distributed
optimization algorithm. The IEEE 118-bus system is used to test the proposed
design procedure in a non trivial scenario.
Influence of dispersive element on phase noise suppression in Talbot effect based optical upconversion scheme
Date: 2020-04-29 00:00:00

The temporal Talbot effect supports the generation of RF signals with high purity in optical domain. This allows for example a remote RF generation without the need for costly high-end electronic circuits and resource sharing. However, one of the most interesting features of the approach is its inherent ability to suppress phase noise during the carrier generation process. Besides the comb laser source, the dispersive element is the key component in this upconversion scheme. Ideally, it provides the right amount of dispersion over the whole spectral range of the comb. Broader combs are expected to allow a higher degree of phase noise suppression. The simulation of phase noise of an RF tone generated by an optical scheme is challenging in terms of computational and memory effort. Therefore, a simulation tool taking advantage of the properties of the comb and the Talbot effect had to be developed. In this paper, the dependency of the phase noise suppression on the dispersion characteristic is investigated yielding an important input to the design of these elements. First, the implemented simulation tool and the simulation parameters ensuring a correct estimation of the phase noise before and after dispersion influence are introduced. Then, the effect of different dispersion characteristics on the phase noise behavior is analyzed. It could be shown and explained how the phase noise at different offset frequencies to the carrier is affected differently depending on the dispersion characteristic and also the comb width. Design rules for the required comb width as well as the acceptable variation of the produced dispersion from the ideal value depending on the demands for the phase noise improvements could be developed and will be presented.
A machine learning approach to portfolio pricing and risk management for high-dimensional problems
Authors: Lucio Fernandez-Arjona, Damir Filipović
Date: 2020-04-29 00:00:00
Tasks: Management

We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces. We show results based on polynomial and neural network bases. Both offer superior results to naive Monte Carlo methods and other existing methods like least-squares Monte Carlo and replicating portfolios.
Interstitial cells and neurons respond to variations in hydration
Authors: Maria P McGee, Michael Morykwas, Mary Kerns, Anirudh Vashisht, Ashok N Hegde, Louis Argenta
Date: 2020-04-27 00:00:00

Dehydration and brain interstitial fluid alterations associate to cognitive dysfunction. We now explore whether changes in matrix hydration are a possible common signal for modulation of water-transfer rates and neuron function.
CAR T cells for T-cell leukemias: Insights from mathematical models
Authors: Víctor M. Pérez-García, O. León-Triana, M. Rosa, A. Pérez-Martínez
Date: 2020-04-26 00:00:00

Immunotherapy has the potential to change the way all cancer types are treated and cured. Cancer immunotherapies use elements of the patient immune system to attack tumor cells. One of the most successful types of immunotherapy is CAR-T cells. This treatment works by extracting patient's T-cells and adding to them an antigen receptor allowing tumor cells to be recognized and targeted. These new cells are called CAR-T cells and are re-infused back into the patient after expansion in-vitro. This approach has been successfully used to treat B-cell malignancies (B-cell leukemias and lymphomas). However, its application to the treatment of T-cell leukemias faces several problems. One of these is fratricide, since the CAR-T cells target both tumor and other CAR-T cells. This leads to nonlinear dynamical phenomena amenable to mathematical modeling. In this paper we construct a mathematical model describing the competition of CAR-T, tumor and normal T-cells and studied some basic properties of the model and its practical implications. Specifically, we found that the model reproduced the observed difficulties for in-vitro expansion of the therapeutic cells found in the laboratory. The mathematical model predicted that CAR-T cell expansion in the patient would be possible due to the initial presence of a large number of targets. We also show that, in the context of our mathematical approach, CAR-T cells could control tumor growth but not eradicate the disease.
Getting Beyond 5G: A Feasibility Study for 6G Using On-Chip Wireless THz Systems
Date: 2020-04-20 00:00:00

A proposed Line of Sight and Non Line of Sight model for Terahertz
Communication with achievable data rates of upto 100GBps using Photonic
Topological Insulator.
Therapeutic Management of Sacro-coccygeal compression in a Green Iguana (Iguana iguana)
Authors: Senthilkumar. K, Ruchika Lakshmanan, Mohamed Shafiuazama, Arun Prasad. A
Date: 2020-04-11 00:00:00
Tasks: Management

In reptiles, especially during their early stage of life when reared under captive conditions, nutritional deficiencies are the most common problems reported in them affecting the spinal cord and the musculoskeletal system. More often green iguanas and snakes are very much vulnerable to it. Case description: A three and a half months old green iguana (Iguana iguana) was brought to the Avian and Exotic Pet Unit, Madras Veterinary College with the history of dragging hind limbs and not passed faeces for the past four days. Findings, treatment and outcome: On examination of hind limbs, it was found that perching reflex was absent and the abdomen was distended. It was tentatively diagnosed for hind limb paralysis but the radiological examination revealed that the iguana was suffering from Sacro-coccygeal compression. The condition could have been aggravated due to poor nutritional management also. In order to reduce the pain, the iguana was treated with Meloxicam orally @ 0.1 mg/kg for a week. In addition, it was successfully treated orally with multivitamin syrup and calcium syrup simultaneously for a month. The iguana was subjected to infrared therapy of 3 minutes for a period of one week. The reptile showed complete recovery without dragging its hind limb after 2 months. Conclusion: It could be concluded that prompt intervention and dietary management may be necessary for musculo-skeletal disorders in Iguana. .
The energy of muscle contraction. II. Transverse compression and work
Authors: D. S. Ryan, S. Domínguez, S. A. Ross, N. Nigam, J. M. Wakeling
Date: 2020-04-28 00:00:00

In this study we reproduced this compression-induced reduction in muscle force through the use of a three-dimensional finite element model of contracting muscle. The model used the principle of minimum total energy and allowed for the redistribution of energy through different strain energy-densities; this allowed us to determine the importance of the strain energy-densities to the transverse forces developed by the muscle. Furthermore, we were able to study how external work done on the muscle by transverse compression affects the internal work and strain-energy distribution of the muscle.
A New Physically Triggered Cell Death via Transbarrier Contactless Cold Atmospheric Plasma Treatment of Cancer Cells
Authors: Dayun Yan, Qihui Wang, Manish Adhikari, Alisa Malyavko, Li Lin, Denis B. Zolotukhin, Xiaoliang Yao, Megan Kirschner, Jonathan H. Sherman, Michael Keidar
Date: 2020-03-30 00:00:00

For years, extensive efforts have been made to discover effective, non-invasive anti-cancer therapies. Cold atmospheric plasma (CAP), is a near room temperature ionized gas composed of reactive species, charged particles, neutral particles, and electrons. CAP also has several physical factors including thermal radiation, ultraviolet (UV) radiation, and electromagnetic (EM) waves. Most of the previously reported biological effects of CAP have relied on direct contact between bulk plasma and cells, resulting in the chemical effects generally seen after CAP treatment. In this paper, we demonstrate that the electromagnetic emission produced by CAP can lead to the death of B16F10 melanoma cancer cells via a transbarrier contactless method. When compared with the effect of reactive species, the effect of the physical factors causes much greater growth inhibition. The physical-triggered growth inhibition is due to a new type of cell death, characterized by rapid leakage of bulk water from the cells, resulting in bubbles on the cell membrane, and cytoplasm shrinkage. The results of this study introduce a new possible mechanism of CAP induced cancer cell death and build a foundation for CAP to be used as a non-invasive cancer treatment in the future.
Representation of Uncertainty in Electric Energy Market Models: Pricing Implication and Formulation
Date: 2020-04-29 00:00:00
Tasks: Management, Scheduling

Modern market management systems continue to evolve due to the intentions to
improve system security and reliability. This evolvement has been leading to a
transition of market auction models from a deterministic structure with
approximations on the reliability criteria (e.g., acquirement of contingency
reserve through proxy reserve policies) to explicit representation of
contingencies (e.g., estimation of post-contingency states via participation
factors and stochastic programming). This paper proposes a comprehensive
framework to establish various procedures for evaluating: (i) transparency and
incentive compatibility of different contingency modeling approaches, and (ii)
efficiency of two possible stochastic market designs. First, the concept of
securitized LMP is presented to solve the issue of how market participants
should be compensated for providing N-1 reliability services. Then, pricing
implications and settlements of three market models are compared: (i) a
deterministic market model with proxy serve policies, (ii) state-of-the-art
market models with estimated post-contingency states, and (iii) a two-stage
stochastic market model. Second, this paper evaluates two stochastic market
models while accounting for potential adjustments from day-ahead scheduling to
real-time operation: (i) minimizing expected operating cost of all N-1
scenarios, and (ii) minimizing the base-case (or no contingency) cost. These
analyses are conducted on IEEE 118-bus test system.