pwc-archive/papers-with-abstracts huggingface dataset:
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling
Authors: Wei Li, Denis Mike Becker
Date: 2021-01-13 00:00:00
Tasks: Feature Importance, feature selection, Prediction, Time Series, Time Series Analysis

The availability of accurate day-ahead electricity price forecasts is pivotal for electricity market participants. In the context of trade liberalisation and market harmonisation in the European markets, accurate price forecasting becomes difficult for electricity market participants to obtain because electricity forecasting requires the consideration of features from ever-growing coupling markets. This study provides a method of exploring the influence of market coupling on electricity price prediction. We apply state-of-the-art long short-term memory (LSTM) deep neural networks combined with feature selection algorithms for electricity price prediction under the consideration of market coupling. LSTM models have a good performance in handling nonlinear and complex problems and processing time series data. In our empirical study of the Nordic market, the proposed models obtain considerably accurate results. The results show that feature selection is essential to achieving accurate prediction, and features from integrated markets have an impact on prediction. The feature importance analysis implies that the German market has a salient role in the price generation of Nord Pool.
Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT
Authors: Simone Bendazzoli, Irene Brusini, Mehdi Astaraki, Mats Persson, Jimmy Yu, Bryan Connolly, Sven Nyrén, Fredrik Strand, Örjan Smedby, Chunliang Wang
Date: 2020-12-29 00:00:00
Tasks: Lesion Segmentation, Segmentation

Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent. However, manual segmentation can be very time consuming and subjective, given the lesions' large variation in shape, size and position. On the other hand, we still lack large manually segmented datasets that could be used for training machine learning-based models for fully automatic segmentation. In this work, we propose a new interactive and user-friendly tool for COVID-19 lesion segmentation, which works by alternating automatic steps (based on level-set segmentation and statistical shape modeling) with manual correction steps. The present software was tested by two different expertise groups: one group of three radiologists and one of three users with an engineering background. Promising segmentation results were obtained by both groups, which achieved satisfactory agreement both between- and within-group. Moreover, our interactive tool was shown to significantly speed up the lesion segmentation process, when compared to fully manual segmentation. Finally, we investigated inter-observer variability and how it is strongly influenced by several subjective factors, showing the importance for AI researchers and clinical doctors to be aware of the uncertainty in lesion segmentation results.
Membrane Trafficking of Integral Cell Junction Proteins and its Functional Consequences
Authors: Arie Horowitz
Date: 2021-01-13 00:00:00
Tasks: Specificity

Though membrane trafficking of cell junction proteins has been studied extensively for more than two decades, the accumulated knowledge remains fragmentary. The goal of this review is to synthesize published studies on the membrane trafficking of the five major junction transmembrane proteins: claudins, occludin, and junction adhesion molecules (JAMs) in tight junctions; cadherins and nectins in adherens junctions; to identify underlying common mechanisms; to highlight their functional consequences on barrier function; and to identify knowledge gaps. Clathrin-mediated endocytosis appears to be the main, but not exclusive, mode of internalization. Caveolin-mediated endocytosis and macropinocytosis are employed less frequently. PDZ-domain binding is the predominant mode of interaction between junction protein cytoplasmic tails and scaffold proteins. It is shared by claudins, the largest family of junction integral proteins, by junction adhesion molecules A, B, and C, and by the three nectins. All eight proteins are destined to either recycling via Rab4/Rab11 GTPases or to degradation. The sorting mechanisms that underlie the specificity of their endocytic pathways and determine their fates are not fully known. New data is presented to introduce an emerging role of junction-associated scaffold proteins in claudin membrane trafficking.
An analytical perturbative solution to the Merton Garman model using symmetries
Authors: Xavier Calmet, Nathaniel Wiesendanger Shaw
Date: 2019-09-03 00:00:00

In this paper, we introduce an analytical perturbative solution to the Merton Garman model. It is obtained by doing perturbation theory around the exact analytical solution of a model which possesses a two-dimensional Galilean symmetry. We compare our perturbative solution of the Merton Garman model to Monte Carlo simulations and find that our solutions performs surprisingly well for a wide range of parameters. We also show how to use symmetries to build option pricing models. Our results demonstrate that the concept of symmetry is important in mathematical finance.
Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network
Authors: Bank G. Fenyves, Gabor S. Szilagyi, Zsolt Vassy, Csaba Soti, Peter Csermely
Date: 2021-01-13 00:00:00

Graph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the ionotropic chemical synapse connectome of C. elegans (3,638 connections and 20,589 synapses total), incorporating available presynaptic neurotransmitter and postsynaptic receptor gene expression data for three major neurotransmitter systems. We made predictions for more than two-thirds of these chemical synapses and observed an excitatory-inhibitory (E:I) ratio close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.
High-resolution agent-based modeling of COVID-19 spreading in a small town
Authors: Agnieszka Truszkowska, Brandon Behring, Jalil Hasanyan, Lorenzo Zino, Sachit Butail, Emanuele Caroppo, Zhong-Ping Jiang, Alessandro Rizzo, Maurizio Porfiri
Date: 2021-01-13 00:00:00
Tasks: Decision Making

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY -- one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches -- in hospitals or drive-through facilities -- and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.
Use Energy Storage for Primary Frequency Control in Power Grids
Authors: Shutang You
Date: 2021-01-13 00:00:00

Frequency stability of power systems becomes more vulnerable with the increase of solar photovoltaic (PV). Energy storage provides an option to mitigate the impact of high PV penetration. Using the U.S. Eastern Interconnection (EI) and Texas Interconnection (ERCOT) power grid models, this paper investigates the capabilities of using energy storage to improve frequency response under high PV penetration. The study result helps to identify the potential and impact factors in utilizing energy storage to improve frequency response in high renewable penetration power grids.
On the Computational Properties of Obviously Strategy-Proof Mechanisms
Authors: Louis Golowich, Shengwu Li
Date: 2021-01-13 00:00:00

We present a polynomial-time algorithm that determines, given some choice rule, whether there exists an obviously strategy-proof mechanism for that choice rule.
Self-Synchronising On-Off-Keying Visible Light Communication System For Intra and Inter-Vehicle Data Transmission
Authors: Shamin Achari, Alice Yi Yang, James Goodhead, Brendon Swanepoel, Ling Cheng
Date: 2021-01-13 00:00:00

Visible Light Communication (VLC) is a current technology which allows data to be transmitted by modulating information onto a light source. It has many advantages over traditional radio frequency communication and up to 10,000 times larger bandwidth. Existing research in visible light communication assumes a synchronised channel, however, this is not always easily achieved. In this paper, a novel synchronised intra and inter-vehicle VLC system is proposed to ensure reliable communication in both inter and intra-vehicle communication for Infotainment Systems (IS). The protocol achieves synchronisation at the symbol level using the transistor-transistor logic protocol and achieves frame synchronisations with markers. Consequently, the deployment of the protocol in both inter and intra-vehicle communication presents numerous advantages over existing data transmission processes. A practical application, where VLC is used for media streaming is also previewed. In addition, various regions of possible data transmission are determined with the intention to infer forward error correction schemes to ensure reliable communication.
PID passivity-based droop control of power converters: Large-signal stability, robustness and performance
Authors: Daniele Zonetti, Gilbert Bergna-Diaz, Romeo Ortega, Nima Monshizadeh
Date: 2021-01-13 00:00:00

We present a full review of PID passivity-based controllers (PBC) applied to power electronic converters, discussing limitations, unprecedented merits and potential improvements in terms of large-signal stability, robustness and performance. We provide four main contributions. The nominal case is first considered and it is shown, under the assumption of perfect knowledge of the system parameters, that the PID-PBC is able to guarantee global exponential stability of a desired operating point for any positive gains. Second, we analyze robustness of the controller to parameters uncertainty for a specific class of power converters, by establishing precise stability margins. Third, we propose a modification of the controller by introducing a leakage, in order to overcome some of the intrinsic performance and robustness limitations. Interestingly, such controller can be interpreted at steady-state as a droop between the input and the passive output, similar to traditional primary controllers. Fourth, we robustify the design against saturation of the control input via an appropriate monotone transformation of the controller. The obtained results are thoroughly discussed and validated by simulations on two relevant power applications: a dc/dc boost converter and an HVDC grid-connected voltage source converter.
Analyzing and Mitigating the Impacts of GMD and EMP Events on the Electrical Grid with PowerModelsGMD.jl
Authors: Adam Mate, Arthur K. Barnes, Russell W. Bent, Eduardo Cotilla-Sanchez
Date: 2021-01-13 00:00:00

Geomagnetic disturbances and E3 high-altitude electromagnetic pulse events pose a substantial threat to the electrical grid by adversely impacting and damaging high-voltage transmission networks and equipment. To evaluate the risks and mitigate the potential effects of these hazards, this work proposes PowerModelsGMD.jl (abbr. PMsGMD). PMsGMD is an open-source Julia package that solves for quasi-dc line flow and ac power flow problems in a system subjected to geomagnetically induced currents. Unlike commercially available software solutions, it is extensible and applicable to a variety of problems. The flexibility of this framework is demonstrated by applying it to the problem of identifying mitigation strategies via line switching for a time-extended transformer heating problem. An overview of PMsGMD is presented in this paper: introduction to its design, validation of its implementation, demonstration of its performance and effectiveness, and a description of how it may be applied to aid system-operation decisions.
An integral sliding-mode parallel control approach for general nonlinear systems via piecewise affine linear models
Authors: ChunYang Zhang, Qing Gao, Yue Deng, Jianbin Qiu
Date: 2021-01-13 00:00:00

The fundamental problem of stabilizing a general nonaffine continuous-time nonlinear system is investigated via piecewise affine linear models (PALMs) in this article. A novel integral sliding-mode parallel control (ISMPC) approach is developed, where an uncertain piecewise affine system (PWA) is constructed to model a nonaffine continuous-time nonlinear system equivalently on a compact region containing the origin. A piecewise sliding-mode parallel controller is designed to globally stabilize the PALM and, consequently, to semiglobally stabilize the original nonlinear system. The proposed scheme enjoys three favorable features: (i) some restrictions on the system input channel are eliminated, thus the developed method is more relaxed compared with the published approaches; (ii) it is convenient to be used to deal with both matched and unmatched uncertainties of the system; and (iii) the proposed piecewise parallel controller generates smooth control signals even around the boundaries between different subspaces, which makes the developed control strategy more implementable and reliable. Moreover, we provide discussions about the universality analysis of the developed control strategy for two kinds of typical nonlinear systems. Simulation results from two numerical examples further demonstrate the performance of the developed control approach.
Instantaneous PSD Estimation for Speech Enhancement based on Generalized Principal Components
Date: 2020-07-01 00:00:00
Tasks: Speech Enhancement

Power spectral density (PSD) estimates of various microphone signal components are essential to many speech enhancement procedures. As speech is highly non-nonstationary, performance improvements may be gained by maintaining time-variations in PSD estimates. In this paper, we propose an instantaneous PSD estimation approach based on generalized principal components. Similarly to other eigenspace-based PSD estimation approaches, we rely on recursive averaging in order to obtain a microphone signal correlation matrix estimate to be decomposed. However, instead of estimating the PSDs directly from the temporally smooth generalized eigenvalues of this matrix, yielding temporally smooth PSD estimates, we propose to estimate the PSDs from newly defined instantaneous generalized eigenvalues, yielding instantaneous PSD estimates. The instantaneous generalized eigenvalues are defined from the generalized principal components, i.e. a generalized eigenvector-based transform of the microphone signals. We further show that the smooth generalized eigenvalues can be understood as a recursive average of the instantaneous generalized eigenvalues. Simulation results comparing the multi-channel Wiener filter (MWF) with smooth and instantaneous PSD estimates indicate better speech enhancement performance for the latter. A MATLAB implementation is available online.
Predictive learning via rule ensembles
Authors: Jerome H. Friedman, Bogdan E. Popescu
Date: 2008-11-11 00:00:00
Tasks: Interpretable Machine Learning

General regression and classification models are constructed as linear combinations of simple rules derived from the data. Each rule consists of a conjunction of a small number of simple statements concerning the values of individual input variables. These rule ensembles are shown to produce predictive accuracy comparable to the best methods. However, their principal advantage lies in interpretation. Because of its simple form, each rule is easy to understand, as is its influence on individual predictions, selected subsets of predictions, or globally over the entire space of joint input variable values. Similarly, the degree of relevance of the respective input variables can be assessed globally, locally in different regions of the input space, or at individual prediction points. Techniques are presented for automatically identifying those variables that are involved in interactions with other variables, the strength and degree of those interactions, as well as the identities of the other variables with which they interact. Graphical representations are used to visualize both main and interaction effects.
Secure Consensus with Distributed Detection via Two-hop Communication
Authors: Liwei Yuan, Hideaki Ishii
Date: 2021-01-13 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, we consider a multi-agent resilient consensus problem, where some of the nodes may behave maliciously. The approach is to equip all nodes with a scheme to detect neighboring nodes when they behave in an abnormal fashion. To this end, the nodes exchange not only their own states but also information regarding their neighbor nodes. Such two-hop communication has long been studied in fault-tolerant algorithms in computer science. We propose two distributed schemes for detection of malicious nodes and resilient consensus with different requirements on resources for communication and the structures of the networks. In particular, the detection schemes become effective under certain connectivity properties in the network so that the non-malicious nodes can share enough information about their neighbors. It is shown that the requirements are however less stringent than those for conventional algorithms. A numerical example is presented to demonstrate the performance of the proposed methods in wireless sensor networks.
Computer Architecture-Aware Optimisation of DNA Analysis Systems
Authors: Hasindu Gamaarachchi
Date: 2021-01-13 00:00:00
Tasks: DNA analysis

DNA sequencing is revolutionising the field of medicine. DNA sequencers, the machines which perform DNA sequencing, have evolved from the size of a fridge to that of a mobile phone over the last two decades. The cost of sequencing a human genome also has reduced from billions of dollars to hundreds of dollars. Despite these improvements, DNA sequencers output hundreds or thousands of gigabytes of data that must be analysed on computers to discover meaningful information with biological implications. Unfortunately, the analysis techniques have not kept the pace with rapidly improving sequencing technologies. Consequently, even today, the process of DNA analysis is performed on high-performance computers, just as it was a couple of decades ago. Such high-performance computers are not portable. Consequently, the full utility of an ultra-portable sequencer for sequencing in-the-field or at the point-of-care is limited by the lack of portable lightweight analytic techniques. This thesis proposes computer architecture-aware optimisation of DNA analysis software. DNA analysis software is inevitably convoluted due to the complexity associated with biological data. Modern computer architectures are also complex. Performing architecture-aware optimisations requires the synergistic use of knowledge from both domains, (i.e, DNA sequence analysis and computer architecture). This thesis aims to draw the two domains together. In this thesis, gold-standard DNA sequence analysis workflows are systematically examined for algorithmic components that cause performance bottlenecks. Identified bottlenecks are resolved through architecture-aware optimisations at different levels, i.e., memory, cache, register and processor. The optimised software tools are used in complete end-to-end analysis workflows and their efficacy is demonstrated by running on prototypical embedded systems.
Signatures of active and passive optimized Le´vy searching in jellyfish
Authors: Andy M. Reynolds
Date: 2014-10-06 00:00:00

Some of the strongest empirical support for Le´vy search theory has come from
telemetry data for the dive patterns of marine predators (sharks, bony fishes,
sea turtles and penguins). The dive patterns of the unusually large jellyfish
Rhizostoma octopus do, however, sit outside of current Le´vy search theory
which predicts that a single search strategy is optimal. When searching the
water column, the movement patterns of these jellyfish change over time.
Movement bouts can be approximated by a variety of Le´vy and Brownian
(exponential) walks. The adaptive value of this variation is not known.
On some occasions movement pattern data are consistent with the jellyfish
prospecting away from a preferred depth, not finding an improvement in con￾ditions elsewhere and so returning to their original depth. This ‘bounce’
behaviour also sits outside of current Le´vy walk search theory. Here, it is
shown that the jellyfish movement patterns are consistent with their using
optimized ‘fast simulated annealing’—a novel kind of Le´vy walk search pat￾tern—to locate the maximum prey concentration in the water column and/
or to locate the strongest of many olfactory trails emanating from more distant
prey. Fast simulated annealing is a powerful stochastic search algorithm
for locating a global maximum that is hidden among many poorer local
maxima in a large search space. This new finding shows that the notion of
active optimized Le´vy walk searching is not limited to the search for randomly
and sparsely distributed resources, as previously thought, but can be extended
to embrace other scenarios, including that of the jellyfish R. octopus. In the pres￾ence of convective currents,it could become energetically favourable to search
the water column by riding the convective currents. Here, it is shown that these
passive movements can be represented accurately by Le´vy walks of the type
occasionally seen in R. octopus. This result vividly illustrates that Le´vy walks
are not necessarily the result of selection pressures for advantageous searching
behaviour but can instead arise freely and naturally from simple processes.
It also shows that the family of Le´vy walkers is vastly larger than previously
thought and includes spores, pollens, seeds and minute wingless arthropods
that on warm days disperse passively within the atmospheric boundary layer.
Joint Modeling of Arguments for Event Understanding
Authors: Yunmo Chen, Tongfei Chen, Benjamin Van Durme
Date: 2020-11-20 00:00:00
Tasks: Sentence

We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.