pwc-archive/papers-with-abstracts huggingface dataset:
Towards automatic detection and classification of orca (Orcinus orca) calls using cross-correlation methods
Authors: Stefano Palmero, Carlo Guidi, Vladimir Kulikovskiy, Matteo Sanguineti, Michele Manghi, Matteo Sommer, Gaia Pesce
Date: 2021-10-29 00:00:00

Orca (Orcinus orca) is known for complex vocalisation. Their social structure consists of pods and clans sharing unique dialects due to geographic isolation. Sound type repertoires are fundamental for monitoring orca populations and are typically created visually and aurally. An orca pod occurring in the Ligurian Sea (Pelagos Sanctuary) in December 2019 provided a unique occasion for long-term recordings. The numerous data collected with the bottom recorder were analysed with a traditional human-driven inspection to create a repertoire of this pod and to compare it to catalogues from different orca populations (Icelandic and Antarctic) investigating its origins. Automatic signal detection and cross-correlation methods (R package warbleR) were used for the first time in orca studies. We found the Pearson cross-correlation method to be efficient for most pairwise calculations (> 85%) but with false positives. One sound type from our repertoire presented a high positive match (range 0.62-0.67) with one from the Icelandic catalogue, which was confirmed visually and aurally. Our first attempt to automatically classify orca sound types presented limitations due to background noise and sound complexity of orca communication. We show cross-correlation methods can be a powerful tool for sound type classification in combination with conventional methods.
SA-SDR: A novel loss function for separation of meeting style data
Authors: Thilo von Neumann, Keisuke Kinoshita, Christoph Boeddeker, Marc Delcroix, Reinhold Haeb-Umbach
Date: 2021-10-29 00:00:00

Many state-of-the-art neural network-based source separation systems use the averaged Signal-to-Distortion Ratio (SDR) as a training objective function. The basic SDR is, however, undefined if the network reconstructs the reference signal perfectly or if the reference signal contains silence, e.g., when a two-output separator processes a single-speaker recording. Many modifications to the plain SDR have been proposed that trade-off between making the loss more robust and distorting its value. We propose to switch from a mean over the SDRs of each individual output channel to a global SDR over all output channels at the same time, which we call source-aggregated SDR (SA-SDR). This makes the loss robust against silence and perfect reconstruction as long as at least one reference signal is not silent. We experimentally show that our proposed SA-SDR is more stable and preferable over other well-known modifications when processing meeting-style data that typically contains many silent or single-speaker regions.
CORAA: a large corpus of spontaneous and prepared speech manually validated for speech recognition in Brazilian Portuguese
Authors: Arnaldo Candido Junior, Edresson Casanova, Anderson Soares, Frederico Santos de Oliveira, Lucas Oliveira, Ricardo Corso Fernandes Junior, Daniel Peixoto Pinto da Silva, Fernando Gorgulho Fayet, Bruno Baldissera Carlotto, Lucas Rafael Stefanel Gris, Sandra Maria Aluísio
Date: 2021-10-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic Speech recognition (ASR) is a complex and challenging task. In recent years, there have been significant advances in the area. In particular, for the Brazilian Portuguese (BP) language, there were about 376 hours public available for ASR task until the second half of 2020. With the release of new datasets in early 2021, this number increased to 574 hours. The existing resources, however, are composed of audios containing only read and prepared speech. There is a lack of datasets including spontaneous speech, which are essential in different ASR applications. This paper presents CORAA (Corpus of Annotated Audios) v1. with 290.77 hours, a publicly available dataset for ASR in BP containing validated pairs (audio-transcription). CORAA also contains European Portuguese audios (4.69 hours). We also present a public ASR model based on Wav2Vec 2.0 XLSR-53 and fine-tuned over CORAA. Our model achieved a Word Error Rate of 24.18% on CORAA test set and 20.08% on Common Voice test set. When measuring the Character Error Rate, we obtained 11.02% and 6.34% for CORAA and Common Voice, respectively. CORAA corpora were assembled to both improve ASR models in BP with phenomena from spontaneous speech and motivate young researchers to start their studies on ASR for Portuguese. All the corpora are publicly available at https://github.com/nilc-nlp/CORAA under the CC BY-NC-ND 4.0 license.
Characterising Linear Spatio-Temporal Dynamical Systems in the Frequency Domain
Authors: Hua-Liang Wei
Date: 2021-10-29 00:00:00

A new concept, called the spatio-temporal transfer function (STTF), is introduced to characterise a class of linear time-invariant (LTI) spatio-temporal dynamical systems. The spatio-temporal transfer function is a natural extension of the ordinary transfer function for classical linear time-invariant control systems. As in the case of the classical transfer function, the spatio-temporal transfer function can be used to characterise, in the frequency domain, the inherent dynamics of linear time-invariant spatio-temporal systems. The introduction of the spatio-temporal transfer function should also facilitate the analysis of the dynamical stability of discrete-time spatio-temporal systems.
Explicit Port-Hamiltonian FEM-Models for Linear Mechanical Systems with Non-Uniform Boundary Conditions
Authors: Tobias Thoma, Paul Kotyczka
Date: 2021-10-29 00:00:00
Tasks: State Space Models

In this contribution we present how to obtain explicit state space models in port-Hamiltonian form when a mixed finite element method is applied to a linear mechanical system with non-uniform boundary conditions. The key is to express the variational problem based on the principle of virtual power, with both the Dirichlet (velocity) and Neumann (stress) boundary conditions imposed in a weak sense. As a consequence, the formal skew-adjointness of the system operator becomes directly visible after integration by parts, and, after compatible FE discretization, the boundary degrees of freedom of both causalities appear as explicit inputs in the resulting state space model. The rationale behind our formulation is illustrated using a lumped parameter example, and numerical experiments on a one-dimensional rod show the properties of the approach in practice.
Data Driven based Dynamic Correction Prediction Model for NOx Emission of Coal Fired Boiler
Authors: Zhenhao Tang, Deyu Zhu, Yang Li
Date: 2021-10-29 00:00:00
Tasks: feature selection

The real-time prediction of NOx emissions is of great significance for pollutant emission control and unit operation of coal-fired power plants. Aiming at dealing with the large time delay and strong nonlinear characteristics of the combustion process, a dynamic correction prediction model considering the time delay is proposed. First, the maximum information coefficient (MIC) is used to calculate the delay time between related parameters and NOx emissions, and the modeling data set is reconstructed; then, an adaptive feature selection algorithm based on Lasso and ReliefF is constructed to filter out the high correlation with NOx emissions. Parameters; Finally, an extreme learning machine (ELM) model combined with error correction was established to achieve the purpose of dynamically predicting the concentration of nitrogen oxides. Experimental results based on actual data show that the same variable has different delay times under load conditions such as rising, falling, and steady; and there are differences in model characteristic variables under different load conditions; dynamic error correction strategies effectively improve modeling accuracy; proposed The prediction error of the algorithm under different working conditions is less than 2%, which can accurately predict the NOx concentration at the combustion outlet, and provide guidance for NOx emission monitoring and combustion process optimization.
Nash equilibrium of multi-agent graphical game with a privacy information encrypted learning algorithm
Authors: Kun Zhang, Ji-Feng Zhang, Rong Su, Huaguang Zhang
Date: 2021-10-29 00:00:00
Tasks: Quantization

This paper studies the global Nash equilibrium problem of leader-follower multi-agent dynamics, which yields consensus with a privacy information encrypted learning algorithm. With the secure hierarchical structure, the relationship between the secure consensus problem and global Nash equilibrium is discussed under potential packet loss attacks, and the necessary and sufficient condition for the existence of global Nash equilibrium is provided regarding the soft-constrained graphical game. To achieve the optimal policies, the convergence of decentralized learning algorithm is guaranteed with an iteratively updated pair of decoupled gains. By using the developed quantization scheme and additive-multiplicative property, the encryption-decryption is successfully embedded in the data transmission and computation to overcome the potential privacy violation in unreliable networks. A simulation example is provided to verify the effectiveness of the designed algorithm.
Dual-band Harmonic and Subharmonic Frequency Generation Circuitry for Joint Communication and Localization Applications Under Severe Multipath Environment
Authors: Payman Pahlavan, Najme Ebrahimi
Date: 2021-10-29 00:00:00

The next generation of ultra-dense connected and automated wireless sensor networks (WSN) requires proximity intelligence for many of its applications, especially for identification and localization. This work presents the first bidirectional circuitry for Internet of Things (IoT) transponder that reciprocally generates harmonics and subharmonics, dual-band frequencies. A multi-band or wideband localization system is essential for future intelligent WSN to mitigate the influence of multipath signals for indoor dense environment. The proposed frequency generation circuitry is based on the novel nonlinear ring resonator (NRR) operating based on standing wave resonation. The proposed NRR generates two sustainable oscillation frequencies based on the periodicity of the nonlinear circuit in the ring configuration. Due to the symmetry and reciprocity of the ring layout, the two bidirectional ports can excite the circuit at the two opposite nodes while maintaining the required boundary conditions for oscillation. The sustainable resonance conditions occur by creating zero, short impedance, or pole, infinite impedance, at subharmonic and harmonic excitation ports. The NRR circuit consumes zero DC power and covers two communication frequency plans interchangeably, which makes it a premier technique compared to the conventional ultra-wideband (UWB) localization system and conventional single-band nonlinear passive circuitry. The latter is narrowband due to the tunning limitation of the nonlinear varactor while the former is power-hungry approach with complex hardware requirements.
Newtonian Mechanics Based Transient Stability PART VI: Machine Transformation
Authors: Songyan Wang, Jilai Yu, Aoife Foley, Jingrui Zhang
Date: 2021-10-29 00:00:00

This paper focuses on the transformations from the individual machine to the equivalent machine through the "correction" perspective of the inner-group machine. The machines are first classified as the real machine with equation of motion and the pseudo machine without equation of motion. Then, it is clarified that both individual machine and equivalent machine are real machines, while the superimposed machine is a pseudo machine. Based on the classifications of the machines, two types of machine transformations are provided. The two types of machine transformations are based on the "energy correction" and "trajectory correction" of the inner-group machine, respectively. For the energy correction case, it is clarified that the trajectory transformation completely fails, while the energy transformation mathematically holds yet it is physically meaningless. For the trajectory correction case, it is clarified that both energy transformation and trajectory transformation are established. The reason is that each trajectory-correction based individual machine has the same equation of motion, i.e., the motion of the equivalent Machine-CR. Simulation results show that the machine transformation from the individual machine to the equivalent machine can be realized only through trajectory correction.
Coordinated Frequency and Voltage Regulation of Grid-Following and Grid-Forming Inverters
Authors: Ankit Singhal, Thanh Long Vu, Wei Du
Date: 2020-12-12 00:00:00

In a purely inverter-based microgrid, both grid-forming (GFM) and grid-following (GFL) inverters will have a crucial role to play in frequency/voltage regulation and maintaining power sharing through their grid support capabilities. Consequently, the coordination between these two technologies becomes increasingly important for optimal system performance. However, the existing work does not consider GFL's potential to participate in a secondary control in coordination with GFM, thus not able to utilize the full capability of inverter resources. In this paper, we show that it is possible to fully coordinate the GFL and GFM inverters to achieve accurate power sharing, frequency/voltage regulation, and circulating var mitigation in networked microgrids even without the support of any synchronous generators or the bulk power system. We use the leader-follower consensus framework to develop a GFM-GFL coordination control. The effectiveness of the proposed coordination is verified under different disturbances and communication degradation. In addition, we find that the proposed fully-coordinated secondary control outperforms other approaches such as un-coordinated and partially-coordinated secondary controls, in aspects of load sharing and frequency and voltage regulation. Overall, this study emphasizes the need and benefits of GFL-GFM coordination in microgrids.
Adaptive Coalition Formation-Based Coordinated Voltage Regulation in Distribution Networks
Authors: Yao Long, Ryan T. Elliott, Daniel S. Kirschen
Date: 2021-10-28 00:00:00

High penetrations of photovoltaic (PV) systems can cause severe voltage quality problems in distribution networks. This paper proposes a distributed control strategy based on the dynamic formation of coalitions to coordinate a large number of PV inverters for voltage regulation. In this strategy, a rule-based coalition formation scheme deals with the zonal voltage difference caused by the uneven integration of PV capacity. Under this scheme, PV inverters form into separate voltage regulation coalitions autonomously according to local, neighbor as well as coalition voltage magnitude and regulation capacity information. To coordinate control within each coalition, we develop a feedback-based leader-follower consensus algorithm which eliminates the voltage violations caused by the fast fluctuations of load and PV generation. This algorithm allocates the required reactive power contribution among the PV inverters according to their maximum available capacity to promote an effective and fair use of the overall voltage regulation capacity. Case studies based on realistic distribution networks and field-recorded data validate the effectiveness of the proposed control strategy. Moreover, comparison with a centralized network decomposition-based scheme shows the flexibility of coalition formation in organizing the distributed PV inverters. The robustness and generalizability of the proposed strategy are also demonstrated.
Process Design and Economics of Production of p-Aminophenol
Authors: Chinmay Ghoroi, Jay Shah, Devanshu Thakar, Sakshi Baheti
Date: 2021-10-29 00:00:00

Para-Aminophenol is one of the key chemicals required for the synthesis of Paracetamol, an analgesic and antipyretic drug. Data shows a large fraction of India's demand for Para-Aminophenol being met through imports from China. The uncertainty in the India-China relations would affect the supply and price of this "Key Starting Material." This report is a detailed business plan for setting up a plant and producing Para-Aminophenol in India at a competitive price. The plant is simulated in AspenPlus V8 and different Material Balances and Energy Balances calculations are carried out. The plant produces 22.7 kmols Para-Aminophenol per hour with a purity of 99.9%. Along with the simulation, economic analysis is carried out for this plant to determine the financial parameters like Payback Period and Return on Investment.
Market Areas in General Equilibrium
Authors: Gianandrea Lanzara, Matteo Santacesaria
Date: 2021-10-29 00:00:00

This paper proposes a spatial model with a realistic geography where a continuous distribution of agents (e.g., farmers) engages in economic interactions with one location from a finite set (e.g., cities). The spatial structure of the equilibrium consists of a tessellation, i.e., a partition of space into a collection of mutually exclusive market areas. After proving the existence of a unique equilibrium, we characterize how the location of borders and, in the case with mobile labor, the set of inhabited cities change in response to economic shocks. To deal with a two-dimensional space, we draw on tools from computational geometry and from the theory of shape optimization. Finally, we provide an empirical application to illustrate the usefulness of the framework for applied work.
Choice and Market Design
Authors: Samson Alva, Battal Doğan
Date: 2021-10-28 00:00:00

A textbook chapter on modeling choice behavior and designing institutional choice functions for matching and market design. The chapter is to appear in: Online and Matching-Based Market Design. Federico Echenique, Nicole Immorlica and Vijay V. Vazirani, Editors. Cambridge University Press. 2021
Singularity and Similarity Detection from Signals Using Wavelet Transform
Authors: Hua-Liang Wei
Date: 2021-10-29 00:00:00

The wavelet transform and related techniques are used to analyze singular and fractal signals. The normalized wavelet scalogram is introduced to detect singularities including jumps, cusps and other sharply changing points. The wavelet auto-covariance is applied to estimate the self-similarity exponent for statistical self-affine signals.
Tractability from overparametrization: The example of the negative perceptron
Authors: Andrea Montanari, Yiqiao Zhong, Kangjie Zhou
Date: 2021-10-28 00:00:00

In the negative perceptron problem we are given $n$ data points $({\boldsymbol x}_i,y_i)$, where ${\boldsymbol x}_i$ is a $d$-dimensional vector and $y_i\in\{+1,-1\}$ is a binary label. The data are not linearly separable and hence we content ourselves to find a linear classifier with the largest possible \emph{negative} margin. In other words, we want to find a unit norm vector ${\boldsymbol \theta}$ that maximizes $\min_{i\le n}y_i\langle {\boldsymbol \theta},{\boldsymbol x}_i\rangle$. This is a non-convex optimization problem (it is equivalent to finding a maximum norm vector in a polytope), and we study its typical properties under two random models for the data. We consider the proportional asymptotics in which $n,d\to \infty$ with $n/d\to\delta$, and prove upper and lower bounds on the maximum margin $\kappa_{\text{s}}(\delta)$ or -- equivalently -- on its inverse function $\delta_{\text{s}}(\kappa)$. In other words, $\delta_{\text{s}}(\kappa)$ is the overparametrization threshold: for $n/d\le \delta_{\text{s}}(\kappa)-\varepsilon$ a classifier achieving vanishing training error exists with high probability, while for $n/d\ge \delta_{\text{s}}(\kappa)+\varepsilon$ it does not. Our bounds on $\delta_{\text{s}}(\kappa)$ match to the leading order as $\kappa\to -\infty$. We then analyze a linear programming algorithm to find a solution, and characterize the corresponding threshold $\delta_{\text{lin}}(\kappa)$. We observe a gap between the interpolation threshold $\delta_{\text{s}}(\kappa)$ and the linear programming threshold $\delta_{\text{lin}}(\kappa)$, raising the question of the behavior of other algorithms.
Viscoelasticity and cell swirling motion
Authors: Ivana Pajic-Lijakovic, Milan Milivojevic
Date: 2021-10-29 00:00:00

Although collective cell migration (CCM) is a highly coordinated and fine-tuned migratory mode, instabilities in the form of cell swirling motion (CSM) often occur. The CSM represents a product of the active turbulence obtained at low Reynolds number which has a feedback impact to various processes such as morphogenesis, wound healing, and cancer invasion. The cause of this phenomenon is related to the viscoelasticity of multicellular systems in the context of cell residual stress accumulation. Particular interest of this work is to: (1) emphasize the roles of cell shear and normal residual stress accumulated during CCM in the appearance of CSM and (2) consider the dynamics of CSM from the standpoint of rheology. Inhomogeneous distribution of the cell residual stress leads to a generation the viscoelastic force which acts to suppress CCM and can induces the system rapid stiffening. This force together with the surface tension force and traction force is responsible for the appearance of the CSM. In this work, a review of existing literature about viscoelasticity caused by CCM is given along with assortment of published experimental findings, in order to invite experimentalists to test given theoretical considerations in multicellular systems.
An optimized protocol for single cell transcriptional profiling by combinatorial indexing
Authors: Beth K. Martin, Chengxiang Qiu, Eva Nichols, Melissa Phung, Rula Green-Gladden, Sanjay Srivatsan, Ronnie Blecher-Gonen, Brian J. Beliveau, Cole Trapnell, Junyue Cao, Jay Shendure
Date: 2021-10-28 00:00:00

Single cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here we report a simplified, optimized version of the three-level sci-RNA-seq protocol that is faster, higher yield, more robust, and more sensitive, than the original sci-RNA-seq3 protocol, with reagent costs on the order of 1 cent per cell or less. We showcase the optimized protocol via whole organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a "tiny sci-*" protocol for experiments where input is extremely limited.
Learning physics confers pose-sensitivity in structure-based virtual screening
Authors: Pawel Gniewek, Bradley Worley, Kate Stafford, Henry van den Bedem, Brandon Anderson
Date: 2021-10-28 00:00:00
Tasks: Drug Discovery, Sensitivity

In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or "hits" for therapeutic protein targets from docked poses at specific binding sites. However, while generally successful at this task, many deep learning methods are known to be insensitive to protein-ligand interactions, decreasing the reliability of hit detection and hindering discovery at novel binding sites. Here, we overcome this limitation by introducing a class of models with two key features: 1) we condition bioactivity on pose quality score, and 2) we present poor poses of true binders to the model as negative examples. The conditioning forces the model to learn details of physical interactions. We evaluate these models on a new benchmark designed to detect pose-sensitivity.