pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic quantized consensus under DoS attacks: Towards a tight zooming-out factor
Authors: Shuai Feng, Maopeng Ran, Hideaki Ishii, Shengyuan Xu
Date: 2023-06-01 00:00:00
Tasks: Quantization

This paper deals with dynamic quantized consensus of dynamical agents in a general form under packet losses induced by Denial-of-Service (DoS) attacks. The communication channel has limited bandwidth and hence the transmitted signals over the network are subject to quantization. To deal with agent's output, an observer is implemented at each node. The state of the observer is quantized by a finite-level quantizer and then transmitted over the network. To solve the problem of quantizer overflow under malicious packet losses, a zooming-in and out dynamic quantization mechanism is designed. By the new quantized controller proposed in the paper, the zooming-out factor is lower bounded by the spectral radius of the agent's dynamic matrix. A sufficient condition of quantization range is provided under which the finite-level quantizer is free of overflow. A sufficient condition of tolerable DoS attacks for achieving consensus is also provided. At last, we study scalar dynamical agents as a special case and further tighten the zooming-out factor to a value smaller than the agent's dynamic parameter. Under such a zooming-out factor, it is possible to recover the level of tolerable DoS attacks to that of unquantized consensus, and the quantizer is free of overflow.
Graph Neural Networks-Based User Pairing in Wireless Communication Systems
Authors: Sharan Mourya, Pavan Reddy, SaiDhiraj Amuru, Kiran Kumar Kuchi
Date: 2023-05-14 00:00:00
Tasks: Graph Neural Network, Scheduling

Recently, deep neural networks have emerged as a solution to solve NP-hard wireless resource allocation problems in real-time. However, multi-layer perceptron (MLP) and convolutional neural network (CNN) structures, which are inherited from image processing tasks, are not optimized for wireless network problems. As network size increases, these methods get harder to train and generalize. User pairing is one such essential NP-hard optimization problem in wireless communication systems that entails selecting users to be scheduled together while minimizing interference and maximizing throughput. In this paper, we propose an unsupervised graph neural network (GNN) approach to efficiently solve the user pairing problem. Our proposed method utilizes the Erdos goes neural pipeline to significantly outperform other scheduling methods such as k-means and semi-orthogonal user scheduling (SUS). At 20 dB SNR, our proposed approach achieves a 49% better sum rate than k-means and a staggering 95% better sum rate than SUS while consuming minimal time and resources. The scalability of the proposed method is also explored as our model can handle dynamic changes in network size without experiencing a substantial decrease in performance. Moreover, our model can accomplish this without being explicitly trained for larger or smaller networks facilitating a dynamic functionality that cannot be achieved using CNNs or MLPs.
Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model
Authors: Xiaohuai Le, Tong Lei, Li Chen, Yiqing Guo, Chao He, Cheng Chen, Xianjun Xia, Hua Gao, Yijian Xiao, Piao Ding, Shenyi Song, Jing Lu
Date: 2023-06-01 00:00:00
Tasks: Retrieval, Speech Enhancement

With fewer feature dimensions, filter banks are often used in light-weight full-band speech enhancement models. In order to further enhance the coarse speech in the sub-band domain, it is necessary to apply a post-filtering for harmonic retrieval. The signal processing-based comb filters used in RNNoise and PercepNet have limited performance and may cause speech quality degradation due to inaccurate fundamental frequency estimation. To tackle this problem, we propose a learnable comb filter to enhance harmonics. Based on the sub-band model, we design a DNN-based fundamental frequency estimator to estimate the discrete fundamental frequencies and a comb filter for harmonic enhancement, which are trained via an end-to-end pattern. The experiments show the advantages of our proposed method over PecepNet and DeepFilterNet.
Spoken Language Identification System for English-Mandarin Code-Switching Child-Directed Speech
Authors: Shashi Kant Gupta, Sushant Hiray, Prashant Kukde
Date: 2023-06-01 00:00:00
Tasks: Decoder, Language Identification, Spoken language identification

This work focuses on improving the Spoken Language Identification (LangId) system for a challenge that focuses on developing robust language identification systems that are reliable for non-standard, accented (Singaporean accent), spontaneous code-switched, and child-directed speech collected via Zoom. We propose a two-stage Encoder-Decoder-based E2E model. The encoder module consists of 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with a global context. The decoder module uses an attentive temporal pooling mechanism to get fixed length time-independent feature representation. The total number of parameters in the model is around 22.1 M, which is relatively light compared to using some large-scale pre-trained speech models. We achieved an EER of 15.6% in the closed track and 11.1% in the open track (baseline system 22.1%). We also curated additional LangId data from YouTube videos (having Singaporean speakers), which will be released for public use.
A Teacher-Student approach for extracting informative speaker embeddings from speech mixtures
Authors: Tobias Cord-Landwehr, Christoph Boeddeker, Cătălin Zorilă, Rama Doddipatla, Reinhold Haeb-Umbach
Date: 2023-06-01 00:00:00

We introduce a monaural neural speaker embeddings extractor that computes an embedding for each speaker present in a speech mixture. To allow for supervised training, a teacher-student approach is employed: the teacher computes the target embeddings from each speaker's utterance before the utterances are added to form the mixture, and the student embedding extractor is then tasked to reproduce those embeddings from the speech mixture at its input. The system much more reliably verifies the presence or absence of a given speaker in a mixture than a conventional speaker embedding extractor, and even exhibits comparable performance to a multi-channel approach that exploits spatial information for embedding extraction. Further, it is shown that a speaker embedding computed from a mixture can be used to check for the presence of that speaker in another mixture.
Speaker verification using attentive multi-scale convolutional recurrent network
Authors: Yanxiong Li, Zhongjie Jiang, Wenchang Cao, Qisheng Huang
Date: 2023-06-01 00:00:00
Tasks: Speaker Verification

In this paper, we propose a speaker verification method by an Attentive Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can acquire both local spatial information and global sequential information from the input speech recordings. In the proposed method, logarithm Mel spectrum is extracted from each speech recording and then fed to the proposed AMCRN for learning speaker embedding. Afterwards, the learned speaker embedding is fed to the back-end classifier (such as cosine similarity metric) for scoring in the testing stage. The proposed method is compared with state-of-the-art methods for speaker verification. Experimental data are three public datasets that are selected from two large-scale speech corpora (VoxCeleb1 and VoxCeleb2). Experimental results show that our method exceeds baseline methods in terms of equal error rate and minimal detection cost function, and has advantages over most of baseline methods in terms of computational complexity and memory requirement. In addition, our method generalizes well across truncated speech segments with different durations, and the speaker embedding learned by the proposed AMCRN has stronger generalization ability across two back-end classifiers.
General SIS diffusion process with indirect spreading pathways on a hypergraph
Authors: Shaoxuan Cui, Fangzhou Liu, Hildeberto Jardón-Kojakhmetov, Ming Cao
Date: 2023-06-01 00:00:00

While conventional graphs only characterize pairwise interactions, higher-order networks (hypergraph, simplicial complex) capture multi-body interactions, which is a potentially more suitable modeling framework for a complex real system. However, the introduction of higher-order interactions brings new challenges for the rigorous analysis of such systems on a higher-order network. In this paper, we study a series of SIS-type diffusion processes with both indirect and direct pathways on a directed hypergraph. In a concrete case, the model we propose is based on a specific choice (polynomial) of interaction function (how several agents influence each other when they are in a hyperedge). Then, by the same choice of interaction function, we further extend the system and propose a bi-virus competing model on a directed hypergraph by coupling two single-virus models together. Finally, the most general model in this paper considers an abstract interaction function under single-virus and bi-virus settings. For the single-virus model, we provide the results regarding healthy state and endemic equilibrium. For the bi-virus setting, we further give an analysis of the existence and stability of the healthy state, dominant endemic equilibria, and coexisting equilibria. All theoretical results are finally supported by some numerical examples.
Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion Tokamaks
Authors: Christopher A. Orrico, Matthijs van Berkel, Thomas O. S. J. Bosman, W. P. M. H. Heemels, Dinesh Krishnamoorthy
Date: 2023-06-01 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) is promising for fueling and core density feedback control in nuclear fusion tokamaks, where the primary actuators, frozen hydrogen fuel pellets fired into the plasma, are discrete. Previous density feedback control approaches have only approximated pellet injection as a continuous input due to the complexity that it introduces. In this letter, we model plasma density and pellet injection as a hybrid system and propose two MPC strategies for density control: mixed-integer (MI) MPC using a conventional mixed-integer programming (MIP) solver and MPC utilizing our novel modification of the penalty term homotopy (PTH) algorithm. By relaxing the integer requirements, the PTH algorithm transforms the MIP problem into a series of continuous optimization problems, reducing computational complexity. Our novel modification to the PTH algorithm ensures that it can handle path constraints, making it viable for constrained hybrid MPC in general. Both strategies perform well with regards to reference tracking without violating path constraints and satisfy the computation time limit for real-time control of the pellet injection system. However, the computation time of the PTH-based MPC strategy consistently outpaces the conventional MI-MPC strategy.
A Bi-level Decision Framework for Incentive-Based Demand Response in Distribution Systems
Authors: Vipin Chandra Pandey, Nikhil Gupta, Khaleequr Rehman Niazi, Anil Swarnkar, Tanuj Rawat, Charalambos Konstantinou
Date: 2023-06-01 00:00:00

In a growing retail electricity market, demand response (DR) is becoming an integral part of the system to enhance economic and operational performances. This is rendered as incentive-based DR (IBDR) in the proposed study. It presents a bi-level decision framework under the ambit of multiple demand response providers (DRPs) in the retail competition. It is formulated as a multi-leader-multi-follower game, where multiple DRPs, as the DR stakeholders, are strategically interacting to optimize load serving entity cost at the upper level, and individual DRP as the aggregated customers is optimizing its cost at the lower level. The strategic behavior of DRPs is modeled in a game-theoretic framework using a generalized Stackelberg game. Further, the existence and uniqueness of the game are validated using variational inequalities. It is presented as a nonlinear problem to consider AC network constraints. An equilibrium problem with equilibrium constraints is used as a mathematical program to model the multi-leader-multi-follower, bi-level problem, which is simultaneously solved for all DRPs. The diagonalization method is employed to solve the problem. The detailed numerical analyses are conducted on IEEE 33-bus test and Indian-108 bus distribution systems to demonstrate the applicability and scalability of the proposed model and the suggested method.
Virtual and Real Data Populated Intersection Visualization and Testing Tool for V2X Application Development
Authors: Sukru Yaren Gelbal, Mustafa Ridvan Cantas, Bilin Aksun Guvenc, Levent Guvenc
Date: 2023-05-31 00:00:00
Tasks: Autonomous Driving

The capability afforded by Vehicle-to-Vehicle communication improves situational awareness and provides advantages for many of the traffic problems caused by reduced visibility or No-Line-of-Sight situations, being useful for both autonomous and non-autonomous driving. Additionally, with the traffic light Signal Phase and Timing and Map Datainformation and other advisory information provided with Vehicle-to-Infrastructure (V2I) communication, outcomes which benefit the driver in the long run, such as reducing fuel consumption with speed regulation or decreasing traffic congestion through optimal speed advisories, providing red light violation warning messages and intersection motion assist messages for collision-free intersection maneuvering are all made possible. However, developing applications to obtain these benefits requires an intensive development process within a lengthy testing period. Understanding the intersection better is a large part of this development process. Being able to see what information is broadcasted and how this information translates into the real world would both benefit the development of these highly useful applications and also ensure faster evaluation, when presented visually, using an easy to use and interactive tool. Moreover, recordings of this broadcasted information can be modified and used for repeated testing. Modification of the data makes it flexible and allows us to use it for a variety of testing scenarios at a virtually populated intersection. Based on this premise, this paper presents and demonstrates visualization tools to project SPaT, MAP and Basic Safety Message information into easy to read real-world based graphs. Also, it provides information about the modification of the real-world data to allow creation of a virtually populated intersection, along with the capability to also inject virtual vehicles at this intersection.
Suppression of chaos in a partially driven recurrent neural network
Authors: Shotaro Takasu, Toshio Aoyagi
Date: 2023-06-01 00:00:00

The dynamics of recurrent neural networks (RNNs), and particularly their response to inputs, play a critical role in information processing. In many applications of RNNs, only a specific subset of the neurons generally receive inputs. However, it remains to be theoretically clarified how the restriction of the input to a specific subset of neurons affects the network dynamics. Considering RNNs with such restricted input, we investigate how the proportion, $p$, of the neurons receiving inputs (the "inputs neurons") and the strength of the input signals affect the dynamics by analytically deriving the conditional maximum Lyapunov exponent. Our results show that for sufficiently large $p$, the maximum Lyapunov exponent decreases monotonically as a function of the input strength, indicating the suppression of chaos, but if $p$ is smaller than a critical threshold, $p_c$, even significantly amplified inputs cannot suppress spontaneous chaotic dynamics. Furthermore, although the value of $p_c$ is seemingly dependent on several model parameters, such as the sparseness and strength of recurrent connections, it is proved to be intrinsically determined solely by the strength of chaos in spontaneous activity of the RNN. This is to say, despite changes in these model parameters, it is possible to represent the value of $p_c$ as a common invariant function by appropriately scaling these parameters to yield the same strength of spontaneous chaos. Our study suggests that if $p$ is above $p_c$, we can bring the neural network to the edge of chaos, thereby maximizing its information processing capacity, by amplifying inputs.
Reconstructing Sea Surface Temperature Images: A Masked Autoencoder Approach for Cloud Masking and Reconstruction
Authors: Angelina Agabin, J. Xavier Prochaska
Date: 2023-05-28 00:00:00

This thesis presents a new algorithm to mitigate cloud masking in the analysis of sea surface temperature (SST) data generated by remote sensing technologies, e.g., Clouds interfere with the analysis of all remote sensing data using wavelengths shorter than 12 microns, significantly limiting the quantity of usable data and creating a biased geographical distribution (towards equatorial and coastal regions). To address this issue, we propose an unsupervised machine learning algorithm called Enki which uses a Vision Transformer with Masked Autoencoding to reconstruct masked pixels. We train four different models of Enki with varying mask ratios (t) of 10%, 35%, 50%, and 75% on the generated Ocean General Circulation Model (OGCM) dataset referred to as LLC4320. To evaluate performance, we reconstruct a validation set of LLC4320 SST images with random ``clouds'' corrupting p=10%, 20%, 30%, 40%, 50% of the images with individual patches of 4x4 pixel^2. We consistently find that at all levels of p there is one or multiple models that reconstruct the images with a mean RMSE of less than 0.03K, i.e. lower than the estimated sensor error of VIIRS data. Similarly, at the individual patch level, the reconstructions have RMSE 8x smaller than the fluctuations in the patch. And, as anticipated, reconstruction errors are larger for images with a higher degree of complexity. Our analysis also reveals that patches along the image border have systematically higher reconstruction error; we recommend ignoring these in production. We conclude that Enki shows great promise to surpass in-painting as a means of reconstructing cloud masking. Future research will develop Enki to reconstruct real-world data.
Inference in Predictive Quantile Regressions
Authors: Alex Maynard, Katsumi Shimotsu, Nina Kuriyama
Date: 2023-06-01 00:00:00
Tasks: quantile regression, regression, Unity

This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein-Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has a reliable size in small samples and good power. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors - the dividend price ratio, earnings price ratio, and book-to-market ratio - to predict the median, shoulders, and tails of the stock return distribution.
Life after (Soft) Default
Authors: Giacomo De Giorgi, Costanza Naguib
Date: 2023-06-01 00:00:00
Tasks: Credit score

Soft default, defined as a delinquency of 90 days or more, is a relatively common event in the credit market, in 2010 such episodes affected about 3 million individuals. Yet we lack a detailed understanding of what happens afterward. We use credit report data, on approximately 2 million individuals from 2004 to 2020, to shed light on individual trajectories after such event, and document enduring negative impacts. These effects persist for up to ten years post-event and manifest in lower credit scores, reduced total credit limits, lower homeownership rates, lower income, and relocation to less economically active zip codes. It appears that those who are overextended in their mortgage lines, and with larger delinquent amounts, suffer the harshest consequences.
Examination of Supernets to Facilitate International Trade for Indian Exports to Brazil
Authors: Evan Winter, Anupam Shah, Ujjwal Gupta, Anshul Kumar, Deepayan Mohanty, Juan Carlos Uribe, Aishwary Gupta, Mini P. Thomas
Date: 2023-06-01 00:00:00

The objective of this paper is to investigate a more efficient cross-border payment and document handling process for the export of Indian goods to Brazil. The paper is structured into two sections: first, to explain the problems unique to the India-Brazil international trade corridor by highlighting the obstacles of compliance, speed, and payments; and second, to propose a digital solution for India-brazil trade utilizing Supernets, focusing on the use case of Indian exports. The solution assumes that stakeholders will be onboarded as permissioned actors (i.e. nodes) on a Polygon Supernet. By engaging trade and banking stakeholders, we ensure that the digital solution results in export benefits for Indian exporters, and a lawful channel to receive hard currency payments. The involvement of Brazilian and Indian banks ensures that Letter of Credit (LC) processing time and document handling occur at the speed of blockchain technology. The ultimate goal is to achieve faster settlement and negotiation period while maintaining a regulatory-compliant outcome, so that the end result is faster and easier, yet otherwise identical to the real-world process in terms of export benefits and compliance.
SolarEV City Concept for Paris: A promising idea?
Authors: Paul Deroubaix, Takuro Kobashi, Léna Gurriaran, Fouzi Benkhelifa, Philippe Ciais, Katsumasa Tanaka
Date: 2023-05-31 00:00:00

Urban decarbonization is one of the pillars for strategies to achieve carbon neutrality around the world. However, the current speed of urban decarbonization is insufficient to keep pace with efforts to achieve this goal. Rooftop PVs integrated with electric vehicles (EVs) as battery is a promising technology capable to supply CO2-free, affordable, and dispatchable electricity in urban environments (SolarEV City Concept). Here, we evaluated Paris, France for the decarbonization potentials of rooftop PV + EV in comparison to the surrounding suburban area Ile-de-France and Kyoto, Japan. We assessed various scenarios by calculating the energy sufficiency, self-consumption, self-sufficiency, cost savings, and CO2 emission reduction of the PV + EV system or PV only system. The combination of EVs with PVs by V2H or V2B systems at the city or region level was found to be more effective in Ile-de-France than in Paris suggesting that SolarEV City is more effective for geographically larger area including Paris. If implemented at a significant scale, they can add substantial values to rooftop PV economics and keep a high self-consumption and self-sufficiency, which also allows bypassing the classical battery storage that is too expensive to be profitable. Furthermore, the systems potentially allow rapid CO2 emissions reduction; however, with already low-carbon electricity of France by nuclear power, CO2 abatement (0.020 kgCO2kWh-1 reduction from 0.063 kgCO2kWh-1) by PV + EV system can be limited, in comparison to that (0.270 kgCO2kWh-1 reduction from 0.352 kgCO2kWh-1) of Kyoto, also because of the Paris low insolation and high demands in higher latitude winter. While the SolarEV City Concept can help Paris to move one step closer to the carbon neutrality goal, there are also implementation challenges for installing PVs in Paris.