pwc-archive/papers-with-abstracts huggingface dataset:
Chebyshev distances associated to the second members of systems of Max-product/Lukasiewicz Fuzzy relational equations
Authors: Ismaïl Baaj
Date: 2023-01-30 00:00:00

In this article, we study the inconsistency of a system of $\max$-product fuzzy relational equations and of a system of $\max$-Lukasiewicz fuzzy relational equations. For a system of $\max-\min$ fuzzy relational equations $A \Box_{\min}^{\max} x = b$ and using the $L_\infty$ norm, (Baaj, 2023) showed that the Chebyshev distance $\Delta = \inf_{c \in \mathcal{C}} \Vert b - c \Vert$, where $\mathcal{C}$ is the set of second members of consistent systems defined with the same matrix $A$, can be computed by an explicit analytical formula according to the components of the matrix $A$ and its second member $b$. In this article, we give analytical formulas analogous to that of (Baaj, 2023) to compute the Chebyshev distance associated to the second member of a system of $\max$-product fuzzy relational equations and that associated to the second member of a system of $\max$-Lukasiewicz fuzzy relational equations.
Neuro-symbolic Meta Reinforcement Learning for Trading
Authors: S I Harini, Gautam Shroff, Ashwin Srinivasan, Prayushi Faldu, Lovekesh Vig
Date: 2023-01-15 00:00:00
Tasks: Decision Making, Meta Reinforcement Learning, Program induction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

We model short-duration (e.g. day) trading in financial markets as a sequential decision-making problem under uncertainty, with the added complication of continual concept-drift. We, therefore, employ meta reinforcement learning via the RL2 algorithm. It is also known that human traders often rely on frequently occurring symbolic patterns in price series. We employ logical program induction to discover symbolic patterns that occur frequently as well as recently, and explore whether using such features improves the performance of our meta reinforcement learning algorithm. We report experiments on real data indicating that meta-RL is better than vanilla RL and also benefits from learned symbolic features.
Invoice discounting using kelly criterion by automated market makers-like implementations
Authors: Peplluis R. Esteva, Alberto Ballesteros Rodríguez
Date: 2023-01-30 00:00:00

There is a persistent lack of funding, especially for SMEs, that cyclically worsens. The factoring and invoice discounting market appears to address delays in paying commercial invoices: sellers bring still-to-be-paid invoices to financial organizations, intermediaries, typically banks that provide an advance payment. This article contains research on novel decentralized approaches to said lending services without intermediaries by using liquidity pools and its associated heuristics, creating an Automated Market Maker. In our approach, the contributed collateral and the invoice trades with risk is measured with a formula: The Kelly criterion is used to calculate the optimal premium to be contributed to a liquidity pool in the funding of the said invoices. The behavior of the algorithm is studied in several scenarios of streams of invoices with representative amounts, collaterals, payment delays, and nonpayments rates or mora. We completed the study with hack scenarios with bogus, nonpayable invoices. As a result, we have created a resilient solution that performs the best with partially collateralized invoices. The outcome is decentralized market developed with the Kelly criterion that is reasonably resilient to a wide variety of the invoicing cases that provides sound profit to liquidity providers, and several premium distribution policies were checked that contributed with extra resilience to the performance of the algorithm.
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax
Authors: Keqi Deng, Philip C. Woodland
Date: 2023-02-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

End-to-end (E2E) automatic speech recognition (ASR) implicitly learns the token sequence distribution of paired audio-transcript training data. However, it still suffers from domain shifts from training to testing, and domain adaptation is still challenging. To alleviate this problem, this paper designs a replaceable internal language model (RILM) method, which makes it feasible to directly replace the internal language model (LM) of E2E ASR models with a target-domain LM in the decoding stage when a domain shift is encountered. Furthermore, this paper proposes a residual softmax (R-softmax) that is designed for CTC-based E2E ASR models to adapt to the target domain without re-training during inference. For E2E ASR models trained on the LibriSpeech corpus, experiments showed that the proposed methods gave a 2.6% absolute WER reduction on the Switchboard data and a 1.0% WER reduction on the AESRC2020 corpus while maintaining intra-domain ASR results.
Speaker Change Detection for Transformer Transducer ASR
Authors: Jian Wu, Zhuo Chen, Min Hu, Xiong Xiao, Jinyu Li
Date: 2023-02-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Change Detection, speech-recognition, Speech Recognition

Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word sequence into paragraphs at speaker change points. Existing SCD solutions either require additional ensemble for the time based decisions and recognized word sequences, or implement a tight integration between ASR and SCD, limiting the potential optimum performance for both tasks. To address these issues, we propose a novel framework for the SCD task, where an additional SCD module is built on top of an existing Transformer Transducer ASR (TT-ASR) network. Two variants of the SCD network are explored in this framework that naturally estimate speaker change probability for each word, while allowing the ASR and SCD to have independent optimization scheme for the best performance. Experiments show that our methods can significantly improve the F1 score on LibriCSS and Microsoft call center data sets without ASR degradation, compared with a joint SCD and ASR baseline.
Output Voltage Regulation of a Fuel Cell/Boost Converter System: A PI-PBC Approach
Authors: Rafael Cisneros, Romeo Ortega, Carlo A. Beltrán, Diego Langarica-Córdoba, Luis H. Díaz-Saldierna
Date: 2023-02-17 00:00:00

In this paper we consider the problem of voltage regulation of a proton exchange membrane fuel cell connected to an uncertain load through a boost converter. We show that, in spite of the inherent nonlinearities in the current-voltage behaviour of the fuel cell, the voltage of a fuel cell/boost converter system can be regulated with a simple proportional-integral (PI) action designed following the {Passivity-based Control (PBC) approach. The system under consideration consists of a DC-DC converter interfacing a fuel cell with a resistive load. We show that the output voltage of the converter converges to its desired constant value for all the systems initial conditions -- with convergence ensured for all positive values of the PI gains. This latter feature facilitates the, usually difficult, task of tuning the gains of the PI. An Immersion and Invariance parameter estimator is afterwards proposed which allows the operation of the PI-PBC when the load is unknown, maintaining the output voltage at the desired level. The stable operation of the overall system is proved and the approach is validated with extensive numerical simulations considering real-life scenarios, where a robust behavior in spite of load variations is obtained.
Adaptive Safety-Critical Control for a Class of Nonlinear Systems with Parametric Uncertainties: A Control Barrier Function Approach
Authors: Yujie Wang, Xiangru Xu
Date: 2023-02-16 00:00:00

This paper presents a novel approach for the safe control design of systems with parametric uncertainties in both drift terms and control-input matrices. The method combines control barrier functions and adaptive laws to generate a safe controller through a nonlinear program with an explicitly given closed-form solution. The proposed approach verifies the non-emptiness of the admissible control set independently of online parameter estimations, which can ensure the safe controller is singularity-free. A data-driven algorithm is also developed to improve the performance of the proposed controller by tightening the bounds of the unknown parameters. The effectiveness of the control scheme is demonstrated through numerical simulations.
Set-based MPC for discrete-time LTI systems with maximal domain of attraction and minimal predictive control horizon
Authors: Alejandro Anderson, Agustina D'Jorge, Alejandro H. González, Antonio Ferramosca, Marcelo Actis
Date: 2019-10-01 00:00:00
Tasks: Model Predictive Control

This paper presents a novel set-based model predictive control for tracking, which provides the largest domain of attraction, even with the minimal predictive/control horizon. The formulation - which consists of a single optimization problem - shows a dual behavior: one operating inside the maximal controllable set to the feasible equilibrium set, and the other operating at the N-controllable set to the same equilibrium set. Based on some finite-time convergence results, asymptotic stability of the resulting closed-loop is proved, while recursive feasibility is ensured for any change of the setpoint. The properties and advantages of the proposal have been tested on simulation models.
Smart 6G Sky for Green Mobile IOT Networks
Authors: Qusai Bshiwa
Date: 2023-02-15 00:00:00

6G is envisioned to connect everything and yet to be a hundred times more energy efficient than the 5G. Thanks for its ability to use sources of ambient energy, energy harvesting (EH) is promising in alleviating the challenge of meeting such conflicting demands. Moreover, when it comes to the Internet of things (IoT), one of the foundations for enabling connecting everything, the need for EH may become inevitable. IoT involves connecting not only devices that are large in number, but also hard to reach. The good news, nevertheless, is that the unmanned aerial vehicle (UAV), owning to its flexibility and ease of deployment is emerging to offer communication services when infrastructure is lacking. Merging the UAV and IoT is of quite interest as the former could not just enable flexible connectivity for the IoT but also powering them in spite of any restrictions. However, managing the UAV assisted IoT resources to meet certain data communications and EH quality measures while keeping the UAV consumed energy minimized is a major challenge as this corresponds to a non-convex optimization problem. Things, obviously, become even worse when the IoT network devices are mobile. Owing to the success of artificial intelligence (AI) in solving complicated problems, in this project we rely on the deep deterministic policy gradient (DDPG) technique, to manage the UAV assisted IoT resources. Our results show that DDPG achieves joint optimization of three objectives, namely sum data rate and harvested energy maximization, and energy consumption minimization, while out performing traditional mathematical schemes. The code of this project is made publicly accessible at https://github.com/QusaiBshiwa/Smart-6G- Sky-for-Green-Mobile-IOT-Networks
Numerical analysis of a multistable capsule system under the delayed feedback control with a constant delay
Authors: Zhi Zhang, Joseph Páez Chávez, Jan Sieber, Yang Liu
Date: 2023-02-13 00:00:00
Tasks: Event Detection

The vibro-impact capsule system is a self-propelled mechanism that has abundant coexisting attractors and moves rectilinearly under periodic excitation when overcoming environmental resistance. In this paper, we study the control of coexisting attractors in this system by using a delayed feedback controller (DFC) with a constant delay. The aim of our control is to steer this complex system toward an attractor with preferable performance characteristics among multiple coexisting attractors, e.g., a periodically fast forward progression. For this purpose, we give an example of a feedback-controlled transition from a period-3 motion with low progression speed to a period-1 motion with high progression speed at the system parameters where both responses coexist. The effectiveness of this controller is investigated numerically by considering its convergence time and the required control energy input to achieve transition. We combine pseudo-spectral approximation of the delay, event detection for the discontinuities and path-following (continuation) techniques for non-smooth delay dynamical systems to carry out bifurcation analysis. We systematically study the dynamical performance of the controlled system when varying its control gain and delay time. Our numerical simulations show the effectiveness of DFC under a wide range of system parameters. We find that the desired period-1 motion is achievable in a range of control delays between a period-doubling and a grazing bifurcation. Therefore, two-parameter continuation of these two bifurcations with respect to the control delay and control gain is conducted to identify the delay-gain parameter region where the period-1 motion is stable. The findings of this work can be used for tuning control parameters in experiments, and similar analysis can be carried out for other non-smooth dynamical systems with a constant delay term.
Firm-level supply chains to minimize unemployment and economic losses in rapid decarbonization scenarios
Authors: Johannes Stangl, András Borsos, Christian Diem, Tobias Reisch, Stefan Thurner
Date: 2023-02-17 00:00:00

Urgently needed carbon emissions reductions might lead to strict command-and-control decarbonization strategies with potentially negative economic consequences. Analysing the entire firm-level production network of a European economy, we have explored how the worst outcomes of such approaches can be avoided. We compared the systemic relevance of every firm in Hungary with its annual CO2 emissions to identify optimal emission-reducing strategies with a minimum of additional unemployment and economic losses. Setting specific reduction targets, we studied various decarbonization scenarios and quantified their economic consequences. We determined that for an emissions reduction of 20%, the most effective strategy leads to losses of about 2% of jobs and 2% of economic output. In contrast, a naive scenario targeting the largest emitters first results in 28% job losses and 33% output reduction for the same target. This demonstrates that it is possible to use firm-level production networks to design highly effective decarbonization strategies that practically preserve employment and economic output.
Great year, bad Sharpe? A note on the joint distribution of performance and risk-adjusted return
Authors: Matteo Smerlak
Date: 2023-02-17 00:00:00

Returns distributions are heavy-tailed across asset classes. In this note, I examine the implications of this well-known stylized fact for the joint statistics of performance (absolute return) and Sharpe ratio (risk-adjusted return). Using both synthetic and real data, I show that, all other things being equal, the investments with the best in-sample performance are never associated with the best in-sample Sharpe ratios (and vice versa). This counter-intuitive effect is unrelated to the risk-return tradeoff familiar from portfolio theory: it is, rather, a consequence of asymptotic correlations between the sample mean and sample standard deviation of heavy-tailed variables. In addition to its large sample noise, this non-monotonic association of the Sharpe ratio with performance puts into question its status as the gold standard metric of investment quality.
A tale of two tails: 130 years of growth-at-risk
Authors: Martin Gächter, Elias Hasler, Florian Huber
Date: 2023-02-17 00:00:00
Tasks: Vocal Bursts Valence Prediction

We extend the existing growth-at-risk (GaR) literature by examining a long time period of 130 years in a time-varying parameter regression model. We identify several important insights for policymakers. First, both the level as well as the determinants of GaR vary significantly over time. Second, the stability of upside risks to GDP growth reported in earlier research is specific to the period known as the Great Moderation, with the distribution of risks being more balanced before the 1970s. Third, the distribution of GDP growth has significantly narrowed since the end of the Bretton Woods system. Fourth, financial stress is always linked to higher downside risks, but it does not affect upside risks. Finally, other risk indicators, such as credit growth and house prices, not only drive downside risks, but also contribute to increased upside risks during boom periods. In this context, the paper also adds to the financial cycle literature by completing the picture of drivers (and risks) for both booms and recessions over time.
Stable Marriage, Children, and Intrahousehold Allocations
Authors: Mikhail Freer, Khushboo Surana
Date: 2023-02-16 00:00:00

We present a revealed preference framework to study sharing of resources in households with children. We explicitly model the impact of the presence of children in the context of stable marriage markets under both potential types of custody arrangement - joint custody and sole custody. Our models deliver testable revealed preference conditions and allow for the identification of intrahousehold allocation of resources. Empirical applications to household data from the Netherlands (joint custody) and Russia (sole custody) show the methods' potential to identify intrahousehold allocation.
Contest in Multitasking: An Evidence from Chinese County Officials' Promotion Assessment
Authors: Yuanhao Zhang
Date: 2023-02-17 00:00:00

Real-world observed contests often take the form of multi-task contests rather than single-task contests, and existing theories are insufficient to explain the incentive for extending the task dimension. This paper proposes a new effect of multi-task contests compared to single-tasking contests: the specialization effect (SE). By establishing a multi-task contest model with heterogeneous competitor costs, this paper shows that after expanding the new competition dimension, competitors will choose the dimension with greater relative comparative advantage rather than absolute advantage and pay more effort, which eventually leads to competitors choosing higher effort levels in both the original dimension and the extended dimension. The paper then uses staggered Difference-in-Difference (DID) method on China's county officers' promotion assessment from 2001 to 2022 as an entry point to discuss the empirical evidence for specialization effect. Through models and empirical studies, the specialization effect studied in this paper do exists in promotion assessments, and may also explain many other real-world scenarios, such as sports events, competition between corporate compensation and employee benefits and competition for R&D expenses.
Semantic Communications with Variable-Length Coding for Extended Reality
Authors: BoWen Zhang, Zhijin Qin, Geoffrey Ye Li
Date: 2023-02-17 00:00:00
Tasks: Semantic Communication

Wireless extended reality (XR) has attracted wide attentions as a promising technology to improve users' mobility and quality of experience. However, the ultra-high data rate requirement of wireless XR has hindered its development for many years. To overcome this challenge, we develop a semantic communication framework, where semantically-unimportant information is highly-compressed or discarded in semantic coders, significantly improving the transmission efficiency. Besides, considering the fact that some source content may have less amount of semantic information or have higher tolerance to channel noise, we propose a universal variable-length semantic-channel coding method. In particular, we first use a rate allocation network to estimate the best code length for semantic information and then adjust the coding process accordingly. By adopting some proxy functions, the whole framework is trained in an end-to-end manner. Numerical results show that our semantic system significantly outperforms traditional transmission methods and the proposed variable-length coding scheme is superior to the fixed-length coding methods.
QARV: Quantization-Aware ResNet VAE for Lossy Image Compression
Authors: Zhihao Duan, Ming Lu, Jack Ma, Yuning Huang, Zhan Ma, Fengqing Zhu
Date: 2023-02-16 00:00:00
Tasks: Image Compression, Quantization

This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications. We start by reviewing the framework of variational autoencoders (VAEs), a powerful class of generative probabilistic models that has a deep connection to lossy compression. Based on VAEs, we develop a novel scheme for lossy image compression, which we name quantization-aware ResNet VAE (QARV). Our method incorporates a hierarchical VAE architecture integrated with test-time quantization and quantization-aware training, without which efficient entropy coding would not be possible. In addition, we design the neural network architecture of QARV specifically for fast decoding and propose an adaptive normalization operation for variable-rate compression. Extensive experiments are conducted, and results show that QARV achieves variable-rate compression, high-speed decoding, and a better rate-distortion performance than existing baseline methods. The code of our method is publicly accessible at https://github.com/duanzhiihao/lossy-vae
On Frequency-Domain Implementation of Digital FIR Filters Using Overlap-Add and Overlap-Save Techniques
Authors: Hakan Johansson, Oscar Gustafsson
Date: 2023-02-17 00:00:00

In this paper, new insights in frequency-domain implementations of digital finite-length impulse response filtering (linear convolution) using overlap-add and overlap-save techniques are provided. It is shown that, in practical finite-wordlength implementations, the overall system corresponds to a time-varying system that can be represented in essentially two different ways. One way is to represent the system with a distortion function and aliasing functions, which in this paper is derived from multirate filter bank representations. The other way is to use a periodically time-varying impulse-response representation or, equivalently, a set of time-invariant impulse responses and the corresponding frequency responses. The paper provides systematic derivations and analyses of these representations along with filter impulse response properties and design examples. The representations are particularly useful when analyzing the effect of coefficient quantizations as well as the use of shorter DFT lengths than theoretically required. A comprehensive computational-complexity analysis is also provided, and accurate formulas for estimating the optimal DFT lengths for given filter lengths are derived. Using optimal DFT lengths, it is shown that the frequency-domain implementations have lower computational complexities (multiplication rates) than the corresponding time-domain implementations for filter lengths that are shorter than those reported earlier in the literature. In particular, for general (unsymmetric) filters, the frequency-domain implementations are shown to be more efficient for all filter lengths. This opens up for new considerations when comparing complexities of different filter implementations.