pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Large Language Models for Collective Decision-Making
Authors: Marios Papachristou, Longqi Yang, Chin-Chia Hsu
Date: 2023-11-03 00:00:00
Tasks: Decision Making, Scheduling

In various work contexts, such as meeting scheduling, collaborating, and project planning, collective decision-making is essential but often challenging due to diverse individual preferences, varying work focuses, and power dynamics among members. To address this, we propose a system leveraging Large Language Models (LLMs) to facilitate group decision-making by managing conversations and balancing preferences among individuals. Our system aims to extract individual preferences from each member's conversation with the system and suggest options that satisfy the preferences of the members. We specifically apply this system to corporate meeting scheduling. We create synthetic employee profiles and simulate conversations at scale, leveraging LLMs to evaluate the system performance as a novel approach to conducting a user study. Our results indicate efficient coordination with reduced interactions between the members and the LLM-based system. The system refines and improves its proposed options over time, ensuring that many of the members' individual preferences are satisfied in an equitable way. Finally, we conduct a survey study involving human participants to assess our system's ability to aggregate preferences and reasoning about them. Our findings show that the system exhibits strong performance in both dimensions.
The Impact of Preference Agreement in Reinforcement Learning from Human Feedback: A Case Study in Summarization
Authors: Sian Gooding, Hassan Mansoor
Date: 2023-11-02 00:00:00
Tasks: Text Generation, Text Summarization

Reinforcement Learning from Human Feedback (RLHF) can be used to capture complex and nuanced properties of text generation quality. As a result, the task of text summarization has been identified as a good candidate for this process. In this paper, we explore how preference agreement impacts the efficacy of RLHF for summarization. We show that sampling human preferences to include a range of annotator agreement results in (1) higher accuracy reward models and (2) alters the characteristics of quality captured. We additionally show improvements in downstream generation when using a reward model trained with a range of preference agreements. Our contributions have implications for the design of synthetic datasets as well as the importance of considering quality differentials in comparison-based data.
Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
Authors: Yoon Kyung Lee, Inju Lee, Minjung Shin, Seoyeon Bae, Sowon Hahn
Date: 2023-11-02 00:00:00

We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce Large Language Models (LLMs) to reason about human emotional states. This method is inspired by various psychotherapy approaches including Cognitive Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality Therapy (RT), each leading to different patterns of interpreting clients' mental states. LLMs without reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathetic responses aligned with the different reasoning patterns of each psychotherapy model. The CBT based CoE resulted in the most balanced generation of empathetic responses. The findings underscore the importance of understanding the emotional context and how it affects human and AI communication. Our research contributes to understanding how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.
From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems
Authors: Samyar Janatian, Hannes Westermann, Jinzhe Tan, Jaromir Savelka, Karim Benyekhlef
Date: 2023-11-01 00:00:00

Encoding legislative text in a formal representation is an important prerequisite to different tasks in the field of AI & Law. For example, rule-based expert systems focused on legislation can support laypeople in understanding how legislation applies to them and provide them with helpful context and information. However, the process of analyzing legislation and other sources to encode it in the desired formal representation can be time-consuming and represents a bottleneck in the development of such systems. Here, we investigate to what degree large language models (LLMs), such as GPT-4, are able to automatically extract structured representations from legislation. We use LLMs to create pathways from legislation, according to the JusticeBot methodology for legal decision support systems, evaluate the pathways and compare them to manually created pathways. The results are promising, with 60% of generated pathways being rated as equivalent or better than manually created ones in a blind comparison. The approach suggests a promising path to leverage the capabilities of LLMs to ease the costly development of systems based on symbolic approaches that are transparent and explainable.
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion
Authors: Mohammad Bokaei, Saeed Razavikia, Stefano Rini, Arash Amini, Hamid Behrouzi
Date: 2023-11-08 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Retrieval

In this paper, we investigate the problem of recovering the frequency components of a mixture of $K$ complex sinusoids from a random subset of $N$ equally-spaced time-domain samples. Because of the random subset, the samples are effectively non-uniform. Besides, the frequency values of each of the $K$ complex sinusoids are assumed to vary continuously within a given range. For this problem, we propose a two-step strategy: (i) we first lift the incomplete set of uniform samples (unavailable samples are treated as missing data) into a structured matrix with missing entries, which is potentially low-rank; then (ii) we complete the matrix using a weighted nuclear minimization problem. We call the method a \emph{ weighted lifted-structured (WLi) low-rank matrix recovery}. Our approach can be applied to a range of matrix structures such as Hankel and double-Hankel, among others, and provides improvement over the unweighted existing schemes such as EMaC and DEMaC. We provide theoretical guarantees for the proposed method, as well as numerical simulations in both noiseless and noisy settings. Both the theoretical and the numerical results confirm the superiority of the proposed approach.
Design and Control of a VTOL Aerial Vehicle Tilting its Rotors Only with Rotor Thrusts and a Passive Joint
Authors: Takumi Ito, Riku Funada, Mitsuji Sampei
Date: 2023-11-09 00:00:00

This paper presents a novel VTOL UAV that owns a link connecting four rotors and a fuselage by a passive joint, allowing the control of the rotor's tilting angle by adjusting the rotors' thrust. This unique structure contributes to eliminating additional actuators, such as servo motors, to control the tilting angles of rotors, resulting in the UAV's weight lighter and simpler structure. We first derive the dynamical model of the newly designed UAV and analyze its controllability. Then, we design the controller that leverages the tiltable link with four rotors to accelerate the UAV while suppressing a deviation of the UAV's angle of attack from the desired value to restrain the change of the aerodynamic force. Finally, the validity of the proposed control strategy is evaluated in simulation study.
Fuel-Optimal Powered Descent Guidance for Hazardous Terrain
Authors: Sheikh Zeeshan Basar, Satadal Ghosh
Date: 2023-11-09 00:00:00

Future interplanetary missions will carry more and more sensitive equipment critical for setting up bases for crewed missions. The ability to manoeuvre around hazardous terrain thus becomes a critical mission aspect. However, large diverts and manoeuvres consume a significant amount of fuel, leading to less fuel remaining for emergencies or return missions. Thus, requiring more fuel to be carried onboard. This work presents fuel-optimal guidance to avoid hazardous terrain and safely land at the desired location. We approximate the hazardous terrain as step-shaped polygons and define barriers around the terrain. Using an augmented cost functional, fuel-optimal guidance command, which avoids the terrain, is derived. The results are validated using computer simulations and tested against many initial conditions to prove their effectiveness.
Dynamic Adaptation Gains for Nonlinear Systems with Unmatched Uncertainties
Authors: Brett T. Lopez, Jean-Jacques Slotine
Date: 2023-11-09 00:00:00

We present a new direct adaptive control approach for nonlinear systems with unmatched and matched uncertainties. The method relies on adjusting the adaptation gains of individual unmatched parameters whose adaptation transients would otherwise destabilize the closed-loop system. The approach also guarantees the restoration of the adaptation gains to their nominal values and can readily incorporate direct adaptation laws for matched uncertainties. The proposed framework is general as it only requires stabilizability for all possible models.
Improving Whispered Speech Recognition Performance using Pseudo-whispered based Data Augmentation
Authors: Zhaofeng Lin, Tanvina Patel, Odette Scharenborg
Date: 2023-11-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

Whispering is a distinct form of speech known for its soft, breathy, and hushed characteristics, often used for private communication. The acoustic characteristics of whispered speech differ substantially from normally phonated speech and the scarcity of adequate training data leads to low automatic speech recognition (ASR) performance. To address the data scarcity issue, we use a signal processing-based technique that transforms the spectral characteristics of normal speech to those of pseudo-whispered speech. We augment an End-to-End ASR with pseudo-whispered speech and achieve an 18.2% relative reduction in word error rate for whispered speech compared to the baseline. Results for the individual speaker groups in the wTIMIT database show the best results for US English. Further investigation showed that the lack of glottal information in whispered speech has the largest impact on whispered speech ASR performance.
Risk-aware Scheduling and Dispatch of Flexibility Events in Buildings
Authors: Paul Scharnhorst, Baptiste Schubnel, Rafael E. Carrillo, Pierre-Jean Alet, Colin N. Jones
Date: 2023-11-09 00:00:00
Tasks: Scheduling

Residential and commercial buildings, equipped with systems such as heat pumps (HPs), hot water tanks, or stationary energy storage, have a large potential to offer their consumption flexibility as grid services. In this work, we leverage this flexibility to react to consumption requests related to maximizing self-consumption and reducing peak loads. We employ a data-driven virtual storage modeling approach for flexibility prediction in the form of flexibility envelopes for individual buildings. The risk-awareness of this prediction is inherited by the proposed scheduling algorithm. A Mixed-integer Linear Program (MILP) is formulated to schedule the activation of a pool of buildings in order to best respond to an external aggregated consumption request. This aggregated request is then dispatched to the active individual buildings, based on the previously determined schedule. The effectiveness of the approach is demonstrated by coordinating up to 500 simulated buildings using the Energym Python library and observing about 1.5 times peak power reduction in comparison with a baseline approach while maintaining comfort more robustly. We demonstrate the scalability of the approach by solving problems with 2000 buildings in about 21 seconds, with solving times being approximately linear in the number of considered assets.
Efficacy Analysis of Power Swing Blocking and Out-of-Step Tripping Functions in Grid-Following VSC Systems
Authors: Yongxin Xiong, Heng Wu, Xiongfei Wang, Yifei Li
Date: 2023-11-09 00:00:00
Tasks: Blocking

Power flow oscillations can occur in power systems after major disturbances such as system faults, which may result in significant power swings and even lead to system collapse. This paper provides a detailed analysis of the efficacy of the legacy power swing blocking and out-of-step tripping functions in grid-following voltage-source converter (GFL-VSC) systems. First, the power swing dynamics of GFL-VSC are characterized, considering the outer power control loops and phase-locked loop (PLL). It is found that the loss of synchronism (LOS) of the GFL-VSC system is caused by the divergence of the output angle of the PLL, which is fundamentally different from that of synchronous generator (SG)-based systems, whose LOS originates from the divergence of power angle differences between power sources. Therefore, the legacy setting principles for power swing blocking and out-of-step tripping function designed for SG-based systems can not be directly applied to GFL-VSC systems, and may even malfunction. Time-domain simulations are performed in the PSCAD/EMTDC platform to validate the correctness of the theoretical analysis.
On the security of randomly transformed quadratic programs for privacy-preserving cloud-based control
Authors: Philipp Binfet, Nils Schlüter, Moritz Schulze Darup
Date: 2023-11-09 00:00:00
Tasks: Model Predictive Control, Privacy Preserving

Control related data, such as system states and inputs or controller specifications, is often sensitive. Meanwhile, the increasing connectivity of cloud-based or networked control results in vast amounts of such data, which poses a privacy threat, especially when evaluation on external platforms is considered. In this context, a cipher based on a random affine transformation gained attention, which is supposed to enable privacy-preserving evaluations of quadratic programs (QPs) with little computational overhead compared to other methods. This paper deals with the security of such randomly transformed QPs in the context of model predictive control (MPC). In particular, we show how to construct attacks against this cipher and thereby underpin concerns regarding its security in a practical setting. To this end, we exploit invariants under the transformations and common specifications of MPC-related QPs. Our numerical examples then illustrate that these two ingredients suffice to extract information from ciphertexts.
Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity markets
Authors: Vineet Jagadeesan Nair, Priyank Srivastava, Anuradha Annaswamy
Date: 2023-11-09 00:00:00

We propose using a hierarchical retail market structure to alert and dispatch resources to mitigate cyber-physical attacks on a distribution grid. We simulate attacks where a number of generation nodes in a distribution grid are attacked. We show that the market is able to successfully meet the shortfall between demand and supply by utilizing the flexibility of remaining resources while minimizing any extra power that needs to be imported from the main transmission grid. This includes utilizing upward flexibility or reserves of remaining online generators and some curtailment or shifting of flexible loads, which results in higher costs. Using price signals and market-based coordination, the grid operator can achieve its objectives without direct control over distributed energy resources and is able to accurately compensate prosumers for the grid support they provide.
Reinforcement Learning Generalization for Nonlinear Systems Through Dual-Scale Homogeneity Transformations
Authors: Abdel Gafoor Haddad, Igor Boiko, Yahya Zweiri
Date: 2023-11-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Reinforcement learning is an emerging approach to control dynamical systems for which classical approaches are difficult to apply. However, trained agents may not generalize against the variations of system parameters. This paper presents the concept of dual-scale homogeneity, an important property in understating the scaling behavior of nonlinear systems. Furthermore, it also presents an effective yet simple approach to designing a parameter-dependent control law that homogenizes a nonlinear system. The presented approach is applied to two systems, demonstrating its ability to provide a consistent performance irrespective of parameters variations. To demonstrate the practicality of the proposed approach, the control policy is generated by a deep deterministic policy gradient to control the load position of a quadrotor with a slung load. The proposed synergy between the homogeneity transformations and reinforcement learning yields superior performance compared to other recent learning-based control techniques. It achieves a success rate of 96% in bringing the load to its designated target with a 3D RMSE of 0.0253 m. The video that shows the experimental results along with a summary of the paper is available at this link.
An non-uniform sampling strategy for physiological signals component analysis
Authors: Molin Jia, Chaoyang Wang, Kui-Ting Chen, Takaaki Baba
Date: 2013-01-01 00:00:00
Proceeding: IEEE International Conference on Consumer Electronics (ICCE) 2013 1

The conventional approach cannot meet the requirement of physiological signal analysis to extract the main component of the acquired signal. This paper proposes a non-uniform sampling strategy with corresponding fast Fourier transform (FFT) for signal component analysis. The simulation results of different approaches are analyzed and compared. The proposed strategy has the superior anti-aliasing performance than conventional approach with lower sampling rate.
Towards a Taxonomy of Large Language Model based Business Model Transformations
Authors: Jochen Wulf, Juerg Meierhofer
Date: 2023-11-09 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model

Research on the role of Large Language Models (LLMs) in business models and services is limited. Previous studies have utilized econometric models, technical showcases, and literature reviews. However, this research is pioneering in its empirical examination of the influence of LLMs at the firm level. The study introduces a detailed taxonomy that can guide further research on the criteria for successful LLM-based business model implementation and deepen understanding of LLM-driven business transformations. Existing knowledge on this subject is sparse and general. This research offers a more detailed business model design framework based on LLM-driven transformations. This taxonomy is not only beneficial for academic research but also has practical implications. It can act as a strategic tool for businesses, offering insights and best practices. Businesses can lev-erage this taxonomy to make informed decisions about LLM initiatives, ensuring that technology in-vestments align with strategic goals.
Workplace diversity and innovation performance: current state of affairs and future directions
Authors: Christian R. Østergaard, Bram Timmermans
Date: 2023-11-09 00:00:00
Tasks: Diversity

Over the last 10 years, there has been a growing interest in diversity in human capital. Fueled by the business case for diversity, there is an increasing interest in understanding how the combination of people with different backgrounds fosters the innovation performance of firms. Studies have measured diversity on a wide range of personal-level characteristics, at different levels of the organization, and in particular kinds of settings. Innovation performance has been measured using an arsenal of indicators, often drawing on a large range of databases. This paper takes stock of this research, identifying the current state of affairs and proposing future research trajectories in the field of diversity and innovation
City formation by dual migration of firms and workers
Authors: Kensuke Ohtake
Date: 2023-11-09 00:00:00

This paper studies a mathematical model of city formation by migration of firms and workers. The Core-Periphery model in the new economic geography, which considers migration of workers driven by real wage inequality among regions, is extended to incorporate migration of firms driven by real profit inequality among regions. Spatially homogeneous distributions of firms and workers become destabilized and eventually forms several cities in which both the firms and workers agglomerate, and the number of the cities decreases as transport costs become lower.
Uncertainty-Aware Bayes' Rule and Its Applications
Authors: Shixiong Wang
Date: 2023-11-09 00:00:00
Tasks: Philosophy

Bayes' rule has enabled innumerable powerful algorithms of statistical signal processing and statistical machine learning. However, when model misspecifications exist in prior and/or data distributions, the direct application of Bayes' rule is questionable. Philosophically, the key is to balance the relative importance between prior and data distributions when calculating posterior distributions: if prior distributions are overly conservative (i.e., exceedingly spread), we upweight the prior belief; if prior distributions are overly opportunistic (i.e., exceedingly concentrated), we downweight the prior belief. The same operation also applies to data distributions. This paper studies a generalized Bayes' rule, called uncertainty-aware Bayes' rule, to technically realize the above philosophy, thus combating the model uncertainties in prior and/or data distributions. Applications of the uncertainty-aware Bayes' rule on classification and estimation are discussed: In particular, the uncertainty-aware Bayes classifier, the uncertainty-aware Kalman filter, the uncertainty-aware particle filter, and the uncertainty-aware interactive-multiple-model filter are suggested and experimentally validated.
Energy-Efficient Analog Beamforming for RF-WET with Charging Time Constraint
Authors: Osmel Martínez Rosabal, Onel L. Alcaraz López, Hirley Alves
Date: 2023-11-09 00:00:00

Internet of Things (IoT) sustainability may hinge on radio frequency wireless energy transfer (RF-WET). However, energy-efficient charging strategies are still needed, motivating our work. Specifically, this letter proposes a time division scheme to efficiently charge low-power devices in an IoT network. For this, a multi-antenna power beacon (PB) drives the devices' energy harvesting circuit to the highest power conversion efficiency point via energy beamforming, thus achieving minimum energy consumption. Herein, we adopt the analog multi-antenna architecture due to its low complexity, cost, and energy consumption. The proposal includes a simple yet accurate model for the transfer characteristic of the energy harvesting circuit, enabling the optimization framework. The results evince the effectiveness of our RF-WET strategy over a benchmark scheme where the PB charges all the IoT devices simultaneously. Furthermore, the performance increases with the number of PB antennas.