pwc-archive/papers-with-abstracts huggingface dataset:
Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition
Authors: Hao Yue, Yingtao Wu, Min Wang, Hesuan Hu, Weimin Wu, Jihui Zhang
Date: 2024-06-28 00:00:00
Tasks: Problem Decomposition, Service Composition

Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve the efficiency and reduce the cost in CMCP, we propose a new framework which considers the collaboration among services with the same functionality. A mathematical evaluation formulation for the service composition and service usage scheme is constructed with the following critical indexes: completion time, cost, and number of selected services. Subsequently, a problem decomposition based genetic algorithm is designed to obtain the optimal service compositions with service usage schemes. A smart clothing customization case is illustrated so as to show the effectiveness and efficiency of the method proposed in this paper. Finally, the results of simulation experiments and comparisons show that these solutions obtained by our method are with the minimum time, a lower cost, and the fewer selected services.
A Thermal Study of Terahertz Induced Protein Interactions
Authors: Hadeel Elayan, Samar Elmaadawy, Andrew W. Eckford, Raviraj Adve, Josep Jornet
Date: 2024-06-27 00:00:00

Proteins can be regarded as thermal nanosensors in an intra-body network. Upon being stimulated by Terahertz (THz) frequencies that match their vibrational modes, protein molecules experience resonant absorption and dissipate their energy as heat, undergoing a thermal process. This paper aims to analyze the effect of THz signaling on the protein heat dissipation mechanism. We therefore deploy a mathematical framework based on the heat diffusion model to characterize how proteins absorb THz-electromagnetic (EM) energy from the stimulating EM fields and subsequently release this energy as heat to their immediate surroundings. We also conduct a parametric study to explain the impact of the signal power, pulse duration, and interparticle distance on the protein thermal analysis. In addition, we demonstrate the relationship between the change in temperature and the opening probability of thermally-gated ion channels. Our results indicate that a controlled temperature change can be achieved in an intra-body environment by exciting protein particles at their resonant frequencies. We further verify our results numerically using COMSOL Multiphysics and introduce an experimental framework that assesses the effects of THz radiation on protein particles. We conclude that under controlled heating, protein molecules can serve as hotspots that impact thermally-gated ion channels. Through the presented work, we infer that the heating process can be engineered on different time and length scales by controlling the THz-EM signal input.
Microgrid Planner: A Distributed Energy Resource Sizing Method
Authors: Daniel Reich
Date: 2024-06-27 00:00:00
Tasks: Heuristic Search

We present a heuristic search method for distrubuted energy resource sizing, released in Microgrid Planner, an open-source software platform. Our method is constructed to identify a wide range of microgrid design options that satisfy a given set of power load requirements, allowing a decision maker to weigh trade-offs between potential designs and select preferred solutions. We introduce a global binary search algorithm to build a diverse set of microgrid design options and refine them using a local linear search method.
Three Scores and 15 Years (1948-2023) of Rao's Score Test: A Brief History
Authors: Anil K. Bera, Yannis Bilias
Date: 2024-06-28 00:00:00
Tasks: Econometrics

Rao (1948) introduced the score test statistic as an alternative to the likelihood ratio and Wald test statistics. In spite of the optimality properties of the score statistic shown in Rao and Poti (1946), the Rao score (RS) test remained unnoticed for almost 20 years. Today, the RS test is part of the ``Holy Trinity'' of hypothesis testing and has found its place in the Statistics and Econometrics textbooks and related software. Reviewing the history of the RS test we note that remarkable test statistics proposed in the literature earlier or around the time of Rao (1948) mostly from intuition, such as Pearson (1900) goodness-fit-test, Moran (1948) I test for spatial dependence and Durbin and Watson (1950) test for serial correlation, can be given RS test statistic interpretation. At the same time, recent developments in the robust hypothesis testing under certain forms of misspecification, make the RS test an active area of research in Statistics and Econometrics. From our brief account of the history the RS test we conclude that its impact in science goes far beyond its calendar starting point with promising future research activities for many years to come.
Dynamically Consistent Analysis of Realized Covariations in Term Structure Models
Authors: Dennis Schroers
Date: 2024-06-17 00:00:00

In this article we show how to analyze the covariation of bond prices nonparametrically and robustly, staying consistent with a general no-arbitrage setting. This is, in particular, motivated by the problem of identifying the number of statistically relevant factors in the bond market under minimal conditions. We apply this method in an empirical study which suggests that a high number of factors is needed to describe the term structure evolution and that the term structure of volatility varies over time.
Single Transferable Vote and Paradoxes of Negative and Positive Involvement
Authors: David McCune
Date: 2024-06-28 00:00:00

We analyze a type of voting paradox which we term an involvement paradox, in which a candidate who loses an election could be made into a winner if more of the candidate's non-supporters participated in the election, or a winner could be made into a loser if more of the candidate's supporters participated. Such paradoxical outcomes are possible under the voting method of single transferable vote (STV), which is widely used for political elections throughout the world. We provide a worst-case analysis of involvement paradoxes under STV and show several interesting examples of these paradoxes from elections in Scotland.
Non-Linearities in International Spillovers of the ECB$^\prime$s Monetary Policy. The Case of Non-ERM II Countries and Anti-Fragmentation Policy
Authors: Iones Kelanemer Holban
Date: 2024-06-28 00:00:00

We investigate the presence of sign and size non-linearities in the impact of the European Central Bank$^\prime$s Anti-Fragmentation Policy on non-ERM II, EU countries. After identifying three orthogonal monetary policy shock using the method of Fanelli and Marsi [2022], we then select an optimal specification and estimate both linear and non linear impulse response functions using local projections (Dufour and Renault [1998], Goncalves et al. [2021]). The choice of non-linear transformations to separate sign and size effects is based on Caravello and Martinez-Bruera [Working Paper, 2024]. Lastly we compare the linear model to the non-linear ones using a battery of Wald tests and find significant evidence of sign non-linearities in the international spillovers of ECB policy.
Common Identification and Common Learning
Authors: Martin W. Cripps
Date: 2024-06-28 00:00:00

Cripps, Ely, Mailath and Samuelson (2008) showed that if there are finitely many states, and the signals are i.i.d and finite, then individual learning is sufficient for common learning. In this note we describe what is commonly learned when this sufficient condition does not hold.
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging
Authors: Takashi Matsuda, Ryo Hayakawa, Youji Iiguni
Date: 2024-06-28 00:00:00
Tasks: Denoising, Video Segmentation, Video Semantic Segmentation

Snapshot compressive imaging (SCI) captures high-dimensional data efficiently by compressing it into two-dimensional observations and reconstructing high-dimensional data from two-dimensional observations with various algorithms. The plug-and-play (PnP) method is a promising approach for the video SCI reconstruction because it can leverage both observation models and denoising methods for videos. Since the reconstruction accuracy significantly depends on the choice of noise level parameters, this paper proposes a deep unfolding-based method for tuning these parameters in PnP-based video SCI. For the training of the parameters, we prepare training data from the densely annotated video segmentation dataset, reparametrize the noise level parameters, and apply the checkpointing technique to reduce the required memory. Simulation results show that the trained noise level parameters via the proposed approach exhibit a non-monotonic pattern, which is different from the assumptions in the conventional convergence analyses of PnP-based algorithms. These findings provide new insights into both the application of deep unfolding and the theoretical basis of PnP algorithms.
Frequency Modulation for Task-Oriented Communications and Multiple Access
Authors: Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Date: 2024-06-28 00:00:00

In the context of task-oriented communications we advocate the development of waveforms for Federated Edge Learning (FEEL). Over-the-air computing (AirComp) has emerged as a communication scheme that allows to compute a function out of distributed data and can be applied to FEEL. However, the design of modulations for AirComp is still in its infancy and most of the literature ignores this topic. In this work we employ frequency modulation (FM) and type based multiple access (TMBA) for FEEL and demonstrate its advantages with respect to the state of the art in terms of convergence and peak-to-average power ratio (PAPR).
Reconfigurable Intelligent Surfaces for 6G Mobile Networks: An Industry R&D Perspective
Authors: Maik Sode, Michael Ponschab, Lucas N. Ribeiro, Sven Haesloop, Ehsan Tohidi, Michael Peter, Sławomir Stańczak, Bilal H. Mohamed, Wilhelm Keusgen, Heinz Mellein, Eslam Yassin, Bernd Schroeder
Date: 2024-06-28 00:00:00

The reconfigurable intelligent surface (RIS) technology is a potential solution to enhance network capacity and coverage without significant investment in additional infrastructure in 6G networks. This work highlights the interest of the mobile communication industry in RIS, and discusses the development of liquid crystal-based RIS for improved energy efficiency and coverage in the millimeter-wave band. Furthermore, the paper discusses perspectives and insights from an industry R&D point of view, addressing relevant use cases, technical requirements, implementation challenges, and practical considerations for RIS deployment optimization in the context of 6G networks. A hardware design of an RIS with liquid crystal at 28 GHz is presented. A propagation model for RIS as a new part of the system architecture is discussed, with approaches of semi-empirical models, geometric models, and their combination through the application of artificial intelligence/machine learning. Finally, a channel model for deployment optimization and dimensioning is presented, with the findings that a rather large RIS is favorable for coverage improvement, as well as greater attenuation at higher frequencies combined with a smaller RIS size.
LUT-Assisted Clock Data Recovery and Equalization for Burst-Mode 50-100 Gbit/s Bandwidth-Limited Flexible PON
Authors: Yanlu Huang, Liyan Wu, Shangya Han, Kai Jin, Kun Xu, Yanni Ou
Date: 2024-06-28 00:00:00
Tasks: Sensitivity

We demonstrated LUT-assisted CDR and equalization for burst-mode 50-100 Gbit/s bandwidth-limited PON, achieving signal recovery under large 100 ppm frequency offsets and 0.5 UI phase mismatch using reduced 50ns preambles, with 0.3dB sensitivity penalty only.
Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels
Authors: Jie Zhang, Jun Li, Zhe Wang, Yu Han, Long Shi, Bin Cao
Date: 2024-06-28 00:00:00
Tasks: Reinforcement Learning (RL)

In this paper, we propose a novel diffusion-decision transformer (D2T) architecture to optimize the beamforming strategies for intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) communication systems. The first challenge lies in the expensive computation cost to recover the real-time channel state information (CSI) from the received pilot signals, which usually requires prior knowledge of the channel distributions. To reduce the channel estimation complexity, we adopt a diffusion model to automatically learn the mapping between the received pilot signals and channel matrices in a model-free manner. The second challenge is that, the traditional optimization or reinforcement learning (RL) algorithms cannot guarantee the optimality of the beamforming policies once the channel distribution changes, and it is costly to resolve the optimized strategies. To enhance the generality of the decision models over varying channel distributions, we propose an offline pre-training and online fine-tuning decision transformer (DT) framework, wherein we first pre-train the DT offline with the data samples collected by the RL algorithms under diverse channel distributions, and then fine-tune the DT online with few-shot samples under a new channel distribution for a generalization purpose. Simulation results demonstrate that, compared with retraining RL algorithms, the proposed D2T algorithm boosts the convergence speed by 3 times with only a few samples from the new channel distribution while enhancing the average user data rate by 6%.
Functional Basis Analysis for the Characterization of Power System Signal Dynamics: Formulation, Implementation and Validation
Authors: Alexandra Karpilow, Asja Derviskadic, Mario Paolone
Date: 2024-06-28 00:00:00

With the integration of distributed energy resources and the trend towards low-inertia power grids, the frequency and severity of grid dynamics is expected to increase. Conventional phasor-based signal processing methods are proving to be insufficient in the analysis of non-stationary AC voltage and current waveforms, while the computational complexity of many dynamic signal analysis techniques hinders their deployment in operational embedded systems. This paper presents the Functional Basis Analysis (FBA), a signal processing tool capable of capturing the full broadband nature of signal dynamics in power grids while maintaining a streamlined design for real-time monitoring applications. Relying on the Hilbert transform and optimization techniques, the FBA can be user-engineered to identify and characterize combinations of several of the most common signal dynamics in power grids, including amplitude/phase modulations, frequency ramps and steps. This paper describes the theoretical basis and design of the FBA as well as the deployment of the algorithm in embedded hardware systems, with adaptations made to consider latency requirements, finite memory capacity, and fixed-point precision arithmetic. For validation, a PMU calibrator is used to evaluate and compare the algorithm's performance to state-of-the-art static and dynamic phasor methods. The test outcomes demonstrate the FBA method's suitability for implementation in embedded systems to improve grid situational awareness during severe grid events.
Localization in Multipath Environments via Active Sensing with Reconfigurable Intelligent Surfaces
Authors: Yinghan Li, Wei Yu
Date: 2024-06-27 00:00:00
Tasks: Indoor Localization

This letter investigates an uplink pilot-based wireless indoor localization problem in a multipath environment for a single-input single-output (SISO) narrowband communication system aided by reconfigurable intelligent surface (RIS). The indoor localization problem is challenging because the uplink channel consists of multiple overlapping propagation paths with varying amplitudes and phases, which are not easy to differentiate. This letter proposes the use of RIS capable of adaptively changing its reflection pattern to sense such a multiple-path environment. Toward this end, we train a long-short-term-memory (LSTM) based controller to perform adaptive sequential reconfigurations of the RIS over multiple stages and propose to group multiple pilots as input in each stage. Information from the multiple paths is captured by training the LSTM to generate multiple RIS configurations to align to the different paths within each stage. Experimental results show that the proposed approach is effective in significantly reducing training complexity while maintaining localization performance at fixed number of pilots.
Object Space is Embodied
Authors: Shan Xu, Xinran Feng, Yuannan Li, Jia Liu
Date: 2024-06-28 00:00:00
Tasks: Object

The perceived similarity between objects has often been attributed to their physical and conceptual features, such as appearance and animacy, and the theoretical framework of object space is accordingly conceived. Here, we extend this framework by proposing that object space may also be defined by embodied features, specifically action possibilities that objects afford to an agent (i.e., affordance) and their spatial relation with the agent (i.e., situatedness). To test this proposal, we quantified the embodied features with a set of action atoms. We found that embodied features explained the subjective similarity among familiar objects along with the objects' visual features. This observation was further replicated with novel objects. Our study demonstrates that embodied features, which place objects within an ecological context, are essential in constructing object space in the human visual system, emphasizing the importance of incorporating embodiment as a fundamental dimension in our understanding of the visual world.
Enhancing Terrestrial Net Primary Productivity Estimation with EXP-CASA: A Novel Light Use Efficiency Model Approach
Authors: Guanzhou Chen, Kaiqi Zhang, Xiaodong Zhang, Hong Xie, Haobo Yang, Xiaoliang Tan, Tong Wang, Yule Ma, Qing Wang, Jinzhou Cao, Weihong Cui
Date: 2024-06-28 00:00:00

The Light Use Efficiency model, epitomized by the CASA model, is extensively applied in the quantitative estimation of vegetation Net Primary Productivity. However, the classic CASA model is marked by significant complexity: the estimation of environmental stress parameters, in particular, necessitates multi-source observation data, adding to the complexity and uncertainty of the model's operation. Additionally, the saturation effect of the Normalized Difference Vegetation Index (NDVI), a key variable in the CASA model, weakened the accuracy of CASA's NPP predictions in densely vegetated areas. To address these limitations, this study introduces the Exponential-CASA (EXP-CASA) model. The EXP-CASA model effectively improves the CASA model by using novel functions for estimating the fraction of absorbed photosynthetically active radiation (FPAR) and environmental stress, by utilizing long-term observational data from FLUXNET and MODIS surface reflectance data. In a comparative analysis of NPP estimation accuracy among four different NPP products, EXP-CASA ($R^2 = 0.68, RMSE= 1.1gC\cdot m^{-2} \cdot d^{-1}$) outperforms others, followed by GLASS-NPP, and lastly MODIS-NPP and classic CASA. Additionally, this research assesses the EXP-CASA model's adaptability to various vegetation indices, evaluates the sensitivity and stability of its parameters over time, and compares its accuracy against other leading NPP estimation products. The findings reveal that the EXP-CASA model exhibits strong adaptability to diverse vegetation indices and stability of model parameters over time series. By introducing a novel estimation approach that optimizes model construction, the EXP-CASA model remarkably improves the accuracy of NPP estimations and paves the way for global-scale, consistent, and continuous assessment of vegetation NPP.
ProtSolM: Protein Solubility Prediction with Multi-modal Features
Authors: Yang Tan, Jia Zheng, Liang Hong, Bingxin Zhou
Date: 2024-06-28 00:00:00
Tasks: Prediction

Understanding protein solubility is essential for their functional applications. Computational methods for predicting protein solubility are crucial for reducing experimental costs and enhancing the efficiency and success rates of protein engineering. Existing methods either construct a supervised learning scheme on small-scale datasets with manually processed physicochemical properties, or blindly apply pre-trained protein language models to extract amino acid interaction information. The scale and quality of available training datasets leave significant room for improvement in terms of accuracy and generalization. To address these research gaps, we propose \sol, a novel deep learning method that combines pre-training and fine-tuning schemes for protein solubility prediction. ProtSolM integrates information from multiple dimensions, including physicochemical properties, amino acid sequences, and protein backbone structures. Our model is trained using \data, the largest solubility dataset that we have constructed. PDBSol includes over $60,000$ protein sequences and structures. We provide a comprehensive leaderboard of existing statistical learning and deep learning methods on independent datasets with computational and experimental labels. ProtSolM achieved state-of-the-art performance across various evaluation metrics, demonstrating its potential to significantly advance the accuracy of protein solubility prediction.
An empirical study of market risk factors for Bitcoin
Authors: Shubham Singh
Date: 2024-05-24 00:00:00

The study examines whether fama-french equity factors can effectively explain the idiosyncratic risk and return characteristics of Bitcoin. By incorporating Fama-french factors, the explanatory power of these factors on Bitcoin's excess returns over various moving average periods is tested through applications of several statistical methods. The analysis aims to determine if equity market factors are significant in explaining and modeling systemic risk in Bitcoin.
Optimal consumption under loss-averse multiplicative habit-formation preferences
Authors: Bahman Angoshtari, Xiang Yu, Fengyi Yuan
Date: 2024-06-28 00:00:00

This paper studies a loss-averse version of the multiplicative habit formation preference and the corresponding optimal investment and consumption strategies over an infinite horizon. The agent's consumption preference is depicted by a general S-shaped utility function of her consumption-to-habit ratio. By considering the concave envelope of the S-shaped utility and the associated dual value function, we provide a thorough analysis of the HJB equation for the concavified problem via studying a related nonlinear free boundary problem. Based on established properties of the solution to this free boundary problem, we obtain the optimal consumption and investment policies in feedback form. Some new and technical verification arguments are developed to cope with generality of the utility function. The equivalence between the original problem and the concavified problem readily follows from the structure of the feedback controls. We also discuss some quantitative properties of the optimal policies, complemented by illustrative numerical examples and their financial implications.