pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Intelligence, Scientific Discovery, and Product Innovation
Authors: Aidan Toner-Rodgers
Date: 2024-12-21 00:00:00
Tasks: scientific discovery

This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of "idea-generation" tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.
Emergent poverty traps and inequality at multiple levels impedes social mobility
Authors: Charles Dupont, Debraj Roy
Date: 2024-12-09 00:00:00

Eradicating extreme poverty and inequality are the key leverage points to achieve the seventeen Sustainable Development goals. Yet, the reduction in extreme poverty and inequality are vulnerable to shocks such as the pandemic and climate change. We find that that these vulnerabilities emerge from the interaction between individual and institutional mechanisms. Individual characteristics like risk aversion, attention, and saving propensity can lead to sub-optimal diversification and low capital accumulation. These individual drivers are reinforced by institutional mechanisms such as lack of financial inclusion, access to technology, and economic segregation, leading to persistent inequality and poverty traps. Our experiments demonstrate that addressing above factors yields 'double dividend' - reducing poverty and inequality within-and-between communities and create positive feedback that can withstand shocks.
Calibrating the Subjective
Authors: Mark Whitmeyer
Date: 2024-12-24 00:00:00

I conduct Rabin's (2000) calibration exercise in the subjective expected utility realm. I show that the rejection of some risky bet by a risk-averse agent only implies the rejection of more extreme and less desirable bets and nothing more.
Robust Equilibria in Generic Extensive form Games
Authors: Lucas Pahl, Carlos Pimienta
Date: 2024-12-24 00:00:00
Tasks: Form

We prove the 2-player, generic extensive-form case of the conjecture of Govindan and Wilson (1997a,b) and Hauk and Hurkens (2002) stating that an equilibrium component is essential in every equivalent game if and only if the index of the component is nonzero. This provides an index-theoretic characterization of the concept of hyperstable components of equilibria in generic extensive-form games, first formulated by Kohlberg and Mertens (1986). We also illustrate how to compute hyperstable equilibria in multiple economically relevant examples and show how the predictions of hyperstability compare with other solution concepts.
Profit Allocation in the We Media Value Chain: A Shapley Value-Based Approach
Authors: Jianfei Xu, Rui Zhang, Junhui Fan
Date: 2024-12-24 00:00:00

The study takes the social media industry as its research subject and examines the impact of scientific innovation capabilities on profit distribution within the value chain of the social media industry. It proposes a specific solution to the profit distribution problem using an improved Shapley value method. Additionally, the AHP (Analytic Hierarchy Process) is employed to evaluate the profit distribution model, allowing the improved Shapley value method to better address the issue of profit allocation within the value chain of the social media industry. This approach ensures that each member receives a fair share of the profits, fostering strong cooperative relationships among members. Moreover, it compensates for the shortcomings of the traditional Shapley value method in addressing such problems to a certain extent.
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction
Authors: Stefano Damiano, Federico Miotello, Mirco Pezzoli, Alberto Bernardini, Fabio Antonacci, Augusto Sarti, Toon van Waterschoot
Date: 2024-12-24 00:00:00
Tasks: Dictionary Learning

Sound field reconstruction aims to estimate pressure fields in areas lacking direct measurements. Existing techniques often rely on strong assumptions or face challenges related to data availability or the explicit modeling of physical properties. To bridge these gaps, this study introduces a zero-shot, physics-informed dictionary learning approach to perform sound field reconstruction. Our method relies only on a few sparse measurements to learn a dictionary, without the need for additional training data. Moreover, by enforcing the Helmholtz equation during the optimization process, the proposed approach ensures that the reconstructed sound field is represented as a linear combination of a few physically meaningful atoms. Evaluations on real-world data show that our approach achieves comparable performance to state-of-the-art dictionary learning techniques, with the advantage of requiring only a few observations of the sound field and no training on a dataset.
Optimizing Prompt Strategies for SAM: Advancing lesion Segmentation Across Diverse Medical Imaging Modalities
Authors: Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai, Sophie Yao, Zhusi Zhong, Zishu Zhang, Jing Wu, Aaron Maxwell, Scott Collins, Zhicheng Jiao, Harrison X. Bai
Date: 2024-12-23 00:00:00
Tasks: Lesion Segmentation, Reinforcement Learning (RL), Segmentation

Purpose: To evaluate various Segmental Anything Model (SAM) prompt strategies across four lesions datasets and to subsequently develop a reinforcement learning (RL) agent to optimize SAM prompt placement. Materials and Methods: This retrospective study included patients with four independent ovarian, lung, renal, and breast tumor datasets. Manual segmentation and SAM-assisted segmentation were performed for all lesions. A RL model was developed to predict and select SAM points to maximize segmentation performance. Statistical analysis of segmentation was conducted using pairwise t-tests. Results: Results show that increasing the number of prompt points significantly improves segmentation accuracy, with Dice coefficients rising from 0.272 for a single point to 0.806 for five or more points in ovarian tumors. The prompt location also influenced performance, with surface and union-based prompts outperforming center-based prompts, achieving mean Dice coefficients of 0.604 and 0.724 for ovarian and breast tumors, respectively. The RL agent achieved a peak Dice coefficient of 0.595 for ovarian tumors, outperforming random and alternative RL strategies. Additionally, it significantly reduced segmentation time, achieving a nearly 10-fold improvement compared to manual methods using SAM. Conclusion: While increased SAM prompts and non-centered prompts generally improved segmentation accuracy, each pathology and modality has specific optimal thresholds and placement strategies. Our RL agent achieved superior performance compared to other agents while achieving a significant reduction in segmentation time.
LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency
Authors: Achintha Wijesinghe, Suchinthaka Wanninayaka, Weiwei Wang, Yu-Chieh Chao, Songyang Zhang, Zhi Ding
Date: 2024-12-18 00:00:00
Tasks: Edge-computing, Generative Adversarial Network

The recent rise of semantic-style communications includes the development of goal-oriented communications (GOCOMs) remarkably efficient multimedia information transmissions. The concept of GO-COMS leverages advanced artificial intelligence (AI) tools to address the rising demand for bandwidth efficiency in applications, such as edge computing and Internet-of-Things (IoT). Unlike traditional communication systems focusing on source data accuracy, GO-COMs provide intelligent message delivery catering to the special needs critical to accomplishing downstream tasks at the receiver. In this work, we present a novel GO-COM framework, namely LaMI-GO that utilizes emerging generative AI for better quality-of-service (QoS) with ultra-high communication efficiency. Specifically, we design our LaMI-GO system backbone based on a latent diffusion model followed by a vector-quantized generative adversarial network (VQGAN) for efficient latent embedding and information representation. The system trains a common feature codebook the receiver side. Our experimental results demonstrate substantial improvement in perceptual quality, accuracy of downstream tasks, and bandwidth consumption over the state-of-the-art GOCOM systems and establish the power of our proposed LaMI-GO communication framework.
Hilbert Transform on Graphs: Let There Be Phase
Authors: Chun Hei Michael Chan, Alexandre Cionca, Dimitri Van De Ville
Date: 2024-12-24 00:00:00

In the past years, many signal processing operations have been successfully adapted to the graph setting. One elegant and effective approach is to exploit the eigendecomposition of a graph shift operator (GSO), such as the adjacency or Laplacian operator, to define a graph Fourier transform when projecting graph signals on the corresponding basis. However, the extension of this scheme to directed graphs is challenging since the associated GSO is non-symmetric and, in general, not diagonalizable. Here, we build upon a recent framework that adds a minimal number of edges to allow diagonalization of the GSO and thus provide a proper graph Fourier transform. Furthermore, we show that such minimal addition of edges creates a cycle cover and that it is essential for the phase analysis of a signal throughout the graph. Concurrently, we propose a generalization of the Hilbert transform interpreted over the newfound cycle cover, which re-establishes intuitions from traditional Hilbert Transform, equivalent to the generalized Hilbert Transform on a single cycle. This generalization leads to a number of simple and elegant recipes to effectively exploit the phase information of graph signals provided by the graph Fourier transform. The feasibility of the approach is demonstrated on several examples.
Extremum Encoding for Joint Baseband Signal Compression and Time-Delay Estimation for Distributed Systems
Authors: Amir Weiss, Yuval Kochman, Gregory W. Wornell
Date: 2024-12-24 00:00:00

The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compression-estimation scheme, we address the critical extension to complex-valued signals, suitable for radio-frequency (RF) baseband processing. This extension introduces new challenges, e.g., due to unknown phase of the signal of interest and random phase of the noise, rendering a na\"ive application of the original scheme inapplicable and irrelevant. In the face of these challenges, we propose a judiciously adapted, though natural, extension of the scheme, paving its way to RF applications. While our extension leads to a different statistical analysis, including extremes of non-Gaussian distributions, we show that, ultimately, its asymptotic behavior is akin to the original scheme. We derive an exponentially tight upper bound on its error probability, corroborate our results via simulation experiments, and demonstrate the superior performance compared to two benchmark approaches.
Achieving Robustness in Blind Modulo Analog-to-Digital Conversion
Authors: Amir Weiss
Date: 2024-12-24 00:00:00
Tasks: Quantization

The need to digitize signals with intricate spectral characteristics often challenges traditional analog-to-digital converters (ADCs). The recently proposed modulo-ADC architecture offers a promising alternative by leveraging inherent features of the input signals. This approach can dramatically reduce the number of bits required for the conversion while maintaining the desired fidelity. However, the core algorithm of this architecture, which utilizes a prediction filter, functions properly only when the respective prediction error is bounded. In practice, this assumption may not always hold, leading to considerable instability and performance degradation. To address this limitation, we propose an enhanced modulo-unfolding solution without this assumption. We develop a reliable detector to successfully unfold the signals, yielding a robust solution. Consequently, the reinforced system maintains proper operation in scenarios where the original approach fails, while also reducing the quantization noise. We present simulation results that demonstrate the superior performance of our approach in a representative setting.
Towards Fault Diagnosis in Induction Motor using Fractional Fourier Transform
Authors: Usman Ali
Date: 2024-12-24 00:00:00
Tasks: Fault Diagnosis

A method for determining the current signature faults using Fractional Fourier Transform (FrFT) has been developed. The method has been applied to the real-time steady-state current of the inverter-fed high power induction motor for fault determination. The method incorporates calculating the relative norm error to find the threshold value between healthy and unhealthy induction motor at different operating frequencies. The experimental results demonstrate that the total harmonics distortion of unhealthy motor is much larger than the healthy motor, and the threshold relative norm error value of different healthy induction motors is less than 0.3, and the threshold relative norm error value of unhealthy induction motor is greater than 0.5. The developed method can function as a simple operator-assisted tool for determining induction motor faults in real-time.
Unsupervised Learning Approach for Beamforming in Cell-Free Integrated Sensing and Communication
Authors: Mohamed Elrashidy, Mudassir Masood, Ali Arshad Nasir
Date: 2024-12-24 00:00:00
Tasks: CPU, Integrated sensing and communication, ISAC

Cell-free massive multiple input multiple output (MIMO) systems can provide reliable connectivity and increase user throughput and spectral efficiency of integrated sensing and communication (ISAC) systems. This can only be achieved through intelligent beamforming design. While many works have proposed optimization methods to design beamformers for cell-free systems, the underlying algorithms are computationally complex and potentially increase fronthaul link loads. To address this concern, we propose an unsupervised learning algorithm to jointly design the communication and sensing beamformers for cell-free ISAC system. Specifically, we adopt a teacher-student training model to guarantee a balanced maximization of sensing signal to noise ratio (SSNR) and signal to interference plus noise ratio (SINR), which represent the sensing and communication metrics, respectively. The proposed scheme is decentralized, which can reduce the load on the central processing unit (CPU) and the required fronthaul links. To avoid the tradeoff problem between sensing and communication counterparts of the cell-free system, we first train two identical models (teacher models) each biased towards one of the two tasks. A third identical model (a student model) is trained based on the maximum sensing and communication performance information obtained by the teacher models. While the results show that our proposed unsupervised DL approach yields a performance close to the state-of-the-art solution, the proposed approach is more computationally efficient than the state of the art by at least three orders of magnitude.
Computation-and-Communication Efficient Coordinated Multicast Beamforming in Massive MIMO Networks
Authors: Shiqi Yin, Min Dong
Date: 2024-12-24 00:00:00
Tasks: Distributed Computing

The main challenges in designing downlink coordinated multicast beamforming in massive multiple-input multiple output (MIMO) cellular networks are the complex computational solutions and significant fronthaul overhead for centralized coordination. This paper proposes a coordinated multicast beamforming solution that is both computation and communication efficient. For joint BS coordination with individual base station transmit power budgets, we first obtain the optimal structure of coordinated multicast beamforming. It reveals that the beamformer at each BS is naturally distributed and only depends on the local channel state information (CSI) at its serving BS. Moreover, the optimal beamformer is a weighted minimum mean square error (MMSE) beamformer with a low-dimensional structure of unknown weights to be optimized, independent of the number of BS antennas. Utilizing the optimal structural properties, we propose fast algorithms to determine the unknown parameters for the optimal beamformer. The main iterative algorithm decomposes the problem into small subproblems, yielding only closed/semi-closed form updates. Furthermore, we propose a semi-distributed computing approach for the proposed algorithm that allows each BS to compute its beamformer based on the local CSI without the need for global CSI sharing, resulting in the fronthaul overhead independent of the number of BS antennas. We further extend our results to the design under the imperfect CSI and other coordination scenarios. Simulation results demonstrate that our proposed methods can achieve near-optimal performance with significantly lower computational time for massive MIMO systems than the conventional approaches.
FOGNA: An effective Sum-Difference Co-Array Design Based on Fourth-Order Cumulants
Authors: Si Wang, GuoQiang Xiao
Date: 2024-12-24 00:00:00

Array structures based on the fourth-order difference co-array (FODCA) provide more degrees of freedom (DOF). However, since the growth of DOF is limited by a single case of fourth-order cumulant in FODCA, this paper aims to design a sparse linear array (SLA) with higher DOF via exploring different cases of fourth-order cumulants. This paper presents a mathematical framework based on fourth-order cumulant to devise a fourth-order extend co-array (FOECA), which is equivalent to FODCA. A novel SLA, namely fourth-order generalized nested array (FOGNA), is proposed based on FOECA to provide closed-form expressions for the sensor locations and enhance DOF to resolve more signal sources in direction of arrival (DOA) estimation. FOGNA is consisted of three subarrays, where the first is a concatenated nested array and the other two subarrays are SLA with big inter-spacing between sensors. When the total physical sensors of FOGNA are given, the number of sensors in each subarray is determined by the designed method, which can obtain the maximum DOF under the proposed array structure and derive closed-form expressions for the sensor locations of FOGNA. The proposed array structure not only achieves higher DOF than those of existing FODCAs but also reduces mutual coupling effects. Numerical simulations are conducted to verify the superiority of FOGNA on DOA estimation performance and enhanced DOF over other existing FODCAs.
PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation
Authors: Yan Jiang, Xiaoyu Ji, Yancheng Jiang, Kai Wang, Chenren Xu, Wenyuan Xu
Date: 2024-12-24 00:00:00
Tasks: Intrusion Detection

Sensors are key components enabling various applications, e.g., home intrusion detection and environmental monitoring. While various software defenses and physical protections are used to prevent sensor manipulation, this paper introduces a new threat vector, PowerRadio, that bypasses existing protections and changes sensor readings from a distance. PowerRadio leverages interconnected ground (GND) wires, a standard practice for electrical safety at home, to inject malicious signals. The injected signal is coupled by the sensor's analog measurement wire and eventually survives the noise filters, inducing incorrect measurement. We present three methods to manipulate sensors by inducing static bias, periodical signals, or pulses. For instance, we show adding stripes into the captured images of a surveillance camera or injecting inaudible voice commands into conference microphones. We study the underlying principles of PowerRadio and identify its root causes: (1) the lack of shielding between ground and data signal wires and (2) the asymmetry of circuit impedance that enables interference to bypass filtering. We validate PowerRadio against a surveillance system, broadcast systems, and various sensors. We believe that PowerRadio represents an emerging threat, exhibiting the advantages of both radiated and conducted EMI, e.g., expanding the effective attack distance of radiated EMI yet eliminating the requirement of line-of-sight or approaching physically. Our insights shall provide guidance for enhancing the sensors' security and power wiring during the design phases.
Updatable Closed-Form Evaluation of Arbitrarily Complex Multi-Port Network Connections
Authors: Hugo Prod'homme, Philipp del Hougne
Date: 2024-12-23 00:00:00
Tasks: Form

The design of large complex wave systems (filters, networks, vacuum-electronic devices, metamaterials, smart radio environments, etc.) requires repeated evaluations of the scattering parameters resulting from complex connections between constituent subsystems. Instead of starting each new evaluation from scratch, we propose a computationally efficient method that updates the outcomes of previous evaluations using the Woodbury matrix identity. To enable this method, we begin by identifying a closed-form approach capable of evaluating arbitrarily complex connection schemes of multi-port networks. We pedagogically present unified equivalence principles for interpretations of system connections, as well as techniques to reduce the computational burden of the closed-form approach using these equivalence principles. Along the way, we also achieve the closed-form retrieval of the power waves traveling through connected ports. We illustrate our techniques considering a complex meta-network involving serial, parallel and cyclic connections between multi-port subsystems. We further validate all results with physics-compliant calculations considering graph-based subsystems, and we conduct exhaustive statistical analyses of computational benefits originating from the reducibility and updatability enabled by our approach. Finally, we find that working with scattering parameters (as opposed to impedance or admittance parameters) presents a fundamental advantage regarding an important class of connection schemes whose closed-form analysis requires the treatment of some connections as delayless, lossless, reflectionless and reciprocal two-port scattering systems. We expect our results to benefit the design (and characterization) of large composite (reconfigurable) wave systems.