pwc-archive/papers-with-abstracts huggingface dataset:
An Optical XNOR-Bitcount Based Accelerator for Efficient Inference of Binary Neural Networks
Authors: Sairam Sri Vatsavai, Venkata Sai Praneeth Karempudi, Ishan Thakkar
Date: 2023-02-03 00:00:00

Binary Neural Networks (BNNs) are increasingly preferred over full-precision Convolutional Neural Networks(CNNs) to reduce the memory and computational requirements of inference processing with minimal accuracy drop. BNNs convert CNN model parameters to 1-bit precision, allowing inference of BNNs to be processed with simple XNOR and bitcount operations. This makes BNNs amenable to hardware acceleration. Several photonic integrated circuits (PICs) based BNN accelerators have been proposed. Although these accelerators provide remarkably higher throughput and energy efficiency than their electronic counterparts, the utilized XNOR and bitcount circuits in these accelerators need to be further enhanced to improve their area, energy efficiency, and throughput. This paper aims to fulfill this need. For that, we invent a single-MRR-based optical XNOR gate (OXG). Moreover, we present a novel design of bitcount circuit which we refer to as Photo-Charge Accumulator (PCA). We employ multiple OXGs in a cascaded manner using dense wavelength division multiplexing (DWDM) and connect them to the PCA, to forge a novel Optical XNOR-Bitcount based Binary Neural Network Accelerator (OXBNN). Our evaluation for the inference of four modern BNNs indicates that OXBNN provides improvements of up to 62x and 7.6x in frames-per-second (FPS) and FPS/W (energy efficiency), respectively, on geometric mean over two PIC-based BNN accelerators from prior work. We developed a transaction-level, event-driven python-based simulator for evaluation of accelerators (https://github.com/uky-UCAT/B_ONN_SIM).
Software-Defined MIMO OFDM Joint Radar-Communication Platform with Fully Digital mmWave Architecture
Authors: Ceyhun D. Ozkaptan, Haocheng Zhu, Eylem Ekici, Onur Altintas
Date: 2023-02-11 00:00:00
Tasks: Joint Radar-Communication

Large-scale deployment of connected vehicles with cooperative sensing and maneuvering technologies increases the demand for vehicle-to-everything communication (V2X) band in 5.9 GHz. Besides the V2X spectrum, the under-utilized millimeter-wave (mmWave) bands at 24 and 77 GHz can be leveraged to supplement V2X communication and support high data rates for emerging broadband applications. For this purpose, joint radar-communication (JRC) systems have been proposed in the literature to perform both functions using the same waveform and hardware. In this work, we present a software-defined multiple-input and multiple-output (MIMO) JRC with orthogonal frequency division multiplexing (OFDM) for the 24 GHz mmWave band. We implement a real-time operating full-duplex JRC platform using commercially available software-defined radios and custom-built mmWave front-ends. With fully digital MIMO architecture, we demonstrate simultaneous data transmission and high-resolution radar imaging capabilities of MIMO OFDM JRC in the mmWave band.
Local retail electricity markets for distribution grid services
Authors: Vineet Jagadeesan Nair, Anuradha Annaswamy
Date: 2023-02-13 00:00:00
Tasks: valid

We propose a hierarchical local electricity market (LEM) at the primary and secondary feeder levels in a distribution grid, to optimally coordinate and schedule distributed energy resources (DER) and provide valuable grid services like voltage control. At the primary level, we use a current injection-based model that is valid for both radial and meshed, balanced and unbalanced, multi-phase systems. The primary and secondary markets leverage the flexibility offered by DERs to optimize grid operation and maximize social welfare. Numerical simulations on an IEEE-123 bus modified to include DERs, show that the LEM successfully achieves voltage control and reduces overall network costs, while also allowing us to decompose the price and value associated with different grid services so as to accurately compensate DERs.
A Tale of Two Currencies: Cash and Crypto
Authors: Ravi Kashyap
Date: 2023-02-13 00:00:00
Tasks: Management, Vocal Bursts Valence Prediction

We discuss numerous justifications for why crypto-currencies would be highly conducive for the smooth functioning of today's society. We provide several comparisons between cryptocurrencies issued by blockchain projects, crypto, and conventional government issued currencies, cash or fiat. We summarize seven fundamental innovations that would be required for participants to have greater confidence in decentralized finance (DeFi) and to obtain wealth appreciation coupled with better risk management. The conceptual ideas we discuss outline an approach to: 1) Strengthened Security Blueprint; 2) Rebalancing and Trade Execution Suited for Blockchain Nuances 3) Volatility and Variance Adjusted Weight Calculation 4) Accommodating Investor Preferences and Risk Parity Construction; 5) Profit Sharing and Investor Protection; 6) Concentration Risk Indicator and Performance Metrics; 7) Multi-chain expansion and Select Strategic Initiatives including the notion of a Decentralized Autonomous Organization (DAO). Incorporating these concepts into several projects would also facilitate the growth of the overall blockchain eco-system so that this technology can, have wider mainstream adoption and, fulfill its potential in transforming all aspects of human interactions.
Zero-Knowledge Mechanisms
Authors: Ran Canetti, Amos Fiat, Yannai A. Gonczarowski
Date: 2023-02-11 00:00:00

A powerful feature in mechanism design is the ability to irrevocably commit to the rules of a mechanism. Commitment is achieved by public declaration, which enables players to verify incentive properties in advance and the outcome in retrospect. However, public declaration can reveal superfluous information that the mechanism designer might prefer not to disclose, such as her target function or private costs. Avoiding this may be possible via a trusted mediator; however, the availability of a trusted mediator, especially if mechanism secrecy must be maintained for years, might be unrealistic. We propose a new approach to commitment, and show how to commit to, and run, any given mechanism without disclosing it, while enabling the verification of incentive properties and the outcome -- all without the need for any mediators. Our framework is based on zero-knowledge proofs -- a cornerstone of modern cryptographic theory. Applications include non-mediated bargaining with hidden yet binding offers.
Minding rights: Mapping ethical and legal foundations of 'neurorights'
Authors: Sjors Ligthart, Marcello Ienca, Gerben Meynen, Fruzsina Molnar-Gabor, Roberto Andorno, Christoph Bublitz, Paul Catley, Lisa Claydon, Thomas Douglas, Nita Farahany, Joseph J. Fins, Sara Goering, Pim Haselager, Fabrice Jotterand, Andrea Lavazza, Allan McCay, Abel Wajnerman Paz, Stephen Rainey, Jesper Ryberg, Philipp Kellmeyer
Date: 2023-02-13 00:00:00

The rise of neurotechnologies, especially in combination with AI-based methods for brain data analytics, has given rise to concerns around the protection of mental privacy, mental integrity and cognitive liberty - often framed as 'neurorights' in ethical, legal and policy discussions. Several states are now looking at including 'neurorights' into their constitutional legal frameworks and international institutions and organizations, such as UNESCO and the Council of Europe, are taking an active interest in developing international policy and governance guidelines on this issue. However, in many discussions of 'neurorights' the philosophical assumptions, ethical frames of reference and legal interpretation are either not made explicit or are in conflict with each other. The aim of this multidisciplinary work here is to provide conceptual, ethical and legal foundations that allow for facilitating a common minimalist conceptual understanding of mental privacy, mental integrity and cognitive liberty to facilitate scholarly, legal and policy discussions.
A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
Authors: Shuvro Chowdhury, Andrea Grimaldi, Navid Anjum Aadit, Shaila Niazi, Masoud Mohseni, Shun Kanai, Hideo Ohno, Shunsuke Fukami, Luke Theogarajan, Giovanni Finocchio, Supriyo Datta, Kerem Y. Camsari
Date: 2023-02-13 00:00:00
Tasks: Combinatorial Optimization

The transistor celebrated its 75${}^\text{th}$ birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.
Intelligent Reflecting Surface Aided Wireless Sensing: Applications and Design Issues
Authors: Xiaodan Shao, Changsheng You, Rui Zhang
Date: 2023-02-12 00:00:00

Intelligent reflecting surface (IRS) is an emerging technology that is able to significantly improve the performance of wireless communications, by smartly tuning signal reflections at a large number of passive reflecting elements. On the other hand, with ubiquitous wireless devices and ambient radio-frequency signals, wireless sensing has become a promising new application for the next-generation/6G wireless networks. By synergizing low-cost IRS and fertile wireless sensing applications, this article proposes a new IRS-aided sensing paradigm for enhancing the performance of wireless sensing cost-effectively. First, we provide an overview of wireless sensing applications and the new opportunities of utilizing IRS for overcoming their performance limitations in practical scenarios. Next, we discuss IRS-aided sensing schemes based on three approaches, namely, passive sensing, semi-passive sensing, and active sensing. We compare their pros and cons in terms of performance, hardware cost and implementation complexity, and outline their main design issues including IRS deployment, channel acquisition and reflection design, as well as sensing algorithms. Finally, numerical results are presented to demonstrate the great potential of IRS for improving wireless sensing accuracy and the superior performance of IRS active sensing compared to other schemes.
Propagation of weakly advantageous mutations in cancer cell population
Authors: Andrzej Polanski, Mateusz Kania, Jarosław Gil, Wojciech Łabaj, Ewa Lach, Agnieszka Szczęsna
Date: 2023-02-13 00:00:00

Research into somatic mutations in cancer cell DNA and their role in tumour growth and progression between successive stages is crucial for improving our understanding of cancer evolution. Mathematical and computer modelling can provide valuable insights into the scenarios of cancer growth, the roles of somatic mutations, and the types and strengths of evolutionary forces they introduce. Previous studies have developed mathematical models of cancer evolution, incorporating driver and passenger somatic mutations. Driver mutations were assumed to have a strong advantageous effect on the growth of the cancer cell population, while passenger mutations were considered fully neutral or mildly deleterious. However, according to several studies, passenger mutations may have a weakly advantageous effect on tumour growth. In this paper, we develop models of cancer evolution with somatic mutations that introduce a weakly advantageous force to the evolution of cancer cells. The models used in this study can be classified into two categories: deterministic and stochastic. Deterministic models are based on systems of differential equations that balance the average number of cells and mutations during evolution. To verify the results of our deterministic modelling, we use a stochastic model based on the Gillespie algorithm. We compare the predictions of our modelling with some observational data on cancer evolution.
Robust Scheduling with GFlowNets
Authors: David W. Zhang, Corrado Rainone, Markus Peschl, Roberto Bondesan
Date: 2023-01-17 00:00:00
Tasks: Compiler Optimization, Diversity, Scheduling

Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be very time-consuming. Traditional approaches as well as previous machine learning ones typically optimize proxy metrics, which are fast to evaluate but can lead to bad schedules when tested on the target hardware. In this work, we propose a new approach to scheduling by sampling proportionally to the proxy metric using a novel GFlowNet method. We introduce a technique to control the trade-off between diversity and goodness of the proposed schedules at inference time and demonstrate empirically that the pure optimization baselines can lead to subpar performance with respect to our approach when tested on a target model. Furthermore, we show that conditioning the GFlowNet on the computation graph enables generalization to unseen scheduling problems for both synthetic and real-world compiler datasets.
Fast and small footprint Hybrid HMM-HiFiGAN based system for speech synthesis in Indian languages
Authors: Sudhanshu Srivastava, Ishika Gupta, Anusha Prakash, Jom Kuriakose, Hema A. Murthy
Date: 2023-02-13 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

Hidden-Markov-model (HMM) based text-to-speech (HTS) offers flexibility in speaking styles along with fast training and synthesis while being computationally less intense. HTS performs well even in low-resource scenarios. The primary drawback is that the voice quality is poor compared to that of E2E systems. A hybrid approach combining HMM-based feature generation and neural-network-based HiFi-GAN vocoder to improve HTS synthesis quality is proposed. HTS is trained on high-resolution mel-spectrograms instead of conventional mel generalized coefficients (MGC), and the output mel-spectrogram corresponding to the input text is used in a HiFi-GAN vocoder trained on Indic languages, to produce naturalness that is equivalent to that of E2E systems, as evidenced from the DMOS and PC tests.
Improved Decoding of Attentional Selection in Multi-Talker Environments with Self-Supervised Learned Speech Representation
Authors: Cong Han, Vishal Choudhari, Yinghao Aaron Li, Nima Mesgarani
Date: 2023-02-11 00:00:00

Auditory attention decoding (AAD) is a technique used to identify and amplify the talker that a listener is focused on in a noisy environment. This is done by comparing the listener's brainwaves to a representation of all the sound sources to find the closest match. The representation is typically the waveform or spectrogram of the sounds. The effectiveness of these representations for AAD is uncertain. In this study, we examined the use of self-supervised learned speech representation in improving the accuracy and speed of AAD. We recorded the brain activity of three subjects using invasive electrocorticography (ECoG) as they listened to two conversations and focused on one. We used WavLM to extract a latent representation of each talker and trained a spatiotemporal filter to map brain activity to intermediate representations of speech. During the evaluation, the reconstructed representation is compared to each speaker's representation to determine the target speaker. Our results indicate that speech representation from WavLM provides better decoding accuracy and speed than the speech envelope and spectrogram. Our findings demonstrate the advantages of self-supervised learned speech representation for auditory attention decoding and pave the way for developing brain-controlled hearable technologies.
On the Difficulty of Characterizing Network Formation with Endogenous Behavior
Authors: Benjamin Golub, Yu-Chi Hsieh, Evan Sadler
Date: 2023-02-12 00:00:00
Tasks: Math

Bolletta (2021, Math. Soc. Sci. 114:1-10) studies a model in which a network is strategically formed and then agents play a linear best-response investment game in it. The model is motivated by an application in which people choose both their study partners and their levels of educational effort. Agents have different one-dimensional types $\unicode{x2013}$ private returns to effort. A main result claims that pairwise Nash stable networks have a locally complete structure consisting of possibly overlapping cliques: if two agents are linked, they are part of a clique composed of all agents with types between theirs. We offer a counterexample showing that the claimed characterization is incorrect, highlight where the analysis errs, and discuss implications for network formation models.
Explicit formulas for the Variance of the State of a Linearized Power System driven by Gaussian stochastic disturbances
Authors: Xian Wu, Kaihua Xi, Aijie Cheng, Hai Xiang Lin, Jan H van Schuppen, Chenghui Zhang
Date: 2023-02-13 00:00:00

We look into the fluctuations caused by disturbances in power systems. In the linearized system of the power systems, the disturbance is modeled by a Brownian motion process, and the fluctuations are described by the covariance matrix of the associated stochastic process at the invariant probability distribution. We derive explicit formulas for the covariance matrix for the system with a uniform damping-inertia ratio. The variance of the frequency at the node with the disturbance is significantly bigger than the sum of those at all the other nodes, indicating the disturbance effects the node most, according to research on the variances in complete graphs and star graphs. Additionally, it is shown that adding new nodes typically does not aid in reducing the variations at the disturbance's source node. Finally, it is shown by the explicit formulas that the line capacity affect the variation of the frequency and the inertia affects the variance of the phase differences.
Using SHAP Values and Machine Learning to Understand Trends in the Transient Stability Limit
Authors: Robert I. Hamilton, Panagiotis N. Papadopoulos
Date: 2023-02-13 00:00:00

Machine learning (ML) for transient stability assessment has gained traction due to the significant increase in computational requirements as renewables connect to power systems. To achieve a high degree of accuracy; black-box ML models are often required - inhibiting interpretation of predictions and consequently reducing confidence in the use of such methods. This paper proposes the use of SHapley Additive exPlanations (SHAP) - a unifying interpretability framework based on Shapley values from cooperative game theory - to provide insights into ML models that are trained to predict critical clearing time (CCT). We use SHAP to obtain explanations of location-specific ML models trained to predict CCT at each busbar on the network. This can provide unique insights into power system variables influencing the entire stability boundary under increasing system complexity and uncertainty. Subsequently, the covariance between a variable of interest and the corresponding SHAP values from each location-specific ML model - can reveal how a change in that variable impacts the stability boundary throughout the network. Such insights can inform planning and/or operational decisions. The case study provided demonstrates the method using a highly accurate opaque ML algorithm in the IEEE 39-bus test network with Type IV wind generation.
Optimal Charging Profile Design for Solar-Powered Sustainable UAV Communication Networks
Authors: Longxin Wang, Saugat Tripathi, Ran Zhang, Nan Cheng, Miao Wang
Date: 2023-02-13 00:00:00
Tasks: Deep Reinforcement Learning

This work studies optimal solar charging for solar-powered self-sustainable UAV communication networks, considering the day-scale time-variability of solar radiation and user service demand. The objective is to optimally trade off between the user coverage performance and the net energy loss of the network by proactively assigning UAVs to serve, charge, or land. Specifically, the studied problem is first formulated into a time-coupled mixed-integer non-convex optimization problem, and further decoupled into two sub-problems for tractability. To solve the challenge caused by time-coupling, deep reinforcement learning (DRL) algorithms are respectively designed for the two sub-problems. Particularly, a relaxation mechanism is put forward to overcome the "dimension curse" incurred by the large discrete action space in the second sub-problem. At last, simulation results demonstrate the efficacy of our designed DRL algorithms in trading off the communication performance against the net energy loss, and the impact of different parameters on the tradeoff performance.
Locating the Sources of Sub-synchronous Oscillations Induced by the Control of Voltage Source Converters Based on Energy Structure and Nonlinearity Detection
Authors: Zetian Zheng, Shaowei Huang, Jun Yan, Qiangsheng Bu, Chen Shen, Mingzhong Zheng, Ye Liu
Date: 2023-02-11 00:00:00

The oscillation phenomena associated with the control of voltage source converters (VSCs) are widely concerning, and locating the source of these oscillations is crucial to suppressing them; therefore, this paper presents a locating scheme, based on the energy structure and nonlinearity detection. On the one hand, the energy structure, which conforms with the principle of the energy-based method and dissipativity theory, is constructed to describe the transient energy flow for VSCs, and on this basis, a defined characteristic quantity is implemented to narrow the scope of oscillation source location; on the other hand, according to the self-sustained oscillation characteristics of VSCs, an index for nonlinearity detection is applied to locate the VSCs which produce the oscillation energy. The combination of the energy structure and nonlinearity detection could distinguish the contributions of different VSCs to the oscillation. The results of a case study implemented by the PSCAD/EMTDC simulation validate the proposed scheme.
A Globally Convergent Estimator of the Parameters of the Classical Model of a Continuous Stirred Tank Reactor
Authors: Anton Pyrkin, Alexey Bobtsov, Romeo Ortega, Jose Guadalupe Romero, Denis Dochain
Date: 2023-02-11 00:00:00
Tasks: regression

In this paper we provide the first solution to the challenging problem of designing a globally exponentially convergent estimator for the parameters of the standard model of a continuous stirred tank reactor. Because of the presence of non-separable exponential nonlinearities in the system dynamics that appear in Arrhenius law, none of the existing parameter estimators is able to deal with them in an efficient way and, in spite of many attempts, the problem was open for many years. To establish our result we propose a novel procedure to obtain a suitable nonlinearly parameterized regression equation and introduce a radically new estimation algorithm - derived applying the Immersion and Invariance methodology - that is applicable to these regression equations. A further contribution of the paper is that parameter convergence is guaranteed with weak excitation requirements.