pwc-archive/papers-with-abstracts huggingface dataset:
Synthesis of separation processes with reinforcement learning
Authors: Stephan C. P. A. van Kalmthout, Laurence I. Midgley, Meik B. Franke
Date: 2022-11-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence. The aim of the SAC agent was to separate a hydrocarbon mixture in its individual components by utilising distillation. While doing so it tries to maximise the profit produced by the distillation sequence. All actions of the agent were set by the SAC agent in Python and communicated in Aspen Plus via an API. Here the distillation column was simulated by use of the build-in RADFRAC column. With this a connection was established for data transfer between Python and Aspen and the agent succeeded to show learning behaviour, while increasing profit. Although results were generated, the use of Aspen was slow (190 hours) and Aspen was found unsuitable for parallelisation. This makes that Aspen is incompatible for solving RL problems. Code and thesis are available at https://github.com/lollcat/Aspen-RL
Fairness-aware Regression Robust to Adversarial Attacks
Authors: Yulu Jin, Lifeng Lai
Date: 2022-11-04 00:00:00
Tasks: Fairness, regression

In this paper, we take a first step towards answering the question of how to design fair machine learning algorithms that are robust to adversarial attacks. Using a minimax framework, we aim to design an adversarially robust fair regression model that achieves optimal performance in the presence of an attacker who is able to add a carefully designed adversarial data point to the dataset or perform a rank-one attack on the dataset. By solving the proposed nonsmooth nonconvex-nonconcave minimax problem, the optimal adversary as well as the robust fairness-aware regression model are obtained. For both synthetic data and real-world datasets, numerical results illustrate that the proposed adversarially robust fair models have better performance on poisoned datasets than other fair machine learning models in both prediction accuracy and group-based fairness measure.
Will we run out of data? Limits of LLM scaling based on human-generated data
Authors: Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Marius Hobbhahn
Date: 2022-10-26 00:00:00
Tasks: Language Modeling, Language Modelling, Synthetic Data Generation, Transfer Learning

We investigate the potential constraints on LLM scaling posed by the availability of public human-generated text data. We forecast the growing demand for training data based on current trends and estimate the total stock of public human text data. Our findings indicate that if current LLM development trends continue, models will be trained on datasets roughly equal in size to the available stock of public human text data between 2026 and 2032, or slightly earlier if models are overtrained. We explore how progress in language modeling can continue when human-generated text datasets cannot be scaled any further. We argue that synthetic data generation, transfer learning from data-rich domains, and data efficiency improvements might support further progress.
Abstraction-Based Verification of Approximate Pre-Opacity for Control Systems
Authors: Junyao Hou, Siyuan Liu, Xiang Yin, Majid Zamani
Date: 2022-11-08 00:00:00

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Pre-opacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions.
Cross-Attention is all you need: Real-Time Streaming Transformers for Personalised Speech Enhancement
Authors: Shucong Zhang, Malcolm Chadwick, Alberto Gil C. P. Ramos, Sourav Bhattacharya
Date: 2022-11-08 00:00:00
Tasks: All, Speech Enhancement

Personalised speech enhancement (PSE), which extracts only the speech of a target user and removes everything else from a recorded audio clip, can potentially improve users' experiences of audio AI modules deployed in the wild. To support a large variety of downstream audio tasks, such as real-time ASR and audio-call enhancement, a PSE solution should operate in a streaming mode, i.e., input audio cleaning should happen in real-time with a small latency and real-time factor. Personalisation is typically achieved by extracting a target speaker's voice profile from an enrolment audio, in the form of a static embedding vector, and then using it to condition the output of a PSE model. However, a fixed target speaker embedding may not be optimal under all conditions. In this work, we present a streaming Transformer-based PSE model and propose a novel cross-attention approach that gives adaptive target speaker representations. We present extensive experiments and show that our proposed cross-attention approach outperforms competitive baselines consistently, even when our model is only approximately half the size.
Pushing the limits of self-supervised speaker verification using regularized distillation framework
Authors: Yafeng Chen, Siqi Zheng, Hui Wang, Luyao Cheng, Qian Chen
Date: 2022-11-08 00:00:00
Tasks: Data Augmentation, Diversity, Self-Supervised Learning, Speaker Verification

Training robust speaker verification systems without speaker labels has long been a challenging task. Previous studies observed a large performance gap between self-supervised and fully supervised methods. In this paper, we apply a non-contrastive self-supervised learning framework called DIstillation with NO labels (DINO) and propose two regularization terms applied to embeddings in DINO. One regularization term guarantees the diversity of the embeddings, while the other regularization term decorrelates the variables of each embedding. The effectiveness of various data augmentation techniques are explored, on both time and frequency domain. A range of experiments conducted on the VoxCeleb datasets demonstrate the superiority of the regularized DINO framework in speaker verification. Our method achieves the state-of-the-art speaker verification performance under a single-stage self-supervised setting on VoxCeleb. Code has been made publicly available at https://github.com/alibaba-damo-academy/3D-Speaker.
On Negative Sampling for Contrastive Audio-Text Retrieval
Authors: Huang Xie, Okko Räsänen, Tuomas Virtanen
Date: 2022-11-08 00:00:00
Tasks: Audio to Text Retrieval, Contrastive Learning, Retrieval, Text Retrieval

This paper investigates negative sampling for contrastive learning in the context of audio-text retrieval. The strategy for negative sampling refers to selecting negatives (either audio clips or textual descriptions) from a pool of candidates for a positive audio-text pair. We explore sampling strategies via model-estimated within-modality and cross-modality relevance scores for audio and text samples. With a constant training setting on the retrieval system from [1], we study eight sampling strategies, including hard and semi-hard negative sampling. Experimental results show that retrieval performance varies dramatically among different strategies. Particularly, by selecting semi-hard negatives with cross-modality scores, the retrieval system gains improved performance in both text-to-audio and audio-to-text retrieval. Besides, we show that feature collapse occurs while sampling hard negatives with cross-modality scores.
EVs and ERCOT: Future Adoption Scenarios and Grid Implications
Authors: Kelsey Nelson, Pedro Moura, Javad Mohammadi
Date: 2022-11-08 00:00:00
Tasks: Management

Electric vehicles (EVs) are becoming more commonplace in Texas, mainly due to their increasing attractiveness to consumers and pushes from the state's governing bodies to incentivize further adoption. Meanwhile, service from Texas's electric grid, ERCOT, has been seeing increases in power demand due to a growing population, increased air conditioning use, and pushes for electrification across other industries. The electrification of vehicles will only add to this demand increase. This paper focuses on evaluating different EV adoption, charging management, and policy scenarios, and how they will be expected to impact ERCOT, particularly with respect to peak demand increases. A strong increase in the peak demand can lead to challenges to keep the electrical grid's reliability, making it an important consideration for electrification in any sector. The anticipated impacts of EV adoption on peak demand are quantified using ERCOT's data on past generation and planned installations, the approximated effectiveness of EV incentives, EV charging profiles, and travel patterns. The results showcase the fact that the achievement of ambitious EV market share goals will be manageable on a statewide level regarding electricity supply into 2030, but will eventually necessitate ambitious charging management strategies in order to limit the EV fleet's potentially heavy impact on peak demand looking forward into 2050 and beyond.
Synchronization of Diverse Agents via Phase Analysis
Authors: Dan Wang, Wei Chen, Li Qiu
Date: 2022-11-08 00:00:00
Tasks: Diversity

In this paper, the synchronization of heterogeneous agents interacting over a dynamical network is studied. The edge dynamics can model the inter-agent communications which are often heterogeneous by nature. They can also model the controllers of the agents which may be different for each agent or uniform for all the agents. Novel synchronization conditions are obtained for both cases from a phase perspective by exploiting a recently developed small phase theorem. The conditions scale well with the network and reveal the trade-off between the phases of node dynamics and edge dynamics. We also study the synchronizability problem which aims to characterize the allowable diversity of the agents for which controllers can be designed so as to achieve synchronization. The allowable diversity is captured in terms of phase conditions engaging the residue matrices of the agents at their persistent modes. Controller design algorithms are provided for the cases of agent-dependent and uniform controllers, respectively.
MetaLoc: Learning to Learn Wireless Localization
Authors: Jun Gao, Dongze Wu, Feng Yin, Qinglei Kong, Lexi Xu, Shuguang Cui
Date: 2022-11-08 00:00:00
Tasks: Meta-Learning

Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather accurate in certain environments. However, these methods, whether based on pure statistical signal processing or data-driven approaches, often struggle to generalize to new environments, which results in considerable time and effort being wasted. To address this challenge, we propose MetaLoc, which is the first fingerprinting-based localization framework that leverages the Model-Agnostic Meta-Learning (MAML). Specifically, built on a deep neural network with strong representation capabilities, MetaLoc is trained on historical data sourced from well-calibrated environments, employing a two-loop optimization mechanism to obtain the meta-parameters. These meta-parameters act as the initialization for quick adaptation in new environments, reducing the need for much human effort. The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately. Furthermore, the advanced distributed paradigm modifies the vanilla MAML loss function to ensure that the reduction of loss occurs in a consistent direction across various training domains, thus facilitating faster convergence during training. Our experiments on both synthetic and real datasets demonstrate that MetaLoc outperforms baseline methods in terms of localization accuracy, robustness, and cost-effectiveness. The code and datasets used in this study are publicly available.
Impact of Virtual Inertia on DC Grid Stability with Constant Power Loads
Authors: Hao Tu, Hui Yu, Srdjan Lukic
Date: 2022-11-08 00:00:00

Virtual inertia is an effective control approach to attenuate sudden voltage changes during transient events in low-inertia DC grids. While methods have been proposed to implement virtual inertia, its impact on DC grid stability in the presence of constant power loads (CPLs) remains unclear. In this paper, we perform a rigorous stability analysis for DC grids with CPLs powered by virtual-inertia-enhanced converters. We derive a closed-form stability criterion that can be used to evaluate the impact of virtual inertia on the system stability, and demonstrate that, given a set of system parameters, the stability of a DC grid powering CPLs can be improved for a range of virtual inertia designs. We provide analytical expressions for the optimal virtual inertia that improves stability and for the maximum virtual inertia that does not deteriorate stability. In addition, we present a step-by-step guideline to design a stable DC grid with virtual inertia. Test results are presented to validate the analysis.
AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities
Authors: Bo Tang, Vijay K. Shah, Vuk Marojevic, Jeffrey H. Reed
Date: 2022-11-08 00:00:00
Tasks: Decision Making

Openness and intelligence are two enabling features to be introduced in next generation wireless networks, e.g. Beyond 5G and 6G, to support service heterogeneity, open hardware, optimal resource utilization, and on-demand service deployment. The open radio access network (O-RAN) is a promising RAN architecture to achieve both openness and intelligence through virtualized network elements and well-defined interfaces. While deploying artificial intelligence (AI) models is becoming easier in O-RAN, one significant challenge that has been long neglected is the comprehensive testing of their performance in realistic environments. This article presents a general automated, distributed and AI-enabled testing framework to test AI models deployed in O-RAN in terms of their decision-making performance, vulnerability and security. This framework adopts a master-actor architecture to manage a number of end devices for distributed testing. More importantly, it leverages AI to automatically and intelligently explore the decision space of AI models in O-RAN. Both software simulation testing and software-defined radio hardware testing are supported, enabling rapid proof of concept research and experimental research on wireless research platforms.
Distributed Computing for Scalable Optimal Power Flow in Large Radial Electric Power Distribution Systems with Distributed Energy Resources
Authors: Rabayet Sadnan, Anamika Dubey
Date: 2022-11-08 00:00:00
Tasks: Distributed Computing

Solving the non-convex optimal power flow (OPF) problem for large-scale power distribution systems is computationally expensive. An alternative is to solve the relaxed convex problem or linear approximated problem, but these methods lead to sub-optimal or power flow infeasible solutions. In this paper, we propose a fast method to solve the OPF problem using distributed computing algorithms combined with a decomposition technique. The full network-level OPF problem is decomposed into multiple smaller sub-problems defined for each decomposed area or node that can be easily solved using off-the-shelf nonlinear programming (NLP) solvers. Distributed computing approach is proposed via which sub-problems achieve consensus and converge to network-level optimal solutions. The novelty lies in leveraging the nature of power flow equations in radial network topologies to design effective decomposition techniques that reduce the number of iterations required to achieve consensus by an order of magnitude.
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids
Authors: Shweta Dahale, Sai Munikoti, Balasubramaniam Natarajan, Rui Yang
Date: 2022-11-07 00:00:00
Tasks: Time Series, Time Series Analysis

Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time monitoring, control, and protection. However, these measurements are typically irregularly sampled. These measurements may also be intermittent due to communication bandwidth limitations. To tackle this problem, this paper proposes a novel latent neural ordinary differential equations (LODE) approach to aggregate the unevenly sampled multivariate time-series measurements. The proposed approach is flexible in performing both imputations and predictions while being computationally efficient. Simulation results on IEEE 37 bus test systems illustrate the efficiency of the proposed approach.
Optimal Merging Control of an Autonomous Vehicle in Mixed Traffic: an Optimal Index Policy
Authors: Ehsan Sabouni, Christos G. Cassandras
Date: 2022-11-07 00:00:00

We consider the problem of a single Autonomous Vehicle (AV) merging into traffic consisting only of Human Driven Vehicles (HDVs) with the goal of minimizing both the travel time and energy consumption of the entire group of vehicles involved in the merging process. This is done by controlling only the AV and determining both the optimal merging sequence and the optimal AV trajectory associated with it. We derive an optimal index policy which prescribes the merging position of the AV within the group of HDVs. We also specify conditions under which the optimal index corresponds to the AV merging before all HDVs or after all HDVs, in which case no interaction of the AV with the HDVs is required. Simulation results are included to validate the optimal index policy and demonstrate cases where optimal merging can be achieved without requiring any explicit assumptions regarding human driving behavior.
ISAR imaging of space objects using encoded apertures
Authors: M. Roueinfar, M. H. Kahaei
Date: 2022-11-07 00:00:00
Tasks: Compressive Sensing

A major threat to satellites is space debris with their low mass and high rotational speed. Accordingly, the short observation time of these objects is a major limitation in space research for appropriate detection and decision. As a result, these objects do not fully illuminated, leading to their incomplete images at any snapshot. In this paper, we propose a method to decrease the number of snapshots in a given observation time and using a limited number of spot beams per snapshot called the encoded aperture. To recover the space debris images, an inverse problem is defined based on compressive sensing methods. Also, we show that for satellite imaging the T V norm is more appropriate. We develop a procedure to recover space debris and satellites using L1 and T V norms. Using simulation results, we compare the results with the well-known SBL and SL0 norm in terms of the number of snapshots, MSE, SNR, and running time. It is shown that our proposed method can successfully recover the space objects images using a fewer number of snapshots.
Modelling chromosome-wide target search
Authors: Lucas Hedström, Ludvig Lizana
Date: 2022-11-08 00:00:00

The most common gene regulation mechanism is when a transcription factor protein binds to a regulatory sequence to increase or decrease RNA transcription. However, transcription factors face two main challenges when searching for these sequences. First, they are vanishingly short relative to the genome length. Second, many nearly identical sequences are scattered across the genome, causing proteins to suspend the search. But as pointed out in a computational study of LacI regulation in Escherichia coli, such almost-targets may lower search times if considering DNA looping. In this paper, we explore if this also occurs over chromosome-wide distances. To this end, we developed a cross-scale computational framework that combines established facilitated-diffusion models for basepair-level search and a network model capturing chromosome-wide leaps. To make our model realistic, we used Hi-C data sets as a proxy for 3D proximity between long-ranged DNA segments and binding profiles for more than 100 transcription factors. Using our cross-scale model, we found that median search times to individual targets critically depend on a network metric combining node strength (sum of link weights) and local dissociation rates. Also, by randomizing these rates, we found that some actual 3D target configurations stand out as considerably faster or slower than their random counterparts. This finding hints that chromosomes' 3D structure funnels essential transcription factors to relevant DNA regions.
Regimes of charged particle dynamics in current sheets: the machine learning approach
Authors: Alexander Lukin, Anton Artemyev, Dmitri Vainchtein, Anatoli Petrukovich
Date: 2022-10-30 00:00:00

Current sheets are spatially localized almost-1D structures with intense plasma currents. They play a key role in storing the magnetic field energy and they separate different plasma populations in planetary magnetospheres, the solar wind, and the solar corona. Current sheets are primary regions for the magnetic field line reconnection responsible for plasma heating and charged particle acceleration. One of the most interesting and widely observed type of 1D current sheets is the rotational discontinuity, that can be force-free or include plasma compression. Theoretical models of such 1D current sheets are based on the assumption of adiabatic motion of ions, i.e. ion adiabatic invariants are conserved. We focus on three current sheet configurations, widely observed in the Earth magnetopause and magnetotail and in the near-Earth solar wind. Magnetic field in such current sheets is supported by currents carried by transient ions, which exist only when there is a sufficient number of invariants. In this paper, we apply a novel machine learning approach, AI Poincar'e, to determine parametrical domains where adiabatic invariants are conserved. For all three current sheet configurations, these domains are quite narrow and do not cover the entire parametrical range of observed current sheets. We discuss possible interpretation of obtained results indicating that 1D current sheets are dynamical rather than static plasma equilibria.